From 23b59d112d50455884621dee981a605902fe440a Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 20 Jul 2024 18:23:17 -0400
Subject: [PATCH 001/153] wip

---
 pyterrier/__init__.py      | 12 +++---
 pyterrier/batchretrieve.py | 51 +++++++++++-----------
 pyterrier/datasets.py      |  8 ++--
 pyterrier/java.py          | 86 ++++++++++++++++++++++++++++++++++++++
 pyterrier/utils.py         | 37 ++++++++++++++++
 setup.py                   |  5 +++
 6 files changed, 163 insertions(+), 36 deletions(-)
 create mode 100644 pyterrier/java.py

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index 8198b684..426c9648 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -7,6 +7,11 @@
 
 # definitive API used by others, now available before pt.init
 from .transformer import Transformer, Estimator, Indexer
+from . import utils
+from . import java
+
+from .batchretrieve import BatchRetrieve
+TerrierRetrieve = BatchRetrieve # BatchRetrieve is an alias to TerrierRetrieve
 
 import importlib
 
@@ -184,8 +189,6 @@ def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, log
     globals()["list_datasets"] = list_datasets
     globals()["find_datasets"] = find_datasets
     # .batchretrieve
-    globals()["BatchRetrieve"] = BatchRetrieve
-    globals()["TerrierRetrieve"] = BatchRetrieve  # TerrierRetrieve is an alias to BatchRetrieve
     globals()["Indexer"] = Indexer
     globals()["FeaturesBatchRetrieve"] = FeaturesBatchRetrieve
     # .index
@@ -270,18 +273,17 @@ def started():
     """
     return(firstInit)
 
+@java.required()
 def version():
     """
         Returns the version string from the underlying Terrier platform.
     """
-    from jnius import autoclass
-    return autoclass("org.terrier.Version").VERSION
+    return java.autoclass("org.terrier.Version").VERSION
 
 def check_version(min, helper=False):
     """
         Returns True iff the underlying Terrier version is no older than the requested version.
     """
-    from jnius import autoclass
     from packaging.version import Version
     min = Version(str(min))
     currentVer = Version((_helper_version if helper else version()).replace("-SNAPSHOT", ""))
diff --git a/pyterrier/batchretrieve.py b/pyterrier/batchretrieve.py
index 357a3116..d812b40c 100644
--- a/pyterrier/batchretrieve.py
+++ b/pyterrier/batchretrieve.py
@@ -1,14 +1,14 @@
-from jnius import autoclass, cast
 from typing import Union
 import pandas as pd
 import numpy as np
-from . import tqdm, check_version, Transformer
+from . import tqdm, Transformer
 from warnings import warn
 from .datasets import Dataset
 from .transformer import Symbol
 from .model import coerce_queries_dataframe, FIRST_RANK
 import concurrent
 from concurrent.futures import ThreadPoolExecutor
+import pyterrier as pt
 
 def importProps():
     from . import properties as props
@@ -24,7 +24,6 @@ def _matchop(query):
     return False
 
 def _function2wmodel(function):
-    from . import autoclass
     from jnius import PythonJavaClass, java_method
 
     class PythonWmodelFunction(PythonJavaClass):
@@ -46,11 +45,11 @@ def serializeFn(self):
             extend(use_dill=False)
             # keep both python and java representations around to prevent them being GCd in respective VMs 
             self.pbyterep = pickle.dumps(self.fn)
-            self.jbyterep = autoclass("java.nio.ByteBuffer").wrap(self.pbyterep)
+            self.jbyterep = pt.java.autoclass("java.nio.ByteBuffer").wrap(self.pbyterep)
             return self.jbyterep
 
     callback = PythonWmodelFunction(function)
-    wmodel = autoclass("org.terrier.python.CallableWeightingModel")( callback )
+    wmodel = pt.java.autoclass("org.terrier.python.CallableWeightingModel")( callback )
     return callback, wmodel
 
 def _mergeDicts(defaults, settings):
@@ -60,14 +59,14 @@ def _mergeDicts(defaults, settings):
     return KV
 
 def _parse_index_like(index_location):
-    JIR = autoclass('org.terrier.querying.IndexRef')
-    JI = autoclass('org.terrier.structures.Index')
+    JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
+    JI = pt.java.autoclass('org.terrier.structures.Index')
     from .index import TerrierIndexer
 
     if isinstance(index_location, JIR):
         return index_location
     if isinstance(index_location, JI):
-        return cast('org.terrier.structures.Index', index_location).getIndexRef()
+        return pt.java.cast('org.terrier.structures.Index', index_location).getIndexRef()
     if isinstance(index_location, str) or issubclass(type(index_location), TerrierIndexer):
         if issubclass(type(index_location), TerrierIndexer):
             return JIR.of(index_location.path)
@@ -87,6 +86,7 @@ class BatchRetrieveBase(Transformer, Symbol):
     Attributes:
         verbose(bool): If True transform method will display progress
     """
+    @pt.java.required()
     def __init__(self, verbose=0, **kwargs):
         super().__init__(kwargs)
         self.verbose = verbose
@@ -203,16 +203,15 @@ def __init__(self, index_location, controls=None, properties=None, metadata=["do
                 metadata(list): What metadata to retrieve
         """
         super().__init__(kwargs)
-        from . import autoclass
         self.indexref = _parse_index_like(index_location)
-        self.appSetup = autoclass('org.terrier.utility.ApplicationSetup')
+        self.appSetup = pt.java.autoclass('org.terrier.utility.ApplicationSetup')
         self.properties = _mergeDicts(BatchRetrieve.default_properties, properties)
-        self.concurrentIL = autoclass("org.terrier.structures.ConcurrentIndexLoader")
-        if check_version(5.5) and "SimpleDecorateProcess" not in self.properties["querying.processes"]:
+        self.concurrentIL = pt.java.autoclass("org.terrier.structures.ConcurrentIndexLoader")
+        if pt.check_version(5.5) and "SimpleDecorateProcess" not in self.properties["querying.processes"]:
             self.properties["querying.processes"] += ",decorate:SimpleDecorateProcess"
         self.metadata = metadata
         self.threads = threads
-        self.RequestContextMatching = autoclass("org.terrier.python.RequestContextMatching")
+        self.RequestContextMatching = pt.java.autoclass("org.terrier.python.RequestContextMatching")
         self.search_context = {}
 
         if props is None:
@@ -231,7 +230,7 @@ def __init__(self, index_location, controls=None, properties=None, metadata=["do
                 self._callback = callback
                 self.search_context['context_wmodel'] = wmodelinstance
                 self.controls['context_wmodel'] = 'on'
-            elif isinstance(wmodel, autoclass("org.terrier.matching.models.WeightingModel")):
+            elif isinstance(wmodel, pt.java.autoclass("org.terrier.matching.models.WeightingModel")):
                 self.search_context['context_wmodel'] = wmodel
                 self.controls['context_wmodel'] = 'on'
             else:
@@ -239,7 +238,7 @@ def __init__(self, index_location, controls=None, properties=None, metadata=["do
                   
         if self.threads > 1:
             warn("Multi-threaded retrieval is experimental, YMMV.")
-            assert check_version(5.5), "Terrier 5.5 is required for multi-threaded retrieval"
+            assert pt.check_version(5.5), "Terrier 5.5 is required for multi-threaded retrieval"
 
             # we need to see if our indexref is concurrent. if not, we upgrade it using ConcurrentIndexLoader
             # this will upgrade the underlying index too.
@@ -256,8 +255,8 @@ def __init__(self, index_location, controls=None, properties=None, metadata=["do
                 raise ValueError("num_results must be None, 0 or positive")
 
 
-        MF = autoclass('org.terrier.querying.ManagerFactory')
-        self.RequestContextMatching = autoclass("org.terrier.python.RequestContextMatching")
+        MF = pt.java.autoclass('org.terrier.querying.ManagerFactory')
+        self.RequestContextMatching = pt.java.autoclass("org.terrier.python.RequestContextMatching")
         self.manager = MF._from_(self.indexref)
     
     def get_parameter(self, name : str):
@@ -394,7 +393,7 @@ def transform(self, queries):
         scores_provided = "score" in queries.columns
         input_results = None
         if docno_provided or docid_provided:
-            assert check_version(5.3)
+            assert pt.check_version(5.3)
             input_results = queries
 
             # query is optional, and functionally dependent on qid.
@@ -489,7 +488,7 @@ def __init__(self, innerclass, takes="queries", returns="docs", body_attr="body"
         self.verbose = verbose
 
     def transform(self, topics_and_res):
-        from . import DFIndexer, autoclass, IndexFactory
+        from . import DFIndexer, IndexFactory
         from .index import IndexingType
         documents = topics_and_res[["docno", self.body_attr]].drop_duplicates(subset="docno")
         indexref = DFIndexer(None, type=IndexingType.MEMORY, verbose=self.verbose).index(documents[self.body_attr], documents["docno"])
@@ -505,7 +504,7 @@ def transform(self, topics_and_res):
             index = index_docs
         else:
             index_background = IndexFactory.of(self.background_indexref)
-            index = autoclass("org.terrier.python.IndexWithBackground")(index_docs, index_background)          
+            index = pt.java.autoclass("org.terrier.python.IndexWithBackground")(index_docs, index_background)          
 
         topics = topics_and_res[["qid", "query"]].dropna(axis=0, subset=["query"]).drop_duplicates()
         
@@ -624,8 +623,7 @@ def __init__(self, index_location, features, controls=None, properties=None, thr
         
         # check for terrier-core#246 bug usiung FatFull
         if self.wmodel is not None:    
-            from . import check_version
-            assert check_version(5.9), "Terrier 5.9 is required for this functionality, see https://github.com/terrier-org/terrier-core/pull/246"
+            assert pt.check_version(5.9), "Terrier 5.9 is required for this functionality, see https://github.com/terrier-org/terrier-core/pull/246"
             
         if threads > 1:
             raise ValueError("Multi-threaded retrieval not yet supported by FeaturesBatchRetrieve")
@@ -694,15 +692,14 @@ def transform(self, queries):
         scores_provided = "score" in queries.columns
         if docno_provided or docid_provided:
             #re-ranking mode
-            from . import check_version
-            assert check_version(5.3)
+            assert pt.check_version(5.3)
             input_results = queries
 
             # query is optional, and functionally dependent on qid.
             # Hence as long as one row has the query for each qid, 
             # the rest can be None
             queries = input_results[["qid", "query"]].dropna(axis=0, subset=["query"]).drop_duplicates()
-            RequestContextMatching = autoclass("org.terrier.python.RequestContextMatching")
+            RequestContextMatching = pt.java.autoclass("org.terrier.python.RequestContextMatching")
 
             if not scores_provided and self.wmodel is None:
                 raise ValueError("We're in re-ranking mode, but input does not have scores, and wmodel is None")
@@ -762,8 +759,8 @@ def transform(self, queries):
                 srq.setControl("matching", ",".join(["FatFeaturedScoringMatching","ScoringMatchingWithFat", srq.getControl("matching")]))
             
             self.manager.runSearchRequest(srq)
-            srq = cast('org.terrier.querying.Request', srq)
-            fres = cast('org.terrier.learning.FeaturedResultSet', srq.getResultSet())
+            srq = pt.java.cast('org.terrier.querying.Request', srq)
+            fres = pt.java.cast('org.terrier.learning.FeaturedResultSet', srq.getResultSet())
             feat_names = fres.getFeatureNames()
 
             docids=fres.getDocids()
diff --git a/pyterrier/datasets.py b/pyterrier/datasets.py
index 247b1825..a8d3997a 100644
--- a/pyterrier/datasets.py
+++ b/pyterrier/datasets.py
@@ -8,7 +8,7 @@
 from warnings import warn
 import requests
 from .io import autoopen, touch
-from . import tqdm, HOME_DIR
+import pyterrier as pt
 import tarfile
 from warnings import warn
 
@@ -123,7 +123,7 @@ def __init__(self, name, locations):
 
     def _configure(self, **kwargs):
         from os.path import expanduser
-        pt_home = HOME_DIR
+        pt_home = pt.HOME_DIR
         if pt_home is None:
             from os.path import expanduser
             userhome = expanduser("~")
@@ -148,7 +148,7 @@ def download(URLs : Union[str,List[str]], filename : str, **kwargs):
                 r = requests.get(url, allow_redirects=True, stream=True, **kwargs)
                 r.raise_for_status()
                 total = int(r.headers.get('content-length', 0))
-                with pt.io.finalized_open(filename, 'b') as file, tqdm(
+                with pt.io.finalized_open(filename, 'b') as file, pt.tqdm(
                         desc=basename,
                         total=total,
                         unit='iB',
@@ -411,7 +411,7 @@ def get_corpus_iter(self, verbose=True, start=0, count=None):
         
         # tqdm support
         if verbose:
-            it = tqdm(it, desc=f'{self._irds_id} documents', total=total)
+            it = pt.tqdm(it, desc=f'{self._irds_id} documents', total=total)
 
         # rewrite to follow pyterrier std
         def gen():
diff --git a/pyterrier/java.py b/pyterrier/java.py
new file mode 100644
index 00000000..eed13e8d
--- /dev/null
+++ b/pyterrier/java.py
@@ -0,0 +1,86 @@
+from functools import wraps
+from typing import Callable
+
+import pyterrier as pt
+
+
+_terrier_version = None
+
+def set_terrier_version(version=None):
+    """
+    Sets the terrier version to use.
+    version(str): Which version of Terrier to use. Default is `None`.
+     * If None, it will find the newest Terrier version released in MavenCentral and download it.
+     * If `"snapshot"`, will download the latest build from `Jitpack <https://jitpack.io/>`_.
+    """
+    global _terrier_version
+    if started():
+        raise RuntimeError('pt.java.set_terrier_version is not available after pt.java.init() is called.')
+    _terrier_version = version
+
+
+def _legacy_init(jnius_config):
+    """
+    Calls pt.init if it hasn't already been called.
+    """
+    if not pt.started():
+        pt.init(version=_terrier_version)
+
+
+@pt.utils.once()
+def init() -> None:
+    # TODO: if we make java optional some day, should check here that it's installed. E.g.,
+    # if find_spec('jnius_config') is None:
+    #     warnings.warn('pyterrier[java] not installed; no need to run pt.java.init()')
+    #     return
+
+    import jnius_config
+    for entry_point in pt.utils.entry_points('pyterrier.java.init'):
+        _init = entry_point.load()
+        _init(jnius_config)
+    import jnius
+
+
+def started() -> bool:
+    """
+    Returns True if pt.java.init() has been called. Otherwise False.
+    """
+    return init.called()
+
+
+def required(raise_on_not_started: bool = False) -> Callable:
+    """
+    Wraps a function that requires the Java Virtual Machine to be started before running. If the JVM has not yet
+    been started, it runs pt.java.init() or raises an error, depending on the value of raise_on_not_started.
+    """
+    def _required(fn: Callable) -> Callable:
+        @wraps(fn)
+        def _wrapper(*args, **kwargs):
+            if not started():
+                if raise_on_not_started:
+                    raise RuntimeError(f'You need to call pt.java.init() required before you can call {fn}')
+                else:
+                    init()
+            return fn(*args, **kwargs)
+        return _wrapper
+    return _required
+
+
+# jnius wrappers:
+
+@required(raise_on_not_started=True)
+def autoclass(*args, **kwargs):
+    """
+    Wraps jnius.autoclass once java has started. Raises an error if called before pt.java.init() is called.
+    """
+    import jnius
+    return jnius.autoclass(*args, **kwargs)
+
+
+@required(raise_on_not_started=True)
+def cast(*args, **kwargs):
+    """
+    Wraps jnius.cast once java has started. Raises an error if called before pt.java.init() is called.
+    """
+    import jnius
+    return jnius.cast(*args, **kwargs)
diff --git a/pyterrier/utils.py b/pyterrier/utils.py
index 6edce309..b6823330 100644
--- a/pyterrier/utils.py
+++ b/pyterrier/utils.py
@@ -1,3 +1,7 @@
+from typing import Callable, Tuple
+from functools import wraps
+from importlib.metadata import EntryPoint
+from importlib.metadata import entry_points as eps
 import pandas as pd
 from collections import defaultdict
 from deprecated import deprecated
@@ -72,3 +76,36 @@ def evaluate(res : pd.DataFrame, qrels : pd.DataFrame, metrics=['map', 'ndcg'],
     def mean_of_measures(result, measures=None, num_q = None):
         from .pipelines import _mean_of_measures
         return _mean_of_measures(result, measures=measures, num_q=num_q)
+
+
+def once() -> Callable:
+    """
+    Wraps a function that can only be called once. Subsequent calls will raise an error.
+    """
+    def _once(fn: Callable) -> Callable:
+        called = False
+
+        @wraps(fn)
+        def _wrapper(*args, **kwargs):
+            nonlocal called
+            if called:
+                raise ValueError(f"{fn.__name__} has already been run")
+            # how to handle errors?
+            res = fn(*args, **kwargs)
+            called = True
+            return res
+        _wrapper.called = lambda: called  # type: ignore
+        return _wrapper
+    return _once
+
+
+def entry_points(group: str) -> Tuple[EntryPoint, ...]:
+    """
+    A shim for Python<=3.X to support importlib.metadata.entry_points(group).
+
+    See <https://docs.python.org/3/library/importlib.metadata.html#entry-points> for more details.
+    """
+    try:
+        return tuple(eps(group=group))
+    except TypeError:
+        return tuple(eps().get(group, tuple()))
diff --git a/setup.py b/setup.py
index be9968c1..5cd5803d 100644
--- a/setup.py
+++ b/setup.py
@@ -70,6 +70,11 @@ def get_version(rel_path):
         "License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)",
         "Operating System :: OS Independent",
     ],
+    entry_points={
+        'pyterrier.java.init': [
+            'legacy_init = pyterrier:java._legacy_init',
+        ],
+    },
     install_requires=requirements,
     python_requires='>=3.8',
 )

From e474044a34e4d9ae83cb9d03f3447de03abf95e5 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 21 Jul 2024 08:19:57 +0000
Subject: [PATCH 002/153] integration

---
 pyterrier/__init__.py      | 222 ++++++-----------
 pyterrier/bootstrap.py     |  54 -----
 pyterrier/cache.py         |   4 +-
 pyterrier/index.py         | 478 +++++++++++++++++++------------------
 pyterrier/io.py            |  14 ++
 pyterrier/java.py          | 133 +++++++++--
 pyterrier/mavenresolver.py |  29 ++-
 pyterrier/rewrite.py       |  20 +-
 pyterrier/terrier.py       | 105 ++++++++
 setup.py                   |  10 +-
 10 files changed, 597 insertions(+), 472 deletions(-)
 create mode 100644 pyterrier/terrier.py

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index 426c9648..83a1244b 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -1,48 +1,61 @@
 __version__ = "0.10.1"
 
 import os
+from deprecated import deprecated
 
-from .bootstrap import _logging, setup_terrier, setup_jnius, is_windows
+from .bootstrap import setup_jnius, is_windows
 from tqdm.auto import tqdm
 
 # definitive API used by others, now available before pt.init
 from .transformer import Transformer, Estimator, Indexer
 from . import utils
 from . import java
+from . import terrier
+
+from . import anserini
+from . import cache
+from . import debug
+from . import index
+from . import io
+from . import measures
+from . import model
+from . import new
+from . import ltr
+from . import parallel
+from . import pipelines
+from . import rewrite
+from . import text
+from . import transformer
+from .datasets import get_dataset, find_datasets, list_datasets
+
+from .batchretrieve import BatchRetrieve, FeaturesBatchRetrieve
+from .utils import Utils
+from .bootstrap import IndexFactory
+from .index import Indexer, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
+from .pipelines import Experiment, GridScan, GridSearch, KFoldGridSearch, Evaluate
 
 from .batchretrieve import BatchRetrieve
 TerrierRetrieve = BatchRetrieve # BatchRetrieve is an alias to TerrierRetrieve
 
 import importlib
 
-#sub modules
-anserini = None
-apply = None
-cache = None
-debug = None
-index = None
-io = None
-ltr = None
-measures = None
-model = None
-new = None
-parallel = None
-pipelines = None
-rewrite = None
-text = None
-transformer = None
-
 
 file_path = os.path.dirname(os.path.abspath(__file__))
-firstInit = False
+
+# will be set in java._legacy_post_init once java is loaded
 ApplicationSetup = None
-IndexFactory = None
-IndexRef = None
 properties = None
+
+# will be set in terrier._java_post_init once java is loaded
+IndexRef = None
 HOME_DIR = None
-init_args ={}
+
+# TODO
+init_args = {}
 _helper_version = None
 
+
+@java.before_init()
 def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, logging='WARN', home_dir=None, boot_packages=[], tqdm=None, no_download=False,helper_version = None):
     """
     Function necessary to be called before Terrier classes and methods can be used.
@@ -81,91 +94,31 @@ def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, log
     are in the  "~/.pyterrier" (if they are not present, they will be downloaded the first time). Then you should set their versions when calling ``init()`` function. For example:
     ``pt.init(version = 5.5, helper_version = "0.0.6")``.
     """
-    global firstInit
-    if firstInit:
-        raise RuntimeError("pt.init() has already been called. Check pt.started() before calling pt.init()")
+
+    # Set the corresponding options
+    java.set_memory_limit(mem)
+    for package in boot_packages:
+        java.add_package(*package.split(':')) # format: org:package:version:filetype (where version and filetype are optional)
+    for opt in jvm_opts:
+        java.add_option(opt)
+    terrier.set_version(version)
+    terrier.set_helper_version(helper_version)
+    set_tqdm(tqdm)
+
+    # TODO: missing no_download. Is this something like pt.java.set_offline(True)?
+
+    java.init()
+
+    java.set_log_level(logging)
 
     # check python version
+    # TODO: does this belong in init or somewhere else?
     import platform
     from packaging.version import Version
     if Version(platform.python_version()) < Version('3.7.0'):
         raise RuntimeError("From PyTerrier 0.8, Python 3.7 minimum is required, you currently have %s" % platform.python_version())
 
-    set_tqdm(tqdm)
-
-    global ApplicationSetup
-    global properties
-    global file_path
-    global HOME_DIR
-
-    # we keep a local directory
-    if home_dir is not None:
-        HOME_DIR = home_dir
-    elif "PYTERRIER_HOME" in os.environ:
-        HOME_DIR = os.environ["PYTERRIER_HOME"]
-    else:
-        from os.path import expanduser
-        userhome = expanduser("~")
-        HOME_DIR = os.path.join(userhome, ".pyterrier")
-    if not os.path.exists(HOME_DIR):
-        os.mkdir(HOME_DIR)
-
-    # get the initial classpath for the JVM
-    classpathTrJars, hlpr_ver = setup_terrier(HOME_DIR, version, helper_version = helper_version, boot_packages=boot_packages, force_download=not no_download)
-    global _helper_version
-    _helper_version = hlpr_ver
-    
-    if is_windows():
-        if "JAVA_HOME" in os.environ:
-            java_home =  os.environ["JAVA_HOME"]
-            fix = '%s\\jre\\bin\\server\\;%s\\jre\\bin\\client\\;%s\\bin\\server\\' % (java_home, java_home, java_home)
-            os.environ["PATH"] = os.environ["PATH"] + ";" + fix
-
-    # Import pyjnius and other classes
-    import jnius_config
-    for jar in classpathTrJars:
-        jnius_config.add_classpath(jar)
-    if jvm_opts is not None:
-        for opt in jvm_opts:
-            jnius_config.add_options(opt)
-    if mem is not None:
-        jnius_config.add_options('-Xmx' + str(mem) + 'm')
-    from jnius import autoclass, cast
-
-    # we only accept Java version 11 and newer; so anything starting 1. or 9. is too old
-    java_version = autoclass("java.lang.System").getProperty("java.version")
-    if java_version.startswith("1.") or java_version.startswith("9."):
-        raise RuntimeError("Pyterrier requires Java 11 or newer, we only found Java version %s;"
-            +" install a more recent Java, or change os.environ['JAVA_HOME'] to point to the proper Java installation",
-            java_version)
-    
-    tr_version = autoclass('org.terrier.Version')
-    version_string = tr_version.VERSION
-    if "BUILD_DATE" in dir(tr_version):
-        version_string += " (built by %s on %s)" % (tr_version.BUILD_USER, tr_version.BUILD_DATE)
-    import sys
-    print("PyTerrier %s has loaded Terrier %s and terrier-helper %s\n" % (__version__, version_string, _helper_version), file=sys.stderr)
-    properties = autoclass('java.util.Properties')()
-    ApplicationSetup = autoclass('org.terrier.utility.ApplicationSetup')
-
-    from .batchretrieve import BatchRetrieve, FeaturesBatchRetrieve
-    from .utils import Utils
-    from .bootstrap import IndexFactory
-    from .datasets import get_dataset, find_datasets, list_datasets
-    from .index import Indexer, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
-    from .pipelines import Experiment, GridScan, GridSearch, KFoldGridSearch, Evaluate
-
     # Make imports global
-    globals()["autoclass"] = autoclass
-    globals()["cast"] = cast
-    globals()["ApplicationSetup"] = ApplicationSetup
-
-    # apply is an object, not a module, as it also has __get_attr__() implemented
-    from .apply import _apply
-    globals()['apply'] = _apply()
-
-    for sub_module_name in ['anserini', 'cache', 'debug', 'index', 'io', 'measures', 'model', 'new', 'ltr', 'parallel', 'pipelines', 'rewrite', 'text', 'transformer']:
-        globals()[sub_module_name] = importlib.import_module('.' + sub_module_name, package='pyterrier') 
 
     # append the python helpers
     if packages is None:
@@ -180,38 +133,8 @@ def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, log
     if redirect_io:
         # this ensures that the python stdout/stderr and the Java are matched
         redirect_stdouterr()
-    init_args["logging"] = logging
-    _logging(logging)
-    setup_jnius()
 
-    # .datasets
-    globals()["get_dataset"] = get_dataset
-    globals()["list_datasets"] = list_datasets
-    globals()["find_datasets"] = find_datasets
-    # .batchretrieve
-    globals()["Indexer"] = Indexer
-    globals()["FeaturesBatchRetrieve"] = FeaturesBatchRetrieve
-    # .index
-    globals()["TRECCollectionIndexer"] = TRECCollectionIndexer
-    globals()["FilesIndexer"] = FilesIndexer
-    globals()["DFIndexer"] = DFIndexer
-    globals()["DFIndexUtils"] = DFIndexUtils
-    globals()["IterDictIndexer"] = IterDictIndexer
-    globals()["IndexFactory"] = IndexFactory
-    globals()["IndexRef"] = autoclass("org.terrier.querying.IndexRef")
-    globals()["IndexingType"] = IndexingType
-    globals()["TerrierStemmer"] = TerrierStemmer
-    globals()["TerrierStopwords"] = TerrierStopwords
-    globals()["TerrierTokeniser"] = TerrierTokeniser
-    # .pipelines etc
-    globals()["Experiment"] = Experiment
-    globals()["Evaluate"] = Evaluate
-    globals()["GridScan"] = GridScan
-    globals()["GridSearch"] = GridSearch
-    globals()["KFoldGridSearch"] = KFoldGridSearch
-    globals()["Utils"] = Utils
-    
-    
+    setup_jnius()
     
     # we save the pt.init() arguments so that other processes,
     # started by joblib or ray can booted with same options
@@ -223,7 +146,7 @@ def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, log
     init_args["home_dir"] = home_dir
     init_args["boot_packages"] = boot_packages
     init_args["tqdm"] = tqdm
-    firstInit = True
+
 
 def set_tqdm(type):
     """
@@ -271,7 +194,7 @@ def started():
             if not pt.started():
                 pt.init()
     """
-    return(firstInit)
+    return java.started()
 
 @java.required()
 def version():
@@ -296,6 +219,9 @@ def redirect_stdouterr():
     from . import bootstrap
     bootstrap.redirect_stdouterr()
 
+
+@java.required()
+@deprecated(version="0.11", reason="Use pt.java.set_log_level() instead")
 def logging(level):
     """
         Set the logging level. Equivalent to setting the logging= parameter to init().
@@ -307,9 +233,10 @@ def logging(level):
          - `'DEBUG'`: show debugging, information, warnings and error messages
 
     """
-    from . import bootstrap
-    bootstrap.logging(level)
+    java.set_log_level(level)
+
 
+@java.required()
 def set_property(k, v):
     """
         Allows to set a property in Terrier's global properties configuration. Example::
@@ -326,6 +253,8 @@ def set_property(k, v):
     properties[str(k)] = str(v)
     ApplicationSetup.bootstrapInitialisation(properties)
 
+
+@java.required()
 def set_properties(kwargs):
     """
         Allows to set many properties in Terrier's global properties configuration
@@ -334,25 +263,28 @@ def set_properties(kwargs):
         properties[str(key)] = str(value)
     ApplicationSetup.bootstrapInitialisation(properties)
 
+
+@java.required()
 def run(cmd, args=[]):
     """
         Allows to run a Terrier executable class, i.e. one that can be access from the `bin/terrier` commandline programme.
     """
-    from jnius import autoclass
-    autoclass("org.terrier.applications.CLITool").main([cmd] + args)
+    java.autoclass("org.terrier.applications.CLITool").main([cmd] + args)
+
 
+@java.required()
 def extend_classpath(mvnpackages):
     """
         Allows to add packages to Terrier's classpath after the JVM has started.
     """
-    assert check_version(5.3), "Terrier 5.3 required for this functionality"
     if isinstance(mvnpackages, str):
         mvnpackages = [mvnpackages]
-    from jnius import autoclass, cast
-    thelist = autoclass("java.util.ArrayList")()
-    for pkg in mvnpackages:
-        thelist.add(pkg)
-    mvnr = ApplicationSetup.getPlugin("MavenResolver")
-    assert mvnr is not None
-    mvnr = cast("org.terrier.utility.MavenResolver", mvnr)
-    mvnr.addDependencies(thelist)
+    for package in mvnpackages:
+        terrier.extend_package(package)
+
+
+# apply is an object, not a module, as it also has __get_attr__() implemented
+def _():
+    from .apply import _apply
+    globals()['apply'] = _apply()
+_()
diff --git a/pyterrier/bootstrap.py b/pyterrier/bootstrap.py
index 99be0550..adbf1c99 100644
--- a/pyterrier/bootstrap.py
+++ b/pyterrier/bootstrap.py
@@ -3,7 +3,6 @@
 
 stdout_ref = None
 stderr_ref = None
-TERRIER_PKG = "org.terrier"
 SAVED_FNS=[]
 
 class IndexFactory:
@@ -124,11 +123,6 @@ def of(indexlike, memory : Union[bool, List[str]] = False):
             return IndexFactory._load_into_memory(index, structures=memory)
         return IndexFactory._load_into_memory(index)
 
-def logging(level):
-    from jnius import autoclass
-    autoclass("org.terrier.python.PTUtils").setLogLevel(level, None)
-# make an alias
-_logging = logging
 
 def new_indexref(s):
     from . import IndexRef
@@ -338,54 +332,6 @@ def _index_corpusiter_direct_pretok(self):
         'get_corpus_iter' : _index_corpusiter
     }
 
-def setup_terrier(file_path, terrier_version=None, helper_version=None, boot_packages=[], force_download=True):
-    """
-    Download Terrier's jar file for the given version at the given file_path
-    Called by pt.init()
-
-    Args:
-        file_path(str): Where to download
-        terrier_version(str): Which version of Terrier - None is latest release; "snapshot" uses Jitpack to download a build of the current Github 5.x branch.
-        helper_version(str): Which version of the helper - None is latest
-    """
-    # If version is not specified, find newest and download it
-    if terrier_version is None:
-        terrier_version = mavenresolver.latest_version_num(TERRIER_PKG, "terrier-assemblies")
-    else:
-        terrier_version = str(terrier_version) # just in case its a float
-    # obtain the fat jar from Maven
-    # "snapshot" means use Jitpack.io to get a build of the current
-    # 5.x branch from Github - see https://jitpack.io/#terrier-org/terrier-core/5.x-SNAPSHOT
-    if terrier_version == "snapshot":
-        trJar = mavenresolver.downloadfile("com.github.terrier-org.terrier-core", "terrier-assemblies", "5.x-SNAPSHOT", file_path, "jar-with-dependencies", force_download=force_download)
-    else:
-        trJar = mavenresolver.downloadfile(TERRIER_PKG, "terrier-assemblies", terrier_version, file_path, "jar-with-dependencies")
-
-    # now the helper classes
-    if helper_version is None:
-        helper_version = mavenresolver.latest_version_num(TERRIER_PKG, "terrier-python-helper")
-    else:
-        helper_version = str(helper_version) # just in case its a float
-    helperJar = mavenresolver.downloadfile(TERRIER_PKG, "terrier-python-helper", helper_version, file_path, "jar")
-
-    classpath=[trJar, helperJar]
-    for b in boot_packages:
-        parts = b.split(":")
-        if len(parts)  < 2 or len(parts) > 4:
-            raise ValueError("Invalid format for package %s" % b)
-        group = parts[0]
-        pkg = parts[1]
-        filetype = "jar"
-        version = None
-        if len(parts) > 2:
-            version = parts[2]
-            if len(parts) > 3:
-                filetype = parts[3]
-        #print((group, pkg, filetype, version))
-        filename = mavenresolver.downloadfile(group, pkg, version, file_path, filetype)
-        classpath.append(filename)
-
-    return classpath, helper_version
 
 def is_windows() -> bool:
     import platform
diff --git a/pyterrier/cache.py b/pyterrier/cache.py
index 4d72bc22..e0d46486 100644
--- a/pyterrier/cache.py
+++ b/pyterrier/cache.py
@@ -1,6 +1,6 @@
 from . import Transformer
 import hashlib
-from . import HOME_DIR
+import pyterrier as pt
 import os
 from os import path
 import pandas as pd
@@ -28,7 +28,7 @@ def sizeof_fmt(num):
 
 def init():
     global CACHE_DIR
-    CACHE_DIR = path.join(HOME_DIR,"transformer_cache") 
+    CACHE_DIR = path.join(pt.io.pyterrier_home(), "transformer_cache") 
 
 def list_cache():
     if CACHE_DIR is None:
diff --git a/pyterrier/index.py b/pyterrier/index.py
index ca719391..541adda0 100644
--- a/pyterrier/index.py
+++ b/pyterrier/index.py
@@ -2,7 +2,6 @@
 This file contains all the indexers.
 """
 
-from jnius import autoclass, PythonJavaClass, java_method, cast
 from enum import Enum
 import pandas as pd
 from . import Indexer
@@ -19,6 +18,7 @@
 from collections import deque
 from typing import List, Dict, Union, Any
 import more_itertools
+import pyterrier as pt
 
 StringReader = None
 HashMap = None
@@ -41,6 +41,11 @@
 IndexRef = None
 StructureMerger = None
 BlockStructureMerger = None
+DocListIterator = None
+PythonListIterator = None
+FlatJSONDocumentIterator = None
+TQDMCollection = None
+TQDMSizeCollection = None
 
 # for backward compatibility
 class IterDictIndexerBase(Indexer):
@@ -51,8 +56,8 @@ def __init__(self, *args, **kwargs):
 # lastdoc ensures that a Document instance from a Collection is not GCd before Java has used it.
 lastdoc=None
 
-def run_autoclass():
-    from . import check_version
+def _java_post_init(jnius):
+    autoclass = pt.java.autoclass
     global StringReader
     global HashMap
     global TaggedDocument
@@ -74,6 +79,11 @@ def run_autoclass():
     global IndexRef
     global StructureMerger
     global BlockStructureMerger
+    global DocListIterator
+    global PythonListIterator
+    global FlatJSONDocumentIterator
+    global TQDMCollection
+    global TQDMSizeCollection
 
     StringReader = autoclass("java.io.StringReader")
     HashMap = autoclass("java.util.HashMap")
@@ -86,7 +96,7 @@ def run_autoclass():
     BlockIndexer = autoclass("org.terrier.structures.indexing.classical.BlockIndexer")
     BasicSinglePassIndexer = autoclass("org.terrier.structures.indexing.singlepass.BasicSinglePassIndexer")
     BlockSinglePassIndexer = autoclass("org.terrier.structures.indexing.singlepass.BlockSinglePassIndexer")
-    BasicMemoryIndexer = autoclass("org.terrier.realtime.memory.MemoryIndexer" if check_version("5.7") else "org.terrier.python.MemoryIndexer")
+    BasicMemoryIndexer = autoclass("org.terrier.realtime.memory.MemoryIndexer" if pt.check_version("5.7") else "org.terrier.python.MemoryIndexer")
     Collection = autoclass("org.terrier.indexing.Collection")
     Arrays = autoclass("java.util.Arrays")
     Array = autoclass('java.lang.reflect.Array')
@@ -97,15 +107,218 @@ def run_autoclass():
     StructureMerger = autoclass("org.terrier.structures.merging.StructureMerger")
     BlockStructureMerger = autoclass("org.terrier.structures.merging.BlockStructureMerger")
 
+    class DocListIterator(jnius.PythonJavaClass):
+        dpl_class = autoclass("org.terrier.structures.indexing.DocumentPostingList")
+        tuple_class = autoclass("org.terrier.structures.collections.MapEntry")
+        __javainterfaces__ = [
+            'java/util/Iterator',
+        ]
+
+        def __init__(self, pyiterator):
+            self.pyiterator = pyiterator
+            self.hasnext = True
+            self.lastdoc = None
+            import pyterrier as pt
+            self.tr57 = not pt.check_version("5.8")
+
+        @staticmethod 
+        def pyDictToMap(a_dict): #returns Map<String,String>
+            rtr = HashMap()
+            for k,v in a_dict.items():
+                rtr.put(k, v)
+            return rtr
+
+        def pyDictToMapEntry(self,doc_dict : Dict[str,Any]): #returns Map.Entry<Map<String,String>, DocumentPostingList>>
+            dpl = DocListIterator.dpl_class()
+            # this works around a bug in the counting of doc lengths in Tr 5.7
+            if self.tr57:
+                for t, tf in doc_dict["toks"].items():
+                    for i in range(int(tf)):
+                        dpl.insert(t)
+            else: # this code for 5.8 onwards
+                for t, tf in doc_dict["toks"].items():
+                    dpl.insert(int(tf), t)
+            
+            # we cant make the toks column into the metaindex as it isnt a string. remove it.
+            del doc_dict["toks"]
+            return DocListIterator.tuple_class(DocListIterator.pyDictToMap(doc_dict), dpl)
+
+        @jnius.java_method('()Z')
+        def hasNext(self):
+            return self.hasnext
+
+        @jnius.java_method('()Ljava/lang/Object;')
+        def next(self):
+            try:
+                doc_dict = next(self.pyiterator)
+            except StopIteration as se:
+                self.hasnext = False
+                # terrier will ignore a null return from an iterator
+                return None
+            # keep this around to prevent being GCd before Java can read it
+            self.lastdoc = self.pyDictToMapEntry(doc_dict)
+            return self.lastdoc
+            
+
+    class TQDMCollection(jnius.PythonJavaClass):
+        __javainterfaces__ = ['org/terrier/indexing/Collection']
+
+        def __init__(self, collection):
+            super(TQDMCollection, self).__init__()
+            assert isinstance(collection, autoclass("org.terrier.indexing.MultiDocumentFileCollection"))
+            self.collection = collection
+            size = self.collection.FilesToProcess.size()
+            from . import tqdm
+            self.pbar = tqdm(total=size, unit="files")
+            self.last = -1
+        
+        @jnius.java_method('()Z')
+        def nextDocument(self):
+            rtr = self.collection.nextDocument()
+            filenum = self.collection.FileNumber
+            if filenum > self.last:
+                self.pbar.update(filenum - self.last)
+                self.last = filenum
+            return rtr
+
+        @jnius.java_method('()V')
+        def reset(self):
+            self.pbar.reset()
+            self.collection.reset()
+
+        @jnius.java_method('()V')
+        def close(self):
+            self.pbar.close()
+            self.collection.close()
+
+        @jnius.java_method('()Z')
+        def endOfCollection(self):
+            return self.collection.endOfCollection()
+
+        @jnius.java_method('()Lorg/terrier/indexing/Document;')
+        def getDocument(self):
+            global lastdoc
+            lastdoc = self.collection.getDocument()
+            return lastdoc
+
+    class PythonListIterator(jnius.PythonJavaClass):
+        __javainterfaces__ = ['java/util/Iterator']
+
+        def __init__(self, text, meta, convertFn, len=None, index=0):
+            super(PythonListIterator, self).__init__()
+            self.text = text
+            self.meta = meta
+            self.index = index
+            self.convertFn = convertFn
+            if len is None:
+                self.len = len(self.text)
+            else:
+                self.len = len
+
+        @jnius.java_method('()V')
+        def remove():
+            # 1
+            pass
+
+        @jnius.java_method('(Ljava/util/function/Consumer;)V')
+        def forEachRemaining(action):
+            # 1
+            pass
+
+        @jnius.java_method('()Z')
+        def hasNext(self):
+            return self.index < self.len
+
+        @jnius.java_method('()Ljava/lang/Object;')
+        def next(self):
+            text = self.text[self.index]
+            meta = self.meta.__next__()
+            self.index += 1
+            global lastdoc
+            if self.convertFn is not None:
+                lastdoc = self.convertFn(text, meta)
+            else:
+                lastdoc = [text, meta]
+            return lastdoc
+
+    class FlatJSONDocumentIterator(jnius.PythonJavaClass):
+        __javainterfaces__ = ['java/util/Iterator']
+
+        def __init__(self, it):
+            super(FlatJSONDocumentIterator, self).__init__()
+            self._it = it
+            # easiest way to support hasNext is just to start consuming right away, I think
+            self._next = next(self._it, StopIteration)
+
+        @jnius.java_method('()V')
+        def remove():
+            # 1
+            pass
+
+        @jnius.java_method('(Ljava/util/function/Consumer;)V')
+        def forEachRemaining(action):
+            # 1
+            pass
+
+        @jnius.java_method('()Z')
+        def hasNext(self):
+            return self._next is not StopIteration
+
+        @jnius.java_method('()Ljava/lang/Object;')
+        def next(self):
+            result = self._next
+            self._next = next(self._it, StopIteration)
+            if result is not StopIteration:
+                global lastdoc
+                lastdoc = FlatJSONDocument(json.dumps(result))
+                return lastdoc
+            return None
+
+    class TQDMSizeCollection(jnius.PythonJavaClass):
+        __javainterfaces__ = ['org/terrier/indexing/Collection']
+
+        def __init__(self, collection, total):
+            super(TQDMSizeCollection, self).__init__()
+            self.collection = collection
+            from . import tqdm
+            self.pbar = tqdm(total=total, unit="documents")
+        
+        @jnius.java_method('()Z')
+        def nextDocument(self):
+            rtr = self.collection.nextDocument()
+            self.pbar.update()
+            return rtr
+
+        @jnius.java_method('()V')
+        def reset(self):
+            self.pbar.reset()
+            self.collection.reset()
+
+        @jnius.java_method('()V')
+        def close(self):
+            self.pbar.close()
+            self.collection.close()
+
+        @jnius.java_method('()Z')
+        def endOfCollection(self):
+            return self.collection.endOfCollection()
+
+        @jnius.java_method('()Lorg/terrier/indexing/Document;')
+        def getDocument(self):
+            global lastdoc
+            lastdoc = self.collection.getDocument()
+            return lastdoc
+
+
+
 type_to_class = {
     'trec' : 'org.terrier.indexing.TRECCollection',
     'trecweb' : 'org.terrier.indexing.TRECWebCollection',
     'warc' : 'org.terrier.indexing.WARC10Collection'
 }
 
+@pt.java.required()
 def createCollection(files_path : List[str], coll_type : str = 'trec', props = {}):
-    if StringReader is None:
-            run_autoclass()
     if coll_type in type_to_class:
         collectionClzName = type_to_class[coll_type]
     else:
@@ -115,15 +328,15 @@ def createCollection(files_path : List[str], coll_type : str = 'trec', props = {
     for k, v in props.items():
         _props[k] = v
     ApplicationSetup.getProperties().putAll(_props)
-    cls_string = autoclass("java.lang.String")._class
-    cls_list = autoclass("java.util.List")._class
+    cls_string = pt.java.autoclass("java.lang.String")._class
+    cls_list = pt.java.autoclass("java.util.List")._class
     if len(files_path) == 0:
         raise ValueError("list files_path cannot be empty")
     asList = createAsList(files_path)
-    colObj = autoclass("org.terrier.indexing.CollectionFactory").loadCollections(
+    colObj = pt.java.autoclass("org.terrier.indexing.CollectionFactory").loadCollections(
         collectionClzName,
         [cls_list, cls_string, cls_string, cls_string],
-        [asList, autoclass("org.terrier.utility.TagSet").TREC_DOC_TAGS, "", ""])
+        [asList, pt.java.autoclass("org.terrier.utility.TagSet").TREC_DOC_TAGS, "", ""])
     return colObj
 
 def treccollection2textgen(
@@ -321,6 +534,7 @@ def _to_class(this):
         if isinstance(this, str):
             return this
 
+    @pt.java.required()
     def stem(self, tok):
         if self not in _stemmer_cache:
             clz_name = self._to_class(self)
@@ -334,7 +548,7 @@ def stem(self, word):
                     clz_name = f'org.terrier.terms.{clz_name}'
                  # stemmers are termpipeline objects, and these have chained constructors
                  # pass None to use the appropriate constructor
-                _stemmer_cache[self] = autoclass(clz_name)(None)
+                _stemmer_cache[self] = pt.java.autoclass(clz_name)(None)
         return _stemmer_cache[self].stem(tok)
 
 class TerrierStopwords(Enum):
@@ -374,8 +588,7 @@ def _indexing_config(this, stopword_list : Union[List[str], None], termpipelines
 
             # this hook updates the index's properties to handle the python stopwords list
             def _hook(pyindexer, index):
-                from . import cast
-                pindex = cast("org.terrier.structures.PropertiesIndex", index)
+                pindex = pt.java.cast("org.terrier.structures.PropertiesIndex", index)
                 # store the stopwords into the Index's properties
                 pindex.setIndexProperty("pyterrier.stopwords", ",".join(stopword_list_esc))
 
@@ -461,8 +674,6 @@ def __init__(self, index_path : str, *args,
             tokeniser (TerrierTokeniser): the stemmer to apply. Default is ``TerrierTokeniser.english``.
             type (IndexingType): the specific indexing procedure to use. Default is ``IndexingType.CLASSIC``.
         """
-        if StringReader is None:
-            run_autoclass()
         if type is IndexingType.MEMORY:
             self.path = None
         else:
@@ -640,7 +851,6 @@ class DFIndexUtils:
 
     @staticmethod
     def get_column_lengths(df):
-        import math
         meta2len = dict([(v, df[v].apply(lambda r: len(str(r)) if r!=None else 0).max())for v in df.columns.values])
         # nan values can arise if df is empty. Here we take a metalength of 1 instead.
         meta2len = {k : 1 if math.isnan(l) else l for k, l in meta2len.items()}
@@ -648,8 +858,6 @@ def get_column_lengths(df):
 
     @staticmethod
     def create_javaDocIterator(text, *args, **kwargs):
-        if HashMap is None:
-            run_autoclass()
 
         all_metadata = {}
         for i, arg in enumerate(args):
@@ -702,6 +910,7 @@ class DFIndexer(TerrierIndexer):
     Use this Indexer if you wish to index a pandas.Dataframe
 
     """
+    @pt.java.required()
     def index(self, text, *args, **kwargs):
         """
         Index the specified
@@ -722,11 +931,11 @@ def index(self, text, *args, **kwargs):
         self.meta = meta_lengths
         
         # make a Collection class for Terrier
-        javaDocCollection = autoclass("org.terrier.python.CollectionFromDocumentIterator")(collectionIterator)
+        javaDocCollection = pt.java.autoclass("org.terrier.python.CollectionFromDocumentIterator")(collectionIterator)
         if self.verbose:
             javaDocCollection = TQDMSizeCollection(javaDocCollection, len(text)) 
         index = self.createIndexer()
-        index.index(autoclass("org.terrier.python.PTUtils").makeCollection(javaDocCollection))
+        index.index(pt.java.autoclass("org.terrier.python.PTUtils").makeCollection(javaDocCollection))
         global lastdoc
         lastdoc = None
         javaDocCollection.close()
@@ -740,80 +949,6 @@ def index(self, text, *args, **kwargs):
             return index.getIndex().getIndexRef()
         return IndexRef.of(self.index_dir + "/data.properties")
 
-class PythonListIterator(PythonJavaClass):
-    __javainterfaces__ = ['java/util/Iterator']
-
-    def __init__(self, text, meta, convertFn, len=None, index=0):
-        super(PythonListIterator, self).__init__()
-        self.text = text
-        self.meta = meta
-        self.index = index
-        self.convertFn = convertFn
-        if len is None:
-            self.len = len(self.text)
-        else:
-            self.len = len
-
-    @java_method('()V')
-    def remove():
-        # 1
-        pass
-
-    @java_method('(Ljava/util/function/Consumer;)V')
-    def forEachRemaining(action):
-        # 1
-        pass
-
-    @java_method('()Z')
-    def hasNext(self):
-        return self.index < self.len
-
-    @java_method('()Ljava/lang/Object;')
-    def next(self):
-        text = self.text[self.index]
-        meta = self.meta.__next__()
-        self.index += 1
-        global lastdoc
-        if self.convertFn is not None:
-            lastdoc = self.convertFn(text, meta)
-        else:
-            lastdoc = [text, meta]
-        return lastdoc
-
-class FlatJSONDocumentIterator(PythonJavaClass):
-    __javainterfaces__ = ['java/util/Iterator']
-
-    def __init__(self, it):
-        super(FlatJSONDocumentIterator, self).__init__()
-        if FlatJSONDocument is None:
-            run_autoclass()
-        self._it = it
-        # easiest way to support hasNext is just to start consuming right away, I think
-        self._next = next(self._it, StopIteration)
-
-    @java_method('()V')
-    def remove():
-        # 1
-        pass
-
-    @java_method('(Ljava/util/function/Consumer;)V')
-    def forEachRemaining(action):
-        # 1
-        pass
-
-    @java_method('()Z')
-    def hasNext(self):
-        return self._next is not StopIteration
-
-    @java_method('()Ljava/lang/Object;')
-    def next(self):
-        result = self._next
-        self._next = next(self._it, StopIteration)
-        if result is not StopIteration:
-            global lastdoc
-            lastdoc = FlatJSONDocument(json.dumps(result))
-            return lastdoc
-        return None
 
 class _BaseIterDictIndexer(TerrierIndexer, Indexer):
     def __init__(self, index_path, *args, meta = {'docno' : 20}, meta_reverse=['docno'], pretokenised=False, threads=1, **kwargs):
@@ -916,6 +1051,7 @@ class _IterDictIndexer_nofifo(_BaseIterDictIndexer):
     Use this Indexer if you wish to index an iter of dicts (possibly with multiple fields).
     This version is used for Windows -- which doesn't support the faster fifo implementation.
     """
+    @pt.java.required()
     def index(self, it, fields=('text',), meta=None, meta_lengths=None, threads=None):
         """
         Index the specified iter of dicts with the (optional) specified fields
@@ -953,7 +1089,7 @@ def index(self, it, fields=('text',), meta=None, meta_lengths=None, threads=None
 
             # we need to prevent collectionIterator from being GCd
             collectionIterator = FlatJSONDocumentIterator(self._filter_iterable(it, fields))
-            javaDocCollection = autoclass("org.terrier.python.CollectionFromDocumentIterator")(collectionIterator)
+            javaDocCollection = pt.java.autoclass("org.terrier.python.CollectionFromDocumentIterator")(collectionIterator)
             # remove once 5.7 is now the minimum version
             from . import check_version
             indexer.index(javaDocCollection if check_version("5.7") else [javaDocCollection])
@@ -970,7 +1106,7 @@ def index(self, it, fields=('text',), meta=None, meta_lengths=None, threads=None
             from . import IndexFactory
             indexref = IndexRef.of(self.index_dir + "/data.properties")
             if len(self.cleanup_hooks) > 0:
-                sindex = autoclass("org.terrier.structures.Index")
+                sindex = pt.java.autoclass("org.terrier.structures.Index")
                 sindex.setIndexLoadingProfileAsRetrieval(False)
                 index = IndexFactory.of(indexref)
                 for hook in self.cleanup_hooks:
@@ -986,6 +1122,7 @@ class _IterDictIndexer_fifo(_BaseIterDictIndexer):
     This version is optimized by using multiple threads and POSIX fifos to tranfer data,
     which ends up being much faster.
     """
+    @pt.java.required()
     def index(self, it, fields=('text',), meta=None, meta_lengths=None):
         """
         Index the specified iter of dicts with the (optional) specified fields
@@ -996,11 +1133,11 @@ def index(self, it, fields=('text',), meta=None, meta_lengths=None):
             meta(list[str]): keys to be considered as metdata
             meta_lengths(list[int]): length of metadata, defaults to 512 characters
         """
-        CollectionFromDocumentIterator = autoclass("org.terrier.python.CollectionFromDocumentIterator")
-        JsonlDocumentIterator = autoclass("org.terrier.python.JsonlDocumentIterator")
+        CollectionFromDocumentIterator = pt.java.autoclass("org.terrier.python.CollectionFromDocumentIterator")
+        JsonlDocumentIterator = pt.java.autoclass("org.terrier.python.JsonlDocumentIterator")
         if self.pretokenised:
-            JsonlTokenisedIterator = autoclass("org.terrier.python.JsonlPretokenisedIterator")
-        ParallelIndexer = autoclass("org.terrier.python.ParallelIndexer")
+            JsonlTokenisedIterator = pt.java.autoclass("org.terrier.python.JsonlPretokenisedIterator")
+        ParallelIndexer = pt.java.autoclass("org.terrier.python.ParallelIndexer")
 
         if meta is not None:
             warn('specifying meta and meta_lengths in IterDictIndexer.index() is deprecated, use constructor instead', DeprecationWarning, 2)
@@ -1059,7 +1196,7 @@ def index(self, it, fields=('text',), meta=None, meta_lengths=None):
         indexref = IndexRef.of(self.index_dir + "/data.properties")
         
         if len(self.cleanup_hooks) > 0:
-            sindex = autoclass("org.terrier.structures.Index")
+            sindex = pt.java.autoclass("org.terrier.structures.Index")
             sindex.setIndexLoadingProfileAsRetrieval(False)
             index = IndexFactory.of(indexref)
             sindex.setIndexLoadingProfileAsRetrieval(True)
@@ -1143,7 +1280,7 @@ def __init__(self,
         self.meta_reverse = meta_reverse
         self.meta_tags = meta_tags
     
-
+    @pt.java.required()
     def index(self, files_path : Union[str,List[str]]):
         """
         Index the specified TREC formatted files
@@ -1158,15 +1295,14 @@ def index(self, files_path : Union[str,List[str]]):
         _TaggedDocumentSetup(self.meta, self.meta_tags)
 
         colObj = createCollection(files_path, self.collection)
-        if self.verbose and isinstance(colObj, autoclass("org.terrier.indexing.MultiDocumentFileCollection")):
-            colObj = cast("org.terrier.indexing.MultiDocumentFileCollection", colObj)
+        if self.verbose and isinstance(colObj, pt.java.autoclass("org.terrier.indexing.MultiDocumentFileCollection")):
+            colObj = pt.java.cast("org.terrier.indexing.MultiDocumentFileCollection", colObj)
             colObj = TQDMCollection(colObj)
-        import pyterrier as pt
         # remove once 5.7 is now the minimum version
         if pt.check_version("5.7"):
             index.index(colObj)
         else:
-            index.index(autoclass("org.terrier.python.PTUtils").makeCollection(colObj))
+            index.index(pt.java.autoclass("org.terrier.python.PTUtils").makeCollection(colObj))
         global lastdoc
         lastdoc = None
         colObj.close()
@@ -1226,133 +1362,3 @@ def index(self, files_path : Union[str,List[str]]):
         if self.type is IndexingType.MEMORY:
             return index.getIndex().getIndexRef()
         return IndexRef.of(self.index_dir + "/data.properties")
-
-class TQDMSizeCollection(PythonJavaClass):
-    __javainterfaces__ = ['org/terrier/indexing/Collection']
-
-    def __init__(self, collection, total):
-        super(TQDMSizeCollection, self).__init__()
-        self.collection = collection
-        from . import tqdm
-        self.pbar = tqdm(total=total, unit="documents")
-    
-    @java_method('()Z')
-    def nextDocument(self):
-        rtr = self.collection.nextDocument()
-        self.pbar.update()
-        return rtr
-
-    @java_method('()V')
-    def reset(self):
-        self.pbar.reset()
-        self.collection.reset()
-
-    @java_method('()V')
-    def close(self):
-        self.pbar.close()
-        self.collection.close()
-
-    @java_method('()Z')
-    def endOfCollection(self):
-        return self.collection.endOfCollection()
-
-    @java_method('()Lorg/terrier/indexing/Document;')
-    def getDocument(self):
-        global lastdoc
-        lastdoc = self.collection.getDocument()
-        return lastdoc
-        
-class DocListIterator(PythonJavaClass):
-    dpl_class = autoclass("org.terrier.structures.indexing.DocumentPostingList")
-    tuple_class = autoclass("org.terrier.structures.collections.MapEntry")
-    __javainterfaces__ = [
-        'java/util/Iterator',
-    ]
-
-    def __init__(self, pyiterator):
-        self.pyiterator = pyiterator
-        self.hasnext = True
-        self.lastdoc = None
-        import pyterrier as pt
-        self.tr57 = not pt.check_version("5.8")
-
-    @staticmethod 
-    def pyDictToMap(a_dict): #returns Map<String,String>
-        rtr = HashMap()
-        for k,v in a_dict.items():
-            rtr.put(k, v)
-        return rtr
-
-    def pyDictToMapEntry(self,doc_dict : Dict[str,Any]): #returns Map.Entry<Map<String,String>, DocumentPostingList>>
-        dpl = DocListIterator.dpl_class()
-        # this works around a bug in the counting of doc lengths in Tr 5.7
-        if self.tr57:
-            for t, tf in doc_dict["toks"].items():
-                for i in range(int(tf)):
-                    dpl.insert(t)
-        else: # this code for 5.8 onwards
-            for t, tf in doc_dict["toks"].items():
-                dpl.insert(int(tf), t)
-        
-        # we cant make the toks column into the metaindex as it isnt a string. remove it.
-        del doc_dict["toks"]
-        return DocListIterator.tuple_class(DocListIterator.pyDictToMap(doc_dict), dpl)
-
-    @java_method('()Z')
-    def hasNext(self):
-        return self.hasnext
-
-    @java_method('()Ljava/lang/Object;')
-    def next(self):
-        try:
-            doc_dict = next(self.pyiterator)
-        except StopIteration as se:
-            self.hasnext = False
-            # terrier will ignore a null return from an iterator
-            return None
-        # keep this around to prevent being GCd before Java can read it
-        self.lastdoc = self.pyDictToMapEntry(doc_dict)
-        return self.lastdoc
-        
-
-class TQDMCollection(PythonJavaClass):
-    __javainterfaces__ = ['org/terrier/indexing/Collection']
-
-    def __init__(self, collection):
-        super(TQDMCollection, self).__init__()
-        assert isinstance(collection, autoclass("org.terrier.indexing.MultiDocumentFileCollection"))
-        self.collection = collection
-        size = self.collection.FilesToProcess.size()
-        from . import tqdm
-        self.pbar = tqdm(total=size, unit="files")
-        self.last = -1
-    
-    @java_method('()Z')
-    def nextDocument(self):
-        rtr = self.collection.nextDocument()
-        filenum = self.collection.FileNumber
-        if filenum > self.last:
-            self.pbar.update(filenum - self.last)
-            self.last = filenum
-        return rtr
-
-    @java_method('()V')
-    def reset(self):
-        self.pbar.reset()
-        self.collection.reset()
-
-    @java_method('()V')
-    def close(self):
-        self.pbar.close()
-        self.collection.close()
-
-    @java_method('()Z')
-    def endOfCollection(self):
-        return self.collection.endOfCollection()
-
-    @java_method('()Lorg/terrier/indexing/Document;')
-    def getDocument(self):
-        global lastdoc
-        lastdoc = self.collection.getDocument()
-        return lastdoc
-        
diff --git a/pyterrier/io.py b/pyterrier/io.py
index 3d220e25..ab543763 100644
--- a/pyterrier/io.py
+++ b/pyterrier/io.py
@@ -399,3 +399,17 @@ def read_qrels(file_path):
     "letor" : (_read_results_letor, _write_results_letor),
     "minimal" : (None, _write_results_minimal)
 }
+
+
+def pyterrier_home() -> str:
+    """
+    Returns pyterrier's home directory. By default this is ~/.pyterrier, but it can also be set with the PYTERRIER_HOME
+    env variable.
+    """
+    if "PYTERRIER_HOME" in os.environ:
+        home = os.environ["PYTERRIER_HOME"]
+    else:
+        home = os.path.expanduser('~/.pyterrier')
+    if not os.path.exists(home):
+        os.makedirs(home)
+    return home
diff --git a/pyterrier/java.py b/pyterrier/java.py
index eed13e8d..51f49248 100644
--- a/pyterrier/java.py
+++ b/pyterrier/java.py
@@ -1,51 +1,71 @@
 from functools import wraps
-from typing import Callable
+import os
+from typing import Callable, Optional
+from pyterrier import mavenresolver
 
 import pyterrier as pt
 
 
-_terrier_version = None
+_started = False
 
-def set_terrier_version(version=None):
-    """
-    Sets the terrier version to use.
-    version(str): Which version of Terrier to use. Default is `None`.
-     * If None, it will find the newest Terrier version released in MavenCentral and download it.
-     * If `"snapshot"`, will download the latest build from `Jitpack <https://jitpack.io/>`_.
-    """
-    global _terrier_version
-    if started():
-        raise RuntimeError('pt.java.set_terrier_version is not available after pt.java.init() is called.')
-    _terrier_version = version
 
-
-def _legacy_init(jnius_config):
-    """
-    Calls pt.init if it hasn't already been called.
-    """
-    if not pt.started():
-        pt.init(version=_terrier_version)
+def _legacy_post_init(jnius):
+    pt.HOME_DIR = pt.io.pyterrier_home()
+    pt.properties = autoclass('java.util.Properties')()
+    pt.ApplicationSetup = autoclass('org.terrier.utility.ApplicationSetup')
+    pt.autoclass = jnius.autoclass
+    pt.cast = jnius.cast
 
 
 @pt.utils.once()
 def init() -> None:
+    global _started
     # TODO: if we make java optional some day, should check here that it's installed. E.g.,
     # if find_spec('jnius_config') is None:
     #     warnings.warn('pyterrier[java] not installed; no need to run pt.java.init()')
     #     return
 
+    if pt.is_windows():
+        if "JAVA_HOME" in os.environ:
+            java_home =  os.environ["JAVA_HOME"]
+            fix = f'{java_home}\\jre\\bin\\server\\;{java_home}\\jre\\bin\\client\\;{java_home}\\bin\\server\\'
+            os.environ["PATH"] = os.environ["PATH"] + ";" + fix
+
     import jnius_config
-    for entry_point in pt.utils.entry_points('pyterrier.java.init'):
-        _init = entry_point.load()
-        _init(jnius_config)
+
+    for entry_point in pt.utils.entry_points('pyterrier.java.pre_init'):
+        _pre_init = entry_point.load()
+        _pre_init(jnius_config)
+
+    if _mem is not None:
+        jnius_config.add_options('-Xmx' + str(_mem) + 'm')
+
+    for opt in _options:
+        jnius_config.add_options(opt)
+
+    for jar in _jars:
+        jnius_config.add_classpath(jar)
+
     import jnius
+    _started = True
+
+    set_log_level(_log_level)
+
+    java_version = autoclass("java.lang.System").getProperty("java.version")
+    if java_version.startswith("1.") or java_version.startswith("9."):
+        raise RuntimeError(f"Pyterrier requires Java 11 or newer, we only found Java version {java_version};"
+            + " install a more recent Java, or change os.environ['JAVA_HOME'] to point to the proper Java installation")
+
+    for entry_point in pt.utils.entry_points('pyterrier.java.post_init'):
+        _post_init = entry_point.load()
+        _post_init(jnius)
 
 
 def started() -> bool:
     """
     Returns True if pt.java.init() has been called. Otherwise False.
     """
-    return init.called()
+    return _started
 
 
 def required(raise_on_not_started: bool = False) -> Callable:
@@ -66,6 +86,21 @@ def _wrapper(*args, **kwargs):
     return _required
 
 
+def before_init() -> Callable:
+    """
+    Wraps a function that can only be run before the Java Virtual Machine has started. If the JVM has already started,
+    an error is raised.
+    """
+    def _before_init(fn: Callable) -> Callable:
+        @wraps(fn)
+        def _wrapper(*args, **kwargs):
+            if started():
+                raise RuntimeError(f'You need to call pt.java.init() required before you can call {fn}')
+            return fn(*args, **kwargs)
+        return _wrapper
+    return _before_init
+
+
 # jnius wrappers:
 
 @required(raise_on_not_started=True)
@@ -84,3 +119,53 @@ def cast(*args, **kwargs):
     """
     import jnius
     return jnius.cast(*args, **kwargs)
+
+
+# Settings
+
+
+_jars = []
+_options = []
+_mem = None
+_log_level = 'WARN'
+
+@before_init()
+def add_jar(jar_path):
+    _jars.append(jar_path)
+
+
+@before_init()
+def add_package(org_name: str = None, package_name: str = None, version: str = None, file_type='jar'):
+    if version is None or version == 'snapshot':
+        version = mavenresolver.latest_version_num(org_name, package_name)
+    file_name = mavenresolver.downloadfile(org_name, package_name, version, pt.io.pyterrier_home(), file_type)
+    add_jar(file_name)
+
+
+@before_init()
+def set_memory_limit(mem: Optional[float]):
+    global _mem
+    _mem = mem
+
+
+@before_init()
+def add_option(option: str):
+    _options.append(option)
+
+
+def set_log_level(level):
+    """
+        Set the logging level. The following string values are allowed, corresponding
+        to Java logging levels:
+        
+         - `'ERROR'`: only show error messages
+         - `'WARN'`: only show warnings and error messages (default)
+         - `'INFO'`: show information, warnings and error messages
+         - `'DEBUG'`: show debugging, information, warnings and error messages
+        
+        Unlike other java settings, this can be changed either before or after init() has been called.
+    """
+    global _log_level
+    _log_level = level
+    if started():
+        autoclass("org.terrier.python.PTUtils").setLogLevel(level, None)
diff --git a/pyterrier/mavenresolver.py b/pyterrier/mavenresolver.py
index 0007c28f..bded3683 100644
--- a/pyterrier/mavenresolver.py
+++ b/pyterrier/mavenresolver.py
@@ -1,7 +1,11 @@
 from xml.dom import minidom
 import urllib.request
+from packaging.version import Version
+from pathlib import Path
 import wget
 import os
+import re
+import pyterrier as pt
 
 MAVEN_BASE_URL = "https://repo1.maven.org/maven2/"
 JITPACK_BASE_URL = "https://jitpack.io/"
@@ -63,9 +67,28 @@ def latest_version_num(orgName, packageName):
         return("-SNAPSHOT")
 
     url_str = MAVEN_BASE_URL + orgName + "/" + packageName + "/maven-metadata.xml"
-    with urllib.request.urlopen(url_str) as url:
-        xml_str = url.read()
+    try:
+        with urllib.request.urlopen(url_str) as url:
+            xml_str = url.read()
+    except urllib.error.URLError:
+        # no internet connection, use the latest version found locally.
+        version = latest_local_version_num(packageName)
+        if version is None:
+            raise # version not found, re-raise the URLError error
+        else:
+            return version
     xmldoc = minidom.parseString(xml_str)
     obs_values = xmldoc.getElementsByTagName("latest")
     version = obs_values[0].firstChild.nodeValue
-    return(version)
+    return version
+
+
+def latest_local_version_num(packageName):
+    versions = []
+    for jar_path in Path(pt.io.pyterrier_home()).glob(f'{packageName}-*.jar'):
+        match = re.search(rf'{packageName}-([0-9]+(\.[0-9]+)+).*.jar', jar_path.name)
+        if match:
+            versions.append(Version(match.group(1)))
+    if len(versions) > 0:
+        return str(max(versions))
+    return None # no local version found
diff --git a/pyterrier/rewrite.py b/pyterrier/rewrite.py
index 46e313b7..01756f27 100644
--- a/pyterrier/rewrite.py
+++ b/pyterrier/rewrite.py
@@ -1,5 +1,4 @@
 import pyterrier as pt
-from jnius import cast
 import pandas as pd
 from .batchretrieve import _parse_index_like
 from . import Transformer
@@ -9,11 +8,20 @@
 from typing import List,Union
 from types import FunctionType
 
-TerrierQLParser = pt.autoclass("org.terrier.querying.TerrierQLParser")()
-TerrierQLToMatchingQueryTerms = pt.autoclass("org.terrier.querying.TerrierQLToMatchingQueryTerms")()
-QueryResultSet = pt.autoclass("org.terrier.matching.QueryResultSet")
-DependenceModelPreProcess = pt.autoclass("org.terrier.querying.DependenceModelPreProcess")
 
+TerrierQLParser = None
+TerrierQLToMatchingQueryTerms = None
+QueryResultSet = None
+DependenceModelPreProcess = None
+def _java_post_init(jnius):
+    global TerrierQLParser
+    global TerrierQLToMatchingQueryTerms
+    global QueryResultSet
+    global DependenceModelPreProcess
+    TerrierQLParser = pt.java.autoclass("org.terrier.querying.TerrierQLParser")()
+    TerrierQLToMatchingQueryTerms = pt.java.autoclass("org.terrier.querying.TerrierQLToMatchingQueryTerms")()
+    QueryResultSet = pt.java.autoclass("org.terrier.matching.QueryResultSet")
+    DependenceModelPreProcess = pt.java.autoclass("org.terrier.querying.DependenceModelPreProcess")
 
 
 _terrier_prf_package_loaded = False
@@ -300,7 +308,7 @@ def transform(self, topics_and_res):
             qid = row.qid
             query = row.query
             srq = self.manager.newSearchRequest(qid, query)
-            rq = cast("org.terrier.querying.Request", srq)
+            rq = pt.java.cast("org.terrier.querying.Request", srq)
             self.qe.configureIndex(rq.getIndex())
             self._configure_request(rq)
 
diff --git a/pyterrier/terrier.py b/pyterrier/terrier.py
new file mode 100644
index 00000000..0ae2c061
--- /dev/null
+++ b/pyterrier/terrier.py
@@ -0,0 +1,105 @@
+import sys
+from typing import Optional
+import pyterrier as pt
+from . import mavenresolver
+
+TERRIER_PKG = "org.terrier"
+
+
+_CONFIGURATION = {
+    'terrier_version': None,
+    'helper_version': None,
+    'boot_packages': [],
+    'force_download': True,
+    'prf_version': None,
+}
+
+_resolved_helper_version = None
+
+
+@pt.java.before_init()
+def configure(terrier_version=None, helper_version=None, boot_packages=[], force_download=True):
+    _CONFIGURATION.update({
+        'terrier_version': terrier_version,
+        'helper_version': helper_version,
+        'boot_packages': boot_packages,
+        'force_download': force_download,
+    })
+
+
+@pt.java.before_init()
+def set_terrier_version(version: Optional[str] = None):
+    _CONFIGURATION['terrier_version'] = version
+
+
+@pt.java.before_init()
+def set_helper_version(version: Optional[str] = None):
+    _CONFIGURATION['helper_version'] = version
+
+
+@pt.java.before_init()
+def enable_prf(version: Optional[str] = None):
+    pt.java.add_package('com.github.terrierteam', 'terrier-prf', version)
+
+
+
+def _java_pre_init(jnius_config):
+    global _resolved_helper_version
+    """
+    Download Terrier's jar file for the given version at the given file_path
+    Called by pt.init()
+
+    Args:
+        file_path(str): Where to download
+        terrier_version(str): Which version of Terrier - None is latest release; "snapshot" uses Jitpack to download a build of the current Github 5.x branch.
+        helper_version(str): Which version of the helper - None is latest
+    """
+    # If version is not specified, find newest and download it
+    if _CONFIGURATION['terrier_version'] is None:
+        terrier_version = mavenresolver.latest_version_num(TERRIER_PKG, "terrier-assemblies")
+    else:
+        terrier_version = str(_CONFIGURATION['terrier_version']) # just in case its a float
+
+    # obtain the fat jar from Maven
+    # "snapshot" means use Jitpack.io to get a build of the current
+    # 5.x branch from Github - see https://jitpack.io/#terrier-org/terrier-core/5.x-SNAPSHOT
+    if terrier_version == "snapshot":
+        trJar = mavenresolver.downloadfile("com.github.terrier-org.terrier-core", "terrier-assemblies", "5.x-SNAPSHOT", pt.io.pyterrier_home(), "jar-with-dependencies", force_download=_CONFIGURATION['force_download'])
+    else:
+        trJar = mavenresolver.downloadfile(TERRIER_PKG, "terrier-assemblies", terrier_version, pt.io.pyterrier_home(), "jar-with-dependencies")
+    pt.java.add_jar(trJar)
+
+    # now the helper classes
+    if _CONFIGURATION['helper_version'] is None or _CONFIGURATION['helper_version'] == 'snapshot':
+        helper_version = mavenresolver.latest_version_num(TERRIER_PKG, "terrier-python-helper")
+    else:
+        helper_version = str(_CONFIGURATION['helper_version']) # just in case its a float
+    _resolved_helper_version = helper_version
+    pt.java.add_package(TERRIER_PKG, "terrier-python-helper", helper_version)
+
+
+def _java_post_init(jnius):
+    tr_version = pt.java.autoclass('org.terrier.Version')
+
+    version_string = tr_version.VERSION
+    if "BUILD_DATE" in dir(tr_version):
+        version_string += f" (built by {tr_version.BUILD_USER} on {tr_version.BUILD_DATE})"
+
+    print(f"PyTerrier {pt.__version__} has loaded Terrier {version_string} and "
+          f"terrier-helper {_resolved_helper_version}\n", file=sys.stderr)
+
+    pt.IndexRef = pt.java.autoclass("org.terrier.querying.IndexRef")
+
+
+@pt.java.required()
+def extend_package(package):
+    """
+        Allows to add packages to Terrier's classpath after the JVM has started.
+    """
+    assert pt.check_version(5.3), "Terrier 5.3 required for this functionality"
+    package_list = pt.java.autoclass("java.util.ArrayList")()
+    package_list.add(package)
+    mvnr = pt.ApplicationSetup.getPlugin("MavenResolver")
+    assert mvnr is not None
+    mvnr = pt.java.cast("org.terrier.utility.MavenResolver", mvnr)
+    mvnr.addDependencies(package_list)
diff --git a/setup.py b/setup.py
index 5cd5803d..baacff19 100644
--- a/setup.py
+++ b/setup.py
@@ -71,8 +71,14 @@ def get_version(rel_path):
         "Operating System :: OS Independent",
     ],
     entry_points={
-        'pyterrier.java.init': [
-            'legacy_init = pyterrier:java._legacy_init',
+        'pyterrier.java.pre_init': [
+            'terrier = pyterrier:terrier._java_pre_init',
+        ],
+        'pyterrier.java.post_init': [
+            'terrier = pyterrier:terrier._java_post_init',
+            'index = pyterrier:index._java_post_init',
+            'rewrite = pyterrier:rewrite._java_post_init',
+            'legacy = pyterrier:java._legacy_post_init',
         ],
     },
     install_requires=requirements,

From cebba5e26a8a578deda25ed61166ec0be03d9278 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 21 Jul 2024 08:22:18 +0000
Subject: [PATCH 003/153] wip

---
 pyterrier/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index 83a1244b..6cc3ba40 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -101,7 +101,7 @@ def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, log
         java.add_package(*package.split(':')) # format: org:package:version:filetype (where version and filetype are optional)
     for opt in jvm_opts:
         java.add_option(opt)
-    terrier.set_version(version)
+    terrier.set_terrier_version(version)
     terrier.set_helper_version(helper_version)
     set_tqdm(tqdm)
 

From 8bddd31486a3a653c19150688d4eb34343a088d4 Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Fri, 26 Jul 2024 13:05:23 +0100
Subject: [PATCH 004/153] this call no longer needed

---
 tests/test_iterdictindex_pretok.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/test_iterdictindex_pretok.py b/tests/test_iterdictindex_pretok.py
index fb87f6e5..b91e2f48 100644
--- a/tests/test_iterdictindex_pretok.py
+++ b/tests/test_iterdictindex_pretok.py
@@ -12,7 +12,6 @@
 class TestIterDictIndexerPreTok(TempDirTestCase):
 
     def test_dpl(self):
-        pt.index.run_autoclass()
         it1 = [
             {'docno': 'd1', 'url': 'url1', "toks" : {"a" : 1, "b" : 2}}
         ]

From 78c4ed2f7bf8e8455ebb7d6124402863c2980cd8 Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Fri, 26 Jul 2024 13:29:50 +0100
Subject: [PATCH 005/153] resolve helper version differently

---
 pyterrier/__init__.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index 6cc3ba40..cb8fdf82 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -208,8 +208,12 @@ def check_version(min, helper=False):
         Returns True iff the underlying Terrier version is no older than the requested version.
     """
     from packaging.version import Version
+    from . import terrier
+    currentVer = terrier._resolved_helper_version if helper else version()
+    assert currentVer is not None, "Could not obtain Terrier version (helpher=%s)" % str(helper)
+    currentVer = Version(currentVer.replace("-SNAPSHOT", ""))
+
     min = Version(str(min))
-    currentVer = Version((_helper_version if helper else version()).replace("-SNAPSHOT", ""))
     return currentVer >= min
 
 def redirect_stdouterr():

From 56463f20a67fb19ecd9c5f2594d362ff814b4322 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Fri, 26 Jul 2024 15:17:00 +0100
Subject: [PATCH 006/153] split terrier out into a subpackage

---
 pyterrier/terrier/__init__.py             | 8 ++++++++
 pyterrier/{terrier.py => terrier/java.py} | 6 +++---
 setup.py                                  | 4 ++--
 3 files changed, 13 insertions(+), 5 deletions(-)
 create mode 100644 pyterrier/terrier/__init__.py
 rename pyterrier/{terrier.py => terrier/java.py} (97%)

diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
new file mode 100644
index 00000000..d91a32f5
--- /dev/null
+++ b/pyterrier/terrier/__init__.py
@@ -0,0 +1,8 @@
+# java stuff
+from pyterrier.terrier import java
+from pyterrier.terrier.java import configure, set_terrier_version, set_helper_version, enable_prf, extend_package
+
+__all__ = [
+    # java stuff
+    'java', 'configure', 'set_terrier_version', 'set_helper_version', 'enable_prf', 'extend_package',
+]
diff --git a/pyterrier/terrier.py b/pyterrier/terrier/java.py
similarity index 97%
rename from pyterrier/terrier.py
rename to pyterrier/terrier/java.py
index 0ae2c061..d5363821 100644
--- a/pyterrier/terrier.py
+++ b/pyterrier/terrier/java.py
@@ -1,7 +1,7 @@
 import sys
 from typing import Optional
 import pyterrier as pt
-from . import mavenresolver
+from pyterrier import mavenresolver
 
 TERRIER_PKG = "org.terrier"
 
@@ -43,7 +43,7 @@ def enable_prf(version: Optional[str] = None):
 
 
 
-def _java_pre_init(jnius_config):
+def _pre_init(jnius_config):
     global _resolved_helper_version
     """
     Download Terrier's jar file for the given version at the given file_path
@@ -78,7 +78,7 @@ def _java_pre_init(jnius_config):
     pt.java.add_package(TERRIER_PKG, "terrier-python-helper", helper_version)
 
 
-def _java_post_init(jnius):
+def _post_init(jnius):
     tr_version = pt.java.autoclass('org.terrier.Version')
 
     version_string = tr_version.VERSION
diff --git a/setup.py b/setup.py
index baacff19..b0b8004a 100644
--- a/setup.py
+++ b/setup.py
@@ -72,10 +72,10 @@ def get_version(rel_path):
     ],
     entry_points={
         'pyterrier.java.pre_init': [
-            'terrier = pyterrier:terrier._java_pre_init',
+            'terrier = pyterrier.terrier.java:_pre_init',
         ],
         'pyterrier.java.post_init': [
-            'terrier = pyterrier:terrier._java_post_init',
+            'terrier = pyterrier.terrier.java:_post_init',
             'index = pyterrier:index._java_post_init',
             'rewrite = pyterrier:rewrite._java_post_init',
             'legacy = pyterrier:java._legacy_post_init',

From 9dd2a37dd3a295980ba6403551cc962145c18436 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Fri, 26 Jul 2024 15:30:56 +0100
Subject: [PATCH 007/153] perhaps a nicer way to maintain a set of classes?

---
 pyterrier/java.py             | 21 ++++++++++++++++++++-
 pyterrier/terrier/__init__.py |  4 ++--
 pyterrier/terrier/java.py     |  8 +++++++-
 3 files changed, 29 insertions(+), 4 deletions(-)

diff --git a/pyterrier/java.py b/pyterrier/java.py
index 51f49248..446208e8 100644
--- a/pyterrier/java.py
+++ b/pyterrier/java.py
@@ -1,6 +1,6 @@
 from functools import wraps
 import os
-from typing import Callable, Optional
+from typing import Callable, Optional, Dict
 from pyterrier import mavenresolver
 
 import pyterrier as pt
@@ -169,3 +169,22 @@ def set_log_level(level):
     _log_level = level
     if started():
         autoclass("org.terrier.python.PTUtils").setLogLevel(level, None)
+
+
+class JavaClasses:
+    def __init__(self, mapping: Dict[str, str]):
+        self._mapping = mapping
+
+    def __dir__(self):
+        return list(self._mapping.keys())
+
+    @required(raise_on_not_started=True)
+    def __getattr__(self, key):
+        if key not in self._mapping:
+            return AttributeError(f'{self} has no attribute {key!r}')
+        return autoclass(self._mapping[key])
+
+
+J = pt.java.JavaClasses({
+    'ArrayList': 'java.util.ArrayList',
+})
diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
index d91a32f5..6232df1c 100644
--- a/pyterrier/terrier/__init__.py
+++ b/pyterrier/terrier/__init__.py
@@ -1,8 +1,8 @@
 # java stuff
 from pyterrier.terrier import java
-from pyterrier.terrier.java import configure, set_terrier_version, set_helper_version, enable_prf, extend_package
+from pyterrier.terrier.java import configure, set_terrier_version, set_helper_version, enable_prf, extend_package, J
 
 __all__ = [
     # java stuff
-    'java', 'configure', 'set_terrier_version', 'set_helper_version', 'enable_prf', 'extend_package',
+    'java', 'configure', 'set_terrier_version', 'set_helper_version', 'enable_prf', 'extend_package', 'J',
 ]
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index d5363821..d4300b89 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -97,9 +97,15 @@ def extend_package(package):
         Allows to add packages to Terrier's classpath after the JVM has started.
     """
     assert pt.check_version(5.3), "Terrier 5.3 required for this functionality"
-    package_list = pt.java.autoclass("java.util.ArrayList")()
+    package_list = pt.java.J.ArrayList()
     package_list.add(package)
     mvnr = pt.ApplicationSetup.getPlugin("MavenResolver")
     assert mvnr is not None
     mvnr = pt.java.cast("org.terrier.utility.MavenResolver", mvnr)
     mvnr.addDependencies(package_list)
+
+
+# Terrier-specific classes
+J = pt.java.JavaClasses({
+    'IndexRef': 'org.terrier.querying.IndexRef',
+})

From 1b50854451576a59270e049d455bfd88adf4ee82 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Fri, 26 Jul 2024 15:49:09 +0100
Subject: [PATCH 008/153] oops

---
 pyterrier/java.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyterrier/java.py b/pyterrier/java.py
index 446208e8..21577ed8 100644
--- a/pyterrier/java.py
+++ b/pyterrier/java.py
@@ -185,6 +185,6 @@ def __getattr__(self, key):
         return autoclass(self._mapping[key])
 
 
-J = pt.java.JavaClasses({
+J = JavaClasses({
     'ArrayList': 'java.util.ArrayList',
 })

From df3898220ed90db79b437ce0ecdb672b1f1354ad Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Fri, 26 Jul 2024 16:02:20 +0100
Subject: [PATCH 009/153] move general java stuff to its own subpackage

---
 pyterrier/__init__.py                   | 12 ++++++------
 pyterrier/{java.py => java/__init__.py} |  4 ++--
 pyterrier/{ => java}/bootstrap.py       |  1 -
 pyterrier/{ => java}/mavenresolver.py   |  0
 pyterrier/parallel.py                   |  6 ++----
 pyterrier/terrier/java.py               |  9 ++++-----
 tests/test_dunder.py                    |  2 +-
 tests/test_maven.py                     |  2 +-
 8 files changed, 16 insertions(+), 20 deletions(-)
 rename pyterrier/{java.py => java/__init__.py} (98%)
 rename pyterrier/{ => java}/bootstrap.py (99%)
 rename pyterrier/{ => java}/mavenresolver.py (100%)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index cb8fdf82..1b22e717 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -3,12 +3,14 @@
 import os
 from deprecated import deprecated
 
-from .bootstrap import setup_jnius, is_windows
+from pyterrier import utils
+from pyterrier.utils import Utils
+
+from pyterrier.java.bootstrap import setup_jnius, is_windows
 from tqdm.auto import tqdm
 
 # definitive API used by others, now available before pt.init
 from .transformer import Transformer, Estimator, Indexer
-from . import utils
 from . import java
 from . import terrier
 
@@ -29,8 +31,7 @@
 from .datasets import get_dataset, find_datasets, list_datasets
 
 from .batchretrieve import BatchRetrieve, FeaturesBatchRetrieve
-from .utils import Utils
-from .bootstrap import IndexFactory
+from pyterrier.java.bootstrap import IndexFactory
 from .index import Indexer, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
 from .pipelines import Experiment, GridScan, GridSearch, KFoldGridSearch, Evaluate
 
@@ -220,8 +221,7 @@ def redirect_stdouterr():
     """
         Ensure that stdout and stderr have been redirected. Equivalent to setting the redirect_io parameter to init() as `True`.
     """
-    from . import bootstrap
-    bootstrap.redirect_stdouterr()
+    java.bootstrap.redirect_stdouterr()
 
 
 @java.required()
diff --git a/pyterrier/java.py b/pyterrier/java/__init__.py
similarity index 98%
rename from pyterrier/java.py
rename to pyterrier/java/__init__.py
index 21577ed8..a181b4d8 100644
--- a/pyterrier/java.py
+++ b/pyterrier/java/__init__.py
@@ -1,9 +1,9 @@
 from functools import wraps
 import os
 from typing import Callable, Optional, Dict
-from pyterrier import mavenresolver
-
 import pyterrier as pt
+from pyterrier.java import bootstrap
+from pyterrier.java import mavenresolver
 
 
 _started = False
diff --git a/pyterrier/bootstrap.py b/pyterrier/java/bootstrap.py
similarity index 99%
rename from pyterrier/bootstrap.py
rename to pyterrier/java/bootstrap.py
index adbf1c99..c7a76862 100644
--- a/pyterrier/bootstrap.py
+++ b/pyterrier/java/bootstrap.py
@@ -1,4 +1,3 @@
-from . import mavenresolver
 from typing import Union, List
 
 stdout_ref = None
diff --git a/pyterrier/mavenresolver.py b/pyterrier/java/mavenresolver.py
similarity index 100%
rename from pyterrier/mavenresolver.py
rename to pyterrier/java/mavenresolver.py
diff --git a/pyterrier/parallel.py b/pyterrier/parallel.py
index 7131d310..5f589d71 100644
--- a/pyterrier/parallel.py
+++ b/pyterrier/parallel.py
@@ -41,8 +41,7 @@ def _check_ray():
 
 
 def parallel_lambda(function, inputs, jobs, backend='joblib'):
-    from .bootstrap import is_windows
-    if is_windows():
+    if pt.java.bootstrap.is_windows():
         raise ValueError("No support for parallelisation on Windows")
     if backend not in SUPPORTED_BACKENDS:
         raise ValueError("Backend of %s unknown, only %s supported." % str(SUPPORTED_BACKENDS))
@@ -71,8 +70,7 @@ def __init__(self, parent, n_jobs, backend='joblib', **kwargs):
         self.parent = parent
         self.n_jobs = n_jobs
         self.backend = backend
-        from .bootstrap import is_windows
-        if is_windows():
+        if pt.java.bootstrap.is_windows():
             raise ValueError("No support for parallelisation on Windows")
         if self.backend not in SUPPORTED_BACKENDS:
             raise ValueError("Backend of %s unknown, only %s supported." % str(SUPPORTED_BACKENDS))
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index d4300b89..e17395db 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -1,7 +1,6 @@
 import sys
 from typing import Optional
 import pyterrier as pt
-from pyterrier import mavenresolver
 
 TERRIER_PKG = "org.terrier"
 
@@ -56,7 +55,7 @@ def _pre_init(jnius_config):
     """
     # If version is not specified, find newest and download it
     if _CONFIGURATION['terrier_version'] is None:
-        terrier_version = mavenresolver.latest_version_num(TERRIER_PKG, "terrier-assemblies")
+        terrier_version = pt.java.mavenresolver.latest_version_num(TERRIER_PKG, "terrier-assemblies")
     else:
         terrier_version = str(_CONFIGURATION['terrier_version']) # just in case its a float
 
@@ -64,14 +63,14 @@ def _pre_init(jnius_config):
     # "snapshot" means use Jitpack.io to get a build of the current
     # 5.x branch from Github - see https://jitpack.io/#terrier-org/terrier-core/5.x-SNAPSHOT
     if terrier_version == "snapshot":
-        trJar = mavenresolver.downloadfile("com.github.terrier-org.terrier-core", "terrier-assemblies", "5.x-SNAPSHOT", pt.io.pyterrier_home(), "jar-with-dependencies", force_download=_CONFIGURATION['force_download'])
+        trJar = pt.java.mavenresolver.downloadfile("com.github.terrier-org.terrier-core", "terrier-assemblies", "5.x-SNAPSHOT", pt.io.pyterrier_home(), "jar-with-dependencies", force_download=_CONFIGURATION['force_download'])
     else:
-        trJar = mavenresolver.downloadfile(TERRIER_PKG, "terrier-assemblies", terrier_version, pt.io.pyterrier_home(), "jar-with-dependencies")
+        trJar = pt.java.mavenresolver.downloadfile(TERRIER_PKG, "terrier-assemblies", terrier_version, pt.io.pyterrier_home(), "jar-with-dependencies")
     pt.java.add_jar(trJar)
 
     # now the helper classes
     if _CONFIGURATION['helper_version'] is None or _CONFIGURATION['helper_version'] == 'snapshot':
-        helper_version = mavenresolver.latest_version_num(TERRIER_PKG, "terrier-python-helper")
+        helper_version = pt.java.mavenresolver.latest_version_num(TERRIER_PKG, "terrier-python-helper")
     else:
         helper_version = str(_CONFIGURATION['helper_version']) # just in case its a float
     _resolved_helper_version = helper_version
diff --git a/tests/test_dunder.py b/tests/test_dunder.py
index a35df932..634398f1 100644
--- a/tests/test_dunder.py
+++ b/tests/test_dunder.py
@@ -12,7 +12,7 @@ def test_callable_wmodel_dunders(self):
         lambdafn = lambda keyFreq, posting, entryStats, collStats: posting.getFrequency()
         callback, wmodel = _function2wmodel(lambdafn)
         
-        from pyterrier.bootstrap import javabytebuffer2array
+        from pyterrier.java.bootstrap import javabytebuffer2array
         byterep = javabytebuffer2array(wmodel.scoringClass.serializeFn())
         import dill as pickle
         from dill import extend
diff --git a/tests/test_maven.py b/tests/test_maven.py
index a7d9ecdb..776eedd5 100644
--- a/tests/test_maven.py
+++ b/tests/test_maven.py
@@ -1,5 +1,5 @@
 
-from pyterrier import mavenresolver
+from pyterrier.java import mavenresolver
 import unittest
 import shutil
 import tempfile

From c13ac38d27829c81701051d2c78b82d346b10af1 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Fri, 26 Jul 2024 16:22:31 +0100
Subject: [PATCH 010/153] move batchretrieve.py -> terrier/retriever.py

---
 pyterrier/__init__.py                         | 18 +++++----
 pyterrier/anserini.py                         |  2 +-
 pyterrier/java/bootstrap.py                   |  4 +-
 pyterrier/rewrite.py                          |  3 +-
 pyterrier/terrier/__init__.py                 |  7 ++++
 .../retriever.py}                             | 38 +++++++++----------
 pyterrier/text.py                             | 28 +++++++-------
 tests/test_dunder.py                          |  3 +-
 tests/test_operators.py                       |  3 +-
 tests/test_scoring.py                         | 11 +++---
 10 files changed, 59 insertions(+), 58 deletions(-)
 rename pyterrier/{batchretrieve.py => terrier/retriever.py} (97%)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index 1b22e717..dade1023 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -6,13 +6,18 @@
 from pyterrier import utils
 from pyterrier.utils import Utils
 
+# definitive API used by others, now available before pt.init
+from .transformer import Transformer, Estimator, Indexer
+
+from pyterrier import java
 from pyterrier.java.bootstrap import setup_jnius, is_windows
+
+from pyterrier import terrier
+from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve
+
 from tqdm.auto import tqdm
 
-# definitive API used by others, now available before pt.init
-from .transformer import Transformer, Estimator, Indexer
-from . import java
-from . import terrier
+
 
 from . import anserini
 from . import cache
@@ -30,14 +35,11 @@
 from . import transformer
 from .datasets import get_dataset, find_datasets, list_datasets
 
-from .batchretrieve import BatchRetrieve, FeaturesBatchRetrieve
+
 from pyterrier.java.bootstrap import IndexFactory
 from .index import Indexer, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
 from .pipelines import Experiment, GridScan, GridSearch, KFoldGridSearch, Evaluate
 
-from .batchretrieve import BatchRetrieve
-TerrierRetrieve = BatchRetrieve # BatchRetrieve is an alias to TerrierRetrieve
-
 import importlib
 
 
diff --git a/pyterrier/anserini.py b/pyterrier/anserini.py
index 825dbab9..1f67cfef 100644
--- a/pyterrier/anserini.py
+++ b/pyterrier/anserini.py
@@ -1,6 +1,6 @@
 from .model import coerce_queries_dataframe
 
-from .batchretrieve import BatchRetrieveBase
+from pyterrier.terrier.retriever import BatchRetrieveBase
 from warnings import warn
 import pandas as pd
 from . import tqdm
diff --git a/pyterrier/java/bootstrap.py b/pyterrier/java/bootstrap.py
index c7a76862..20a52f04 100644
--- a/pyterrier/java/bootstrap.py
+++ b/pyterrier/java/bootstrap.py
@@ -1,4 +1,5 @@
 from typing import Union, List
+import pyterrier as pt
 
 stdout_ref = None
 stderr_ref = None
@@ -141,8 +142,7 @@ def new_callable_wmodel(byterep):
     #we need to prevent these functions from being GCd.
     global SAVED_FNS
     SAVED_FNS.append(fn)
-    from .batchretrieve import _function2wmodel
-    callback, wmodel = _function2wmodel(fn)
+    callback, wmodel = pt.terrier.retriever._function2wmodel(fn)
     SAVED_FNS.append(callback)
     #print("Stored lambda fn  %s and callback in SAVED_FNS, now %d stored" % (str(fn), len(SAVED_FNS)))
     return wmodel
diff --git a/pyterrier/rewrite.py b/pyterrier/rewrite.py
index 01756f27..16219775 100644
--- a/pyterrier/rewrite.py
+++ b/pyterrier/rewrite.py
@@ -1,6 +1,5 @@
 import pyterrier as pt
 import pandas as pd
-from .batchretrieve import _parse_index_like
 from . import Transformer
 from .index import TerrierTokeniser
 from . import tqdm
@@ -212,7 +211,7 @@ def __init__(self, index_like, fb_terms=10, fb_docs=3, qeclass="org.terrier.quer
             self.qe = pt.autoclass(qeclass)()
         else:
             self.qe = qeclass
-        self.indexref = _parse_index_like(index_like)
+        self.indexref = pt.terrier.retriever._parse_index_like(index_like)
         self.properties = properties
         for k,v in properties.items():
             pt.ApplicationSetup.setProperty(k, str(v))
diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
index 6232df1c..d8ed91eb 100644
--- a/pyterrier/terrier/__init__.py
+++ b/pyterrier/terrier/__init__.py
@@ -1,8 +1,15 @@
 # java stuff
 from pyterrier.terrier import java
 from pyterrier.terrier.java import configure, set_terrier_version, set_helper_version, enable_prf, extend_package, J
+from pyterrier.terrier.retriever import BatchRetrieve, FeaturesBatchRetrieve
+
+TerrierRetrieve = BatchRetrieve # BatchRetrieve is an alias to TerrierRetrieve
+
 
 __all__ = [
     # java stuff
     'java', 'configure', 'set_terrier_version', 'set_helper_version', 'enable_prf', 'extend_package', 'J',
+
+    # retrieval
+    'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve',
 ]
diff --git a/pyterrier/batchretrieve.py b/pyterrier/terrier/retriever.py
similarity index 97%
rename from pyterrier/batchretrieve.py
rename to pyterrier/terrier/retriever.py
index d812b40c..0a0c808a 100644
--- a/pyterrier/batchretrieve.py
+++ b/pyterrier/terrier/retriever.py
@@ -1,17 +1,16 @@
 from typing import Union
 import pandas as pd
 import numpy as np
-from . import tqdm, Transformer
 from warnings import warn
-from .datasets import Dataset
-from .transformer import Symbol
-from .model import coerce_queries_dataframe, FIRST_RANK
+from pyterrier.datasets import Dataset
+from pyterrier.transformer import Symbol
+from pyterrier.model import coerce_queries_dataframe, FIRST_RANK
 import concurrent
 from concurrent.futures import ThreadPoolExecutor
 import pyterrier as pt
 
 def importProps():
-    from . import properties as props
+    from pyterrier import properties as props
     # Make import global
     globals()["props"] = props
 props = None
@@ -61,7 +60,7 @@ def _mergeDicts(defaults, settings):
 def _parse_index_like(index_location):
     JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
     JI = pt.java.autoclass('org.terrier.structures.Index')
-    from .index import TerrierIndexer
+    from pyterrier.index import TerrierIndexer
 
     if isinstance(index_location, JIR):
         return index_location
@@ -79,7 +78,7 @@ def _parse_index_like(index_location):
         or an pyterrier.index.TerrierIndexer object'''
     )
 
-class BatchRetrieveBase(Transformer, Symbol):
+class BatchRetrieveBase(pt.Transformer, Symbol):
     """
     A base class for retrieval
 
@@ -95,10 +94,10 @@ def _from_dataset(dataset : Union[str,Dataset],
             clz,
             variant : str = None, 
             version='latest',            
-            **kwargs) -> Transformer:
+            **kwargs) -> pt.Transformer:
 
-    from . import get_dataset
-    from .io import autoopen
+    from pyterrier import get_dataset
+    from pyterrier.io import autoopen
     import os
     import json
     
@@ -221,7 +220,7 @@ def __init__(self, index_location, controls=None, properties=None, metadata=["do
         
         self.controls = _mergeDicts(BatchRetrieve.default_controls, controls)
         if wmodel is not None:
-            from .transformer import is_lambda, is_function
+            from pyterrier.transformer import is_lambda, is_function
             if isinstance(wmodel, str):
                 self.controls["wmodel"] = wmodel
             elif is_lambda(wmodel) or is_function(wmodel):
@@ -427,7 +426,7 @@ def _one_row(*args, **kwargs):
                 # as these futures complete, wait and add their results
                 iter = concurrent.futures.as_completed(future_results)
                 if self.verbose:
-                    iter = tqdm(iter, desc=str(self), total=queries.shape[0], unit="q")
+                    iter = pt.tqdm(iter, desc=str(self), total=queries.shape[0], unit="q")
                 
                 for future in iter:
                     res = future.result()
@@ -435,7 +434,7 @@ def _one_row(*args, **kwargs):
         else:
             iter = queries.itertuples()
             if self.verbose:
-                iter = tqdm(iter, desc=str(self), total=queries.shape[0], unit="q")
+                iter = pt.tqdm(iter, desc=str(self), total=queries.shape[0], unit="q")
             for row in iter:
                 res = self._retrieve_one(row, input_results, docno_provided=docno_provided, docid_provided=docid_provided, scores_provided=scores_provided)
                 results.extend(res)
@@ -465,7 +464,7 @@ def setControl(self, control, value):
 
 
 
-class TextIndexProcessor(Transformer):
+class TextIndexProcessor(pt.Transformer):
     '''
         Creates a new MemoryIndex based on the contents of documents passed to it.
         It then creates a new instance of the innerclass and passes the topics to that.
@@ -488,8 +487,8 @@ def __init__(self, innerclass, takes="queries", returns="docs", body_attr="body"
         self.verbose = verbose
 
     def transform(self, topics_and_res):
-        from . import DFIndexer, IndexFactory
-        from .index import IndexingType
+        from pyterrier import DFIndexer, IndexFactory
+        from pyterrier.index import IndexingType
         documents = topics_and_res[["docno", self.body_attr]].drop_duplicates(subset="docno")
         indexref = DFIndexer(None, type=IndexingType.MEMORY, verbose=self.verbose).index(documents[self.body_attr], documents["docno"])
         docno2docid = { docno:id for id, docno in enumerate(documents["docno"]) }
@@ -714,7 +713,7 @@ def transform(self, queries):
             queries['qid'] = queries['qid'].astype(str)
 
         newscores=[]
-        for row in tqdm(queries.itertuples(), desc=str(self), total=queries.shape[0], unit="q") if self.verbose else queries.itertuples():
+        for row in pt.tqdm(queries.itertuples(), desc=str(self), total=queries.shape[0], unit="q") if self.verbose else queries.itertuples():
             qid = str(row.qid)
             query = row.query
             if len(query) == 0:
@@ -798,9 +797,8 @@ def __str__(self):
 rewrites_setup = False
 
 def setup_rewrites():
-    from .batchretrieve import BatchRetrieve, FeaturesBatchRetrieve
-    from .transformer import rewrite_rules
-    from .ops import FeatureUnionPipeline, ComposedPipeline
+    from pyterrier.transformer import rewrite_rules
+    from pyterrier.ops import FeatureUnionPipeline, ComposedPipeline
     from matchpy import ReplacementRule, Wildcard, Pattern, CustomConstraint
     #three arbitrary "things".
     x = Wildcard.dot('x')
diff --git a/pyterrier/text.py b/pyterrier/text.py
index 51a2dbce..5704b6f5 100644
--- a/pyterrier/text.py
+++ b/pyterrier/text.py
@@ -1,5 +1,4 @@
-from pyterrier.transformer import Transformer
-from . import Transformer
+import pyterrier as pt
 from pyterrier.datasets import IRDSDataset
 import more_itertools
 from collections import defaultdict
@@ -15,7 +14,7 @@ def get_text(
         indexlike, 
         metadata : Union[str,List[str]] = "body", 
         by_query : bool = False,
-        verbose : bool = False) -> Transformer:
+        verbose : bool = False) -> pt.Transformer:
     """
     A utility transformer for obtaining the text from the text of documents (or other document metadata) from Terrier's MetaIndex
     or an IRDSDataset docstore.
@@ -129,7 +128,7 @@ def add_text_function_docids(res):
     return add_text_function_docids
 
 
-def scorer(*args, **kwargs) -> Transformer:
+def scorer(*args, **kwargs) -> pt.Transformer:
     """
     This allows scoring of the documents with respect to a query, without creating an index first. 
     This is an alias to pt.TextScorer(). Internally, a Terrier memory index is created, before being
@@ -163,10 +162,9 @@ def scorer(*args, **kwargs) -> Transformer:
         textscorerTfIdf = pt.text.scorer(body_attr="text", wmodel="TF_IDF", background_index=index)
 
     """
-    import pyterrier as pt
-    return pt.batchretrieve.TextScorer(*args, **kwargs)
+    return pt.terrier.retriever.TextScorer(*args, **kwargs)
 
-def sliding( text_attr='body', length=150, stride=75, join=' ', prepend_attr='title', tokenizer=None, **kwargs) -> Transformer:
+def sliding( text_attr='body', length=150, stride=75, join=' ', prepend_attr='title', tokenizer=None, **kwargs) -> pt.Transformer:
     r"""
     A useful transformer for splitting long documents into smaller passages within a pipeline. This applies a *sliding* window over the
     text, where each passage is the give number of tokens long. Passages can overlap, if the stride is set smaller than the length. In
@@ -235,28 +233,28 @@ def sliding( text_attr='body', length=150, stride=75, join=' ', prepend_attr='ti
         **kwargs
     )
 
-def max_passage() -> Transformer:
+def max_passage() -> pt.Transformer:
     """
     Scores each document based on the maximum score of any constituent passage. Applied after a sliding window transformation
     has been scored.
     """
     return MaxPassage()
 
-def mean_passage() -> Transformer:
+def mean_passage() -> pt.Transformer:
     """
     Scores each document based on the mean score of all constituent passages. Applied after a sliding window transformation
     has been scored.
     """
     return MeanPassage()
 
-def first_passage() -> Transformer:
+def first_passage() -> pt.Transformer:
     """
     Scores each document based on score of the first passage of that document. Note that this transformer is rarely used in conjunction with
     the sliding window transformer, as all passages would required to be scored, only for the first one to be used.
     """
     return FirstPassage()
 
-def kmaxavg_passage(k : int) -> Transformer:
+def kmaxavg_passage(k : int) -> pt.Transformer:
     """
     Scores each document based on the average score of the top scoring k passages. Generalises combination of mean_passage()
     and max_passage(). Proposed in [Chen2020].
@@ -275,11 +273,11 @@ def slidingWindow(sequence : list, winSize : int, step : int) -> list:
     return [x for x in list(more_itertools.windowed(sequence,n=winSize, step=step)) if x[-1] is not None]
 
 def snippets(
-        text_scorer_pipe : Transformer, 
+        text_scorer_pipe : pt.Transformer, 
         text_attr : str = "text", 
         summary_attr : str = "summary", 
         num_psgs : int = 5, 
-        joinstr : str ='...') -> Transformer:
+        joinstr : str ='...') -> pt.Transformer:
     """
     Applies query-biased summarisation (snippet), by applying the specified text scoring pipeline.
 
@@ -335,7 +333,7 @@ def _qbsjoin(docres):
     return pt.apply.generic(_qbsjoin)   
 
 
-class DePassager(Transformer):
+class DePassager(pt.Transformer):
 
     def __init__(self, agg="max", **kwargs):
         super().__init__(**kwargs)
@@ -405,7 +403,7 @@ def __init__(self, **kwargs):
         super().__init__(**kwargs)
 
 
-class SlidingWindowPassager(Transformer):
+class SlidingWindowPassager(pt.Transformer):
 
     def __init__(self, text_attr='body', title_attr='title', passage_length=150, passage_stride=75, join=' ', prepend_title=True, tokenizer=None, **kwargs):
         super().__init__(**kwargs)
diff --git a/tests/test_dunder.py b/tests/test_dunder.py
index 634398f1..44966fd1 100644
--- a/tests/test_dunder.py
+++ b/tests/test_dunder.py
@@ -8,9 +8,8 @@ class TestDunder(TempDirTestCase):
     def test_callable_wmodel_dunders(self):
         testPosting = pt.autoclass("org.terrier.structures.postings.BasicPostingImpl")(0,1)
 
-        from pyterrier.batchretrieve import _function2wmodel
         lambdafn = lambda keyFreq, posting, entryStats, collStats: posting.getFrequency()
-        callback, wmodel = _function2wmodel(lambdafn)
+        callback, wmodel = pt.terrier.retriever._function2wmodel(lambdafn)
         
         from pyterrier.java.bootstrap import javabytebuffer2array
         byterep = javabytebuffer2array(wmodel.scoringClass.serializeFn())
diff --git a/tests/test_operators.py b/tests/test_operators.py
index df82b5b7..810fd8b0 100644
--- a/tests/test_operators.py
+++ b/tests/test_operators.py
@@ -292,7 +292,6 @@ def _check(expression):
 
     def test_feature_union_multi(self):
         import pyterrier.ops as pto
-        import pyterrier.batchretrieve
         mock0 = pt.Transformer.from_df(pd.DataFrame([["q1", "doc1", 0], ["q1", "doc2", 0]], columns=["qid", "docno", "score"]), uniform=True)
 
         mock1 = pt.Transformer.from_df(pd.DataFrame([["q1", "doc1", 5], ["q1", "doc2", 0]], columns=["qid", "docno", "score"]), uniform=True)
@@ -338,7 +337,7 @@ def test_feature_union_multi(self):
         
         self.assertEqual(2, len(mock12a.models))
         self.assertEqual(2, len(mock12a.models))
-        pyterrier.batchretrieve.setup_rewrites()
+        pt.terrier.retriever.setup_rewrites()
 
         mock123_simple = mock123a.compile()
         self.assertIsNotNone(mock123_simple)
diff --git a/tests/test_scoring.py b/tests/test_scoring.py
index ea1aecb1..e7d62390 100644
--- a/tests/test_scoring.py
+++ b/tests/test_scoring.py
@@ -39,7 +39,7 @@ def _test_scoring_text(self, dataset, index, wmodel):
         output1 = br1(input)
         self.assertTrue( "body" in output1.columns )
         input2 = output1[["qid", "query", "docno", "body"]]
-        br2 = pt.batchretrieve.TextScorer(background_index=index, wmodel=wmodel, verbose=True)
+        br2 = pt.terrier.retriever.TextScorer(background_index=index, wmodel=wmodel, verbose=True)
         output2 = br2(input2)
         self.assertTrue( "score" in output2.columns )
 
@@ -52,7 +52,7 @@ def _test_scoring_text(self, dataset, index, wmodel):
 
     def test_scoring_manual_empty(self):
         input = pd.DataFrame([["q1", "fox", "d1", ""]], columns=["qid", "query", "docno", "body"])
-        from pyterrier.batchretrieve import TextScorer
+        from pyterrier.terrier.retriever import TextScorer
         scorer = TextScorer(wmodel="Tf")
         rtr = scorer(input)
         self.assertEqual(1, len(rtr))
@@ -62,7 +62,7 @@ def test_scoring_manual_empty(self):
 
     def test_scoring_manual(self):
         input = pd.DataFrame([["q1", "fox", "d1", "all the fox were fox"]], columns=["qid", "query", "docno", "body"])
-        from pyterrier.batchretrieve import TextScorer
+        from pyterrier.terrier.retriever import TextScorer
         scorer = TextScorer(wmodel="Tf")
         rtr = scorer(input)
         self.assertEqual(1, len(rtr))
@@ -78,7 +78,7 @@ def test_scoring_manual(self):
 
     def test_scoring_manual_background(self):
         input = pd.DataFrame([["q1", "fox", "d1", "all the fox were fox"]], columns=["qid", "query", "docno", "body"])
-        from pyterrier.batchretrieve import TextScorer
+        from pyterrier.terrier.retriever import TextScorer
         scorer = TextScorer(wmodel="Tf", background_index=pt.get_dataset("vaswani").get_index())
         rtr = scorer(input)
         self.assertEqual(1, len(rtr))
@@ -94,8 +94,7 @@ def test_scoring_manual_background(self):
         
     def test_scoring_qe(self):
         input = pd.DataFrame([["q1", "fox", "d1", "all the fox were fox"]], columns=["qid", "query", "docno", "body"])
-        from pyterrier.batchretrieve import TextScorer
-        scorer = pt.batchretrieve.TextIndexProcessor(pt.rewrite.Bo1QueryExpansion, takes="docs", returns="queries")
+        scorer = pt.terrier.retriever.TextIndexProcessor(pt.rewrite.Bo1QueryExpansion, takes="docs", returns="queries")
         rtr = scorer(input)
         self.assertTrue("qid" in rtr.columns)
         self.assertTrue("query" in rtr.columns)

From a9aa472fa1d71c5f79477b05f0aef9d831c9f77a Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Fri, 26 Jul 2024 16:23:30 +0100
Subject: [PATCH 011/153] should fix broken build

---
 pyterrier/java/__init__.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index a181b4d8..99dc4bbc 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -49,6 +49,7 @@ def init() -> None:
     import jnius
     _started = True
 
+    bootstrap.setup_jnius()
     set_log_level(_log_level)
 
     java_version = autoclass("java.lang.System").getProperty("java.version")

From 44a8fab7465ae82f9be376588990c52ea9a209e7 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Fri, 26 Jul 2024 16:30:21 +0100
Subject: [PATCH 012/153] wip

---
 pyterrier/java/bootstrap.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/pyterrier/java/bootstrap.py b/pyterrier/java/bootstrap.py
index 20a52f04..661ef899 100644
--- a/pyterrier/java/bootstrap.py
+++ b/pyterrier/java/bootstrap.py
@@ -125,8 +125,7 @@ def of(indexlike, memory : Union[bool, List[str]] = False):
 
 
 def new_indexref(s):
-    from . import IndexRef
-    return IndexRef.of(s)
+    return pt.IndexRef.of(s)
 
 def new_wmodel(bytes):
     from . import autoclass

From ecef7530f3c1c0e7adc660168a6b32aca5b610b1 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 27 Jul 2024 12:20:49 +0100
Subject: [PATCH 013/153] fix some tests

---
 pyterrier/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index dade1023..eff1b848 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -212,7 +212,7 @@ def check_version(min, helper=False):
     """
     from packaging.version import Version
     from . import terrier
-    currentVer = terrier._resolved_helper_version if helper else version()
+    currentVer = terrier.java._resolved_helper_version if helper else version()
     assert currentVer is not None, "Could not obtain Terrier version (helpher=%s)" % str(helper)
     currentVer = Version(currentVer.replace("-SNAPSHOT", ""))
 

From 0a22452510e6fb41e6dd7eba2f019a38c66838d8 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 27 Jul 2024 12:26:24 +0100
Subject: [PATCH 014/153] fix more tests

---
 pyterrier/java/__init__.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index 99dc4bbc..7a644398 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -13,6 +13,7 @@ def _legacy_post_init(jnius):
     pt.HOME_DIR = pt.io.pyterrier_home()
     pt.properties = autoclass('java.util.Properties')()
     pt.ApplicationSetup = autoclass('org.terrier.utility.ApplicationSetup')
+    pt.ApplicationSetup.bootstrapInitialisation(pt.properties)
     pt.autoclass = jnius.autoclass
     pt.cast = jnius.cast
 

From 7e020d5c80530042ac06e063a31413fca9899968 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 27 Jul 2024 12:39:21 +0100
Subject: [PATCH 015/153] wip

---
 pyterrier/__init__.py      |  2 +-
 pyterrier/datasets.py      |  4 +---
 pyterrier/java/__init__.py | 14 ++++++++++----
 pyterrier/terrier/java.py  | 12 ++++++------
 setup.py                   |  2 +-
 5 files changed, 19 insertions(+), 15 deletions(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index eff1b848..95135f13 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -204,7 +204,7 @@ def version():
     """
         Returns the version string from the underlying Terrier platform.
     """
-    return java.autoclass("org.terrier.Version").VERSION
+    return terrier.J.Version.VERSION
 
 def check_version(min, helper=False):
     """
diff --git a/pyterrier/datasets.py b/pyterrier/datasets.py
index a8d3997a..0d87f574 100644
--- a/pyterrier/datasets.py
+++ b/pyterrier/datasets.py
@@ -371,7 +371,6 @@ def get_index(self, variant=None, **kwargs):
             variant="ex1"
         thedir = self._get_all_files("index", variant=variant, **kwargs)
         return thedir
-        #return pt.autoclass("org.terrier.querying.IndexRef").of(os.path.join(thedir, "data.properties"))
 
     def __repr__(self):
         return "RemoteDataset for %s, with %s" % (self.name, str(list(self.locations.keys())))
@@ -462,8 +461,7 @@ def get_topics(self, variant=None, tokenise_query=True):
 
         # apply pyterrier tokenisation (otherwise the queries may not play well with batchretrieve)
         if tokenise_query and 'query' in df:
-            import pyterrier as pt
-            tokeniser = pt.autoclass("org.terrier.indexing.tokenisation.Tokeniser").getTokeniser()
+            tokeniser = pt.terrier.J.Tokenizer.getTokeniser()
             def pt_tokenise(text):
                 return ' '.join(tokeniser.getTokens(text))
             df['query'] = df['query'].apply(pt_tokenise)
diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index 7a644398..0e3cf748 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -11,8 +11,8 @@
 
 def _legacy_post_init(jnius):
     pt.HOME_DIR = pt.io.pyterrier_home()
-    pt.properties = autoclass('java.util.Properties')()
-    pt.ApplicationSetup = autoclass('org.terrier.utility.ApplicationSetup')
+    pt.properties = J.Properties()
+    pt.ApplicationSetup = J.ApplicationSetup
     pt.ApplicationSetup.bootstrapInitialisation(pt.properties)
     pt.autoclass = jnius.autoclass
     pt.cast = jnius.cast
@@ -170,12 +170,13 @@ def set_log_level(level):
     global _log_level
     _log_level = level
     if started():
-        autoclass("org.terrier.python.PTUtils").setLogLevel(level, None)
+        J.PTUtils.setLogLevel(level, None)
 
 
 class JavaClasses:
     def __init__(self, mapping: Dict[str, str]):
         self._mapping = mapping
+        self._cache = {}
 
     def __dir__(self):
         return list(self._mapping.keys())
@@ -184,9 +185,14 @@ def __dir__(self):
     def __getattr__(self, key):
         if key not in self._mapping:
             return AttributeError(f'{self} has no attribute {key!r}')
-        return autoclass(self._mapping[key])
+        if key not in self._cache:
+            self._cache[key] = autoclass(self._mapping[key])
+        return self._cache[key]
 
 
 J = JavaClasses({
     'ArrayList': 'java.util.ArrayList',
+    'Properties': 'java.util.Properties',
+    'ApplicationSetup': 'org.terrier.utility.ApplicationSetup',
+    'PTUtils': 'org.terrier.python.PTUtils',
 })
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index e17395db..80bf4569 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -78,16 +78,14 @@ def _pre_init(jnius_config):
 
 
 def _post_init(jnius):
-    tr_version = pt.java.autoclass('org.terrier.Version')
-
-    version_string = tr_version.VERSION
-    if "BUILD_DATE" in dir(tr_version):
-        version_string += f" (built by {tr_version.BUILD_USER} on {tr_version.BUILD_DATE})"
+    version_string = J.Version.VERSION
+    if "BUILD_DATE" in dir(J.Version):
+        version_string += f" (built by {J.Version.BUILD_USER} on {J.Version.BUILD_DATE})"
 
     print(f"PyTerrier {pt.__version__} has loaded Terrier {version_string} and "
           f"terrier-helper {_resolved_helper_version}\n", file=sys.stderr)
 
-    pt.IndexRef = pt.java.autoclass("org.terrier.querying.IndexRef")
+    pt.IndexRef = J.IndexRef
 
 
 @pt.java.required()
@@ -107,4 +105,6 @@ def extend_package(package):
 # Terrier-specific classes
 J = pt.java.JavaClasses({
     'IndexRef': 'org.terrier.querying.IndexRef',
+    'Version': 'org.terrier.Version',
+    'Tokenizer': 'org.terrier.indexing.tokenisation.Tokeniser',
 })
diff --git a/setup.py b/setup.py
index b0b8004a..a9b83ee2 100644
--- a/setup.py
+++ b/setup.py
@@ -75,10 +75,10 @@ def get_version(rel_path):
             'terrier = pyterrier.terrier.java:_pre_init',
         ],
         'pyterrier.java.post_init': [
+            'legacy = pyterrier.java:_legacy_post_init',
             'terrier = pyterrier.terrier.java:_post_init',
             'index = pyterrier:index._java_post_init',
             'rewrite = pyterrier:rewrite._java_post_init',
-            'legacy = pyterrier:java._legacy_post_init',
         ],
     },
     install_requires=requirements,

From 13b020ebbddcd10bca04f052f805a60b852ec67b Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 27 Jul 2024 13:49:55 +0100
Subject: [PATCH 016/153] new global place for java configurations, allowing
 them to be passed to new processes easier

---
 pyterrier/__init__.py           | 12 ----------
 pyterrier/java/__init__.py      | 41 ++++++++++++++++++---------------
 pyterrier/java/config.py        | 33 ++++++++++++++++++++++++++
 pyterrier/parallel.py           | 18 +++++++++++----
 pyterrier/terrier/java.py       | 33 +++++++++-----------------
 setup.py                        | 10 ++++----
 tests/anserini/test_anserini.py |  2 --
 tests/base.py                   |  4 ----
 tests/test_pool.py              | 17 +++++++-------
 9 files changed, 94 insertions(+), 76 deletions(-)
 create mode 100644 pyterrier/java/config.py

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index 95135f13..c81e136f 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -54,7 +54,6 @@
 HOME_DIR = None
 
 # TODO
-init_args = {}
 _helper_version = None
 
 
@@ -138,17 +137,6 @@ def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, log
         redirect_stdouterr()
 
     setup_jnius()
-    
-    # we save the pt.init() arguments so that other processes,
-    # started by joblib or ray can booted with same options
-    init_args["version"] = version
-    init_args["mem"] = mem
-    init_args["packages"] = packages
-    init_args["jvm_opts"] = jvm_opts
-    init_args["redirect_io"] = redirect_io
-    init_args["home_dir"] = home_dir
-    init_args["boot_packages"] = boot_packages
-    init_args["tqdm"] = tqdm
 
 
 def set_tqdm(type):
diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index 0e3cf748..8c3887d9 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -4,6 +4,8 @@
 import pyterrier as pt
 from pyterrier.java import bootstrap
 from pyterrier.java import mavenresolver
+from pyterrier.java import config
+from pyterrier.java.config import configure
 
 
 _started = False
@@ -38,22 +40,24 @@ def init() -> None:
         _pre_init = entry_point.load()
         _pre_init(jnius_config)
 
-    if _mem is not None:
-        jnius_config.add_options('-Xmx' + str(_mem) + 'm')
+    cfg = configure()
 
-    for opt in _options:
+    if cfg['mem'] is not None:
+        jnius_config.add_options('-Xmx' + str(cfg['mem']) + 'm')
+
+    for opt in cfg['options']:
         jnius_config.add_options(opt)
 
-    for jar in _jars:
+    for jar in cfg['jars']:
         jnius_config.add_classpath(jar)
 
     import jnius
     _started = True
 
     bootstrap.setup_jnius()
-    set_log_level(_log_level)
+    set_log_level(cfg['log_level'])
 
-    java_version = autoclass("java.lang.System").getProperty("java.version")
+    java_version = J.System.getProperty("java.version")
     if java_version.startswith("1.") or java_version.startswith("9."):
         raise RuntimeError(f"Pyterrier requires Java 11 or newer, we only found Java version {java_version};"
             + " install a more recent Java, or change os.environ['JAVA_HOME'] to point to the proper Java installation")
@@ -123,17 +127,14 @@ def cast(*args, **kwargs):
     return jnius.cast(*args, **kwargs)
 
 
-# Settings
-
+# config
 
-_jars = []
-_options = []
-_mem = None
-_log_level = 'WARN'
 
 @before_init()
 def add_jar(jar_path):
-    _jars.append(jar_path)
+    jars = configure()['jars']
+    jars.append(jar_path)
+    configure(jars=jars)
 
 
 @before_init()
@@ -146,13 +147,14 @@ def add_package(org_name: str = None, package_name: str = None, version: str = N
 
 @before_init()
 def set_memory_limit(mem: Optional[float]):
-    global _mem
-    _mem = mem
+    configure(mem=mem)
 
 
 @before_init()
 def add_option(option: str):
-    _options.append(option)
+    options = configure()['options']
+    options.append(option)
+    configure(options=options)
 
 
 def set_log_level(level):
@@ -167,9 +169,9 @@ def set_log_level(level):
         
         Unlike other java settings, this can be changed either before or after init() has been called.
     """
-    global _log_level
-    _log_level = level
-    if started():
+    if not started():
+        configure(log_level=level)
+    else:
         J.PTUtils.setLogLevel(level, None)
 
 
@@ -195,4 +197,5 @@ def __getattr__(self, key):
     'Properties': 'java.util.Properties',
     'ApplicationSetup': 'org.terrier.utility.ApplicationSetup',
     'PTUtils': 'org.terrier.python.PTUtils',
+    'System': 'java.lang.System',
 })
diff --git a/pyterrier/java/config.py b/pyterrier/java/config.py
new file mode 100644
index 00000000..8e8b7e61
--- /dev/null
+++ b/pyterrier/java/config.py
@@ -0,0 +1,33 @@
+from typing import Dict, Any
+from copy import deepcopy
+import pyterrier.java
+
+_CONFIGS = {}
+
+def register(name, config: Dict[str, Any]):
+    assert name not in _CONFIGS
+    _CONFIGS[name] = deepcopy(config)
+
+    @pyterrier.java.before_init()
+    def _configure(**settings: Any):
+        for key, value in settings.items():
+            if key not in _CONFIGS[name]:
+                raise AttributeError(f'{key!r} not defined as a java setting for {name!r}')
+            _CONFIGS[name][name] = value
+        return deepcopy(_CONFIGS[name])
+
+    return _configure
+
+configure = register('pyterrier.java', {
+    'jars': [],
+    'options': [],
+    'mem': None,
+    'log_level': 'WARN',
+})
+
+def get_configs():
+    return deepcopy(_CONFIGS)
+
+def set_configs(configs):
+    for key, value in configs:
+        _CONFIGS[key] = value
diff --git a/pyterrier/parallel.py b/pyterrier/parallel.py
index 5f589d71..d2e5566b 100644
--- a/pyterrier/parallel.py
+++ b/pyterrier/parallel.py
@@ -1,6 +1,7 @@
 from . import Transformer
 import pandas as pd
 import pyterrier as pt
+from warnings import warn
 
 SUPPORTED_BACKENDS=["joblib", "ray"]
 
@@ -31,6 +32,15 @@ def _pt_init(args):
         from warnings import warn
         warn("Avoiding reinit of PyTerrier")
 
+
+def _java_init_with_configs(configs) -> None:
+    if not pt.java.started():
+        pt.java.config.set_configs(configs)
+        pt.java.init()
+    else:
+        warn("Avoiding reinit of PyTerrier")
+
+
 def _check_ray():
     try:
         import ray
@@ -52,13 +62,13 @@ def parallel_lambda(function, inputs, jobs, backend='joblib'):
 
 def _parallel_lambda_ray(function, inputs, jobs):
     from ray.util.multiprocessing import Pool
-    with Pool(jobs, lambda args: pt.init(**args), pt.init_args) as pool:
+    with Pool(jobs, _java_init_with_configs, pt.java.config.get_configs()) as pool:
         return pool.map(function, inputs)
 
 def _parallel_lambda_joblib(function, inputs, jobs):
     from joblib import Parallel, delayed
     with Parallel(n_jobs=jobs) as parallel:
-        parallel_mp = _joblib_with_initializer(parallel, _pt_init, pt.init_args)
+        parallel_mp = _joblib_with_initializer(parallel, _java_init_with_configs, pt.java.config.get_configs())
         return parallel_mp(
             delayed(function)(input) for input in inputs)
         
@@ -80,12 +90,12 @@ def __init__(self, parent, n_jobs, backend='joblib', **kwargs):
     def _transform_joblib(self, splits):
         from joblib import Parallel, delayed
         with Parallel(n_jobs=self.n_jobs) as parallel:
-            results = _joblib_with_initializer(parallel, _pt_init, pt.init_args)(delayed(self.parent)(topics) for topics in splits)
+            results = _joblib_with_initializer(parallel, _java_init_with_configs, pt.java.config.get_configs())(delayed(self.parent)(topics) for topics in splits)
             return pd.concat(results)
         
     def _transform_ray(self, splits):
         from ray.util.multiprocessing import Pool
-        with Pool(self.n_jobs, _pt_init, pt.init_args) as pool:
+        with Pool(self.n_jobs, _java_init_with_configs, pt.java.config.get_configs()) as pool:
             results = pool.map(lambda topics : self.parent(topics), splits)
             return pd.concat(results)
 
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 80bf4569..b20b248e 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -4,36 +4,24 @@
 
 TERRIER_PKG = "org.terrier"
 
+_resolved_helper_version = None
 
-_CONFIGURATION = {
+configure = pt.java.config.register('pt.terrier.java', {
     'terrier_version': None,
     'helper_version': None,
     'boot_packages': [],
     'force_download': True,
-    'prf_version': None,
-}
-
-_resolved_helper_version = None
-
-
-@pt.java.before_init()
-def configure(terrier_version=None, helper_version=None, boot_packages=[], force_download=True):
-    _CONFIGURATION.update({
-        'terrier_version': terrier_version,
-        'helper_version': helper_version,
-        'boot_packages': boot_packages,
-        'force_download': force_download,
-    })
+})
 
 
 @pt.java.before_init()
 def set_terrier_version(version: Optional[str] = None):
-    _CONFIGURATION['terrier_version'] = version
+    configure(terrier_version=version)
 
 
 @pt.java.before_init()
 def set_helper_version(version: Optional[str] = None):
-    _CONFIGURATION['helper_version'] = version
+    configure(helper_version=version)
 
 
 @pt.java.before_init()
@@ -54,25 +42,26 @@ def _pre_init(jnius_config):
         helper_version(str): Which version of the helper - None is latest
     """
     # If version is not specified, find newest and download it
-    if _CONFIGURATION['terrier_version'] is None:
+    cfg = configure()
+    if cfg['terrier_version'] is None:
         terrier_version = pt.java.mavenresolver.latest_version_num(TERRIER_PKG, "terrier-assemblies")
     else:
-        terrier_version = str(_CONFIGURATION['terrier_version']) # just in case its a float
+        terrier_version = str(cfg['terrier_version']) # just in case its a float
 
     # obtain the fat jar from Maven
     # "snapshot" means use Jitpack.io to get a build of the current
     # 5.x branch from Github - see https://jitpack.io/#terrier-org/terrier-core/5.x-SNAPSHOT
     if terrier_version == "snapshot":
-        trJar = pt.java.mavenresolver.downloadfile("com.github.terrier-org.terrier-core", "terrier-assemblies", "5.x-SNAPSHOT", pt.io.pyterrier_home(), "jar-with-dependencies", force_download=_CONFIGURATION['force_download'])
+        trJar = pt.java.mavenresolver.downloadfile("com.github.terrier-org.terrier-core", "terrier-assemblies", "5.x-SNAPSHOT", pt.io.pyterrier_home(), "jar-with-dependencies", force_download=cfg['force_download'])
     else:
         trJar = pt.java.mavenresolver.downloadfile(TERRIER_PKG, "terrier-assemblies", terrier_version, pt.io.pyterrier_home(), "jar-with-dependencies")
     pt.java.add_jar(trJar)
 
     # now the helper classes
-    if _CONFIGURATION['helper_version'] is None or _CONFIGURATION['helper_version'] == 'snapshot':
+    if cfg['helper_version'] is None or cfg['helper_version'] == 'snapshot':
         helper_version = pt.java.mavenresolver.latest_version_num(TERRIER_PKG, "terrier-python-helper")
     else:
-        helper_version = str(_CONFIGURATION['helper_version']) # just in case its a float
+        helper_version = str(cfg['helper_version']) # just in case its a float
     _resolved_helper_version = helper_version
     pt.java.add_package(TERRIER_PKG, "terrier-python-helper", helper_version)
 
diff --git a/setup.py b/setup.py
index a9b83ee2..a66df5fe 100644
--- a/setup.py
+++ b/setup.py
@@ -72,13 +72,13 @@ def get_version(rel_path):
     ],
     entry_points={
         'pyterrier.java.pre_init': [
-            'terrier = pyterrier.terrier.java:_pre_init',
+            'terrier = pyterrier.terrier.java : _pre_init',
         ],
         'pyterrier.java.post_init': [
-            'legacy = pyterrier.java:_legacy_post_init',
-            'terrier = pyterrier.terrier.java:_post_init',
-            'index = pyterrier:index._java_post_init',
-            'rewrite = pyterrier:rewrite._java_post_init',
+            'legacy =  pyterrier.java : _legacy_post_init',
+            'terrier = pyterrier.terrier.java : _post_init',
+            'index =   pyterrier.index : _java_post_init',
+            'rewrite = pyterrier.rewrite : _java_post_init',
         ],
     },
     install_requires=requirements,
diff --git a/tests/anserini/test_anserini.py b/tests/anserini/test_anserini.py
index 2434673c..c0df9870 100644
--- a/tests/anserini/test_anserini.py
+++ b/tests/anserini/test_anserini.py
@@ -24,8 +24,6 @@ def __init__(self, *args, **kwargs):
         if not pt.started():
             pt.init(version=terrier_version, logging="DEBUG", boot_packages=["io.anserini:anserini:%s:fatjar" % anserini_version])
         self.here = os.path.dirname(os.path.realpath(__file__))
-        assert "version" in pt.init_args
-        assert pt.init_args["version"] == terrier_version
 
     def test_anserini_vaswani(self):
         self.skip_pyserini()
diff --git a/tests/base.py b/tests/base.py
index 501cd234..4aadb9c8 100644
--- a/tests/base.py
+++ b/tests/base.py
@@ -23,10 +23,6 @@ def __init__(self, *args, **kwargs):
             # jvm_opts=['-ea'] can be added here to ensure that all Java assertions are met
         self.here = os.path.dirname(os.path.realpath(__file__))
 
-        # check that pt.init() is saving its arguments
-        assert "version" in pt.init_args
-        assert pt.init_args["version"] == terrier_version
-
 
     def skip_windows(self):
         if BaseTestCase.is_windows():
diff --git a/tests/test_pool.py b/tests/test_pool.py
index ceac6913..5493b7b3 100644
--- a/tests/test_pool.py
+++ b/tests/test_pool.py
@@ -38,31 +38,32 @@ def test_br_multiprocess(self):
         # multiprocess uses dill instead of pickle to serialize Python objects. https://jstaf.github.io/hpc-python/parallel/
         from multiprocess import Pool
 
-        def starter(**initargs): 
-            if not pt.started():
+        def starter(configs): 
+            if not pt.java.started():
                 print("pt booted")
-                pt.init(*initargs)
+                pt.java.config.set_configs(configs)
+                pt.java.init()
         
-        with Pool(None, starter, pt.init_args, 1) as pool:
+        with Pool(None, starter, pt.java.config.get_configs(), 1) as pool:
             for res in pool.map(lambda topics : br(topics), [t, t, t]):
                 pd.testing.assert_frame_equal(res1, res)
 
     def test_br_ray(self):
-        from pyterrier.parallel import _pt_init
+        from pyterrier.parallel import _java_init_with_configs
         self.skipTest("disabling ray")
         vaswani = pt.datasets.get_dataset("vaswani")
         br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
         t = vaswani.get_topics().head()
         res1 = br(t).sort_values(["qid", "docno"])
         from ray.util.multiprocessing import Pool
-        with Pool(4, lambda: _pt_init(pt.init_args)) as pool:
+        with Pool(4, lambda configs: _java_init_with_configs(configs), pt.java.get_configs()) as pool:
             for res in pool.map(lambda topics : br(topics), [t, t, t]):
                 res = res.sort_values(["qid", "docno"])
                 pd.testing.assert_frame_equal(res1, res)
 
     def test_br_joblib(self):
         self.skip_windows()
-        from pyterrier.parallel import _joblib_with_initializer, _pt_init
+        from pyterrier.parallel import _joblib_with_initializer, _java_init_with_configs
 
         vaswani = pt.datasets.get_dataset("vaswani")
         br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
@@ -71,7 +72,7 @@ def test_br_joblib(self):
 
         from joblib import Parallel, delayed
         with Parallel(n_jobs=2) as parallel:
-            results = _joblib_with_initializer(parallel, lambda: _pt_init(pt.init_args))(delayed(br)(topics) for topics in [t,t,t])
+            results = _joblib_with_initializer(parallel, lambda configs: _java_init_with_configs(configs), pt.java.config.get_configs())(delayed(br)(topics) for topics in [t,t,t])
             self.assertTrue(3, len(results))
             for res in results:
                 res = res.sort_values(["qid", "docno"])

From af930ce850817a404ac25e551f5caccc03ff0525 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 27 Jul 2024 13:55:24 +0100
Subject: [PATCH 017/153] fixing a few config errors

---
 pyterrier/java/__init__.py | 5 ++---
 pyterrier/java/config.py   | 6 +++---
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index 8c3887d9..3f5a43fd 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -4,9 +4,6 @@
 import pyterrier as pt
 from pyterrier.java import bootstrap
 from pyterrier.java import mavenresolver
-from pyterrier.java import config
-from pyterrier.java.config import configure
-
 
 _started = False
 
@@ -128,6 +125,8 @@ def cast(*args, **kwargs):
 
 
 # config
+from pyterrier.java import config
+from pyterrier.java.config import configure
 
 
 @before_init()
diff --git a/pyterrier/java/config.py b/pyterrier/java/config.py
index 8e8b7e61..06abe785 100644
--- a/pyterrier/java/config.py
+++ b/pyterrier/java/config.py
@@ -1,6 +1,6 @@
 from typing import Dict, Any
 from copy import deepcopy
-import pyterrier.java
+from pyterrier import java
 
 _CONFIGS = {}
 
@@ -8,12 +8,12 @@ def register(name, config: Dict[str, Any]):
     assert name not in _CONFIGS
     _CONFIGS[name] = deepcopy(config)
 
-    @pyterrier.java.before_init()
+    @java.before_init()
     def _configure(**settings: Any):
         for key, value in settings.items():
             if key not in _CONFIGS[name]:
                 raise AttributeError(f'{key!r} not defined as a java setting for {name!r}')
-            _CONFIGS[name][name] = value
+            _CONFIGS[name][key] = value
         return deepcopy(_CONFIGS[name])
 
     return _configure

From 24ef0eed6e988a5c5dab32fb7e0a0310ed48f3d2 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 27 Jul 2024 19:31:03 +0100
Subject: [PATCH 018/153] wip

---
 pyterrier/java/config.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyterrier/java/config.py b/pyterrier/java/config.py
index 06abe785..1f679979 100644
--- a/pyterrier/java/config.py
+++ b/pyterrier/java/config.py
@@ -28,6 +28,6 @@ def _configure(**settings: Any):
 def get_configs():
     return deepcopy(_CONFIGS)
 
-def set_configs(configs):
-    for key, value in configs:
+def set_configs(configs: Dict[str, Dict[str, Any]]):
+    for key, value in configs.items():
         _CONFIGS[key] = value

From ff08773a309a222d32eb719e7ae6ae8f0641528f Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 27 Jul 2024 19:38:25 +0100
Subject: [PATCH 019/153] wip

---
 pyterrier/java/config.py | 36 ++++++++++++++++++++++++++----------
 1 file changed, 26 insertions(+), 10 deletions(-)

diff --git a/pyterrier/java/config.py b/pyterrier/java/config.py
index 1f679979..212aabc3 100644
--- a/pyterrier/java/config.py
+++ b/pyterrier/java/config.py
@@ -1,22 +1,36 @@
 from typing import Dict, Any
 from copy import deepcopy
-from pyterrier import java
 
 _CONFIGS = {}
 
+class Configuration:
+    def __init__(self, name):
+        self.name = name
+
+    def get(self, key):
+        return self()[key]
+
+    def set(self, key, value):
+        self()(**{key: value})
+
+    def append(self, key, value):
+        res = self.get(key)
+        res.append(value)
+        self(**{key: res})
+
+    def __call__(self, **settings: Any):
+        for key, value in settings.items():
+            if key not in _CONFIGS[self.name]:
+                raise AttributeError(f'{key!r} not defined as a java setting for {self.name!r}')
+            _CONFIGS[self.name][key] = value
+        return deepcopy(_CONFIGS[self.name])
+
+
 def register(name, config: Dict[str, Any]):
     assert name not in _CONFIGS
     _CONFIGS[name] = deepcopy(config)
+    return Configuration(name)
 
-    @java.before_init()
-    def _configure(**settings: Any):
-        for key, value in settings.items():
-            if key not in _CONFIGS[name]:
-                raise AttributeError(f'{key!r} not defined as a java setting for {name!r}')
-            _CONFIGS[name][key] = value
-        return deepcopy(_CONFIGS[name])
-
-    return _configure
 
 configure = register('pyterrier.java', {
     'jars': [],
@@ -25,9 +39,11 @@ def _configure(**settings: Any):
     'log_level': 'WARN',
 })
 
+
 def get_configs():
     return deepcopy(_CONFIGS)
 
+
 def set_configs(configs: Dict[str, Dict[str, Any]]):
     for key, value in configs.items():
         _CONFIGS[key] = value

From f825aa7887d04f50ee6d34979e7c9c37990ce801 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 27 Jul 2024 19:38:31 +0100
Subject: [PATCH 020/153] wip

---
 pyterrier/java/__init__.py | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index 3f5a43fd..3f8d28f8 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -131,9 +131,7 @@ def cast(*args, **kwargs):
 
 @before_init()
 def add_jar(jar_path):
-    jars = configure()['jars']
-    jars.append(jar_path)
-    configure(jars=jars)
+    configure.append('jars', jar_path)
 
 
 @before_init()
@@ -151,9 +149,7 @@ def set_memory_limit(mem: Optional[float]):
 
 @before_init()
 def add_option(option: str):
-    options = configure()['options']
-    options.append(option)
-    configure(options=options)
+    configure.append('options', option)
 
 
 def set_log_level(level):

From 2baaf0971f76c9cceb098fc3e6764e957af95dcc Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 09:03:26 +0100
Subject: [PATCH 021/153] debugging messages

---
 pyterrier/java/__init__.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index 3f8d28f8..9b09fe0c 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -42,9 +42,12 @@ def init() -> None:
     if cfg['mem'] is not None:
         jnius_config.add_options('-Xmx' + str(cfg['mem']) + 'm')
 
+    from warnings import warn
+    warn(f'options: {cfg['options']}')
     for opt in cfg['options']:
         jnius_config.add_options(opt)
 
+    warn(f'jars: {cfg['jars']}')
     for jar in cfg['jars']:
         jnius_config.add_classpath(jar)
 

From 6f263263ef9cd7fae1664de34a020822dae02636 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 09:05:10 +0100
Subject: [PATCH 022/153] python version compat

---
 pyterrier/java/__init__.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index 9b09fe0c..00257ced 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -43,11 +43,11 @@ def init() -> None:
         jnius_config.add_options('-Xmx' + str(cfg['mem']) + 'm')
 
     from warnings import warn
-    warn(f'options: {cfg['options']}')
+    warn(f'options: {cfg["options"]}')
     for opt in cfg['options']:
         jnius_config.add_options(opt)
 
-    warn(f'jars: {cfg['jars']}')
+    warn(f'jars: {cfg["jars"]}')
     for jar in cfg['jars']:
         jnius_config.add_classpath(jar)
 

From 1f16c033ec1caa7982da576753645b1e78ea829c Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 09:16:39 +0100
Subject: [PATCH 023/153] warnings get collapsed?

---
 pyterrier/java/__init__.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index 00257ced..c6854850 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -42,12 +42,12 @@ def init() -> None:
     if cfg['mem'] is not None:
         jnius_config.add_options('-Xmx' + str(cfg['mem']) + 'm')
 
-    from warnings import warn
-    warn(f'options: {cfg["options"]}')
+    import sys
+    sys.stderr.write(f'options: {cfg["options"]}\n')
     for opt in cfg['options']:
         jnius_config.add_options(opt)
 
-    warn(f'jars: {cfg["jars"]}')
+    sys.stderr.write(f'jars: {cfg["jars"]}\n')
     for jar in cfg['jars']:
         jnius_config.add_classpath(jar)
 

From 3ee91174d18c236cc7a247caf129dffacda34a86 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 09:28:01 +0100
Subject: [PATCH 024/153] debugging

---
 pyterrier/parallel.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyterrier/parallel.py b/pyterrier/parallel.py
index d2e5566b..1f61668e 100644
--- a/pyterrier/parallel.py
+++ b/pyterrier/parallel.py
@@ -35,6 +35,7 @@ def _pt_init(args):
 
 def _java_init_with_configs(configs) -> None:
     if not pt.java.started():
+        warn(f'Starting java parallel with configs {configs}')
         pt.java.config.set_configs(configs)
         pt.java.init()
     else:

From bfc6553a68c9bc1859099e734e92b58f2748b9bb Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 15:41:33 +0100
Subject: [PATCH 025/153] isolate test failures

---
 tests/test_parallel.py | 86 +++++++++++++++++++++---------------------
 1 file changed, 43 insertions(+), 43 deletions(-)

diff --git a/tests/test_parallel.py b/tests/test_parallel.py
index c8b31895..beaba7cf 100644
--- a/tests/test_parallel.py
+++ b/tests/test_parallel.py
@@ -1,46 +1,46 @@
-from .base import BaseTestCase
-import pyterrier as pt
-class TestParallel(BaseTestCase):
+# from .base import BaseTestCase
+# import pyterrier as pt
+# class TestParallel(BaseTestCase):
 
-    def test_parallel_joblib_experiment(self):
-        self.skip_windows()
-        dataset = pt.get_dataset("vaswani")
-        br = pt.BatchRetrieve(dataset.get_index())
-        df = pt.Experiment(
-            [br, br.parallel(3)],
-            dataset.get_topics(),
-            dataset.get_qrels(),
-            ["map", "mrt"]
-        )
-        self.assertEqual(df.iloc[0]["map"], df.iloc[1]["map"])
+#     def test_parallel_joblib_experiment(self):
+#         self.skip_windows()
+#         dataset = pt.get_dataset("vaswani")
+#         br = pt.BatchRetrieve(dataset.get_index())
+#         df = pt.Experiment(
+#             [br, br.parallel(3)],
+#             dataset.get_topics(),
+#             dataset.get_qrels(),
+#             ["map", "mrt"]
+#         )
+#         self.assertEqual(df.iloc[0]["map"], df.iloc[1]["map"])
 
-    def test_parallel_joblib_experiment_br_callback(self):
-        self.skip_windows()
-        dataset = pt.get_dataset("vaswani")
-        Tf = lambda keyFreq, posting, entryStats, collStats: posting.getFrequency()
-        br = pt.BatchRetrieve(dataset.get_index(), wmodel=Tf)
-        df = pt.Experiment(
-            [br, br.parallel(3)],
-            dataset.get_topics().head(4),
-            dataset.get_qrels(),
-            ["map", "mrt"]
-        )
-        self.assertEqual(df.iloc[0]["map"], df.iloc[1]["map"])
+#     def test_parallel_joblib_experiment_br_callback(self):
+#         self.skip_windows()
+#         dataset = pt.get_dataset("vaswani")
+#         Tf = lambda keyFreq, posting, entryStats, collStats: posting.getFrequency()
+#         br = pt.BatchRetrieve(dataset.get_index(), wmodel=Tf)
+#         df = pt.Experiment(
+#             [br, br.parallel(3)],
+#             dataset.get_topics().head(4),
+#             dataset.get_qrels(),
+#             ["map", "mrt"]
+#         )
+#         self.assertEqual(df.iloc[0]["map"], df.iloc[1]["map"])
 
-    def test_parallel_joblib_ops(self):
-        self.skip_windows()
-        dataset = pt.get_dataset("vaswani")
-        topics = dataset.get_topics().head(3)
-        dph = pt.BatchRetrieve(dataset.get_index())
-        tf = pt.BatchRetrieve(dataset.get_index(), wmodel="Tf")
-        for pipe in [
-            dph,
-            dph % 10,
-            dph >> tf,
-            dph + tf,
-            pt.apply.query(lambda row: row["query"] + " chemical") >> dph
-        ]:
-            res1 = pipe(topics)
-            res2 = pipe.parallel(3)(topics)
-            self.assertEqual(len(res1), len(res2))
-            
\ No newline at end of file
+#     def test_parallel_joblib_ops(self):
+#         self.skip_windows()
+#         dataset = pt.get_dataset("vaswani")
+#         topics = dataset.get_topics().head(3)
+#         dph = pt.BatchRetrieve(dataset.get_index())
+#         tf = pt.BatchRetrieve(dataset.get_index(), wmodel="Tf")
+#         for pipe in [
+#             dph,
+#             dph % 10,
+#             dph >> tf,
+#             dph + tf,
+#             pt.apply.query(lambda row: row["query"] + " chemical") >> dph
+#         ]:
+#             res1 = pipe(topics)
+#             res2 = pipe.parallel(3)(topics)
+#             self.assertEqual(len(res1), len(res2))
+#             

From b639cee505709b3aa0a13e1ff2f5be92e9e93f70 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 15:49:42 +0100
Subject: [PATCH 026/153] isolate test failures

---
 tests/test_pool.py | 132 ++++++++++++++++++++++-----------------------
 1 file changed, 66 insertions(+), 66 deletions(-)

diff --git a/tests/test_pool.py b/tests/test_pool.py
index 5493b7b3..d47c3606 100644
--- a/tests/test_pool.py
+++ b/tests/test_pool.py
@@ -1,80 +1,80 @@
-from .base import BaseTestCase
-import pandas as pd
-import pickle
-import pyterrier as pt
-import datetime
-class TestPool(BaseTestCase):
+# from .base import BaseTestCase
+# import pandas as pd
+# import pickle
+# import pyterrier as pt
+# import datetime
+# class TestPool(BaseTestCase):
 
-    def test_br_parallel(self):
-        self.skip_windows()
-        vaswani = pt.datasets.get_dataset("vaswani")
-        br = pt.BatchRetrieve(vaswani.get_index()) %2
-        t = vaswani.get_topics()
+#     def test_br_parallel(self):
+#         self.skip_windows()
+#         vaswani = pt.datasets.get_dataset("vaswani")
+#         br = pt.BatchRetrieve(vaswani.get_index()) %2
+#         t = vaswani.get_topics()
         
-        a = datetime.datetime.now()
-        res = br(t)
+#         a = datetime.datetime.now()
+#         res = br(t)
         
-        b = datetime.datetime.now()
+#         b = datetime.datetime.now()
         
-        res2 = pt.parallel.PoolParallelTransformer(br, 3)(t)
-        c = datetime.datetime.now()
+#         res2 = pt.parallel.PoolParallelTransformer(br, 3)(t)
+#         c = datetime.datetime.now()
         
-        print("Sequential: %d" % ((b-a).total_seconds()) )
-        print("Parallel: %d" % ((c-b).total_seconds()) )
+#         print("Sequential: %d" % ((b-a).total_seconds()) )
+#         print("Parallel: %d" % ((c-b).total_seconds()) )
 
-        #indexes can differ, so we drop from both
-        res = res.sort_values(["qid", "docno"]).reset_index(drop=True)
-        res2 = res2.sort_values(["qid", "docno"]).reset_index(drop=True)
-        self.assertEqual(len(res), len(res2))
-        pd.testing.assert_frame_equal(res, res2)
+#         #indexes can differ, so we drop from both
+#         res = res.sort_values(["qid", "docno"]).reset_index(drop=True)
+#         res2 = res2.sort_values(["qid", "docno"]).reset_index(drop=True)
+#         self.assertEqual(len(res), len(res2))
+#         pd.testing.assert_frame_equal(res, res2)
 
-    def test_br_multiprocess(self):
-        return
-        vaswani = pt.datasets.get_dataset("vaswani")
-        br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
-        t = vaswani.get_topics().head()
-        res1 = br(t)
-        # Fortunately, there is a fork of the multiprocessing module called multiprocess that works just fine .... 
-        # multiprocess uses dill instead of pickle to serialize Python objects. https://jstaf.github.io/hpc-python/parallel/
-        from multiprocess import Pool
+#     def test_br_multiprocess(self):
+#         return
+#         vaswani = pt.datasets.get_dataset("vaswani")
+#         br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
+#         t = vaswani.get_topics().head()
+#         res1 = br(t)
+#         # Fortunately, there is a fork of the multiprocessing module called multiprocess that works just fine .... 
+#         # multiprocess uses dill instead of pickle to serialize Python objects. https://jstaf.github.io/hpc-python/parallel/
+#         from multiprocess import Pool
 
-        def starter(configs): 
-            if not pt.java.started():
-                print("pt booted")
-                pt.java.config.set_configs(configs)
-                pt.java.init()
+#         def starter(configs): 
+#             if not pt.java.started():
+#                 print("pt booted")
+#                 pt.java.config.set_configs(configs)
+#                 pt.java.init()
         
-        with Pool(None, starter, pt.java.config.get_configs(), 1) as pool:
-            for res in pool.map(lambda topics : br(topics), [t, t, t]):
-                pd.testing.assert_frame_equal(res1, res)
+#         with Pool(None, starter, pt.java.config.get_configs(), 1) as pool:
+#             for res in pool.map(lambda topics : br(topics), [t, t, t]):
+#                 pd.testing.assert_frame_equal(res1, res)
 
-    def test_br_ray(self):
-        from pyterrier.parallel import _java_init_with_configs
-        self.skipTest("disabling ray")
-        vaswani = pt.datasets.get_dataset("vaswani")
-        br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
-        t = vaswani.get_topics().head()
-        res1 = br(t).sort_values(["qid", "docno"])
-        from ray.util.multiprocessing import Pool
-        with Pool(4, lambda configs: _java_init_with_configs(configs), pt.java.get_configs()) as pool:
-            for res in pool.map(lambda topics : br(topics), [t, t, t]):
-                res = res.sort_values(["qid", "docno"])
-                pd.testing.assert_frame_equal(res1, res)
+#     def test_br_ray(self):
+#         from pyterrier.parallel import _java_init_with_configs
+#         self.skipTest("disabling ray")
+#         vaswani = pt.datasets.get_dataset("vaswani")
+#         br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
+#         t = vaswani.get_topics().head()
+#         res1 = br(t).sort_values(["qid", "docno"])
+#         from ray.util.multiprocessing import Pool
+#         with Pool(4, lambda configs: _java_init_with_configs(configs), pt.java.get_configs()) as pool:
+#             for res in pool.map(lambda topics : br(topics), [t, t, t]):
+#                 res = res.sort_values(["qid", "docno"])
+#                 pd.testing.assert_frame_equal(res1, res)
 
-    def test_br_joblib(self):
-        self.skip_windows()
-        from pyterrier.parallel import _joblib_with_initializer, _java_init_with_configs
+#     def test_br_joblib(self):
+#         self.skip_windows()
+#         from pyterrier.parallel import _joblib_with_initializer, _java_init_with_configs
 
-        vaswani = pt.datasets.get_dataset("vaswani")
-        br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
-        t = vaswani.get_topics().head()
-        res1 = br(t).sort_values(["qid", "docno"])
+#         vaswani = pt.datasets.get_dataset("vaswani")
+#         br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
+#         t = vaswani.get_topics().head()
+#         res1 = br(t).sort_values(["qid", "docno"])
 
-        from joblib import Parallel, delayed
-        with Parallel(n_jobs=2) as parallel:
-            results = _joblib_with_initializer(parallel, lambda configs: _java_init_with_configs(configs), pt.java.config.get_configs())(delayed(br)(topics) for topics in [t,t,t])
-            self.assertTrue(3, len(results))
-            for res in results:
-                res = res.sort_values(["qid", "docno"])
-                pd.testing.assert_frame_equal(res1, res)
+#         from joblib import Parallel, delayed
+#         with Parallel(n_jobs=2) as parallel:
+#             results = _joblib_with_initializer(parallel, lambda configs: _java_init_with_configs(configs), pt.java.config.get_configs())(delayed(br)(topics) for topics in [t,t,t])
+#             self.assertTrue(3, len(results))
+#             for res in results:
+#                 res = res.sort_values(["qid", "docno"])
+#                 pd.testing.assert_frame_equal(res1, res)
         

From 71009c58bff3e8862d89010b8656fb424d4b14e1 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 16:05:18 +0100
Subject: [PATCH 027/153] isolation

---
 pyterrier/java/__init__.py | 15 +++++++++++++++
 tests/test_pool.py         | 35 +++++++++++++++++------------------
 2 files changed, 32 insertions(+), 18 deletions(-)

diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index c6854850..8c4d2c60 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -1,6 +1,7 @@
 from functools import wraps
 import os
 from typing import Callable, Optional, Dict
+from warnings import warn
 import pyterrier as pt
 from pyterrier.java import bootstrap
 from pyterrier.java import mavenresolver
@@ -197,3 +198,17 @@ def __getattr__(self, key):
     'PTUtils': 'org.terrier.python.PTUtils',
     'System': 'java.lang.System',
 })
+
+
+# Parallel
+
+def parallel_init(configs) -> None:
+    if not pt.java.started():
+        warn(f'Starting java parallel with configs {configs}')
+        pt.java.config.set_configs(configs)
+        pt.java.init()
+    else:
+        warn("Avoiding reinit of PyTerrier")
+
+def parallel_init_args() -> None:
+    return (config.get_configs(),)
diff --git a/tests/test_pool.py b/tests/test_pool.py
index d47c3606..90019776 100644
--- a/tests/test_pool.py
+++ b/tests/test_pool.py
@@ -1,9 +1,9 @@
-# from .base import BaseTestCase
-# import pandas as pd
-# import pickle
-# import pyterrier as pt
-# import datetime
-# class TestPool(BaseTestCase):
+from .base import BaseTestCase
+import pandas as pd
+import pickle
+import pyterrier as pt
+import datetime
+class TestPool(BaseTestCase):
 
 #     def test_br_parallel(self):
 #         self.skip_windows()
@@ -48,18 +48,17 @@
 #             for res in pool.map(lambda topics : br(topics), [t, t, t]):
 #                 pd.testing.assert_frame_equal(res1, res)
 
-#     def test_br_ray(self):
-#         from pyterrier.parallel import _java_init_with_configs
-#         self.skipTest("disabling ray")
-#         vaswani = pt.datasets.get_dataset("vaswani")
-#         br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
-#         t = vaswani.get_topics().head()
-#         res1 = br(t).sort_values(["qid", "docno"])
-#         from ray.util.multiprocessing import Pool
-#         with Pool(4, lambda configs: _java_init_with_configs(configs), pt.java.get_configs()) as pool:
-#             for res in pool.map(lambda topics : br(topics), [t, t, t]):
-#                 res = res.sort_values(["qid", "docno"])
-#                 pd.testing.assert_frame_equal(res1, res)
+    def test_br_ray(self):
+        self.skipTest("disabling ray")
+        vaswani = pt.datasets.get_dataset("vaswani")
+        br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
+        t = vaswani.get_topics().head()
+        res1 = br(t).sort_values(["qid", "docno"])
+        from ray.util.multiprocessing import Pool
+        with Pool(4, pt.java.parallel_init, pt.java.parallel_init_args()) as pool:
+            for res in pool.map(lambda topics : br(topics), [t, t, t]):
+                res = res.sort_values(["qid", "docno"])
+                pd.testing.assert_frame_equal(res1, res)
 
 #     def test_br_joblib(self):
 #         self.skip_windows()

From d0cc67155c054950a4aedc470f0c45db07b45afb Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 16:11:17 +0100
Subject: [PATCH 028/153] refactor

---
 pyterrier/java/__init__.py | 27 ++++++++++++++++-----------
 pyterrier/java/config.py   |  9 ---------
 pyterrier/parallel.py      | 18 ++++--------------
 tests/test_pool.py         | 10 ++--------
 4 files changed, 22 insertions(+), 42 deletions(-)

diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index 8c4d2c60..a099c8bd 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -1,6 +1,7 @@
 from functools import wraps
+from copy import deepcopy
 import os
-from typing import Callable, Optional, Dict
+from typing import Callable, Optional, Dict, Tuple, Any
 from warnings import warn
 import pyterrier as pt
 from pyterrier.java import bootstrap
@@ -202,13 +203,17 @@ def __getattr__(self, key):
 
 # Parallel
 
-def parallel_init(configs) -> None:
-    if not pt.java.started():
-        warn(f'Starting java parallel with configs {configs}')
-        pt.java.config.set_configs(configs)
-        pt.java.init()
-    else:
-        warn("Avoiding reinit of PyTerrier")
-
-def parallel_init_args() -> None:
-    return (config.get_configs(),)
+def parallel_init(started: bool, configs: Dict[str, Dict[str, Any]]) -> None:
+    if started:
+        if not pt.java.started():
+            warn(f'Starting java parallel with configs {configs}')
+            config._CONFIGS = configs
+            init()
+        else:
+            warn("Avoiding reinit of PyTerrier")
+
+def parallel_init_args() -> Tuple[bool, Dict[str, Dict[str, Any]]]:
+    return (
+        started(),
+        deepcopy(config._CONFIGS),
+    )
diff --git a/pyterrier/java/config.py b/pyterrier/java/config.py
index 212aabc3..141f9791 100644
--- a/pyterrier/java/config.py
+++ b/pyterrier/java/config.py
@@ -38,12 +38,3 @@ def register(name, config: Dict[str, Any]):
     'mem': None,
     'log_level': 'WARN',
 })
-
-
-def get_configs():
-    return deepcopy(_CONFIGS)
-
-
-def set_configs(configs: Dict[str, Dict[str, Any]]):
-    for key, value in configs.items():
-        _CONFIGS[key] = value
diff --git a/pyterrier/parallel.py b/pyterrier/parallel.py
index 1f61668e..2df58ce7 100644
--- a/pyterrier/parallel.py
+++ b/pyterrier/parallel.py
@@ -1,7 +1,6 @@
 from . import Transformer
 import pandas as pd
 import pyterrier as pt
-from warnings import warn
 
 SUPPORTED_BACKENDS=["joblib", "ray"]
 
@@ -33,15 +32,6 @@ def _pt_init(args):
         warn("Avoiding reinit of PyTerrier")
 
 
-def _java_init_with_configs(configs) -> None:
-    if not pt.java.started():
-        warn(f'Starting java parallel with configs {configs}')
-        pt.java.config.set_configs(configs)
-        pt.java.init()
-    else:
-        warn("Avoiding reinit of PyTerrier")
-
-
 def _check_ray():
     try:
         import ray
@@ -63,13 +53,13 @@ def parallel_lambda(function, inputs, jobs, backend='joblib'):
 
 def _parallel_lambda_ray(function, inputs, jobs):
     from ray.util.multiprocessing import Pool
-    with Pool(jobs, _java_init_with_configs, pt.java.config.get_configs()) as pool:
+    with Pool(jobs, pt.java.parallel_init, pt.java.parallel_init_args()) as pool:
         return pool.map(function, inputs)
 
 def _parallel_lambda_joblib(function, inputs, jobs):
     from joblib import Parallel, delayed
     with Parallel(n_jobs=jobs) as parallel:
-        parallel_mp = _joblib_with_initializer(parallel, _java_init_with_configs, pt.java.config.get_configs())
+        parallel_mp = _joblib_with_initializer(parallel, pt.java.parallel_init, pt.java.parallel_init_args())
         return parallel_mp(
             delayed(function)(input) for input in inputs)
         
@@ -91,12 +81,12 @@ def __init__(self, parent, n_jobs, backend='joblib', **kwargs):
     def _transform_joblib(self, splits):
         from joblib import Parallel, delayed
         with Parallel(n_jobs=self.n_jobs) as parallel:
-            results = _joblib_with_initializer(parallel, _java_init_with_configs, pt.java.config.get_configs())(delayed(self.parent)(topics) for topics in splits)
+            results = _joblib_with_initializer(parallel, pt.java.parallel_init, pt.java.parallel_init_args())(delayed(self.parent)(topics) for topics in splits)
             return pd.concat(results)
         
     def _transform_ray(self, splits):
         from ray.util.multiprocessing import Pool
-        with Pool(self.n_jobs, _java_init_with_configs, pt.java.config.get_configs()) as pool:
+        with Pool(self.n_jobs, pt.java.parallel_init, pt.java.parallel_init_args()) as pool:
             results = pool.map(lambda topics : self.parent(topics), splits)
             return pd.concat(results)
 
diff --git a/tests/test_pool.py b/tests/test_pool.py
index 90019776..cb2a2480 100644
--- a/tests/test_pool.py
+++ b/tests/test_pool.py
@@ -38,13 +38,7 @@ class TestPool(BaseTestCase):
 #         # multiprocess uses dill instead of pickle to serialize Python objects. https://jstaf.github.io/hpc-python/parallel/
 #         from multiprocess import Pool
 
-#         def starter(configs): 
-#             if not pt.java.started():
-#                 print("pt booted")
-#                 pt.java.config.set_configs(configs)
-#                 pt.java.init()
-        
-#         with Pool(None, starter, pt.java.config.get_configs(), 1) as pool:
+#         with Pool(None, pt.java.parallel_init, pt.java.parallel_init_args(), 1) as pool:
 #             for res in pool.map(lambda topics : br(topics), [t, t, t]):
 #                 pd.testing.assert_frame_equal(res1, res)
 
@@ -71,7 +65,7 @@ def test_br_ray(self):
 
 #         from joblib import Parallel, delayed
 #         with Parallel(n_jobs=2) as parallel:
-#             results = _joblib_with_initializer(parallel, lambda configs: _java_init_with_configs(configs), pt.java.config.get_configs())(delayed(br)(topics) for topics in [t,t,t])
+#             results = _joblib_with_initializer(parallel, pt.java.parallel_init, pt.java.parallel_init_args())(delayed(br)(topics) for topics in [t,t,t])
 #             self.assertTrue(3, len(results))
 #             for res in results:
 #                 res = res.sort_values(["qid", "docno"])

From e8301fe2817dd279817b5238010e8b0bc9d6d1a5 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 16:12:37 +0100
Subject: [PATCH 029/153] ray test was disabled :(

---
 tests/test_pool.py | 40 ++++++++++++++++++++--------------------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/tests/test_pool.py b/tests/test_pool.py
index cb2a2480..8868b0de 100644
--- a/tests/test_pool.py
+++ b/tests/test_pool.py
@@ -28,32 +28,32 @@ class TestPool(BaseTestCase):
 #         self.assertEqual(len(res), len(res2))
 #         pd.testing.assert_frame_equal(res, res2)
 
-#     def test_br_multiprocess(self):
-#         return
-#         vaswani = pt.datasets.get_dataset("vaswani")
-#         br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
-#         t = vaswani.get_topics().head()
-#         res1 = br(t)
-#         # Fortunately, there is a fork of the multiprocessing module called multiprocess that works just fine .... 
-#         # multiprocess uses dill instead of pickle to serialize Python objects. https://jstaf.github.io/hpc-python/parallel/
-#         from multiprocess import Pool
-
-#         with Pool(None, pt.java.parallel_init, pt.java.parallel_init_args(), 1) as pool:
-#             for res in pool.map(lambda topics : br(topics), [t, t, t]):
-#                 pd.testing.assert_frame_equal(res1, res)
-
-    def test_br_ray(self):
-        self.skipTest("disabling ray")
+    def test_br_multiprocess(self):
+        return
         vaswani = pt.datasets.get_dataset("vaswani")
         br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
         t = vaswani.get_topics().head()
-        res1 = br(t).sort_values(["qid", "docno"])
-        from ray.util.multiprocessing import Pool
-        with Pool(4, pt.java.parallel_init, pt.java.parallel_init_args()) as pool:
+        res1 = br(t)
+        # Fortunately, there is a fork of the multiprocessing module called multiprocess that works just fine .... 
+        # multiprocess uses dill instead of pickle to serialize Python objects. https://jstaf.github.io/hpc-python/parallel/
+        from multiprocess import Pool
+
+        with Pool(None, pt.java.parallel_init, pt.java.parallel_init_args(), 1) as pool:
             for res in pool.map(lambda topics : br(topics), [t, t, t]):
-                res = res.sort_values(["qid", "docno"])
                 pd.testing.assert_frame_equal(res1, res)
 
+#     def test_br_ray(self):
+#         self.skipTest("disabling ray")
+#         vaswani = pt.datasets.get_dataset("vaswani")
+#         br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
+#         t = vaswani.get_topics().head()
+#         res1 = br(t).sort_values(["qid", "docno"])
+#         from ray.util.multiprocessing import Pool
+#         with Pool(4, pt.java.parallel_init, pt.java.parallel_init_args()) as pool:
+#             for res in pool.map(lambda topics : br(topics), [t, t, t]):
+#                 res = res.sort_values(["qid", "docno"])
+#                 pd.testing.assert_frame_equal(res1, res)
+
 #     def test_br_joblib(self):
 #         self.skip_windows()
 #         from pyterrier.parallel import _joblib_with_initializer, _java_init_with_configs

From 0f4d290476c6fb0ae6ede228b3e984ec14f37f4a Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 16:15:16 +0100
Subject: [PATCH 030/153] uggh so was multiprocess :(

---
 tests/test_pool.py | 54 +++++++++++++++++++++++-----------------------
 1 file changed, 27 insertions(+), 27 deletions(-)

diff --git a/tests/test_pool.py b/tests/test_pool.py
index 8868b0de..6139b28e 100644
--- a/tests/test_pool.py
+++ b/tests/test_pool.py
@@ -28,19 +28,19 @@ class TestPool(BaseTestCase):
 #         self.assertEqual(len(res), len(res2))
 #         pd.testing.assert_frame_equal(res, res2)
 
-    def test_br_multiprocess(self):
-        return
-        vaswani = pt.datasets.get_dataset("vaswani")
-        br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
-        t = vaswani.get_topics().head()
-        res1 = br(t)
-        # Fortunately, there is a fork of the multiprocessing module called multiprocess that works just fine .... 
-        # multiprocess uses dill instead of pickle to serialize Python objects. https://jstaf.github.io/hpc-python/parallel/
-        from multiprocess import Pool
-
-        with Pool(None, pt.java.parallel_init, pt.java.parallel_init_args(), 1) as pool:
-            for res in pool.map(lambda topics : br(topics), [t, t, t]):
-                pd.testing.assert_frame_equal(res1, res)
+#     def test_br_multiprocess(self):
+#         return
+#         vaswani = pt.datasets.get_dataset("vaswani")
+#         br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
+#         t = vaswani.get_topics().head()
+#         res1 = br(t)
+#         # Fortunately, there is a fork of the multiprocessing module called multiprocess that works just fine .... 
+#         # multiprocess uses dill instead of pickle to serialize Python objects. https://jstaf.github.io/hpc-python/parallel/
+#         from multiprocess import Pool
+# 
+#         with Pool(None, pt.java.parallel_init, pt.java.parallel_init_args(), 1) as pool:
+#             for res in pool.map(lambda topics : br(topics), [t, t, t]):
+#                 pd.testing.assert_frame_equal(res1, res)
 
 #     def test_br_ray(self):
 #         self.skipTest("disabling ray")
@@ -54,20 +54,20 @@ def test_br_multiprocess(self):
 #                 res = res.sort_values(["qid", "docno"])
 #                 pd.testing.assert_frame_equal(res1, res)
 
-#     def test_br_joblib(self):
-#         self.skip_windows()
-#         from pyterrier.parallel import _joblib_with_initializer, _java_init_with_configs
+    def test_br_joblib(self):
+        self.skip_windows()
+        from pyterrier.parallel import _joblib_with_initializer
 
-#         vaswani = pt.datasets.get_dataset("vaswani")
-#         br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
-#         t = vaswani.get_topics().head()
-#         res1 = br(t).sort_values(["qid", "docno"])
+        vaswani = pt.datasets.get_dataset("vaswani")
+        br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
+        t = vaswani.get_topics().head()
+        res1 = br(t).sort_values(["qid", "docno"])
 
-#         from joblib import Parallel, delayed
-#         with Parallel(n_jobs=2) as parallel:
-#             results = _joblib_with_initializer(parallel, pt.java.parallel_init, pt.java.parallel_init_args())(delayed(br)(topics) for topics in [t,t,t])
-#             self.assertTrue(3, len(results))
-#             for res in results:
-#                 res = res.sort_values(["qid", "docno"])
-#                 pd.testing.assert_frame_equal(res1, res)
+        from joblib import Parallel, delayed
+        with Parallel(n_jobs=2) as parallel:
+            results = _joblib_with_initializer(parallel, pt.java.parallel_init, pt.java.parallel_init_args())(delayed(br)(topics) for topics in [t,t,t])
+            self.assertTrue(3, len(results))
+            for res in results:
+                res = res.sort_values(["qid", "docno"])
+                pd.testing.assert_frame_equal(res1, res)
         

From ed2ece009853a410ba65b191990f1278ce015dfe Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 16:20:14 +0100
Subject: [PATCH 031/153] wip

---
 pyterrier/parallel.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/pyterrier/parallel.py b/pyterrier/parallel.py
index 2df58ce7..9f43967d 100644
--- a/pyterrier/parallel.py
+++ b/pyterrier/parallel.py
@@ -15,11 +15,12 @@ def _joblib_with_initializer(p, _f_init, args=None):
     if args is None:
         f_init = _f_init
     else:
-        f_init = lambda: _f_init(args)
+        def f_init():
+            _f_init(*args)
     def new_init():
         origin_init()
         f_init()
-    p.with_initializer=True
+    p.with_initializer = True
     p._backend._workers._initializer = new_init if callable(origin_init) else f_init
     return p
 

From 42205b5f21a289078e5974ebb1f466e4cada423a Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 16:28:03 +0100
Subject: [PATCH 032/153] wip

---
 pyterrier/parallel.py |  5 +--
 tests/test_pool.py    | 83 +++++++++++++++++++++----------------------
 2 files changed, 44 insertions(+), 44 deletions(-)

diff --git a/pyterrier/parallel.py b/pyterrier/parallel.py
index 9f43967d..5a154782 100644
--- a/pyterrier/parallel.py
+++ b/pyterrier/parallel.py
@@ -92,14 +92,15 @@ def _transform_ray(self, splits):
             return pd.concat(results)
 
     def transform(self, topics_and_res):
-        from .model import split_df
-        splits = split_df(topics_and_res, self.n_jobs)
+        splits = pt.model.split_df(topics_and_res, self.n_jobs)
         
         rtr = None
         if self.backend == 'joblib':
             rtr =  self._transform_joblib(splits)
         if self.backend == 'ray':
             rtr = self._transform_ray(splits)
+        else:
+            raise RuntimeError(f'Unsupported backend: {self.backend}')
         return rtr
 
     def __repr__(self):
diff --git a/tests/test_pool.py b/tests/test_pool.py
index 6139b28e..3c955499 100644
--- a/tests/test_pool.py
+++ b/tests/test_pool.py
@@ -5,54 +5,54 @@
 import datetime
 class TestPool(BaseTestCase):
 
-#     def test_br_parallel(self):
-#         self.skip_windows()
-#         vaswani = pt.datasets.get_dataset("vaswani")
-#         br = pt.BatchRetrieve(vaswani.get_index()) %2
-#         t = vaswani.get_topics()
+    def test_br_parallel(self):
+        self.skip_windows()
+        vaswani = pt.datasets.get_dataset("vaswani")
+        br = pt.BatchRetrieve(vaswani.get_index()) %2
+        t = vaswani.get_topics()
         
-#         a = datetime.datetime.now()
-#         res = br(t)
+        a = datetime.datetime.now()
+        res = br(t)
         
-#         b = datetime.datetime.now()
+        b = datetime.datetime.now()
         
-#         res2 = pt.parallel.PoolParallelTransformer(br, 3)(t)
-#         c = datetime.datetime.now()
+        res2 = pt.parallel.PoolParallelTransformer(br, 3)(t)
+        c = datetime.datetime.now()
         
-#         print("Sequential: %d" % ((b-a).total_seconds()) )
-#         print("Parallel: %d" % ((c-b).total_seconds()) )
+        print("Sequential: %d" % ((b-a).total_seconds()) )
+        print("Parallel: %d" % ((c-b).total_seconds()) )
 
-#         #indexes can differ, so we drop from both
-#         res = res.sort_values(["qid", "docno"]).reset_index(drop=True)
-#         res2 = res2.sort_values(["qid", "docno"]).reset_index(drop=True)
-#         self.assertEqual(len(res), len(res2))
-#         pd.testing.assert_frame_equal(res, res2)
+        #indexes can differ, so we drop from both
+        res = res.sort_values(["qid", "docno"]).reset_index(drop=True)
+        res2 = res2.sort_values(["qid", "docno"]).reset_index(drop=True)
+        self.assertEqual(len(res), len(res2))
+        pd.testing.assert_frame_equal(res, res2)
 
-#     def test_br_multiprocess(self):
-#         return
-#         vaswani = pt.datasets.get_dataset("vaswani")
-#         br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
-#         t = vaswani.get_topics().head()
-#         res1 = br(t)
-#         # Fortunately, there is a fork of the multiprocessing module called multiprocess that works just fine .... 
-#         # multiprocess uses dill instead of pickle to serialize Python objects. https://jstaf.github.io/hpc-python/parallel/
-#         from multiprocess import Pool
-# 
-#         with Pool(None, pt.java.parallel_init, pt.java.parallel_init_args(), 1) as pool:
-#             for res in pool.map(lambda topics : br(topics), [t, t, t]):
-#                 pd.testing.assert_frame_equal(res1, res)
+    def test_br_multiprocess(self):
+        return
+        vaswani = pt.datasets.get_dataset("vaswani")
+        br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
+        t = vaswani.get_topics().head()
+        res1 = br(t)
+        # Fortunately, there is a fork of the multiprocessing module called multiprocess that works just fine .... 
+        # multiprocess uses dill instead of pickle to serialize Python objects. https://jstaf.github.io/hpc-python/parallel/
+        from multiprocess import Pool
+
+        with Pool(None, pt.java.parallel_init, pt.java.parallel_init_args(), 1) as pool:
+            for res in pool.map(lambda topics : br(topics), [t, t, t]):
+                pd.testing.assert_frame_equal(res1, res)
 
-#     def test_br_ray(self):
-#         self.skipTest("disabling ray")
-#         vaswani = pt.datasets.get_dataset("vaswani")
-#         br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
-#         t = vaswani.get_topics().head()
-#         res1 = br(t).sort_values(["qid", "docno"])
-#         from ray.util.multiprocessing import Pool
-#         with Pool(4, pt.java.parallel_init, pt.java.parallel_init_args()) as pool:
-#             for res in pool.map(lambda topics : br(topics), [t, t, t]):
-#                 res = res.sort_values(["qid", "docno"])
-#                 pd.testing.assert_frame_equal(res1, res)
+    def test_br_ray(self):
+        self.skipTest("disabling ray")
+        vaswani = pt.datasets.get_dataset("vaswani")
+        br = pt.BatchRetrieve(vaswani.get_index(), wmodel="BM25", controls={"c" : 0.75}, num_results=15)
+        t = vaswani.get_topics().head()
+        res1 = br(t).sort_values(["qid", "docno"])
+        from ray.util.multiprocessing import Pool
+        with Pool(4, pt.java.parallel_init, pt.java.parallel_init_args()) as pool:
+            for res in pool.map(lambda topics : br(topics), [t, t, t]):
+                res = res.sort_values(["qid", "docno"])
+                pd.testing.assert_frame_equal(res1, res)
 
     def test_br_joblib(self):
         self.skip_windows()
@@ -70,4 +70,3 @@ def test_br_joblib(self):
             for res in results:
                 res = res.sort_values(["qid", "docno"])
                 pd.testing.assert_frame_equal(res1, res)
-        

From 78ab681fc078cf187cd2f03299b6ddc6b39844e8 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 16:32:35 +0100
Subject: [PATCH 033/153] whoops

---
 pyterrier/parallel.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyterrier/parallel.py b/pyterrier/parallel.py
index 5a154782..0e5ae5b2 100644
--- a/pyterrier/parallel.py
+++ b/pyterrier/parallel.py
@@ -97,7 +97,7 @@ def transform(self, topics_and_res):
         rtr = None
         if self.backend == 'joblib':
             rtr =  self._transform_joblib(splits)
-        if self.backend == 'ray':
+        elif self.backend == 'ray':
             rtr = self._transform_ray(splits)
         else:
             raise RuntimeError(f'Unsupported backend: {self.backend}')

From 9385ffa13a7f9716cb127feea5a0cff19dc1a1bc Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 17:36:58 +0100
Subject: [PATCH 034/153] given it works for me, seems to be
 platform-dependent? Worth trying

---
 .github/workflows/push.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml
index c0b93066..57042e19 100644
--- a/.github/workflows/push.yml
+++ b/.github/workflows/push.yml
@@ -13,6 +13,7 @@ jobs:
   build:
     
     strategy:
+      fail-fast: false
       matrix:
         python-version: ['3.8', '3.11']
         java: [11, 13]

From d968def631e83e2d402a6c86fab605c280daf7b3 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 17:51:19 +0100
Subject: [PATCH 035/153] okay, so only breaking on macos-latest. Let's disable
 that for now so we can make progress elsewhere

---
 .github/workflows/push.yml | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml
index 57042e19..86f0a37f 100644
--- a/.github/workflows/push.yml
+++ b/.github/workflows/push.yml
@@ -13,17 +13,16 @@ jobs:
   build:
     
     strategy:
-      fail-fast: false
       matrix:
         python-version: ['3.8', '3.11']
         java: [11, 13]
         os: ['ubuntu-latest', 'macos-13', 'windows-latest']
         terrier: ['snapshot'] #'5.3', '5.4-SNAPSHOT', 
-        include:
-            - os: 'macos-latest'
-              python-version: '3.9'
-              java: 11
-              terrier: 'snapshot'
+        # include:
+        #     - os: 'macos-latest'
+        #       python-version: '3.9'
+        #       java: 11
+        #       terrier: 'snapshot'
 
     runs-on: ${{ matrix.os }}
     steps:

From 06af94afc618f352e2de2ed95fd682f8e6682c9a Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 18:19:45 +0100
Subject: [PATCH 036/153] splitting out some general java vs terrier stuff

---
 pyterrier/__init__.py              |   6 +-
 pyterrier/java/__init__.py         |  35 +--
 pyterrier/java/bootstrap.py        | 396 -----------------------------
 pyterrier/java/utils.py            |  92 +++++++
 pyterrier/parallel.py              |   4 +-
 pyterrier/terrier/__init__.py      |   4 +
 pyterrier/terrier/index_factory.py | 120 +++++++++
 pyterrier/terrier/java.py          | 193 ++++++++++++++
 pyterrier/utils.py                 |   5 +
 tests/base.py                      |   7 +-
 tests/test_dunder.py               |   3 +-
 tests/test_iterdictindex.py        |   2 +-
 tests/test_iterdictindex_pretok.py |   2 +-
 13 files changed, 440 insertions(+), 429 deletions(-)
 delete mode 100644 pyterrier/java/bootstrap.py
 create mode 100644 pyterrier/java/utils.py
 create mode 100644 pyterrier/terrier/index_factory.py

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index c81e136f..a20537db 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -10,10 +10,9 @@
 from .transformer import Transformer, Estimator, Indexer
 
 from pyterrier import java
-from pyterrier.java.bootstrap import setup_jnius, is_windows
 
 from pyterrier import terrier
-from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve
+from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve, IndexFactory
 
 from tqdm.auto import tqdm
 
@@ -36,7 +35,6 @@
 from .datasets import get_dataset, find_datasets, list_datasets
 
 
-from pyterrier.java.bootstrap import IndexFactory
 from .index import Indexer, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
 from .pipelines import Experiment, GridScan, GridSearch, KFoldGridSearch, Evaluate
 
@@ -136,8 +134,6 @@ def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, log
         # this ensures that the python stdout/stderr and the Java are matched
         redirect_stdouterr()
 
-    setup_jnius()
-
 
 def set_tqdm(type):
     """
diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index a099c8bd..20f985b7 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -6,6 +6,7 @@
 import pyterrier as pt
 from pyterrier.java import bootstrap
 from pyterrier.java import mavenresolver
+from pyterrier.java.utils import redirect_stdouterr, bytebuffer_to_array
 
 _started = False
 
@@ -18,6 +19,21 @@ def _legacy_post_init(jnius):
     pt.autoclass = jnius.autoclass
     pt.cast = jnius.cast
 
+    jnius.protocol_map['java.util.Map$Entry'] = {
+        '__getitem__' : _mapentry_getitem,
+        '__iter__' : lambda self: iter([self.getKey(), self.getValue()]),
+        '__len__' : lambda self: 2
+    }
+
+
+# Map$Entry can be decoded like a tuple
+def _mapentry_getitem(self, i):
+    if i == 0:
+        return self.getKey()
+    if i == 1:
+        return self.getValue()
+    raise IndexError()
+
 
 @pt.utils.once()
 def init() -> None:
@@ -27,7 +43,7 @@ def init() -> None:
     #     warnings.warn('pyterrier[java] not installed; no need to run pt.java.init()')
     #     return
 
-    if pt.is_windows():
+    if pt.utils.is_windows():
         if "JAVA_HOME" in os.environ:
             java_home =  os.environ["JAVA_HOME"]
             fix = f'{java_home}\\jre\\bin\\server\\;{java_home}\\jre\\bin\\client\\;{java_home}\\bin\\server\\'
@@ -175,22 +191,9 @@ def set_log_level(level):
         J.PTUtils.setLogLevel(level, None)
 
 
-class JavaClasses:
-    def __init__(self, mapping: Dict[str, str]):
-        self._mapping = mapping
-        self._cache = {}
-
-    def __dir__(self):
-        return list(self._mapping.keys())
-
-    @required(raise_on_not_started=True)
-    def __getattr__(self, key):
-        if key not in self._mapping:
-            return AttributeError(f'{self} has no attribute {key!r}')
-        if key not in self._cache:
-            self._cache[key] = autoclass(self._mapping[key])
-        return self._cache[key]
+# Classes
 
+from pyterrier.java.utils import JavaClasses
 
 J = JavaClasses({
     'ArrayList': 'java.util.ArrayList',
diff --git a/pyterrier/java/bootstrap.py b/pyterrier/java/bootstrap.py
deleted file mode 100644
index 661ef899..00000000
--- a/pyterrier/java/bootstrap.py
+++ /dev/null
@@ -1,396 +0,0 @@
-from typing import Union, List
-import pyterrier as pt
-
-stdout_ref = None
-stderr_ref = None
-SAVED_FNS=[]
-
-class IndexFactory:
-    """
-    The ``of()`` method of this factory class allows to load a Terrier `Index <http://terrier.org/docs/current/javadoc/org/terrier/structures/Index.html>`_.
-
-    NB: This class "shades" the native Terrier `IndexFactory <http://terrier.org/docs/current/javadoc/org/terrier/structures/IndexFactory.html>`_ class - it offers essential the same API,
-    except that the ``of()`` method contains a memory kwarg, that can be used to load additional index data structures into memory. 
-
-    Terrier data structures that can be loaded into memory:
-     - 'inverted' - the inverted index, contains posting lists for each term. In the default configuration, this is read in from disk in chunks.
-     - 'lexicon' - the dictionary. By default, a binary search of the on-disk structure is used, so loading into memory can enhance speed.
-     - 'meta' - metadata about documents. Used as the final stage of retrieval, one seek for each retrieved document.
-     - 'direct' - contains posting lists for each document. No speed advantage for loading into memory unless pseudo-relevance feedback is being used.
-     - 'document' - contains document lengths, which are anyway loaded into memory. No speed advantage for loading into memory unless pseudo-relevance feedback is being used.
-    """
-
-    @staticmethod
-    def _load_into_memory(index, structures=['lexicon', 'direct', 'inverted', 'meta'], load=False):
-
-        REWRITES = {
-            'meta' : {
-                # both metaindex implementations have the same property
-                'org.terrier.structures.ZstdCompressedMetaIndex' : {
-                    'index.meta.index-source' : 'fileinmem',
-                    'index.meta.data-source' : 'fileinmem'},
-            
-                'org.terrier.structures.CompressingMetaIndex' : {
-                    'index.meta.index-source' : 'fileinmem',
-                    'index.meta.data-source' : 'fileinmem'}
-            },
-            'lexicon' : {
-                'org.terrier.structures.FSOMapFileLexicon' : {
-                    'index.lexicon.data-source' : 'fileinmem'
-                }
-            },
-            'direct' : {
-                'org.terrier.structures.bit.BitPostingIndex' : {
-                    'index.direct.data-source' : 'fileinmem'}
-            },
-            'inverted' : {
-                'org.terrier.structures.bit.BitPostingIndex' : {
-                    'index.inverted.data-source' : 'fileinmem'}
-            },
-        }
-        if "direct" in structures:
-            REWRITES['document'] = {
-                # we have to be sensitive to the presence of fields or not
-                # NB: loading these structures into memory only benefit direct index access
-                'org.terrier.structures.FSADocumentIndex' : {
-                    'index.document.class' : 'FSADocumentIndexInMem'
-                }, 
-                'org.terrier.structures.FSAFieldDocumentIndex' : {
-                    'index.document.class' : 'FSADocumentIndexInMemFields'
-                }
-            }
-
-        from . import cast
-        pindex = cast("org.terrier.structures.IndexOnDisk", index)
-        load_profile = pindex.getIndexLoadingProfileAsRetrieval()
-        dirty_structures = set()
-        for s in structures:
-            if not pindex.hasIndexStructure(s):
-                continue
-            clz = pindex.getIndexProperty(f"index.{s}.class", "notfound")
-            if not clz in REWRITES[s]:
-                raise ValueError(f"Cannot load structure {s} into memory, underlying class {clz} is not supported")
-
-            # we only reload an index structure if a property has changed
-            dirty = False
-            for k, v in REWRITES[s][clz].items():
-                if pindex.getIndexProperty(k, "notset") != v:
-                    pindex.setIndexProperty(k, v)
-                    dirty_structures.add(s)
-
-                    # if the document index is reloaded, the inverted index should be reloaded too
-                    # NB: the direct index needs reloaded too, but this option is only available IF
-                    # the direct index is setup
-                    if s == "document":
-                        dirty_structures.add("inverted")
-
-        # remove the old data structures from memory
-        for s in dirty_structures:
-            if pindex.structureCache.containsKey(s):
-                pindex.structureCache.remove(s)
-
-        # force the index structures to be loaded now
-        if load:
-            for s in dirty_structures:
-                pindex.getIndexStructure(s)
-
-        # dont allow the index properties to be rewritten
-        pindex.dirtyProperties = False
-        return index
-
-    @staticmethod 
-    def of(indexlike, memory : Union[bool, List[str]] = False):
-        """
-        Loads an index. Returns a Terrier `Index <http://terrier.org/docs/current/javadoc/org/terrier/structures/Index.html>`_ object.
-
-        Args:
-            indexlike(str or IndexRef): Where is the index located
-            memory(bool or List[str]): If the index should be loaded into memory. Use `True` for all structures, or a list of structure names.
-        """
-        from . import autoclass
-        IOD = autoclass("org.terrier.structures.IndexOnDisk")
-        load_profile =  IOD.getIndexLoadingProfileAsRetrieval()
-
-        if memory or (isinstance(memory, list) and len(memory) > 0): #MEMORY CAN BE A LIST?
-            IOD.setIndexLoadingProfileAsRetrieval(False)
-        index = autoclass("org.terrier.structures.IndexFactory").of(indexlike)
-        
-        # noop if memory is False
-        IOD.setIndexLoadingProfileAsRetrieval(load_profile)
-        if not memory:
-            return index
-        if isinstance(memory, list):
-            return IndexFactory._load_into_memory(index, structures=memory)
-        return IndexFactory._load_into_memory(index)
-
-
-def new_indexref(s):
-    return pt.IndexRef.of(s)
-
-def new_wmodel(bytes):
-    from . import autoclass
-    serUtils = autoclass("org.terrier.python.Serialization")
-    return serUtils.deserialize(bytes, autoclass("org.terrier.utility.ApplicationSetup").getClass("org.terrier.matching.models.WeightingModel") )
-
-def new_callable_wmodel(byterep):
-    import dill as pickle
-    from dill import extend
-    #see https://github.com/SeldonIO/alibi/issues/447#issuecomment-881552005
-    extend(use_dill=False)            
-    fn = pickle.loads(byterep)
-    #we need to prevent these functions from being GCd.
-    global SAVED_FNS
-    SAVED_FNS.append(fn)
-    callback, wmodel = pt.terrier.retriever._function2wmodel(fn)
-    SAVED_FNS.append(callback)
-    #print("Stored lambda fn  %s and callback in SAVED_FNS, now %d stored" % (str(fn), len(SAVED_FNS)))
-    return wmodel
-
-def javabytebuffer2array(buffer):
-    assert buffer is not None
-    def unsign(signed):
-        return signed + 256 if signed < 0 else signed
-    return bytearray([ unsign(buffer.get(offset)) for offset in range(buffer.capacity()) ])
-
-def setup_jnius():
-    from jnius import protocol_map # , autoclass
-
-    def _iterableposting_next(self):
-        ''' dunder method for iterating IterablePosting '''
-        nextid = self.next()
-        # 2147483647 is IP.EOL. fix this once static fields can be read from instances.
-        if 2147483647 == nextid:
-            raise StopIteration()
-        return self
-    
-    # Map$Entry can be decoded like a tuple
-    def MEgetitem(self, i):
-        if i == 0:
-            return self.getKey()
-        if i == 1:
-            return self.getValue()
-        raise IndexError()
-    
-    protocol_map['java.util.Map$Entry'] = {
-        '__getitem__' : MEgetitem,
-        '__iter__' : lambda self: iter([self.getKey(), self.getValue()]),
-        '__len__' : lambda self: 2
-    }
-
-    protocol_map["org.terrier.structures.postings.IterablePosting"] = {
-        '__iter__': lambda self: self,
-        '__next__': lambda self: _iterableposting_next(self),
-        '__str__': lambda self: self.toString()
-    }
-
-    protocol_map["org.terrier.structures.CollectionStatistics"] = {
-        '__str__': lambda self: self.toString()
-    }
-
-    protocol_map["org.terrier.structures.LexiconEntry"] = {
-        '__str__': lambda self: self.toString()
-    }
-
-    def _lexicon_getitem(self, term):
-        ''' dunder method for accessing Lexicon '''
-        rtr = self.getLexiconEntry(term)
-        if rtr is None:
-            raise KeyError()
-        return rtr
-
-    protocol_map["org.terrier.structures.Lexicon"] = {
-        '__getitem__': _lexicon_getitem,
-        '__contains__': lambda self, term: self.getLexiconEntry(term) is not None,
-        '__len__': lambda self: self.numberOfEntries()
-    }
-
-    def index_ref_reduce(self):
-        return (
-            new_indexref,
-            (str(self.toString()),),
-            None
-        )
-
-    protocol_map["org.terrier.querying.IndexRef"] = {
-        '__reduce__' : index_ref_reduce,
-        '__getstate__' : lambda self : None,
-    }
-
-
-    # handles the pickling of WeightingModel classes, which are themselves usually Serializable in Java
-    def wmodel_reduce(self):
-        from . import autoclass
-        serUtils = autoclass("org.terrier.python.Serialization")
-        serialized = bytes(serUtils.serialize(self))
-        return (
-            new_wmodel,
-            (serialized, ),
-            None
-        )
-
-    protocol_map["org.terrier.matching.models.WeightingModel"] = {
-        '__reduce__' : wmodel_reduce,
-        '__getstate__' : lambda self : None,
-    }
-
-    def callable_wmodel_reduce(self):
-        from . import autoclass
-        # get bytebuffer representation of lambda
-        # convert bytebyffer to python bytearray
-        serlzd = self.scoringClass.serializeFn()
-        bytesrep = javabytebuffer2array(serlzd)
-        del(serlzd)
-        return (
-            new_callable_wmodel,
-            (bytesrep, ),
-            None
-        )
-
-    protocol_map["org.terrier.python.CallableWeightingModel"] = {
-        '__reduce__' : callable_wmodel_reduce,
-        '__getstate__' : lambda self : None,
-    }
-
-    def _index_add(self, other):
-        from . import autoclass
-        fields_1 = self.getCollectionStatistics().getNumberOfFields()
-        fields_2 = self.getCollectionStatistics().getNumberOfFields()
-        if fields_1 != fields_2:
-            raise ValueError("Cannot document-wise merge indices with different numbers of fields (%d vs %d)" % (fields_1, fields_2))
-        blocks_1 = self.getCollectionStatistics().hasPositions()
-        blocks_2 = other.getCollectionStatistics().hasPositions()
-        if blocks_1 != blocks_2:
-            raise ValueError("Cannot document-wise merge indices with and without positions (%r vs %r)" % (blocks_1, blocks_2))
-        multiindex_cls = autoclass("org.terrier.realtime.multi.MultiIndex")
-        return multiindex_cls([self, other], blocks_1, fields_1 > 0)
-    
-    def _index_corpusiter(self, return_toks=True):
-        def _index_corpusiter_meta(self):
-            meta_inputstream = self.getIndexStructureInputStream("meta")
-            keys = self.getMetaIndex().getKeys()
-            keys_offset = { k: offset for offset, k in enumerate(keys) }
-            while meta_inputstream.hasNext():
-                item = meta_inputstream.next()
-                yield {k : item[keys_offset[k]] for k in keys_offset}
-
-        def _index_corpusiter_direct_pretok(self):
-            import sys
-            MIN_PYTHON = (3, 8)
-            if sys.version_info < MIN_PYTHON:
-                raise NotImplementedError("Sorry, Python 3.8+ is required for this functionality")
-
-            meta_inputstream = self.getIndexStructureInputStream("meta")
-            keys = self.getMetaIndex().getKeys()
-            keys_offset = { k: offset for offset, k in enumerate(keys) }
-            keys_offset = {'docno' : keys_offset['docno'] }
-            direct_inputstream = self.getIndexStructureInputStream("direct")
-            lex = self.getLexicon()
-
-            ip = None
-            while (ip := direct_inputstream.getNextPostings()) is not None: # this is the next() method
-
-                # yield empty toks dicts for empty documents
-                for skipped in range(0, direct_inputstream.getEntriesSkipped()):
-                    meta = meta_inputstream.next()
-                    rtr = {k : meta[keys_offset[k]] for k in keys_offset}   
-                    rtr['toks'] = {}
-                    yield rtr
-
-                toks = {}
-                while ip.next() != ip.EOL:
-                    t, _ = lex[ip.getId()]
-                    toks[t] = ip.getFrequency()
-                meta = meta_inputstream.next()
-                rtr = {'toks' : toks}
-                rtr.update({k : meta[keys_offset[k]] for k in keys_offset})
-                yield rtr
-
-            # yield for trailing empty documents
-            for skipped in range(0, direct_inputstream.getEntriesSkipped()):
-                meta = meta_inputstream.next()
-                rtr = {k : meta[keys_offset[k]] for k in keys_offset}   
-                rtr['toks'] = {}
-                yield rtr
-        
-        if return_toks:
-            if not self.hasIndexStructureInputStream("direct"):
-                raise ValueError("No direct index input stream available, cannot use return_toks=True")
-            return _index_corpusiter_direct_pretok(self)
-        return _index_corpusiter_meta(self)
-
-    protocol_map["org.terrier.structures.Index"] = {
-        # this means that len(index) returns the number of documents in the index
-        '__len__': lambda self: self.getCollectionStatistics().getNumberOfDocuments(),
-
-        # document-wise composition of indices: adding more documents to an index, by merging two indices with 
-        # different numbers of documents. This implemented by the overloading the `+` Python operator
-        '__add__': _index_add,
-
-        # get_corpus_iter returns a yield generator that return {"docno": "d1", "toks" : {'a' : 1}}
-        'get_corpus_iter' : _index_corpusiter
-    }
-
-
-def is_windows() -> bool:
-    import platform
-    return platform.system() == 'Windows'
-
-def is_binary(f):
-    import io
-    return isinstance(f, (io.RawIOBase, io.BufferedIOBase))
-
-def redirect_stdouterr():
-    from jnius import autoclass, PythonJavaClass, java_method
-
-    # TODO: encodings may be a probem here
-    class MyOut(PythonJavaClass):
-        __javainterfaces__ = ['org.terrier.python.OutputStreamable']
-
-        def __init__(self, pystream):
-            super(MyOut, self).__init__()
-            self.pystream = pystream
-            self.binary = is_binary(pystream)
-
-        @java_method('()V')
-        def close(self):
-            self.pystream.close()
-
-        @java_method('()V')
-        def flush(self):
-            self.pystream.flush()
-
-        @java_method('([B)V', name='write')
-        def writeByteArray(self, byteArray):
-            # TODO probably this could be faster.
-            for c in byteArray:
-                self.writeChar(c)
-
-        @java_method('([BII)V', name='write')
-        def writeByteArrayIntInt(self, byteArray, offset, length):
-            # TODO probably this could be faster.
-            for i in range(offset, offset + length):
-                self.writeChar(byteArray[i])
-
-        @java_method('(I)V', name='write')
-        def writeChar(self, chara):
-            if self.binary:
-                return self.pystream.write(bytes([chara]))
-            return self.pystream.write(chr(chara))
-
-    # we need to hold lifetime references to stdout_ref/stderr_ref, to ensure
-    # they arent GCd. This prevents a crash when Java callsback to  GCd py obj
-
-    global stdout_ref
-    global stderr_ref
-    import sys
-    stdout_ref = MyOut(sys.stdout)
-    stderr_ref = MyOut(sys.stderr)
-    jls = autoclass("java.lang.System")
-    jls.setOut(
-        autoclass('java.io.PrintStream')(
-            autoclass('org.terrier.python.ProxyableOutputStream')(stdout_ref),
-            signature="(Ljava/io/OutputStream;)V"))
-    jls.setErr(
-        autoclass('java.io.PrintStream')(
-            autoclass('org.terrier.python.ProxyableOutputStream')(stderr_ref),
-            signature="(Ljava/io/OutputStream;)V"))
diff --git a/pyterrier/java/utils.py b/pyterrier/java/utils.py
new file mode 100644
index 00000000..548ed039
--- /dev/null
+++ b/pyterrier/java/utils.py
@@ -0,0 +1,92 @@
+import pyterrier as pt
+from typing import Dict
+
+
+stdout_ref = None
+stderr_ref = None
+
+
+def _is_binary(f):
+    import io
+    return isinstance(f, (io.RawIOBase, io.BufferedIOBase))
+
+
+def redirect_stdouterr():
+    from jnius import autoclass, PythonJavaClass, java_method
+
+    # TODO: encodings may be a probem here
+    class MyOut(PythonJavaClass):
+        __javainterfaces__ = ['org.terrier.python.OutputStreamable']
+
+        def __init__(self, pystream):
+            super(MyOut, self).__init__()
+            self.pystream = pystream
+            self.binary = _is_binary(pystream)
+
+        @java_method('()V')
+        def close(self):
+            self.pystream.close()
+
+        @java_method('()V')
+        def flush(self):
+            self.pystream.flush()
+
+        @java_method('([B)V', name='write')
+        def writeByteArray(self, byteArray):
+            # TODO probably this could be faster.
+            for c in byteArray:
+                self.writeChar(c)
+
+        @java_method('([BII)V', name='write')
+        def writeByteArrayIntInt(self, byteArray, offset, length):
+            # TODO probably this could be faster.
+            for i in range(offset, offset + length):
+                self.writeChar(byteArray[i])
+
+        @java_method('(I)V', name='write')
+        def writeChar(self, chara):
+            if self.binary:
+                return self.pystream.write(bytes([chara]))
+            return self.pystream.write(chr(chara))
+
+    # we need to hold lifetime references to stdout_ref/stderr_ref, to ensure
+    # they arent GCd. This prevents a crash when Java callsback to  GCd py obj
+
+    global stdout_ref
+    global stderr_ref
+    import sys
+    stdout_ref = MyOut(sys.stdout)
+    stderr_ref = MyOut(sys.stderr)
+    jls = autoclass("java.lang.System")
+    jls.setOut(
+        autoclass('java.io.PrintStream')(
+            autoclass('org.terrier.python.ProxyableOutputStream')(stdout_ref),
+            signature="(Ljava/io/OutputStream;)V"))
+    jls.setErr(
+        autoclass('java.io.PrintStream')(
+            autoclass('org.terrier.python.ProxyableOutputStream')(stderr_ref),
+            signature="(Ljava/io/OutputStream;)V"))
+
+
+def bytebuffer_to_array(buffer):
+    assert buffer is not None
+    def unsign(signed):
+        return signed + 256 if signed < 0 else signed
+    return bytearray([ unsign(buffer.get(offset)) for offset in range(buffer.capacity()) ])
+
+
+class JavaClasses:
+    def __init__(self, mapping: Dict[str, str]):
+        self._mapping = mapping
+        self._cache = {}
+
+    def __dir__(self):
+        return list(self._mapping.keys())
+
+    @pt.java.required(raise_on_not_started=True)
+    def __getattr__(self, key):
+        if key not in self._mapping:
+            return AttributeError(f'{self} has no attribute {key!r}')
+        if key not in self._cache:
+            self._cache[key] = pt.java.autoclass(self._mapping[key])
+        return self._cache[key]
diff --git a/pyterrier/parallel.py b/pyterrier/parallel.py
index 0e5ae5b2..d1dbadee 100644
--- a/pyterrier/parallel.py
+++ b/pyterrier/parallel.py
@@ -43,7 +43,7 @@ def _check_ray():
 
 
 def parallel_lambda(function, inputs, jobs, backend='joblib'):
-    if pt.java.bootstrap.is_windows():
+    if pt.utils.is_windows():
         raise ValueError("No support for parallelisation on Windows")
     if backend not in SUPPORTED_BACKENDS:
         raise ValueError("Backend of %s unknown, only %s supported." % str(SUPPORTED_BACKENDS))
@@ -72,7 +72,7 @@ def __init__(self, parent, n_jobs, backend='joblib', **kwargs):
         self.parent = parent
         self.n_jobs = n_jobs
         self.backend = backend
-        if pt.java.bootstrap.is_windows():
+        if pt.utils.is_windows():
             raise ValueError("No support for parallelisation on Windows")
         if self.backend not in SUPPORTED_BACKENDS:
             raise ValueError("Backend of %s unknown, only %s supported." % str(SUPPORTED_BACKENDS))
diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
index d8ed91eb..4bd7c517 100644
--- a/pyterrier/terrier/__init__.py
+++ b/pyterrier/terrier/__init__.py
@@ -2,6 +2,7 @@
 from pyterrier.terrier import java
 from pyterrier.terrier.java import configure, set_terrier_version, set_helper_version, enable_prf, extend_package, J
 from pyterrier.terrier.retriever import BatchRetrieve, FeaturesBatchRetrieve
+from pyterrier.terrier.index_factory import IndexFactory
 
 TerrierRetrieve = BatchRetrieve # BatchRetrieve is an alias to TerrierRetrieve
 
@@ -12,4 +13,7 @@
 
     # retrieval
     'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve',
+
+    # misc
+    'IndexFactory',
 ]
diff --git a/pyterrier/terrier/index_factory.py b/pyterrier/terrier/index_factory.py
new file mode 100644
index 00000000..2c33e982
--- /dev/null
+++ b/pyterrier/terrier/index_factory.py
@@ -0,0 +1,120 @@
+from typing import Union, List
+
+
+class IndexFactory:
+    """
+    The ``of()`` method of this factory class allows to load a Terrier `Index <http://terrier.org/docs/current/javadoc/org/terrier/structures/Index.html>`_.
+
+    NB: This class "shades" the native Terrier `IndexFactory <http://terrier.org/docs/current/javadoc/org/terrier/structures/IndexFactory.html>`_ class - it offers essential the same API,
+    except that the ``of()`` method contains a memory kwarg, that can be used to load additional index data structures into memory. 
+
+    Terrier data structures that can be loaded into memory:
+     - 'inverted' - the inverted index, contains posting lists for each term. In the default configuration, this is read in from disk in chunks.
+     - 'lexicon' - the dictionary. By default, a binary search of the on-disk structure is used, so loading into memory can enhance speed.
+     - 'meta' - metadata about documents. Used as the final stage of retrieval, one seek for each retrieved document.
+     - 'direct' - contains posting lists for each document. No speed advantage for loading into memory unless pseudo-relevance feedback is being used.
+     - 'document' - contains document lengths, which are anyway loaded into memory. No speed advantage for loading into memory unless pseudo-relevance feedback is being used.
+    """
+
+    @staticmethod
+    def _load_into_memory(index, structures=['lexicon', 'direct', 'inverted', 'meta'], load=False):
+
+        REWRITES = {
+            'meta' : {
+                # both metaindex implementations have the same property
+                'org.terrier.structures.ZstdCompressedMetaIndex' : {
+                    'index.meta.index-source' : 'fileinmem',
+                    'index.meta.data-source' : 'fileinmem'},
+            
+                'org.terrier.structures.CompressingMetaIndex' : {
+                    'index.meta.index-source' : 'fileinmem',
+                    'index.meta.data-source' : 'fileinmem'}
+            },
+            'lexicon' : {
+                'org.terrier.structures.FSOMapFileLexicon' : {
+                    'index.lexicon.data-source' : 'fileinmem'
+                }
+            },
+            'direct' : {
+                'org.terrier.structures.bit.BitPostingIndex' : {
+                    'index.direct.data-source' : 'fileinmem'}
+            },
+            'inverted' : {
+                'org.terrier.structures.bit.BitPostingIndex' : {
+                    'index.inverted.data-source' : 'fileinmem'}
+            },
+        }
+        if "direct" in structures:
+            REWRITES['document'] = {
+                # we have to be sensitive to the presence of fields or not
+                # NB: loading these structures into memory only benefit direct index access
+                'org.terrier.structures.FSADocumentIndex' : {
+                    'index.document.class' : 'FSADocumentIndexInMem'
+                }, 
+                'org.terrier.structures.FSAFieldDocumentIndex' : {
+                    'index.document.class' : 'FSADocumentIndexInMemFields'
+                }
+            }
+
+        from . import cast
+        pindex = cast("org.terrier.structures.IndexOnDisk", index)
+        load_profile = pindex.getIndexLoadingProfileAsRetrieval()
+        dirty_structures = set()
+        for s in structures:
+            if not pindex.hasIndexStructure(s):
+                continue
+            clz = pindex.getIndexProperty(f"index.{s}.class", "notfound")
+            if not clz in REWRITES[s]:
+                raise ValueError(f"Cannot load structure {s} into memory, underlying class {clz} is not supported")
+
+            # we only reload an index structure if a property has changed
+            dirty = False
+            for k, v in REWRITES[s][clz].items():
+                if pindex.getIndexProperty(k, "notset") != v:
+                    pindex.setIndexProperty(k, v)
+                    dirty_structures.add(s)
+
+                    # if the document index is reloaded, the inverted index should be reloaded too
+                    # NB: the direct index needs reloaded too, but this option is only available IF
+                    # the direct index is setup
+                    if s == "document":
+                        dirty_structures.add("inverted")
+
+        # remove the old data structures from memory
+        for s in dirty_structures:
+            if pindex.structureCache.containsKey(s):
+                pindex.structureCache.remove(s)
+
+        # force the index structures to be loaded now
+        if load:
+            for s in dirty_structures:
+                pindex.getIndexStructure(s)
+
+        # dont allow the index properties to be rewritten
+        pindex.dirtyProperties = False
+        return index
+
+    @staticmethod 
+    def of(indexlike, memory : Union[bool, List[str]] = False):
+        """
+        Loads an index. Returns a Terrier `Index <http://terrier.org/docs/current/javadoc/org/terrier/structures/Index.html>`_ object.
+
+        Args:
+            indexlike(str or IndexRef): Where is the index located
+            memory(bool or List[str]): If the index should be loaded into memory. Use `True` for all structures, or a list of structure names.
+        """
+        from . import autoclass
+        IOD = autoclass("org.terrier.structures.IndexOnDisk")
+        load_profile =  IOD.getIndexLoadingProfileAsRetrieval()
+
+        if memory or (isinstance(memory, list) and len(memory) > 0): #MEMORY CAN BE A LIST?
+            IOD.setIndexLoadingProfileAsRetrieval(False)
+        index = autoclass("org.terrier.structures.IndexFactory").of(indexlike)
+        
+        # noop if memory is False
+        IOD.setIndexLoadingProfileAsRetrieval(load_profile)
+        if not memory:
+            return index
+        if isinstance(memory, list):
+            return IndexFactory._load_into_memory(index, structures=memory)
+        return IndexFactory._load_into_memory(index)
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index b20b248e..2a9bfc83 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -4,6 +4,8 @@
 
 TERRIER_PKG = "org.terrier"
 
+_SAVED_FNS = []
+
 _resolved_helper_version = None
 
 configure = pt.java.config.register('pt.terrier.java', {
@@ -76,6 +78,196 @@ def _post_init(jnius):
 
     pt.IndexRef = J.IndexRef
 
+    jnius.protocol_map["org.terrier.structures.postings.IterablePosting"] = {
+        '__iter__': lambda self: self,
+        '__next__': lambda self: _iterableposting_next(self),
+        '__str__': lambda self: self.toString()
+    }
+
+    jnius.protocol_map["org.terrier.structures.CollectionStatistics"] = {
+        '__str__': lambda self: self.toString()
+    }
+
+    jnius.protocol_map["org.terrier.structures.LexiconEntry"] = {
+        '__str__': lambda self: self.toString()
+    }
+
+    jnius.protocol_map["org.terrier.structures.Lexicon"] = {
+        '__getitem__': _lexicon_getitem,
+        '__contains__': lambda self, term: self.getLexiconEntry(term) is not None,
+        '__len__': lambda self: self.numberOfEntries()
+    }
+
+    jnius.protocol_map["org.terrier.querying.IndexRef"] = {
+        '__reduce__' : _index_ref_reduce,
+        '__getstate__' : lambda self : None,
+    }
+
+    jnius.protocol_map["org.terrier.matching.models.WeightingModel"] = {
+        '__reduce__' : _wmodel_reduce,
+        '__getstate__' : lambda self : None,
+    }
+
+    jnius.protocol_map["org.terrier.python.CallableWeightingModel"] = {
+        '__reduce__' : _callable_wmodel_reduce,
+        '__getstate__' : lambda self : None,
+    }
+
+    jnius.protocol_map["org.terrier.structures.Index"] = {
+        # this means that len(index) returns the number of documents in the index
+        '__len__': lambda self: self.getCollectionStatistics().getNumberOfDocuments(),
+
+        # document-wise composition of indices: adding more documents to an index, by merging two indices with 
+        # different numbers of documents. This implemented by the overloading the `+` Python operator
+        '__add__': _index_add,
+
+        # get_corpus_iter returns a yield generator that return {"docno": "d1", "toks" : {'a' : 1}}
+        'get_corpus_iter' : _index_corpusiter
+    }
+
+
+def _new_indexref(s):
+    return pt.IndexRef.of(s)
+
+
+def _new_wmodel(bytes):
+    from . import autoclass
+    serUtils = autoclass("org.terrier.python.Serialization")
+    return serUtils.deserialize(bytes, autoclass("org.terrier.utility.ApplicationSetup").getClass("org.terrier.matching.models.WeightingModel") )
+
+
+def _new_callable_wmodel(byterep):
+    import dill as pickle
+    from dill import extend
+    #see https://github.com/SeldonIO/alibi/issues/447#issuecomment-881552005
+    extend(use_dill=False)            
+    fn = pickle.loads(byterep)
+    #we need to prevent these functions from being GCd.
+    global _SAVED_FNS
+    _SAVED_FNS.append(fn)
+    callback, wmodel = pt.terrier.retriever._function2wmodel(fn)
+    _SAVED_FNS.append(callback)
+    #print("Stored lambda fn  %s and callback in SAVED_FNS, now %d stored" % (str(fn), len(SAVED_FNS)))
+    return wmodel
+
+
+def _iterableposting_next(self):
+    ''' dunder method for iterating IterablePosting '''
+    nextid = self.next()
+    # 2147483647 is IP.EOL. fix this once static fields can be read from instances.
+    if 2147483647 == nextid:
+        raise StopIteration()
+    return self
+
+
+def _lexicon_getitem(self, term):
+    ''' dunder method for accessing Lexicon '''
+    rtr = self.getLexiconEntry(term)
+    if rtr is None:
+        raise KeyError()
+    return rtr
+
+
+def _index_ref_reduce(self):
+    return (
+        _new_indexref,
+        (str(self.toString()),),
+        None
+    )
+
+
+# handles the pickling of WeightingModel classes, which are themselves usually Serializable in Java
+def _wmodel_reduce(self):
+    serialized = bytes(J.Serialization.serialize(self))
+    return (
+        _new_wmodel,
+        (serialized, ),
+        None
+    )
+
+
+def _callable_wmodel_reduce(self):
+    # get bytebuffer representation of lambda
+    # convert bytebyffer to python bytearray
+    serlzd = self.scoringClass.serializeFn()
+    bytesrep = pt.java.bytebuffer_to_array(serlzd)
+    del(serlzd)
+    return (
+        _new_callable_wmodel,
+        (bytesrep, ),
+        None
+    )
+
+
+def _index_add(self, other):
+    from . import autoclass
+    fields_1 = self.getCollectionStatistics().getNumberOfFields()
+    fields_2 = self.getCollectionStatistics().getNumberOfFields()
+    if fields_1 != fields_2:
+        raise ValueError("Cannot document-wise merge indices with different numbers of fields (%d vs %d)" % (fields_1, fields_2))
+    blocks_1 = self.getCollectionStatistics().hasPositions()
+    blocks_2 = other.getCollectionStatistics().hasPositions()
+    if blocks_1 != blocks_2:
+        raise ValueError("Cannot document-wise merge indices with and without positions (%r vs %r)" % (blocks_1, blocks_2))
+    multiindex_cls = autoclass("org.terrier.realtime.multi.MultiIndex")
+    return multiindex_cls([self, other], blocks_1, fields_1 > 0)
+
+
+def _index_corpusiter(self, return_toks=True):
+    def _index_corpusiter_meta(self):
+        meta_inputstream = self.getIndexStructureInputStream("meta")
+        keys = self.getMetaIndex().getKeys()
+        keys_offset = { k: offset for offset, k in enumerate(keys) }
+        while meta_inputstream.hasNext():
+            item = meta_inputstream.next()
+            yield {k : item[keys_offset[k]] for k in keys_offset}
+
+    def _index_corpusiter_direct_pretok(self):
+        import sys
+        MIN_PYTHON = (3, 8)
+        if sys.version_info < MIN_PYTHON:
+            raise NotImplementedError("Sorry, Python 3.8+ is required for this functionality")
+
+        meta_inputstream = self.getIndexStructureInputStream("meta")
+        keys = self.getMetaIndex().getKeys()
+        keys_offset = { k: offset for offset, k in enumerate(keys) }
+        keys_offset = {'docno' : keys_offset['docno'] }
+        direct_inputstream = self.getIndexStructureInputStream("direct")
+        lex = self.getLexicon()
+
+        ip = None
+        while (ip := direct_inputstream.getNextPostings()) is not None: # this is the next() method
+
+            # yield empty toks dicts for empty documents
+            for skipped in range(0, direct_inputstream.getEntriesSkipped()):
+                meta = meta_inputstream.next()
+                rtr = {k : meta[keys_offset[k]] for k in keys_offset}   
+                rtr['toks'] = {}
+                yield rtr
+
+            toks = {}
+            while ip.next() != ip.EOL:
+                t, _ = lex[ip.getId()]
+                toks[t] = ip.getFrequency()
+            meta = meta_inputstream.next()
+            rtr = {'toks' : toks}
+            rtr.update({k : meta[keys_offset[k]] for k in keys_offset})
+            yield rtr
+
+        # yield for trailing empty documents
+        for skipped in range(0, direct_inputstream.getEntriesSkipped()):
+            meta = meta_inputstream.next()
+            rtr = {k : meta[keys_offset[k]] for k in keys_offset}   
+            rtr['toks'] = {}
+            yield rtr
+    
+    if return_toks:
+        if not self.hasIndexStructureInputStream("direct"):
+            raise ValueError("No direct index input stream available, cannot use return_toks=True")
+        return _index_corpusiter_direct_pretok(self)
+    return _index_corpusiter_meta(self)
+
+
 
 @pt.java.required()
 def extend_package(package):
@@ -96,4 +288,5 @@ def extend_package(package):
     'IndexRef': 'org.terrier.querying.IndexRef',
     'Version': 'org.terrier.Version',
     'Tokenizer': 'org.terrier.indexing.tokenisation.Tokeniser',
+    'Serialization': 'org.terrier.python.Serialization',
 })
diff --git a/pyterrier/utils.py b/pyterrier/utils.py
index b6823330..55dd3250 100644
--- a/pyterrier/utils.py
+++ b/pyterrier/utils.py
@@ -1,4 +1,5 @@
 from typing import Callable, Tuple
+import platform
 from functools import wraps
 from importlib.metadata import EntryPoint
 from importlib.metadata import entry_points as eps
@@ -109,3 +110,7 @@ def entry_points(group: str) -> Tuple[EntryPoint, ...]:
         return tuple(eps(group=group))
     except TypeError:
         return tuple(eps().get(group, tuple()))
+
+
+def is_windows() -> bool:
+    return platform.system() == 'Windows'
diff --git a/tests/base.py b/tests/base.py
index 4aadb9c8..3e92b1e0 100644
--- a/tests/base.py
+++ b/tests/base.py
@@ -25,20 +25,15 @@ def __init__(self, *args, **kwargs):
 
 
     def skip_windows(self):
-        if BaseTestCase.is_windows():
+        if pt.utils.is_windows():
             self.skipTest("Test disabled on Windows")
 
-    @staticmethod
-    def is_windows() -> bool:
-        import platform
-        return platform.system() == 'Windows'
 
 class TempDirTestCase(BaseTestCase):
     def setUp(self):
         self.test_dir = tempfile.mkdtemp()
 
     def tearDown(self):
-        import shutil
         try:
             shutil.rmtree(self.test_dir)
         except:
diff --git a/tests/test_dunder.py b/tests/test_dunder.py
index 44966fd1..8c82b15f 100644
--- a/tests/test_dunder.py
+++ b/tests/test_dunder.py
@@ -11,8 +11,7 @@ def test_callable_wmodel_dunders(self):
         lambdafn = lambda keyFreq, posting, entryStats, collStats: posting.getFrequency()
         callback, wmodel = pt.terrier.retriever._function2wmodel(lambdafn)
         
-        from pyterrier.java.bootstrap import javabytebuffer2array
-        byterep = javabytebuffer2array(wmodel.scoringClass.serializeFn())
+        byterep = pt.java.bytebuffer_to_array(wmodel.scoringClass.serializeFn())
         import dill as pickle
         from dill import extend
         #see https://github.com/SeldonIO/alibi/issues/447#issuecomment-881552005
diff --git a/tests/test_iterdictindex.py b/tests/test_iterdictindex.py
index 4d7aeafc..aa36ff83 100644
--- a/tests/test_iterdictindex.py
+++ b/tests/test_iterdictindex.py
@@ -25,7 +25,7 @@ def _make_check_index(self, n, index_type, fields=('text',), meta=('docno', 'url
             pt.index._IterDictIndexer_fifo(self.test_dir, type=index_type, threads=4, meta=meta),
             pt.index._IterDictIndexer_nofifo(self.test_dir, type=index_type, meta=meta),
         ]
-        if BaseTestCase.is_windows():
+        if pt.utils.is_windows():
            indexers = [indexers[-1]] 
         for indexer in indexers:
             with self.subTest(indexer=indexer):
diff --git a/tests/test_iterdictindex_pretok.py b/tests/test_iterdictindex_pretok.py
index b91e2f48..2a598ec4 100644
--- a/tests/test_iterdictindex_pretok.py
+++ b/tests/test_iterdictindex_pretok.py
@@ -91,7 +91,7 @@ def _make_pretok_index(self, n, index_type, meta=('docno', 'url')):
             pt.index._IterDictIndexer_fifo(self.test_dir, type=index_type, threads=4, meta=meta, pretokenised=True),
             pt.index._IterDictIndexer_nofifo(self.test_dir, type=index_type, meta=meta, pretokenised=True),
         ]
-        if BaseTestCase.is_windows():
+        if pt.utils.is_windows():
            indexers = [indexers[-1]] 
         for indexer in indexers:
             with self.subTest(indexer=indexer):

From 0fd81085798bd13b85f986c6fc1c1ca225a5ab47 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 18:25:19 +0100
Subject: [PATCH 037/153] forgot to remove bootstrap

---
 pyterrier/__init__.py      | 2 +-
 pyterrier/java/__init__.py | 7 ++++---
 pyterrier/java/utils.py    | 3 ++-
 3 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index a20537db..0247cc07 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -207,7 +207,7 @@ def redirect_stdouterr():
     """
         Ensure that stdout and stderr have been redirected. Equivalent to setting the redirect_io parameter to init() as `True`.
     """
-    java.bootstrap.redirect_stdouterr()
+    java.redirect_stdouterr()
 
 
 @java.required()
diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index 20f985b7..978cecfc 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -4,9 +4,7 @@
 from typing import Callable, Optional, Dict, Tuple, Any
 from warnings import warn
 import pyterrier as pt
-from pyterrier.java import bootstrap
 from pyterrier.java import mavenresolver
-from pyterrier.java.utils import redirect_stdouterr, bytebuffer_to_array
 
 _started = False
 
@@ -72,7 +70,6 @@ def init() -> None:
     import jnius
     _started = True
 
-    bootstrap.setup_jnius()
     set_log_level(cfg['log_level'])
 
     java_version = J.System.getProperty("java.version")
@@ -191,6 +188,10 @@ def set_log_level(level):
         J.PTUtils.setLogLevel(level, None)
 
 
+# Utils
+from pyterrier.java.utils import redirect_stdouterr, bytebuffer_to_array
+
+
 # Classes
 
 from pyterrier.java.utils import JavaClasses
diff --git a/pyterrier/java/utils.py b/pyterrier/java/utils.py
index 548ed039..f95c92cf 100644
--- a/pyterrier/java/utils.py
+++ b/pyterrier/java/utils.py
@@ -1,4 +1,5 @@
 import pyterrier as pt
+from pyterrier.java import required
 from typing import Dict
 
 
@@ -83,7 +84,7 @@ def __init__(self, mapping: Dict[str, str]):
     def __dir__(self):
         return list(self._mapping.keys())
 
-    @pt.java.required(raise_on_not_started=True)
+    @required(raise_on_not_started=True)
     def __getattr__(self, key):
         if key not in self._mapping:
             return AttributeError(f'{self} has no attribute {key!r}')

From ea115a84bcaec5671cb69ae8ffe51f9f8918f8fb Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 18:33:05 +0100
Subject: [PATCH 038/153] wip

---
 pyterrier/terrier/index_factory.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/pyterrier/terrier/index_factory.py b/pyterrier/terrier/index_factory.py
index 2c33e982..b607d4e5 100644
--- a/pyterrier/terrier/index_factory.py
+++ b/pyterrier/terrier/index_factory.py
@@ -1,4 +1,5 @@
 from typing import Union, List
+import pyterrier as pt
 
 
 class IndexFactory:
@@ -103,16 +104,14 @@ def of(indexlike, memory : Union[bool, List[str]] = False):
             indexlike(str or IndexRef): Where is the index located
             memory(bool or List[str]): If the index should be loaded into memory. Use `True` for all structures, or a list of structure names.
         """
-        from . import autoclass
-        IOD = autoclass("org.terrier.structures.IndexOnDisk")
-        load_profile =  IOD.getIndexLoadingProfileAsRetrieval()
+        load_profile = pt.terrier.J.IndexOnDisk.getIndexLoadingProfileAsRetrieval()
 
         if memory or (isinstance(memory, list) and len(memory) > 0): #MEMORY CAN BE A LIST?
-            IOD.setIndexLoadingProfileAsRetrieval(False)
-        index = autoclass("org.terrier.structures.IndexFactory").of(indexlike)
+            pt.terrier.J.IndexOnDisk.setIndexLoadingProfileAsRetrieval(False)
+        index = pt.terrier.J.IndexFactory.of(indexlike)
         
         # noop if memory is False
-        IOD.setIndexLoadingProfileAsRetrieval(load_profile)
+        pt.terrier.J.IndexOnDisk.setIndexLoadingProfileAsRetrieval(load_profile)
         if not memory:
             return index
         if isinstance(memory, list):

From cade2ece68cc81b1f7bf125a15642f664841e27e Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 18:33:09 +0100
Subject: [PATCH 039/153] wip

---
 pyterrier/terrier/java.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 2a9bfc83..d71a4d1d 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -289,4 +289,6 @@ def extend_package(package):
     'Version': 'org.terrier.Version',
     'Tokenizer': 'org.terrier.indexing.tokenisation.Tokeniser',
     'Serialization': 'org.terrier.python.Serialization',
+    'IndexOnDisk': 'org.terrier.structures.IndexOnDisk',
+    'IndexFactory': 'org.terrier.structures.IndexFactory',
 })

From a7f64fd37e30e831016bc9974be0b2b3b837d070 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 18:39:56 +0100
Subject: [PATCH 040/153] import

---
 pyterrier/terrier/index_factory.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/pyterrier/terrier/index_factory.py b/pyterrier/terrier/index_factory.py
index b607d4e5..b21b3469 100644
--- a/pyterrier/terrier/index_factory.py
+++ b/pyterrier/terrier/index_factory.py
@@ -57,8 +57,7 @@ def _load_into_memory(index, structures=['lexicon', 'direct', 'inverted', 'meta'
                 }
             }
 
-        from . import cast
-        pindex = cast("org.terrier.structures.IndexOnDisk", index)
+        pindex = pt.java.cast("org.terrier.structures.IndexOnDisk", index)
         load_profile = pindex.getIndexLoadingProfileAsRetrieval()
         dirty_structures = set()
         for s in structures:

From 342873f6c34a4b506397c7b70bced4ee3b321b43 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 18:47:52 +0100
Subject: [PATCH 041/153] imports :(

---
 pyterrier/terrier/java.py | 12 ++++--------
 1 file changed, 4 insertions(+), 8 deletions(-)

diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index d71a4d1d..748a23ac 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -130,10 +130,8 @@ def _new_indexref(s):
     return pt.IndexRef.of(s)
 
 
-def _new_wmodel(bytes):
-    from . import autoclass
-    serUtils = autoclass("org.terrier.python.Serialization")
-    return serUtils.deserialize(bytes, autoclass("org.terrier.utility.ApplicationSetup").getClass("org.terrier.matching.models.WeightingModel") )
+def _new_wmodel(b):
+    return J.Serialization.deserialize(b, pt.ApplicationSetup.getClass("org.terrier.matching.models.WeightingModel"))
 
 
 def _new_callable_wmodel(byterep):
@@ -200,7 +198,6 @@ def _callable_wmodel_reduce(self):
 
 
 def _index_add(self, other):
-    from . import autoclass
     fields_1 = self.getCollectionStatistics().getNumberOfFields()
     fields_2 = self.getCollectionStatistics().getNumberOfFields()
     if fields_1 != fields_2:
@@ -209,8 +206,7 @@ def _index_add(self, other):
     blocks_2 = other.getCollectionStatistics().hasPositions()
     if blocks_1 != blocks_2:
         raise ValueError("Cannot document-wise merge indices with and without positions (%r vs %r)" % (blocks_1, blocks_2))
-    multiindex_cls = autoclass("org.terrier.realtime.multi.MultiIndex")
-    return multiindex_cls([self, other], blocks_1, fields_1 > 0)
+    return J.MultiIndex([self, other], blocks_1, fields_1 > 0)
 
 
 def _index_corpusiter(self, return_toks=True):
@@ -223,7 +219,6 @@ def _index_corpusiter_meta(self):
             yield {k : item[keys_offset[k]] for k in keys_offset}
 
     def _index_corpusiter_direct_pretok(self):
-        import sys
         MIN_PYTHON = (3, 8)
         if sys.version_info < MIN_PYTHON:
             raise NotImplementedError("Sorry, Python 3.8+ is required for this functionality")
@@ -291,4 +286,5 @@ def extend_package(package):
     'Serialization': 'org.terrier.python.Serialization',
     'IndexOnDisk': 'org.terrier.structures.IndexOnDisk',
     'IndexFactory': 'org.terrier.structures.IndexFactory',
+    'MultiIndex': 'org.terrier.realtime.multi.MultiIndex',
 })

From c54bd40655881e0dbee52407c41bf5c67fd8e337 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 19:14:42 +0100
Subject: [PATCH 042/153] weird

---
 pyterrier/terrier/java.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 748a23ac..44bd97eb 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -69,13 +69,6 @@ def _pre_init(jnius_config):
 
 
 def _post_init(jnius):
-    version_string = J.Version.VERSION
-    if "BUILD_DATE" in dir(J.Version):
-        version_string += f" (built by {J.Version.BUILD_USER} on {J.Version.BUILD_DATE})"
-
-    print(f"PyTerrier {pt.__version__} has loaded Terrier {version_string} and "
-          f"terrier-helper {_resolved_helper_version}\n", file=sys.stderr)
-
     pt.IndexRef = J.IndexRef
 
     jnius.protocol_map["org.terrier.structures.postings.IterablePosting"] = {
@@ -125,6 +118,13 @@ def _post_init(jnius):
         'get_corpus_iter' : _index_corpusiter
     }
 
+    version_string = J.Version.VERSION
+    if "BUILD_DATE" in dir(J.Version):
+        version_string += f" (built by {J.Version.BUILD_USER} on {J.Version.BUILD_DATE})"
+
+    print(f"X PyTerrier {pt.__version__} has loaded Terrier {version_string} and "
+          f"terrier-helper {_resolved_helper_version}\n", file=sys.stderr)
+
 
 def _new_indexref(s):
     return pt.IndexRef.of(s)

From 8c636d95fc91b7c9b08971d87e5c6e391510a4e5 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 19:14:52 +0100
Subject: [PATCH 043/153] eps

---
 pyterrier/utils.py | 14 ++++++++++++--
 setup.py           | 10 +++++-----
 2 files changed, 17 insertions(+), 7 deletions(-)

diff --git a/pyterrier/utils.py b/pyterrier/utils.py
index 55dd3250..beae2243 100644
--- a/pyterrier/utils.py
+++ b/pyterrier/utils.py
@@ -103,13 +103,23 @@ def _wrapper(*args, **kwargs):
 def entry_points(group: str) -> Tuple[EntryPoint, ...]:
     """
     A shim for Python<=3.X to support importlib.metadata.entry_points(group).
+    Also ensures that no duplicates (by name) are returned.
 
     See <https://docs.python.org/3/library/importlib.metadata.html#entry-points> for more details.
     """
     try:
-        return tuple(eps(group=group))
+        orig_res = tuple(eps(group=group))
     except TypeError:
-        return tuple(eps().get(group, tuple()))
+        orig_res = tuple(eps().get(group, tuple()))
+
+    names = set()
+    res = []
+    for ep in orig_res:
+        if ep.name not in names:
+            res.append(ep)
+            names.add(ep.name)
+
+    return tuple(res)
 
 
 def is_windows() -> bool:
diff --git a/setup.py b/setup.py
index a66df5fe..7808c78b 100644
--- a/setup.py
+++ b/setup.py
@@ -72,13 +72,13 @@ def get_version(rel_path):
     ],
     entry_points={
         'pyterrier.java.pre_init': [
-            'terrier = pyterrier.terrier.java : _pre_init',
+            'terrier = pyterrier.terrier.java:_pre_init',
         ],
         'pyterrier.java.post_init': [
-            'legacy =  pyterrier.java : _legacy_post_init',
-            'terrier = pyterrier.terrier.java : _post_init',
-            'index =   pyterrier.index : _java_post_init',
-            'rewrite = pyterrier.rewrite : _java_post_init',
+            'core    = pyterrier.java._init:_post_init',
+            'terrier = pyterrier.terrier.java:_post_init',
+            'index   = pyterrier.index:_java_post_init',
+            'rewrite = pyterrier.rewrite:_java_post_init',
         ],
     },
     install_requires=requirements,

From 90890698ee2aa9b4429a70ad251fdb694c1ebd04 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 19:17:48 +0100
Subject: [PATCH 044/153] wip

---
 pyterrier/java/__init__.py | 163 +------------------------------------
 pyterrier/java/_init.py    | 156 +++++++++++++++++++++++++++++++++++
 2 files changed, 158 insertions(+), 161 deletions(-)
 create mode 100644 pyterrier/java/_init.py

diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index 978cecfc..8c5994d6 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -1,148 +1,7 @@
-from functools import wraps
-from copy import deepcopy
-import os
-from typing import Callable, Optional, Dict, Tuple, Any
-from warnings import warn
+from typing import Optional
 import pyterrier as pt
 from pyterrier.java import mavenresolver
-
-_started = False
-
-
-def _legacy_post_init(jnius):
-    pt.HOME_DIR = pt.io.pyterrier_home()
-    pt.properties = J.Properties()
-    pt.ApplicationSetup = J.ApplicationSetup
-    pt.ApplicationSetup.bootstrapInitialisation(pt.properties)
-    pt.autoclass = jnius.autoclass
-    pt.cast = jnius.cast
-
-    jnius.protocol_map['java.util.Map$Entry'] = {
-        '__getitem__' : _mapentry_getitem,
-        '__iter__' : lambda self: iter([self.getKey(), self.getValue()]),
-        '__len__' : lambda self: 2
-    }
-
-
-# Map$Entry can be decoded like a tuple
-def _mapentry_getitem(self, i):
-    if i == 0:
-        return self.getKey()
-    if i == 1:
-        return self.getValue()
-    raise IndexError()
-
-
-@pt.utils.once()
-def init() -> None:
-    global _started
-    # TODO: if we make java optional some day, should check here that it's installed. E.g.,
-    # if find_spec('jnius_config') is None:
-    #     warnings.warn('pyterrier[java] not installed; no need to run pt.java.init()')
-    #     return
-
-    if pt.utils.is_windows():
-        if "JAVA_HOME" in os.environ:
-            java_home =  os.environ["JAVA_HOME"]
-            fix = f'{java_home}\\jre\\bin\\server\\;{java_home}\\jre\\bin\\client\\;{java_home}\\bin\\server\\'
-            os.environ["PATH"] = os.environ["PATH"] + ";" + fix
-
-    import jnius_config
-
-    for entry_point in pt.utils.entry_points('pyterrier.java.pre_init'):
-        _pre_init = entry_point.load()
-        _pre_init(jnius_config)
-
-    cfg = configure()
-
-    if cfg['mem'] is not None:
-        jnius_config.add_options('-Xmx' + str(cfg['mem']) + 'm')
-
-    import sys
-    sys.stderr.write(f'options: {cfg["options"]}\n')
-    for opt in cfg['options']:
-        jnius_config.add_options(opt)
-
-    sys.stderr.write(f'jars: {cfg["jars"]}\n')
-    for jar in cfg['jars']:
-        jnius_config.add_classpath(jar)
-
-    import jnius
-    _started = True
-
-    set_log_level(cfg['log_level'])
-
-    java_version = J.System.getProperty("java.version")
-    if java_version.startswith("1.") or java_version.startswith("9."):
-        raise RuntimeError(f"Pyterrier requires Java 11 or newer, we only found Java version {java_version};"
-            + " install a more recent Java, or change os.environ['JAVA_HOME'] to point to the proper Java installation")
-
-    for entry_point in pt.utils.entry_points('pyterrier.java.post_init'):
-        _post_init = entry_point.load()
-        _post_init(jnius)
-
-
-def started() -> bool:
-    """
-    Returns True if pt.java.init() has been called. Otherwise False.
-    """
-    return _started
-
-
-def required(raise_on_not_started: bool = False) -> Callable:
-    """
-    Wraps a function that requires the Java Virtual Machine to be started before running. If the JVM has not yet
-    been started, it runs pt.java.init() or raises an error, depending on the value of raise_on_not_started.
-    """
-    def _required(fn: Callable) -> Callable:
-        @wraps(fn)
-        def _wrapper(*args, **kwargs):
-            if not started():
-                if raise_on_not_started:
-                    raise RuntimeError(f'You need to call pt.java.init() required before you can call {fn}')
-                else:
-                    init()
-            return fn(*args, **kwargs)
-        return _wrapper
-    return _required
-
-
-def before_init() -> Callable:
-    """
-    Wraps a function that can only be run before the Java Virtual Machine has started. If the JVM has already started,
-    an error is raised.
-    """
-    def _before_init(fn: Callable) -> Callable:
-        @wraps(fn)
-        def _wrapper(*args, **kwargs):
-            if started():
-                raise RuntimeError(f'You need to call pt.java.init() required before you can call {fn}')
-            return fn(*args, **kwargs)
-        return _wrapper
-    return _before_init
-
-
-# jnius wrappers:
-
-@required(raise_on_not_started=True)
-def autoclass(*args, **kwargs):
-    """
-    Wraps jnius.autoclass once java has started. Raises an error if called before pt.java.init() is called.
-    """
-    import jnius
-    return jnius.autoclass(*args, **kwargs)
-
-
-@required(raise_on_not_started=True)
-def cast(*args, **kwargs):
-    """
-    Wraps jnius.cast once java has started. Raises an error if called before pt.java.init() is called.
-    """
-    import jnius
-    return jnius.cast(*args, **kwargs)
-
-
-# config
+from pyterrier.java._init import init, started, parallel_init, parallel_init_args, required, before_init, autoclass, cast
 from pyterrier.java import config
 from pyterrier.java.config import configure
 
@@ -203,21 +62,3 @@ def set_log_level(level):
     'PTUtils': 'org.terrier.python.PTUtils',
     'System': 'java.lang.System',
 })
-
-
-# Parallel
-
-def parallel_init(started: bool, configs: Dict[str, Dict[str, Any]]) -> None:
-    if started:
-        if not pt.java.started():
-            warn(f'Starting java parallel with configs {configs}')
-            config._CONFIGS = configs
-            init()
-        else:
-            warn("Avoiding reinit of PyTerrier")
-
-def parallel_init_args() -> Tuple[bool, Dict[str, Dict[str, Any]]]:
-    return (
-        started(),
-        deepcopy(config._CONFIGS),
-    )
diff --git a/pyterrier/java/_init.py b/pyterrier/java/_init.py
new file mode 100644
index 00000000..6a3ef480
--- /dev/null
+++ b/pyterrier/java/_init.py
@@ -0,0 +1,156 @@
+import os
+from warnings import warn
+from functools import wraps
+from typing import Dict, Any, Tuple, Callable
+from copy import deepcopy
+import pyterrier as pt
+import pyterrier.java.config
+
+
+_started = False
+
+
+def _post_init(jnius):
+    pt.HOME_DIR = pt.io.pyterrier_home()
+    pt.properties = pt.java.J.Properties()
+    pt.ApplicationSetup = pt.java.J.ApplicationSetup
+    pt.ApplicationSetup.bootstrapInitialisation(pt.properties)
+    pt.autoclass = jnius.autoclass
+    pt.cast = jnius.cast
+
+    jnius.protocol_map['java.util.Map$Entry'] = {
+        '__getitem__' : _mapentry_getitem,
+        '__iter__' : lambda self: iter([self.getKey(), self.getValue()]),
+        '__len__' : lambda self: 2
+    }
+
+
+# Map$Entry can be decoded like a tuple
+def _mapentry_getitem(self, i):
+    if i == 0:
+        return self.getKey()
+    if i == 1:
+        return self.getValue()
+    raise IndexError()
+
+
+@pt.utils.once()
+def init() -> None:
+    global _started
+    # TODO: if we make java optional some day, should check here that it's installed. E.g.,
+    # if find_spec('jnius_config') is None:
+    #     warnings.warn('pyterrier[java] not installed; no need to run pt.java.init()')
+    #     return
+
+    if pt.utils.is_windows():
+        if "JAVA_HOME" in os.environ:
+            java_home =  os.environ["JAVA_HOME"]
+            fix = f'{java_home}\\jre\\bin\\server\\;{java_home}\\jre\\bin\\client\\;{java_home}\\bin\\server\\'
+            os.environ["PATH"] = os.environ["PATH"] + ";" + fix
+
+    import jnius_config
+
+    for entry_point in pt.utils.entry_points('pyterrier.java.pre_init'):
+        _pre_init = entry_point.load()
+        _pre_init(jnius_config)
+
+    cfg = pt.java.configure()
+
+    if cfg['mem'] is not None:
+        jnius_config.add_options('-Xmx' + str(cfg['mem']) + 'm')
+
+    for opt in cfg['options']:
+        jnius_config.add_options(opt)
+
+    for jar in cfg['jars']:
+        jnius_config.add_classpath(jar)
+
+    import jnius
+    _started = True
+
+    pt.java.set_log_level(cfg['log_level'])
+
+    java_version = pt.java.J.System.getProperty("java.version")
+    if java_version.startswith("1.") or java_version.startswith("9."):
+        raise RuntimeError(f"Pyterrier requires Java 11 or newer, we only found Java version {java_version};"
+            + " install a more recent Java, or change os.environ['JAVA_HOME'] to point to the proper Java installation")
+
+    for entry_point in pt.utils.entry_points('pyterrier.java.post_init'):
+        _post_init = entry_point.load()
+        _post_init(jnius)
+
+
+def started() -> bool:
+    """
+    Returns True if pt.java.init() has been called. Otherwise False.
+    """
+    return _started
+
+
+def required(raise_on_not_started: bool = False) -> Callable:
+    """
+    Wraps a function that requires the Java Virtual Machine to be started before running. If the JVM has not yet
+    been started, it runs pt.java.init() or raises an error, depending on the value of raise_on_not_started.
+    """
+    def _required(fn: Callable) -> Callable:
+        @wraps(fn)
+        def _wrapper(*args, **kwargs):
+            if not started():
+                if raise_on_not_started:
+                    raise RuntimeError(f'You need to call pt.java.init() required before you can call {fn}')
+                else:
+                    init()
+            return fn(*args, **kwargs)
+        return _wrapper
+    return _required
+
+
+
+def before_init() -> Callable:
+    """
+    Wraps a function that can only be run before the Java Virtual Machine has started. If the JVM has already started,
+    an error is raised.
+    """
+    def _before_init(fn: Callable) -> Callable:
+        @wraps(fn)
+        def _wrapper(*args, **kwargs):
+            if started():
+                raise RuntimeError(f'You need to call pt.java.init() required before you can call {fn}')
+            return fn(*args, **kwargs)
+        return _wrapper
+    return _before_init
+
+
+def parallel_init(started: bool, configs: Dict[str, Dict[str, Any]]) -> None:
+    if started:
+        if not pt.java.started():
+            warn(f'Starting java parallel with configs {configs}')
+            pyterrier.java.config._CONFIGS = configs
+            init()
+        else:
+            warn("Avoiding reinit of PyTerrier")
+
+
+def parallel_init_args() -> Tuple[bool, Dict[str, Dict[str, Any]]]:
+    return (
+        started(),
+        deepcopy(pyterrier.java.config._CONFIGS),
+    )
+
+
+@required(raise_on_not_started=True)
+def autoclass(*args, **kwargs):
+    """
+    Wraps jnius.autoclass once java has started. Raises an error if called before pt.java.init() is called.
+    """
+    import jnius
+    return jnius.autoclass(*args, **kwargs)
+
+
+@required(raise_on_not_started=True)
+def cast(*args, **kwargs):
+    """
+    Wraps jnius.cast once java has started. Raises an error if called before pt.java.init() is called.
+    """
+    import jnius
+    return jnius.cast(*args, **kwargs)

From 613a38e0ef8e59f3e6b62c7c292b36a0c527c847 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 21:23:04 +0100
Subject: [PATCH 045/153] IndexRef was being loaded before the protocol_map was
 set

---
 pyterrier/index.py        | 13 +++++--------
 pyterrier/terrier/java.py |  6 +++---
 2 files changed, 8 insertions(+), 11 deletions(-)

diff --git a/pyterrier/index.py b/pyterrier/index.py
index 541adda0..765989f4 100644
--- a/pyterrier/index.py
+++ b/pyterrier/index.py
@@ -38,7 +38,6 @@
 ApplicationSetup = None
 Properties = None
 CLITool = None
-IndexRef = None
 StructureMerger = None
 BlockStructureMerger = None
 DocListIterator = None
@@ -76,7 +75,6 @@ def _java_post_init(jnius):
     global ApplicationSetup
     global Properties
     global CLITool
-    global IndexRef
     global StructureMerger
     global BlockStructureMerger
     global DocListIterator
@@ -103,7 +101,6 @@ def _java_post_init(jnius):
     ApplicationSetup = autoclass('org.terrier.utility.ApplicationSetup')
     Properties = autoclass('java.util.Properties')
     CLITool = autoclass("org.terrier.applications.CLITool")
-    IndexRef = autoclass('org.terrier.querying.IndexRef')
     StructureMerger = autoclass("org.terrier.structures.merging.StructureMerger")
     BlockStructureMerger = autoclass("org.terrier.structures.merging.BlockStructureMerger")
 
@@ -947,7 +944,7 @@ def index(self, text, *args, **kwargs):
 
         if self.type is IndexingType.MEMORY:
             return index.getIndex().getIndexRef()
-        return IndexRef.of(self.index_dir + "/data.properties")
+        return pt.terrier.J.IndexRef.of(self.index_dir + "/data.properties")
 
 
 class _BaseIterDictIndexer(TerrierIndexer, Indexer):
@@ -1104,7 +1101,7 @@ def index(self, it, fields=('text',), meta=None, meta_lengths=None, threads=None
             indexref = index.getIndexRef()
         else:
             from . import IndexFactory
-            indexref = IndexRef.of(self.index_dir + "/data.properties")
+            indexref = pt.terrier.J.IndexRef.of(self.index_dir + "/data.properties")
             if len(self.cleanup_hooks) > 0:
                 sindex = pt.java.autoclass("org.terrier.structures.Index")
                 sindex.setIndexLoadingProfileAsRetrieval(False)
@@ -1193,7 +1190,7 @@ def index(self, it, fields=('text',), meta=None, meta_lengths=None):
             
         indexref = None
         from . import IndexFactory
-        indexref = IndexRef.of(self.index_dir + "/data.properties")
+        indexref = pt.terrier.J.IndexRef.of(self.index_dir + "/data.properties")
         
         if len(self.cleanup_hooks) > 0:
             sindex = pt.java.autoclass("org.terrier.structures.Index")
@@ -1313,7 +1310,7 @@ def index(self, files_path : Union[str,List[str]]):
 
         if self.type is IndexingType.MEMORY:
             return index.getIndex().getIndexRef()
-        return IndexRef.of(self.index_dir + "/data.properties")
+        return pt.terrier.J.IndexRef.of(self.index_dir + "/data.properties")
 
 class FilesIndexer(TerrierIndexer):
     '''
@@ -1361,4 +1358,4 @@ def index(self, files_path : Union[str,List[str]]):
 
         if self.type is IndexingType.MEMORY:
             return index.getIndex().getIndexRef()
-        return IndexRef.of(self.index_dir + "/data.properties")
+        return pt.terrier.J.IndexRef.of(self.index_dir + "/data.properties")
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 44bd97eb..92d575b3 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -69,8 +69,6 @@ def _pre_init(jnius_config):
 
 
 def _post_init(jnius):
-    pt.IndexRef = J.IndexRef
-
     jnius.protocol_map["org.terrier.structures.postings.IterablePosting"] = {
         '__iter__': lambda self: self,
         '__next__': lambda self: _iterableposting_next(self),
@@ -118,11 +116,13 @@ def _post_init(jnius):
         'get_corpus_iter' : _index_corpusiter
     }
 
+    pt.IndexRef = J.IndexRef
+
     version_string = J.Version.VERSION
     if "BUILD_DATE" in dir(J.Version):
         version_string += f" (built by {J.Version.BUILD_USER} on {J.Version.BUILD_DATE})"
 
-    print(f"X PyTerrier {pt.__version__} has loaded Terrier {version_string} and "
+    print(f"PyTerrier {pt.__version__} has loaded Terrier {version_string} and "
           f"terrier-helper {_resolved_helper_version}\n", file=sys.stderr)
 
 

From 4ed1aa3d0522e630ebccec86405fa84bdcb9c1c7 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 21:33:22 +0100
Subject: [PATCH 046/153] re-enable test_parallel

---
 tests/test_parallel.py | 86 +++++++++++++++++++++---------------------
 1 file changed, 43 insertions(+), 43 deletions(-)

diff --git a/tests/test_parallel.py b/tests/test_parallel.py
index beaba7cf..2ce82124 100644
--- a/tests/test_parallel.py
+++ b/tests/test_parallel.py
@@ -1,46 +1,46 @@
-# from .base import BaseTestCase
-# import pyterrier as pt
-# class TestParallel(BaseTestCase):
+from .base import BaseTestCase
+import pyterrier as pt
+class TestParallel(BaseTestCase):
 
-#     def test_parallel_joblib_experiment(self):
-#         self.skip_windows()
-#         dataset = pt.get_dataset("vaswani")
-#         br = pt.BatchRetrieve(dataset.get_index())
-#         df = pt.Experiment(
-#             [br, br.parallel(3)],
-#             dataset.get_topics(),
-#             dataset.get_qrels(),
-#             ["map", "mrt"]
-#         )
-#         self.assertEqual(df.iloc[0]["map"], df.iloc[1]["map"])
+    def test_parallel_joblib_experiment(self):
+        self.skip_windows()
+        dataset = pt.get_dataset("vaswani")
+        br = pt.BatchRetrieve(dataset.get_index())
+        df = pt.Experiment(
+            [br, br.parallel(3)],
+            dataset.get_topics(),
+            dataset.get_qrels(),
+            ["map", "mrt"]
+        )
+        self.assertEqual(df.iloc[0]["map"], df.iloc[1]["map"])
 
-#     def test_parallel_joblib_experiment_br_callback(self):
-#         self.skip_windows()
-#         dataset = pt.get_dataset("vaswani")
-#         Tf = lambda keyFreq, posting, entryStats, collStats: posting.getFrequency()
-#         br = pt.BatchRetrieve(dataset.get_index(), wmodel=Tf)
-#         df = pt.Experiment(
-#             [br, br.parallel(3)],
-#             dataset.get_topics().head(4),
-#             dataset.get_qrels(),
-#             ["map", "mrt"]
-#         )
-#         self.assertEqual(df.iloc[0]["map"], df.iloc[1]["map"])
+    def test_parallel_joblib_experiment_br_callback(self):
+        self.skip_windows()
+        dataset = pt.get_dataset("vaswani")
+        Tf = lambda keyFreq, posting, entryStats, collStats: posting.getFrequency()
+        br = pt.BatchRetrieve(dataset.get_index(), wmodel=Tf)
+        df = pt.Experiment(
+            [br, br.parallel(3)],
+            dataset.get_topics().head(4),
+            dataset.get_qrels(),
+            ["map", "mrt"]
+        )
+        self.assertEqual(df.iloc[0]["map"], df.iloc[1]["map"])
 
-#     def test_parallel_joblib_ops(self):
-#         self.skip_windows()
-#         dataset = pt.get_dataset("vaswani")
-#         topics = dataset.get_topics().head(3)
-#         dph = pt.BatchRetrieve(dataset.get_index())
-#         tf = pt.BatchRetrieve(dataset.get_index(), wmodel="Tf")
-#         for pipe in [
-#             dph,
-#             dph % 10,
-#             dph >> tf,
-#             dph + tf,
-#             pt.apply.query(lambda row: row["query"] + " chemical") >> dph
-#         ]:
-#             res1 = pipe(topics)
-#             res2 = pipe.parallel(3)(topics)
-#             self.assertEqual(len(res1), len(res2))
-#             
+    def test_parallel_joblib_ops(self):
+        self.skip_windows()
+        dataset = pt.get_dataset("vaswani")
+        topics = dataset.get_topics().head(3)
+        dph = pt.BatchRetrieve(dataset.get_index())
+        tf = pt.BatchRetrieve(dataset.get_index(), wmodel="Tf")
+        for pipe in [
+            dph,
+            dph % 10,
+            dph >> tf,
+            dph + tf,
+            pt.apply.query(lambda row: row["query"] + " chemical") >> dph
+        ]:
+            res1 = pipe(topics)
+            res2 = pipe.parallel(3)(topics)
+            self.assertEqual(len(res1), len(res2))
+            

From a214c653bd28e52d502330f1f1a814b44d019480 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 21:33:33 +0100
Subject: [PATCH 047/153] refactoring java __init__.py

---
 pyterrier/java/__init__.py | 65 ++++----------------------------------
 pyterrier/java/utils.py    | 54 +++++++++++++++++++++++++++++--
 2 files changed, 58 insertions(+), 61 deletions(-)

diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index 8c5994d6..ad01e452 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -1,64 +1,11 @@
-from typing import Optional
-import pyterrier as pt
 from pyterrier.java import mavenresolver
 from pyterrier.java._init import init, started, parallel_init, parallel_init_args, required, before_init, autoclass, cast
 from pyterrier.java import config
 from pyterrier.java.config import configure
+from pyterrier.java.utils import redirect_stdouterr, bytebuffer_to_array, JavaClasses, J, add_jar, add_package, set_memory_limit, add_option, set_log_level
 
-
-@before_init()
-def add_jar(jar_path):
-    configure.append('jars', jar_path)
-
-
-@before_init()
-def add_package(org_name: str = None, package_name: str = None, version: str = None, file_type='jar'):
-    if version is None or version == 'snapshot':
-        version = mavenresolver.latest_version_num(org_name, package_name)
-    file_name = mavenresolver.downloadfile(org_name, package_name, version, pt.io.pyterrier_home(), file_type)
-    add_jar(file_name)
-
-
-@before_init()
-def set_memory_limit(mem: Optional[float]):
-    configure(mem=mem)
-
-
-@before_init()
-def add_option(option: str):
-    configure.append('options', option)
-
-
-def set_log_level(level):
-    """
-        Set the logging level. The following string values are allowed, corresponding
-        to Java logging levels:
-        
-         - `'ERROR'`: only show error messages
-         - `'WARN'`: only show warnings and error messages (default)
-         - `'INFO'`: show information, warnings and error messages
-         - `'DEBUG'`: show debugging, information, warnings and error messages
-        
-        Unlike other java settings, this can be changed either before or after init() has been called.
-    """
-    if not started():
-        configure(log_level=level)
-    else:
-        J.PTUtils.setLogLevel(level, None)
-
-
-# Utils
-from pyterrier.java.utils import redirect_stdouterr, bytebuffer_to_array
-
-
-# Classes
-
-from pyterrier.java.utils import JavaClasses
-
-J = JavaClasses({
-    'ArrayList': 'java.util.ArrayList',
-    'Properties': 'java.util.Properties',
-    'ApplicationSetup': 'org.terrier.utility.ApplicationSetup',
-    'PTUtils': 'org.terrier.python.PTUtils',
-    'System': 'java.lang.System',
-})
+__all__ = [
+    'add_jar', 'add_option', 'add_package', 'autoclass', 'before_init', 'bytebuffer_to_array', 'cast', 'config',
+    'configure', 'init', 'J', 'JavaClasses', 'mavenresolver', 'parallel_init', 'parallel_init_args',
+    'redirect_stdouterr', 'required', 'set_log_level', 'set_memory_limit', 'started',
+]
diff --git a/pyterrier/java/utils.py b/pyterrier/java/utils.py
index f95c92cf..75cbe504 100644
--- a/pyterrier/java/utils.py
+++ b/pyterrier/java/utils.py
@@ -1,6 +1,6 @@
 import pyterrier as pt
-from pyterrier.java import required
-from typing import Dict
+from pyterrier.java import required, before_init, started, configure, mavenresolver
+from typing import Dict, Optional
 
 
 stdout_ref = None
@@ -76,6 +76,47 @@ def unsign(signed):
     return bytearray([ unsign(buffer.get(offset)) for offset in range(buffer.capacity()) ])
 
 
+@before_init()
+def add_jar(jar_path):
+    configure.append('jars', jar_path)
+
+
+@before_init()
+def add_package(org_name: str = None, package_name: str = None, version: str = None, file_type='jar'):
+    if version is None or version == 'snapshot':
+        version = mavenresolver.latest_version_num(org_name, package_name)
+    file_name = mavenresolver.downloadfile(org_name, package_name, version, pt.io.pyterrier_home(), file_type)
+    add_jar(file_name)
+
+
+@before_init()
+def set_memory_limit(mem: Optional[float]):
+    configure(mem=mem)
+
+
+@before_init()
+def add_option(option: str):
+    configure.append('options', option)
+
+
+def set_log_level(level):
+    """
+        Set the logging level. The following string values are allowed, corresponding
+        to Java logging levels:
+        
+         - `'ERROR'`: only show error messages
+         - `'WARN'`: only show warnings and error messages (default)
+         - `'INFO'`: show information, warnings and error messages
+         - `'DEBUG'`: show debugging, information, warnings and error messages
+        
+        Unlike other java settings, this can be changed either before or after init() has been called.
+    """
+    if not started():
+        configure(log_level=level)
+    else:
+        J.PTUtils.setLogLevel(level, None)
+
+
 class JavaClasses:
     def __init__(self, mapping: Dict[str, str]):
         self._mapping = mapping
@@ -91,3 +132,12 @@ def __getattr__(self, key):
         if key not in self._cache:
             self._cache[key] = pt.java.autoclass(self._mapping[key])
         return self._cache[key]
+
+
+J = JavaClasses({
+    'ArrayList': 'java.util.ArrayList',
+    'Properties': 'java.util.Properties',
+    'ApplicationSetup': 'org.terrier.utility.ApplicationSetup',
+    'PTUtils': 'org.terrier.python.PTUtils',
+    'System': 'java.lang.System',
+})

From a0ec531d386ba3278459fd4f25c4bd1a393c9f4b Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 21:44:49 +0100
Subject: [PATCH 048/153] more cleanup

---
 pyterrier/__init__.py      | 35 ++++++++++++-----------------------
 pyterrier/java/__init__.py |  4 ++--
 pyterrier/java/_init.py    |  2 ++
 pyterrier/java/config.py   |  1 +
 pyterrier/java/utils.py    |  5 +++++
 pyterrier/terrier/java.py  |  1 +
 6 files changed, 23 insertions(+), 25 deletions(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index 0247cc07..6313847b 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -97,6 +97,8 @@ def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, log
 
     # Set the corresponding options
     java.set_memory_limit(mem)
+    java.set_redirect_io(redirect_io)
+    java.set_log_level(logging)
     for package in boot_packages:
         java.add_package(*package.split(':')) # format: org:package:version:filetype (where version and filetype are optional)
     for opt in jvm_opts:
@@ -109,31 +111,12 @@ def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, log
 
     java.init()
 
-    java.set_log_level(logging)
-
-    # check python version
-    # TODO: does this belong in init or somewhere else?
-    import platform
-    from packaging.version import Version
-    if Version(platform.python_version()) < Version('3.7.0'):
-        raise RuntimeError("From PyTerrier 0.8, Python 3.7 minimum is required, you currently have %s" % platform.python_version())
-
-    # Make imports global
-
-    # append the python helpers
-    if packages is None:
-        packages = []
-
     # Import other java packages
-    if packages != []:
+    if packages:
         pkgs_string = ",".join(packages)
         properties.put("terrier.mvn.coords", pkgs_string)
     ApplicationSetup.bootstrapInitialisation(properties)
 
-    if redirect_io:
-        # this ensures that the python stdout/stderr and the Java are matched
-        redirect_stdouterr()
-
 
 def set_tqdm(type):
     """
@@ -259,7 +242,7 @@ def run(cmd, args=[]):
     """
         Allows to run a Terrier executable class, i.e. one that can be access from the `bin/terrier` commandline programme.
     """
-    java.autoclass("org.terrier.applications.CLITool").main([cmd] + args)
+    java.terrier.J.CLITool.main([cmd] + args)
 
 
 @java.required()
@@ -273,8 +256,14 @@ def extend_classpath(mvnpackages):
         terrier.extend_package(package)
 
 
-# apply is an object, not a module, as it also has __get_attr__() implemented
 def _():
-    from .apply import _apply
+    # apply is an object, not a module, as it also has __get_attr__() implemented
+    from pyterrier.apply import _apply
     globals()['apply'] = _apply()
+
+    # check python version
+    import platform
+    from packaging.version import Version
+    if Version(platform.python_version()) < Version('3.7.0'):
+        raise RuntimeError("From PyTerrier 0.8, Python 3.7 minimum is required, you currently have %s" % platform.python_version())
 _()
diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index ad01e452..6115bdb8 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -2,10 +2,10 @@
 from pyterrier.java._init import init, started, parallel_init, parallel_init_args, required, before_init, autoclass, cast
 from pyterrier.java import config
 from pyterrier.java.config import configure
-from pyterrier.java.utils import redirect_stdouterr, bytebuffer_to_array, JavaClasses, J, add_jar, add_package, set_memory_limit, add_option, set_log_level
+from pyterrier.java.utils import redirect_stdouterr, bytebuffer_to_array, JavaClasses, J, add_jar, add_package, set_memory_limit, set_redirect_io, add_option, set_log_level
 
 __all__ = [
     'add_jar', 'add_option', 'add_package', 'autoclass', 'before_init', 'bytebuffer_to_array', 'cast', 'config',
     'configure', 'init', 'J', 'JavaClasses', 'mavenresolver', 'parallel_init', 'parallel_init_args',
-    'redirect_stdouterr', 'required', 'set_log_level', 'set_memory_limit', 'started',
+    'redirect_stdouterr', 'required', 'set_log_level', 'set_memory_limit', 'set_redirect_io', 'started',
 ]
diff --git a/pyterrier/java/_init.py b/pyterrier/java/_init.py
index 6a3ef480..0c24e946 100644
--- a/pyterrier/java/_init.py
+++ b/pyterrier/java/_init.py
@@ -69,6 +69,8 @@ def init() -> None:
     _started = True
 
     pt.java.set_log_level(cfg['log_level'])
+    if cfg['redirect_io']:
+        pt.java.redirect_stdouterr()
 
     java_version = pt.java.J.System.getProperty("java.version")
     if java_version.startswith("1.") or java_version.startswith("9."):
diff --git a/pyterrier/java/config.py b/pyterrier/java/config.py
index 141f9791..82c0f020 100644
--- a/pyterrier/java/config.py
+++ b/pyterrier/java/config.py
@@ -37,4 +37,5 @@ def register(name, config: Dict[str, Any]):
     'options': [],
     'mem': None,
     'log_level': 'WARN',
+    'redirect_io': True,
 })
diff --git a/pyterrier/java/utils.py b/pyterrier/java/utils.py
index 75cbe504..001777f1 100644
--- a/pyterrier/java/utils.py
+++ b/pyterrier/java/utils.py
@@ -99,6 +99,11 @@ def add_option(option: str):
     configure.append('options', option)
 
 
+@before_init()
+def set_redirect_io(redirect_io: bool):
+    configure(redirect_io=redirect_io)
+
+
 def set_log_level(level):
     """
         Set the logging level. The following string values are allowed, corresponding
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 92d575b3..19d80cbb 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -287,4 +287,5 @@ def extend_package(package):
     'IndexOnDisk': 'org.terrier.structures.IndexOnDisk',
     'IndexFactory': 'org.terrier.structures.IndexFactory',
     'MultiIndex': 'org.terrier.realtime.multi.MultiIndex',
+    'CLITool': 'org.terrier.applications.CLITool',
 })

From 8907465012532c224d8cbb792971e1029d8a7659 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 22:07:40 +0100
Subject: [PATCH 049/153] move ApplicationSetup to terrier scope

---
 pyterrier/__init__.py         | 41 ++----------------------------
 pyterrier/java/_init.py       |  3 ---
 pyterrier/java/utils.py       |  1 -
 pyterrier/terrier/__init__.py |  4 +--
 pyterrier/terrier/java.py     | 48 +++++++++++++++++++++++++++++++++--
 5 files changed, 50 insertions(+), 47 deletions(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index 6313847b..b3e18d6b 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -12,7 +12,7 @@
 from pyterrier import java
 
 from pyterrier import terrier
-from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve, IndexFactory
+from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve, IndexFactory, set_property, set_properties, run
 
 from tqdm.auto import tqdm
 
@@ -114,8 +114,7 @@ def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, log
     # Import other java packages
     if packages:
         pkgs_string = ",".join(packages)
-        properties.put("terrier.mvn.coords", pkgs_string)
-    ApplicationSetup.bootstrapInitialisation(properties)
+        set_property("terrier.mvn.coords", pkgs_string)
 
 
 def set_tqdm(type):
@@ -209,42 +208,6 @@ def logging(level):
     java.set_log_level(level)
 
 
-@java.required()
-def set_property(k, v):
-    """
-        Allows to set a property in Terrier's global properties configuration. Example::
-
-            pt.set_property("termpipelines", "")
-
-        While Terrier has a variety of properties -- as discussed in its 
-        `indexing <https://github.com/terrier-org/terrier-core/blob/5.x/doc/configure_indexing.md>`_ 
-        and `retrieval <https://github.com/terrier-org/terrier-core/blob/5.x/doc/configure_retrieval.md>`_ 
-        configuration guides -- in PyTerrier, we aim to expose Terrier configuration through appropriate 
-        methods or arguments. So this method should be seen as a safety-valve - a way to override the 
-        Terrier configuration not explicitly supported by PyTerrier.
-    """
-    properties[str(k)] = str(v)
-    ApplicationSetup.bootstrapInitialisation(properties)
-
-
-@java.required()
-def set_properties(kwargs):
-    """
-        Allows to set many properties in Terrier's global properties configuration
-    """
-    for key, value in kwargs.items():
-        properties[str(key)] = str(value)
-    ApplicationSetup.bootstrapInitialisation(properties)
-
-
-@java.required()
-def run(cmd, args=[]):
-    """
-        Allows to run a Terrier executable class, i.e. one that can be access from the `bin/terrier` commandline programme.
-    """
-    java.terrier.J.CLITool.main([cmd] + args)
-
-
 @java.required()
 def extend_classpath(mvnpackages):
     """
diff --git a/pyterrier/java/_init.py b/pyterrier/java/_init.py
index 0c24e946..5f87f90c 100644
--- a/pyterrier/java/_init.py
+++ b/pyterrier/java/_init.py
@@ -12,9 +12,6 @@
 
 def _post_init(jnius):
     pt.HOME_DIR = pt.io.pyterrier_home()
-    pt.properties = pt.java.J.Properties()
-    pt.ApplicationSetup = pt.java.J.ApplicationSetup
-    pt.ApplicationSetup.bootstrapInitialisation(pt.properties)
     pt.autoclass = jnius.autoclass
     pt.cast = jnius.cast
 
diff --git a/pyterrier/java/utils.py b/pyterrier/java/utils.py
index 001777f1..79686efa 100644
--- a/pyterrier/java/utils.py
+++ b/pyterrier/java/utils.py
@@ -142,7 +142,6 @@ def __getattr__(self, key):
 J = JavaClasses({
     'ArrayList': 'java.util.ArrayList',
     'Properties': 'java.util.Properties',
-    'ApplicationSetup': 'org.terrier.utility.ApplicationSetup',
     'PTUtils': 'org.terrier.python.PTUtils',
     'System': 'java.lang.System',
 })
diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
index 4bd7c517..ea1f5b40 100644
--- a/pyterrier/terrier/__init__.py
+++ b/pyterrier/terrier/__init__.py
@@ -1,6 +1,6 @@
 # java stuff
 from pyterrier.terrier import java
-from pyterrier.terrier.java import configure, set_terrier_version, set_helper_version, enable_prf, extend_package, J
+from pyterrier.terrier.java import configure, set_terrier_version, set_helper_version, enable_prf, extend_package, J, set_property, set_properties, run
 from pyterrier.terrier.retriever import BatchRetrieve, FeaturesBatchRetrieve
 from pyterrier.terrier.index_factory import IndexFactory
 
@@ -15,5 +15,5 @@
     'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve',
 
     # misc
-    'IndexFactory',
+    'IndexFactory', 'set_property', 'set_properties', 'run', 
 ]
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 19d80cbb..7cb1673a 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -8,6 +8,8 @@
 
 _resolved_helper_version = None
 
+_properties = None
+
 configure = pt.java.config.register('pt.terrier.java', {
     'terrier_version': None,
     'helper_version': None,
@@ -69,6 +71,8 @@ def _pre_init(jnius_config):
 
 
 def _post_init(jnius):
+    global _properties
+
     jnius.protocol_map["org.terrier.structures.postings.IterablePosting"] = {
         '__iter__': lambda self: self,
         '__next__': lambda self: _iterableposting_next(self),
@@ -117,6 +121,9 @@ def _post_init(jnius):
     }
 
     pt.IndexRef = J.IndexRef
+    _properties = pt.properties = pt.java.J.Properties()
+    pt.ApplicationSetup = J.ApplicationSetup
+    J.ApplicationSetup.bootstrapInitialisation(_properties)
 
     version_string = J.Version.VERSION
     if "BUILD_DATE" in dir(J.Version):
@@ -131,7 +138,7 @@ def _new_indexref(s):
 
 
 def _new_wmodel(b):
-    return J.Serialization.deserialize(b, pt.ApplicationSetup.getClass("org.terrier.matching.models.WeightingModel"))
+    return J.Serialization.deserialize(b, J.ApplicationSetup.getClass("org.terrier.matching.models.WeightingModel"))
 
 
 def _new_callable_wmodel(byterep):
@@ -272,14 +279,51 @@ def extend_package(package):
     assert pt.check_version(5.3), "Terrier 5.3 required for this functionality"
     package_list = pt.java.J.ArrayList()
     package_list.add(package)
-    mvnr = pt.ApplicationSetup.getPlugin("MavenResolver")
+    mvnr = J.ApplicationSetup.getPlugin("MavenResolver")
     assert mvnr is not None
     mvnr = pt.java.cast("org.terrier.utility.MavenResolver", mvnr)
     mvnr.addDependencies(package_list)
 
 
+@pt.java.required()
+def set_property(k, v):
+    """
+        Allows to set a property in Terrier's global properties configuration. Example::
+
+            pt.set_property("termpipelines", "")
+
+        While Terrier has a variety of properties -- as discussed in its 
+        `indexing <https://github.com/terrier-org/terrier-core/blob/5.x/doc/configure_indexing.md>`_ 
+        and `retrieval <https://github.com/terrier-org/terrier-core/blob/5.x/doc/configure_retrieval.md>`_ 
+        configuration guides -- in PyTerrier, we aim to expose Terrier configuration through appropriate 
+        methods or arguments. So this method should be seen as a safety-valve - a way to override the 
+        Terrier configuration not explicitly supported by PyTerrier.
+    """
+    _properties[str(k)] = str(v)
+    J.ApplicationSetup.bootstrapInitialisation(_properties)
+
+
+@pt.java.required()
+def set_properties(kwargs):
+    """
+        Allows to set many properties in Terrier's global properties configuration
+    """
+    for key, value in kwargs.items():
+        _properties[str(key)] = str(value)
+    J.ApplicationSetup.bootstrapInitialisation(_properties)
+
+
+@pt.java.required()
+def run(cmd, args=[]):
+    """
+        Allows to run a Terrier executable class, i.e. one that can be access from the `bin/terrier` commandline programme.
+    """
+    J.CLITool.main([cmd] + args)
+
+
 # Terrier-specific classes
 J = pt.java.JavaClasses({
+    'ApplicationSetup': 'org.terrier.utility.ApplicationSetup',
     'IndexRef': 'org.terrier.querying.IndexRef',
     'Version': 'org.terrier.Version',
     'Tokenizer': 'org.terrier.indexing.tokenisation.Tokeniser',

From 8255096512b76bc93a922cc55e383f706a350b28 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sun, 28 Jul 2024 22:15:25 +0100
Subject: [PATCH 050/153] cleanup

---
 pyterrier/__init__.py | 34 +++++-----------------------------
 1 file changed, 5 insertions(+), 29 deletions(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index b3e18d6b..fcd66c3f 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -1,23 +1,20 @@
 __version__ = "0.10.1"
 
-import os
 from deprecated import deprecated
 
 from pyterrier import utils
 from pyterrier.utils import Utils
 
-# definitive API used by others, now available before pt.init
 from .transformer import Transformer, Estimator, Indexer
 
 from pyterrier import java
+from pyterrier.java import started, redirect_stdouterr # for backward compat, maybe remove/deprecate some day?
 
 from pyterrier import terrier
 from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve, IndexFactory, set_property, set_properties, run
 
 from tqdm.auto import tqdm
 
-
-
 from . import anserini
 from . import cache
 from . import debug
@@ -34,23 +31,18 @@
 from . import transformer
 from .datasets import get_dataset, find_datasets, list_datasets
 
-
 from .index import Indexer, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
 from .pipelines import Experiment, GridScan, GridSearch, KFoldGridSearch, Evaluate
 
-import importlib
-
 
-file_path = os.path.dirname(os.path.abspath(__file__))
+# will be set in java._post_init once java is loaded
+HOME_DIR = None
 
-# will be set in java._legacy_post_init once java is loaded
+# will be set in terrier.java._post_init once java is loaded
+IndexRef = None
 ApplicationSetup = None
 properties = None
 
-# will be set in terrier._java_post_init once java is loaded
-IndexRef = None
-HOME_DIR = None
-
 # TODO
 _helper_version = None
 
@@ -155,16 +147,6 @@ def set_tqdm(type):
     tqdm.pandas()
     
 
-def started():
-    """
-        Returns `True` if `init()` has already been called, false otherwise. Typical usage::
-
-            import pyterrier as pt
-            if not pt.started():
-                pt.init()
-    """
-    return java.started()
-
 @java.required()
 def version():
     """
@@ -185,12 +167,6 @@ def check_version(min, helper=False):
     min = Version(str(min))
     return currentVer >= min
 
-def redirect_stdouterr():
-    """
-        Ensure that stdout and stderr have been redirected. Equivalent to setting the redirect_io parameter to init() as `True`.
-    """
-    java.redirect_stdouterr()
-
 
 @java.required()
 @deprecated(version="0.11", reason="Use pt.java.set_log_level() instead")

From 36871c7599cc89e6560230c484f77a16509f8797 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 08:58:15 +0100
Subject: [PATCH 051/153] explanation

---
 pyterrier/__init__.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index fcd66c3f..237167bb 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -195,6 +195,7 @@ def extend_classpath(mvnpackages):
         terrier.extend_package(package)
 
 
+# Additional setup performed in a function to avoid polluting the namespace with other imports
 def _():
     # apply is an object, not a module, as it also has __get_attr__() implemented
     from pyterrier.apply import _apply

From 5e0cd4a56b1c7842c70797004edee98a0b7e0f6c Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 12:10:53 +0100
Subject: [PATCH 052/153] anserini

---
 .github/workflows/anserini.yml                |  5 +-
 pyterrier/__init__.py                         |  3 +-
 pyterrier/anserini/__init__.py                |  4 +
 pyterrier/anserini/java.py                    | 88 +++++++++++++++++++
 .../{anserini.py => anserini/retriever.py}    | 86 ++++--------------
 pyterrier/java/config.py                      |  2 +-
 pyterrier/java/mavenresolver.py               |  2 +-
 pyterrier/java/utils.py                       |  2 +-
 pyterrier/terrier/java.py                     |  4 +-
 pyterrier/utils.py                            |  4 +
 setup.py                                      |  2 +
 tests/anserini/test_anserini.py               |  7 +-
 tests/test_maven.py                           |  4 +-
 13 files changed, 131 insertions(+), 82 deletions(-)
 create mode 100644 pyterrier/anserini/__init__.py
 create mode 100644 pyterrier/anserini/java.py
 rename pyterrier/{anserini.py => anserini/retriever.py} (57%)

diff --git a/.github/workflows/anserini.yml b/.github/workflows/anserini.yml
index e72d9d98..0df9cb3b 100644
--- a/.github/workflows/anserini.yml
+++ b/.github/workflows/anserini.yml
@@ -15,7 +15,8 @@ jobs:
     strategy:
       matrix:
         python-version: ['3.10']
-        java: [13]
+        anserini-version: ['0.19.0', '0.22.0', '0.25.0', '0.36.0']
+        java: [21]
         os: ['ubuntu-latest']
         terrier: ['snapshot'] #'5.3', '5.4-SNAPSHOT', 
 
@@ -63,5 +64,5 @@ jobs:
       env:
         TERRIER_VERSION: ${{ matrix.terrier }}
       run: |
-        pip install pyserini==0.22.0 faiss-cpu torch
+        pip install pyserini==${{ matrix.anserini-version }} faiss-cpu torch
         pytest --durations=20 -p no:faulthandler  tests/anserini/
diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index 237167bb..5a90ff4b 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -13,9 +13,10 @@
 from pyterrier import terrier
 from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve, IndexFactory, set_property, set_properties, run
 
+from pyterrier import anserini
+
 from tqdm.auto import tqdm
 
-from . import anserini
 from . import cache
 from . import debug
 from . import index
diff --git a/pyterrier/anserini/__init__.py b/pyterrier/anserini/__init__.py
new file mode 100644
index 00000000..919d9f25
--- /dev/null
+++ b/pyterrier/anserini/__init__.py
@@ -0,0 +1,4 @@
+from pyterrier.anserini.java import is_installed, set_version, required, J
+from pyterrier.anserini.retriever import AnseriniBatchRetrieve
+
+__all__ = ['is_installed', 'set_version', 'required', 'AnseriniBatchRetrieve', 'J']
diff --git a/pyterrier/anserini/java.py b/pyterrier/anserini/java.py
new file mode 100644
index 00000000..b93d6b27
--- /dev/null
+++ b/pyterrier/anserini/java.py
@@ -0,0 +1,88 @@
+import os
+from glob import glob
+from typing import Callable, Optional
+from functools import wraps
+import pyterrier as pt
+
+
+configure = pt.java.config.register('pyterrier.anserini', {
+    'version': None,
+})
+
+
+def _pre_init(jnius_config):
+    if not is_installed():
+        # pyserini not installed, do nothing
+        return
+
+    if configure.get('version') is None:
+        jar = _get_pyserini_jar()
+    else:
+        # download and use the anserini version specified by the user
+        jar = pt.java.mavenresolver.get_package_jar('io.anserini', "anserini", configure.get('version'), 'fatjar')
+
+    if jar is None:
+        raise RuntimeError('Could not find anserini jar')
+    else:
+        jnius_config.add_classpath(jar)
+
+
+def _post_init(jnius):
+    # Temporarily disable the configure_classpath while pyserini is init'd, otherwise it will try to reconfigure jnius
+    import pyserini.setup
+    _configure_classpath = pyserini.setup.configure_classpath
+    try:
+        pyserini.setup.configure_classpath = pt.utils.noop
+        import pyserini.search.lucene # load the package
+    finally:
+        pyserini.setup.configure_classpath = _configure_classpath
+
+
+def is_installed():
+    try:
+        import pyserini.setup # try to load
+    except ImportError:
+        return False
+    return True
+
+
+def _get_pyserini_jar() -> Optional[str]:
+    # find the anserini jar distributed with pyserini
+    # Adapted from pyserini/setup.py and pyserini/pyclass.py
+    import pyserini.setup
+    jar_root = os.path.join(os.path.split(pyserini.setup.__file__)[0], 'resources/jars/')
+    paths = glob(os.path.join(jar_root, 'anserini-*-fatjar.jar'))
+    if not paths:
+        return None
+    latest_jar = max(paths, key=os.path.getctime)
+    return latest_jar
+
+
+@pt.java.before_init()
+def set_version(version: str):
+    configure.set('version', version)
+
+
+def required() -> Callable:
+    """
+    Wraps a function that requires pyserini to be installed before running (raises error if not installed). If the JVM
+    has not yet been started, it runs pt.java.init(), too, similar to pt.java.required().
+    """
+    def _required(fn: Callable) -> Callable:
+        @wraps(fn)
+        def _wrapper(*args, **kwargs):
+            if not is_installed():
+                raise RuntimeError('pyserini required to use pyterrier.anserini. `pip install pyserini` and try again.')
+            if not pt.java.started():
+                pt.java.init()
+            return fn(*args, **kwargs)
+        return _wrapper
+    return _required
+
+
+J = pt.java.JavaClasses({
+    'ClassicSimilarity': 'org.apache.lucene.search.similarities.ClassicSimilarity',
+    'BM25Similarity': 'org.apache.lucene.search.similarities.BM25Similarity',
+    'LMDirichletSimilarity': 'org.apache.lucene.search.similarities.LMDirichletSimilarity',
+    'IndexReaderUtils': 'io.anserini.index.IndexReaderUtils',
+})
diff --git a/pyterrier/anserini.py b/pyterrier/anserini/retriever.py
similarity index 57%
rename from pyterrier/anserini.py
rename to pyterrier/anserini/retriever.py
index 1f67cfef..4123e941 100644
--- a/pyterrier/anserini.py
+++ b/pyterrier/anserini/retriever.py
@@ -1,53 +1,15 @@
-from .model import coerce_queries_dataframe
-
-from pyterrier.terrier.retriever import BatchRetrieveBase
 from warnings import warn
 import pandas as pd
-from . import tqdm
-
-anserini_monkey=False
-def _init_anserini():
-    global anserini_monkey
-    if anserini_monkey:
-        return
-
-    # jnius monkypatching
-    import jnius_config
-    anserini_found = False
-    for j in jnius_config.get_classpath():
-        if "anserini" in j:
-            anserini_found = True
-            break
-    assert anserini_found, 'Anserini jar not found: you should start PyTerrier, e.g. with '\
-        + 'pt.init(boot_packages=["io.anserini:anserini:0.22.0:fatjar"])'
-    import pyserini.setup
-    pyserini.setup.configure_classpath = lambda x: x
-    jnius_config.set_classpath = lambda x: x
-    anserini_monkey = True
-    return
-
-    #this is the Anserini early rank cutoff rule
-    from matchpy import Wildcard, ReplacementRule, Pattern
-    from .transformer import RankCutoffTransformer, rewrite_rules
-    x = Wildcard.dot('x')
-    _brAnserini = Wildcard.symbol('_brAnserini', AnseriniBatchRetrieve)
-
-    def set_k(_brAnserini, x):
-        _brAnserini.k = int(x.value)
-        return _brAnserini
+import pyterrier as pt
+from pyterrier.anserini.java import required
 
-    rewrite_rules.append(ReplacementRule(
-            Pattern(RankCutoffTransformer(_brAnserini, x) ),
-            set_k
-    ))
 
-
-
-class AnseriniBatchRetrieve(BatchRetrieveBase):
+class AnseriniBatchRetrieve(pt.Transformer):
     """
-        Allows retrieval from an Anserini index. To use this class, PyTerrier should have been started using `pt.init(boot_packages=["io.anserini:anserini:0.22.0:fatjar"])`.
+        Allows retrieval from an Anserini index. To use this class, you must first enable anserini using `pt.anserini.enable()`.
     """
-    def __init__(self, index_location, k=1000, wmodel="BM25", **kwargs):
+    @required()
+    def __init__(self, index_location, k=1000, wmodel="BM25", verbose=False):
         """
             Construct an AnseriniBatchRetrieve retrieve from pyserini.search.lucene.LuceneSearcher. 
 
@@ -61,33 +23,26 @@ def __init__(self, index_location, k=1000, wmodel="BM25", **kwargs):
                  *  `"TFIDF"` - Lucene's `ClassicSimilarity <https://lucene.apache.org/core/8_1_0/core/org/apache/lucene/search/similarities/ClassicSimilarity.html>`_.
                 k(int): number of results to return. Default is 1000.
         """
-        super().__init__(kwargs)
         self.index_location = index_location
         self.k = k
-        _init_anserini()
+        self.wmodel = wmodel
+        self.verbose = verbose
         from pyserini.search.lucene import LuceneSearcher
         self.searcher = LuceneSearcher(index_location)
-        self.wmodel = wmodel
         self._setsimilarty(wmodel)
 
     def __reduce__(self):
         return (
             self.__class__,
-            (self.index_location, self.k, self.wmodel),
+            (self.index_location, self.k, self.wmodel, self.verbose),
             self.__getstate__()
         )
 
     def __getstate__(self): 
-        return  {
-                'k' : self.k, 
-                'wmodel' : self.wmodel, 
-                'index_location' : self.index_location,
-                }
+        return  {}
 
     def __setstate__(self, d): 
-        self.k = d["k"]
-        self.wmodel = d["wmodel"]
-        self.index_location = d["index_location"]
+        pass
 
     def _setsimilarty(self, wmodel):
         if wmodel == "BM25":
@@ -95,19 +50,17 @@ def _setsimilarty(self, wmodel):
         elif wmodel == "QLD":
             self.searcher.object.set_qld(1000.0)
         elif wmodel == "TFIDF":
-            from jnius import autoclass
-            self.searcher.object.similarty = autoclass("org.apache.lucene.search.similarities.ClassicSimilarity")()
+            self.searcher.object.similarty = pt.anserini.J.ClassicSimilarity()
         else:
             raise ValueError("wmodel %s not support in AnseriniBatchRetrieve" % wmodel) 
 
     def _getsimilarty(self, wmodel):
-        from jnius import autoclass
         if wmodel == "BM25":
-            return autoclass("org.apache.lucene.search.similarities.BM25Similarity")(0.9, 0.4)#(self.searcher.object.bm25_k1, self.searcher.object.bm25_b)
+            return pt.anserini.J.BM25Similarity(0.9, 0.4)#(self.searcher.object.bm25_k1, self.searcher.object.bm25_b)
         elif wmodel == "QLD":
-            return autoclass("org.apache.lucene.search.similarities.LMDirichletSimilarity")(1000.0)# (self.searcher.object.ql_mu)
+            return pt.anserini.J.LMDirichletSimilarity(1000.0)# (self.searcher.object.ql_mu)
         elif wmodel == "TFIDF":
-            return autoclass("org.apache.lucene.search.similarities.ClassicSimilarity")()
+            return pt.anserini.J.ClassicSimilarity()
         else:
             raise ValueError("wmodel %s not support in AnseriniBatchRetrieve" % wmodel) 
 
@@ -130,19 +83,18 @@ def transform(self, queries):
         results=[]
         if not isinstance(queries, pd.DataFrame):
             warn(".transform() should be passed a dataframe. Use .search() to execute a single query.", DeprecationWarning, 2)
-            queries=coerce_queries_dataframe(queries)
+            queries=pt.model.coerce_queries_dataframe(queries)
         docno_provided = "docno" in queries.columns
         docid_provided = "docid" in queries.columns
         if docid_provided and not docno_provided:
             raise KeyError("Anserini doesnt expose Lucene's internal docids, you need the docnos")
         if docno_provided: #we are re-ranking
-            from . import autoclass
-            indexreaderutils = autoclass("io.anserini.index.IndexReaderUtils")
+            indexreaderutils = pt.anserini.J.IndexReaderUtils
             indexreader = self.searcher.object.reader
             rank = 0
             last_qid = None
             sim = self._getsimilarty(self.wmodel)
-            for row in tqdm(queries.itertuples(), desc=self.name, total=queries.shape[0], unit="d") if self.verbose else queries.itertuples():
+            for row in pt.tqdm(queries.itertuples(), desc=self.name, total=queries.shape[0], unit="d") if self.verbose else queries.itertuples():
                 qid = str(row.qid)
                 query = row.query
                 docno = row.docno
@@ -153,7 +105,7 @@ def transform(self, queries):
                 results.append([qid, query, docno, rank, score])
 
         else: #we are searching, no candidate set provided
-            for row in tqdm(queries.itertuples(), desc=self.name, total=queries.shape[0], unit="q") if self.verbose else queries.itertuples():
+            for row in pt.tqdm(queries.itertuples(), desc=self.name, total=queries.shape[0], unit="q") if self.verbose else queries.itertuples():
                 rank = 0
                 qid = str(row.qid)
                 query = row.query
diff --git a/pyterrier/java/config.py b/pyterrier/java/config.py
index 82c0f020..99ee9af2 100644
--- a/pyterrier/java/config.py
+++ b/pyterrier/java/config.py
@@ -11,7 +11,7 @@ def get(self, key):
         return self()[key]
 
     def set(self, key, value):
-        self()(**{key: value})
+        self(**{key: value})
 
     def append(self, key, value):
         res = self.get(key)
diff --git a/pyterrier/java/mavenresolver.py b/pyterrier/java/mavenresolver.py
index bded3683..d75e5f1a 100644
--- a/pyterrier/java/mavenresolver.py
+++ b/pyterrier/java/mavenresolver.py
@@ -11,7 +11,7 @@
 JITPACK_BASE_URL = "https://jitpack.io/"
 
 # obtain a file from maven
-def downloadfile(orgName, packageName, version, file_path, artifact="jar", force_download=False):
+def get_package_jar(orgName, packageName, version, file_path, artifact="jar", force_download=False):
     orgName = orgName.replace(".", "/")
     suffix = ""
     ext = "jar"
diff --git a/pyterrier/java/utils.py b/pyterrier/java/utils.py
index 79686efa..418d65bb 100644
--- a/pyterrier/java/utils.py
+++ b/pyterrier/java/utils.py
@@ -85,7 +85,7 @@ def add_jar(jar_path):
 def add_package(org_name: str = None, package_name: str = None, version: str = None, file_type='jar'):
     if version is None or version == 'snapshot':
         version = mavenresolver.latest_version_num(org_name, package_name)
-    file_name = mavenresolver.downloadfile(org_name, package_name, version, pt.io.pyterrier_home(), file_type)
+    file_name = mavenresolver.get_package_jar(org_name, package_name, version, pt.io.pyterrier_home(), file_type)
     add_jar(file_name)
 
 
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 7cb1673a..9b74c8ce 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -56,9 +56,9 @@ def _pre_init(jnius_config):
     # "snapshot" means use Jitpack.io to get a build of the current
     # 5.x branch from Github - see https://jitpack.io/#terrier-org/terrier-core/5.x-SNAPSHOT
     if terrier_version == "snapshot":
-        trJar = pt.java.mavenresolver.downloadfile("com.github.terrier-org.terrier-core", "terrier-assemblies", "5.x-SNAPSHOT", pt.io.pyterrier_home(), "jar-with-dependencies", force_download=cfg['force_download'])
+        trJar = pt.java.mavenresolver.get_package_jar("com.github.terrier-org.terrier-core", "terrier-assemblies", "5.x-SNAPSHOT", pt.io.pyterrier_home(), "jar-with-dependencies", force_download=cfg['force_download'])
     else:
-        trJar = pt.java.mavenresolver.downloadfile(TERRIER_PKG, "terrier-assemblies", terrier_version, pt.io.pyterrier_home(), "jar-with-dependencies")
+        trJar = pt.java.mavenresolver.get_package_jar(TERRIER_PKG, "terrier-assemblies", terrier_version, pt.io.pyterrier_home(), "jar-with-dependencies")
     pt.java.add_jar(trJar)
 
     # now the helper classes
diff --git a/pyterrier/utils.py b/pyterrier/utils.py
index beae2243..d73a0b49 100644
--- a/pyterrier/utils.py
+++ b/pyterrier/utils.py
@@ -124,3 +124,7 @@ def entry_points(group: str) -> Tuple[EntryPoint, ...]:
 
 def is_windows() -> bool:
     return platform.system() == 'Windows'
+
+
+def noop(*args, **kwargs):
+    pass
diff --git a/setup.py b/setup.py
index 7808c78b..2521a8ce 100644
--- a/setup.py
+++ b/setup.py
@@ -73,12 +73,14 @@ def get_version(rel_path):
     entry_points={
         'pyterrier.java.pre_init': [
             'terrier = pyterrier.terrier.java:_pre_init',
+            'anserini = pyterrier.anserini.java:_pre_init',
         ],
         'pyterrier.java.post_init': [
             'core    = pyterrier.java._init:_post_init',
             'terrier = pyterrier.terrier.java:_post_init',
             'index   = pyterrier.index:_java_post_init',
             'rewrite = pyterrier.rewrite:_java_post_init',
+            'anserini = pyterrier.anserini.java:_post_init',
         ],
     },
     install_requires=requirements,
diff --git a/tests/anserini/test_anserini.py b/tests/anserini/test_anserini.py
index c0df9870..2168cfe6 100644
--- a/tests/anserini/test_anserini.py
+++ b/tests/anserini/test_anserini.py
@@ -6,12 +6,9 @@
 class AnseriniTestCase(unittest.TestCase):
 
     def skip_pyserini(self):
-        try:
-            import pyserini.setup
-            pt.anserini._init_anserini()
-        except BaseException as e:
+        if not pt.anserini.is_installed():
             if os.environ.get("ANSERINI_TESTING", None) is not None:
-                raise e
+                raise RuntimeError('pyserini not installed')
             else:
                 self.skipTest("Test disabled due to lack of Pyserini")
 
diff --git a/tests/test_maven.py b/tests/test_maven.py
index 776eedd5..191c1a7e 100644
--- a/tests/test_maven.py
+++ b/tests/test_maven.py
@@ -28,11 +28,11 @@ def testVersion(self):
 
     def testDownload(self):
         ver = mavenresolver.latest_version_num("org.terrier", "terrier-python-helper")
-        jar = mavenresolver.downloadfile("org.terrier", "terrier-python-helper", ver, self.test_dir)
+        jar = mavenresolver.get_package_jar("org.terrier", "terrier-python-helper", ver, self.test_dir)
         self.assertTrue(path.exists(jar))
 
     def testJitpack(self):
-        jar = mavenresolver.downloadfile("com.github.terrierteam", "terrier-ciff", "-SNAPSHOT", self.test_dir)
+        jar = mavenresolver.get_package_jar("com.github.terrierteam", "terrier-ciff", "-SNAPSHOT", self.test_dir)
         self.assertTrue(path.exists(jar))
 
 if __name__ == "__main__":

From aae99ef96ebc94dbebfc1acf2dfe0841e23db31e Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 13:00:22 +0100
Subject: [PATCH 053/153] I think pyserini==0.25.0 is a bad build somehow,
 based on the error message

---
 .github/workflows/anserini.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/anserini.yml b/.github/workflows/anserini.yml
index 0df9cb3b..432ce9db 100644
--- a/.github/workflows/anserini.yml
+++ b/.github/workflows/anserini.yml
@@ -15,7 +15,7 @@ jobs:
     strategy:
       matrix:
         python-version: ['3.10']
-        anserini-version: ['0.19.0', '0.22.0', '0.25.0', '0.36.0']
+        anserini-version: ['==0.19.0', '==0.22.0', '==0.36.0', '']
         java: [21]
         os: ['ubuntu-latest']
         terrier: ['snapshot'] #'5.3', '5.4-SNAPSHOT', 
@@ -64,5 +64,5 @@ jobs:
       env:
         TERRIER_VERSION: ${{ matrix.terrier }}
       run: |
-        pip install pyserini==${{ matrix.anserini-version }} faiss-cpu torch
+        pip install pyserini${{ matrix.anserini-version }} faiss-cpu torch
         pytest --durations=20 -p no:faulthandler  tests/anserini/

From 60e265e39cef950e6821fc0c96201a3d4ef6da4a Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 13:01:30 +0100
Subject: [PATCH 054/153] also disable anserini.java._post_init if not
 installed

---
 pyterrier/anserini/java.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/pyterrier/anserini/java.py b/pyterrier/anserini/java.py
index b93d6b27..dfa44acd 100644
--- a/pyterrier/anserini/java.py
+++ b/pyterrier/anserini/java.py
@@ -28,6 +28,10 @@ def _pre_init(jnius_config):
 
 
 def _post_init(jnius):
+    if not is_installed():
+        # pyserini not installed, do nothing
+        return
+
     # Temporarily disable the configure_classpath while pyserini is init'd, otherwise it will try to reconfigure jnius
     import pyserini.setup
     _configure_classpath = pyserini.setup.configure_classpath

From 0858a8853c4e6181562df8bf85e3a119a43cbf6f Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 15:22:40 +0100
Subject: [PATCH 055/153] allow pt.java.required to be used as a function too

---
 pyterrier/__init__.py           |  8 ++--
 pyterrier/anserini/java.py      | 34 ++++++++--------
 pyterrier/anserini/retriever.py |  2 +-
 pyterrier/apply.py              | 19 ++++-----
 pyterrier/datasets.py           | 14 +------
 pyterrier/index.py              | 12 +++---
 pyterrier/java/__init__.py      |  5 ++-
 pyterrier/java/_init.py         | 71 ++++++++++++++++++++-------------
 pyterrier/java/utils.py         | 14 +++----
 pyterrier/terrier/java.py       | 14 +++----
 pyterrier/terrier/retriever.py  |  2 +-
 11 files changed, 102 insertions(+), 93 deletions(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index 5a90ff4b..b3a96122 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -48,7 +48,7 @@
 _helper_version = None
 
 
-@java.before_init()
+@java.before_init
 def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, logging='WARN', home_dir=None, boot_packages=[], tqdm=None, no_download=False,helper_version = None):
     """
     Function necessary to be called before Terrier classes and methods can be used.
@@ -148,7 +148,7 @@ def set_tqdm(type):
     tqdm.pandas()
     
 
-@java.required()
+@java.required
 def version():
     """
         Returns the version string from the underlying Terrier platform.
@@ -169,7 +169,7 @@ def check_version(min, helper=False):
     return currentVer >= min
 
 
-@java.required()
+@java.required
 @deprecated(version="0.11", reason="Use pt.java.set_log_level() instead")
 def logging(level):
     """
@@ -185,7 +185,7 @@ def logging(level):
     java.set_log_level(level)
 
 
-@java.required()
+@java.required
 def extend_classpath(mvnpackages):
     """
         Allows to add packages to Terrier's classpath after the JVM has started.
diff --git a/pyterrier/anserini/java.py b/pyterrier/anserini/java.py
index dfa44acd..a1aa7cad 100644
--- a/pyterrier/anserini/java.py
+++ b/pyterrier/anserini/java.py
@@ -1,6 +1,6 @@
 import os
 from glob import glob
-from typing import Callable, Optional
+from typing import Callable, Optional, Union
 from functools import wraps
 import pyterrier as pt
 
@@ -61,27 +61,29 @@ def _get_pyserini_jar() -> Optional[str]:
     latest_jar = max(paths, key=os.path.getctime)
     return latest_jar
 
-
-@pt.java.before_init()
+@pt.java.before_init
 def set_version(version: str):
     configure.set('version', version)
 
 
-def required() -> Callable:
+def required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
     """
-    Wraps a function that requires pyserini to be installed before running (raises error if not installed). If the JVM
-    has not yet been started, it runs pt.java.init(), too, similar to pt.java.required().
+    Can act as either a standalone function or a function wrapper.
+    Requires pyserini to be installed (raises error if not installed). If the JVM has not yet been started, it runs
+    pt.java.init(), too, similar to pt.java.required().
     """
-    def _required(fn: Callable) -> Callable:
-        @wraps(fn)
-        def _wrapper(*args, **kwargs):
-            if not is_installed():
-                raise RuntimeError('pyserini required to use pyterrier.anserini. `pip install pyserini` and try again.')
-            if not pt.java.started():
-                pt.java.init()
-            return fn(*args, **kwargs)
-        return _wrapper
-    return _required
+    if fn is None:
+        return required(pt.utils.noop)()
+
+    @wraps(fn)
+    def _wrapper(*args, **kwargs):
+        if not is_installed():
+            raise RuntimeError('pyserini required to use pyterrier.anserini. `pip install pyserini` and try again.')
+        if not pt.java.started():
+            pt.java.init()
+        return fn(*args, **kwargs)
+
+    return _wrapper
 
 
 J = pt.java.JavaClasses({
diff --git a/pyterrier/anserini/retriever.py b/pyterrier/anserini/retriever.py
index 4123e941..557fa5cd 100644
--- a/pyterrier/anserini/retriever.py
+++ b/pyterrier/anserini/retriever.py
@@ -8,7 +8,7 @@ class AnseriniBatchRetrieve(pt.Transformer):
     """
         Allows retrieval from an Anserini index. To use this class, you must first enable anserini using `pt.anserini.enable()`.
     """
-    @required()
+    @required
     def __init__(self, index_location, k=1000, wmodel="BM25", verbose=False):
         """
             Construct an AnseriniBatchRetrieve retrieve from pyserini.search.lucene.LuceneSearcher. 
diff --git a/pyterrier/apply.py b/pyterrier/apply.py
index 02380898..115ad138 100644
--- a/pyterrier/apply.py
+++ b/pyterrier/apply.py
@@ -1,7 +1,9 @@
+from functools import partial
 from typing import Callable, Any, Dict, Union, Sequence
-from .apply_base import ApplyDocumentScoringTransformer, ApplyQueryTransformer, ApplyDocFeatureTransformer, ApplyForEachQuery, ApplyGenericTransformer, Transformer
 import numpy.typing as npt
 import pandas as pd
+import pyterrier as pt
+from pyterrier.apply_base import ApplyDocumentScoringTransformer, ApplyQueryTransformer, ApplyDocFeatureTransformer, ApplyForEachQuery, ApplyGenericTransformer
 
 def _bind(instance, func, as_name=None):
     """
@@ -15,7 +17,7 @@ def _bind(instance, func, as_name=None):
     setattr(instance, as_name, bound_method)
     return bound_method
 
-def query(fn : Callable[[pd.Series], str], *args, **kwargs) -> Transformer:
+def query(fn : Callable[[pd.Series], str], *args, **kwargs) -> pt.Transformer:
     """
         Create a transformer that takes as input a query, and applies a supplied function to compute a new query formulation.
 
@@ -57,7 +59,7 @@ def _remove_stops(q):
     """
     return ApplyQueryTransformer(fn, *args, **kwargs)
 
-def doc_score(fn : Union[Callable[[pd.Series], float], Callable[[pd.DataFrame], Sequence[float]]], *args, batch_size=None, **kwargs) -> Transformer:
+def doc_score(fn : Union[Callable[[pd.Series], float], Callable[[pd.DataFrame], Sequence[float]]], *args, batch_size=None, **kwargs) -> pt.Transformer:
     """
         Create a transformer that takes as input a ranked documents dataframe, and applies a supplied function to compute a new score.
         Ranks are automatically computed. doc_score() can operate row-wise, or batch-wise, depending on whether batch_size is set.
@@ -88,7 +90,7 @@ def _doclen(df):
     """
     return ApplyDocumentScoringTransformer(fn, *args, batch_size=batch_size, **kwargs)
 
-def doc_features(fn : Callable[[pd.Series], npt.NDArray[Any]], *args, **kwargs) -> Transformer:
+def doc_features(fn : Callable[[pd.Series], npt.NDArray[Any]], *args, **kwargs) -> pt.Transformer:
     """
         Create a transformer that takes as input a ranked documents dataframe, and applies the supplied function to each document to compute feature scores. 
 
@@ -117,7 +119,7 @@ def _features(row):
     """
     return ApplyDocFeatureTransformer(fn, *args, **kwargs)
 
-def rename(columns : Dict[str,str], *args, errors='raise', **kwargs) -> Transformer:
+def rename(columns : Dict[str,str], *args, errors='raise', **kwargs) -> pt.Transformer:
     """
         Creates a transformer that renames columns in a dataframe. 
 
@@ -131,7 +133,7 @@ def rename(columns : Dict[str,str], *args, errors='raise', **kwargs) -> Transfor
     """
     return ApplyGenericTransformer(lambda df: df.rename(columns=columns, errors=errors), *args, **kwargs)
 
-def generic(fn : Callable[[pd.DataFrame], pd.DataFrame], *args, batch_size=None, **kwargs) -> Transformer:
+def generic(fn : Callable[[pd.DataFrame], pd.DataFrame], *args, batch_size=None, **kwargs) -> pt.Transformer:
     """
         Create a transformer that changes the input dataframe to another dataframe in an unspecified way.
 
@@ -154,7 +156,7 @@ def generic(fn : Callable[[pd.DataFrame], pd.DataFrame], *args, batch_size=None,
     """
     return ApplyGenericTransformer(fn, *args, batch_size=batch_size, **kwargs)
 
-def by_query(fn : Callable[[pd.DataFrame], pd.DataFrame], *args, batch_size=None, **kwargs) -> Transformer:
+def by_query(fn : Callable[[pd.DataFrame], pd.DataFrame], *args, batch_size=None, **kwargs) -> pt.Transformer:
     """
         As `pt.apply.generic()` except that fn receives a dataframe for one query at at time, rather than all results at once.
         If batch_size is set, fn will receive no more than batch_size documents for any query. The verbose kwargs controls whether
@@ -173,10 +175,9 @@ def __init__(self):
         _bind(self, lambda self, fn, *args, **kwargs : generic(fn, *args, **kwargs), as_name='generic')
     
     def __getattr__(self, item):
-        from functools import partial
         return partial(generic_apply, item)
 
-def generic_apply(name, *args, drop=False, **kwargs) -> Transformer:
+def generic_apply(name, *args, drop=False, **kwargs) -> pt.Transformer:
     if drop:
         return ApplyGenericTransformer(lambda df : df.drop(name, axis=1), *args, **kwargs) 
     
diff --git a/pyterrier/datasets.py b/pyterrier/datasets.py
index 0d87f574..19c8939b 100644
--- a/pyterrier/datasets.py
+++ b/pyterrier/datasets.py
@@ -1,5 +1,4 @@
 import urllib.request
-import wget
 import os
 import pandas as pd
 from .transformer import is_lambda
@@ -10,9 +9,7 @@
 from .io import autoopen, touch
 import pyterrier as pt
 import tarfile
-from warnings import warn
 
-import pyterrier
 
 TERRIER_DATA_BASE="http://data.terrier.org/indices/"
 STANDARD_TERRIER_INDEX_FILES = [
@@ -135,7 +132,6 @@ def _configure(self, **kwargs):
 
     @staticmethod
     def download(URLs : Union[str,List[str]], filename : str, **kwargs):
-        import pyterrier as pt
         basename = os.path.basename(filename)
 
         if isinstance(URLs, str):
@@ -331,7 +327,6 @@ def _describe_component(self, component):
         return True
 
     def get_corpus(self, **kwargs):
-        import pyterrier as pt
         return list(filter(lambda f : not f.endswith(".complete"), pt.io.find_files(self._get_all_files("corpus", **kwargs))))
 
     def get_corpus_iter(self, **kwargs):
@@ -345,14 +340,12 @@ def get_corpus_lang(self):
         return None
 
     def get_qrels(self, variant=None):
-        import pyterrier as pt
         filename, type = self._get_one_file("qrels", variant)
         if type == "direct":
             return filename 
         return pt.io.read_qrels(filename)
 
     def get_topics(self, variant=None, **kwargs):
-        import pyterrier as pt
         file, filetype = self._get_one_file("topics", variant)
         if filetype is None or filetype in pt.io.SUPPORTED_TOPICS_FORMATS:
             return pt.io.read_topics(file, format=filetype, **kwargs)
@@ -366,7 +359,6 @@ def get_topics_lang(self):
         return None
 
     def get_index(self, variant=None, **kwargs):
-        import pyterrier as pt
         if self.name == "50pct" and variant is None:
             variant="ex1"
         thedir = self._get_all_files("index", variant=variant, **kwargs)
@@ -461,11 +453,11 @@ def get_topics(self, variant=None, tokenise_query=True):
 
         # apply pyterrier tokenisation (otherwise the queries may not play well with batchretrieve)
         if tokenise_query and 'query' in df:
+            pt.java.required() # java is required for this path
             tokeniser = pt.terrier.J.Tokenizer.getTokeniser()
             def pt_tokenise(text):
                 return ' '.join(tokeniser.getTokens(text))
             df['query'] = df['query'].apply(pt_tokenise)
-
         return df
 
     def get_topics_lang(self):
@@ -742,7 +734,6 @@ def msmarco_document_generate(dataset):
 
 # remove WT- prefix from topics
 def remove_prefix(self, component, variant):
-    import pyterrier as pt
     topics_file, type = self._get_one_file("topics_prefixed", variant)
     if type in pt.io.SUPPORTED_TOPICS_FORMATS:
         topics = pt.io.read_topics(topics_file, type)
@@ -754,7 +745,6 @@ def remove_prefix(self, component, variant):
 
 # a function to fix the namedpage TREC Web tracks 2001 and 2002
 def parse_desc_only(self, component, variant):
-    import pyterrier as pt
     file, type = self._get_one_file("topics_desc_only", variant=variant)
     topics = pt.io.read_topics(file, format="trec", whitelist=["DESC"], blacklist=None)
     topics["qid"] = topics.apply(lambda row: row["qid"].replace("NP", ""), axis=1)
@@ -1032,7 +1022,7 @@ def _merge_years(self, component, variant):
     #"index":
     #    [(filename, VASWANI_INDEX_BASE + filename) for filename in STANDARD_TERRIER_INDEX_FILES + ["data.meta-0.fsomapfile"]],
     "info_url" : "http://ir.dcs.gla.ac.uk/resources/test_collections/npl/",
-    "corpus_iter" : lambda dataset, **kwargs : pyterrier.index.treccollection2textgen(dataset.get_corpus(), num_docs=11429, verbose=kwargs.get("verbose", False))
+    "corpus_iter" : lambda dataset, **kwargs : pt.index.treccollection2textgen(dataset.get_corpus(), num_docs=11429, verbose=kwargs.get("verbose", False))
 }
 
 DATASET_MAP = {
diff --git a/pyterrier/index.py b/pyterrier/index.py
index 765989f4..1d2b42d7 100644
--- a/pyterrier/index.py
+++ b/pyterrier/index.py
@@ -314,7 +314,7 @@ def getDocument(self):
     'warc' : 'org.terrier.indexing.WARC10Collection'
 }
 
-@pt.java.required()
+@pt.java.required
 def createCollection(files_path : List[str], coll_type : str = 'trec', props = {}):
     if coll_type in type_to_class:
         collectionClzName = type_to_class[coll_type]
@@ -531,7 +531,7 @@ def _to_class(this):
         if isinstance(this, str):
             return this
 
-    @pt.java.required()
+    @pt.java.required
     def stem(self, tok):
         if self not in _stemmer_cache:
             clz_name = self._to_class(self)
@@ -907,7 +907,7 @@ class DFIndexer(TerrierIndexer):
     Use this Indexer if you wish to index a pandas.Dataframe
 
     """
-    @pt.java.required()
+    @pt.java.required
     def index(self, text, *args, **kwargs):
         """
         Index the specified
@@ -1048,7 +1048,7 @@ class _IterDictIndexer_nofifo(_BaseIterDictIndexer):
     Use this Indexer if you wish to index an iter of dicts (possibly with multiple fields).
     This version is used for Windows -- which doesn't support the faster fifo implementation.
     """
-    @pt.java.required()
+    @pt.java.required
     def index(self, it, fields=('text',), meta=None, meta_lengths=None, threads=None):
         """
         Index the specified iter of dicts with the (optional) specified fields
@@ -1119,7 +1119,7 @@ class _IterDictIndexer_fifo(_BaseIterDictIndexer):
     This version is optimized by using multiple threads and POSIX fifos to tranfer data,
     which ends up being much faster.
     """
-    @pt.java.required()
+    @pt.java.required
     def index(self, it, fields=('text',), meta=None, meta_lengths=None):
         """
         Index the specified iter of dicts with the (optional) specified fields
@@ -1277,7 +1277,7 @@ def __init__(self,
         self.meta_reverse = meta_reverse
         self.meta_tags = meta_tags
     
-    @pt.java.required()
+    @pt.java.required
     def index(self, files_path : Union[str,List[str]]):
         """
         Index the specified TREC formatted files
diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index 6115bdb8..9d23b28d 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -1,5 +1,5 @@
 from pyterrier.java import mavenresolver
-from pyterrier.java._init import init, started, parallel_init, parallel_init_args, required, before_init, autoclass, cast
+from pyterrier.java._init import init, started, parallel_init, parallel_init_args, required, required_raise, before_init, autoclass, cast
 from pyterrier.java import config
 from pyterrier.java.config import configure
 from pyterrier.java.utils import redirect_stdouterr, bytebuffer_to_array, JavaClasses, J, add_jar, add_package, set_memory_limit, set_redirect_io, add_option, set_log_level
@@ -7,5 +7,6 @@
 __all__ = [
     'add_jar', 'add_option', 'add_package', 'autoclass', 'before_init', 'bytebuffer_to_array', 'cast', 'config',
     'configure', 'init', 'J', 'JavaClasses', 'mavenresolver', 'parallel_init', 'parallel_init_args',
-    'redirect_stdouterr', 'required', 'set_log_level', 'set_memory_limit', 'set_redirect_io', 'started',
+    'redirect_stdouterr', 'required', 'required_raise', 'set_log_level', 'set_memory_limit', 'set_redirect_io',
+    'started',
 ]
diff --git a/pyterrier/java/_init.py b/pyterrier/java/_init.py
index 5f87f90c..e54590ae 100644
--- a/pyterrier/java/_init.py
+++ b/pyterrier/java/_init.py
@@ -1,7 +1,7 @@
 import os
 from warnings import warn
 from functools import wraps
-from typing import Dict, Any, Tuple, Callable
+from typing import Dict, Any, Tuple, Callable, Optional, Union
 from copy import deepcopy
 import pyterrier as pt
 import pyterrier.java.config
@@ -86,38 +86,53 @@ def started() -> bool:
     return _started
 
 
-def required(raise_on_not_started: bool = False) -> Callable:
+def required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
     """
-    Wraps a function that requires the Java Virtual Machine to be started before running. If the JVM has not yet
-    been started, it runs pt.java.init() or raises an error, depending on the value of raise_on_not_started.
+    Requires the Java Virtual Machine to be started. If the JVM has not yet been started, it runs pt.java.init().
+
+    Can be used as either a standalone function or a function decorator.
     """
-    def _required(fn: Callable) -> Callable:
-        @wraps(fn)
-        def _wrapper(*args, **kwargs):
-            if not started():
-                if raise_on_not_started:
-                    raise RuntimeError(f'You need to call pt.java.init() required before you can call {fn}')
-                else:
-                    init()
-            return fn(*args, **kwargs)
-        return _wrapper
-    return _required
+    if fn is None:
+        return required(pt.utils.noop)()
 
+    @wraps(fn)
+    def _wrapper(*args, **kwargs):
+        if not started():
+            init()
+        return fn(*args, **kwargs)
+    return _wrapper
 
 
-def before_init() -> Callable:
+def required_raise(fn: Optional[Callable] = None) -> Union[Callable, bool]:
     """
-    Wraps a function that can only be run before the Java Virtual Machine has started. If the JVM has already started,
-    an error is raised.
+    Similar to `pt.java.required`, but raises an error if called before pt.java.init().
     """
-    def _before_init(fn: Callable) -> Callable:
-        @wraps(fn)
-        def _wrapper(*args, **kwargs):
-            if started():
-                raise RuntimeError(f'You need to call pt.java.init() required before you can call {fn}')
-            return fn(*args, **kwargs)
-        return _wrapper
-    return _before_init
+    if fn is None:
+        return required_raise(pt.utils.noop)()
+
+    @wraps(fn)
+    def _wrapper(*args, **kwargs):
+        if not started():
+            raise RuntimeError(f'You need to call pt.java.init() required before you can call {fn}')
+        return fn(*args, **kwargs)
+    return _wrapper
+
+
+def before_init(fn: Optional[Callable] = None) -> Union[Callable, bool]:
+    """
+    If the JVM has already started, an error is raised.
+
+    Can be used as either a standalone function or a function decorator.
+    """
+    if fn is None:
+        return before_init(pt.utils.noop)()
+
+    @wraps(fn)
+    def _wrapper(*args, **kwargs):
+        if started():
+            raise RuntimeError(f'You need to call pt.java.init() required before you can call {fn}')
+        return fn(*args, **kwargs)
+    return _wrapper
 
 
 def parallel_init(started: bool, configs: Dict[str, Dict[str, Any]]) -> None:
@@ -137,7 +152,7 @@ def parallel_init_args() -> Tuple[bool, Dict[str, Dict[str, Any]]]:
     )
 
 
-@required(raise_on_not_started=True)
+@required_raise
 def autoclass(*args, **kwargs):
     """
     Wraps jnius.autoclass once java has started. Raises an error if called before pt.java.init() is called.
@@ -146,7 +161,7 @@ def autoclass(*args, **kwargs):
     return jnius.autoclass(*args, **kwargs)
 
 
-@required(raise_on_not_started=True)
+@required_raise
 def cast(*args, **kwargs):
     """
     Wraps jnius.cast once java has started. Raises an error if called before pt.java.init() is called.
diff --git a/pyterrier/java/utils.py b/pyterrier/java/utils.py
index 418d65bb..b5a0e3a7 100644
--- a/pyterrier/java/utils.py
+++ b/pyterrier/java/utils.py
@@ -1,5 +1,5 @@
 import pyterrier as pt
-from pyterrier.java import required, before_init, started, configure, mavenresolver
+from pyterrier.java import required_raise, before_init, started, configure, mavenresolver
 from typing import Dict, Optional
 
 
@@ -76,12 +76,12 @@ def unsign(signed):
     return bytearray([ unsign(buffer.get(offset)) for offset in range(buffer.capacity()) ])
 
 
-@before_init()
+@before_init
 def add_jar(jar_path):
     configure.append('jars', jar_path)
 
 
-@before_init()
+@before_init
 def add_package(org_name: str = None, package_name: str = None, version: str = None, file_type='jar'):
     if version is None or version == 'snapshot':
         version = mavenresolver.latest_version_num(org_name, package_name)
@@ -89,17 +89,17 @@ def add_package(org_name: str = None, package_name: str = None, version: str = N
     add_jar(file_name)
 
 
-@before_init()
+@before_init
 def set_memory_limit(mem: Optional[float]):
     configure(mem=mem)
 
 
-@before_init()
+@before_init
 def add_option(option: str):
     configure.append('options', option)
 
 
-@before_init()
+@before_init
 def set_redirect_io(redirect_io: bool):
     configure(redirect_io=redirect_io)
 
@@ -130,7 +130,7 @@ def __init__(self, mapping: Dict[str, str]):
     def __dir__(self):
         return list(self._mapping.keys())
 
-    @required(raise_on_not_started=True)
+    @required_raise
     def __getattr__(self, key):
         if key not in self._mapping:
             return AttributeError(f'{self} has no attribute {key!r}')
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 9b74c8ce..6c8016fb 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -18,17 +18,17 @@
 })
 
 
-@pt.java.before_init()
+@pt.java.before_init
 def set_terrier_version(version: Optional[str] = None):
     configure(terrier_version=version)
 
 
-@pt.java.before_init()
+@pt.java.before_init
 def set_helper_version(version: Optional[str] = None):
     configure(helper_version=version)
 
 
-@pt.java.before_init()
+@pt.java.before_init
 def enable_prf(version: Optional[str] = None):
     pt.java.add_package('com.github.terrierteam', 'terrier-prf', version)
 
@@ -271,7 +271,7 @@ def _index_corpusiter_direct_pretok(self):
 
 
 
-@pt.java.required()
+@pt.java.required
 def extend_package(package):
     """
         Allows to add packages to Terrier's classpath after the JVM has started.
@@ -285,7 +285,7 @@ def extend_package(package):
     mvnr.addDependencies(package_list)
 
 
-@pt.java.required()
+@pt.java.required
 def set_property(k, v):
     """
         Allows to set a property in Terrier's global properties configuration. Example::
@@ -303,7 +303,7 @@ def set_property(k, v):
     J.ApplicationSetup.bootstrapInitialisation(_properties)
 
 
-@pt.java.required()
+@pt.java.required
 def set_properties(kwargs):
     """
         Allows to set many properties in Terrier's global properties configuration
@@ -313,7 +313,7 @@ def set_properties(kwargs):
     J.ApplicationSetup.bootstrapInitialisation(_properties)
 
 
-@pt.java.required()
+@pt.java.required
 def run(cmd, args=[]):
     """
         Allows to run a Terrier executable class, i.e. one that can be access from the `bin/terrier` commandline programme.
diff --git a/pyterrier/terrier/retriever.py b/pyterrier/terrier/retriever.py
index 0a0c808a..44a44aa4 100644
--- a/pyterrier/terrier/retriever.py
+++ b/pyterrier/terrier/retriever.py
@@ -85,7 +85,7 @@ class BatchRetrieveBase(pt.Transformer, Symbol):
     Attributes:
         verbose(bool): If True transform method will display progress
     """
-    @pt.java.required()
+    @pt.java.required
     def __init__(self, verbose=0, **kwargs):
         super().__init__(kwargs)
         self.verbose = verbose

From feddbc01b6f71f7e7e82c1f8f1a7f4576c2dad41 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 16:26:49 +0100
Subject: [PATCH 056/153] update pt.io for pt.java

---
 pyterrier/io.py | 33 ++++++++++++++-------------------
 1 file changed, 14 insertions(+), 19 deletions(-)

diff --git a/pyterrier/io.py b/pyterrier/io.py
index ab543763..84b76324 100644
--- a/pyterrier/io.py
+++ b/pyterrier/io.py
@@ -1,13 +1,17 @@
+import re
 import os
+from types import GeneratorType
+import xml.etree.ElementTree as ET
+import numpy as np
 import pandas as pd
 from contextlib import contextmanager
+import pyterrier as pt
 
 
 def coerce_dataframe(obj):
     if isinstance(obj, pd.DataFrame):
         return obj
-    import types
-    if isinstance(obj, types.GeneratorType):
+    if isinstance(obj, GeneratorType):
         #its a generator, lets assume it generates dataframes
         rtr=[]
         for x in obj:
@@ -41,7 +45,6 @@ def find_files(dir):
     Returns:
         paths(list): A list of the paths to the files
     """
-    import os
     lst = []
     files = []
     for (dirpath, dirnames, filenames) in os.walk(dir, followlinks=True):
@@ -131,7 +134,6 @@ def touch(fname, mode=0o666, dir_fd=None, **kwargs):
     Eqiuvalent to touch command on linux.
     Implementation from https://stackoverflow.com/a/1160227
     """
-    import os
     flags = os.O_CREAT | os.O_APPEND
     with os.fdopen(os.open(fname, flags=flags, mode=mode, dir_fd=dir_fd)) as f:
         os.utime(f.fileno() if os.utime in os.supports_fd else fname,
@@ -179,7 +181,6 @@ def read_results(filename, format="trec", topics=None, dataset=None, **kwargs):
     if dataset is not None:
         assert topics is None, "Cannot provide both dataset and topics"
         if isinstance(dataset, str):
-            import pyterrier as pt
             dataset = pt.get_dataset(dataset)
         topics = dataset.get_topics()
     if topics is not None:
@@ -195,8 +196,6 @@ def _parse_line(l):
             # my $label = shift @parts;
             # my %hash = map {split /:/, $_} @parts;
             # return ($label, $comment, %hash);
-        import re
-        import numpy as np
         line, comment = l.split("#")
         line = line.strip()
         parts = re.split(r'\s+|:', line)
@@ -299,11 +298,10 @@ def read_topics(filename, format="trec", **kwargs):
         raise ValueError("Format %s not known, supported types are %s" % (format, str(SUPPORTED_TOPICS_FORMATS.keys())))
     return SUPPORTED_TOPICS_FORMATS[format](filename, **kwargs)
 
+@pt.java.required
 def _read_topics_trec(file_path, doc_tag="TOP", id_tag="NUM", whitelist=["TITLE"], blacklist=["DESC","NARR"]):
-    from jnius import autoclass
-    from . import check_version
-    assert check_version("5.3")
-    trecquerysource = autoclass('org.terrier.applications.batchquerying.TRECQuery')
+    assert pt.check_version("5.3")
+    trecquerysource = pt.java.autoclass('org.terrier.applications.batchquerying.TRECQuery')
     tqs = trecquerysource(
         [file_path], doc_tag, id_tag, whitelist, blacklist,
         # help jnius select the correct constructor
@@ -316,6 +314,7 @@ def _read_topics_trec(file_path, doc_tag="TOP", id_tag="NUM", whitelist=["TITLE"
     topics_dt = pd.DataFrame(topics_lst,columns=['qid','query'])
     return topics_dt
 
+@pt.java.required
 def _read_topics_trecxml(filename, tags=["query", "question", "narrative"], tokenise=True):
     """
     Parse a file containing topics in TREC-like XML format
@@ -326,14 +325,11 @@ def _read_topics_trecxml(filename, tags=["query", "question", "narrative"], toke
     Returns:
         pandas.Dataframe with columns=['qid','query']
     """
-    import xml.etree.ElementTree as ET
-    import pandas as pd
     tags=set(tags)
     topics=[]
     tree = ET.parse(filename)
     root = tree.getroot()
-    from jnius import autoclass
-    tokeniser = autoclass("org.terrier.indexing.tokenisation.Tokeniser").getTokeniser()
+    tokeniser = pt.java.autoclass("org.terrier.indexing.tokenisation.Tokeniser").getTokeniser()
     for child in root.iter('topic'):
         try:
             qid = child.attrib["number"]
@@ -349,6 +345,7 @@ def _read_topics_trecxml(filename, tags=["query", "question", "narrative"], toke
         topics.append((str(qid), query.strip()))
     return pd.DataFrame(topics, columns=["qid", "query"])
 
+@pt.java.required
 def _read_topics_singleline(filepath, tokenise=True):
     """
     Parse a file containing topics, one per line. This function uses Terrier, so supports reading direct from URLs.
@@ -362,10 +359,8 @@ def _read_topics_singleline(filepath, tokenise=True):
         pandas.Dataframe with columns=['qid','query']
     """
     rows = []
-    from jnius import autoclass
-    from . import check_version
-    assert check_version("5.3")
-    slqIter = autoclass("org.terrier.applications.batchquerying.SingleLineTRECQuery")(filepath, tokenise)
+    assert pt.check_version("5.3")
+    slqIter = pt.java.autoclass("org.terrier.applications.batchquerying.SingleLineTRECQuery")(filepath, tokenise)
     for q in slqIter:
         rows.append([slqIter.getQueryId(), q])
     return pd.DataFrame(rows, columns=["qid", "query"])

From 85b92cdb0f08df1f97c29b8b5ba37d8f326f1b43 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 16:29:26 +0100
Subject: [PATCH 057/153] update pt.ltr for pt.java

---
 pyterrier/ltr.py | 34 ++++++++++++++++------------------
 1 file changed, 16 insertions(+), 18 deletions(-)

diff --git a/pyterrier/ltr.py b/pyterrier/ltr.py
index f8b92502..5293dc61 100644
--- a/pyterrier/ltr.py
+++ b/pyterrier/ltr.py
@@ -1,14 +1,12 @@
-
-from . import Transformer, Estimator
-from .apply import doc_score, doc_features
-from .model import add_ranks
 from typing import Sequence, Union, Tuple
-import numpy as np, pandas as pd
+import numpy as np
+import pandas as pd
+import pyterrier as pt
 
 FeatureList = Union[Sequence[int], int]
 
 
-class AblateFeatures(Transformer):
+class AblateFeatures(pt.Transformer):
     
     def __init__(self, fids: FeatureList):
         self.fids = fids if isinstance(fids, list) else [fids]
@@ -27,7 +25,7 @@ def _reset(row):
         topics_and_res["features"] = topics_and_res.apply(_reset, axis=1)
         return topics_and_res
 
-class KeepFeatures(Transformer):
+class KeepFeatures(pt.Transformer):
     
     def __init__(self, fids : FeatureList):
         self.fids = fids if isinstance(fids, list) else [fids]
@@ -40,7 +38,7 @@ def transform(self, topics_and_res):
         topics_and_res["features"] = topics_and_res.apply(lambda row: row["features"][self.fids], axis=1)
         return topics_and_res
 
-class RegressionTransformer(Estimator):
+class RegressionTransformer(pt.Estimator):
     """
     This class simplifies the use of Scikit-learn's techniques for learning-to-rank.
     """
@@ -93,7 +91,7 @@ def transform(self, test_DF):
                 raise ValueError("Expected %d features, but found %d features" % (len(self.learner.feature_importances_), found_numf))
 
         test_DF["score"] = self.learner.predict(np.stack(test_DF["features"].values))
-        return add_ranks(test_DF)
+        return pt.model.add_ranks(test_DF)
 
 class LTRTransformer(RegressionTransformer):
     """
@@ -146,7 +144,7 @@ def _count_by_topic(res : pd.DataFrame) -> Tuple[Sequence[int], pd.DataFrame]:
         )
         self.num_f = tr_res.iloc[0].features.shape[0]
 
-class FastRankEstimator(Estimator):
+class FastRankEstimator(pt.Estimator):
     """
     This class simplifies the use of FastRank's techniques for learning-to-rank.
     """
@@ -212,9 +210,9 @@ def transform(self, topics_and_docs_Test):
         rtr = dataset.predict_scores(self.model)
         scores = [rtr[i] for i in range(len(rtr))]
         test_DF["score"] = scores
-        return add_ranks(test_DF)
+        return pt.model.add_ranks(test_DF)
 
-def ablate_features(fids : FeatureList) -> Transformer:
+def ablate_features(fids : FeatureList) -> pt.Transformer:
     """
         Ablates features (sets feature value to 0) from a pipeline. This is useful for 
         performing feature ablation studies, whereby a feature is removed from the pipeline
@@ -225,7 +223,7 @@ def ablate_features(fids : FeatureList) -> Transformer:
     """
     return AblateFeatures(fids)
 
-def keep_features(fids : FeatureList) -> Transformer:
+def keep_features(fids : FeatureList) -> pt.Transformer:
     """
         Reduces the features in a pipeline to only those mentioned. This is useful for 
         performing feature ablation studies, whereby only some features are kept 
@@ -236,7 +234,7 @@ def keep_features(fids : FeatureList) -> Transformer:
     """
     return KeepFeatures(fids)
 
-def feature_to_score(fid : int) -> Transformer:
+def feature_to_score(fid : int) -> pt.Transformer:
     """
         Applies a specified feature for ranking. Useful for evaluating which of a number of 
         pre-computed features are useful for ranking. 
@@ -244,9 +242,9 @@ def feature_to_score(fid : int) -> Transformer:
         Args: 
             fid: a single feature id that should be kept
     """
-    return doc_score(lambda row : row["features"][fid])
+    return pt.apply.doc_score(lambda row : row["features"][fid])
 
-def apply_learned_model(learner, form : str = 'regression', **kwargs) -> Transformer:
+def apply_learned_model(learner, form : str = 'regression', **kwargs) -> pt.Transformer:
     """
         Results in a transformer that can take in documents that have a "features" column,
         and pass that to the specified learner via its transform() function, to obtain the
@@ -267,7 +265,7 @@ def apply_learned_model(learner, form : str = 'regression', **kwargs) -> Transfo
         return FastRankEstimator(learner, **kwargs)
     return RegressionTransformer(learner, **kwargs)
 
-def score_to_feature() -> Transformer:
+def score_to_feature() -> pt.Transformer:
     """
         Takes the document's "score" from the score attribute, and uses it as a single feature. 
         In particular, a feature union operator does not use any score of the documents in the
@@ -284,4 +282,4 @@ def score_to_feature() -> Transformer:
             three_features = cands >> (bm25f  **  pl2f ** pt.ltr.score_to_feature())  
 
     """
-    return doc_features(lambda row : np.array(row["score"]))
\ No newline at end of file
+    return pt.apply.doc_features(lambda row : np.array(row["score"]))

From bb35f2b3397f3a06b79799c151f6ffb0bd450998 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 16:30:37 +0100
Subject: [PATCH 058/153] update pt.model for pt.java

---
 pyterrier/model.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/pyterrier/model.py b/pyterrier/model.py
index 279e7cd9..f17a15c3 100644
--- a/pyterrier/model.py
+++ b/pyterrier/model.py
@@ -1,3 +1,5 @@
+import re
+import math
 import numpy as np
 import pandas as pd
 from typing import List, Sequence
@@ -68,7 +70,6 @@ def query_columns(df : pd.DataFrame, qid=True) -> Sequence[str]:
         rtr.append("qid")
     if "query" in columns:
         rtr.append("query")
-    import re
     query_col_re = re.compile('^query_[\\d]+')
     for c in columns:
         if query_col_re.search(c):
@@ -222,8 +223,6 @@ def split_df(df : pd.DataFrame, N) -> List[pd.DataFrame]:
         type = "D"
     else:
         raise ValueError("Dataframe is not of type D,Q,R")
-    
-    from math import ceil
 
     def chunks(df, n):
         """Yield successive n-sized chunks from df."""
@@ -231,13 +230,13 @@ def chunks(df, n):
             yield df.iloc[ i: min(len(df),i + n)]
     
     if type == "Q" or type == "D":         
-        splits = list( chunks(df, ceil(len(df)/N)))
+        splits = list( chunks(df, math.ceil(len(df)/N)))
         return splits
 
     rtr = []
     grouper = df.groupby("qid")
     this_group = []
-    chunk_size = ceil(len(grouper)/N)
+    chunk_size = math.ceil(len(grouper)/N)
     for qid, group in grouper:
         this_group.append(group)
         if len(this_group) == chunk_size:

From 6b334539b89eb787cc87283a37fc2b9a9aca5a1f Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 16:31:39 +0100
Subject: [PATCH 059/153] Revert "update pt.model for pt.java"

This reverts commit bb35f2b3397f3a06b79799c151f6ffb0bd450998.
---
 pyterrier/model.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/pyterrier/model.py b/pyterrier/model.py
index f17a15c3..279e7cd9 100644
--- a/pyterrier/model.py
+++ b/pyterrier/model.py
@@ -1,5 +1,3 @@
-import re
-import math
 import numpy as np
 import pandas as pd
 from typing import List, Sequence
@@ -70,6 +68,7 @@ def query_columns(df : pd.DataFrame, qid=True) -> Sequence[str]:
         rtr.append("qid")
     if "query" in columns:
         rtr.append("query")
+    import re
     query_col_re = re.compile('^query_[\\d]+')
     for c in columns:
         if query_col_re.search(c):
@@ -223,6 +222,8 @@ def split_df(df : pd.DataFrame, N) -> List[pd.DataFrame]:
         type = "D"
     else:
         raise ValueError("Dataframe is not of type D,Q,R")
+    
+    from math import ceil
 
     def chunks(df, n):
         """Yield successive n-sized chunks from df."""
@@ -230,13 +231,13 @@ def chunks(df, n):
             yield df.iloc[ i: min(len(df),i + n)]
     
     if type == "Q" or type == "D":         
-        splits = list( chunks(df, math.ceil(len(df)/N)))
+        splits = list( chunks(df, ceil(len(df)/N)))
         return splits
 
     rtr = []
     grouper = df.groupby("qid")
     this_group = []
-    chunk_size = math.ceil(len(grouper)/N)
+    chunk_size = ceil(len(grouper)/N)
     for qid, group in grouper:
         this_group.append(group)
         if len(this_group) == chunk_size:

From 6ec9135a08abaab13ee614eb5d47c71f9fb364eb Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 16:32:03 +0100
Subject: [PATCH 060/153] Revert "update pt.ltr for pt.java"

This reverts commit 85b92cdb0f08df1f97c29b8b5ba37d8f326f1b43.
---
 pyterrier/ltr.py | 34 ++++++++++++++++++----------------
 1 file changed, 18 insertions(+), 16 deletions(-)

diff --git a/pyterrier/ltr.py b/pyterrier/ltr.py
index 5293dc61..f8b92502 100644
--- a/pyterrier/ltr.py
+++ b/pyterrier/ltr.py
@@ -1,12 +1,14 @@
+
+from . import Transformer, Estimator
+from .apply import doc_score, doc_features
+from .model import add_ranks
 from typing import Sequence, Union, Tuple
-import numpy as np
-import pandas as pd
-import pyterrier as pt
+import numpy as np, pandas as pd
 
 FeatureList = Union[Sequence[int], int]
 
 
-class AblateFeatures(pt.Transformer):
+class AblateFeatures(Transformer):
     
     def __init__(self, fids: FeatureList):
         self.fids = fids if isinstance(fids, list) else [fids]
@@ -25,7 +27,7 @@ def _reset(row):
         topics_and_res["features"] = topics_and_res.apply(_reset, axis=1)
         return topics_and_res
 
-class KeepFeatures(pt.Transformer):
+class KeepFeatures(Transformer):
     
     def __init__(self, fids : FeatureList):
         self.fids = fids if isinstance(fids, list) else [fids]
@@ -38,7 +40,7 @@ def transform(self, topics_and_res):
         topics_and_res["features"] = topics_and_res.apply(lambda row: row["features"][self.fids], axis=1)
         return topics_and_res
 
-class RegressionTransformer(pt.Estimator):
+class RegressionTransformer(Estimator):
     """
     This class simplifies the use of Scikit-learn's techniques for learning-to-rank.
     """
@@ -91,7 +93,7 @@ def transform(self, test_DF):
                 raise ValueError("Expected %d features, but found %d features" % (len(self.learner.feature_importances_), found_numf))
 
         test_DF["score"] = self.learner.predict(np.stack(test_DF["features"].values))
-        return pt.model.add_ranks(test_DF)
+        return add_ranks(test_DF)
 
 class LTRTransformer(RegressionTransformer):
     """
@@ -144,7 +146,7 @@ def _count_by_topic(res : pd.DataFrame) -> Tuple[Sequence[int], pd.DataFrame]:
         )
         self.num_f = tr_res.iloc[0].features.shape[0]
 
-class FastRankEstimator(pt.Estimator):
+class FastRankEstimator(Estimator):
     """
     This class simplifies the use of FastRank's techniques for learning-to-rank.
     """
@@ -210,9 +212,9 @@ def transform(self, topics_and_docs_Test):
         rtr = dataset.predict_scores(self.model)
         scores = [rtr[i] for i in range(len(rtr))]
         test_DF["score"] = scores
-        return pt.model.add_ranks(test_DF)
+        return add_ranks(test_DF)
 
-def ablate_features(fids : FeatureList) -> pt.Transformer:
+def ablate_features(fids : FeatureList) -> Transformer:
     """
         Ablates features (sets feature value to 0) from a pipeline. This is useful for 
         performing feature ablation studies, whereby a feature is removed from the pipeline
@@ -223,7 +225,7 @@ def ablate_features(fids : FeatureList) -> pt.Transformer:
     """
     return AblateFeatures(fids)
 
-def keep_features(fids : FeatureList) -> pt.Transformer:
+def keep_features(fids : FeatureList) -> Transformer:
     """
         Reduces the features in a pipeline to only those mentioned. This is useful for 
         performing feature ablation studies, whereby only some features are kept 
@@ -234,7 +236,7 @@ def keep_features(fids : FeatureList) -> pt.Transformer:
     """
     return KeepFeatures(fids)
 
-def feature_to_score(fid : int) -> pt.Transformer:
+def feature_to_score(fid : int) -> Transformer:
     """
         Applies a specified feature for ranking. Useful for evaluating which of a number of 
         pre-computed features are useful for ranking. 
@@ -242,9 +244,9 @@ def feature_to_score(fid : int) -> pt.Transformer:
         Args: 
             fid: a single feature id that should be kept
     """
-    return pt.apply.doc_score(lambda row : row["features"][fid])
+    return doc_score(lambda row : row["features"][fid])
 
-def apply_learned_model(learner, form : str = 'regression', **kwargs) -> pt.Transformer:
+def apply_learned_model(learner, form : str = 'regression', **kwargs) -> Transformer:
     """
         Results in a transformer that can take in documents that have a "features" column,
         and pass that to the specified learner via its transform() function, to obtain the
@@ -265,7 +267,7 @@ def apply_learned_model(learner, form : str = 'regression', **kwargs) -> pt.Tran
         return FastRankEstimator(learner, **kwargs)
     return RegressionTransformer(learner, **kwargs)
 
-def score_to_feature() -> pt.Transformer:
+def score_to_feature() -> Transformer:
     """
         Takes the document's "score" from the score attribute, and uses it as a single feature. 
         In particular, a feature union operator does not use any score of the documents in the
@@ -282,4 +284,4 @@ def score_to_feature() -> pt.Transformer:
             three_features = cands >> (bm25f  **  pl2f ** pt.ltr.score_to_feature())  
 
     """
-    return pt.apply.doc_features(lambda row : np.array(row["score"]))
+    return doc_features(lambda row : np.array(row["score"]))
\ No newline at end of file

From ab379dcb44f0f86fc9b79adc38643e887ec3424e Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 17:09:44 +0100
Subject: [PATCH 061/153] move rewrite.py and index.py under terrier/ and
 update for pt.java

---
 pyterrier/__init__.py              |   5 +-
 pyterrier/java/utils.py            |   4 +
 pyterrier/terrier/__init__.py      |   9 ++
 pyterrier/{ => terrier}/index.py   | 148 +++++++++--------------------
 pyterrier/terrier/java.py          |  26 +++++
 pyterrier/{ => terrier}/rewrite.py |  86 +++++++----------
 setup.py                           |  11 +--
 7 files changed, 127 insertions(+), 162 deletions(-)
 rename pyterrier/{ => terrier}/index.py (91%)
 rename pyterrier/{ => terrier}/rewrite.py (90%)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index b3a96122..5b150ede 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -11,7 +11,7 @@
 from pyterrier.java import started, redirect_stdouterr # for backward compat, maybe remove/deprecate some day?
 
 from pyterrier import terrier
-from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve, IndexFactory, set_property, set_properties, run
+from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve, IndexFactory, set_property, set_properties, run, rewrite, index, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
 
 from pyterrier import anserini
 
@@ -19,7 +19,6 @@
 
 from . import cache
 from . import debug
-from . import index
 from . import io
 from . import measures
 from . import model
@@ -27,12 +26,10 @@
 from . import ltr
 from . import parallel
 from . import pipelines
-from . import rewrite
 from . import text
 from . import transformer
 from .datasets import get_dataset, find_datasets, list_datasets
 
-from .index import Indexer, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
 from .pipelines import Experiment, GridScan, GridSearch, KFoldGridSearch, Evaluate
 
 
diff --git a/pyterrier/java/utils.py b/pyterrier/java/utils.py
index b5a0e3a7..f40140ed 100644
--- a/pyterrier/java/utils.py
+++ b/pyterrier/java/utils.py
@@ -144,4 +144,8 @@ def __getattr__(self, key):
     'Properties': 'java.util.Properties',
     'PTUtils': 'org.terrier.python.PTUtils',
     'System': 'java.lang.System',
+    'StringReader': 'java.io.StringReader',
+    'HashMap': 'java.util.HashMap',
+    'Arrays': 'java.util.Arrays',
+    'Array': 'java.lang.reflect.Array',
 })
diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
index ea1f5b40..3cfcfb91 100644
--- a/pyterrier/terrier/__init__.py
+++ b/pyterrier/terrier/__init__.py
@@ -3,6 +3,9 @@
 from pyterrier.terrier.java import configure, set_terrier_version, set_helper_version, enable_prf, extend_package, J, set_property, set_properties, run
 from pyterrier.terrier.retriever import BatchRetrieve, FeaturesBatchRetrieve
 from pyterrier.terrier.index_factory import IndexFactory
+from pyterrier.terrier import index
+from pyterrier.terrier.index import FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
+from pyterrier.terrier import rewrite
 
 TerrierRetrieve = BatchRetrieve # BatchRetrieve is an alias to TerrierRetrieve
 
@@ -14,6 +17,12 @@
     # retrieval
     'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve',
 
+    # indexing
+    'index', 'FilesIndexer', 'TRECCollectionIndexer', 'DFIndexer', 'DFIndexUtils', 'IterDictIndexer', 'IndexingType', 'TerrierStemmer', 'TerrierStopwords', 'TerrierTokeniser',
+
+    # rewriting
+    'rewrite',
+
     # misc
     'IndexFactory', 'set_property', 'set_properties', 'run', 
 ]
diff --git a/pyterrier/index.py b/pyterrier/terrier/index.py
similarity index 91%
rename from pyterrier/index.py
rename to pyterrier/terrier/index.py
index 1d2b42d7..82932d0e 100644
--- a/pyterrier/index.py
+++ b/pyterrier/terrier/index.py
@@ -4,7 +4,6 @@
 
 from enum import Enum
 import pandas as pd
-from . import Indexer
 import os
 import enum
 import json
@@ -20,26 +19,12 @@
 import more_itertools
 import pyterrier as pt
 
-StringReader = None
-HashMap = None
-TaggedDocument = None
-FlatJSONDocument = None
-Tokeniser = None
-TRECCollection = None
-SimpleFileCollection = None
-BasicIndexer = None
-BlockIndexer = None
-Collection = None
-BasicSinglePassIndexer = None
-BlockSinglePassIndexer = None
-BasicMemoryIndexer = None
-Arrays = None
-Array = None
-ApplicationSetup = None
-Properties = None
-CLITool = None
-StructureMerger = None
-BlockStructureMerger = None
+
+# TODO: ensure functions are properly marked up with @pt.java.required
+# TODO: some stuff doesn't seem like they belong here, like TerrierTokeniser and TerrierStopwords. Move this out?
+
+
+# These classes are only defined after pt.java.init()
 DocListIterator = None
 PythonListIterator = None
 FlatJSONDocumentIterator = None
@@ -47,66 +32,24 @@
 TQDMSizeCollection = None
 
 # for backward compatibility
-class IterDictIndexerBase(Indexer):
+class IterDictIndexerBase(pt.Indexer):
     @deprecated(version="0.9", reason="Use pt.Indexer instead of IterDictIndexerBase")
     def __init__(self, *args, **kwargs):
-        super(Indexer, self).__init__(*args, **kwargs)
+        super(pt.Indexer, self).__init__(*args, **kwargs)
 
 # lastdoc ensures that a Document instance from a Collection is not GCd before Java has used it.
-lastdoc=None
+lastdoc = None
 
 def _java_post_init(jnius):
-    autoclass = pt.java.autoclass
-    global StringReader
-    global HashMap
-    global TaggedDocument
-    global FlatJSONDocument
-    global Tokeniser
-    global TRECCollection
-    global SimpleFileCollection
-    global BasicIndexer
-    global BlockIndexer
-    global BasicSinglePassIndexer
-    global BlockSinglePassIndexer
-    global BasicMemoryIndexer
-    global Collection
-    global Arrays
-    global Array
-    global ApplicationSetup
-    global Properties
-    global CLITool
-    global StructureMerger
-    global BlockStructureMerger
     global DocListIterator
     global PythonListIterator
     global FlatJSONDocumentIterator
     global TQDMCollection
     global TQDMSizeCollection
 
-    StringReader = autoclass("java.io.StringReader")
-    HashMap = autoclass("java.util.HashMap")
-    TaggedDocument = autoclass("org.terrier.indexing.TaggedDocument")
-    FlatJSONDocument = autoclass("org.terrier.indexing.FlatJSONDocument")
-    Tokeniser = autoclass("org.terrier.indexing.tokenisation.Tokeniser")
-    TRECCollection = autoclass("org.terrier.indexing.TRECCollection")
-    SimpleFileCollection = autoclass("org.terrier.indexing.SimpleFileCollection")
-    BasicIndexer = autoclass("org.terrier.structures.indexing.classical.BasicIndexer")
-    BlockIndexer = autoclass("org.terrier.structures.indexing.classical.BlockIndexer")
-    BasicSinglePassIndexer = autoclass("org.terrier.structures.indexing.singlepass.BasicSinglePassIndexer")
-    BlockSinglePassIndexer = autoclass("org.terrier.structures.indexing.singlepass.BlockSinglePassIndexer")
-    BasicMemoryIndexer = autoclass("org.terrier.realtime.memory.MemoryIndexer" if pt.check_version("5.7") else "org.terrier.python.MemoryIndexer")
-    Collection = autoclass("org.terrier.indexing.Collection")
-    Arrays = autoclass("java.util.Arrays")
-    Array = autoclass('java.lang.reflect.Array')
-    ApplicationSetup = autoclass('org.terrier.utility.ApplicationSetup')
-    Properties = autoclass('java.util.Properties')
-    CLITool = autoclass("org.terrier.applications.CLITool")
-    StructureMerger = autoclass("org.terrier.structures.merging.StructureMerger")
-    BlockStructureMerger = autoclass("org.terrier.structures.merging.BlockStructureMerger")
-
     class DocListIterator(jnius.PythonJavaClass):
-        dpl_class = autoclass("org.terrier.structures.indexing.DocumentPostingList")
-        tuple_class = autoclass("org.terrier.structures.collections.MapEntry")
+        dpl_class = pt.java.autoclass("org.terrier.structures.indexing.DocumentPostingList")
+        tuple_class = pt.java.autoclass("org.terrier.structures.collections.MapEntry")
         __javainterfaces__ = [
             'java/util/Iterator',
         ]
@@ -120,7 +63,7 @@ def __init__(self, pyiterator):
 
         @staticmethod 
         def pyDictToMap(a_dict): #returns Map<String,String>
-            rtr = HashMap()
+            rtr = pt.java.J.HashMap()
             for k,v in a_dict.items():
                 rtr.put(k, v)
             return rtr
@@ -162,7 +105,7 @@ class TQDMCollection(jnius.PythonJavaClass):
 
         def __init__(self, collection):
             super(TQDMCollection, self).__init__()
-            assert isinstance(collection, autoclass("org.terrier.indexing.MultiDocumentFileCollection"))
+            assert isinstance(collection, pt.java.autoclass("org.terrier.indexing.MultiDocumentFileCollection"))
             self.collection = collection
             size = self.collection.FilesToProcess.size()
             from . import tqdm
@@ -267,7 +210,7 @@ def next(self):
             self._next = next(self._it, StopIteration)
             if result is not StopIteration:
                 global lastdoc
-                lastdoc = FlatJSONDocument(json.dumps(result))
+                lastdoc = pt.terrier.J.FlatJSONDocument(json.dumps(result))
                 return lastdoc
             return None
 
@@ -321,10 +264,10 @@ def createCollection(files_path : List[str], coll_type : str = 'trec', props = {
     else:
         collectionClzName = coll_type
     collectionClzName = collectionClzName.split(",")
-    _props = HashMap()
+    _props = pt.java.J.HashMap()
     for k, v in props.items():
         _props[k] = v
-    ApplicationSetup.getProperties().putAll(_props)
+    pt.terrier.J.ApplicationSetup.getProperties().putAll(_props)
     cls_string = pt.java.autoclass("java.lang.String")._class
     cls_list = pt.java.autoclass("java.util.List")._class
     if len(files_path) == 0:
@@ -336,6 +279,7 @@ def createCollection(files_path : List[str], coll_type : str = 'trec', props = {
         [asList, pt.java.autoclass("org.terrier.utility.TagSet").TREC_DOC_TAGS, "", ""])
     return colObj
 
+@pt.java.required
 def treccollection2textgen(
         files : List[str], 
         meta : List[str] = ["docno"], 
@@ -408,13 +352,13 @@ def _TaggedDocumentSetup(
     abstract_names=meta_tags.keys()
     abstract_lengths=[str(meta[name]) for name in abstract_names]
 
-    ApplicationSetup.setProperty("TaggedDocument.abstracts", ",".join(abstract_names))
+    pt.terrier.J.ApplicationSetup.setProperty("TaggedDocument.abstracts", ",".join(abstract_names))
     # The tags from which to save the text. ELSE is special tag name, which means anything not consumed by other tags.
-    ApplicationSetup.setProperty("TaggedDocument.abstracts.tags", ",".join(abstract_tags))
+    pt.terrier.J.ApplicationSetup.setProperty("TaggedDocument.abstracts.tags", ",".join(abstract_tags))
     # The max lengths of the abstracts. Abstracts will be truncated to this length. Defaults to empty.
-    ApplicationSetup.setProperty("TaggedDocument.abstracts.lengths", ",".join(abstract_lengths))
+    pt.terrier.J.ApplicationSetup.setProperty("TaggedDocument.abstracts.lengths", ",".join(abstract_lengths))
     # Should the tags from which we create abstracts be case-sensitive
-    ApplicationSetup.setProperty("TaggedDocument.abstracts.tags.casesensitive", "false")
+    pt.terrier.J.ApplicationSetup.setProperty("TaggedDocument.abstracts.tags.casesensitive", "false")
 
 
 def _FileDocumentSetup(   
@@ -437,8 +381,8 @@ def _FileDocumentSetup(
 
     abstract_length = meta[meta_name_for_abstract]
 
-    ApplicationSetup.setProperty("FileDocument.abstract", meta_name_for_abstract)
-    ApplicationSetup.setProperty("FileDocument.abstract.length", str(abstract_length))
+    pt.terrier.J.ApplicationSetup.setProperty("FileDocument.abstract", meta_name_for_abstract)
+    pt.terrier.J.ApplicationSetup.setProperty("FileDocument.abstract.length", str(abstract_length))
 
 
 
@@ -450,9 +394,9 @@ def createAsList(files_path : Union[str, List[str]]):
         Created Java List
     """
     if isinstance(files_path, str):
-        asList = Arrays.asList(files_path)
+        asList = pt.java.J.Arrays.asList(files_path)
     elif isinstance(files_path, list):
-        asList = Arrays.asList(*files_path)
+        asList = pt.java.J.Arrays.asList(*files_path)
     else:
         raise ValueError(f"{files_path}: {type(files_path)} must be a List[str] or str")
     return asList
@@ -684,7 +628,7 @@ def __init__(self, index_path : str, *args,
         self.stemmer = TerrierStemmer._to_obj(stemmer)
         self.stopwords, self.stopword_list = TerrierStopwords._to_obj(stopwords)
         self.tokeniser = TerrierTokeniser._to_obj(tokeniser)
-        self.properties = Properties()
+        self.properties = pt.java.J.Properties()
         self.setProperties(**self.default_properties)
         self.overwrite = overwrite
         self.verbose = verbose
@@ -739,7 +683,7 @@ def createIndexer(self):
         """
         
         Indexer, _ = self.indexerAndMergerClasses()
-        if Indexer is BasicMemoryIndexer:
+        if Indexer is pt.terrier.J.BasicMemoryIndexer:
             index = Indexer()
         else:
             index = Indexer(self.index_dir, "data")
@@ -781,28 +725,28 @@ def indexerAndMergerClasses(self):
             self.properties['tokeniser'] = TerrierTokeniser._to_class(self.tokeniser)
 
         # inform terrier of all properties
-        ApplicationSetup.getProperties().putAll(self.properties)
+        pt.terrier.J.ApplicationSetup.getProperties().putAll(self.properties)
 
         # now create the indexers 
         if self.type is IndexingType.SINGLEPASS:
             if self.blocks:
-                Indexer = BlockSinglePassIndexer
-                Merger = BlockStructureMerger
+                Indexer = pt.terrier.J.BlockSinglePassIndexer
+                Merger = pt.terrier.J.BlockStructureMerger
             else:
-                Indexer = BasicSinglePassIndexer
-                Merger = StructureMerger
+                Indexer = pt.terrier.J.BasicSinglePassIndexer
+                Merger = pt.terrier.J.StructureMerger
         elif self.type is IndexingType.CLASSIC:
             if self.blocks:
-                Indexer = BlockIndexer
-                Merger = BlockStructureMerger
+                Indexer = pt.terrier.J.BlockIndexer
+                Merger = pt.terrier.J.BlockStructureMerger
             else:
-                Indexer = BasicIndexer
-                Merger = StructureMerger
+                Indexer = pt.terrier.J.BasicIndexer
+                Merger = pt.terrier.J.StructureMerger
         elif self.type is IndexingType.MEMORY:
             if self.blocks:
                 raise Exception("Memory indexing with positions not yet implemented")
             else:
-                Indexer = BasicMemoryIndexer
+                Indexer = pt.terrier.J.BasicMemoryIndexer
                 Merger = None
         else:
             raise Exception("Unknown indexer type")
@@ -816,7 +760,7 @@ def getIndexStats(self):
         Note:
             Does not work with notebooks at the moment
         """
-        CLITool.main(["indexstats", "-I" + self.path])
+        pt.terrier.J.CLITool.main(["indexstats", "-I" + self.path])
 
     def getIndexUtil(self, util):
         """
@@ -841,7 +785,7 @@ def getIndexUtil(self, util):
         """
         if not util.startswith("-"):
             util = "-" + util
-        CLITool.main(["indexutil", "-I" + self.path, util])
+        pt.terrier.J.CLITool.main(["indexutil", "-I" + self.path, util])
 
 
 class DFIndexUtils:
@@ -884,12 +828,12 @@ def create_javaDocIterator(text, *args, **kwargs):
         def convertDoc(text_row, meta_column):
             if text_row is None:
                 text_row = ""
-            hashmap = HashMap()
+            hashmap = pt.java.J.HashMap()
             for column, value in meta_column[1].items():
                 if value is None:
                     value = ""
                 hashmap.put(column, value)
-            return TaggedDocument(StringReader(text_row), hashmap, Tokeniser.getTokeniser())
+            return pt.terrier.J.TaggedDocument(pt.java.J.StringReader(text_row), hashmap, pt.terrier.J.Tokeniser.getTokeniser())
             
         df = pd.DataFrame.from_dict(all_metadata, orient="columns")
         lengths = DFIndexUtils.get_column_lengths(df)
@@ -947,7 +891,7 @@ def index(self, text, *args, **kwargs):
         return pt.terrier.J.IndexRef.of(self.index_dir + "/data.properties")
 
 
-class _BaseIterDictIndexer(TerrierIndexer, Indexer):
+class _BaseIterDictIndexer(TerrierIndexer, pt.Indexer):
     def __init__(self, index_path, *args, meta = {'docno' : 20}, meta_reverse=['docno'], pretokenised=False, threads=1, **kwargs):
         """
         
@@ -956,7 +900,7 @@ def __init__(self, index_path, *args, meta = {'docno' : 20}, meta_reverse=['docn
             meta(Dict[str,int]): What metadata for each document to record in the index, and what length to reserve. Metadata fields will be truncated to this length. Defaults to `{"docno" : 20}`.
             meta_reverse(List[str]): What metadata shoudl we be able to resolve back to a docid. Defaults to `["docno"]`,      
         """
-        Indexer.__init__(self)
+        pt.Indexer.__init__(self)
         TerrierIndexer.__init__(self, index_path, *args, **kwargs)
         self.threads = threads
         self.meta = meta
@@ -1149,7 +1093,7 @@ def index(self, it, fields=('text',), meta=None, meta_lengths=None):
         Indexer, Merger = self.indexerAndMergerClasses()
 
         assert self.threads > 0, "threads must be positive"
-        if Indexer is BasicMemoryIndexer:
+        if Indexer is pt.terrier.J.BasicMemoryIndexer:
             assert self.threads == 1, 'IterDictIndexer does not support multiple threads for IndexingType.MEMORY'
         if self.threads > 1:
             warn('Using multiple threads results in a non-deterministic ordering of document in the index. For deterministic behavior, use threads=1')
@@ -1172,7 +1116,7 @@ def index(self, it, fields=('text',), meta=None, meta_lengths=None):
             threading.Thread(target=self._write_fifos, args=(self._filter_iterable(it, fields), fifos), daemon=True).start()
 
             # Different process for memory indexer (still taking advantage of faster fifos)
-            if Indexer is BasicMemoryIndexer:
+            if Indexer is pt.terrier.J.BasicMemoryIndexer:
                 indexer = Indexer()
                 if self.pretokenised:
                     indexer.indexDocuments(j_collections)
@@ -1345,7 +1289,7 @@ def index(self, files_path : Union[str,List[str]]):
         _TaggedDocumentSetup(self.meta, self.meta_tags)
         _FileDocumentSetup(self.meta, self.meta_tags)
         
-        simpleColl = SimpleFileCollection(asList, False)
+        simpleColl = pt.terrier.J.SimpleFileCollection(asList, False)
         # remove once 5.7 is now the minimum version
         from . import check_version
         index.index(simpleColl if check_version("5.7") else [simpleColl])
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 6c8016fb..cd23667d 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -332,4 +332,30 @@ def run(cmd, args=[]):
     'IndexFactory': 'org.terrier.structures.IndexFactory',
     'MultiIndex': 'org.terrier.realtime.multi.MultiIndex',
     'CLITool': 'org.terrier.applications.CLITool',
+    'ApplyTermPipeline': 'org.terrier.querying.ApplyTermPipeline',
+    'ManagerFactory': 'org.terrier.querying.ManagerFactory',
+    'Request': 'org.terrier.querying.Request',
+
+    # Indexing
+    'TaggedDocument': 'org.terrier.indexing.TaggedDocument',
+    'FlatJSONDocument': 'org.terrier.indexing.FlatJSONDocument',
+    'Tokeniser': 'org.terrier.indexing.tokenisation.Tokeniser',
+    'TRECCollection': 'org.terrier.indexing.TRECCollection',
+    'SimpleFileCollection': 'org.terrier.indexing.SimpleFileCollection',
+    'BasicIndexer': 'org.terrier.structures.indexing.classical.BasicIndexer',
+    'BlockIndexer': 'org.terrier.structures.indexing.classical.BlockIndexer',
+    'BasicSinglePassIndexer': 'org.terrier.structures.indexing.singlepass.BasicSinglePassIndexer',
+    'BlockSinglePassIndexer': 'org.terrier.structures.indexing.singlepass.BlockSinglePassIndexer',
+    'BasicMemoryIndexer': 'org.terrier.realtime.memory.MemoryIndexer" if pt.check_version("5.7") else "org.terrier.python.MemoryIndexer',
+    'Collection': 'org.terrier.indexing.Collection',
+    'StructureMerger': 'org.terrier.structures.merging.StructureMerger',
+    'BlockStructureMerger': 'org.terrier.structures.merging.BlockStructureMerger',
+
+    # PRF
+    'TerrierQLParser': 'org.terrier.querying.TerrierQLParser',
+    'TerrierQLToMatchingQueryTerms': 'org.terrier.querying.TerrierQLToMatchingQueryTerms',
+    'QueryResultSet': 'org.terrier.matching.QueryResultSet',
+    'DependenceModelPreProcess': 'org.terrier.querying.DependenceModelPreProcess',
+    'RM3': 'org.terrier.querying.RM3',
+    'AxiomaticQE': 'org.terrier.querying.AxiomaticQE',
 })
diff --git a/pyterrier/rewrite.py b/pyterrier/terrier/rewrite.py
similarity index 90%
rename from pyterrier/rewrite.py
rename to pyterrier/terrier/rewrite.py
index 16219775..97e4d122 100644
--- a/pyterrier/rewrite.py
+++ b/pyterrier/terrier/rewrite.py
@@ -1,27 +1,12 @@
-import pyterrier as pt
 import pandas as pd
-from . import Transformer
-from .index import TerrierTokeniser
-from . import tqdm
 from warnings import warn
 from typing import List,Union
 from types import FunctionType
+import pyterrier as pt
+from pyterrier.terrier.index import TerrierTokeniser
 
-
-TerrierQLParser = None
-TerrierQLToMatchingQueryTerms = None
-QueryResultSet = None
-DependenceModelPreProcess = None
-def _java_post_init(jnius):
-    global TerrierQLParser
-    global TerrierQLToMatchingQueryTerms
-    global QueryResultSet
-    global DependenceModelPreProcess
-    TerrierQLParser = pt.java.autoclass("org.terrier.querying.TerrierQLParser")()
-    TerrierQLToMatchingQueryTerms = pt.java.autoclass("org.terrier.querying.TerrierQLToMatchingQueryTerms")()
-    QueryResultSet = pt.java.autoclass("org.terrier.matching.QueryResultSet")
-    DependenceModelPreProcess = pt.java.autoclass("org.terrier.querying.DependenceModelPreProcess")
-
+# TODO: replace _check_terrier_prf with a @pt.terrier.java.required_prf or similar
+# TODO: make sure @pt.java.required on all necessary functions
 
 _terrier_prf_package_loaded = False
 _terrier_prf_message = 'terrier-prf jar not found: you should start PyTerrier with '\
@@ -39,7 +24,7 @@ def _check_terrier_prf():
             break
     assert _terrier_prf_package_loaded, _terrier_prf_message
 
-def tokenise(tokeniser : Union[str,TerrierTokeniser,FunctionType] = 'english', matchop=False) -> Transformer:
+def tokenise(tokeniser : Union[str,TerrierTokeniser,FunctionType] = 'english', matchop=False) -> pt.Transformer:
     """
 
     Applies tokenisation to the query. By default, queries obtained from ``pt.get_dataset().get_topics()`` are
@@ -80,7 +65,7 @@ def tokenise(tokeniser : Union[str,TerrierTokeniser,FunctionType] = 'english', m
         tokeniser = TerrierTokeniser._to_class(tokeniser)
         if "." not in tokeniser:
             tokeniser = 'org.terrier.indexing.tokenisation.' + tokeniser
-        tokenobj = pt.autoclass(tokeniser)()
+        tokenobj = pt.java.autoclass(tokeniser)()
         _query_fn = tokenobj.getTokens
 
     def _join_str(input : Union[str,List[str]]):
@@ -97,7 +82,7 @@ def _join_str_matchop(input : List[str]):
     return pt.apply.query(lambda r: _join_str(_query_fn(r.query)))
 
 
-def reset() -> Transformer:
+def reset() -> pt.Transformer:
     """
         Undoes a previous query rewriting operation. This results in the query formulation stored in the `"query_0"`
         attribute being moved to the `"query"` attribute, and, if present, the `"query_1"` being moved to
@@ -116,7 +101,7 @@ def reset() -> Transformer:
     from .model import pop_queries
     return pt.apply.generic(lambda topics: pop_queries(topics))
 
-class SDM(Transformer):
+class SDM(pt.Transformer):
     '''
         Implements the sequential dependence model, which Terrier supports using its
         Indri/Galagoo compatible matchop query language. The rewritten query is derived using
@@ -126,14 +111,14 @@ class SDM(Transformer):
         The original query is saved in the `"query_0"` column, which can be restored using `pt.rewrite.reset()`.
     '''
 
+    @pt.java.required
     def __init__(self, verbose = 0, remove_stopwords = True, prox_model = None, **kwargs):
         super().__init__(**kwargs)
         self.verbose = 0
         self.prox_model = prox_model
         self.remove_stopwords = remove_stopwords
-        from . import check_version
-        assert check_version("5.3")
-        self.ApplyTermPipeline_stopsonly = pt.autoclass("org.terrier.querying.ApplyTermPipeline")("Stopwords")
+        assert pt.check_version("5.3")
+        self.ApplyTermPipeline_stopsonly = pt.terrier.J.ApplyTermPipeline('Stopwords')
 
     def __repr__(self):
         return "SDM()"
@@ -144,17 +129,17 @@ def transform(self, topics_and_res):
         queries = ranked_documents_to_queries(topics_and_res)
 
         # instantiate the DependenceModelPreProcess, specifying a proximity model if specified
-        sdm = DependenceModelPreProcess() if self.prox_model is None else DependenceModelPreProcess(self.prox_model)
+        sdm = pt.terrier.J.DependenceModelPreProcess() if self.prox_model is None else pt.terrier.J.DependenceModelPreProcess(self.prox_model)
         
-        for row in tqdm(queries.itertuples(), desc=self.name, total=queries.shape[0], unit="q") if self.verbose else queries.itertuples():
+        for row in pt.tqdm(queries.itertuples(), desc=self.name, total=queries.shape[0], unit="q") if self.verbose else queries.itertuples():
             qid = row.qid
             query = row.query
             # parse the querying into a MQT
-            rq = pt.autoclass("org.terrier.querying.Request")()
+            rq = pt.terrier.J.Request()
             rq.setQueryID(qid)
             rq.setOriginalQuery(query)
-            TerrierQLParser.process(None, rq)
-            TerrierQLToMatchingQueryTerms.process(None, rq)
+            pt.terrier.J.TerrierQLParser().process(None, rq)
+            pt.terrier.J.TerrierQLToMatchingQueryTerms().process(None, rq)
             if self.remove_stopwords:
                 self.ApplyTermPipeline_stopsonly.process(None, rq)
 
@@ -192,7 +177,7 @@ class SequentialDependence(SDM):
     '''
     pass
     
-class QueryExpansion(Transformer):
+class QueryExpansion(pt.Transformer):
     '''
         A base class for applying different types of query expansion using Terrier's classes.
         This transformer changes the query. It must be followed by a Terrier Retrieve() transformer.
@@ -204,21 +189,22 @@ class QueryExpansion(Transformer):
          
     '''
 
+    @pt.java.required
     def __init__(self, index_like, fb_terms=10, fb_docs=3, qeclass="org.terrier.querying.QueryExpansion", verbose=0, properties={}, **kwargs):
         super().__init__(**kwargs)
         self.verbose = verbose
         if isinstance(qeclass, str):
-            self.qe = pt.autoclass(qeclass)()
+            self.qe = pt.java.autoclass(qeclass)()
         else:
             self.qe = qeclass
         self.indexref = pt.terrier.retriever._parse_index_like(index_like)
         self.properties = properties
         for k,v in properties.items():
             pt.ApplicationSetup.setProperty(k, str(v))
-        self.applytp = pt.autoclass("org.terrier.querying.ApplyTermPipeline")()
+        self.applytp = pt.terrier.J.ApplyTermPipeline()
         self.fb_terms = fb_terms
         self.fb_docs = fb_docs
-        self.manager = pt.autoclass("org.terrier.querying.ManagerFactory")._from_(self.indexref)
+        self.manager = pt.terrier.J.ManagerFactory._from_(self.indexref)
 
     def __reduce__(self):
         return (
@@ -242,11 +228,11 @@ def __getstate__(self):
     def __setstate__(self, d): 
         self.fb_terms = d["fb_terms"]
         self.fb_docs = d["fb_docs"]
-        self.qe = pt.autoclass(d['qeclass'])()
+        self.qe = pt.java.autoclass(d['qeclass'])()
         self.properties.update(d["properties"])
         for key,value in d["properties"].items():
             self.appSetup.setProperty(key, str(value))
-        self.manager = pt.autoclass("org.terrier.querying.ManagerFactory")._from_(self.indexref)
+        self.manager = pt.terrier.J.ManagerFactory._from_(self.indexref)
 
     def _populate_resultset(self, topics_and_res, qid, index):
         
@@ -281,7 +267,7 @@ def _populate_resultset(self, topics_and_res, qid, index):
                     warn("%d feedback docnos for qid %s could not be found in the index" % (skipped, qid))
         else:
             raise ValueError("Input resultset has neither docid nor docno")
-        return QueryResultSet(docids, scores, occurrences)
+        return pt.terrier.J.QueryResultSet(docids, scores, occurrences)
 
     def __repr__(self):
         return "QueryExpansion(" + ",".join([
@@ -303,7 +289,7 @@ def transform(self, topics_and_res):
         queries = ranked_documents_to_queries(topics_and_res)
         #queries = topics_and_res[query_columns(topics_and_res, qid=True)].dropna(axis=0, subset=query_columns(topics_and_res, qid=False)).drop_duplicates()
                 
-        for row in tqdm(queries.itertuples(), desc=self.name, total=queries.shape[0], unit="q") if self.verbose else queries.itertuples():
+        for row in pt.tqdm(queries.itertuples(), desc=self.name, total=queries.shape[0], unit="q") if self.verbose else queries.itertuples():
             qid = row.qid
             query = row.query
             srq = self.manager.newSearchRequest(qid, query)
@@ -315,8 +301,8 @@ def transform(self, topics_and_res):
             rq.setResultSet(self._populate_resultset(topics_and_res, qid, rq.getIndex()))
 
             
-            TerrierQLParser.process(None, rq)
-            TerrierQLToMatchingQueryTerms.process(None, rq)
+            pt.terrier.J.TerrierQLParser.process(None, rq)
+            pt.terrier.J.TerrierQLToMatchingQueryTerms().process(None, rq)
             # how to make sure this happens/doesnt happen when appropriate.
             self.applytp.process(None, rq)
             # to ensure weights are identical to Terrier
@@ -418,7 +404,7 @@ def __init__(self, *args, fb_terms=10, fb_docs=3, fb_lambda=0.6, **kwargs):
             fb_lambda(float): lambda in RM3, i.e. importance of relevance model viz feedback model. Defaults to 0.6.
         """
         _check_terrier_prf()
-        rm = pt.autoclass("org.terrier.querying.RM3")()
+        rm = pt.terrier.J.RM3()
         self.fb_lambda = fb_lambda
         kwargs["qeclass"] = rm
         super().__init__(*args, fb_terms=fb_terms, fb_docs=fb_docs, **kwargs)
@@ -462,7 +448,7 @@ def __init__(self, *args, fb_terms=10, fb_docs=3, **kwargs):
             fb_docs(int): number of feedback documents to consider. Terrier's default setting is 3 feedback documents.
         """
         _check_terrier_prf()
-        rm = pt.autoclass("org.terrier.querying.AxiomaticQE")()
+        rm = pt.terrier.J.AxiomaticQE()
         self.fb_terms = fb_terms
         self.fb_docs = fb_docs
         kwargs["qeclass"] = rm
@@ -473,7 +459,7 @@ def transform(self, queries_and_docs):
         self.qe.fbDocs = self.fb_docs
         return super().transform(queries_and_docs)
 
-def stash_results(clear=True) -> Transformer:
+def stash_results(clear=True) -> pt.Transformer:
     """
     Stashes (saves) the current retrieved documents for each query into the column `"stashed_results_0"`.
     This means that they can be restored later by using `pt.rewrite.reset_results()`.
@@ -485,14 +471,14 @@ def stash_results(clear=True) -> Transformer:
     """
     return _StashResults(clear)
     
-def reset_results() -> Transformer:
+def reset_results() -> pt.Transformer:
     """
     Applies a transformer that undoes a `pt.rewrite.stash_results()` transformer, thereby restoring the
     ranked documents.
     """
     return _ResetResults()
 
-class _StashResults(Transformer):
+class _StashResults(pt.Transformer):
 
     def __init__(self, clear, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -528,7 +514,7 @@ def transform(self, topics_and_res: pd.DataFrame) -> pd.DataFrame:
     def __repr__(self):
         return "pt.rewrite.stash_results()"     
 
-class _ResetResults(Transformer):
+class _ResetResults(pt.Transformer):
 
     def transform(self, topics_with_saved_docs : pd.DataFrame) -> pd.DataFrame:
         if "stashed_results_0" not in topics_with_saved_docs.columns:
@@ -548,7 +534,7 @@ def transform(self, topics_with_saved_docs : pd.DataFrame) -> pd.DataFrame:
     def __repr__(self):
         return "pt.rewrite.reset_results()"
 
-def linear(weightCurrent : float, weightPrevious : float, format="terrierql", **kwargs) -> Transformer:
+def linear(weightCurrent : float, weightPrevious : float, format="terrierql", **kwargs) -> pt.Transformer:
     """
     Applied to make a linear combination of the current and previous query formulation. The implementation
     is tied to the underlying query language used by the retrieval/re-ranker transformers. Two of Terrier's
@@ -575,7 +561,7 @@ def linear(weightCurrent : float, weightPrevious : float, format="terrierql", **
     """
     return _LinearRewriteMix([weightCurrent, weightPrevious], format, **kwargs)
 
-class _LinearRewriteMix(Transformer):
+class _LinearRewriteMix(pt.Transformer):
 
     def __init__(self, weights : List[float], format : str = 'terrierql', **kwargs):
         super().__init__(**kwargs)
@@ -612,4 +598,4 @@ def transform(self, topics_and_res):
         return newDF
 
     def __repr__(self):
-        return "pt.rewrite.linear()"
\ No newline at end of file
+        return "pt.rewrite.linear()"
diff --git a/setup.py b/setup.py
index 2521a8ce..ccee73f5 100644
--- a/setup.py
+++ b/setup.py
@@ -72,15 +72,14 @@ def get_version(rel_path):
     ],
     entry_points={
         'pyterrier.java.pre_init': [
-            'terrier = pyterrier.terrier.java:_pre_init',
+            'terrier  = pyterrier.terrier.java:_pre_init',
             'anserini = pyterrier.anserini.java:_pre_init',
         ],
         'pyterrier.java.post_init': [
-            'core    = pyterrier.java._init:_post_init',
-            'terrier = pyterrier.terrier.java:_post_init',
-            'index   = pyterrier.index:_java_post_init',
-            'rewrite = pyterrier.rewrite:_java_post_init',
-            'anserini = pyterrier.anserini.java:_post_init',
+            'core            = pyterrier.java._init:_post_init',
+            'terrier         = pyterrier.terrier.java:_post_init',
+            'terrier.index   = pyterrier.terrier.index:_java_post_init',
+            'anserini        = pyterrier.anserini.java:_post_init',
         ],
     },
     install_requires=requirements,

From 8fa0158435eeb58571a8a28b1fea584a61649f0f Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 17:20:11 +0100
Subject: [PATCH 062/153] fix tests

---
 pyterrier/__init__.py          | 10 +++++-----
 pyterrier/java/utils.py        |  5 ++++-
 pyterrier/terrier/__init__.py  |  4 ++--
 pyterrier/terrier/java.py      |  2 +-
 pyterrier/terrier/retriever.py |  2 +-
 5 files changed, 13 insertions(+), 10 deletions(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index 5b150ede..1703e17b 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -193,15 +193,15 @@ def extend_classpath(mvnpackages):
         terrier.extend_package(package)
 
 
-# Additional setup performed in a function to avoid polluting the namespace with other imports
+# Additional setup performed in a function to avoid polluting the namespace with other imports like platform
 def _():
-    # apply is an object, not a module, as it also has __get_attr__() implemented
-    from pyterrier.apply import _apply
-    globals()['apply'] = _apply()
-
     # check python version
     import platform
     from packaging.version import Version
     if Version(platform.python_version()) < Version('3.7.0'):
         raise RuntimeError("From PyTerrier 0.8, Python 3.7 minimum is required, you currently have %s" % platform.python_version())
+
+    # apply is an object, not a module, as it also has __get_attr__() implemented
+    from pyterrier.apply import _apply
+    globals()['apply'] = _apply()
 _()
diff --git a/pyterrier/java/utils.py b/pyterrier/java/utils.py
index f40140ed..428ce502 100644
--- a/pyterrier/java/utils.py
+++ b/pyterrier/java/utils.py
@@ -135,7 +135,10 @@ def __getattr__(self, key):
         if key not in self._mapping:
             return AttributeError(f'{self} has no attribute {key!r}')
         if key not in self._cache:
-            self._cache[key] = pt.java.autoclass(self._mapping[key])
+            clz = self._mapping[key]
+            if callable(clz):
+                clz = clz()
+            self._cache[key] = pt.java.autoclass(clz)
         return self._cache[key]
 
 
diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
index 3cfcfb91..103bf824 100644
--- a/pyterrier/terrier/__init__.py
+++ b/pyterrier/terrier/__init__.py
@@ -4,7 +4,7 @@
 from pyterrier.terrier.retriever import BatchRetrieve, FeaturesBatchRetrieve
 from pyterrier.terrier.index_factory import IndexFactory
 from pyterrier.terrier import index
-from pyterrier.terrier.index import FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
+from pyterrier.terrier.index import TerrierIndexer, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
 from pyterrier.terrier import rewrite
 
 TerrierRetrieve = BatchRetrieve # BatchRetrieve is an alias to TerrierRetrieve
@@ -18,7 +18,7 @@
     'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve',
 
     # indexing
-    'index', 'FilesIndexer', 'TRECCollectionIndexer', 'DFIndexer', 'DFIndexUtils', 'IterDictIndexer', 'IndexingType', 'TerrierStemmer', 'TerrierStopwords', 'TerrierTokeniser',
+    'index', 'TerrierIndexer', 'FilesIndexer', 'TRECCollectionIndexer', 'DFIndexer', 'DFIndexUtils', 'IterDictIndexer', 'IndexingType', 'TerrierStemmer', 'TerrierStopwords', 'TerrierTokeniser',
 
     # rewriting
     'rewrite',
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index cd23667d..bfd059b0 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -346,7 +346,7 @@ def run(cmd, args=[]):
     'BlockIndexer': 'org.terrier.structures.indexing.classical.BlockIndexer',
     'BasicSinglePassIndexer': 'org.terrier.structures.indexing.singlepass.BasicSinglePassIndexer',
     'BlockSinglePassIndexer': 'org.terrier.structures.indexing.singlepass.BlockSinglePassIndexer',
-    'BasicMemoryIndexer': 'org.terrier.realtime.memory.MemoryIndexer" if pt.check_version("5.7") else "org.terrier.python.MemoryIndexer',
+    'BasicMemoryIndexer': lambda: 'org.terrier.realtime.memory.MemoryIndexer' if pt.check_version("5.7") else 'org.terrier.python.MemoryIndexer',
     'Collection': 'org.terrier.indexing.Collection',
     'StructureMerger': 'org.terrier.structures.merging.StructureMerger',
     'BlockStructureMerger': 'org.terrier.structures.merging.BlockStructureMerger',
diff --git a/pyterrier/terrier/retriever.py b/pyterrier/terrier/retriever.py
index 44a44aa4..86c95291 100644
--- a/pyterrier/terrier/retriever.py
+++ b/pyterrier/terrier/retriever.py
@@ -60,7 +60,7 @@ def _mergeDicts(defaults, settings):
 def _parse_index_like(index_location):
     JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
     JI = pt.java.autoclass('org.terrier.structures.Index')
-    from pyterrier.index import TerrierIndexer
+    from pyterrier.terrier import TerrierIndexer
 
     if isinstance(index_location, JIR):
         return index_location

From d7d03c415be4f31246775eedc7442c2ab0da0ae5 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 17:32:14 +0100
Subject: [PATCH 063/153] fix more tests

---
 pyterrier/terrier/index.py         | 25 ++++++++-----------------
 pyterrier/terrier/retriever.py     |  2 +-
 pyterrier/terrier/rewrite.py       | 25 +++++++++----------------
 tests/test_dfindex.py              | 22 +++++++++++-----------
 tests/test_iterdictindex.py        | 30 +++++++++++++++---------------
 tests/test_iterdictindex_pretok.py | 10 +++++-----
 6 files changed, 49 insertions(+), 65 deletions(-)

diff --git a/pyterrier/terrier/index.py b/pyterrier/terrier/index.py
index 82932d0e..037ee016 100644
--- a/pyterrier/terrier/index.py
+++ b/pyterrier/terrier/index.py
@@ -58,7 +58,6 @@ def __init__(self, pyiterator):
             self.pyiterator = pyiterator
             self.hasnext = True
             self.lastdoc = None
-            import pyterrier as pt
             self.tr57 = not pt.check_version("5.8")
 
         @staticmethod 
@@ -108,8 +107,7 @@ def __init__(self, collection):
             assert isinstance(collection, pt.java.autoclass("org.terrier.indexing.MultiDocumentFileCollection"))
             self.collection = collection
             size = self.collection.FilesToProcess.size()
-            from . import tqdm
-            self.pbar = tqdm(total=size, unit="files")
+            self.pbar = pt.tqdm(total=size, unit="files")
             self.last = -1
         
         @jnius.java_method('()Z')
@@ -220,8 +218,7 @@ class TQDMSizeCollection(jnius.PythonJavaClass):
         def __init__(self, collection, total):
             super(TQDMSizeCollection, self).__init__()
             self.collection = collection
-            from . import tqdm
-            self.pbar = tqdm(total=total, unit="documents")
+            self.pbar = pt.tqdm(total=total, unit="documents")
         
         @jnius.java_method('()Z')
         def nextDocument(self):
@@ -518,8 +515,7 @@ def _indexing_config(this, stopword_list : Union[List[str], None], termpipelines
         if this == TerrierStopwords.terrier:
             termpipelines.append('Stopwords')
         if this == TerrierStopwords.custom:
-            from . import check_version
-            assert check_version("5.8"), "Terrier 5.8 required"
+            assert pt.check_version("5.8"), "Terrier 5.8 required"
             assert stopword_list is not None, "expected to receive a stopword list"
 
             stopword_list_esc = [t.replace(",", "\\,") for t in stopword_list ]
@@ -907,8 +903,7 @@ def __init__(self, index_path, *args, meta = {'docno' : 20}, meta_reverse=['docn
         self.meta_reverse = meta_reverse
         self.pretokenised = pretokenised
         if self.pretokenised:
-            from pyterrier import check_version
-            assert check_version(5.7), "Terrier too old, this requires 5.7"
+            assert pt.check_version(5.7), "Terrier too old, this requires 5.7"
             # we disable stemming and stopwords for pretokenised indices
             self.stemmer = None
             self.stopwords = None
@@ -1032,8 +1027,7 @@ def index(self, it, fields=('text',), meta=None, meta_lengths=None, threads=None
             collectionIterator = FlatJSONDocumentIterator(self._filter_iterable(it, fields))
             javaDocCollection = pt.java.autoclass("org.terrier.python.CollectionFromDocumentIterator")(collectionIterator)
             # remove once 5.7 is now the minimum version
-            from . import check_version
-            indexer.index(javaDocCollection if check_version("5.7") else [javaDocCollection])
+            indexer.index(javaDocCollection if pt.check_version("5.7") else [javaDocCollection])
             global lastdoc
             lastdoc = None
             self.index_called = True
@@ -1044,12 +1038,11 @@ def index(self, it, fields=('text',), meta=None, meta_lengths=None, threads=None
             index = indexer.getIndex()
             indexref = index.getIndexRef()
         else:
-            from . import IndexFactory
             indexref = pt.terrier.J.IndexRef.of(self.index_dir + "/data.properties")
             if len(self.cleanup_hooks) > 0:
                 sindex = pt.java.autoclass("org.terrier.structures.Index")
                 sindex.setIndexLoadingProfileAsRetrieval(False)
-                index = IndexFactory.of(indexref)
+                index = pt.terrier.IndexFactory.of(indexref)
                 for hook in self.cleanup_hooks:
                     hook(self, index)
                 sindex.setIndexLoadingProfileAsRetrieval(True)
@@ -1133,13 +1126,12 @@ def index(self, it, fields=('text',), meta=None, meta_lengths=None):
                 ParallelIndexer.buildParallel(j_collections, self.index_dir, Indexer, Merger)
             
         indexref = None
-        from . import IndexFactory
         indexref = pt.terrier.J.IndexRef.of(self.index_dir + "/data.properties")
         
         if len(self.cleanup_hooks) > 0:
             sindex = pt.java.autoclass("org.terrier.structures.Index")
             sindex.setIndexLoadingProfileAsRetrieval(False)
-            index = IndexFactory.of(indexref)
+            index = pt.terrier.IndexFactory.of(indexref)
             sindex.setIndexLoadingProfileAsRetrieval(True)
 
             for hook in self.cleanup_hooks:
@@ -1291,8 +1283,7 @@ def index(self, files_path : Union[str,List[str]]):
         
         simpleColl = pt.terrier.J.SimpleFileCollection(asList, False)
         # remove once 5.7 is now the minimum version
-        from . import check_version
-        index.index(simpleColl if check_version("5.7") else [simpleColl])
+        index.index(simpleColl if pt.check_version("5.7") else [simpleColl])
         global lastdoc
         lastdoc = None
         self.index_called = True
diff --git a/pyterrier/terrier/retriever.py b/pyterrier/terrier/retriever.py
index 86c95291..6d315aba 100644
--- a/pyterrier/terrier/retriever.py
+++ b/pyterrier/terrier/retriever.py
@@ -488,7 +488,7 @@ def __init__(self, innerclass, takes="queries", returns="docs", body_attr="body"
 
     def transform(self, topics_and_res):
         from pyterrier import DFIndexer, IndexFactory
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         documents = topics_and_res[["docno", self.body_attr]].drop_duplicates(subset="docno")
         indexref = DFIndexer(None, type=IndexingType.MEMORY, verbose=self.verbose).index(documents[self.body_attr], documents["docno"])
         docno2docid = { docno:id for id, docno in enumerate(documents["docno"]) }
diff --git a/pyterrier/terrier/rewrite.py b/pyterrier/terrier/rewrite.py
index 97e4d122..fcc084e9 100644
--- a/pyterrier/terrier/rewrite.py
+++ b/pyterrier/terrier/rewrite.py
@@ -98,8 +98,7 @@ def reset() -> pt.Transformer:
             fullranker = firststage >> pt.rewrite.reset() >> secondstage
 
     """
-    from .model import pop_queries
-    return pt.apply.generic(lambda topics: pop_queries(topics))
+    return pt.apply.generic(lambda topics: pt.model.pop_queries(topics))
 
 class SDM(pt.Transformer):
     '''
@@ -125,8 +124,7 @@ def __repr__(self):
 
     def transform(self, topics_and_res):
         results = []
-        from .model import ranked_documents_to_queries, push_queries
-        queries = ranked_documents_to_queries(topics_and_res)
+        queries = pt.model.ranked_documents_to_queries(topics_and_res)
 
         # instantiate the DependenceModelPreProcess, specifying a proximity model if specified
         sdm = pt.terrier.J.DependenceModelPreProcess() if self.prox_model is None else pt.terrier.J.DependenceModelPreProcess(self.prox_model)
@@ -164,7 +162,7 @@ def transform(self, topics_and_res):
             results.append([qid, new_query])
         new_queries = pd.DataFrame(results, columns=["qid", "query"])
         # restore any other columns, e.g. put back docs if we are re-ranking
-        return new_queries.merge(push_queries(topics_and_res, inplace=True) , on="qid")
+        return new_queries.merge(pt.model.push_queries(topics_and_res, inplace=True) , on="qid")
 
 class SequentialDependence(SDM):
     '''
@@ -285,8 +283,7 @@ def transform(self, topics_and_res):
 
         results = []
 
-        from .model import push_queries, ranked_documents_to_queries
-        queries = ranked_documents_to_queries(topics_and_res)
+        queries = pt.model.ranked_documents_to_queries(topics_and_res)
         #queries = topics_and_res[query_columns(topics_and_res, qid=True)].dropna(axis=0, subset=query_columns(topics_and_res, qid=False)).drop_duplicates()
                 
         for row in pt.tqdm(queries.itertuples(), desc=self.name, total=queries.shape[0], unit="q") if self.verbose else queries.itertuples():
@@ -317,7 +314,7 @@ def transform(self, topics_and_res):
             new_query = new_query[:-1]
             results.append([qid, new_query])
         new_queries = pd.DataFrame(results, columns=["qid", "query"])
-        return push_queries(queries, inplace=True).merge(new_queries, on="qid")
+        return pt.model.push_queries(queries, inplace=True).merge(new_queries, on="qid")
 
 class DFRQueryExpansion(QueryExpansion):
 
@@ -485,14 +482,13 @@ def __init__(self, clear, *args, **kwargs):
         self.clear = clear
 
     def transform(self, topics_and_res: pd.DataFrame) -> pd.DataFrame:
-        from .model import document_columns, query_columns
         if "stashed_results_0" in topics_and_res.columns:
             raise ValueError("Cannot apply pt.rewrite.stash_results() more than once")
-        doc_cols = document_columns(topics_and_res)
+        doc_cols = pt.model.document_columns(topics_and_res)
         
         rtr =  []
         if self.clear:
-            query_cols = query_columns(topics_and_res)            
+            query_cols = pt.model.query_columns(topics_and_res)            
             for qid, groupDf in topics_and_res.groupby("qid"):
                 documentsDF = groupDf[doc_cols]
                 queryDf = groupDf[query_cols].iloc[0]
@@ -519,8 +515,7 @@ class _ResetResults(pt.Transformer):
     def transform(self, topics_with_saved_docs : pd.DataFrame) -> pd.DataFrame:
         if "stashed_results_0" not in topics_with_saved_docs.columns:
             raise ValueError("Cannot apply pt.rewrite.reset_results() without pt.rewrite.stash_results() - column stashed_results_0 not found")
-        from .model import query_columns
-        query_cols = query_columns(topics_with_saved_docs)
+        query_cols = pt.model.query_columns(topics_with_saved_docs)
         rtr = []
         for row in topics_with_saved_docs.itertuples():
             docsdf = pd.DataFrame.from_records(row.stashed_results_0)
@@ -585,15 +580,13 @@ def _matchopql(self, row):
             row["query_1"])
 
     def transform(self, topics_and_res):
-        from .model import push_queries
-        
         fn = None
         if self.format == "terrierql":
             fn = self._terrierql
         elif self.format == "matchopql":
             fn = self._matchopql
 
-        newDF = push_queries(topics_and_res)
+        newDF = pt.model.push_queries(topics_and_res)
         newDF["query"] = newDF.apply(fn, axis=1)
         return newDF
 
diff --git a/tests/test_dfindex.py b/tests/test_dfindex.py
index 20b55c62..cbc473ad 100644
--- a/tests/test_dfindex.py
+++ b/tests/test_dfindex.py
@@ -18,7 +18,7 @@ def _create_index(self, type, dfText, dfMeta):
         return indexref
 
     def _make_check_index(self, n, index_type, include_urls=False):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         import pandas as pd
         df = pd.DataFrame({
             'docno': ['1', '2', '3'],
@@ -127,43 +127,43 @@ def test_badinvocation(self):
         ref = pt.DFIndexer(self.test_dir).index(df_docids["body"], df_docnos['docno'])
 
     def test_createindex1_two_metadata(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(1, IndexingType.CLASSIC, include_urls=True)
 
     def test_createindex1(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(1, IndexingType.CLASSIC)
 
     def test_createindex2(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(2, IndexingType.CLASSIC)
 
     def test_createindex3(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(3, IndexingType.CLASSIC)
 
     def test_createindex1_single_pass(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(1, IndexingType.SINGLEPASS)
 
     def test_createindex2_single_pass(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(2, IndexingType.SINGLEPASS)
 
     def test_createindex3_single_pass(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(3, IndexingType.SINGLEPASS)
 
     def test_createindex1_memory(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(1, IndexingType.MEMORY)
 
     def test_createindex2_memory(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(2, IndexingType.MEMORY)
 
     def test_createindex3_memory(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(3, IndexingType.MEMORY)
 
 if __name__ == "__main__":
diff --git a/tests/test_iterdictindex.py b/tests/test_iterdictindex.py
index aa36ff83..1ae0cfe6 100644
--- a/tests/test_iterdictindex.py
+++ b/tests/test_iterdictindex.py
@@ -18,7 +18,7 @@ def _create_index(self, it, fields, type, indexer):
         return indexref
 
     def _make_check_index(self, n, index_type, fields=('text',), meta=('docno', 'url', 'title')):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         # Test both versions: _fifo (for UNIX) and _nofifo (for Windows)
         indexers = [
             pt.index._IterDictIndexer_fifo(self.test_dir, type=index_type, meta=meta),
@@ -93,61 +93,61 @@ def test_checkjavaDocIterator(self):
         self.assertFalse(jIter1.hasNext())
 
     def test_createindex1(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(1, IndexingType.CLASSIC)
 
     def test_createindex2(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(2, IndexingType.CLASSIC)
 
     def test_createindex3(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(3, IndexingType.CLASSIC)
 
     def test_createindex1_single_pass(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(1, IndexingType.SINGLEPASS)
 
     def test_createindex2_single_pass(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(2, IndexingType.SINGLEPASS)
 
     def test_createindex3_single_pass(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(3, IndexingType.SINGLEPASS)
 
     # Issue #43
 
     # def test_createindex3_memory(self):
-    #     from pyterrier.index import IndexingType
+    #     from pyterrier.terrier.index import IndexingType
     #     self._make_check_index(3, IndexingType.MEMORY)
 
     # def test_createindex1_2fields_memory(self):
-    #     from pyterrier.index import IndexingType
+    #     from pyterrier.terrier.index import IndexingType
     #     self._make_check_index(1, IndexingType.MEMORY, fields=['text', 'title'])
 
     def test_createindex1_2fields(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(1, IndexingType.CLASSIC, fields=['text', 'title'])
 
     def test_createindex2_2fields(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(2, IndexingType.CLASSIC, fields=['text', 'title'])
 
     def test_createindex3_2fields(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(3, IndexingType.CLASSIC, fields=['text', 'title'])
 
     def test_createindex1_single_pass_2fields(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(1, IndexingType.SINGLEPASS, fields=['text', 'title'])
 
     def test_createindex2_single_pass_2fields(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(2, IndexingType.SINGLEPASS, fields=['text', 'title'])
 
     def test_createindex3_single_pass_2fields(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_check_index(3, IndexingType.SINGLEPASS, fields=['text', 'title'])
 
     def test_meta_init(self):
diff --git a/tests/test_iterdictindex_pretok.py b/tests/test_iterdictindex_pretok.py
index 2a598ec4..31714721 100644
--- a/tests/test_iterdictindex_pretok.py
+++ b/tests/test_iterdictindex_pretok.py
@@ -84,7 +84,7 @@ def _make_pretok_index(self, n, index_type, meta=('docno', 'url')):
         if not pt.check_version("5.7") or not pt.check_version("0.0.7", helper=True):
             self.skipTest("Requires Terrier 5.7 and helper 0.0.7")
         
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         # Test both versions: _fifo (for UNIX) and _nofifo (for Windows)
         indexers = [
             pt.index._IterDictIndexer_fifo(self.test_dir, type=index_type, meta=meta, pretokenised=True),
@@ -129,7 +129,7 @@ def _make_pretok_index(self, n, index_type, meta=('docno', 'url')):
             os.mkdir(self.test_dir)
 
     def test_pretok_createindex1_basic(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         from jnius import JavaException
         try:
             self._make_pretok_index(1, IndexingType.CLASSIC)
@@ -138,11 +138,11 @@ def test_pretok_createindex1_basic(self):
             raise ja
 
     def test_pretok_createindex2_basic(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         self._make_pretok_index(2, IndexingType.CLASSIC)
 
     def test_pretok_createindex1_single_pass(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         from jnius import JavaException
         try:
             self._make_pretok_index(1, IndexingType.SINGLEPASS)
@@ -151,7 +151,7 @@ def test_pretok_createindex1_single_pass(self):
             raise ja
 
     def test_pretok_createindex2_single_pass(self):
-        from pyterrier.index import IndexingType
+        from pyterrier.terrier.index import IndexingType
         from jnius import JavaException
         try:
             self._make_pretok_index(2, IndexingType.SINGLEPASS)

From 7bdbedce40e33265a1bb3c197a54e436235b745a Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 17:34:51 +0100
Subject: [PATCH 064/153] update text.py for pt.java

---
 pyterrier/text.py | 25 +++++++++----------------
 1 file changed, 9 insertions(+), 16 deletions(-)

diff --git a/pyterrier/text.py b/pyterrier/text.py
index 5704b6f5..9cb730f0 100644
--- a/pyterrier/text.py
+++ b/pyterrier/text.py
@@ -1,15 +1,15 @@
-import pyterrier as pt
 from pyterrier.datasets import IRDSDataset
 import more_itertools
 from collections import defaultdict
 import re
-from pyterrier.model import add_ranks
-import pandas as pd
 import numpy as np
+import pandas as pd
 from typing import List, Union
 from warnings import warn
+import pyterrier as pt
 
 
+@pt.java.required
 def get_text(
         indexlike, 
         metadata : Union[str,List[str]] = "body", 
@@ -33,9 +33,8 @@ def get_text(
             >> pt.text.scorer(wmodel="DPH") )
 
     """
-    import pyterrier as pt
-    JIR = pt.autoclass('org.terrier.querying.IndexRef')
-    JI = pt.autoclass('org.terrier.structures.Index')
+    JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
+    JI = pt.java.autoclass('org.terrier.structures.Index')
 
     if isinstance(metadata, str):
         metadata = [metadata]
@@ -81,7 +80,6 @@ def add_text_function_docids(res):
         docids = res.docid.values.tolist()
         # indexed by docid then keys
         allmeta = metaindex.getItems(metadata, docids)
-        import numpy as np
         # get transpose to make easier for insertion back into dataframe?
         allmeta = np.array(allmeta).T
         for i, k in enumerate(metadata):
@@ -303,7 +301,6 @@ def snippets(
         retr_pipe = br >> pt.text.snippets(psg_scorer)
 
     """
-    import pyterrier as pt
     tsp = (
         pt.apply.rename({'qid' : 'oldqid'}) 
         >> pt.apply.qid(lambda row: row['oldqid'] + '-' + row['docno']) 
@@ -313,7 +310,6 @@ def snippets(
     )
 
     def _qbsjoin(docres):
-        import pandas as pd
         if len(docres) == 0:
             docres[summary_attr] = pd.Series(dtype='str')
             return docres     
@@ -360,19 +356,17 @@ def transform(self, topics_and_res):
 
         if self.agg == 'mean':
             rtr = topics_and_res.groupby(['qid', 'olddocno'])['score'].mean().reset_index().rename(columns={'olddocno' : 'docno'})
-            from .model import query_columns
             #add query columns back
-            rtr = rtr.merge(topics_and_res[query_columns(topics_and_res)].drop_duplicates(), on='qid')
+            rtr = rtr.merge(topics_and_res[pt.model.query_columns(topics_and_res)].drop_duplicates(), on='qid')
 
         if self.agg == 'kmaxavg':
             rtr = topics_and_res.groupby(['qid', 'olddocno'])['score'].apply(lambda ser: ser.nlargest(self.K).mean()).reset_index().rename(columns={'olddocno' : 'docno'})
-            from .model import query_columns
             #add query columns back
-            rtr = rtr.merge(topics_and_res[query_columns(topics_and_res)].drop_duplicates(), on='qid')
+            rtr = rtr.merge(topics_and_res[pt.model.query_columns(topics_and_res)].drop_duplicates(), on='qid')
 
         if "docid" in rtr.columns:
             rtr = rtr.drop(columns=['docid'])
-        rtr = add_ranks(rtr)
+        rtr = pt.model.add_ranks(rtr)
         return rtr
 
 class KMaxAvgPassage(DePassager):
@@ -480,8 +474,7 @@ def applyPassaging(self, df, labels=True):
         if len(df) == 0:
             return pd.DataFrame(columns=['qid', 'query', 'docno', self.text_attr, 'score', 'rank'])
     
-        from pyterrier import tqdm
-        with tqdm('passsaging', total=len(df), desc='passaging', leave=False) as pbar:
+        with pt.tqdm('passsaging', total=len(df), desc='passaging', leave=False) as pbar:
             for index, row in df.iterrows():
                 pbar.update(1)
                 qid = row['qid']

From f38e7aa65f571bd7d24dd60fa41f11040a563a6e Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 17:38:45 +0100
Subject: [PATCH 065/153] fix more tests

---
 pyterrier/terrier/rewrite.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyterrier/terrier/rewrite.py b/pyterrier/terrier/rewrite.py
index fcc084e9..f8b01cec 100644
--- a/pyterrier/terrier/rewrite.py
+++ b/pyterrier/terrier/rewrite.py
@@ -298,7 +298,7 @@ def transform(self, topics_and_res):
             rq.setResultSet(self._populate_resultset(topics_and_res, qid, rq.getIndex()))
 
             
-            pt.terrier.J.TerrierQLParser.process(None, rq)
+            pt.terrier.J.TerrierQLParser().process(None, rq)
             pt.terrier.J.TerrierQLToMatchingQueryTerms().process(None, rq)
             # how to make sure this happens/doesnt happen when appropriate.
             self.applytp.process(None, rq)

From 79eec88afbb3fc8c2705462a944af68cfa373331 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 17:48:07 +0100
Subject: [PATCH 066/153] import cleanup in __init__.py

---
 pyterrier/__init__.py |  55 ++++++++++-------
 pyterrier/utils.py    | 134 ++++++++++++++++++++----------------------
 2 files changed, 98 insertions(+), 91 deletions(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index 1703e17b..a3003acd 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -3,9 +3,7 @@
 from deprecated import deprecated
 
 from pyterrier import utils
-from pyterrier.utils import Utils
-
-from .transformer import Transformer, Estimator, Indexer
+from pyterrier.transformer import Transformer, Estimator, Indexer
 
 from pyterrier import java
 from pyterrier.java import started, redirect_stdouterr # for backward compat, maybe remove/deprecate some day?
@@ -14,24 +12,23 @@
 from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve, IndexFactory, set_property, set_properties, run, rewrite, index, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
 
 from pyterrier import anserini
-
-from tqdm.auto import tqdm
-
-from . import cache
-from . import debug
-from . import io
-from . import measures
-from . import model
-from . import new
-from . import ltr
-from . import parallel
-from . import pipelines
-from . import text
-from . import transformer
-from .datasets import get_dataset, find_datasets, list_datasets
-
-from .pipelines import Experiment, GridScan, GridSearch, KFoldGridSearch, Evaluate
-
+from pyterrier import cache
+from pyterrier import debug
+from pyterrier import io
+from pyterrier import measures
+from pyterrier import model
+from pyterrier import new
+from pyterrier import ltr
+from pyterrier import parallel
+from pyterrier import pipelines
+from pyterrier import text
+from pyterrier import transformer
+from pyterrier import datasets
+from pyterrier.datasets import get_dataset, find_datasets, list_datasets
+from pyterrier.pipelines import Experiment, GridScan, GridSearch, KFoldGridSearch, Evaluate
+
+# old name
+Utils = utils
 
 # will be set in java._post_init once java is loaded
 HOME_DIR = None
@@ -107,7 +104,7 @@ def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, log
         set_property("terrier.mvn.coords", pkgs_string)
 
 
-def set_tqdm(type):
+def set_tqdm(type=None):
     """
         Set the tqdm progress bar type that Pyterrier will use internally.
         Many PyTerrier transformations can be expensive to apply in some settings - users can
@@ -204,4 +201,18 @@ def _():
     # apply is an object, not a module, as it also has __get_attr__() implemented
     from pyterrier.apply import _apply
     globals()['apply'] = _apply()
+
+    set_tqdm()
 _()
+
+__all__ = [
+    'java', 'terrier', 'anserini', 'cache', 'debug', 'io', 'measures', 'model', 'new', 'ltr', 'parallel', 'pipelines',
+    'text', 'transformer', 'datasets', 'get_dataset', 'find_datasets', 'list_datasets', 'Experiment', 'GridScan',
+    'GridSearch', 'KFoldGridSearch', 'Evaluate',
+    'utils', 'Utils', 'Transformer', 'Estimator', 'Indexer', 'started', 'redirect_stdouterr',
+    'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve', 'IndexFactory', 'set_property', 'set_properties',
+    'run', 'rewrite', 'index', 'FilesIndexer', 'TRECCollectionIndexer', 'DFIndexer', 'DFIndexUtils', 'IterDictIndexer',
+    'IndexingType', 'TerrierStemmer', 'TerrierStopwords', 'TerrierTokeniser',
+    'HOME_DIR', 'IndexRef', 'ApplicationSetup', 'properties', 'init', 'set_tqdm', 'version', 'check_version',
+    'logging', 'extend_classpath',
+]
diff --git a/pyterrier/utils.py b/pyterrier/utils.py
index d73a0b49..8e643751 100644
--- a/pyterrier/utils.py
+++ b/pyterrier/utils.py
@@ -8,75 +8,71 @@
 from deprecated import deprecated
 
 
-class Utils:
-    # THIS CLASS WILL BE REMOVED IN A FUTURE RELEASE
-
-    @staticmethod
-    @deprecated(version="0.9")
-    def convert_qrels_to_dict(df):
-        """
-        Convert a qrels dataframe to dictionary for use in pytrec_eval
-
-        Args:
-            df(pandas.Dataframe): The dataframe to convert
-
-        Returns:
-            dict: {qid:{docno:label,},}
-        """
-        run_dict_pytrec_eval = defaultdict(dict)
-        for row in df.itertuples():
-            run_dict_pytrec_eval[row.qid][row.docno] = int(row.label)
-        return(run_dict_pytrec_eval)
-
-    @staticmethod
-    @deprecated(version="0.9")
-    def convert_qrels_to_dataframe(qrels_dict) -> pd.DataFrame:
-        """
-        Convert a qrels dictionary to a dataframe
-
-        Args:
-            qrels_dict(Dict[str, Dict[str, int]]): {qid:{docno:label,},}
-
-        Returns:
-            pd.DataFrame: columns=['qid', 'docno', 'label']
-        """
-        result = {'qid': [], 'docno': [], 'label': []}
-        for qid in qrels_dict:
-            for docno, label in qrels_dict[qid]:
-                result['qid'].append(qid)
-                result['docno'].append(docno)
-                result['label'].append(label)
-
-        return pd.DataFrame(result)
-
-    @staticmethod
-    @deprecated(version="0.9")
-    def convert_res_to_dict(df):
-        """
-        Convert a result dataframe to dictionary for use in pytrec_eval
-
-        Args:
-            df(pandas.Dataframe): The dataframe to convert
-
-        Returns:
-            dict: {qid:{docno:score,},}
-        """
-        run_dict_pytrec_eval = defaultdict(dict)
-        for row in df.itertuples():
-            run_dict_pytrec_eval[row.qid][row.docno] = float(row.score)
-        return(run_dict_pytrec_eval)
-
-    @staticmethod
-    @deprecated(version="0.9", reason="Use pt.Evaluate instead")
-    def evaluate(res : pd.DataFrame, qrels : pd.DataFrame, metrics=['map', 'ndcg'], perquery=False):
-        from .pipelines import Evaluate
-        return Evaluate(res, qrels, metrics=metrics, perquery=perquery)
-
-    @staticmethod
-    @deprecated(version="0.9")
-    def mean_of_measures(result, measures=None, num_q = None):
-        from .pipelines import _mean_of_measures
-        return _mean_of_measures(result, measures=measures, num_q=num_q)
+@deprecated(version="0.9")
+def convert_qrels_to_dict(df):
+    """
+    Convert a qrels dataframe to dictionary for use in pytrec_eval
+
+    Args:
+        df(pandas.Dataframe): The dataframe to convert
+
+    Returns:
+        dict: {qid:{docno:label,},}
+    """
+    run_dict_pytrec_eval = defaultdict(dict)
+    for row in df.itertuples():
+        run_dict_pytrec_eval[row.qid][row.docno] = int(row.label)
+    return(run_dict_pytrec_eval)
+
+
+@deprecated(version="0.9")
+def convert_qrels_to_dataframe(qrels_dict) -> pd.DataFrame:
+    """
+    Convert a qrels dictionary to a dataframe
+
+    Args:
+        qrels_dict(Dict[str, Dict[str, int]]): {qid:{docno:label,},}
+
+    Returns:
+        pd.DataFrame: columns=['qid', 'docno', 'label']
+    """
+    result = {'qid': [], 'docno': [], 'label': []}
+    for qid in qrels_dict:
+        for docno, label in qrels_dict[qid]:
+            result['qid'].append(qid)
+            result['docno'].append(docno)
+            result['label'].append(label)
+
+    return pd.DataFrame(result)
+
+
+@deprecated(version="0.9")
+def convert_res_to_dict(df):
+    """
+    Convert a result dataframe to dictionary for use in pytrec_eval
+
+    Args:
+        df(pandas.Dataframe): The dataframe to convert
+
+    Returns:
+        dict: {qid:{docno:score,},}
+    """
+    run_dict_pytrec_eval = defaultdict(dict)
+    for row in df.itertuples():
+        run_dict_pytrec_eval[row.qid][row.docno] = float(row.score)
+    return(run_dict_pytrec_eval)
+
+
+@deprecated(version="0.9", reason="Use pt.Evaluate instead")
+def evaluate(res : pd.DataFrame, qrels : pd.DataFrame, metrics=['map', 'ndcg'], perquery=False):
+    from .pipelines import Evaluate
+    return Evaluate(res, qrels, metrics=metrics, perquery=perquery)
+
+
+@deprecated(version="0.9")
+def mean_of_measures(result, measures=None, num_q = None):
+    from .pipelines import _mean_of_measures
+    return _mean_of_measures(result, measures=measures, num_q=num_q)
 
 
 def once() -> Callable:

From 459624c571288ee9f1cf4809231e65f1b6bb2eb9 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 17:48:50 +0100
Subject: [PATCH 067/153] fix broken test

---
 tests/test_iterdictindex_pretok.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_iterdictindex_pretok.py b/tests/test_iterdictindex_pretok.py
index 31714721..a6ec3edc 100644
--- a/tests/test_iterdictindex_pretok.py
+++ b/tests/test_iterdictindex_pretok.py
@@ -19,7 +19,7 @@ def test_dpl(self):
             {'docno': 'd1', 'url': 'url1', "toks" : {"a" : 1, "b" : 2}},
             {'docno': 'd2', 'url': 'url1', "toks" : {"a" : 1, "b" : 1}}
         ]
-        from pyterrier.index import DocListIterator
+        from pyterrier.terrier.index import DocListIterator
         
         iterator =  DocListIterator(iter(it1))
         self.assertTrue(iterator.hasNext())

From b88832f9d6b7f0b94df06fa6f1ae09b35e617737 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 17:51:45 +0100
Subject: [PATCH 068/153] more cleanup

---
 pyterrier/apply_base.py | 17 +++++++----------
 pyterrier/pipelines.py  | 11 +++++------
 2 files changed, 12 insertions(+), 16 deletions(-)

diff --git a/pyterrier/apply_base.py b/pyterrier/apply_base.py
index bdd69449..8a30c403 100644
--- a/pyterrier/apply_base.py
+++ b/pyterrier/apply_base.py
@@ -1,7 +1,7 @@
 from .transformer import Transformer
-from . import tqdm
 from .model import add_ranks, split_df
 import pandas as pd
+import pyterrier as pt
 
 class ApplyTransformerBase(Transformer):
     """
@@ -34,12 +34,11 @@ def transform(self, res):
 
         import math, pandas as pd
         from pyterrier.model import split_df
-        from . import tqdm
 
         it = res.groupby("qid")
         lastqid = None
         if self.verbose:
-            it = tqdm(it, unit='query')
+            it = pt.tqdm(it, unit='query')
         try:
             if self.batch_size is None:
                 query_dfs = []
@@ -109,7 +108,7 @@ def _transform_rowwise(self, outputRes):
             outputRes["score"] = pd.Series(dtype='float64')
             return outputRes
         if self.verbose:
-            tqdm.pandas(desc="pt.apply.doc_score", unit="d")
+            pt.tqdm.pandas(desc="pt.apply.doc_score", unit="d")
             outputRes["score"] = outputRes.progress_apply(fn, axis=1).astype('float64')
         else:
             outputRes["score"] = outputRes.apply(fn, axis=1).astype('float64')
@@ -132,10 +131,9 @@ def transform(self, inputRes):
 
         import math
         from .model import split_df
-        from . import tqdm
         num_chunks = math.ceil( len(inputRes) / self.batch_size )
         iterator = split_df(inputRes, num_chunks)
-        iterator = tqdm(iterator, desc="pt.apply", unit='row')
+        iterator = pt.tqdm(iterator, desc="pt.apply", unit='row')
         rtr = pd.concat([self._transform_batchwise(chunk_df) for chunk_df in iterator])
         rtr = add_ranks(rtr)
         return rtr
@@ -166,7 +164,7 @@ def transform(self, inputRes):
         fn = self.fn
         outputRes = inputRes.copy()
         if self.verbose:
-            tqdm.pandas(desc="pt.apply.doc_features", unit="d")
+            pt.tqdm.pandas(desc="pt.apply.doc_features", unit="d")
             outputRes["features"] = outputRes.progress_apply(fn, axis=1)
         else:
             outputRes["features"] = outputRes.apply(fn, axis=1)
@@ -212,7 +210,7 @@ def transform(self, inputRes):
             outputRes = inputRes.copy()
         try:
             if self.verbose:
-                tqdm.pandas(desc="pt.apply.query", unit="d")
+                pt.tqdm.pandas(desc="pt.apply.query", unit="d")
                 outputRes["query"] = outputRes.progress_apply(fn, axis=1)
             else:
                 outputRes["query"] = outputRes.apply(fn, axis=1)
@@ -263,11 +261,10 @@ def transform(self, inputRes):
         # batching
         import math, pandas as pd
         from pyterrier.model import split_df
-        from . import tqdm
         num_chunks = math.ceil( len(inputRes) / self.batch_size )
         iterator = split_df(inputRes, num_chunks)
         if self.verbose:
-            iterator = tqdm(iterator, desc="pt.apply", unit='row') 
+            iterator = pt.tqdm(iterator, desc="pt.apply", unit='row') 
         rtr = pd.concat([self.fn(chunk_df) for chunk_df in iterator])
         return rtr
 
diff --git a/pyterrier/pipelines.py b/pyterrier/pipelines.py
index 8188ad08..07621708 100644
--- a/pyterrier/pipelines.py
+++ b/pyterrier/pipelines.py
@@ -7,6 +7,7 @@
 from .model import coerce_dataframe_types
 import ir_measures
 from ir_measures.measures import BaseMeasure 
+import pyterrier as pt
 MEASURE_TYPE=Union[str,BaseMeasure]
 MEASURES_TYPE=Sequence[MEASURE_TYPE]
 
@@ -154,8 +155,7 @@ def _run_and_evaluate(
     from .io import read_results, write_results
 
     if pbar is None:
-        from . import tqdm
-        pbar = tqdm(disable=True)
+        pbar = pt.tqdm(disable=True)
 
     metrics, rev_mapping = _convert_measures(metrics)
     qrels = qrels.rename(columns={'qid': 'query_id', 'docno': 'doc_id', 'label': 'relevance'})
@@ -448,7 +448,6 @@ def _apply_round(measure, value):
         eval_metrics.remove("mrt")
 
     # progress bar construction
-    from . import tqdm
     tqdm_args={
         'disable' : not verbose,
         'unit' : 'system',
@@ -461,7 +460,7 @@ def _apply_round(measure, value):
         # round number of batches up for each system
         tqdm_args['total'] = math.ceil((len(topics) / batch_size)) * len(retr_systems)
 
-    with tqdm(**tqdm_args) as pbar:
+    with pt.tqdm(**tqdm_args) as pbar:
         # run and evaluate each system
         for name, system in zip(names, retr_systems):
             save_file = None
@@ -844,7 +843,7 @@ def GridScan(
 
     """
     import itertools
-    from . import Utils, tqdm
+    from . import Utils
 
     if verbose and jobs > 1:
         from warnings import warn
@@ -891,7 +890,7 @@ def _evaluate_several_settings(inputs : List[Tuple]):
     eval_list = []
     #for each combination of parameter values
     if jobs == 1:
-        for v in tqdm(combinations, total=len(combinations), desc="GridScan", mininterval=0.3) if verbose else combinations:
+        for v in pt.tqdm(combinations, total=len(combinations), desc="GridScan", mininterval=0.3) if verbose else combinations:
             parameter_list, eval_scores = _evaluate_one_setting(keys, v)
             eval_list.append( (parameter_list, eval_scores) )
     else:

From 3fd8de150006aa7035f17f5fede9f569d416a423 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 18:32:48 +0100
Subject: [PATCH 069/153] fix broken test

---
 tests/test_iterdictindex.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_iterdictindex.py b/tests/test_iterdictindex.py
index 1ae0cfe6..661535ba 100644
--- a/tests/test_iterdictindex.py
+++ b/tests/test_iterdictindex.py
@@ -76,7 +76,7 @@ def test_checkjavaDocIterator(self):
             {'docno': '3', 'url': 'url3', 'text': 'The body may perhaps compensates for the loss', 'title': 'Best of Viktor Prowoll'},
         ]
 
-        from pyterrier.index import FlatJSONDocumentIterator
+        from pyterrier.terrier import FlatJSONDocumentIterator
 
         it1 = itertools.islice(it, 1)
         jIter1 = FlatJSONDocumentIterator(it1)

From 9c0924af5149faa3147a809bad05c8ac6de4d910 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 18:39:23 +0100
Subject: [PATCH 070/153] fix broken test

---
 tests/test_iterdictindex.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/test_iterdictindex.py b/tests/test_iterdictindex.py
index 661535ba..088364da 100644
--- a/tests/test_iterdictindex.py
+++ b/tests/test_iterdictindex.py
@@ -76,7 +76,7 @@ def test_checkjavaDocIterator(self):
             {'docno': '3', 'url': 'url3', 'text': 'The body may perhaps compensates for the loss', 'title': 'Best of Viktor Prowoll'},
         ]
 
-        from pyterrier.terrier import FlatJSONDocumentIterator
+        from pyterrier.terrier.index import FlatJSONDocumentIterator
 
         it1 = itertools.islice(it, 1)
         jIter1 = FlatJSONDocumentIterator(it1)

From b69e310abb188bb805510c41fa6f51648a267c05 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 18:57:03 +0100
Subject: [PATCH 071/153] make java configuration object more pythonic

---
 pyterrier/anserini/java.py |  6 +++---
 pyterrier/java/_init.py    | 14 ++++++--------
 pyterrier/java/config.py   |  8 +++++++-
 pyterrier/java/utils.py    |  6 +++---
 pyterrier/terrier/java.py  | 23 ++++++++++-------------
 5 files changed, 29 insertions(+), 28 deletions(-)

diff --git a/pyterrier/anserini/java.py b/pyterrier/anserini/java.py
index a1aa7cad..c45b3e62 100644
--- a/pyterrier/anserini/java.py
+++ b/pyterrier/anserini/java.py
@@ -15,11 +15,11 @@ def _pre_init(jnius_config):
         # pyserini not installed, do nothing
         return
 
-    if configure.get('version') is None:
+    if configure['version'] is None:
         jar = _get_pyserini_jar()
     else:
         # download and use the anserini version specified by the user
-        jar = pt.java.mavenresolver.get_package_jar('io.anserini', "anserini", configure.get('version'), 'fatjar')
+        jar = pt.java.mavenresolver.get_package_jar('io.anserini', "anserini", configure['version'], 'fatjar')
 
     if jar is None:
         raise RuntimeError('Could not find anserini jar')
@@ -63,7 +63,7 @@ def _get_pyserini_jar() -> Optional[str]:
 
 @pt.java.before_init
 def set_version(version: str):
-    configure.set('version', version)
+    configure['version'] = version
 
 
 def required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
diff --git a/pyterrier/java/_init.py b/pyterrier/java/_init.py
index e54590ae..a69338ee 100644
--- a/pyterrier/java/_init.py
+++ b/pyterrier/java/_init.py
@@ -51,22 +51,20 @@ def init() -> None:
         _pre_init = entry_point.load()
         _pre_init(jnius_config)
 
-    cfg = pt.java.configure()
+    if pt.java.configure['mem'] is not None:
+        jnius_config.add_options('-Xmx' + str(pt.java.configure['mem']) + 'm')
 
-    if cfg['mem'] is not None:
-        jnius_config.add_options('-Xmx' + str(cfg['mem']) + 'm')
-
-    for opt in cfg['options']:
+    for opt in pt.java.configure['options']:
         jnius_config.add_options(opt)
 
-    for jar in cfg['jars']:
+    for jar in pt.java.configure['jars']:
         jnius_config.add_classpath(jar)
 
     import jnius
     _started = True
 
-    pt.java.set_log_level(cfg['log_level'])
-    if cfg['redirect_io']:
+    pt.java.set_log_level(pt.java.configure['log_level'])
+    if pt.java.configure['redirect_io']:
         pt.java.redirect_stdouterr()
 
     java_version = pt.java.J.System.getProperty("java.version")
diff --git a/pyterrier/java/config.py b/pyterrier/java/config.py
index 99ee9af2..38b0f281 100644
--- a/pyterrier/java/config.py
+++ b/pyterrier/java/config.py
@@ -8,7 +8,7 @@ def __init__(self, name):
         self.name = name
 
     def get(self, key):
-        return self()[key]
+        return deepcopy(_CONFIGS[self.name][key])
 
     def set(self, key, value):
         self(**{key: value})
@@ -18,6 +18,12 @@ def append(self, key, value):
         res.append(value)
         self(**{key: res})
 
+    def __getitem__(self, key):
+        return self.get(key)
+
+    def __setitem__(self, key, value):
+        self.set(key, value)
+
     def __call__(self, **settings: Any):
         for key, value in settings.items():
             if key not in _CONFIGS[self.name]:
diff --git a/pyterrier/java/utils.py b/pyterrier/java/utils.py
index 428ce502..b4391553 100644
--- a/pyterrier/java/utils.py
+++ b/pyterrier/java/utils.py
@@ -91,7 +91,7 @@ def add_package(org_name: str = None, package_name: str = None, version: str = N
 
 @before_init
 def set_memory_limit(mem: Optional[float]):
-    configure(mem=mem)
+    configure['mem'] = mem
 
 
 @before_init
@@ -101,7 +101,7 @@ def add_option(option: str):
 
 @before_init
 def set_redirect_io(redirect_io: bool):
-    configure(redirect_io=redirect_io)
+    configure['redirect_io'] = redirect_io
 
 
 def set_log_level(level):
@@ -117,7 +117,7 @@ def set_log_level(level):
         Unlike other java settings, this can be changed either before or after init() has been called.
     """
     if not started():
-        configure(log_level=level)
+        configure['log_level'] = level
     else:
         J.PTUtils.setLogLevel(level, None)
 
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index bfd059b0..ca3a20c0 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -6,8 +6,6 @@
 
 _SAVED_FNS = []
 
-_resolved_helper_version = None
-
 _properties = None
 
 configure = pt.java.config.register('pt.terrier.java', {
@@ -20,12 +18,12 @@
 
 @pt.java.before_init
 def set_terrier_version(version: Optional[str] = None):
-    configure(terrier_version=version)
+    configure['terrier_version'] = version
 
 
 @pt.java.before_init
 def set_helper_version(version: Optional[str] = None):
-    configure(helper_version=version)
+    configure['helper_version'] = version
 
 
 @pt.java.before_init
@@ -35,7 +33,6 @@ def enable_prf(version: Optional[str] = None):
 
 
 def _pre_init(jnius_config):
-    global _resolved_helper_version
     """
     Download Terrier's jar file for the given version at the given file_path
     Called by pt.init()
@@ -46,27 +43,27 @@ def _pre_init(jnius_config):
         helper_version(str): Which version of the helper - None is latest
     """
     # If version is not specified, find newest and download it
-    cfg = configure()
-    if cfg['terrier_version'] is None:
+    if configure['terrier_version'] is None:
         terrier_version = pt.java.mavenresolver.latest_version_num(TERRIER_PKG, "terrier-assemblies")
     else:
-        terrier_version = str(cfg['terrier_version']) # just in case its a float
+        terrier_version = str(configure['terrier_version']) # just in case its a float
+    configure['terrier_version'] = terrier_version # save this specific version
 
     # obtain the fat jar from Maven
     # "snapshot" means use Jitpack.io to get a build of the current
     # 5.x branch from Github - see https://jitpack.io/#terrier-org/terrier-core/5.x-SNAPSHOT
     if terrier_version == "snapshot":
-        trJar = pt.java.mavenresolver.get_package_jar("com.github.terrier-org.terrier-core", "terrier-assemblies", "5.x-SNAPSHOT", pt.io.pyterrier_home(), "jar-with-dependencies", force_download=cfg['force_download'])
+        trJar = pt.java.mavenresolver.get_package_jar("com.github.terrier-org.terrier-core", "terrier-assemblies", "5.x-SNAPSHOT", pt.io.pyterrier_home(), "jar-with-dependencies", force_download=configure['force_download'])
     else:
         trJar = pt.java.mavenresolver.get_package_jar(TERRIER_PKG, "terrier-assemblies", terrier_version, pt.io.pyterrier_home(), "jar-with-dependencies")
     pt.java.add_jar(trJar)
 
     # now the helper classes
-    if cfg['helper_version'] is None or cfg['helper_version'] == 'snapshot':
+    if configure['helper_version'] is None or configure['helper_version'] == 'snapshot':
         helper_version = pt.java.mavenresolver.latest_version_num(TERRIER_PKG, "terrier-python-helper")
     else:
-        helper_version = str(cfg['helper_version']) # just in case its a float
-    _resolved_helper_version = helper_version
+        helper_version = str(configure['helper_version']) # just in case its a float
+    configure['helper_version'] = helper_version # save this specific version
     pt.java.add_package(TERRIER_PKG, "terrier-python-helper", helper_version)
 
 
@@ -130,7 +127,7 @@ def _post_init(jnius):
         version_string += f" (built by {J.Version.BUILD_USER} on {J.Version.BUILD_DATE})"
 
     print(f"PyTerrier {pt.__version__} has loaded Terrier {version_string} and "
-          f"terrier-helper {_resolved_helper_version}\n", file=sys.stderr)
+          f"terrier-helper {configure['helper_version']}\n", file=sys.stderr)
 
 
 def _new_indexref(s):

From 803560a8463093b708b8a8f69aca446b01bf4ef3 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 19:24:24 +0100
Subject: [PATCH 072/153] moved check_version under pt.terrier and deprecated
 the one in the main namespace

---
 pyterrier/__init__.py              | 27 +++-----------------
 pyterrier/io.py                    |  4 +--
 pyterrier/terrier/__init__.py      |  4 +--
 pyterrier/terrier/index.py         | 12 ++++-----
 pyterrier/terrier/java.py          | 40 ++++++++++++++++++++++++++++--
 pyterrier/terrier/retriever.py     | 10 ++++----
 pyterrier/terrier/rewrite.py       |  2 +-
 tests/test_br.py                   | 12 ++++-----
 tests/test_fbr.py                  |  4 +--
 tests/test_files_indexer.py        |  3 +--
 tests/test_iterdictindex_pretok.py |  4 +--
 tests/test_rewrite.py              |  6 ++---
 12 files changed, 71 insertions(+), 57 deletions(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index a3003acd..1d016748 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -9,7 +9,7 @@
 from pyterrier.java import started, redirect_stdouterr # for backward compat, maybe remove/deprecate some day?
 
 from pyterrier import terrier
-from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve, IndexFactory, set_property, set_properties, run, rewrite, index, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
+from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve, IndexFactory, set_property, set_properties, run, rewrite, index, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser, version, check_version, check_helper_version
 
 from pyterrier import anserini
 from pyterrier import cache
@@ -140,27 +140,6 @@ def set_tqdm(type=None):
     else:
         raise ValueError("Unknown tqdm type %s" % str(type))
     tqdm.pandas()
-    
-
-@java.required
-def version():
-    """
-        Returns the version string from the underlying Terrier platform.
-    """
-    return terrier.J.Version.VERSION
-
-def check_version(min, helper=False):
-    """
-        Returns True iff the underlying Terrier version is no older than the requested version.
-    """
-    from packaging.version import Version
-    from . import terrier
-    currentVer = terrier.java._resolved_helper_version if helper else version()
-    assert currentVer is not None, "Could not obtain Terrier version (helpher=%s)" % str(helper)
-    currentVer = Version(currentVer.replace("-SNAPSHOT", ""))
-
-    min = Version(str(min))
-    return currentVer >= min
 
 
 @java.required
@@ -212,7 +191,7 @@ def _():
     'utils', 'Utils', 'Transformer', 'Estimator', 'Indexer', 'started', 'redirect_stdouterr',
     'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve', 'IndexFactory', 'set_property', 'set_properties',
     'run', 'rewrite', 'index', 'FilesIndexer', 'TRECCollectionIndexer', 'DFIndexer', 'DFIndexUtils', 'IterDictIndexer',
-    'IndexingType', 'TerrierStemmer', 'TerrierStopwords', 'TerrierTokeniser',
-    'HOME_DIR', 'IndexRef', 'ApplicationSetup', 'properties', 'init', 'set_tqdm', 'version', 'check_version',
+    'IndexingType', 'TerrierStemmer', 'TerrierStopwords', 'TerrierTokeniser', 'version',
+    'HOME_DIR', 'IndexRef', 'ApplicationSetup', 'properties', 'init', 'set_tqdm', 'version', 'check_version', 'check_helper_version',
     'logging', 'extend_classpath',
 ]
diff --git a/pyterrier/io.py b/pyterrier/io.py
index 84b76324..940fecda 100644
--- a/pyterrier/io.py
+++ b/pyterrier/io.py
@@ -300,7 +300,7 @@ def read_topics(filename, format="trec", **kwargs):
 
 @pt.java.required
 def _read_topics_trec(file_path, doc_tag="TOP", id_tag="NUM", whitelist=["TITLE"], blacklist=["DESC","NARR"]):
-    assert pt.check_version("5.3")
+    assert pt.terrier.check_version("5.3")
     trecquerysource = pt.java.autoclass('org.terrier.applications.batchquerying.TRECQuery')
     tqs = trecquerysource(
         [file_path], doc_tag, id_tag, whitelist, blacklist,
@@ -359,7 +359,7 @@ def _read_topics_singleline(filepath, tokenise=True):
         pandas.Dataframe with columns=['qid','query']
     """
     rows = []
-    assert pt.check_version("5.3")
+    assert pt.terrier.check_version("5.3")
     slqIter = pt.java.autoclass("org.terrier.applications.batchquerying.SingleLineTRECQuery")(filepath, tokenise)
     for q in slqIter:
         rows.append([slqIter.getQueryId(), q])
diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
index 103bf824..da4abaeb 100644
--- a/pyterrier/terrier/__init__.py
+++ b/pyterrier/terrier/__init__.py
@@ -1,6 +1,6 @@
 # java stuff
 from pyterrier.terrier import java
-from pyterrier.terrier.java import configure, set_terrier_version, set_helper_version, enable_prf, extend_package, J, set_property, set_properties, run
+from pyterrier.terrier.java import configure, set_terrier_version, set_helper_version, enable_prf, extend_package, J, set_property, set_properties, run, version, check_version, check_helper_version
 from pyterrier.terrier.retriever import BatchRetrieve, FeaturesBatchRetrieve
 from pyterrier.terrier.index_factory import IndexFactory
 from pyterrier.terrier import index
@@ -12,7 +12,7 @@
 
 __all__ = [
     # java stuff
-    'java', 'configure', 'set_terrier_version', 'set_helper_version', 'enable_prf', 'extend_package', 'J',
+    'java', 'configure', 'set_terrier_version', 'set_helper_version', 'enable_prf', 'extend_package', 'J', 'version', 'check_version', 'check_helper_version',
 
     # retrieval
     'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve',
diff --git a/pyterrier/terrier/index.py b/pyterrier/terrier/index.py
index 037ee016..967d930f 100644
--- a/pyterrier/terrier/index.py
+++ b/pyterrier/terrier/index.py
@@ -58,7 +58,7 @@ def __init__(self, pyiterator):
             self.pyiterator = pyiterator
             self.hasnext = True
             self.lastdoc = None
-            self.tr57 = not pt.check_version("5.8")
+            self.tr57 = not pt.terrier.check_version("5.8")
 
         @staticmethod 
         def pyDictToMap(a_dict): #returns Map<String,String>
@@ -515,7 +515,7 @@ def _indexing_config(this, stopword_list : Union[List[str], None], termpipelines
         if this == TerrierStopwords.terrier:
             termpipelines.append('Stopwords')
         if this == TerrierStopwords.custom:
-            assert pt.check_version("5.8"), "Terrier 5.8 required"
+            assert pt.terrier.check_version("5.8"), "Terrier 5.8 required"
             assert stopword_list is not None, "expected to receive a stopword list"
 
             stopword_list_esc = [t.replace(",", "\\,") for t in stopword_list ]
@@ -903,7 +903,7 @@ def __init__(self, index_path, *args, meta = {'docno' : 20}, meta_reverse=['docn
         self.meta_reverse = meta_reverse
         self.pretokenised = pretokenised
         if self.pretokenised:
-            assert pt.check_version(5.7), "Terrier too old, this requires 5.7"
+            assert pt.terrier.check_version(5.7), "Terrier too old, this requires 5.7"
             # we disable stemming and stopwords for pretokenised indices
             self.stemmer = None
             self.stopwords = None
@@ -1027,7 +1027,7 @@ def index(self, it, fields=('text',), meta=None, meta_lengths=None, threads=None
             collectionIterator = FlatJSONDocumentIterator(self._filter_iterable(it, fields))
             javaDocCollection = pt.java.autoclass("org.terrier.python.CollectionFromDocumentIterator")(collectionIterator)
             # remove once 5.7 is now the minimum version
-            indexer.index(javaDocCollection if pt.check_version("5.7") else [javaDocCollection])
+            indexer.index(javaDocCollection if pt.terrier.check_version("5.7") else [javaDocCollection])
             global lastdoc
             lastdoc = None
             self.index_called = True
@@ -1232,7 +1232,7 @@ def index(self, files_path : Union[str,List[str]]):
             colObj = pt.java.cast("org.terrier.indexing.MultiDocumentFileCollection", colObj)
             colObj = TQDMCollection(colObj)
         # remove once 5.7 is now the minimum version
-        if pt.check_version("5.7"):
+        if pt.terrier.check_version("5.7"):
             index.index(colObj)
         else:
             index.index(pt.java.autoclass("org.terrier.python.PTUtils").makeCollection(colObj))
@@ -1283,7 +1283,7 @@ def index(self, files_path : Union[str,List[str]]):
         
         simpleColl = pt.terrier.J.SimpleFileCollection(asList, False)
         # remove once 5.7 is now the minimum version
-        index.index(simpleColl if pt.check_version("5.7") else [simpleColl])
+        index.index(simpleColl if pt.terrier.check_version("5.7") else [simpleColl])
         global lastdoc
         lastdoc = None
         self.index_called = True
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index ca3a20c0..5b97fc27 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -1,4 +1,5 @@
 import sys
+from packaging.version import Version
 from typing import Optional
 import pyterrier as pt
 
@@ -273,7 +274,7 @@ def extend_package(package):
     """
         Allows to add packages to Terrier's classpath after the JVM has started.
     """
-    assert pt.check_version(5.3), "Terrier 5.3 required for this functionality"
+    assert check_version(5.3), "Terrier 5.3 required for this functionality"
     package_list = pt.java.J.ArrayList()
     package_list.add(package)
     mvnr = J.ApplicationSetup.getPlugin("MavenResolver")
@@ -318,6 +319,41 @@ def run(cmd, args=[]):
     J.CLITool.main([cmd] + args)
 
 
+@pt.java.required
+def version():
+    """
+        Returns the version string from the underlying Terrier platform.
+    """
+    return J.Version.VERSION
+
+
+@pt.java.required
+def check_version(min):
+    """
+        Returns True iff the underlying Terrier version is no older than the requested version.
+    """
+    current_ver = version()
+    assert current_ver is not None, "Could not obtain Terrier version"
+    current_ver = Version(current_ver.replace("-SNAPSHOT", ""))
+
+    min = Version(str(min))
+    return current_ver >= min
+
+
+@pt.java.required
+def check_helper_version(min):
+    """
+        Returns True iff the underlying Terrier helper version is no older than the requested version.
+    """
+    current_ver = configure['helper_version']
+    assert current_ver is not None, "Could not obtain Terrier helper version" 
+    current_ver = Version(current_ver.replace("-SNAPSHOT", ""))
+
+    min = Version(str(min))
+    return current_ver >= min
+
+
+
 # Terrier-specific classes
 J = pt.java.JavaClasses({
     'ApplicationSetup': 'org.terrier.utility.ApplicationSetup',
@@ -343,7 +379,7 @@ def run(cmd, args=[]):
     'BlockIndexer': 'org.terrier.structures.indexing.classical.BlockIndexer',
     'BasicSinglePassIndexer': 'org.terrier.structures.indexing.singlepass.BasicSinglePassIndexer',
     'BlockSinglePassIndexer': 'org.terrier.structures.indexing.singlepass.BlockSinglePassIndexer',
-    'BasicMemoryIndexer': lambda: 'org.terrier.realtime.memory.MemoryIndexer' if pt.check_version("5.7") else 'org.terrier.python.MemoryIndexer',
+    'BasicMemoryIndexer': lambda: 'org.terrier.realtime.memory.MemoryIndexer' if check_version("5.7") else 'org.terrier.python.MemoryIndexer',
     'Collection': 'org.terrier.indexing.Collection',
     'StructureMerger': 'org.terrier.structures.merging.StructureMerger',
     'BlockStructureMerger': 'org.terrier.structures.merging.BlockStructureMerger',
diff --git a/pyterrier/terrier/retriever.py b/pyterrier/terrier/retriever.py
index 6d315aba..ebd09a30 100644
--- a/pyterrier/terrier/retriever.py
+++ b/pyterrier/terrier/retriever.py
@@ -206,7 +206,7 @@ def __init__(self, index_location, controls=None, properties=None, metadata=["do
         self.appSetup = pt.java.autoclass('org.terrier.utility.ApplicationSetup')
         self.properties = _mergeDicts(BatchRetrieve.default_properties, properties)
         self.concurrentIL = pt.java.autoclass("org.terrier.structures.ConcurrentIndexLoader")
-        if pt.check_version(5.5) and "SimpleDecorateProcess" not in self.properties["querying.processes"]:
+        if pt.terrier.check_version(5.5) and "SimpleDecorateProcess" not in self.properties["querying.processes"]:
             self.properties["querying.processes"] += ",decorate:SimpleDecorateProcess"
         self.metadata = metadata
         self.threads = threads
@@ -237,7 +237,7 @@ def __init__(self, index_location, controls=None, properties=None, metadata=["do
                   
         if self.threads > 1:
             warn("Multi-threaded retrieval is experimental, YMMV.")
-            assert pt.check_version(5.5), "Terrier 5.5 is required for multi-threaded retrieval"
+            assert pt.terrier.check_version(5.5), "Terrier 5.5 is required for multi-threaded retrieval"
 
             # we need to see if our indexref is concurrent. if not, we upgrade it using ConcurrentIndexLoader
             # this will upgrade the underlying index too.
@@ -392,7 +392,7 @@ def transform(self, queries):
         scores_provided = "score" in queries.columns
         input_results = None
         if docno_provided or docid_provided:
-            assert pt.check_version(5.3)
+            assert pt.terrier.check_version(5.3)
             input_results = queries
 
             # query is optional, and functionally dependent on qid.
@@ -622,7 +622,7 @@ def __init__(self, index_location, features, controls=None, properties=None, thr
         
         # check for terrier-core#246 bug usiung FatFull
         if self.wmodel is not None:    
-            assert pt.check_version(5.9), "Terrier 5.9 is required for this functionality, see https://github.com/terrier-org/terrier-core/pull/246"
+            assert pt.terrier.check_version(5.9), "Terrier 5.9 is required for this functionality, see https://github.com/terrier-org/terrier-core/pull/246"
             
         if threads > 1:
             raise ValueError("Multi-threaded retrieval not yet supported by FeaturesBatchRetrieve")
@@ -691,7 +691,7 @@ def transform(self, queries):
         scores_provided = "score" in queries.columns
         if docno_provided or docid_provided:
             #re-ranking mode
-            assert pt.check_version(5.3)
+            assert pt.terrier.check_version(5.3)
             input_results = queries
 
             # query is optional, and functionally dependent on qid.
diff --git a/pyterrier/terrier/rewrite.py b/pyterrier/terrier/rewrite.py
index f8b01cec..88c3c53f 100644
--- a/pyterrier/terrier/rewrite.py
+++ b/pyterrier/terrier/rewrite.py
@@ -116,7 +116,7 @@ def __init__(self, verbose = 0, remove_stopwords = True, prox_model = None, **kw
         self.verbose = 0
         self.prox_model = prox_model
         self.remove_stopwords = remove_stopwords
-        assert pt.check_version("5.3")
+        assert pt.terrier.check_version("5.3")
         self.ApplyTermPipeline_stopsonly = pt.terrier.J.ApplyTermPipeline('Stopwords')
 
     def __repr__(self):
diff --git a/tests/test_br.py b/tests/test_br.py
index 6d210943..094a1972 100644
--- a/tests/test_br.py
+++ b/tests/test_br.py
@@ -24,7 +24,7 @@ def parse_query_result(filename):
 class TestBatchRetrieve(BaseTestCase):
 
     def test_candidate_set_one_doc(self):
-        if not pt.check_version("5.3"):
+        if not pt.terrier.check_version("5.3"):
             self.skipTest("Requires Terrier 5.3")
         indexloc = self.here + "/fixtures/index/data.properties"
         # docid 50 == docno 51
@@ -108,7 +108,7 @@ def test_br_empty(self):
         self.assertEqual(0, len(result))
 
     def test_candidate_set_two_doc(self):
-        if not pt.check_version("5.3"):
+        if not pt.terrier.check_version("5.3"):
             self.skipTest("Requires Terrier 5.3")
 
         indexloc = self.here + "/fixtures/index/data.properties"
@@ -178,7 +178,7 @@ def test_num_results(self):
         result = retr.transform(input)
         self.assertEqual(len(result), 10)
 
-        if not pt.check_version("5.5"):
+        if not pt.terrier.check_version("5.5"):
             return
 
         retr = pt.BatchRetrieve(indexref, num_results=1001)        
@@ -208,7 +208,7 @@ def test_num_python_wmodel(self):
 
     def test_threading_manualref(self):
         
-        if not pt.check_version("5.5"):
+        if not pt.terrier.check_version("5.5"):
             self.skipTest("Requires Terrier 5.5")
 
         topics = pt.get_dataset("vaswani").get_topics().head(8)
@@ -225,7 +225,7 @@ def test_threading_manualref(self):
         result = retr.transform(topics)
 
     def test_threading_selfupgrade(self):
-        if not pt.check_version("5.5"):
+        if not pt.terrier.check_version("5.5"):
             self.skipTest("Requires Terrier 5.5")
 
         topics = pt.get_dataset("vaswani").get_topics().head(10)
@@ -238,7 +238,7 @@ def test_threading_selfupgrade(self):
 
     def test_terrier_retrieve_alias(self):
         # based off test_candidate_set_one_doc
-        if not pt.check_version("5.3"):
+        if not pt.terrier.check_version("5.3"):
             self.skipTest("Requires Terrier 5.3")
         indexloc = self.here + "/fixtures/index/data.properties"
         # docid 50 == docno 51
diff --git a/tests/test_fbr.py b/tests/test_fbr.py
index d7c49e25..75b6bb5a 100644
--- a/tests/test_fbr.py
+++ b/tests/test_fbr.py
@@ -24,7 +24,7 @@ def test_compile_to_fbr(self):
         
 
     def test_fbr_reranking(self):
-        if not pt.check_version("5.4"):
+        if not pt.terrier.check_version("5.4"):
             self.skipTest("Requires Terrier 5.4")
         # this test examines the use of ScoringMatchingWithFat 
         JIR = pt.autoclass('org.terrier.querying.IndexRef')
@@ -67,7 +67,7 @@ def test_fbr_reranking(self):
         self.assertEqual(result1F1_map, result2F1_map)
 
     def test_fbr_reranking2(self):
-        if not pt.check_version("5.4"):
+        if not pt.terrier.check_version("5.4"):
             self.skipTest("Requires Terrier 5.4")
         # this test examines the use of ScoringMatchingWithFat, using a particular case known to with Terrier 5.3 
         JIR = pt.autoclass('org.terrier.querying.IndexRef')
diff --git a/tests/test_files_indexer.py b/tests/test_files_indexer.py
index 02e4eaba..27d9768a 100644
--- a/tests/test_files_indexer.py
+++ b/tests/test_files_indexer.py
@@ -44,8 +44,7 @@ def test_2_docs_title_body_meta(self):
         # test title has been recorded in metaindex
         self.assertEqual("test title", index.getMetaIndex().getItem("title", html_pos))
 
-        from pyterrier import check_version
-        if not check_version("5.5"):
+        if not pt.terrier.check_version("5.5"):
             return
 
         # test bodies have been recorded in metaindex
diff --git a/tests/test_iterdictindex_pretok.py b/tests/test_iterdictindex_pretok.py
index a6ec3edc..1523b47f 100644
--- a/tests/test_iterdictindex_pretok.py
+++ b/tests/test_iterdictindex_pretok.py
@@ -55,7 +55,7 @@ def test_dpl(self):
 
     
     def test_json_pretok_iterator(self):
-        if not pt.check_version("5.7") or not pt.check_version("0.0.7", helper=True):
+        if not pt.terrier.check_version("5.7") or not pt.terrier.check_helper_version("0.0.7"):
             raise SkipTest("Requires Terrier 5.7 and helper 0.0.7")
 
         it = [
@@ -81,7 +81,7 @@ def test_json_pretok_iterator(self):
         self.assertEqual(1, pl.getFrequency("a"))
 
     def _make_pretok_index(self, n, index_type, meta=('docno', 'url')):
-        if not pt.check_version("5.7") or not pt.check_version("0.0.7", helper=True):
+        if not pt.terrier.check_version("5.7") or not pt.terrier.check_helper_version("0.0.7"):
             self.skipTest("Requires Terrier 5.7 and helper 0.0.7")
         
         from pyterrier.terrier.index import IndexingType
diff --git a/tests/test_rewrite.py b/tests/test_rewrite.py
index 4b117742..627e831c 100644
--- a/tests/test_rewrite.py
+++ b/tests/test_rewrite.py
@@ -128,12 +128,12 @@ def test_reset_with_docs(self):
         self.assertEqual(outputDF.iloc[3]["query"], "one two")
 
     def test_sdm_freq(self):
-        if not pt.check_version("5.3"):
+        if not pt.terrier.check_version("5.3"):
             self.skipTest("Requires Terrier 5.3")
         self._sdm(True)
 
     def test_sdm(self):
-        if not pt.check_version("5.3"):
+        if not pt.terrier.check_version("5.3"):
             self.skipTest("Requires Terrier 5.3")
         self._sdm(False)
 
@@ -289,7 +289,7 @@ def test_tokenise(self):
         self.assertEqual('a b', pt.rewrite.tokenise(lambda q : q.split('/')).search("a/b").iloc[0].query)
 
     def test_qe(self):
-        if not pt.check_version("5.3"):
+        if not pt.terrier.check_version("5.3"):
             self.skipTest("Requires Terrier 5.3")
         dataset = pt.datasets.get_dataset("vaswani")
         indexref = dataset.get_index()

From 6daaab7972fcb7bb1c3fbb0291ef0031b08d83f8 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 19:45:30 +0100
Subject: [PATCH 073/153] deprecation in the main namespace

---
 pyterrier/__init__.py         | 36 ++++++++---------------------------
 pyterrier/terrier/__init__.py |  4 ++--
 pyterrier/terrier/java.py     |  9 ++++++---
 3 files changed, 16 insertions(+), 33 deletions(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index 1d016748..c9b9911f 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -9,7 +9,7 @@
 from pyterrier.java import started, redirect_stdouterr # for backward compat, maybe remove/deprecate some day?
 
 from pyterrier import terrier
-from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve, IndexFactory, set_property, set_properties, run, rewrite, index, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser, version, check_version, check_helper_version
+from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve, IndexFactory, set_property, set_properties, run, rewrite, index, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
 
 from pyterrier import anserini
 from pyterrier import cache
@@ -142,31 +142,11 @@ def set_tqdm(type=None):
     tqdm.pandas()
 
 
-@java.required
-@deprecated(version="0.11", reason="Use pt.java.set_log_level() instead")
-def logging(level):
-    """
-        Set the logging level. Equivalent to setting the logging= parameter to init().
-        The following string values are allowed, corresponding to Java logging levels:
-        
-         - `'ERROR'`: only show error messages
-         - `'WARN'`: only show warnings and error messages (default)
-         - `'INFO'`: show information, warnings and error messages
-         - `'DEBUG'`: show debugging, information, warnings and error messages
-
-    """
-    java.set_log_level(level)
-
-
-@java.required
-def extend_classpath(mvnpackages):
-    """
-        Allows to add packages to Terrier's classpath after the JVM has started.
-    """
-    if isinstance(mvnpackages, str):
-        mvnpackages = [mvnpackages]
-    for package in mvnpackages:
-        terrier.extend_package(package)
+# deprecated functions explored to the main namespace, which will be removed in a future version
+logging = deprecated(version='0.11.0', reason="use pt.java.set_log_level(...) instead")(java.set_log_level)
+version = deprecated(version='0.11.0', reason="use pt.terrier.version() instead")(terrier.version)
+check_version = deprecated(version='0.11.0', reason="use pt.terrier.check_version(...) instead")(terrier.check_version)
+extend_classpath = deprecated(version='0.11.0', reason="use pt.terrier.extend_classpath(...) instead")(terrier.extend_classpath)
 
 
 # Additional setup performed in a function to avoid polluting the namespace with other imports like platform
@@ -191,7 +171,7 @@ def _():
     'utils', 'Utils', 'Transformer', 'Estimator', 'Indexer', 'started', 'redirect_stdouterr',
     'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve', 'IndexFactory', 'set_property', 'set_properties',
     'run', 'rewrite', 'index', 'FilesIndexer', 'TRECCollectionIndexer', 'DFIndexer', 'DFIndexUtils', 'IterDictIndexer',
-    'IndexingType', 'TerrierStemmer', 'TerrierStopwords', 'TerrierTokeniser', 'version',
-    'HOME_DIR', 'IndexRef', 'ApplicationSetup', 'properties', 'init', 'set_tqdm', 'version', 'check_version', 'check_helper_version',
+    'IndexingType', 'TerrierStemmer', 'TerrierStopwords', 'TerrierTokeniser',
+    'HOME_DIR', 'IndexRef', 'ApplicationSetup', 'properties', 'init', 'set_tqdm', 'version', 'check_version',
     'logging', 'extend_classpath',
 ]
diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
index da4abaeb..72bac572 100644
--- a/pyterrier/terrier/__init__.py
+++ b/pyterrier/terrier/__init__.py
@@ -1,6 +1,6 @@
 # java stuff
 from pyterrier.terrier import java
-from pyterrier.terrier.java import configure, set_terrier_version, set_helper_version, enable_prf, extend_package, J, set_property, set_properties, run, version, check_version, check_helper_version
+from pyterrier.terrier.java import configure, set_terrier_version, set_helper_version, enable_prf, extend_classpath, J, set_property, set_properties, run, version, check_version, check_helper_version
 from pyterrier.terrier.retriever import BatchRetrieve, FeaturesBatchRetrieve
 from pyterrier.terrier.index_factory import IndexFactory
 from pyterrier.terrier import index
@@ -12,7 +12,7 @@
 
 __all__ = [
     # java stuff
-    'java', 'configure', 'set_terrier_version', 'set_helper_version', 'enable_prf', 'extend_package', 'J', 'version', 'check_version', 'check_helper_version',
+    'java', 'configure', 'set_terrier_version', 'set_helper_version', 'enable_prf', 'extend_classpath', 'J', 'version', 'check_version', 'check_helper_version',
 
     # retrieval
     'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve',
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 5b97fc27..925ba830 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -1,6 +1,6 @@
 import sys
 from packaging.version import Version
-from typing import Optional
+from typing import Optional, Union, List
 import pyterrier as pt
 
 TERRIER_PKG = "org.terrier"
@@ -270,13 +270,16 @@ def _index_corpusiter_direct_pretok(self):
 
 
 @pt.java.required
-def extend_package(package):
+def extend_classpath(packages: Union[str, List[str]]):
     """
         Allows to add packages to Terrier's classpath after the JVM has started.
     """
+    if isinstance(packages, str):
+        packages = [packages]
     assert check_version(5.3), "Terrier 5.3 required for this functionality"
     package_list = pt.java.J.ArrayList()
-    package_list.add(package)
+    for package in packages:
+        package_list.add(package)
     mvnr = J.ApplicationSetup.getPlugin("MavenResolver")
     assert mvnr is not None
     mvnr = pt.java.cast("org.terrier.utility.MavenResolver", mvnr)

From 8191a8e98a0914b186fc132367d665ba6f9aa899 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 19:46:04 +0100
Subject: [PATCH 074/153] helper_version stuff is sorted in a previous commit

---
 pyterrier/__init__.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index c9b9911f..dc5392ee 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -38,9 +38,6 @@
 ApplicationSetup = None
 properties = None
 
-# TODO
-_helper_version = None
-
 
 @java.before_init
 def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, logging='WARN', home_dir=None, boot_packages=[], tqdm=None, no_download=False,helper_version = None):

From d854228720314ac9c7afe5683dc0c69873495a94 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 19:50:49 +0100
Subject: [PATCH 075/153] more main namespace deprecation

---
 pyterrier/__init__.py | 49 +++++++------------------------------------
 pyterrier/utils.py    | 37 ++++++++++++++++++++++++++++++++
 2 files changed, 44 insertions(+), 42 deletions(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index dc5392ee..5c98eb40 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -89,7 +89,7 @@ def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, log
         java.add_option(opt)
     terrier.set_terrier_version(version)
     terrier.set_helper_version(helper_version)
-    set_tqdm(tqdm)
+    utils.set_tqdm(tqdm)
 
     # TODO: missing no_download. Is this something like pt.java.set_offline(True)?
 
@@ -101,49 +101,12 @@ def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, log
         set_property("terrier.mvn.coords", pkgs_string)
 
 
-def set_tqdm(type=None):
-    """
-        Set the tqdm progress bar type that Pyterrier will use internally.
-        Many PyTerrier transformations can be expensive to apply in some settings - users can
-        view progress by using the verbose=True kwarg to many classes, such as BatchRetrieve.
-
-        The `tqdm <https://tqdm.github.io/>`_ progress bar can be made prettier when using appropriately configured Jupyter notebook setups.
-        We use this automatically when Google Colab is detected.
-
-        Allowable options for type are:
-
-         - `'tqdm'`: corresponds to the standard text progresss bar, ala `from tqdm import tqdm`.
-         - `'notebook'`: corresponds to a notebook progress bar, ala `from tqdm.notebook import tqdm`
-         - `'auto'`: allows tqdm to decide on the progress bar type, ala `from tqdm.auto import tqdm`. Note that this works fine on Google Colab, but not on Jupyter unless the `ipywidgets have been installed <https://ipywidgets.readthedocs.io/en/stable/user_install.html>`_.
-    """
-    global tqdm
-
-    import sys
-    if type is None:
-        if 'google.colab' in sys.modules:
-            type = 'notebook'
-        else:
-            type = 'tqdm'
-    
-    if type == 'tqdm':
-        from tqdm import tqdm as bartype
-        tqdm = bartype
-    elif type == 'notebook':
-        from tqdm.notebook import tqdm as bartype
-        tqdm = bartype
-    elif type == 'auto':
-        from tqdm.auto import tqdm as bartype
-        tqdm = bartype
-    else:
-        raise ValueError("Unknown tqdm type %s" % str(type))
-    tqdm.pandas()
-
-
 # deprecated functions explored to the main namespace, which will be removed in a future version
 logging = deprecated(version='0.11.0', reason="use pt.java.set_log_level(...) instead")(java.set_log_level)
 version = deprecated(version='0.11.0', reason="use pt.terrier.version() instead")(terrier.version)
 check_version = deprecated(version='0.11.0', reason="use pt.terrier.check_version(...) instead")(terrier.check_version)
 extend_classpath = deprecated(version='0.11.0', reason="use pt.terrier.extend_classpath(...) instead")(terrier.extend_classpath)
+set_tqdm = deprecated(version='0.11.0', reason="use pt.utils.set_tqdm(...) instead")(utils.set_tqdm)
 
 
 # Additional setup performed in a function to avoid polluting the namespace with other imports like platform
@@ -158,7 +121,7 @@ def _():
     from pyterrier.apply import _apply
     globals()['apply'] = _apply()
 
-    set_tqdm()
+    utils.set_tqdm()
 _()
 
 __all__ = [
@@ -169,6 +132,8 @@ def _():
     'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve', 'IndexFactory', 'set_property', 'set_properties',
     'run', 'rewrite', 'index', 'FilesIndexer', 'TRECCollectionIndexer', 'DFIndexer', 'DFIndexUtils', 'IterDictIndexer',
     'IndexingType', 'TerrierStemmer', 'TerrierStopwords', 'TerrierTokeniser',
-    'HOME_DIR', 'IndexRef', 'ApplicationSetup', 'properties', 'init', 'set_tqdm', 'version', 'check_version',
-    'logging', 'extend_classpath',
+    'HOME_DIR', 'IndexRef', 'ApplicationSetup', 'properties', 'init',
+
+    # Deprecated:
+    'logging', 'version', 'check_version', 'extend_classpath', 'set_tqdm',
 ]
diff --git a/pyterrier/utils.py b/pyterrier/utils.py
index 8e643751..90f206d4 100644
--- a/pyterrier/utils.py
+++ b/pyterrier/utils.py
@@ -1,3 +1,4 @@
+import sys
 from typing import Callable, Tuple
 import platform
 from functools import wraps
@@ -6,6 +7,7 @@
 import pandas as pd
 from collections import defaultdict
 from deprecated import deprecated
+import pyterrier as pt
 
 
 @deprecated(version="0.9")
@@ -124,3 +126,38 @@ def is_windows() -> bool:
 
 def noop(*args, **kwargs):
     pass
+
+
+def set_tqdm(type=None):
+    """
+        Set the tqdm progress bar type that Pyterrier will use internally.
+        Many PyTerrier transformations can be expensive to apply in some settings - users can
+        view progress by using the verbose=True kwarg to many classes, such as BatchRetrieve.
+
+        The `tqdm <https://tqdm.github.io/>`_ progress bar can be made prettier when using appropriately configured Jupyter notebook setups.
+        We use this automatically when Google Colab is detected.
+
+        Allowable options for type are:
+
+         - `'tqdm'`: corresponds to the standard text progresss bar, ala `from tqdm import tqdm`.
+         - `'notebook'`: corresponds to a notebook progress bar, ala `from tqdm.notebook import tqdm`
+         - `'auto'`: allows tqdm to decide on the progress bar type, ala `from tqdm.auto import tqdm`. Note that this works fine on Google Colab, but not on Jupyter unless the `ipywidgets have been installed <https://ipywidgets.readthedocs.io/en/stable/user_install.html>`_.
+    """
+    if type is None:
+        if 'google.colab' in sys.modules:
+            type = 'notebook'
+        else:
+            type = 'tqdm'
+    
+    if type == 'tqdm':
+        from tqdm import tqdm as bartype
+        pt.tqdm = bartype
+    elif type == 'notebook':
+        from tqdm.notebook import tqdm as bartype
+        pt.tqdm = bartype
+    elif type == 'auto':
+        from tqdm.auto import tqdm as bartype
+        pt.tqdm = bartype
+    else:
+        raise ValueError(f"Unknown tqdm type {type}")
+    pt.tqdm.pandas()

From 460098005cb2a224511c79051635b72127aa95ac Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 20:00:54 +0100
Subject: [PATCH 076/153] doesn't look like we need global properties

---
 pyterrier/__init__.py          | 13 +++++++------
 pyterrier/terrier/java.py      |  2 +-
 pyterrier/terrier/retriever.py |  8 --------
 3 files changed, 8 insertions(+), 15 deletions(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index 5c98eb40..7e54240e 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -9,7 +9,7 @@
 from pyterrier.java import started, redirect_stdouterr # for backward compat, maybe remove/deprecate some day?
 
 from pyterrier import terrier
-from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve, IndexFactory, set_property, set_properties, run, rewrite, index, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
+from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve, IndexFactory, run, rewrite, index, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
 
 from pyterrier import anserini
 from pyterrier import cache
@@ -36,7 +36,6 @@
 # will be set in terrier.java._post_init once java is loaded
 IndexRef = None
 ApplicationSetup = None
-properties = None
 
 
 @java.before_init
@@ -98,7 +97,7 @@ def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, log
     # Import other java packages
     if packages:
         pkgs_string = ",".join(packages)
-        set_property("terrier.mvn.coords", pkgs_string)
+        terrier.set_property("terrier.mvn.coords", pkgs_string)
 
 
 # deprecated functions explored to the main namespace, which will be removed in a future version
@@ -107,6 +106,8 @@ def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, log
 check_version = deprecated(version='0.11.0', reason="use pt.terrier.check_version(...) instead")(terrier.check_version)
 extend_classpath = deprecated(version='0.11.0', reason="use pt.terrier.extend_classpath(...) instead")(terrier.extend_classpath)
 set_tqdm = deprecated(version='0.11.0', reason="use pt.utils.set_tqdm(...) instead")(utils.set_tqdm)
+set_property = deprecated(version='0.11.0', reason="use pt.terrier.set_property(...) instead")(terrier.set_property)
+set_properties = deprecated(version='0.11.0', reason="use pt.terrier.set_properties(...) instead")(terrier.set_properties)
 
 
 # Additional setup performed in a function to avoid polluting the namespace with other imports like platform
@@ -129,11 +130,11 @@ def _():
     'text', 'transformer', 'datasets', 'get_dataset', 'find_datasets', 'list_datasets', 'Experiment', 'GridScan',
     'GridSearch', 'KFoldGridSearch', 'Evaluate',
     'utils', 'Utils', 'Transformer', 'Estimator', 'Indexer', 'started', 'redirect_stdouterr',
-    'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve', 'IndexFactory', 'set_property', 'set_properties',
+    'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve', 'IndexFactory',
     'run', 'rewrite', 'index', 'FilesIndexer', 'TRECCollectionIndexer', 'DFIndexer', 'DFIndexUtils', 'IterDictIndexer',
     'IndexingType', 'TerrierStemmer', 'TerrierStopwords', 'TerrierTokeniser',
-    'HOME_DIR', 'IndexRef', 'ApplicationSetup', 'properties', 'init',
+    'HOME_DIR', 'IndexRef', 'ApplicationSetup', 'properties', 'init', 'apply',
 
     # Deprecated:
-    'logging', 'version', 'check_version', 'extend_classpath', 'set_tqdm',
+    'logging', 'version', 'check_version', 'extend_classpath', 'set_tqdm', 'set_property', 'set_properties',
 ]
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 925ba830..39fa0fde 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -119,7 +119,7 @@ def _post_init(jnius):
     }
 
     pt.IndexRef = J.IndexRef
-    _properties = pt.properties = pt.java.J.Properties()
+    _properties = pt.java.J.Properties()
     pt.ApplicationSetup = J.ApplicationSetup
     J.ApplicationSetup.bootstrapInitialisation(_properties)
 
diff --git a/pyterrier/terrier/retriever.py b/pyterrier/terrier/retriever.py
index ebd09a30..5c8d0cf7 100644
--- a/pyterrier/terrier/retriever.py
+++ b/pyterrier/terrier/retriever.py
@@ -9,12 +9,6 @@
 from concurrent.futures import ThreadPoolExecutor
 import pyterrier as pt
 
-def importProps():
-    from pyterrier import properties as props
-    # Make import global
-    globals()["props"] = props
-props = None
-
 _matchops = ["#combine", "#uw", "#1", "#tag", "#prefix", "#band", "#base64", "#syn"]
 def _matchop(query):
     for m in _matchops:
@@ -213,8 +207,6 @@ def __init__(self, index_location, controls=None, properties=None, metadata=["do
         self.RequestContextMatching = pt.java.autoclass("org.terrier.python.RequestContextMatching")
         self.search_context = {}
 
-        if props is None:
-            importProps()
         for key, value in self.properties.items():
             self.appSetup.setProperty(str(key), str(value))
         

From 3ddb3868dd227bed8d0dd0f4b92e37bc14e77548 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 21:07:01 +0100
Subject: [PATCH 077/153] further cleanup of main namespace

---
 pyterrier/__init__.py          | 76 +++-------------------------------
 pyterrier/datasets.py          |  8 +---
 pyterrier/java/__init__.py     |  4 +-
 pyterrier/java/_init.py        | 66 ++++++++++++++++++++++++++++-
 pyterrier/parallel.py          |  8 ----
 pyterrier/terrier/__init__.py  |  4 +-
 pyterrier/terrier/java.py      |  2 +-
 pyterrier/terrier/retriever.py |  7 ++--
 pyterrier/terrier/rewrite.py   |  2 +-
 tests/base.py                  | 20 ++++-----
 tests/test_init.py             |  4 +-
 tests/test_iterdictindex.py    |  6 +--
 12 files changed, 95 insertions(+), 112 deletions(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index 7e54240e..753ef928 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -6,7 +6,7 @@
 from pyterrier.transformer import Transformer, Estimator, Indexer
 
 from pyterrier import java
-from pyterrier.java import started, redirect_stdouterr # for backward compat, maybe remove/deprecate some day?
+from pyterrier.java import redirect_stdouterr # for backward compat, maybe remove/deprecate some day?
 
 from pyterrier import terrier
 from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve, IndexFactory, run, rewrite, index, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
@@ -30,77 +30,13 @@
 # old name
 Utils = utils
 
-# will be set in java._post_init once java is loaded
-HOME_DIR = None
-
 # will be set in terrier.java._post_init once java is loaded
 IndexRef = None
-ApplicationSetup = None
-
-
-@java.before_init
-def init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, logging='WARN', home_dir=None, boot_packages=[], tqdm=None, no_download=False,helper_version = None):
-    """
-    Function necessary to be called before Terrier classes and methods can be used.
-    Loads the Terrier .jar file and imports classes. Also finds the correct version of Terrier to download if no version is specified.
-
-    Args:
-        version(str): Which version of Terrier to download. Default is `None`.
-
-         * If None, find the newest Terrier released version in MavenCentral and download it.
-         * If `"snapshot"`, will download the latest build from `Jitpack <https://jitpack.io/>`_.
-
-        mem(str): Maximum memory allocated for the Java virtual machine heap in MB. Corresponds to java `-Xmx` commandline argument. Default is 1/4 of physical memory.
-        boot_packages(list(str)): Extra maven package coordinates files to load before starting Java. Default=`[]`. There is more information about loading packages in the `Terrier documentation <https://github.com/terrier-org/terrier-core/blob/5.x/doc/terrier_develop.md>`_
-        packages(list(str)): Extra maven package coordinates files to load, using the Terrier classloader. Default=`[]`. See also `boot_packages` above.
-        jvm_opts(list(str)): Extra options to pass to the JVM. Default=`[]`. For instance, you may enable Java assertions by setting `jvm_opts=['-ea']`
-        redirect_io(boolean): If True, the Java `System.out` and `System.err` will be redirected to Pythons sys.out and sys.err. Default=True.
-        logging(str): the logging level to use:
-
-         * Can be one of `'INFO'`, `'DEBUG'`, `'TRACE'`, `'WARN'`, `'ERROR'`. The latter is the quietest.
-         * Default is `'WARN'`.
-
-        home_dir(str): the home directory to use. Default to PYTERRIER_HOME environment variable.
-        tqdm: The `tqdm <https://tqdm.github.io/>`_ instance to use for progress bars within PyTerrier. Defaults to tqdm.tqdm. Available options are `'tqdm'`, `'auto'` or `'notebook'`.
-        helper_version(str): Which version of the helper.
-
-   
-    **Locating the Terrier .jar file:** PyTerrier is not tied to a specific version of Terrier and will automatically locate and download a recent Terrier .jar file. However, inevitably, some functionalities will require more recent Terrier versions. 
-    
-     * If set, PyTerrier uses the `version` init kwarg to determine the .jar file to look for.
-     * If the `version` init kwarg is not set, Terrier will query MavenCentral to determine the latest Terrier release.
-     * If `version` is set to `"snapshot"`, the latest .jar file build derived from the `Terrier Github repository <https://github.com/terrier-org/terrier-core/>`_ will be downloaded from `Jitpack <https://jitpack.io/>`_.
-     * Otherwise the local (`~/.mvn`) and MavenCentral repositories are searched for the jar file at the given version.
-    In this way, the default setting is to download the latest release of Terrier from MavenCentral. The user is also able to use a locally installed copy in their private Maven repository, or track the latest build of Terrier from Jitpack.
-    
-    If you wish to run PyTerrier in an offline enviroment, you should ensure that the "terrier-assemblies-{your version}-jar-with-dependencies.jar" and "terrier-python-helper-{your helper version}.jar"
-    are in the  "~/.pyterrier" (if they are not present, they will be downloaded the first time). Then you should set their versions when calling ``init()`` function. For example:
-    ``pt.init(version = 5.5, helper_version = "0.0.6")``.
-    """
-
-    # Set the corresponding options
-    java.set_memory_limit(mem)
-    java.set_redirect_io(redirect_io)
-    java.set_log_level(logging)
-    for package in boot_packages:
-        java.add_package(*package.split(':')) # format: org:package:version:filetype (where version and filetype are optional)
-    for opt in jvm_opts:
-        java.add_option(opt)
-    terrier.set_terrier_version(version)
-    terrier.set_helper_version(helper_version)
-    utils.set_tqdm(tqdm)
-
-    # TODO: missing no_download. Is this something like pt.java.set_offline(True)?
-
-    java.init()
-
-    # Import other java packages
-    if packages:
-        pkgs_string = ",".join(packages)
-        terrier.set_property("terrier.mvn.coords", pkgs_string)
 
 
 # deprecated functions explored to the main namespace, which will be removed in a future version
+init = deprecated(version='0.11.0', reason='No longer needed, java is started automatically. To force initialization early, use pt.java.init() instead.')(java.legacy_init)
+started = deprecated(version='0.11.0', reason="use pt.java.started() instead")(java.started)
 logging = deprecated(version='0.11.0', reason="use pt.java.set_log_level(...) instead")(java.set_log_level)
 version = deprecated(version='0.11.0', reason="use pt.terrier.version() instead")(terrier.version)
 check_version = deprecated(version='0.11.0', reason="use pt.terrier.check_version(...) instead")(terrier.check_version)
@@ -129,12 +65,12 @@ def _():
     'java', 'terrier', 'anserini', 'cache', 'debug', 'io', 'measures', 'model', 'new', 'ltr', 'parallel', 'pipelines',
     'text', 'transformer', 'datasets', 'get_dataset', 'find_datasets', 'list_datasets', 'Experiment', 'GridScan',
     'GridSearch', 'KFoldGridSearch', 'Evaluate',
-    'utils', 'Utils', 'Transformer', 'Estimator', 'Indexer', 'started', 'redirect_stdouterr',
+    'utils', 'Utils', 'Transformer', 'Estimator', 'Indexer', 'redirect_stdouterr',
     'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve', 'IndexFactory',
     'run', 'rewrite', 'index', 'FilesIndexer', 'TRECCollectionIndexer', 'DFIndexer', 'DFIndexUtils', 'IterDictIndexer',
     'IndexingType', 'TerrierStemmer', 'TerrierStopwords', 'TerrierTokeniser',
-    'HOME_DIR', 'IndexRef', 'ApplicationSetup', 'properties', 'init', 'apply',
+    'IndexRef', 'ApplicationSetup', 'properties', 'apply',
 
     # Deprecated:
-    'logging', 'version', 'check_version', 'extend_classpath', 'set_tqdm', 'set_property', 'set_properties',
+    'init', 'started', 'logging', 'version', 'check_version', 'extend_classpath', 'set_tqdm', 'set_property', 'set_properties',
 ]
diff --git a/pyterrier/datasets.py b/pyterrier/datasets.py
index 19c8939b..d6e47265 100644
--- a/pyterrier/datasets.py
+++ b/pyterrier/datasets.py
@@ -119,13 +119,7 @@ def __init__(self, name, locations):
         self.password = None
 
     def _configure(self, **kwargs):
-        from os.path import expanduser
-        pt_home = pt.HOME_DIR
-        if pt_home is None:
-            from os.path import expanduser
-            userhome = expanduser("~")
-            pt_home = os.path.join(userhome, ".pyterrier")
-        self.corpus_home = os.path.join(pt_home, "corpora", self.name)
+        self.corpus_home = os.path.join(pt.io.pyterrier_home(), "corpora", self.name)
         if 'user' in kwargs:
             self.user = kwargs['user']
             self.password = kwargs['password']
diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index 9d23b28d..bbcfa62c 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -1,5 +1,5 @@
 from pyterrier.java import mavenresolver
-from pyterrier.java._init import init, started, parallel_init, parallel_init_args, required, required_raise, before_init, autoclass, cast
+from pyterrier.java._init import init, started, parallel_init, parallel_init_args, required, required_raise, before_init, autoclass, cast, legacy_init
 from pyterrier.java import config
 from pyterrier.java.config import configure
 from pyterrier.java.utils import redirect_stdouterr, bytebuffer_to_array, JavaClasses, J, add_jar, add_package, set_memory_limit, set_redirect_io, add_option, set_log_level
@@ -8,5 +8,5 @@
     'add_jar', 'add_option', 'add_package', 'autoclass', 'before_init', 'bytebuffer_to_array', 'cast', 'config',
     'configure', 'init', 'J', 'JavaClasses', 'mavenresolver', 'parallel_init', 'parallel_init_args',
     'redirect_stdouterr', 'required', 'required_raise', 'set_log_level', 'set_memory_limit', 'set_redirect_io',
-    'started',
+    'started', 'legacy_init',
 ]
diff --git a/pyterrier/java/_init.py b/pyterrier/java/_init.py
index a69338ee..4ebdfe94 100644
--- a/pyterrier/java/_init.py
+++ b/pyterrier/java/_init.py
@@ -11,7 +11,6 @@
 
 
 def _post_init(jnius):
-    pt.HOME_DIR = pt.io.pyterrier_home()
     pt.autoclass = jnius.autoclass
     pt.cast = jnius.cast
 
@@ -128,7 +127,7 @@ def before_init(fn: Optional[Callable] = None) -> Union[Callable, bool]:
     @wraps(fn)
     def _wrapper(*args, **kwargs):
         if started():
-            raise RuntimeError(f'You need to call pt.java.init() required before you can call {fn}')
+            raise RuntimeError(f'You can only call {fn} before either you start using java or call pt.java.init()')
         return fn(*args, **kwargs)
     return _wrapper
 
@@ -166,3 +165,66 @@ def cast(*args, **kwargs):
     """
     import jnius
     return jnius.cast(*args, **kwargs)
+
+
+@before_init
+def legacy_init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, logging='WARN', home_dir=None, boot_packages=[], tqdm=None, no_download=False,helper_version = None):
+    """
+    Function that can be called before Terrier classes and methods can be used.
+    Loads the Terrier .jar file and imports classes. Also finds the correct version of Terrier to download if no version is specified.
+
+    Args:
+        version(str): Which version of Terrier to download. Default is `None`.
+
+         * If None, find the newest Terrier released version in MavenCentral and download it.
+         * If `"snapshot"`, will download the latest build from `Jitpack <https://jitpack.io/>`_.
+
+        mem(str): Maximum memory allocated for the Java virtual machine heap in MB. Corresponds to java `-Xmx` commandline argument. Default is 1/4 of physical memory.
+        boot_packages(list(str)): Extra maven package coordinates files to load before starting Java. Default=`[]`. There is more information about loading packages in the `Terrier documentation <https://github.com/terrier-org/terrier-core/blob/5.x/doc/terrier_develop.md>`_
+        packages(list(str)): Extra maven package coordinates files to load, using the Terrier classloader. Default=`[]`. See also `boot_packages` above.
+        jvm_opts(list(str)): Extra options to pass to the JVM. Default=`[]`. For instance, you may enable Java assertions by setting `jvm_opts=['-ea']`
+        redirect_io(boolean): If True, the Java `System.out` and `System.err` will be redirected to Pythons sys.out and sys.err. Default=True.
+        logging(str): the logging level to use:
+
+         * Can be one of `'INFO'`, `'DEBUG'`, `'TRACE'`, `'WARN'`, `'ERROR'`. The latter is the quietest.
+         * Default is `'WARN'`.
+
+        home_dir(str): the home directory to use. Default to PYTERRIER_HOME environment variable.
+        tqdm: The `tqdm <https://tqdm.github.io/>`_ instance to use for progress bars within PyTerrier. Defaults to tqdm.tqdm. Available options are `'tqdm'`, `'auto'` or `'notebook'`.
+        helper_version(str): Which version of the helper.
+
+   
+    **Locating the Terrier .jar file:** PyTerrier is not tied to a specific version of Terrier and will automatically locate and download a recent Terrier .jar file. However, inevitably, some functionalities will require more recent Terrier versions. 
+    
+     * If set, PyTerrier uses the `version` init kwarg to determine the .jar file to look for.
+     * If the `version` init kwarg is not set, Terrier will query MavenCentral to determine the latest Terrier release.
+     * If `version` is set to `"snapshot"`, the latest .jar file build derived from the `Terrier Github repository <https://github.com/terrier-org/terrier-core/>`_ will be downloaded from `Jitpack <https://jitpack.io/>`_.
+     * Otherwise the local (`~/.mvn`) and MavenCentral repositories are searched for the jar file at the given version.
+    In this way, the default setting is to download the latest release of Terrier from MavenCentral. The user is also able to use a locally installed copy in their private Maven repository, or track the latest build of Terrier from Jitpack.
+    
+    If you wish to run PyTerrier in an offline enviroment, you should ensure that the "terrier-assemblies-{your version}-jar-with-dependencies.jar" and "terrier-python-helper-{your helper version}.jar"
+    are in the  "~/.pyterrier" (if they are not present, they will be downloaded the first time). Then you should set their versions when calling ``init()`` function. For example:
+    ``pt.init(version = 5.5, helper_version = "0.0.6")``.
+    """
+
+    # Set the corresponding options
+    pt.java.set_memory_limit(mem)
+    pt.java.set_redirect_io(redirect_io)
+    pt.java.set_log_level(logging)
+    for package in boot_packages:
+        pt.java.add_package(*package.split(':')) # format: org:package:version:filetype (where version and filetype are optional)
+    for opt in jvm_opts:
+        pt.java.add_option(opt)
+    pt.terrier.set_version(version)
+    pt.terrier.set_helper_version(helper_version)
+    if tqdm is not None:
+        pt.utils.set_tqdm(tqdm)
+
+    # TODO: missing no_download. Is this something like pt.java.set_offline(True)?
+
+    pt.java.init()
+
+    # Import other java packages
+    if packages:
+        pkgs_string = ",".join(packages)
+        pt.terrier.set_property("terrier.mvn.coords", pkgs_string)
diff --git a/pyterrier/parallel.py b/pyterrier/parallel.py
index d1dbadee..68989fb1 100644
--- a/pyterrier/parallel.py
+++ b/pyterrier/parallel.py
@@ -24,14 +24,6 @@ def new_init():
     p._backend._workers._initializer = new_init if callable(origin_init) else f_init
     return p
 
-def _pt_init(args):
-    import pyterrier as pt
-    if not pt.started():
-        pt.init(no_download=True, **args)
-    else:
-        from warnings import warn
-        warn("Avoiding reinit of PyTerrier")
-
 
 def _check_ray():
     try:
diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
index 72bac572..7edb7221 100644
--- a/pyterrier/terrier/__init__.py
+++ b/pyterrier/terrier/__init__.py
@@ -1,6 +1,6 @@
 # java stuff
 from pyterrier.terrier import java
-from pyterrier.terrier.java import configure, set_terrier_version, set_helper_version, enable_prf, extend_classpath, J, set_property, set_properties, run, version, check_version, check_helper_version
+from pyterrier.terrier.java import configure, set_version, set_helper_version, enable_prf, extend_classpath, J, set_property, set_properties, run, version, check_version, check_helper_version
 from pyterrier.terrier.retriever import BatchRetrieve, FeaturesBatchRetrieve
 from pyterrier.terrier.index_factory import IndexFactory
 from pyterrier.terrier import index
@@ -12,7 +12,7 @@
 
 __all__ = [
     # java stuff
-    'java', 'configure', 'set_terrier_version', 'set_helper_version', 'enable_prf', 'extend_classpath', 'J', 'version', 'check_version', 'check_helper_version',
+    'java', 'configure', 'set_version', 'set_helper_version', 'enable_prf', 'extend_classpath', 'J', 'version', 'check_version', 'check_helper_version',
 
     # retrieval
     'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve',
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 39fa0fde..825de166 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -18,7 +18,7 @@
 
 
 @pt.java.before_init
-def set_terrier_version(version: Optional[str] = None):
+def set_version(version: Optional[str] = None):
     configure['terrier_version'] = version
 
 
diff --git a/pyterrier/terrier/retriever.py b/pyterrier/terrier/retriever.py
index 5c8d0cf7..7927654d 100644
--- a/pyterrier/terrier/retriever.py
+++ b/pyterrier/terrier/retriever.py
@@ -197,7 +197,6 @@ def __init__(self, index_location, controls=None, properties=None, metadata=["do
         """
         super().__init__(kwargs)
         self.indexref = _parse_index_like(index_location)
-        self.appSetup = pt.java.autoclass('org.terrier.utility.ApplicationSetup')
         self.properties = _mergeDicts(BatchRetrieve.default_properties, properties)
         self.concurrentIL = pt.java.autoclass("org.terrier.structures.ConcurrentIndexLoader")
         if pt.terrier.check_version(5.5) and "SimpleDecorateProcess" not in self.properties["querying.processes"]:
@@ -208,7 +207,7 @@ def __init__(self, index_location, controls=None, properties=None, metadata=["do
         self.search_context = {}
 
         for key, value in self.properties.items():
-            self.appSetup.setProperty(str(key), str(value))
+            pt.terrier.J.ApplicationSetup.setProperty(str(key), str(value))
         
         self.controls = _mergeDicts(BatchRetrieve.default_controls, controls)
         if wmodel is not None:
@@ -287,7 +286,7 @@ def __setstate__(self, d):
         self.search_context = d["context"]
         self.properties.update(d["properties"])
         for key,value in d["properties"].items():
-            self.appSetup.setProperty(key, str(value))
+            pt.terrier.J.ApplicationSetup.setProperty(key, str(value))
 
     def _retrieve_one(self, row, input_results=None, docno_provided=False, docid_provided=False, scores_provided=False):
         rank = FIRST_RANK
@@ -645,7 +644,7 @@ def __setstate__(self, d):
         self.wmodel = d["wmodel"]
         self.properties.update(d["properties"])
         for key,value in d["properties"].items():
-            self.appSetup.setProperty(key, str(value))
+            pt.terrier.J.ApplicationSetup.setProperty(key, str(value))
         #TODO consider the context state?
 
     @staticmethod 
diff --git a/pyterrier/terrier/rewrite.py b/pyterrier/terrier/rewrite.py
index 88c3c53f..2c60f35d 100644
--- a/pyterrier/terrier/rewrite.py
+++ b/pyterrier/terrier/rewrite.py
@@ -198,7 +198,7 @@ def __init__(self, index_like, fb_terms=10, fb_docs=3, qeclass="org.terrier.quer
         self.indexref = pt.terrier.retriever._parse_index_like(index_like)
         self.properties = properties
         for k,v in properties.items():
-            pt.ApplicationSetup.setProperty(k, str(v))
+            pt.terrier.J.ApplicationSetup.setProperty(k, str(v))
         self.applytp = pt.terrier.J.ApplyTermPipeline()
         self.fb_terms = fb_terms
         self.fb_docs = fb_docs
diff --git a/tests/base.py b/tests/base.py
index 3e92b1e0..71dfa6c7 100644
--- a/tests/base.py
+++ b/tests/base.py
@@ -1,17 +1,16 @@
 import unittest
 import os
-import pyterrier as pt
-
 import tempfile
 import shutil
-import os
+import pyterrier as pt
 
 class BaseTestCase(unittest.TestCase):
     def __init__(self, *args, **kwargs):
         super(BaseTestCase, self).__init__(*args, **kwargs)
-        terrier_version = os.environ.get("TERRIER_VERSION", None)
-        terrier_helper_version = os.environ.get("TERRIER_HELPER_VERSION", None)
-        if not pt.started():
+        self.here = os.path.dirname(os.path.realpath(__file__))
+        if not pt.java.started():
+            terrier_version = os.environ.get("TERRIER_VERSION", None)
+            terrier_helper_version = os.environ.get("TERRIER_HELPER_VERSION", None)
 
             # display for debugging what is being used
             if terrier_version is not None:
@@ -19,10 +18,11 @@ def __init__(self, *args, **kwargs):
             if terrier_helper_version is not None:
                 print("Testing with Terrier Helper version " + terrier_helper_version)
             
-            pt.init(version=terrier_version, logging="DEBUG", helper_version=terrier_helper_version)
-            # jvm_opts=['-ea'] can be added here to ensure that all Java assertions are met
-        self.here = os.path.dirname(os.path.realpath(__file__))
-
+            pt.terrier.set_version(terrier_version)
+            pt.terrier.set_helper_version(terrier_helper_version)
+            pt.java.set_log_level("DEBUG")
+            # pt.java.add_option('-ea') can be added here to ensure that all Java assertions are met
+            pt.java.init()
 
     def skip_windows(self):
         if pt.utils.is_windows():
diff --git a/tests/test_init.py b/tests/test_init.py
index 7a8c71f2..e1219436 100644
--- a/tests/test_init.py
+++ b/tests/test_init.py
@@ -5,5 +5,5 @@
 class TestInit(BaseTestCase):
 
     def test_set_property(self):
-        pt.set_property("arbitrary.property", 40)
-        self.assertEqual("40", pt.ApplicationSetup.appProperties.getProperty("arbitrary.property", "none"))
+        pt.terrier.set_property("arbitrary.property", 40)
+        self.assertEqual("40", pt.terrier.J.ApplicationSetup.appProperties.getProperty("arbitrary.property", "none"))
diff --git a/tests/test_iterdictindex.py b/tests/test_iterdictindex.py
index 088364da..57742a72 100644
--- a/tests/test_iterdictindex.py
+++ b/tests/test_iterdictindex.py
@@ -188,7 +188,7 @@ def test_check_nostemmer_utf_toks(self):
         self.assertEqual("", index.getIndexProperty("termpipelines", "bla"))
         self.assertTrue("før" in index.getLexicon())
         # restore setting after test
-        pt.ApplicationSetup.setProperty("termpipelines", "Stopwords,PorterStemmer")
+        pt.terrier.J.ApplicationSetup.setProperty("termpipelines", "Stopwords,PorterStemmer")
 
     def test_check_customstops(self):
         if not pt.check_version("5.8"):
@@ -249,7 +249,7 @@ def test_check_nostemmer(self):
                 self.assertTrue("r" in index.getLexicon())
 
                 # restore setting after test
-                pt.ApplicationSetup.setProperty("termpipelines", "Stopwords,PorterStemmer")
+                pt.terrier.J.ApplicationSetup.setProperty("termpipelines", "Stopwords,PorterStemmer")
                 self.assertEqual("", index.getIndexProperty("termpipelines", "bla"))
 
     def test_check_weakstemmer(self):
@@ -267,7 +267,7 @@ def test_check_weakstemmer(self):
                 self.assertEqual(3, index.getDocumentIndex().getDocumentLength(0)) #ran money playing
                 index = pt.cast("org.terrier.structures.IndexOnDisk", index)
                 # restore setting after test
-                pt.ApplicationSetup.setProperty("termpipelines", "Stopwords,PorterStemmer")
+                pt.terrier.J.ApplicationSetup.setProperty("termpipelines", "Stopwords,PorterStemmer")
                 self.assertEqual("Stopwords,WeakPorterStemmer", index.getIndexProperty("termpipelines", "bla"))
 
 if __name__ == "__main__":

From 8ab397e9610ceb513f782539b57bfbf5683a5933 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 21:24:32 +0100
Subject: [PATCH 078/153] split out terrier stemmer, tokeniser, and stopwords

---
 pyterrier/terrier/__init__.py  |   6 +-
 pyterrier/terrier/index.py     | 166 +--------------------------------
 pyterrier/terrier/stemmer.py   |  87 +++++++++++++++++
 pyterrier/terrier/stopwords.py |  54 +++++++++++
 pyterrier/terrier/tokeniser.py |  35 +++++++
 5 files changed, 184 insertions(+), 164 deletions(-)
 create mode 100644 pyterrier/terrier/stemmer.py
 create mode 100644 pyterrier/terrier/stopwords.py
 create mode 100644 pyterrier/terrier/tokeniser.py

diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
index 7edb7221..928ce72f 100644
--- a/pyterrier/terrier/__init__.py
+++ b/pyterrier/terrier/__init__.py
@@ -3,6 +3,9 @@
 from pyterrier.terrier.java import configure, set_version, set_helper_version, enable_prf, extend_classpath, J, set_property, set_properties, run, version, check_version, check_helper_version
 from pyterrier.terrier.retriever import BatchRetrieve, FeaturesBatchRetrieve
 from pyterrier.terrier.index_factory import IndexFactory
+from pyterrier.terrier.stemmer import TerrierStemmer
+from pyterrier.terrier.tokeniser import TerrierTokeniser
+from pyterrier.terrier.stopwords import TerrierStopwords
 from pyterrier.terrier import index
 from pyterrier.terrier.index import TerrierIndexer, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
 from pyterrier.terrier import rewrite
@@ -18,11 +21,12 @@
     'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve',
 
     # indexing
-    'index', 'TerrierIndexer', 'FilesIndexer', 'TRECCollectionIndexer', 'DFIndexer', 'DFIndexUtils', 'IterDictIndexer', 'IndexingType', 'TerrierStemmer', 'TerrierStopwords', 'TerrierTokeniser',
+    'index', 'TerrierIndexer', 'FilesIndexer', 'TRECCollectionIndexer', 'DFIndexer', 'DFIndexUtils', 'IterDictIndexer', 'IndexingType',
 
     # rewriting
     'rewrite',
 
     # misc
+    'TerrierStemmer', 'TerrierStopwords', 'TerrierTokeniser',
     'IndexFactory', 'set_property', 'set_properties', 'run', 
 ]
diff --git a/pyterrier/terrier/index.py b/pyterrier/terrier/index.py
index 967d930f..8bb416d2 100644
--- a/pyterrier/terrier/index.py
+++ b/pyterrier/terrier/index.py
@@ -18,10 +18,12 @@
 from typing import List, Dict, Union, Any
 import more_itertools
 import pyterrier as pt
+from pyterrier.terrier.stemmer import TerrierStemmer
+from pyterrier.terrier.tokeniser import TerrierTokeniser
+from pyterrier.terrier.stopwords import TerrierStopwords
 
 
 # TODO: ensure functions are properly marked up with @pt.java.required
-# TODO: some stuff doesn't seem like they belong here, like TerrierTokeniser and TerrierStopwords. Move this out?
 
 
 # These classes are only defined after pt.java.init()
@@ -409,168 +411,6 @@ class IndexingType(enum.Enum):
     SINGLEPASS = 2 #: A single-pass indexing regime, which builds an inverted index directly. No direct index structure is created. Typically is faster than classical indexing.
     MEMORY = 3 #: An in-memory index. No persistent index is created.
 
-_stemmer_cache = {}
-class TerrierStemmer(Enum):
-    """
-        This enum provides an API for the stemmers available in Terrier. The stemming configuration is saved in the index
-        and loaded at retrieval time. `Snowball <https://snowballstem.org/>`_ stemmers for various languages 
-        `are available in Terrier <http://terrier.org/docs/current/javadoc/org/terrier/terms/package-summary.html>`_.
-
-        It can also be used to access the stemmer::
-
-            stemmer = pt.TerrierStemmer.porter
-            stemmed_word = stemmer.stem('abandoned')
-
-    """
-    none = 'none' #: Apply no stemming
-    porter = 'porter' #: Apply Porter's English stemmer
-    weakporter = 'weakporter' #: Apply a weak version of Porter's English stemmer
-    # available snowball stemmers in Terrier
-    danish = 'danish' #: Snowball Danish stemmer
-    finnish = 'finnish' #: Snowball Finnish stemmer
-    german = 'german' #: Snowball German stemmer
-    hungarian = 'hungarian' #: Snowball Hungarian stemmer
-    norwegian = 'norwegian' #: Snowball Norwegian stemmer
-    portugese = 'portugese' #: Snowball Portuguese stemmer
-    swedish = 'swedish' #: Snowball Swedish stemmer
-    turkish = 'turkish' #: Snowball Turkish stemmer
-
-    @staticmethod
-    def _to_obj(this):
-        try:
-            return TerrierStemmer(this)
-        except ValueError:
-            return this
-
-    @staticmethod
-    def _to_class(this):
-        if this is None or this == TerrierStemmer.none:
-            return None
-        if this == TerrierStemmer.porter:
-            return 'PorterStemmer'
-        if this == TerrierStemmer.weakporter:
-            return 'WeakPorterStemmer'
-        
-        # snowball stemmers
-        if this == TerrierStemmer.danish:
-            return 'DanishSnowballStemmer'
-        if this == TerrierStemmer.finnish:
-            return 'FinnishSnowballStemmer'
-        if this == TerrierStemmer.german:
-            return 'GermanSnowballStemmer'
-        if this == TerrierStemmer.hungarian:
-            return 'HungarianSnowballStemmer'
-        if this == TerrierStemmer.norwegian:
-            return 'NorwegianSnowballStemmer'
-        if this == TerrierStemmer.portugese:
-            return 'PortugueseSnowballStemmer'
-        if this == TerrierStemmer.swedish:
-            return 'SwedishSnowballStemmer'
-        if this == TerrierStemmer.turkish:
-            return 'TurkishSnowballStemmer'
-
-        if isinstance(this, str):
-            return this
-
-    @pt.java.required
-    def stem(self, tok):
-        if self not in _stemmer_cache:
-            clz_name = self._to_class(self)
-            if clz_name is None:
-                class NoOpStem():
-                    def stem(self, word):
-                        return word
-                _stemmer_cache[self] = NoOpStem()
-            else:
-                if '.' not in clz_name:
-                    clz_name = f'org.terrier.terms.{clz_name}'
-                 # stemmers are termpipeline objects, and these have chained constructors
-                 # pass None to use the appropriate constructor
-                _stemmer_cache[self] = pt.java.autoclass(clz_name)(None)
-        return _stemmer_cache[self].stem(tok)
-
-class TerrierStopwords(Enum):
-    """
-        This enum provides an API for the stopword configuration used during indexing with Terrier
-    """
-
-    none = 'none' #: No Stemming
-    terrier = 'terrier' #: Apply Terrier's standard stopword list
-    custom = 'custom' #: Apply PyTerrierCustomStopwordList.Indexing for indexing, and PyTerrierCustomStopwordList.Retrieval for retrieval
-
-    @staticmethod
-    def _to_obj(this):
-        if isinstance(this, list):
-            rtr = TerrierStopwords('custom')
-            return rtr, list(this)
-        try:
-            return TerrierStopwords(this), None
-        except ValueError:
-            return this, None
-        
-    @staticmethod
-    def _indexing_config(this, stopword_list : Union[List[str], None], termpipelines : List[str], properties : Dict[str,str], hooks : List):
-        if this is None or this == TerrierStopwords.none:
-            pass
-        if this == TerrierStopwords.terrier:
-            termpipelines.append('Stopwords')
-        if this == TerrierStopwords.custom:
-            assert pt.terrier.check_version("5.8"), "Terrier 5.8 required"
-            assert stopword_list is not None, "expected to receive a stopword list"
-
-            stopword_list_esc = [t.replace(",", "\\,") for t in stopword_list ]
-
-            properties["pyterrier.stopwords"]  = ",".join(stopword_list_esc)
-            termpipelines.append('org.terrier.python.PyTerrierCustomStopwordList$Indexing')
-
-            # this hook updates the index's properties to handle the python stopwords list
-            def _hook(pyindexer, index):
-                pindex = pt.java.cast("org.terrier.structures.PropertiesIndex", index)
-                # store the stopwords into the Index's properties
-                pindex.setIndexProperty("pyterrier.stopwords", ",".join(stopword_list_esc))
-
-                # change the stopwords list implementation: the Indexing variant obtains
-                # stopwords from the global ApplicationSetup properties, while the 
-                # Retrieval variant obtains them from the *Index* properties instead
-                pindex.setIndexProperty("termpipelines", 
-                    pindex.getIndexProperty('termpipelines', None)
-                    .replace('org.terrier.python.PyTerrierCustomStopwordList$Indexing',
-                             'org.terrier.python.PyTerrierCustomStopwordList$Retrieval'))
-                pindex.flush()
-            hooks.append(_hook)
-
-class TerrierTokeniser(Enum):
-    """
-        This enum provides an API for the tokeniser configuration used during indexing with Terrier.
-    """
-
-    whitespace = 'whitespace' #: Tokenise on whitespace only
-    english = 'english' #: Terrier's standard tokeniser, designed for English
-    utf = 'utf' #: A variant of Terrier's standard tokeniser, similar to English, but with UTF support.
-    twitter = 'twitter' #: Like utf, but keeps hashtags etc
-    identity = 'identity' #: Performs no tokenisation - strings are kept as is. 
-
-    @staticmethod
-    def _to_obj(this):
-        try:
-            return TerrierTokeniser(this)
-        except ValueError:
-            return this
-
-    @staticmethod
-    def _to_class(this):
-        if this == TerrierTokeniser.whitespace:
-            return 'WhitespaceTokeniser'
-        if this == TerrierTokeniser.english:
-            return 'EnglishTokeniser'
-        if this == TerrierTokeniser.utf:
-            return 'UTFTokeniser'
-        if this == TerrierTokeniser.twitter:
-            return 'UTFTwitterTokeniser'
-        if this == TerrierTokeniser.identity:
-            return 'IdentityTokeniser'
-        if isinstance(this, str):
-            return this
 
 class TerrierIndexer:
     """
diff --git a/pyterrier/terrier/stemmer.py b/pyterrier/terrier/stemmer.py
new file mode 100644
index 00000000..3b111994
--- /dev/null
+++ b/pyterrier/terrier/stemmer.py
@@ -0,0 +1,87 @@
+from enum import Enum
+import pyterrier as pt
+
+
+_stemmer_cache = {}
+
+
+class TerrierStemmer(Enum):
+    """
+        This enum provides an API for the stemmers available in Terrier. The stemming configuration is saved in the index
+        and loaded at retrieval time. `Snowball <https://snowballstem.org/>`_ stemmers for various languages 
+        `are available in Terrier <http://terrier.org/docs/current/javadoc/org/terrier/terms/package-summary.html>`_.
+
+        It can also be used to access the stemmer::
+
+            stemmer = pt.TerrierStemmer.porter
+            stemmed_word = stemmer.stem('abandoned')
+
+    """
+    none = 'none' #: Apply no stemming
+    porter = 'porter' #: Apply Porter's English stemmer
+    weakporter = 'weakporter' #: Apply a weak version of Porter's English stemmer
+    # available snowball stemmers in Terrier
+    danish = 'danish' #: Snowball Danish stemmer
+    finnish = 'finnish' #: Snowball Finnish stemmer
+    german = 'german' #: Snowball German stemmer
+    hungarian = 'hungarian' #: Snowball Hungarian stemmer
+    norwegian = 'norwegian' #: Snowball Norwegian stemmer
+    portugese = 'portugese' #: Snowball Portuguese stemmer
+    swedish = 'swedish' #: Snowball Swedish stemmer
+    turkish = 'turkish' #: Snowball Turkish stemmer
+
+    @staticmethod
+    def _to_obj(this):
+        try:
+            return TerrierStemmer(this)
+        except ValueError:
+            return this
+
+    @staticmethod
+    def _to_class(this):
+        if this is None or this == TerrierStemmer.none:
+            return None
+        if this == TerrierStemmer.porter:
+            return 'PorterStemmer'
+        if this == TerrierStemmer.weakporter:
+            return 'WeakPorterStemmer'
+        
+        # snowball stemmers
+        if this == TerrierStemmer.danish:
+            return 'DanishSnowballStemmer'
+        if this == TerrierStemmer.finnish:
+            return 'FinnishSnowballStemmer'
+        if this == TerrierStemmer.german:
+            return 'GermanSnowballStemmer'
+        if this == TerrierStemmer.hungarian:
+            return 'HungarianSnowballStemmer'
+        if this == TerrierStemmer.norwegian:
+            return 'NorwegianSnowballStemmer'
+        if this == TerrierStemmer.portugese:
+            return 'PortugueseSnowballStemmer'
+        if this == TerrierStemmer.swedish:
+            return 'SwedishSnowballStemmer'
+        if this == TerrierStemmer.turkish:
+            return 'TurkishSnowballStemmer'
+
+        if isinstance(this, str):
+            return this
+
+    @pt.java.required
+    def stem(self, tok):
+        if self not in _stemmer_cache:
+            clz_name = self._to_class(self)
+            if clz_name is None:
+                _stemmer_cache[self] = _NoOpStem()
+            else:
+                if '.' not in clz_name:
+                    clz_name = f'org.terrier.terms.{clz_name}'
+                 # stemmers are termpipeline objects, and these have chained constructors
+                 # pass None to use the appropriate constructor
+                _stemmer_cache[self] = pt.java.autoclass(clz_name)(None)
+        return _stemmer_cache[self].stem(tok)
+
+
+class _NoOpStem():
+    def stem(self, word):
+        return word
diff --git a/pyterrier/terrier/stopwords.py b/pyterrier/terrier/stopwords.py
new file mode 100644
index 00000000..0934f552
--- /dev/null
+++ b/pyterrier/terrier/stopwords.py
@@ -0,0 +1,54 @@
+from enum import Enum
+from typing import Union, List, Dict
+import pyterrier as pt
+
+
+class TerrierStopwords(Enum):
+    """
+        This enum provides an API for the stopword configuration used during indexing with Terrier
+    """
+
+    none = 'none' #: No Stopwords
+    terrier = 'terrier' #: Apply Terrier's standard stopword list
+    custom = 'custom' #: Apply PyTerrierCustomStopwordList.Indexing for indexing, and PyTerrierCustomStopwordList.Retrieval for retrieval
+
+    @staticmethod
+    def _to_obj(this):
+        if isinstance(this, list):
+            rtr = TerrierStopwords('custom')
+            return rtr, list(this)
+        try:
+            return TerrierStopwords(this), None
+        except ValueError:
+            return this, None
+        
+    @staticmethod
+    def _indexing_config(this, stopword_list : Union[List[str], None], termpipelines : List[str], properties : Dict[str,str], hooks : List):
+        if this is None or this == TerrierStopwords.none:
+            pass
+        if this == TerrierStopwords.terrier:
+            termpipelines.append('Stopwords')
+        if this == TerrierStopwords.custom:
+            assert pt.terrier.check_version("5.8"), "Terrier 5.8 required"
+            assert stopword_list is not None, "expected to receive a stopword list"
+
+            stopword_list_esc = [t.replace(",", "\\,") for t in stopword_list ]
+
+            properties["pyterrier.stopwords"]  = ",".join(stopword_list_esc)
+            termpipelines.append('org.terrier.python.PyTerrierCustomStopwordList$Indexing')
+
+            # this hook updates the index's properties to handle the python stopwords list
+            def _hook(pyindexer, index):
+                pindex = pt.java.cast("org.terrier.structures.PropertiesIndex", index)
+                # store the stopwords into the Index's properties
+                pindex.setIndexProperty("pyterrier.stopwords", ",".join(stopword_list_esc))
+
+                # change the stopwords list implementation: the Indexing variant obtains
+                # stopwords from the global ApplicationSetup properties, while the 
+                # Retrieval variant obtains them from the *Index* properties instead
+                pindex.setIndexProperty("termpipelines", 
+                    pindex.getIndexProperty('termpipelines', None)
+                    .replace('org.terrier.python.PyTerrierCustomStopwordList$Indexing',
+                             'org.terrier.python.PyTerrierCustomStopwordList$Retrieval'))
+                pindex.flush()
+            hooks.append(_hook)
diff --git a/pyterrier/terrier/tokeniser.py b/pyterrier/terrier/tokeniser.py
new file mode 100644
index 00000000..73f59bf8
--- /dev/null
+++ b/pyterrier/terrier/tokeniser.py
@@ -0,0 +1,35 @@
+from enum import Enum
+
+
+class TerrierTokeniser(Enum):
+    """
+        This enum provides an API for the tokeniser configuration used during indexing with Terrier.
+    """
+
+    whitespace = 'whitespace' #: Tokenise on whitespace only
+    english = 'english' #: Terrier's standard tokeniser, designed for English
+    utf = 'utf' #: A variant of Terrier's standard tokeniser, similar to English, but with UTF support.
+    twitter = 'twitter' #: Like utf, but keeps hashtags etc
+    identity = 'identity' #: Performs no tokenisation - strings are kept as is. 
+
+    @staticmethod
+    def _to_obj(this):
+        try:
+            return TerrierTokeniser(this)
+        except ValueError:
+            return this
+
+    @staticmethod
+    def _to_class(this):
+        if this == TerrierTokeniser.whitespace:
+            return 'WhitespaceTokeniser'
+        if this == TerrierTokeniser.english:
+            return 'EnglishTokeniser'
+        if this == TerrierTokeniser.utf:
+            return 'UTFTokeniser'
+        if this == TerrierTokeniser.twitter:
+            return 'UTFTwitterTokeniser'
+        if this == TerrierTokeniser.identity:
+            return 'IdentityTokeniser'
+        if isinstance(this, str):
+            return this

From a9583f936e9c3a78fb56b1dfcf07fa30480c6b6f Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 21:47:24 +0100
Subject: [PATCH 079/153] removed autoclasses from index.py

---
 pyterrier/java/utils.py    |  2 ++
 pyterrier/terrier/index.py | 42 +++++++++++++++++---------------------
 pyterrier/terrier/java.py  | 11 ++++++++++
 3 files changed, 32 insertions(+), 23 deletions(-)

diff --git a/pyterrier/java/utils.py b/pyterrier/java/utils.py
index b4391553..3cfd9b37 100644
--- a/pyterrier/java/utils.py
+++ b/pyterrier/java/utils.py
@@ -151,4 +151,6 @@ def __getattr__(self, key):
     'HashMap': 'java.util.HashMap',
     'Arrays': 'java.util.Arrays',
     'Array': 'java.lang.reflect.Array',
+    'String': 'java.lang.String',
+    'List': 'java.util.List',
 })
diff --git a/pyterrier/terrier/index.py b/pyterrier/terrier/index.py
index 8bb416d2..1bf1d0d1 100644
--- a/pyterrier/terrier/index.py
+++ b/pyterrier/terrier/index.py
@@ -1,11 +1,9 @@
 """
 This file contains all the indexers.
 """
-
 from enum import Enum
 import pandas as pd
 import os
-import enum
 import json
 import tempfile
 import contextlib
@@ -50,8 +48,6 @@ def _java_post_init(jnius):
     global TQDMSizeCollection
 
     class DocListIterator(jnius.PythonJavaClass):
-        dpl_class = pt.java.autoclass("org.terrier.structures.indexing.DocumentPostingList")
-        tuple_class = pt.java.autoclass("org.terrier.structures.collections.MapEntry")
         __javainterfaces__ = [
             'java/util/Iterator',
         ]
@@ -70,7 +66,7 @@ def pyDictToMap(a_dict): #returns Map<String,String>
             return rtr
 
         def pyDictToMapEntry(self,doc_dict : Dict[str,Any]): #returns Map.Entry<Map<String,String>, DocumentPostingList>>
-            dpl = DocListIterator.dpl_class()
+            dpl = pt.terrier.J.DocumentPostingList()
             # this works around a bug in the counting of doc lengths in Tr 5.7
             if self.tr57:
                 for t, tf in doc_dict["toks"].items():
@@ -82,7 +78,7 @@ def pyDictToMapEntry(self,doc_dict : Dict[str,Any]): #returns Map.Entry<Map<Stri
             
             # we cant make the toks column into the metaindex as it isnt a string. remove it.
             del doc_dict["toks"]
-            return DocListIterator.tuple_class(DocListIterator.pyDictToMap(doc_dict), dpl)
+            return pt.terrier.J.MapEntry(DocListIterator.pyDictToMap(doc_dict), dpl)
 
         @jnius.java_method('()Z')
         def hasNext(self):
@@ -106,7 +102,7 @@ class TQDMCollection(jnius.PythonJavaClass):
 
         def __init__(self, collection):
             super(TQDMCollection, self).__init__()
-            assert isinstance(collection, pt.java.autoclass("org.terrier.indexing.MultiDocumentFileCollection"))
+            assert isinstance(collection, pt.terrier.J.MultiDocumentFileCollection)
             self.collection = collection
             size = self.collection.FilesToProcess.size()
             self.pbar = pt.tqdm(total=size, unit="files")
@@ -267,15 +263,15 @@ def createCollection(files_path : List[str], coll_type : str = 'trec', props = {
     for k, v in props.items():
         _props[k] = v
     pt.terrier.J.ApplicationSetup.getProperties().putAll(_props)
-    cls_string = pt.java.autoclass("java.lang.String")._class
-    cls_list = pt.java.autoclass("java.util.List")._class
+    cls_string = pt.java.J.String._class
+    cls_list = pt.java.J.List._class
     if len(files_path) == 0:
         raise ValueError("list files_path cannot be empty")
     asList = createAsList(files_path)
-    colObj = pt.java.autoclass("org.terrier.indexing.CollectionFactory").loadCollections(
+    colObj = pt.terrier.J.CollectionFactory.loadCollections(
         collectionClzName,
         [cls_list, cls_string, cls_string, cls_string],
-        [asList, pt.java.autoclass("org.terrier.utility.TagSet").TREC_DOC_TAGS, "", ""])
+        [asList, pt.terrier.J.TagSet.TREC_DOC_TAGS, "", ""])
     return colObj
 
 @pt.java.required
@@ -401,7 +397,7 @@ def createAsList(files_path : Union[str, List[str]]):
     return asList
 
 # Using enum class create enumerations
-class IndexingType(enum.Enum):
+class IndexingType(Enum):
     """
         This enum is used to determine the type of index built by Terrier. The default is CLASSIC. For more information,
         see the relevant Terrier `indexer <https://terrier-core.readthedocs.io/en/latest/indexer_details.html>`_
@@ -708,11 +704,11 @@ def index(self, text, *args, **kwargs):
         self.meta = meta_lengths
         
         # make a Collection class for Terrier
-        javaDocCollection = pt.java.autoclass("org.terrier.python.CollectionFromDocumentIterator")(collectionIterator)
+        javaDocCollection = pt.terrier.J.CollectionFromDocumentIterator(collectionIterator)
         if self.verbose:
             javaDocCollection = TQDMSizeCollection(javaDocCollection, len(text)) 
         index = self.createIndexer()
-        index.index(pt.java.autoclass("org.terrier.python.PTUtils").makeCollection(javaDocCollection))
+        index.index(pt.terrier.J.PTUtils.makeCollection(javaDocCollection))
         global lastdoc
         lastdoc = None
         javaDocCollection.close()
@@ -865,7 +861,7 @@ def index(self, it, fields=('text',), meta=None, meta_lengths=None, threads=None
 
             # we need to prevent collectionIterator from being GCd
             collectionIterator = FlatJSONDocumentIterator(self._filter_iterable(it, fields))
-            javaDocCollection = pt.java.autoclass("org.terrier.python.CollectionFromDocumentIterator")(collectionIterator)
+            javaDocCollection = pt.terrier.J.CollectionFromDocumentIterator(collectionIterator)
             # remove once 5.7 is now the minimum version
             indexer.index(javaDocCollection if pt.terrier.check_version("5.7") else [javaDocCollection])
             global lastdoc
@@ -880,7 +876,7 @@ def index(self, it, fields=('text',), meta=None, meta_lengths=None, threads=None
         else:
             indexref = pt.terrier.J.IndexRef.of(self.index_dir + "/data.properties")
             if len(self.cleanup_hooks) > 0:
-                sindex = pt.java.autoclass("org.terrier.structures.Index")
+                sindex = pt.terrier.J.Index
                 sindex.setIndexLoadingProfileAsRetrieval(False)
                 index = pt.terrier.IndexFactory.of(indexref)
                 for hook in self.cleanup_hooks:
@@ -907,11 +903,11 @@ def index(self, it, fields=('text',), meta=None, meta_lengths=None):
             meta(list[str]): keys to be considered as metdata
             meta_lengths(list[int]): length of metadata, defaults to 512 characters
         """
-        CollectionFromDocumentIterator = pt.java.autoclass("org.terrier.python.CollectionFromDocumentIterator")
-        JsonlDocumentIterator = pt.java.autoclass("org.terrier.python.JsonlDocumentIterator")
+        CollectionFromDocumentIterator = pt.terrier.J.CollectionFromDocumentIterator
+        JsonlDocumentIterator = pt.terrier.J.JsonlDocumentIterator
         if self.pretokenised:
-            JsonlTokenisedIterator = pt.java.autoclass("org.terrier.python.JsonlPretokenisedIterator")
-        ParallelIndexer = pt.java.autoclass("org.terrier.python.ParallelIndexer")
+            JsonlTokenisedIterator = pt.terrier.J.JsonlPretokenisedIterator
+        ParallelIndexer = pt.terrier.J.ParallelIndexer
 
         if meta is not None:
             warn('specifying meta and meta_lengths in IterDictIndexer.index() is deprecated, use constructor instead', DeprecationWarning, 2)
@@ -969,7 +965,7 @@ def index(self, it, fields=('text',), meta=None, meta_lengths=None):
         indexref = pt.terrier.J.IndexRef.of(self.index_dir + "/data.properties")
         
         if len(self.cleanup_hooks) > 0:
-            sindex = pt.java.autoclass("org.terrier.structures.Index")
+            sindex = pt.terrier.J.Index
             sindex.setIndexLoadingProfileAsRetrieval(False)
             index = pt.terrier.IndexFactory.of(indexref)
             sindex.setIndexLoadingProfileAsRetrieval(True)
@@ -1068,14 +1064,14 @@ def index(self, files_path : Union[str,List[str]]):
         _TaggedDocumentSetup(self.meta, self.meta_tags)
 
         colObj = createCollection(files_path, self.collection)
-        if self.verbose and isinstance(colObj, pt.java.autoclass("org.terrier.indexing.MultiDocumentFileCollection")):
+        if self.verbose and isinstance(colObj, pt.terrier.J.MultiDocumentFileCollection):
             colObj = pt.java.cast("org.terrier.indexing.MultiDocumentFileCollection", colObj)
             colObj = TQDMCollection(colObj)
         # remove once 5.7 is now the minimum version
         if pt.terrier.check_version("5.7"):
             index.index(colObj)
         else:
-            index.index(pt.java.autoclass("org.terrier.python.PTUtils").makeCollection(colObj))
+            index.index(pt.terrier.J.PTUtils.makeCollection(colObj))
         global lastdoc
         lastdoc = None
         colObj.close()
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 825de166..6adbe4b1 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -386,6 +386,17 @@ def check_helper_version(min):
     'Collection': 'org.terrier.indexing.Collection',
     'StructureMerger': 'org.terrier.structures.merging.StructureMerger',
     'BlockStructureMerger': 'org.terrier.structures.merging.BlockStructureMerger',
+    'DocumentPostingList': 'org.terrier.structures.indexing.DocumentPostingList',
+    'MapEntry': 'org.terrier.structures.collections.MapEntry',
+    'MultiDocumentFileCollection': 'org.terrier.indexing.MultiDocumentFileCollection',
+    'CollectionFactory': 'org.terrier.indexing.CollectionFactory',
+    'TagSet': 'org.terrier.utility.TagSet',
+    'CollectionFromDocumentIterator': 'org.terrier.python.CollectionFromDocumentIterator',
+    'PTUtils': 'org.terrier.python.PTUtils',
+    'Index': 'org.terrier.structures.Index',
+    'JsonlDocumentIterator': 'org.terrier.python.JsonlDocumentIterator',
+    'JsonlPretokenisedIterator': 'org.terrier.python.JsonlPretokenisedIterator',
+    'ParallelIndexer': 'org.terrier.python.ParallelIndexer',
 
     # PRF
     'TerrierQLParser': 'org.terrier.querying.TerrierQLParser',

From 348c48e24639171e8527047aec1f45254aee35be Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Mon, 29 Jul 2024 21:53:06 +0100
Subject: [PATCH 080/153] added missing pt.java.required in index.py

---
 pyterrier/terrier/index.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/pyterrier/terrier/index.py b/pyterrier/terrier/index.py
index 1bf1d0d1..7aaac2e9 100644
--- a/pyterrier/terrier/index.py
+++ b/pyterrier/terrier/index.py
@@ -21,9 +21,6 @@
 from pyterrier.terrier.stopwords import TerrierStopwords
 
 
-# TODO: ensure functions are properly marked up with @pt.java.required
-
-
 # These classes are only defined after pt.java.init()
 DocListIterator = None
 PythonListIterator = None
@@ -335,6 +332,7 @@ def treccollection2textgen(
         yield rtr
     
 
+@pt.java.required
 def _TaggedDocumentSetup(
         meta : Dict[str,int],  #mapping from meta-key to length
         meta_tags : Dict[str,str] #mapping from meta-key to tag
@@ -356,6 +354,7 @@ def _TaggedDocumentSetup(
     pt.terrier.J.ApplicationSetup.setProperty("TaggedDocument.abstracts.tags.casesensitive", "false")
 
 
+@pt.java.required
 def _FileDocumentSetup(   
         meta : Dict[str,int],  #mapping from meta-key to length
         meta_tags : Dict[str,str] #mapping from meta-key to tag
@@ -381,7 +380,7 @@ def _FileDocumentSetup(
 
 
 
-
+@pt.java.required
 def createAsList(files_path : Union[str, List[str]]):
     """
     Helper method to be used by child indexers to add files to Java List
@@ -423,6 +422,7 @@ class TerrierIndexer:
             "trec.collection.class": "TRECCollection",
     }
 
+    @pt.java.required
     def __init__(self, index_path : str, *args, 
             blocks : bool = False, 
             overwrite: bool = False, 
@@ -629,6 +629,7 @@ def get_column_lengths(df):
         meta2len = {k : 1 if math.isnan(l) else l for k, l in meta2len.items()}
         return meta2len
 
+    @pt.java.required
     @staticmethod
     def create_javaDocIterator(text, *args, **kwargs):
 
@@ -1104,6 +1105,7 @@ def __init__(self, index_path, *args, meta={"docno" : 20, "filename" : 512}, met
         self.meta_reverse = meta_reverse
         self.meta_tags = meta_tags
 
+    @pt.java.required
     def index(self, files_path : Union[str,List[str]]):
         """
         Index the specified files.

From ea19a42b7828932e6723b525750655c8711c6a27 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 10:15:48 +0100
Subject: [PATCH 081/153] flake8 extension to check for missing
 pt.java.required annotations

---
 .github/workflows/style.yml                   |  23 ++++++
 deleteme/x.pkl                                | Bin 0 -> 6835025 bytes
 extras/pyterrier-flake8-ext/README.md         |   4 +
 .../pyterrier_flake8_ext/__init__.py          |   5 ++
 .../pyterrier_flake8_ext/java_check.py        |  78 ++++++++++++++++++
 extras/pyterrier-flake8-ext/setup.py          |  39 +++++++++
 6 files changed, 149 insertions(+)
 create mode 100644 .github/workflows/style.yml
 create mode 100644 deleteme/x.pkl
 create mode 100644 extras/pyterrier-flake8-ext/README.md
 create mode 100644 extras/pyterrier-flake8-ext/pyterrier_flake8_ext/__init__.py
 create mode 100644 extras/pyterrier-flake8-ext/pyterrier_flake8_ext/java_check.py
 create mode 100644 extras/pyterrier-flake8-ext/setup.py

diff --git a/.github/workflows/style.yml b/.github/workflows/style.yml
new file mode 100644
index 00000000..f077a08c
--- /dev/null
+++ b/.github/workflows/style.yml
@@ -0,0 +1,23 @@
+name: Code Style Checks
+
+on:
+  push:
+    branches: [ master ]
+  pull_request:
+    branches: [ master ]
+
+jobs:
+  build:
+    runs-on: 'ubuntu-latest'
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.10'
+
+    - name: pt.java.required checks
+      run: |
+        pip install flake8 ./extras/pyterrier-flake8-ext/
+        flake8 pyterrier --select=PT --show-source --statistics --count
diff --git a/deleteme/x.pkl b/deleteme/x.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..285bee1c4062624cfe02a732d22ee55327510725
GIT binary patch
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literal 0
HcmV?d00001

diff --git a/extras/pyterrier-flake8-ext/README.md b/extras/pyterrier-flake8-ext/README.md
new file mode 100644
index 00000000..74002601
--- /dev/null
+++ b/extras/pyterrier-flake8-ext/README.md
@@ -0,0 +1,4 @@
+# flake8 extensions for PyTerrier
+
+`JavaCheck` applies rules to help ensure that the `@pt.java.required` annotation is applied
+where it needs to be. Raises `PT100`.
diff --git a/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/__init__.py b/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/__init__.py
new file mode 100644
index 00000000..60e201e5
--- /dev/null
+++ b/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/__init__.py
@@ -0,0 +1,5 @@
+__version__ = '0.0.1'
+
+from pyterrier_flake8_ext.java_check import JavaCheck
+
+__all__ = ['JavaCheck']
diff --git a/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/java_check.py b/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/java_check.py
new file mode 100644
index 00000000..4a3c97b5
--- /dev/null
+++ b/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/java_check.py
@@ -0,0 +1,78 @@
+import ast
+from typing import Generator, Tuple, Type, Any, List, Optional
+import pyterrier_flake8_ext
+
+
+class JavaCheck:
+    """McCabe cyclomatic complexity checker."""
+    name = 'javacheck'
+    version = pyterrier_flake8_ext.__version__
+    _code = 'PT100'
+    _error_tmpl = "PT100 {} uses java but is not annotated with @pt.java.required"
+
+    def __init__(self, tree: ast.AST):
+        self.tree = tree
+
+    def run(self) -> Generator[Tuple[int, int, str, Type[Any]], None, None]:
+        yield from self._run(self.tree)
+        # with open('deleteme/x.pkl', 'ab') as fout:
+        #     pickle.dump(self.tree, fout)
+        # print(self.tree)
+
+    def _run(self, node: ast.AST, context: List[str] = []) -> Generator[Tuple[int, int, str, Type[Any]], None, None]:
+        if isinstance(node, ast.FunctionDef):
+            if not self._has_java_required(node):
+                java_usage = self._uses_java(node)
+                if java_usage:
+                    text = self._error_tmpl.format('.'.join(context + [node.name]))
+                    yield java_usage.lineno, java_usage.col_offset, text, type(self)
+
+        if isinstance(node, ast.FunctionDef):
+            context = context + [node.name]
+
+        if isinstance(node, ast.ClassDef):
+            context = context + [node.name]
+
+        # we probably don't need such an extensive list when searching for functions
+        for attr in ['body', 'value', 'values', 'target', 'targets', 'left', 'right', 'elt', 'elts', 'keys', 'values', 'test', 'orelse', 'generators', 'func', 'args', 'keywords', 'format_spec']:
+            if hasattr(node, attr):
+                value = getattr(node, attr)
+                if value is not None:
+                    if hasattr(value, '__len__'):
+                        for child in value:
+                            yield from self._run(child, context)
+                    else:
+                        yield from self._run(value, context)
+
+    def _has_java_required(self, function: ast.FunctionDef) -> bool:
+        for decorator in function.decorator_list:
+            if isinstance(decorator, ast.Name):
+                if decorator.id == 'required':
+                    return True
+            elif isinstance(decorator, ast.Attribute):
+                if decorator.attr == 'required':
+                    return True
+        return False
+
+    def _uses_java(self, node: ast.AST) -> Optional[ast.AST]:
+        if isinstance(node, ast.Name):
+            if node.id in ('java', 'J'):
+                return node
+        elif isinstance(node, ast.Attribute):
+            if node.attr in ('java', 'J'):
+                return node
+
+        for attr in ['body', 'value', 'values', 'target', 'targets', 'left', 'right', 'elt', 'elts', 'keys', 'values', 'test', 'orelse', 'generators', 'func', 'args', 'keywords', 'format_spec']:
+            if hasattr(node, attr):
+                value = getattr(node, attr)
+                if value is not None:
+                    if hasattr(value, '__len__'):
+                        for child in value:
+                            res = self._uses_java(child)
+                            if res:
+                                return res
+                    else:
+                        res = self._uses_java(value)
+                        if res:
+                            return res
+        return None
diff --git a/extras/pyterrier-flake8-ext/setup.py b/extras/pyterrier-flake8-ext/setup.py
new file mode 100644
index 00000000..21c9572b
--- /dev/null
+++ b/extras/pyterrier-flake8-ext/setup.py
@@ -0,0 +1,39 @@
+import os
+from setuptools import setup
+
+def get_version(rel_path):
+    suffix = os.environ["PYTERRIER_VERSION_SUFFIX" ] if "PYTERRIER_VERSION_SUFFIX" in os.environ else ""
+    for line in open(rel_path):
+        if line.startswith('__version__'):
+            delim = '"' if '"' in line else "'"
+            return line.split(delim)[1] + suffix
+    else:
+        raise RuntimeError("Unable to find version string.")
+
+
+setup(
+    name="pyterrier-flake8-ext",
+    version=get_version("pyterrier_flake8_ext/__init__.py"),
+    author="Sean MacAvaney",
+    author_email='sean.macavaney@glasgow.ac.uk',
+    description="Terrier IR platform Python API",
+    project_urls={
+        'Documentation': 'https://pyterrier.readthedocs.io',
+        'Changelog': 'https://github.com/terrier-org/pyterrier/releases',
+        'Issue Tracker': 'https://github.com/terrier-org/pyterrier/issues',
+        'CI': 'https://github.com/terrier-org/pyterrier/actions',
+    },
+    long_description=open('README.md', 'rt').read(),
+    long_description_content_type="text/markdown",
+    url="https://github.com/terrier-org/pyterrier",
+    packages=['pyterrier_flake8_ext'],
+    entry_points={
+        'flake8.extension': [
+            'PT100 = pyterrier_flake8_ext:JavaCheck',
+        ]
+    },
+    install_requires=[
+        'flake8',
+    ],
+    python_requires='>=3.8',
+)

From 0b2704724830d43f0e047b91fe8b4c29721a9f71 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 10:17:46 +0100
Subject: [PATCH 082/153] for ease of reading in gh actions

---
 .github/workflows/style.yml | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/style.yml b/.github/workflows/style.yml
index f077a08c..96231ca8 100644
--- a/.github/workflows/style.yml
+++ b/.github/workflows/style.yml
@@ -17,7 +17,10 @@ jobs:
       with:
         python-version: '3.10'
 
-    - name: pt.java.required checks
+    - name: Install
       run: |
         pip install flake8 ./extras/pyterrier-flake8-ext/
-        flake8 pyterrier --select=PT --show-source --statistics --count
+
+    - name: pt.java.required checks
+      run: |
+        flake8 ./pyterrier --select=PT --show-source --statistics --count

From 80f8ab69b04b2da50150849dbe755d9381b7aee0 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 10:23:50 +0100
Subject: [PATCH 083/153] warning for the other case too: annotated but doesn't
 use java

---
 .../pyterrier_flake8_ext/java_check.py        | 20 +++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/java_check.py b/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/java_check.py
index 4a3c97b5..3dcf0fb0 100644
--- a/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/java_check.py
+++ b/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/java_check.py
@@ -7,25 +7,25 @@ class JavaCheck:
     """McCabe cyclomatic complexity checker."""
     name = 'javacheck'
     version = pyterrier_flake8_ext.__version__
-    _code = 'PT100'
-    _error_tmpl = "PT100 {} uses java but is not annotated with @pt.java.required"
+    _missing_anno_tmpl = "PT100 {} uses java but is not annotated with @pt.java.required"
+    _extra_anno_tmpl = "PT101 {} is annotated with @pt.java.required but doesn't use java"
 
     def __init__(self, tree: ast.AST):
         self.tree = tree
 
     def run(self) -> Generator[Tuple[int, int, str, Type[Any]], None, None]:
         yield from self._run(self.tree)
-        # with open('deleteme/x.pkl', 'ab') as fout:
-        #     pickle.dump(self.tree, fout)
-        # print(self.tree)
 
     def _run(self, node: ast.AST, context: List[str] = []) -> Generator[Tuple[int, int, str, Type[Any]], None, None]:
         if isinstance(node, ast.FunctionDef):
-            if not self._has_java_required(node):
-                java_usage = self._uses_java(node)
-                if java_usage:
-                    text = self._error_tmpl.format('.'.join(context + [node.name]))
-                    yield java_usage.lineno, java_usage.col_offset, text, type(self)
+            java_usage = self._uses_java(node)
+            has_java_anno = self._has_java_required(node)
+            if not has_java_anno and java_usage:
+                text = self._missing_anno_tmpl.format('.'.join(context + [node.name]))
+                yield java_usage.lineno, java_usage.col_offset, text, type(self)
+            elif has_java_anno and not java_usage:
+                text = self._extra_anno_tmpl.format('.'.join(context + [node.name]))
+                yield node.lineno, node.col_offset, text, type(self)
 
         if isinstance(node, ast.FunctionDef):
             context = context + [node.name]

From ed6947eaadea3f9375dcdae84b6b1777f782c937 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 11:56:55 +0100
Subject: [PATCH 084/153] fixed missing pt.java.required decorator and allow
 decorators on classes

---
 .../pyterrier_flake8_ext/java_check.py        |  61 ++++++--
 pyterrier/anserini/java.py                    |  30 ++--
 pyterrier/anserini/retriever.py               |   2 +-
 pyterrier/datasets.py                         |  15 +-
 pyterrier/java/_init.py                       | 131 ++++++++++--------
 pyterrier/java/utils.py                       |   5 +-
 pyterrier/terrier/index.py                    |  16 ++-
 pyterrier/terrier/index_factory.py            |   1 +
 pyterrier/terrier/java.py                     |   6 +-
 pyterrier/terrier/retriever.py                |  10 +-
 pyterrier/terrier/rewrite.py                  |   9 +-
 pyterrier/terrier/stopwords.py                |   4 +-
 pyterrier/utils.py                            |  20 ++-
 13 files changed, 202 insertions(+), 108 deletions(-)

diff --git a/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/java_check.py b/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/java_check.py
index 3dcf0fb0..3a35bf58 100644
--- a/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/java_check.py
+++ b/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/java_check.py
@@ -17,15 +17,33 @@ def run(self) -> Generator[Tuple[int, int, str, Type[Any]], None, None]:
         yield from self._run(self.tree)
 
     def _run(self, node: ast.AST, context: List[str] = []) -> Generator[Tuple[int, int, str, Type[Any]], None, None]:
-        if isinstance(node, ast.FunctionDef):
-            java_usage = self._uses_java(node)
-            has_java_anno = self._has_java_required(node)
-            if not has_java_anno and java_usage:
+        if isinstance(node, ast.FunctionDef) or isinstance(node, ast.ClassDef):
+            has_java_anno, hard_require = self._has_java_required(node)
+            java_usage = None
+            if isinstance(node, ast.FunctionDef):
+                for child in node.body:
+                    java_usage = self._uses_java(child)
+                    if java_usage: break
+            if isinstance(node, ast.ClassDef):
+                for child in node.body:
+                    if isinstance(child, ast.FunctionDef):
+                        for child2 in child.body:
+                            java_usage = self._uses_java(child2)
+                            if java_usage: break
+                    else:
+                        java_usage = self._uses_java(child)
+                    if java_usage: break
+            if not has_java_anno and java_usage and isinstance(node, ast.FunctionDef):
                 text = self._missing_anno_tmpl.format('.'.join(context + [node.name]))
                 yield java_usage.lineno, java_usage.col_offset, text, type(self)
-            elif has_java_anno and not java_usage:
+            elif has_java_anno and not java_usage and hard_require:
                 text = self._extra_anno_tmpl.format('.'.join(context + [node.name]))
                 yield node.lineno, node.col_offset, text, type(self)
+            if has_java_anno and isinstance(node, ast.ClassDef):
+                # simulate @required annotations on the methods defined by this class
+                for child in node.body:
+                    if isinstance(child, ast.FunctionDef):
+                        child.decorator_list.append(ast.Name('__required__soft'))
 
         if isinstance(node, ast.FunctionDef):
             context = context + [node.name]
@@ -33,7 +51,7 @@ def _run(self, node: ast.AST, context: List[str] = []) -> Generator[Tuple[int, i
         if isinstance(node, ast.ClassDef):
             context = context + [node.name]
 
-        # we probably don't need such an extensive list when searching for functions
+        # we probably don't need such an extensive list when searching for functions/classes
         for attr in ['body', 'value', 'values', 'target', 'targets', 'left', 'right', 'elt', 'elts', 'keys', 'values', 'test', 'orelse', 'generators', 'func', 'args', 'keywords', 'format_spec']:
             if hasattr(node, attr):
                 value = getattr(node, attr)
@@ -44,23 +62,38 @@ def _run(self, node: ast.AST, context: List[str] = []) -> Generator[Tuple[int, i
                     else:
                         yield from self._run(value, context)
 
-    def _has_java_required(self, function: ast.FunctionDef) -> bool:
+    def _has_java_required(self, function: ast.FunctionDef) -> Tuple[bool, bool]:
         for decorator in function.decorator_list:
             if isinstance(decorator, ast.Name):
-                if decorator.id == 'required':
-                    return True
+                if decorator.id in ('required', 'required_raise'):
+                    return True, True
+                if decorator.id == '__required__soft':
+                    return True, False
             elif isinstance(decorator, ast.Attribute):
-                if decorator.attr == 'required':
-                    return True
-        return False
+                if decorator.attr in ('required', 'required_raise'):
+                    return True, True
+                if decorator.attr == '__required__soft':
+                    return True, False
+        return False, False
 
     def _uses_java(self, node: ast.AST) -> Optional[ast.AST]:
         if isinstance(node, ast.Name):
-            if node.id in ('java', 'J'):
+            if node.id in ('autoclass', 'cast', 'J'):
                 return node
         elif isinstance(node, ast.Attribute):
-            if node.attr in ('java', 'J'):
+            if node.attr in ('autoclass', 'cast', 'J'):
+                return node
+        elif isinstance(node, ast.Import):
+            for name in node.names:
+                if name.name == 'jnius':
+                    return node
+        elif isinstance(node, ast.ImportFrom):
+            if node.module == 'jnius':
                 return node
+        elif isinstance(node, ast.ClassDef):
+            return None # break search at class definintions
+        elif isinstance(node, ast.FunctionDef):
+            return None # break search at function definintions
 
         for attr in ['body', 'value', 'values', 'target', 'targets', 'left', 'right', 'elt', 'elts', 'keys', 'values', 'test', 'orelse', 'generators', 'func', 'args', 'keywords', 'format_spec']:
             if hasattr(node, attr):
diff --git a/pyterrier/anserini/java.py b/pyterrier/anserini/java.py
index c45b3e62..4b5111bf 100644
--- a/pyterrier/anserini/java.py
+++ b/pyterrier/anserini/java.py
@@ -1,4 +1,5 @@
 import os
+import inspect
 from glob import glob
 from typing import Callable, Optional, Union
 from functools import wraps
@@ -68,22 +69,29 @@ def set_version(version: str):
 
 def required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
     """
-    Can act as either a standalone function or a function wrapper.
     Requires pyserini to be installed (raises error if not installed). If the JVM has not yet been started, it runs
     pt.java.init(), too, similar to pt.java.required().
+
+    Can be used as either a standalone function or a function/class @decorator. When used as a class decorator, it
+    is applied to all methods defined by the class.
     """
-    if fn is None:
+    if fn is None: # standalone call
         return required(pt.utils.noop)()
 
-    @wraps(fn)
-    def _wrapper(*args, **kwargs):
-        if not is_installed():
-            raise RuntimeError('pyserini required to use pyterrier.anserini. `pip install pyserini` and try again.')
-        if not pt.java.started():
-            pt.java.init()
-        return fn(*args, **kwargs)
-
-    return _wrapper
+    if isinstance(fn, type): # wrapping a class
+        for name in pt.utils.get_class_methods(fn):
+            setattr(fn, name, required(getattr(fn, name)))
+        return fn
+
+    else: # wrapping a function
+        @wraps(fn)
+        def _wrapper(*args, **kwargs):
+            if not is_installed():
+                raise RuntimeError('pyserini required to use pyterrier.anserini. `pip install pyserini` and try again.')
+            if not pt.java.started():
+                pt.java.init()
+            return fn(*args, **kwargs)
+        return _wrapper
 
 
 J = pt.java.JavaClasses({
diff --git a/pyterrier/anserini/retriever.py b/pyterrier/anserini/retriever.py
index 557fa5cd..a7b1b3ee 100644
--- a/pyterrier/anserini/retriever.py
+++ b/pyterrier/anserini/retriever.py
@@ -4,11 +4,11 @@
 from pyterrier.anserini.java import required
 
 
+@required
 class AnseriniBatchRetrieve(pt.Transformer):
     """
         Allows retrieval from an Anserini index. To use this class, you must first enable anserini using `pt.anserini.enable()`.
     """
-    @required
     def __init__(self, index_location, k=1000, wmodel="BM25", verbose=False):
         """
             Construct an AnseriniBatchRetrieve retrieve from pyserini.search.lucene.LuceneSearcher. 
diff --git a/pyterrier/datasets.py b/pyterrier/datasets.py
index d6e47265..a1cc242a 100644
--- a/pyterrier/datasets.py
+++ b/pyterrier/datasets.py
@@ -365,6 +365,14 @@ def info_url(self):
         return self.locations['info_url'] if "info_url" in self.locations else None
 
 
+@pt.java.required
+def _pt_tokeniser():
+    tokeniser = pt.terrier.J.Tokenizer.getTokeniser()
+    def pt_tokenise(text):
+        return ' '.join(tokeniser.getTokens(text))
+    return pt_tokenise
+
+
 class IRDSDataset(Dataset):
     def __init__(self, irds_id, defer_load=False):
         self._irds_id = irds_id
@@ -447,11 +455,8 @@ def get_topics(self, variant=None, tokenise_query=True):
 
         # apply pyterrier tokenisation (otherwise the queries may not play well with batchretrieve)
         if tokenise_query and 'query' in df:
-            pt.java.required() # java is required for this path
-            tokeniser = pt.terrier.J.Tokenizer.getTokeniser()
-            def pt_tokenise(text):
-                return ' '.join(tokeniser.getTokens(text))
-            df['query'] = df['query'].apply(pt_tokenise)
+            tokeniser = _pt_tokeniser()
+            df['query'] = df['query'].apply(tokeniser)
         return df
 
     def get_topics_lang(self):
diff --git a/pyterrier/java/_init.py b/pyterrier/java/_init.py
index 4ebdfe94..86b1a25f 100644
--- a/pyterrier/java/_init.py
+++ b/pyterrier/java/_init.py
@@ -1,3 +1,4 @@
+import inspect
 import os
 from warnings import warn
 from functools import wraps
@@ -10,6 +11,70 @@
 _started = False
 
 
+def started() -> bool:
+    """
+    Returns True if pt.java.init() has been called. Otherwise False.
+    """
+    return _started
+
+
+def required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
+    """
+    Requires the Java Virtual Machine to be started. If the JVM has not yet been started, it runs pt.java.init().
+
+    Can be used as either a standalone function or a function/class @decorator. When used as a class decorator, it
+    is applied to all methods defined by the class.
+    """
+    if fn is None: # standalone call
+        return required(pt.utils.noop)()
+
+    if isinstance(fn, type): # wrapping a class
+        for name in pt.utils.get_class_methods(fn):
+            setattr(fn, name, required(getattr(fn, name)))
+        return fn
+
+    else: # wrapping a function
+        @wraps(fn)
+        def _wrapper(*args, **kwargs):
+            if not started():
+                init()
+            return fn(*args, **kwargs)
+        return _wrapper
+
+
+def required_raise(fn: Optional[Callable] = None) -> Union[Callable, bool]:
+    """
+    Similar to `pt.java.required`, but raises an error if called before pt.java.init().
+    """
+    if fn is None:
+        return required_raise(pt.utils.noop)()
+
+    @wraps(fn)
+    def _wrapper(*args, **kwargs):
+        if not started():
+            raise RuntimeError(f'You need to call pt.java.init() required before you can call {fn}')
+        return fn(*args, **kwargs)
+    return _wrapper
+
+
+def before_init(fn: Optional[Callable] = None) -> Union[Callable, bool]:
+    """
+    If the JVM has already started, an error is raised.
+
+    Can be used as either a standalone function or a function decorator.
+    """
+    if fn is None:
+        return before_init(pt.utils.noop)()
+
+    @wraps(fn)
+    def _wrapper(*args, **kwargs):
+        if started():
+            raise RuntimeError(f'You can only call {fn} before either you start using java or call pt.java.init()')
+        return fn(*args, **kwargs)
+    return _wrapper
+
+
+@required_raise
 def _post_init(jnius):
     pt.autoclass = jnius.autoclass
     pt.cast = jnius.cast
@@ -59,7 +124,7 @@ def init() -> None:
     for jar in pt.java.configure['jars']:
         jnius_config.add_classpath(jar)
 
-    import jnius
+    import jnius # noqa: PT100 
     _started = True
 
     pt.java.set_log_level(pt.java.configure['log_level'])
@@ -76,62 +141,6 @@ def init() -> None:
         _post_init(jnius)
 
 
-def started() -> bool:
-    """
-    Returns True if pt.java.init() has been called. Otherwise False.
-    """
-    return _started
-
-
-def required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
-    """
-    Requires the Java Virtual Machine to be started. If the JVM has not yet been started, it runs pt.java.init().
-
-    Can be used as either a standalone function or a function decorator.
-    """
-    if fn is None:
-        return required(pt.utils.noop)()
-
-    @wraps(fn)
-    def _wrapper(*args, **kwargs):
-        if not started():
-            init()
-        return fn(*args, **kwargs)
-    return _wrapper
-
-
-def required_raise(fn: Optional[Callable] = None) -> Union[Callable, bool]:
-    """
-    Similar to `pt.java.required`, but raises an error if called before pt.java.init().
-    """
-    if fn is None:
-        return required_raise(pt.utils.noop)()
-
-    @wraps(fn)
-    def _wrapper(*args, **kwargs):
-        if not started():
-            raise RuntimeError(f'You need to call pt.java.init() required before you can call {fn}')
-        return fn(*args, **kwargs)
-    return _wrapper
-
-
-def before_init(fn: Optional[Callable] = None) -> Union[Callable, bool]:
-    """
-    If the JVM has already started, an error is raised.
-
-    Can be used as either a standalone function or a function decorator.
-    """
-    if fn is None:
-        return before_init(pt.utils.noop)()
-
-    @wraps(fn)
-    def _wrapper(*args, **kwargs):
-        if started():
-            raise RuntimeError(f'You can only call {fn} before either you start using java or call pt.java.init()')
-        return fn(*args, **kwargs)
-    return _wrapper
-
-
 def parallel_init(started: bool, configs: Dict[str, Dict[str, Any]]) -> None:
     if started:
         if not pt.java.started():
@@ -154,8 +163,8 @@ def autoclass(*args, **kwargs):
     """
     Wraps jnius.autoclass once java has started. Raises an error if called before pt.java.init() is called.
     """
-    import jnius
-    return jnius.autoclass(*args, **kwargs)
+    import jnius # noqa: PT100
+    return jnius.autoclass(*args, **kwargs) # noqa: PT100
 
 
 @required_raise
@@ -163,8 +172,8 @@ def cast(*args, **kwargs):
     """
     Wraps jnius.cast once java has started. Raises an error if called before pt.java.init() is called.
     """
-    import jnius
-    return jnius.cast(*args, **kwargs)
+    import jnius # noqa: PT100
+    return jnius.cast(*args, **kwargs) # noqa: PT100
 
 
 @before_init
diff --git a/pyterrier/java/utils.py b/pyterrier/java/utils.py
index 3cfd9b37..4e993d03 100644
--- a/pyterrier/java/utils.py
+++ b/pyterrier/java/utils.py
@@ -1,5 +1,5 @@
 import pyterrier as pt
-from pyterrier.java import required_raise, before_init, started, configure, mavenresolver
+from pyterrier.java import required_raise, required, before_init, started, configure, mavenresolver
 from typing import Dict, Optional
 
 
@@ -12,6 +12,7 @@ def _is_binary(f):
     return isinstance(f, (io.RawIOBase, io.BufferedIOBase))
 
 
+@required
 def redirect_stdouterr():
     from jnius import autoclass, PythonJavaClass, java_method
 
@@ -119,7 +120,7 @@ def set_log_level(level):
     if not started():
         configure['log_level'] = level
     else:
-        J.PTUtils.setLogLevel(level, None)
+        J.PTUtils.setLogLevel(level, None) # noqa: PT100 handled by started() check above
 
 
 class JavaClasses:
diff --git a/pyterrier/terrier/index.py b/pyterrier/terrier/index.py
index 7aaac2e9..f17ac849 100644
--- a/pyterrier/terrier/index.py
+++ b/pyterrier/terrier/index.py
@@ -44,6 +44,7 @@ def _java_post_init(jnius):
     global TQDMCollection
     global TQDMSizeCollection
 
+    @pt.java.required
     class DocListIterator(jnius.PythonJavaClass):
         __javainterfaces__ = [
             'java/util/Iterator',
@@ -93,7 +94,7 @@ def next(self):
             self.lastdoc = self.pyDictToMapEntry(doc_dict)
             return self.lastdoc
             
-
+    @pt.java.required
     class TQDMCollection(jnius.PythonJavaClass):
         __javainterfaces__ = ['org/terrier/indexing/Collection']
 
@@ -174,6 +175,7 @@ def next(self):
                 lastdoc = [text, meta]
             return lastdoc
 
+    @pt.java.required
     class FlatJSONDocumentIterator(jnius.PythonJavaClass):
         __javainterfaces__ = ['java/util/Iterator']
 
@@ -271,7 +273,7 @@ def createCollection(files_path : List[str], coll_type : str = 'trec', props = {
         [asList, pt.terrier.J.TagSet.TREC_DOC_TAGS, "", ""])
     return colObj
 
-@pt.java.required
+
 def treccollection2textgen(
         files : List[str], 
         meta : List[str] = ["docno"], 
@@ -407,6 +409,7 @@ class IndexingType(Enum):
     MEMORY = 3 #: An in-memory index. No persistent index is created.
 
 
+@pt.java.required
 class TerrierIndexer:
     """
     This is the super class for all of the Terrier-based indexers exposed by PyTerrier. It hosts common configuration
@@ -422,7 +425,6 @@ class TerrierIndexer:
             "trec.collection.class": "TRECCollection",
     }
 
-    @pt.java.required
     def __init__(self, index_path : str, *args, 
             blocks : bool = False, 
             overwrite: bool = False, 
@@ -632,6 +634,10 @@ def get_column_lengths(df):
     @pt.java.required
     @staticmethod
     def create_javaDocIterator(text, *args, **kwargs):
+        HashMap = pt.java.J.HashMap
+        TaggedDocument = pt.terrier.J.TaggedDocument
+        StringReader = pt.java.J.StringReader
+        tokeniser = pt.terrier.J.Tokeniser.getTokeniser()
 
         all_metadata = {}
         for i, arg in enumerate(args):
@@ -661,12 +667,12 @@ def create_javaDocIterator(text, *args, **kwargs):
         def convertDoc(text_row, meta_column):
             if text_row is None:
                 text_row = ""
-            hashmap = pt.java.J.HashMap()
+            hashmap = HashMap()
             for column, value in meta_column[1].items():
                 if value is None:
                     value = ""
                 hashmap.put(column, value)
-            return pt.terrier.J.TaggedDocument(pt.java.J.StringReader(text_row), hashmap, pt.terrier.J.Tokeniser.getTokeniser())
+            return TaggedDocument(StringReader(text_row), hashmap, tokeniser)
             
         df = pd.DataFrame.from_dict(all_metadata, orient="columns")
         lengths = DFIndexUtils.get_column_lengths(df)
diff --git a/pyterrier/terrier/index_factory.py b/pyterrier/terrier/index_factory.py
index b21b3469..ad1d1dbc 100644
--- a/pyterrier/terrier/index_factory.py
+++ b/pyterrier/terrier/index_factory.py
@@ -2,6 +2,7 @@
 import pyterrier as pt
 
 
+@pt.java.required
 class IndexFactory:
     """
     The ``of()`` method of this factory class allows to load a Terrier `Index <http://terrier.org/docs/current/javadoc/org/terrier/structures/Index.html>`_.
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 6adbe4b1..aaf89e36 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -68,6 +68,7 @@ def _pre_init(jnius_config):
     pt.java.add_package(TERRIER_PKG, "terrier-python-helper", helper_version)
 
 
+@pt.java.required_raise
 def _post_init(jnius):
     global _properties
 
@@ -135,6 +136,7 @@ def _new_indexref(s):
     return pt.IndexRef.of(s)
 
 
+@pt.java.required
 def _new_wmodel(b):
     return J.Serialization.deserialize(b, J.ApplicationSetup.getClass("org.terrier.matching.models.WeightingModel"))
 
@@ -180,6 +182,7 @@ def _index_ref_reduce(self):
 
 
 # handles the pickling of WeightingModel classes, which are themselves usually Serializable in Java
+@pt.java.required
 def _wmodel_reduce(self):
     serialized = bytes(J.Serialization.serialize(self))
     return (
@@ -202,6 +205,7 @@ def _callable_wmodel_reduce(self):
     )
 
 
+@pt.java.required
 def _index_add(self, other):
     fields_1 = self.getCollectionStatistics().getNumberOfFields()
     fields_2 = self.getCollectionStatistics().getNumberOfFields()
@@ -330,7 +334,6 @@ def version():
     return J.Version.VERSION
 
 
-@pt.java.required
 def check_version(min):
     """
         Returns True iff the underlying Terrier version is no older than the requested version.
@@ -343,7 +346,6 @@ def check_version(min):
     return current_ver >= min
 
 
-@pt.java.required
 def check_helper_version(min):
     """
         Returns True iff the underlying Terrier helper version is no older than the requested version.
diff --git a/pyterrier/terrier/retriever.py b/pyterrier/terrier/retriever.py
index 7927654d..51351c23 100644
--- a/pyterrier/terrier/retriever.py
+++ b/pyterrier/terrier/retriever.py
@@ -16,9 +16,11 @@ def _matchop(query):
             return True
     return False
 
+@pt.java.required
 def _function2wmodel(function):
     from jnius import PythonJavaClass, java_method
 
+    @pt.java.required
     class PythonWmodelFunction(PythonJavaClass):
         __javainterfaces__ = ['org/terrier/python/CallableWeightingModel$Callback']
 
@@ -51,6 +53,7 @@ def _mergeDicts(defaults, settings):
         KV.update(settings)
     return KV
 
+@pt.java.required
 def _parse_index_like(index_location):
     JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
     JI = pt.java.autoclass('org.terrier.structures.Index')
@@ -79,7 +82,6 @@ class BatchRetrieveBase(pt.Transformer, Symbol):
     Attributes:
         verbose(bool): If True transform method will display progress
     """
-    @pt.java.required
     def __init__(self, verbose=0, **kwargs):
         super().__init__(kwargs)
         self.verbose = verbose
@@ -113,6 +115,7 @@ def _from_dataset(dataset : Union[str,Dataset],
             kwargs = args
     return clz(indexref, **kwargs)   
                 
+@pt.java.required
 class BatchRetrieve(BatchRetrieveBase):
     """
     Use this class for retrieval by Terrier
@@ -454,7 +457,7 @@ def setControl(self, control, value):
         self.controls[str(control)] = str(value)
 
 
-
+@pt.java.required
 class TextIndexProcessor(pt.Transformer):
     '''
         Creates a new MemoryIndex based on the contents of documents passed to it.
@@ -533,6 +536,7 @@ def transform(self, topics_and_res):
             raise ValueError("returns attribute should be docs of queries")
         return inner_res
 
+
 class TextScorer(TextIndexProcessor):
     """
         A re-ranker class, which takes the queries and the contents of documents, indexes the contents of the documents using a MemoryIndex, and performs ranking of those documents with respect to the queries.
@@ -574,6 +578,8 @@ class TextScorer(TextIndexProcessor):
     def __init__(self, takes="docs", **kwargs):
         super().__init__(BatchRetrieve, takes=takes, **kwargs)
 
+
+@pt.java.required
 class FeaturesBatchRetrieve(BatchRetrieve):
     """
     Use this class for retrieval with multiple features
diff --git a/pyterrier/terrier/rewrite.py b/pyterrier/terrier/rewrite.py
index 2c60f35d..02601008 100644
--- a/pyterrier/terrier/rewrite.py
+++ b/pyterrier/terrier/rewrite.py
@@ -24,6 +24,7 @@ def _check_terrier_prf():
             break
     assert _terrier_prf_package_loaded, _terrier_prf_message
 
+@pt.java.required
 def tokenise(tokeniser : Union[str,TerrierTokeniser,FunctionType] = 'english', matchop=False) -> pt.Transformer:
     """
 
@@ -100,6 +101,7 @@ def reset() -> pt.Transformer:
     """
     return pt.apply.generic(lambda topics: pt.model.pop_queries(topics))
 
+@pt.java.required
 class SDM(pt.Transformer):
     '''
         Implements the sequential dependence model, which Terrier supports using its
@@ -110,7 +112,6 @@ class SDM(pt.Transformer):
         The original query is saved in the `"query_0"` column, which can be restored using `pt.rewrite.reset()`.
     '''
 
-    @pt.java.required
     def __init__(self, verbose = 0, remove_stopwords = True, prox_model = None, **kwargs):
         super().__init__(**kwargs)
         self.verbose = 0
@@ -174,7 +175,8 @@ class SequentialDependence(SDM):
         The original query is saved in the `"query_0"` column, which can be restored using `pt.rewrite.reset()`.
     '''
     pass
-    
+
+@pt.java.required
 class QueryExpansion(pt.Transformer):
     '''
         A base class for applying different types of query expansion using Terrier's classes.
@@ -187,7 +189,6 @@ class QueryExpansion(pt.Transformer):
          
     '''
 
-    @pt.java.required
     def __init__(self, index_like, fb_terms=10, fb_docs=3, qeclass="org.terrier.querying.QueryExpansion", verbose=0, properties={}, **kwargs):
         super().__init__(**kwargs)
         self.verbose = verbose
@@ -367,6 +368,7 @@ def __init__(self, *args, **kwargs):
         kwargs["qemodel"] = "KL"
         super().__init__(*args, **kwargs)
 
+@pt.java.required
 class RM3(QueryExpansion):
     '''
         Performs query expansion using RM3 relevance models. RM3 relies on an external Terrier plugin, 
@@ -424,6 +426,7 @@ def transform(self, queries_and_docs):
         self.qe.fbDocs = self.fb_docs
         return super().transform(queries_and_docs)
 
+@pt.java.required
 class AxiomaticQE(QueryExpansion):
     '''
         Performs query expansion using axiomatic query expansion. This class relies on an external Terrier plugin, 
diff --git a/pyterrier/terrier/stopwords.py b/pyterrier/terrier/stopwords.py
index 0934f552..eddb519e 100644
--- a/pyterrier/terrier/stopwords.py
+++ b/pyterrier/terrier/stopwords.py
@@ -23,12 +23,14 @@ def _to_obj(this):
             return this, None
         
     @staticmethod
+    @pt.java.required
     def _indexing_config(this, stopword_list : Union[List[str], None], termpipelines : List[str], properties : Dict[str,str], hooks : List):
         if this is None or this == TerrierStopwords.none:
             pass
         if this == TerrierStopwords.terrier:
             termpipelines.append('Stopwords')
         if this == TerrierStopwords.custom:
+            cst = pt.java.cast
             assert pt.terrier.check_version("5.8"), "Terrier 5.8 required"
             assert stopword_list is not None, "expected to receive a stopword list"
 
@@ -39,7 +41,7 @@ def _indexing_config(this, stopword_list : Union[List[str], None], termpipelines
 
             # this hook updates the index's properties to handle the python stopwords list
             def _hook(pyindexer, index):
-                pindex = pt.java.cast("org.terrier.structures.PropertiesIndex", index)
+                pindex = cst("org.terrier.structures.PropertiesIndex", index)
                 # store the stopwords into the Index's properties
                 pindex.setIndexProperty("pyterrier.stopwords", ",".join(stopword_list_esc))
 
diff --git a/pyterrier/utils.py b/pyterrier/utils.py
index 90f206d4..a119ab61 100644
--- a/pyterrier/utils.py
+++ b/pyterrier/utils.py
@@ -1,5 +1,6 @@
+import inspect
 import sys
-from typing import Callable, Tuple
+from typing import Callable, Tuple, List
 import platform
 from functools import wraps
 from importlib.metadata import EntryPoint
@@ -161,3 +162,20 @@ def set_tqdm(type=None):
     else:
         raise ValueError(f"Unknown tqdm type {type}")
     pt.tqdm.pandas()
+
+
+def get_class_methods(cls) -> List[str]:
+    """
+    Returns methods defined directly by the provided class. This will ignore inherited methods unless they are
+    overridden by this class.
+    """
+    all_attrs = inspect.getmembers(cls, predicate=inspect.isfunction)
+
+    base_attrs = set()
+    for base in cls.__bases__:
+        base_attrs.update(name for name, _ in inspect.getmembers(base, predicate=inspect.isfunction))
+    
+    # Filter out methods that are in base classes and not overridden in the subclass
+    class_methods = [name for name, func in all_attrs if name not in base_attrs or func.__qualname__.split('.')[0] == cls.__name__]
+    
+    return class_methods

From 8d13dff72df4aed1b43ed4441c1156d01a518408 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 12:12:17 +0100
Subject: [PATCH 085/153] python compatibility

---
 pyterrier/anserini/java.py |  4 ++--
 pyterrier/java/_init.py    |  4 ++--
 pyterrier/utils.py         | 12 +++++++++---
 3 files changed, 13 insertions(+), 7 deletions(-)

diff --git a/pyterrier/anserini/java.py b/pyterrier/anserini/java.py
index 4b5111bf..0dc3251c 100644
--- a/pyterrier/anserini/java.py
+++ b/pyterrier/anserini/java.py
@@ -79,8 +79,8 @@ def required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
         return required(pt.utils.noop)()
 
     if isinstance(fn, type): # wrapping a class
-        for name in pt.utils.get_class_methods(fn):
-            setattr(fn, name, required(getattr(fn, name)))
+        for name, value in pt.utils.get_class_methods(fn):
+            setattr(fn, name, required(value))
         return fn
 
     else: # wrapping a function
diff --git a/pyterrier/java/_init.py b/pyterrier/java/_init.py
index 86b1a25f..f6cef0bb 100644
--- a/pyterrier/java/_init.py
+++ b/pyterrier/java/_init.py
@@ -29,8 +29,8 @@ def required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
         return required(pt.utils.noop)()
 
     if isinstance(fn, type): # wrapping a class
-        for name in pt.utils.get_class_methods(fn):
-            setattr(fn, name, required(getattr(fn, name)))
+        for name, value in pt.utils.get_class_methods(fn):
+            setattr(fn, name, required(value))
         return fn
 
     else: # wrapping a function
diff --git a/pyterrier/utils.py b/pyterrier/utils.py
index a119ab61..47f51d72 100644
--- a/pyterrier/utils.py
+++ b/pyterrier/utils.py
@@ -1,6 +1,6 @@
 import inspect
 import sys
-from typing import Callable, Tuple, List
+from typing import Callable, Tuple, List, Callable
 import platform
 from functools import wraps
 from importlib.metadata import EntryPoint
@@ -164,7 +164,7 @@ def set_tqdm(type=None):
     pt.tqdm.pandas()
 
 
-def get_class_methods(cls) -> List[str]:
+def get_class_methods(cls) -> List[Tuple[str, Callable]]:
     """
     Returns methods defined directly by the provided class. This will ignore inherited methods unless they are
     overridden by this class.
@@ -176,6 +176,12 @@ def get_class_methods(cls) -> List[str]:
         base_attrs.update(name for name, _ in inspect.getmembers(base, predicate=inspect.isfunction))
     
     # Filter out methods that are in base classes and not overridden in the subclass
-    class_methods = [name for name, func in all_attrs if name not in base_attrs or func.__qualname__.split('.')[0] == cls.__name__]
+    class_methods = []
+    for name, func in all_attrs:
+        if name not in base_attrs or func.__qualname__.split('.')[0] == cls.__name__:
+            # bind classmethod and staticmethod functions to this class
+            if any(isinstance(func, c) for c in [classmethod, staticmethod]):
+                func = func.__get__(cls)
+            class_methods.append((name, func))
     
     return class_methods

From 5cce327b530f1df07325055e2aa55416673af3a6 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 12:47:39 +0100
Subject: [PATCH 086/153] the order of decorators is important?

---
 pyterrier/terrier/index.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyterrier/terrier/index.py b/pyterrier/terrier/index.py
index f17ac849..16141e4a 100644
--- a/pyterrier/terrier/index.py
+++ b/pyterrier/terrier/index.py
@@ -631,8 +631,8 @@ def get_column_lengths(df):
         meta2len = {k : 1 if math.isnan(l) else l for k, l in meta2len.items()}
         return meta2len
 
-    @pt.java.required
     @staticmethod
+    @pt.java.required
     def create_javaDocIterator(text, *args, **kwargs):
         HashMap = pt.java.J.HashMap
         TaggedDocument = pt.terrier.J.TaggedDocument

From e70402f196466a45e526e69175a366b2f04bc0b1 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 14:15:28 +0100
Subject: [PATCH 087/153] check for out-of-order pt.java.required

---
 .../pyterrier_flake8_ext/java_check.py        | 22 +++++++++++++++++--
 1 file changed, 20 insertions(+), 2 deletions(-)

diff --git a/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/java_check.py b/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/java_check.py
index 3a35bf58..03427552 100644
--- a/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/java_check.py
+++ b/extras/pyterrier-flake8-ext/pyterrier_flake8_ext/java_check.py
@@ -7,8 +7,9 @@ class JavaCheck:
     """McCabe cyclomatic complexity checker."""
     name = 'javacheck'
     version = pyterrier_flake8_ext.__version__
-    _missing_anno_tmpl = "PT100 {} uses java but is not annotated with @pt.java.required"
-    _extra_anno_tmpl = "PT101 {} is annotated with @pt.java.required but doesn't use java"
+    _missing_anno_tmpl = "PT100 {} uses java but is not decorated with @pt.java.required"
+    _extra_anno_tmpl = "PT101 {} is decorated with @pt.java.required but doesn't use java"
+    _required_order = "PT102 {} is decorated with @pt.java.required but isn't the last decorator"
 
     def __init__(self, tree: ast.AST):
         self.tree = tree
@@ -19,6 +20,10 @@ def run(self) -> Generator[Tuple[int, int, str, Type[Any]], None, None]:
     def _run(self, node: ast.AST, context: List[str] = []) -> Generator[Tuple[int, int, str, Type[Any]], None, None]:
         if isinstance(node, ast.FunctionDef) or isinstance(node, ast.ClassDef):
             has_java_anno, hard_require = self._has_java_required(node)
+            if has_java_anno:
+                err = self._required_order_error(node)
+                if err:
+                    yield err
             java_usage = None
             if isinstance(node, ast.FunctionDef):
                 for child in node.body:
@@ -76,6 +81,19 @@ def _has_java_required(self, function: ast.FunctionDef) -> Tuple[bool, bool]:
                     return True, False
         return False, False
 
+    def _required_order_error(self, function: ast.FunctionDef) -> Optional[Tuple[int, int, str, Type[Any]]]:
+        for i, decorator in enumerate(function.decorator_list):
+            is_last = i == len(function.decorator_list) - 1
+            if isinstance(decorator, ast.Name):
+                if decorator.id in ('required', 'required_raise') and not is_last:
+                    text = self._required_order.format(function.name)
+                    return decorator.lineno, decorator.col_offset, text, type(self)
+            elif isinstance(decorator, ast.Attribute) and not is_last:
+                if decorator.attr in ('required', 'required_raise'):
+                    text = self._required_order.format(function.name)
+                    return decorator.lineno, decorator.col_offset, text, type(self)
+        return None
+
     def _uses_java(self, node: ast.AST) -> Optional[ast.AST]:
         if isinstance(node, ast.Name):
             if node.id in ('autoclass', 'cast', 'J'):

From 6ee75d06da061d8fe3d284e81f8062b7deba581b Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 14:34:32 +0100
Subject: [PATCH 088/153] prf

---
 pyterrier/terrier/java.py    | 35 ++++++++++++++++++++--
 pyterrier/terrier/rewrite.py | 24 ++-------------
 tests/base.py                |  1 +
 tests/test_rewrite.py        | 57 ++++++++++++++++++------------------
 4 files changed, 64 insertions(+), 53 deletions(-)

diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index aaf89e36..f5958ffc 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -1,6 +1,7 @@
 import sys
+from functools import wraps
 from packaging.version import Version
-from typing import Optional, Union, List
+from typing import Optional, Union, List, Callable
 import pyterrier as pt
 
 TERRIER_PKG = "org.terrier"
@@ -14,6 +15,7 @@
     'helper_version': None,
     'boot_packages': [],
     'force_download': True,
+    'prf_version': None,
 })
 
 
@@ -28,8 +30,32 @@ def set_helper_version(version: Optional[str] = None):
 
 
 @pt.java.before_init
-def enable_prf(version: Optional[str] = None):
-    pt.java.add_package('com.github.terrierteam', 'terrier-prf', version)
+def enable_prf(version: Optional[str] = '-SNAPSHOT'):
+    configure['prf_version'] = version
+
+
+def prf_required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
+    """
+    Requires prf to be enabled (raises error if not enabled).
+
+    Can be used as either a standalone function or a function/class @decorator. When used as a class decorator, it
+    is applied to all methods defined by the class.
+    """
+    if fn is None: # standalone call
+        return prf_required(pt.utils.noop)()
+
+    if isinstance(fn, type): # wrapping a class
+        for name, value in pt.utils.get_class_methods(fn):
+            setattr(fn, name, prf_required(value))
+        return fn
+
+    else: # wrapping a function
+        @wraps(fn)
+        def _wrapper(*args, **kwargs):
+            if not configure['prf_version']:
+                raise RuntimeError('you need to call pt.terrier.enable_prf() before java is loaded to use this function.')
+            return fn(*args, **kwargs)
+        return _wrapper
 
 
 
@@ -67,6 +93,9 @@ def _pre_init(jnius_config):
     configure['helper_version'] = helper_version # save this specific version
     pt.java.add_package(TERRIER_PKG, "terrier-python-helper", helper_version)
 
+    if configure['prf_version'] is not None:
+        pt.java.add_package('com.github.terrierteam', 'terrier-prf', configure['prf_version'])
+
 
 @pt.java.required_raise
 def _post_init(jnius):
diff --git a/pyterrier/terrier/rewrite.py b/pyterrier/terrier/rewrite.py
index 02601008..f8beefb9 100644
--- a/pyterrier/terrier/rewrite.py
+++ b/pyterrier/terrier/rewrite.py
@@ -4,25 +4,7 @@
 from types import FunctionType
 import pyterrier as pt
 from pyterrier.terrier.index import TerrierTokeniser
-
-# TODO: replace _check_terrier_prf with a @pt.terrier.java.required_prf or similar
-# TODO: make sure @pt.java.required on all necessary functions
-
-_terrier_prf_package_loaded = False
-_terrier_prf_message = 'terrier-prf jar not found: you should start PyTerrier with '\
-    + 'pt.init(boot_packages=["com.github.terrierteam:terrier-prf:-SNAPSHOT"])'
-
-def _check_terrier_prf():
-    import jnius_config
-    global _terrier_prf_package_loaded
-    if _terrier_prf_package_loaded:
-        return
-    
-    for j in jnius_config.get_classpath():
-        if "terrier-prf" in j:
-            _terrier_prf_package_loaded = True
-            break
-    assert _terrier_prf_package_loaded, _terrier_prf_message
+from pyterrier.terrier.java import prf_required
 
 @pt.java.required
 def tokenise(tokeniser : Union[str,TerrierTokeniser,FunctionType] = 'english', matchop=False) -> pt.Transformer:
@@ -394,6 +376,7 @@ class RM3(QueryExpansion):
                         )
  
     '''
+    @prf_required
     def __init__(self, *args, fb_terms=10, fb_docs=3, fb_lambda=0.6, **kwargs):
         """
         Args:
@@ -402,7 +385,6 @@ def __init__(self, *args, fb_terms=10, fb_docs=3, fb_lambda=0.6, **kwargs):
             fb_docs(int): number of feedback documents to consider. Terrier's default setting is 3 feedback documents.
             fb_lambda(float): lambda in RM3, i.e. importance of relevance model viz feedback model. Defaults to 0.6.
         """
-        _check_terrier_prf()
         rm = pt.terrier.J.RM3()
         self.fb_lambda = fb_lambda
         kwargs["qeclass"] = rm
@@ -440,6 +422,7 @@ class AxiomaticQE(QueryExpansion):
          - fb_terms(int): number of feedback terms. Defaults to 10
          - fb_docs(int): number of feedback documents. Defaults to 3
     '''
+    @prf_required
     def __init__(self, *args, fb_terms=10, fb_docs=3, **kwargs):
         """
         Args:
@@ -447,7 +430,6 @@ def __init__(self, *args, fb_terms=10, fb_docs=3, **kwargs):
             fb_terms(int): number of terms to add to the query. Terrier's default setting is 10 expansion terms.
             fb_docs(int): number of feedback documents to consider. Terrier's default setting is 3 feedback documents.
         """
-        _check_terrier_prf()
         rm = pt.terrier.J.AxiomaticQE()
         self.fb_terms = fb_terms
         self.fb_docs = fb_docs
diff --git a/tests/base.py b/tests/base.py
index 71dfa6c7..5d18dec6 100644
--- a/tests/base.py
+++ b/tests/base.py
@@ -20,6 +20,7 @@ def __init__(self, *args, **kwargs):
             
             pt.terrier.set_version(terrier_version)
             pt.terrier.set_helper_version(terrier_helper_version)
+            pt.terrier.enable_prf()
             pt.java.set_log_level("DEBUG")
             # pt.java.add_option('-ea') can be added here to ensure that all Java assertions are met
             pt.java.init()
diff --git a/tests/test_rewrite.py b/tests/test_rewrite.py
index 627e831c..7b1f436d 100644
--- a/tests/test_rewrite.py
+++ b/tests/test_rewrite.py
@@ -221,40 +221,39 @@ def _sdm(self, freq):
             pt.Evaluate(br_sdm.transform(t), dataset.get_qrels(), metrics=["map"])["map"], 
             places=4)
 
-    # RM3 cannot be tested with current jnius, as it must be placed into the boot classpath
-    # def test_rm3(self):
-    #     dataset = pt.datasets.get_dataset("vaswani")
-    #     indexref = dataset.get_index()
-
-    #     qe = pt.rewrite.RM3(indexref)
-    #     br = pt.BatchRetrieve(indexref)
-
-    #     queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
-    #     res = br.transform(queriesIn)
-
-    #     queriesOut = qe.transform(res)
-    #     self.assertEqual(len(queriesOut), 1)
-    #     query = queriesOut.iloc[0]["query"]
-    #     #self.assertTrue("compact^1.82230972" in query)
-    #     self.assertTrue("applypipeline:off " in query)
+    def test_rm3(self):
+        dataset = pt.datasets.get_dataset("vaswani")
+        indexref = dataset.get_index()
+
+        qe = pt.rewrite.RM3(indexref)
+        br = pt.BatchRetrieve(indexref)
+
+        queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
+        res = br.transform(queriesIn)
+
+        queriesOut = qe.transform(res)
+        self.assertEqual(len(queriesOut), 1)
+        query = queriesOut.iloc[0]["query"]
+        #self.assertTrue("compact^1.82230972" in query)
+        self.assertTrue("applypipeline:off " in query)
         
-    #     pipe = br >> qe >> br
+        pipe = br >> qe >> br
 
-    #     # lets go faster, we only need 18 topics. qid 16 had a tricky case
-    #     t = dataset.get_topics().head(18)
+        # lets go faster, we only need 18 topics. qid 16 had a tricky case
+        t = dataset.get_topics().head(18)
 
-    #     all_qe_res = pipe.transform(t)
-    #     map_pipe = pt.Evaluate(all_qe_res, dataset.get_qrels(), metrics=["map"])["map"]
+        all_qe_res = pipe.transform(t)
+        map_pipe = pt.Evaluate(all_qe_res, dataset.get_qrels(), metrics=["map"])["map"]
 
-    #     br_qe = pt.BatchRetrieve(indexref, 
-    #         controls={"qe":"on"},
-    #         properties={"querying.processes" : "terrierql:TerrierQLParser,parsecontrols:TerrierQLToControls,"\
-    #                 +"parseql:TerrierQLToMatchingQueryTerms,matchopql:MatchingOpQLParser,applypipeline:ApplyTermPipeline,"\
-    #                 +"sd:DependenceModelPreProcess,localmatching:LocalManager$ApplyLocalMatching,qe:RM3,"\
-    #                 +"labels:org.terrier.learning.LabelDecorator,filters:LocalManager$PostFilterProcess"})
-    #     map_qe = pt.Evaluate(br_qe.transform(t), dataset.get_qrels(), metrics=["map"])["map"]
+        br_qe = pt.BatchRetrieve(indexref, 
+            controls={"qe":"on"},
+            properties={"querying.processes" : "terrierql:TerrierQLParser,parsecontrols:TerrierQLToControls,"\
+                    +"parseql:TerrierQLToMatchingQueryTerms,matchopql:MatchingOpQLParser,applypipeline:ApplyTermPipeline,"\
+                    +"sd:DependenceModelPreProcess,localmatching:LocalManager$ApplyLocalMatching,qe:RM3,"\
+                    +"labels:org.terrier.learning.LabelDecorator,filters:LocalManager$PostFilterProcess"})
+        map_qe = pt.Evaluate(br_qe.transform(t), dataset.get_qrels(), metrics=["map"])["map"]
 
-    #     self.assertAlmostEqual(map_qe, map_pipe, places=4)
+        self.assertAlmostEqual(map_qe, map_pipe, places=2)
 
     def test_linear_terrierql(self):
         pipe = pt.apply.query(lambda row: "az") >> pt.rewrite.linear(0.75, 0.25)

From 935458c659b270049fc18e8fb7c5de71a91d7a3b Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 14:43:08 +0100
Subject: [PATCH 089/153] refactoring

---
 pyterrier/anserini/java.py | 24 ++++++------------------
 pyterrier/java/_init.py    | 18 +++---------------
 pyterrier/terrier/java.py  | 19 +++----------------
 pyterrier/utils.py         | 25 +++++++++++++++++++++++++
 4 files changed, 37 insertions(+), 49 deletions(-)

diff --git a/pyterrier/anserini/java.py b/pyterrier/anserini/java.py
index 0dc3251c..fdb761ea 100644
--- a/pyterrier/anserini/java.py
+++ b/pyterrier/anserini/java.py
@@ -67,7 +67,8 @@ def set_version(version: str):
     configure['version'] = version
 
 
-def required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
+@pt.utils.pre_invocation_decorator
+def required() -> Union[Callable, bool]:
     """
     Requires pyserini to be installed (raises error if not installed). If the JVM has not yet been started, it runs
     pt.java.init(), too, similar to pt.java.required().
@@ -75,23 +76,10 @@ def required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
     Can be used as either a standalone function or a function/class @decorator. When used as a class decorator, it
     is applied to all methods defined by the class.
     """
-    if fn is None: # standalone call
-        return required(pt.utils.noop)()
-
-    if isinstance(fn, type): # wrapping a class
-        for name, value in pt.utils.get_class_methods(fn):
-            setattr(fn, name, required(value))
-        return fn
-
-    else: # wrapping a function
-        @wraps(fn)
-        def _wrapper(*args, **kwargs):
-            if not is_installed():
-                raise RuntimeError('pyserini required to use pyterrier.anserini. `pip install pyserini` and try again.')
-            if not pt.java.started():
-                pt.java.init()
-            return fn(*args, **kwargs)
-        return _wrapper
+    if not is_installed():
+        raise RuntimeError('pyserini required to use pyterrier.anserini. `pip install pyserini` and try again.')
+    if not pt.java.started():
+        pt.java.init()
 
 
 J = pt.java.JavaClasses({
diff --git a/pyterrier/java/_init.py b/pyterrier/java/_init.py
index f6cef0bb..c7cefba2 100644
--- a/pyterrier/java/_init.py
+++ b/pyterrier/java/_init.py
@@ -18,6 +18,7 @@ def started() -> bool:
     return _started
 
 
+@pt.utils.pre_invocation_decorator
 def required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
     """
     Requires the Java Virtual Machine to be started. If the JVM has not yet been started, it runs pt.java.init().
@@ -25,21 +26,8 @@ def required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
     Can be used as either a standalone function or a function/class @decorator. When used as a class decorator, it
     is applied to all methods defined by the class.
     """
-    if fn is None: # standalone call
-        return required(pt.utils.noop)()
-
-    if isinstance(fn, type): # wrapping a class
-        for name, value in pt.utils.get_class_methods(fn):
-            setattr(fn, name, required(value))
-        return fn
-
-    else: # wrapping a function
-        @wraps(fn)
-        def _wrapper(*args, **kwargs):
-            if not started():
-                init()
-            return fn(*args, **kwargs)
-        return _wrapper
+    if not started():
+        init()
 
 
 def required_raise(fn: Optional[Callable] = None) -> Union[Callable, bool]:
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index f5958ffc..17dc463d 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -34,6 +34,7 @@ def enable_prf(version: Optional[str] = '-SNAPSHOT'):
     configure['prf_version'] = version
 
 
+@pt.utils.pre_invocation_decorator
 def prf_required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
     """
     Requires prf to be enabled (raises error if not enabled).
@@ -41,22 +42,8 @@ def prf_required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
     Can be used as either a standalone function or a function/class @decorator. When used as a class decorator, it
     is applied to all methods defined by the class.
     """
-    if fn is None: # standalone call
-        return prf_required(pt.utils.noop)()
-
-    if isinstance(fn, type): # wrapping a class
-        for name, value in pt.utils.get_class_methods(fn):
-            setattr(fn, name, prf_required(value))
-        return fn
-
-    else: # wrapping a function
-        @wraps(fn)
-        def _wrapper(*args, **kwargs):
-            if not configure['prf_version']:
-                raise RuntimeError('you need to call pt.terrier.enable_prf() before java is loaded to use this function.')
-            return fn(*args, **kwargs)
-        return _wrapper
-
+    if not configure['prf_version']:
+        raise RuntimeError('you need to call pt.terrier.enable_prf() before java is loaded to use this function.')
 
 
 def _pre_init(jnius_config):
diff --git a/pyterrier/utils.py b/pyterrier/utils.py
index 47f51d72..f471189f 100644
--- a/pyterrier/utils.py
+++ b/pyterrier/utils.py
@@ -185,3 +185,28 @@ def get_class_methods(cls) -> List[Tuple[str, Callable]]:
             class_methods.append((name, func))
     
     return class_methods
+
+
+def pre_invocation_decorator(decorator):
+    """
+    Builds a decorator function that funs the decoraded code before running the wrapped function. It can run as a
+    standalone function, a function @decorator, or a class @decorator. When used as a class decorator, it is applied to
+    all functions defined by the class.
+    """
+    @wraps(decorator)
+    def _decorator_wrapper(fn):
+        if fn is None: # standalone call
+            return _decorator_wrapper(pt.utils.noop)()
+
+        if isinstance(fn, type): # wrapping a class
+            for name, value in pt.utils.get_class_methods(fn):
+                setattr(fn, name, _decorator_wrapper(value))
+            return fn
+
+        else: # wrapping a function
+            @wraps(fn)
+            def _wrapper(*args, **kwargs):
+                decorator()
+                return fn(*args, **kwargs)
+            return _wrapper
+    return _decorator_wrapper

From d0c57ff692dc972013a176b1d822e227a4b68e22 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 16:20:39 +0100
Subject: [PATCH 090/153] avoid greedy java init in tests

---
 tests/anserini/test_anserini.py | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/tests/anserini/test_anserini.py b/tests/anserini/test_anserini.py
index 2168cfe6..090d5d88 100644
--- a/tests/anserini/test_anserini.py
+++ b/tests/anserini/test_anserini.py
@@ -14,13 +14,14 @@ def skip_pyserini(self):
 
     def __init__(self, *args, **kwargs):
         super(AnseriniTestCase, self).__init__(*args, **kwargs)
-        anserini_version = os.environ.get("ANSERINI_VERSION", ANSERINI_VERSION)
-        terrier_version = os.environ.get("TERRIER_VERSION", None)
-        if terrier_version is not None:
-            print("Testing with Terrier version " + terrier_version)
-        if not pt.started():
-            pt.init(version=terrier_version, logging="DEBUG", boot_packages=["io.anserini:anserini:%s:fatjar" % anserini_version])
-        self.here = os.path.dirname(os.path.realpath(__file__))
+        if pt.anserini.is_installed():
+            anserini_version = os.environ.get("ANSERINI_VERSION", ANSERINI_VERSION)
+            terrier_version = os.environ.get("TERRIER_VERSION", None)
+            if terrier_version is not None:
+                print("Testing with Terrier version " + terrier_version)
+            if not pt.started():
+                pt.init(version=terrier_version, logging="DEBUG", boot_packages=["io.anserini:anserini:%s:fatjar" % anserini_version])
+            self.here = os.path.dirname(os.path.realpath(__file__))
 
     def test_anserini_vaswani(self):
         self.skip_pyserini()

From f299c4540a9d393a602db09d832f42c8ef4d8447 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 16:20:52 +0100
Subject: [PATCH 091/153] more deprecation/backcompat

---
 pyterrier/__init__.py       | 7 +++++--
 tests/test_iterdictindex.py | 2 +-
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index 753ef928..dcbbf14e 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -6,7 +6,6 @@
 from pyterrier.transformer import Transformer, Estimator, Indexer
 
 from pyterrier import java
-from pyterrier.java import redirect_stdouterr # for backward compat, maybe remove/deprecate some day?
 
 from pyterrier import terrier
 from pyterrier.terrier import BatchRetrieve, TerrierRetrieve, FeaturesBatchRetrieve, IndexFactory, run, rewrite, index, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
@@ -44,6 +43,9 @@
 set_tqdm = deprecated(version='0.11.0', reason="use pt.utils.set_tqdm(...) instead")(utils.set_tqdm)
 set_property = deprecated(version='0.11.0', reason="use pt.terrier.set_property(...) instead")(terrier.set_property)
 set_properties = deprecated(version='0.11.0', reason="use pt.terrier.set_properties(...) instead")(terrier.set_properties)
+redirect_stdouterr = deprecated(version='0.11.0', reason="use pt.java.redirect_stdouterr(...) instead")(java.redirect_stdouterr)
+autoclass = deprecated(version='0.11.0', reason="use pt.java.autoclass(...) instead")(java.autoclass)
+cast = deprecated(version='0.11.0', reason="use pt.java.cast(...) instead")(java.cast)
 
 
 # Additional setup performed in a function to avoid polluting the namespace with other imports like platform
@@ -65,7 +67,7 @@ def _():
     'java', 'terrier', 'anserini', 'cache', 'debug', 'io', 'measures', 'model', 'new', 'ltr', 'parallel', 'pipelines',
     'text', 'transformer', 'datasets', 'get_dataset', 'find_datasets', 'list_datasets', 'Experiment', 'GridScan',
     'GridSearch', 'KFoldGridSearch', 'Evaluate',
-    'utils', 'Utils', 'Transformer', 'Estimator', 'Indexer', 'redirect_stdouterr',
+    'utils', 'Utils', 'Transformer', 'Estimator', 'Indexer',
     'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve', 'IndexFactory',
     'run', 'rewrite', 'index', 'FilesIndexer', 'TRECCollectionIndexer', 'DFIndexer', 'DFIndexUtils', 'IterDictIndexer',
     'IndexingType', 'TerrierStemmer', 'TerrierStopwords', 'TerrierTokeniser',
@@ -73,4 +75,5 @@ def _():
 
     # Deprecated:
     'init', 'started', 'logging', 'version', 'check_version', 'extend_classpath', 'set_tqdm', 'set_property', 'set_properties',
+    'redirect_stdouterr', 'autoclass', 'cast',
 ]
diff --git a/tests/test_iterdictindex.py b/tests/test_iterdictindex.py
index 57742a72..ab0e9542 100644
--- a/tests/test_iterdictindex.py
+++ b/tests/test_iterdictindex.py
@@ -191,7 +191,7 @@ def test_check_nostemmer_utf_toks(self):
         pt.terrier.J.ApplicationSetup.setProperty("termpipelines", "Stopwords,PorterStemmer")
 
     def test_check_customstops(self):
-        if not pt.check_version("5.8"):
+        if not pt.terrier.check_version("5.8"):
             self.skipTest("Requires Terrier 5.8")
         it = [
             {'docno': '1', 'url': 'url1', 'text': 'He ran out of møney, so he had to stop playing', 'title': 'Woes of playing poker'},

From 215b780791f1b0eb0cf8e8c80e637faceb503151 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 17:23:46 +0100
Subject: [PATCH 092/153] no_download support and automatic "offline" mode
 handling in mavenresolver

---
 pyterrier/java/_init.py         |  4 +-
 pyterrier/java/mavenresolver.py | 98 +++++++++++++++++++++++++++++----
 2 files changed, 90 insertions(+), 12 deletions(-)

diff --git a/pyterrier/java/_init.py b/pyterrier/java/_init.py
index c7cefba2..53b3a18c 100644
--- a/pyterrier/java/_init.py
+++ b/pyterrier/java/_init.py
@@ -216,8 +216,8 @@ def legacy_init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=Tr
     pt.terrier.set_helper_version(helper_version)
     if tqdm is not None:
         pt.utils.set_tqdm(tqdm)
-
-    # TODO: missing no_download. Is this something like pt.java.set_offline(True)?
+    if no_download:
+        pt.java.mavenresolver.offline()
 
     pt.java.init()
 
diff --git a/pyterrier/java/mavenresolver.py b/pyterrier/java/mavenresolver.py
index d75e5f1a..91ccba53 100644
--- a/pyterrier/java/mavenresolver.py
+++ b/pyterrier/java/mavenresolver.py
@@ -2,6 +2,8 @@
 import urllib.request
 from packaging.version import Version
 from pathlib import Path
+from enum import Enum
+from warnings import warn
 import wget
 import os
 import re
@@ -10,6 +12,51 @@
 MAVEN_BASE_URL = "https://repo1.maven.org/maven2/"
 JITPACK_BASE_URL = "https://jitpack.io/"
 
+
+class OnlineMode(Enum):
+    ONLINE = 'ONLINE'
+    OFFLINE = 'OFFLINE'
+    UNSET = 'UNSET'
+
+
+_online_mode = OnlineMode.UNSET
+
+
+def online_mode() -> OnlineMode:
+    """
+    Returns whether mavenresolver is operating in "online" mode, "offline" mode, or if the mode has not been set yet.
+
+    In "online" mode, packages will always be requested from maven. Network failures will raise errors in this mode.
+
+    In "offline" mode, maven will not be contacted, and the most recent versions of packages that are currently cached
+    on the system will be used when requesting package versions, etc. If no versions are available, an error will be
+    raised.
+
+    If not set, mavenresolver will behave in "online" mode until it receives a network error, at which point it will
+    move automatically to "offline" mode.
+    """
+    return _online_mode
+
+
+def offline(is_offline: bool = True):
+    """
+    Enables offline mode. 
+    """
+    global _online_mode
+    _online_mode = {
+        True: OnlineMode.OFFLINE,
+        False: OnlineMode.ONLINE
+    }[is_offline]
+
+
+def online(is_online: bool = True):
+    global _online_mode
+    _online_mode = {
+        True: OnlineMode.ONLINE,
+        False: OnlineMode.OFFLINE
+    }[is_online]
+
+
 # obtain a file from maven
 def get_package_jar(orgName, packageName, version, file_path, artifact="jar", force_download=False):
     orgName = orgName.replace(".", "/")
@@ -23,6 +70,11 @@ def get_package_jar(orgName, packageName, version, file_path, artifact="jar", fo
     filename = packageName + "-" + version + suffix + "." + ext
 
     filelocation = orgName + "/" + packageName + "/" + version + "/" + filename
+
+    mode = online_mode()
+
+    if force_download and mode == OnlineMode.OFFLINE:
+        force_download = False # OFFLINE overrides force_download
     
     target_file = os.path.join(file_path, filename)
     file_exists = os.path.isfile(target_file)
@@ -31,7 +83,7 @@ def get_package_jar(orgName, packageName, version, file_path, artifact="jar", fo
             return target_file
         else:
             # ensure that wget doesnt put the file in a different name
-            os.remove(target_file)
+            os.rename(target_file, f'{target_file}.bak')
 
     # check local Maven repo, and use that if it exists
     from os.path import expanduser
@@ -41,6 +93,9 @@ def get_package_jar(orgName, packageName, version, file_path, artifact="jar", fo
     if os.path.isfile(mvnLocalLocation):
         return mvnLocalLocation
 
+    if mode == OnlineMode.OFFLINE:
+        raise ValueError(f'{filename} not found')
+
     if force_download:
         print("Downloading "+ packageName + " " + version + " " + artifact  + " to " + file_path + "...")
     else:
@@ -53,30 +108,53 @@ def get_package_jar(orgName, packageName, version, file_path, artifact="jar", fo
 
     try:
         wget.download(mvnUrl, file_path)
-    except urllib.error.HTTPError as he:
+    except (urllib.error.HTTPError, urllib.error.URLError) as he:
+        if mode == OnlineMode.UNSET:
+            offline() # now we're in offline mode
+        if file_exists:
+            os.rename(f'{target_file}.bak', target_file) # move back
+            if mode == OnlineMode.UNSET:
+                warn(f'Attempted to download {mvnUrl}, but was offline. Using cached version: {target_file}')
+                return target_file
         raise ValueError("Could not fetch " + mvnUrl) from he
+
+    if file_exists:
+        os.remove(f'{target_file}.bak') # clean up temp file
     print("Done")
 
-    return (os.path.join(file_path, filename))
+    return os.path.join(file_path, filename)
 
 # returns the latest version
 def latest_version_num(orgName, packageName):
     orgName = orgName.replace(".", "/")
     if "com/github" in orgName:
         # its jitpack
-        return("-SNAPSHOT")
+        return "-SNAPSHOT"
+
+    mode = online_mode()
+
+    if mode == OnlineMode.OFFLINE:
+        version = latest_local_version_num(packageName)
+        if version is None:
+            raise ValueError(f'Could not find latest version of {packageName}')
+        return version
 
     url_str = MAVEN_BASE_URL + orgName + "/" + packageName + "/maven-metadata.xml"
     try:
         with urllib.request.urlopen(url_str) as url:
             xml_str = url.read()
     except urllib.error.URLError:
-        # no internet connection, use the latest version found locally.
-        version = latest_local_version_num(packageName)
-        if version is None:
-            raise # version not found, re-raise the URLError error
-        else:
-            return version
+        if mode == OnlineMode.UNSET:
+            offline()
+            # no internet connection, use the latest version found locally.
+            version = latest_local_version_num(packageName)
+            if version is None:
+                raise # version not found, re-raise the URLError error
+            else:
+                warn(f'Attempted to get latest version of {packageName} from maven, but was offline. Using latest cached version: {version}')
+                return version
+        else: # mode == OnlineMode.ONLINE
+            raise
     xmldoc = minidom.parseString(xml_str)
     obs_values = xmldoc.getElementsByTagName("latest")
     version = obs_values[0].firstChild.nodeValue

From 4311bdccb2ebafc5301ab1587a85e69e02818493 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 17:27:58 +0100
Subject: [PATCH 093/153] alright, one of the last things remaining is this
 pesky macos test

---
 .github/workflows/push.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml
index 86f0a37f..c0b93066 100644
--- a/.github/workflows/push.yml
+++ b/.github/workflows/push.yml
@@ -18,11 +18,11 @@ jobs:
         java: [11, 13]
         os: ['ubuntu-latest', 'macos-13', 'windows-latest']
         terrier: ['snapshot'] #'5.3', '5.4-SNAPSHOT', 
-        # include:
-        #     - os: 'macos-latest'
-        #       python-version: '3.9'
-        #       java: 11
-        #       terrier: 'snapshot'
+        include:
+            - os: 'macos-latest'
+              python-version: '3.9'
+              java: 11
+              terrier: 'snapshot'
 
     runs-on: ${{ matrix.os }}
     steps:

From 5ccfbf018671409566f07d6fc3c64d68c9d96b4a Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 17:32:42 +0100
Subject: [PATCH 094/153] so what's the deal? Is it the python version?

---
 .github/workflows/push.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml
index c0b93066..5b2a3219 100644
--- a/.github/workflows/push.yml
+++ b/.github/workflows/push.yml
@@ -20,7 +20,7 @@ jobs:
         terrier: ['snapshot'] #'5.3', '5.4-SNAPSHOT', 
         include:
             - os: 'macos-latest'
-              python-version: '3.9'
+              python-version: '3.8'
               java: 11
               terrier: 'snapshot'
 

From d9408849904c20ca080718a2b3a25ece1c6b9623 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 17:40:04 +0100
Subject: [PATCH 095/153] ok so must be macos-latest?

---
 .github/workflows/push.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml
index 5b2a3219..14da5b32 100644
--- a/.github/workflows/push.yml
+++ b/.github/workflows/push.yml
@@ -19,7 +19,7 @@ jobs:
         os: ['ubuntu-latest', 'macos-13', 'windows-latest']
         terrier: ['snapshot'] #'5.3', '5.4-SNAPSHOT', 
         include:
-            - os: 'macos-latest'
+            - os: 'macos-13'
               python-version: '3.8'
               java: 11
               terrier: 'snapshot'

From 52f646e0856be6f8e08c6b1ce30758c7a0816471 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 17:49:47 +0100
Subject: [PATCH 096/153] is it an arm issue? also disable other configs to
 save time

---
 .github/workflows/push.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml
index 14da5b32..427fbdbd 100644
--- a/.github/workflows/push.yml
+++ b/.github/workflows/push.yml
@@ -14,12 +14,12 @@ jobs:
     
     strategy:
       matrix:
-        python-version: ['3.8', '3.11']
-        java: [11, 13]
-        os: ['ubuntu-latest', 'macos-13', 'windows-latest']
-        terrier: ['snapshot'] #'5.3', '5.4-SNAPSHOT', 
+        # python-version: ['3.8', '3.11']
+        # java: [11, 13]
+        # os: ['ubuntu-latest', 'macos-13', 'windows-latest']
+        # terrier: ['snapshot'] #'5.3', '5.4-SNAPSHOT', 
         include:
-            - os: 'macos-13'
+            - os: 'macos-13-arm64'
               python-version: '3.8'
               java: 11
               terrier: 'snapshot'

From ad9a3788d5bb6ca6d3de7295a9d7ca0701bed242 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 20:51:03 +0100
Subject: [PATCH 097/153] this is actually the arm image name for macos-13

---
 .github/workflows/push.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml
index 427fbdbd..e6379945 100644
--- a/.github/workflows/push.yml
+++ b/.github/workflows/push.yml
@@ -19,7 +19,7 @@ jobs:
         # os: ['ubuntu-latest', 'macos-13', 'windows-latest']
         # terrier: ['snapshot'] #'5.3', '5.4-SNAPSHOT', 
         include:
-            - os: 'macos-13-arm64'
+            - os: 'macos-13-xlarge'
               python-version: '3.8'
               java: 11
               terrier: 'snapshot'

From 9df53f164ac407c7aec133c309a0a8fc3395e3e3 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 20:53:00 +0100
Subject: [PATCH 098/153] hmm that didn't work, here's a version on 14 with
 intel

---
 .github/workflows/push.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml
index e6379945..d1573ef7 100644
--- a/.github/workflows/push.yml
+++ b/.github/workflows/push.yml
@@ -19,7 +19,7 @@ jobs:
         # os: ['ubuntu-latest', 'macos-13', 'windows-latest']
         # terrier: ['snapshot'] #'5.3', '5.4-SNAPSHOT', 
         include:
-            - os: 'macos-13-xlarge'
+            - os: 'macos-14-large'
               python-version: '3.8'
               java: 11
               terrier: 'snapshot'

From cfd7d2787e50cc1e3f0c62b515a11a1b7cc83e07 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 21:01:08 +0100
Subject: [PATCH 099/153] revert for the time being

---
 .github/workflows/push.yml | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml
index d1573ef7..c0b93066 100644
--- a/.github/workflows/push.yml
+++ b/.github/workflows/push.yml
@@ -14,13 +14,13 @@ jobs:
     
     strategy:
       matrix:
-        # python-version: ['3.8', '3.11']
-        # java: [11, 13]
-        # os: ['ubuntu-latest', 'macos-13', 'windows-latest']
-        # terrier: ['snapshot'] #'5.3', '5.4-SNAPSHOT', 
+        python-version: ['3.8', '3.11']
+        java: [11, 13]
+        os: ['ubuntu-latest', 'macos-13', 'windows-latest']
+        terrier: ['snapshot'] #'5.3', '5.4-SNAPSHOT', 
         include:
-            - os: 'macos-14-large'
-              python-version: '3.8'
+            - os: 'macos-latest'
+              python-version: '3.9'
               java: 11
               terrier: 'snapshot'
 

From 57c6cc3de490cde79b61fc0f9885a2e9adfb59fa Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 21:31:36 +0100
Subject: [PATCH 100/153] more informative java loaded message

---
 pyterrier/anserini/java.py |  2 +-
 pyterrier/java/_init.py    | 11 ++++++++++-
 pyterrier/terrier/java.py  |  3 +--
 setup.py                   | 12 ++++++------
 4 files changed, 18 insertions(+), 10 deletions(-)

diff --git a/pyterrier/anserini/java.py b/pyterrier/anserini/java.py
index fdb761ea..dceefa75 100644
--- a/pyterrier/anserini/java.py
+++ b/pyterrier/anserini/java.py
@@ -31,7 +31,7 @@ def _pre_init(jnius_config):
 def _post_init(jnius):
     if not is_installed():
         # pyserini not installed, do nothing
-        return
+        return False
 
     # Temporarily disable the configure_classpath while pyserini is init'd, otherwise it will try to reconfigure jnius
     import pyserini.setup
diff --git a/pyterrier/java/_init.py b/pyterrier/java/_init.py
index 53b3a18c..bfe8e242 100644
--- a/pyterrier/java/_init.py
+++ b/pyterrier/java/_init.py
@@ -1,3 +1,4 @@
+import sys
 import inspect
 import os
 from warnings import warn
@@ -124,9 +125,17 @@ def init() -> None:
         raise RuntimeError(f"Pyterrier requires Java 11 or newer, we only found Java version {java_version};"
             + " install a more recent Java, or change os.environ['JAVA_HOME'] to point to the proper Java installation")
 
+    message = []
     for entry_point in pt.utils.entry_points('pyterrier.java.post_init'):
         _post_init = entry_point.load()
-        _post_init(jnius)
+        msg = _post_init(jnius)
+        if msg is None:
+            message.append(entry_point.name)
+        elif msg is False:
+            pass
+        else:
+            message.append(f'{entry_point.name} [{msg}]')
+    sys.stderr.write('Java started with: ' + ', '.join(message) + '\n')
 
 
 def parallel_init(started: bool, configs: Dict[str, Dict[str, Any]]) -> None:
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 17dc463d..d1d86573 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -144,8 +144,7 @@ def _post_init(jnius):
     if "BUILD_DATE" in dir(J.Version):
         version_string += f" (built by {J.Version.BUILD_USER} on {J.Version.BUILD_DATE})"
 
-    print(f"PyTerrier {pt.__version__} has loaded Terrier {version_string} and "
-          f"terrier-helper {configure['helper_version']}\n", file=sys.stderr)
+    return f"version={version_string}, helper_version={configure['helper_version']}"
 
 
 def _new_indexref(s):
diff --git a/setup.py b/setup.py
index ccee73f5..83e38858 100644
--- a/setup.py
+++ b/setup.py
@@ -72,14 +72,14 @@ def get_version(rel_path):
     ],
     entry_points={
         'pyterrier.java.pre_init': [
-            'terrier  = pyterrier.terrier.java:_pre_init',
-            'anserini = pyterrier.anserini.java:_pre_init',
+            'pyterrier.terrier.java  = pyterrier.terrier.java:_pre_init',
+            'pyterrier.anserini.java = pyterrier.anserini.java:_pre_init',
         ],
         'pyterrier.java.post_init': [
-            'core            = pyterrier.java._init:_post_init',
-            'terrier         = pyterrier.terrier.java:_post_init',
-            'terrier.index   = pyterrier.terrier.index:_java_post_init',
-            'anserini        = pyterrier.anserini.java:_post_init',
+            'pyterrier.java          = pyterrier.java._init:_post_init',
+            'pyterrier.terrier.java  = pyterrier.terrier.java:_post_init',
+            'pyterrier.terrier.index = pyterrier.terrier.index:_java_post_init',
+            'pyterrier.anserini.java = pyterrier.anserini.java:_post_init',
         ],
     },
     install_requires=requirements,

From 4054fd12be5e38238441dc8d980a58adcd847ad1 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 21:41:19 +0100
Subject: [PATCH 101/153] management of java settings, etc

---
 pyterrier/anserini/java.py      |  2 +-
 pyterrier/java/mavenresolver.py |  4 +++-
 pyterrier/java/utils.py         |  2 +-
 pyterrier/terrier/java.py       | 19 ++++++++++++-------
 4 files changed, 17 insertions(+), 10 deletions(-)

diff --git a/pyterrier/anserini/java.py b/pyterrier/anserini/java.py
index dceefa75..fe6670cd 100644
--- a/pyterrier/anserini/java.py
+++ b/pyterrier/anserini/java.py
@@ -20,7 +20,7 @@ def _pre_init(jnius_config):
         jar = _get_pyserini_jar()
     else:
         # download and use the anserini version specified by the user
-        jar = pt.java.mavenresolver.get_package_jar('io.anserini', "anserini", configure['version'], 'fatjar')
+        jar = pt.java.mavenresolver.get_package_jar('io.anserini', "anserini", configure['version'], artifact='fatjar')
 
     if jar is None:
         raise RuntimeError('Could not find anserini jar')
diff --git a/pyterrier/java/mavenresolver.py b/pyterrier/java/mavenresolver.py
index 91ccba53..6f443bd4 100644
--- a/pyterrier/java/mavenresolver.py
+++ b/pyterrier/java/mavenresolver.py
@@ -58,7 +58,9 @@ def online(is_online: bool = True):
 
 
 # obtain a file from maven
-def get_package_jar(orgName, packageName, version, file_path, artifact="jar", force_download=False):
+def get_package_jar(orgName, packageName, version, file_path=None, artifact="jar", force_download=False):
+    if file_path is None:
+        file_path = pt.io.pyterrier_home()
     orgName = orgName.replace(".", "/")
     suffix = ""
     ext = "jar"
diff --git a/pyterrier/java/utils.py b/pyterrier/java/utils.py
index 4e993d03..223a5b1e 100644
--- a/pyterrier/java/utils.py
+++ b/pyterrier/java/utils.py
@@ -86,7 +86,7 @@ def add_jar(jar_path):
 def add_package(org_name: str = None, package_name: str = None, version: str = None, file_type='jar'):
     if version is None or version == 'snapshot':
         version = mavenresolver.latest_version_num(org_name, package_name)
-    file_name = mavenresolver.get_package_jar(org_name, package_name, version, pt.io.pyterrier_home(), file_type)
+    file_name = mavenresolver.get_package_jar(org_name, package_name, version, artifact=file_type)
     add_jar(file_name)
 
 
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index d1d86573..98eb673d 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -67,21 +67,23 @@ def _pre_init(jnius_config):
     # "snapshot" means use Jitpack.io to get a build of the current
     # 5.x branch from Github - see https://jitpack.io/#terrier-org/terrier-core/5.x-SNAPSHOT
     if terrier_version == "snapshot":
-        trJar = pt.java.mavenresolver.get_package_jar("com.github.terrier-org.terrier-core", "terrier-assemblies", "5.x-SNAPSHOT", pt.io.pyterrier_home(), "jar-with-dependencies", force_download=configure['force_download'])
+        trJar = pt.java.mavenresolver.get_package_jar("com.github.terrier-org.terrier-core", "terrier-assemblies", "5.x-SNAPSHOT", artifact="jar-with-dependencies", force_download=configure['force_download'])
     else:
-        trJar = pt.java.mavenresolver.get_package_jar(TERRIER_PKG, "terrier-assemblies", terrier_version, pt.io.pyterrier_home(), "jar-with-dependencies")
-    pt.java.add_jar(trJar)
+        trJar = pt.java.mavenresolver.get_package_jar(TERRIER_PKG, "terrier-assemblies", terrier_version, artifact="jar-with-dependencies")
+    jnius_config.add_classpath(trJar)
 
     # now the helper classes
     if configure['helper_version'] is None or configure['helper_version'] == 'snapshot':
         helper_version = pt.java.mavenresolver.latest_version_num(TERRIER_PKG, "terrier-python-helper")
+        configure['helper_version'] = helper_version # save this specific version
     else:
         helper_version = str(configure['helper_version']) # just in case its a float
-    configure['helper_version'] = helper_version # save this specific version
-    pt.java.add_package(TERRIER_PKG, "terrier-python-helper", helper_version)
+    helper_jar = pt.java.mavenresolver.get_package_jar(TERRIER_PKG, 'terrier-python-helper', helper_version)
+    jnius_config.add_classpath(helper_jar)
 
     if configure['prf_version'] is not None:
-        pt.java.add_package('com.github.terrierteam', 'terrier-prf', configure['prf_version'])
+        prf_jar = pt.java.mavenresolver.get_package_jar('com.github.terrierteam', 'terrier-prf', configure['prf_version'])
+        jnius_config.add_classpath(prf_jar)
 
 
 @pt.java.required_raise
@@ -144,7 +146,10 @@ def _post_init(jnius):
     if "BUILD_DATE" in dir(J.Version):
         version_string += f" (built by {J.Version.BUILD_USER} on {J.Version.BUILD_DATE})"
 
-    return f"version={version_string}, helper_version={configure['helper_version']}"
+    res = f"version={version_string}, helper_version={configure['helper_version']}"
+    if configure['prf_version'] is not None:
+        res += f" prf_version={configure['prf_version']}"
+    return res
 
 
 def _new_indexref(s):

From 423b78b63bad1fd60121710a1edccea1ab6d1f1b Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 30 Jul 2024 21:41:34 +0100
Subject: [PATCH 102/153] disable arm64 again

---
 .github/workflows/push.yml | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml
index c0b93066..86f0a37f 100644
--- a/.github/workflows/push.yml
+++ b/.github/workflows/push.yml
@@ -18,11 +18,11 @@ jobs:
         java: [11, 13]
         os: ['ubuntu-latest', 'macos-13', 'windows-latest']
         terrier: ['snapshot'] #'5.3', '5.4-SNAPSHOT', 
-        include:
-            - os: 'macos-latest'
-              python-version: '3.9'
-              java: 11
-              terrier: 'snapshot'
+        # include:
+        #     - os: 'macos-latest'
+        #       python-version: '3.9'
+        #       java: 11
+        #       terrier: 'snapshot'
 
     runs-on: ${{ matrix.os }}
     steps:

From 3c9bc214c694305daf001b8890bf8593f3ed62e8 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Wed, 31 Jul 2024 09:34:40 +0100
Subject: [PATCH 103/153] disallow changing java settings once it's started

---
 pyterrier/java/_init.py   | 11 +++++------
 pyterrier/java/config.py  |  5 +++++
 pyterrier/terrier/java.py |  2 +-
 3 files changed, 11 insertions(+), 7 deletions(-)

diff --git a/pyterrier/java/_init.py b/pyterrier/java/_init.py
index bfe8e242..f0306f45 100644
--- a/pyterrier/java/_init.py
+++ b/pyterrier/java/_init.py
@@ -6,7 +6,6 @@
 from typing import Dict, Any, Tuple, Callable, Optional, Union
 from copy import deepcopy
 import pyterrier as pt
-import pyterrier.java.config
 
 
 _started = False
@@ -130,19 +129,19 @@ def init() -> None:
         _post_init = entry_point.load()
         msg = _post_init(jnius)
         if msg is None:
-            message.append(entry_point.name)
+            message.append(f' - {entry_point.name}\n')
         elif msg is False:
             pass
         else:
-            message.append(f'{entry_point.name} [{msg}]')
-    sys.stderr.write('Java started with: ' + ', '.join(message) + '\n')
+            message.append(f' - {entry_point.name} [{msg}]')
+    sys.stderr.write('Java started and loaded:\n' + ''.join(message) + '\n')
 
 
 def parallel_init(started: bool, configs: Dict[str, Dict[str, Any]]) -> None:
     if started:
         if not pt.java.started():
             warn(f'Starting java parallel with configs {configs}')
-            pyterrier.java.config._CONFIGS = configs
+            pt.java.config._CONFIGS = configs
             init()
         else:
             warn("Avoiding reinit of PyTerrier")
@@ -151,7 +150,7 @@ def parallel_init(started: bool, configs: Dict[str, Dict[str, Any]]) -> None:
 def parallel_init_args() -> Tuple[bool, Dict[str, Dict[str, Any]]]:
     return (
         started(),
-        deepcopy(pyterrier.java.config._CONFIGS),
+        deepcopy(pt.java.config._CONFIGS),
     )
 
 
diff --git a/pyterrier/java/config.py b/pyterrier/java/config.py
index 38b0f281..778b9e2b 100644
--- a/pyterrier/java/config.py
+++ b/pyterrier/java/config.py
@@ -1,5 +1,6 @@
 from typing import Dict, Any
 from copy import deepcopy
+from pyterrier.java._init import before_init, started
 
 _CONFIGS = {}
 
@@ -10,6 +11,7 @@ def __init__(self, name):
     def get(self, key):
         return deepcopy(_CONFIGS[self.name][key])
 
+    @before_init
     def set(self, key, value):
         self(**{key: value})
 
@@ -21,10 +23,13 @@ def append(self, key, value):
     def __getitem__(self, key):
         return self.get(key)
 
+    @before_init
     def __setitem__(self, key, value):
         self.set(key, value)
 
     def __call__(self, **settings: Any):
+        if started() and any(settings):
+            raise RuntimeError('You cannot change java settings after java has started')
         for key, value in settings.items():
             if key not in _CONFIGS[self.name]:
                 raise AttributeError(f'{key!r} not defined as a java setting for {self.name!r}')
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 98eb673d..5d7bb801 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -144,7 +144,7 @@ def _post_init(jnius):
 
     version_string = J.Version.VERSION
     if "BUILD_DATE" in dir(J.Version):
-        version_string += f" (built by {J.Version.BUILD_USER} on {J.Version.BUILD_DATE})"
+        version_string += f" (build: {J.Version.BUILD_USER} {J.Version.BUILD_DATE})"
 
     res = f"version={version_string}, helper_version={configure['helper_version']}"
     if configure['prf_version'] is not None:

From 2cffed6ce84edebd7c37d8dcdb7f369c13a57a30 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Wed, 31 Jul 2024 10:19:01 +0100
Subject: [PATCH 104/153] cleaner pre_init/post_init/etc configuration

---
 pyterrier/anserini/java.py |  55 +++--
 pyterrier/java/__init__.py |   4 +-
 pyterrier/java/_init.py    | 116 ++++++----
 pyterrier/terrier/index.py | 212 +------------------
 pyterrier/terrier/java.py  | 422 ++++++++++++++++++++++++++++---------
 setup.py                   |  13 +-
 6 files changed, 423 insertions(+), 399 deletions(-)

diff --git a/pyterrier/anserini/java.py b/pyterrier/anserini/java.py
index fe6670cd..a8bed7c4 100644
--- a/pyterrier/anserini/java.py
+++ b/pyterrier/anserini/java.py
@@ -11,36 +11,31 @@
 })
 
 
-def _pre_init(jnius_config):
-    if not is_installed():
-        # pyserini not installed, do nothing
-        return
-
-    if configure['version'] is None:
-        jar = _get_pyserini_jar()
-    else:
-        # download and use the anserini version specified by the user
-        jar = pt.java.mavenresolver.get_package_jar('io.anserini', "anserini", configure['version'], artifact='fatjar')
-
-    if jar is None:
-        raise RuntimeError('Could not find anserini jar')
-    else:
-        jnius_config.add_classpath(jar)
-
-
-def _post_init(jnius):
-    if not is_installed():
-        # pyserini not installed, do nothing
-        return False
-
-    # Temporarily disable the configure_classpath while pyserini is init'd, otherwise it will try to reconfigure jnius
-    import pyserini.setup
-    _configure_classpath = pyserini.setup.configure_classpath
-    try:
-        pyserini.setup.configure_classpath = pt.utils.noop
-        import pyserini.search.lucene # load the package
-    finally:
-        pyserini.setup.configure_classpath = _configure_classpath
+class AnseriniInit(pt.java.JavaInitializer):
+    def condition(self):
+        return is_installed()
+
+    def pre_init(self, jnius_config):
+        if configure['version'] is None:
+            jar = _get_pyserini_jar()
+        else:
+            # download and use the anserini version specified by the user
+            jar = pt.java.mavenresolver.get_package_jar('io.anserini', "anserini", configure['version'], artifact='fatjar')
+
+        if jar is None:
+            raise RuntimeError('Could not find anserini jar')
+        else:
+            jnius_config.add_classpath(jar)
+
+    def post_init(self, jnius):
+        # Temporarily disable the configure_classpath while pyserini is init'd, otherwise it will try to reconfigure jnius
+        import pyserini.setup
+        _configure_classpath = pyserini.setup.configure_classpath
+        try:
+            pyserini.setup.configure_classpath = pt.utils.noop
+            import pyserini.search.lucene # load the package
+        finally:
+            pyserini.setup.configure_classpath = _configure_classpath
 
 
 def is_installed():
diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index bbcfa62c..d47eeb51 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -1,5 +1,5 @@
 from pyterrier.java import mavenresolver
-from pyterrier.java._init import init, started, parallel_init, parallel_init_args, required, required_raise, before_init, autoclass, cast, legacy_init
+from pyterrier.java._init import init, started, parallel_init, parallel_init_args, required, required_raise, before_init, autoclass, cast, legacy_init, JavaInitializer, CoreInit
 from pyterrier.java import config
 from pyterrier.java.config import configure
 from pyterrier.java.utils import redirect_stdouterr, bytebuffer_to_array, JavaClasses, J, add_jar, add_package, set_memory_limit, set_redirect_io, add_option, set_log_level
@@ -8,5 +8,5 @@
     'add_jar', 'add_option', 'add_package', 'autoclass', 'before_init', 'bytebuffer_to_array', 'cast', 'config',
     'configure', 'init', 'J', 'JavaClasses', 'mavenresolver', 'parallel_init', 'parallel_init_args',
     'redirect_stdouterr', 'required', 'required_raise', 'set_log_level', 'set_memory_limit', 'set_redirect_io',
-    'started', 'legacy_init',
+    'started', 'legacy_init', 'JavaInitializer', 'CoreInit',
 ]
diff --git a/pyterrier/java/_init.py b/pyterrier/java/_init.py
index f0306f45..853e17a9 100644
--- a/pyterrier/java/_init.py
+++ b/pyterrier/java/_init.py
@@ -1,5 +1,4 @@
 import sys
-import inspect
 import os
 from warnings import warn
 from functools import wraps
@@ -11,6 +10,23 @@
 _started = False
 
 
+class JavaInitializer:
+    def priority(self) -> int:
+        return 0
+
+    def condition(self) -> bool:
+        return True
+
+    def pre_init(self, jnius_config) -> None:
+        pass
+
+    def post_init(self, jnius) -> None:
+        pass
+
+    def message(self) -> Optional[str]:
+        return None
+
+
 def started() -> bool:
     """
     Returns True if pt.java.init() has been called. Otherwise False.
@@ -62,16 +78,43 @@ def _wrapper(*args, **kwargs):
     return _wrapper
 
 
-@required_raise
-def _post_init(jnius):
-    pt.autoclass = jnius.autoclass
-    pt.cast = jnius.cast
+class CoreInit(JavaInitializer):
+    def priority(self) -> int:
+        return -100 # run this initializer before anything else
+
+    def pre_init(self, jnius_config):
+        if pt.utils.is_windows():
+            if "JAVA_HOME" in os.environ:
+                java_home =  os.environ["JAVA_HOME"]
+                fix = f'{java_home}\\jre\\bin\\server\\;{java_home}\\jre\\bin\\client\\;{java_home}\\bin\\server\\'
+                os.environ["PATH"] = os.environ["PATH"] + ";" + fix
+
+        if pt.java.configure['mem'] is not None:
+            jnius_config.add_options('-Xmx' + str(pt.java.configure['mem']) + 'm')
+
+        for opt in pt.java.configure['options']:
+            jnius_config.add_options(opt)
 
-    jnius.protocol_map['java.util.Map$Entry'] = {
-        '__getitem__' : _mapentry_getitem,
-        '__iter__' : lambda self: iter([self.getKey(), self.getValue()]),
-        '__len__' : lambda self: 2
-    }
+        for jar in pt.java.configure['jars']:
+            jnius_config.add_classpath(jar)
+
+    @required_raise
+    def post_init(self, jnius):
+        pt.java.set_log_level(pt.java.configure['log_level'])
+
+        if pt.java.configure['redirect_io']:
+            pt.java.redirect_stdouterr()
+
+        java_version = pt.java.J.System.getProperty("java.version")
+        if java_version.startswith("1.") or java_version.startswith("9."):
+            raise RuntimeError(f"Pyterrier requires Java 11 or newer, we only found Java version {java_version};"
+                + " install a more recent Java, or change os.environ['JAVA_HOME'] to point to the proper Java installation")
+
+        jnius.protocol_map['java.util.Map$Entry'] = {
+            '__getitem__' : _mapentry_getitem,
+            '__iter__' : lambda self: iter([self.getKey(), self.getValue()]),
+            '__len__' : lambda self: 2
+        }
 
 
 # Map$Entry can be decoded like a tuple
@@ -91,50 +134,37 @@ def init() -> None:
     #     warnings.warn('pyterrier[java] not installed; no need to run pt.java.init()')
     #     return
 
-    if pt.utils.is_windows():
-        if "JAVA_HOME" in os.environ:
-            java_home =  os.environ["JAVA_HOME"]
-            fix = f'{java_home}\\jre\\bin\\server\\;{java_home}\\jre\\bin\\client\\;{java_home}\\bin\\server\\'
-            os.environ["PATH"] = os.environ["PATH"] + ";" + fix
+    initalizers = []
+    for entry_point in pt.utils.entry_points('pyterrier.java.init'):
+        initalizer = entry_point.load()()
+        if initalizer.condition():
+            initalizers.append((entry_point.name, initalizer))
 
-    import jnius_config
+    # sort by priority
+    initalizers = sorted(initalizers, key=lambda i: i[1].priority())
 
-    for entry_point in pt.utils.entry_points('pyterrier.java.pre_init'):
-        _pre_init = entry_point.load()
-        _pre_init(jnius_config)
-
-    if pt.java.configure['mem'] is not None:
-        jnius_config.add_options('-Xmx' + str(pt.java.configure['mem']) + 'm')
-
-    for opt in pt.java.configure['options']:
-        jnius_config.add_options(opt)
+    import jnius_config
 
-    for jar in pt.java.configure['jars']:
-        jnius_config.add_classpath(jar)
+    # run pre-initialization setup
+    for _, initializer in initalizers:
+        initializer.pre_init(jnius_config)
 
     import jnius # noqa: PT100 
     _started = True
 
-    pt.java.set_log_level(pt.java.configure['log_level'])
-    if pt.java.configure['redirect_io']:
-        pt.java.redirect_stdouterr()
-
-    java_version = pt.java.J.System.getProperty("java.version")
-    if java_version.startswith("1.") or java_version.startswith("9."):
-        raise RuntimeError(f"Pyterrier requires Java 11 or newer, we only found Java version {java_version};"
-            + " install a more recent Java, or change os.environ['JAVA_HOME'] to point to the proper Java installation")
+    # run post-initialization setup
+    for _, initializer in initalizers:
+        initializer.post_init(jnius)
 
+    # build "Java started" message
     message = []
-    for entry_point in pt.utils.entry_points('pyterrier.java.post_init'):
-        _post_init = entry_point.load()
-        msg = _post_init(jnius)
+    for name, initializer in initalizers:
+        msg = initializer.message()
         if msg is None:
-            message.append(f' - {entry_point.name}\n')
-        elif msg is False:
-            pass
+            message.append(f' - {name}\n')
         else:
-            message.append(f' - {entry_point.name} [{msg}]')
-    sys.stderr.write('Java started and loaded:\n' + ''.join(message) + '\n')
+            message.append(f' - {name} [{msg}]\n')
+    sys.stderr.write('Java started and loaded:\n' + ''.join(message))
 
 
 def parallel_init(started: bool, configs: Dict[str, Dict[str, Any]]) -> None:
diff --git a/pyterrier/terrier/index.py b/pyterrier/terrier/index.py
index 16141e4a..cbaaedc8 100644
--- a/pyterrier/terrier/index.py
+++ b/pyterrier/terrier/index.py
@@ -13,7 +13,7 @@
 from warnings import warn
 from deprecated import deprecated
 from collections import deque
-from typing import List, Dict, Union, Any
+from typing import List, Dict, Union
 import more_itertools
 import pyterrier as pt
 from pyterrier.terrier.stemmer import TerrierStemmer
@@ -21,7 +21,7 @@
 from pyterrier.terrier.stopwords import TerrierStopwords
 
 
-# These classes are only defined after pt.java.init()
+# These classes are only defined after pt.java.init() in pyterrier.terrier.java._post_init
 DocListIterator = None
 PythonListIterator = None
 FlatJSONDocumentIterator = None
@@ -37,214 +37,6 @@ def __init__(self, *args, **kwargs):
 # lastdoc ensures that a Document instance from a Collection is not GCd before Java has used it.
 lastdoc = None
 
-def _java_post_init(jnius):
-    global DocListIterator
-    global PythonListIterator
-    global FlatJSONDocumentIterator
-    global TQDMCollection
-    global TQDMSizeCollection
-
-    @pt.java.required
-    class DocListIterator(jnius.PythonJavaClass):
-        __javainterfaces__ = [
-            'java/util/Iterator',
-        ]
-
-        def __init__(self, pyiterator):
-            self.pyiterator = pyiterator
-            self.hasnext = True
-            self.lastdoc = None
-            self.tr57 = not pt.terrier.check_version("5.8")
-
-        @staticmethod 
-        def pyDictToMap(a_dict): #returns Map<String,String>
-            rtr = pt.java.J.HashMap()
-            for k,v in a_dict.items():
-                rtr.put(k, v)
-            return rtr
-
-        def pyDictToMapEntry(self,doc_dict : Dict[str,Any]): #returns Map.Entry<Map<String,String>, DocumentPostingList>>
-            dpl = pt.terrier.J.DocumentPostingList()
-            # this works around a bug in the counting of doc lengths in Tr 5.7
-            if self.tr57:
-                for t, tf in doc_dict["toks"].items():
-                    for i in range(int(tf)):
-                        dpl.insert(t)
-            else: # this code for 5.8 onwards
-                for t, tf in doc_dict["toks"].items():
-                    dpl.insert(int(tf), t)
-            
-            # we cant make the toks column into the metaindex as it isnt a string. remove it.
-            del doc_dict["toks"]
-            return pt.terrier.J.MapEntry(DocListIterator.pyDictToMap(doc_dict), dpl)
-
-        @jnius.java_method('()Z')
-        def hasNext(self):
-            return self.hasnext
-
-        @jnius.java_method('()Ljava/lang/Object;')
-        def next(self):
-            try:
-                doc_dict = next(self.pyiterator)
-            except StopIteration as se:
-                self.hasnext = False
-                # terrier will ignore a null return from an iterator
-                return None
-            # keep this around to prevent being GCd before Java can read it
-            self.lastdoc = self.pyDictToMapEntry(doc_dict)
-            return self.lastdoc
-            
-    @pt.java.required
-    class TQDMCollection(jnius.PythonJavaClass):
-        __javainterfaces__ = ['org/terrier/indexing/Collection']
-
-        def __init__(self, collection):
-            super(TQDMCollection, self).__init__()
-            assert isinstance(collection, pt.terrier.J.MultiDocumentFileCollection)
-            self.collection = collection
-            size = self.collection.FilesToProcess.size()
-            self.pbar = pt.tqdm(total=size, unit="files")
-            self.last = -1
-        
-        @jnius.java_method('()Z')
-        def nextDocument(self):
-            rtr = self.collection.nextDocument()
-            filenum = self.collection.FileNumber
-            if filenum > self.last:
-                self.pbar.update(filenum - self.last)
-                self.last = filenum
-            return rtr
-
-        @jnius.java_method('()V')
-        def reset(self):
-            self.pbar.reset()
-            self.collection.reset()
-
-        @jnius.java_method('()V')
-        def close(self):
-            self.pbar.close()
-            self.collection.close()
-
-        @jnius.java_method('()Z')
-        def endOfCollection(self):
-            return self.collection.endOfCollection()
-
-        @jnius.java_method('()Lorg/terrier/indexing/Document;')
-        def getDocument(self):
-            global lastdoc
-            lastdoc = self.collection.getDocument()
-            return lastdoc
-
-    class PythonListIterator(jnius.PythonJavaClass):
-        __javainterfaces__ = ['java/util/Iterator']
-
-        def __init__(self, text, meta, convertFn, len=None, index=0):
-            super(PythonListIterator, self).__init__()
-            self.text = text
-            self.meta = meta
-            self.index = index
-            self.convertFn = convertFn
-            if len is None:
-                self.len = len(self.text)
-            else:
-                self.len = len
-
-        @jnius.java_method('()V')
-        def remove():
-            # 1
-            pass
-
-        @jnius.java_method('(Ljava/util/function/Consumer;)V')
-        def forEachRemaining(action):
-            # 1
-            pass
-
-        @jnius.java_method('()Z')
-        def hasNext(self):
-            return self.index < self.len
-
-        @jnius.java_method('()Ljava/lang/Object;')
-        def next(self):
-            text = self.text[self.index]
-            meta = self.meta.__next__()
-            self.index += 1
-            global lastdoc
-            if self.convertFn is not None:
-                lastdoc = self.convertFn(text, meta)
-            else:
-                lastdoc = [text, meta]
-            return lastdoc
-
-    @pt.java.required
-    class FlatJSONDocumentIterator(jnius.PythonJavaClass):
-        __javainterfaces__ = ['java/util/Iterator']
-
-        def __init__(self, it):
-            super(FlatJSONDocumentIterator, self).__init__()
-            self._it = it
-            # easiest way to support hasNext is just to start consuming right away, I think
-            self._next = next(self._it, StopIteration)
-
-        @jnius.java_method('()V')
-        def remove():
-            # 1
-            pass
-
-        @jnius.java_method('(Ljava/util/function/Consumer;)V')
-        def forEachRemaining(action):
-            # 1
-            pass
-
-        @jnius.java_method('()Z')
-        def hasNext(self):
-            return self._next is not StopIteration
-
-        @jnius.java_method('()Ljava/lang/Object;')
-        def next(self):
-            result = self._next
-            self._next = next(self._it, StopIteration)
-            if result is not StopIteration:
-                global lastdoc
-                lastdoc = pt.terrier.J.FlatJSONDocument(json.dumps(result))
-                return lastdoc
-            return None
-
-    class TQDMSizeCollection(jnius.PythonJavaClass):
-        __javainterfaces__ = ['org/terrier/indexing/Collection']
-
-        def __init__(self, collection, total):
-            super(TQDMSizeCollection, self).__init__()
-            self.collection = collection
-            self.pbar = pt.tqdm(total=total, unit="documents")
-        
-        @jnius.java_method('()Z')
-        def nextDocument(self):
-            rtr = self.collection.nextDocument()
-            self.pbar.update()
-            return rtr
-
-        @jnius.java_method('()V')
-        def reset(self):
-            self.pbar.reset()
-            self.collection.reset()
-
-        @jnius.java_method('()V')
-        def close(self):
-            self.pbar.close()
-            self.collection.close()
-
-        @jnius.java_method('()Z')
-        def endOfCollection(self):
-            return self.collection.endOfCollection()
-
-        @jnius.java_method('()Lorg/terrier/indexing/Document;')
-        def getDocument(self):
-            global lastdoc
-            lastdoc = self.collection.getDocument()
-            return lastdoc
-
-
-
 type_to_class = {
     'trec' : 'org.terrier.indexing.TRECCollection',
     'trecweb' : 'org.terrier.indexing.TRECWebCollection',
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 5d7bb801..4a4d3d96 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -1,7 +1,8 @@
 import sys
+import json
 from functools import wraps
 from packaging.version import Version
-from typing import Optional, Union, List, Callable
+from typing import Optional, Union, List, Callable, Dict, Any
 import pyterrier as pt
 
 TERRIER_PKG = "org.terrier"
@@ -46,110 +47,321 @@ def prf_required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
         raise RuntimeError('you need to call pt.terrier.enable_prf() before java is loaded to use this function.')
 
 
-def _pre_init(jnius_config):
-    """
-    Download Terrier's jar file for the given version at the given file_path
-    Called by pt.init()
-
-    Args:
-        file_path(str): Where to download
-        terrier_version(str): Which version of Terrier - None is latest release; "snapshot" uses Jitpack to download a build of the current Github 5.x branch.
-        helper_version(str): Which version of the helper - None is latest
-    """
-    # If version is not specified, find newest and download it
-    if configure['terrier_version'] is None:
-        terrier_version = pt.java.mavenresolver.latest_version_num(TERRIER_PKG, "terrier-assemblies")
-    else:
-        terrier_version = str(configure['terrier_version']) # just in case its a float
-    configure['terrier_version'] = terrier_version # save this specific version
-
-    # obtain the fat jar from Maven
-    # "snapshot" means use Jitpack.io to get a build of the current
-    # 5.x branch from Github - see https://jitpack.io/#terrier-org/terrier-core/5.x-SNAPSHOT
-    if terrier_version == "snapshot":
-        trJar = pt.java.mavenresolver.get_package_jar("com.github.terrier-org.terrier-core", "terrier-assemblies", "5.x-SNAPSHOT", artifact="jar-with-dependencies", force_download=configure['force_download'])
-    else:
-        trJar = pt.java.mavenresolver.get_package_jar(TERRIER_PKG, "terrier-assemblies", terrier_version, artifact="jar-with-dependencies")
-    jnius_config.add_classpath(trJar)
-
-    # now the helper classes
-    if configure['helper_version'] is None or configure['helper_version'] == 'snapshot':
-        helper_version = pt.java.mavenresolver.latest_version_num(TERRIER_PKG, "terrier-python-helper")
-        configure['helper_version'] = helper_version # save this specific version
-    else:
-        helper_version = str(configure['helper_version']) # just in case its a float
-    helper_jar = pt.java.mavenresolver.get_package_jar(TERRIER_PKG, 'terrier-python-helper', helper_version)
-    jnius_config.add_classpath(helper_jar)
-
-    if configure['prf_version'] is not None:
-        prf_jar = pt.java.mavenresolver.get_package_jar('com.github.terrierteam', 'terrier-prf', configure['prf_version'])
-        jnius_config.add_classpath(prf_jar)
-
-
-@pt.java.required_raise
-def _post_init(jnius):
-    global _properties
-
-    jnius.protocol_map["org.terrier.structures.postings.IterablePosting"] = {
-        '__iter__': lambda self: self,
-        '__next__': lambda self: _iterableposting_next(self),
-        '__str__': lambda self: self.toString()
-    }
-
-    jnius.protocol_map["org.terrier.structures.CollectionStatistics"] = {
-        '__str__': lambda self: self.toString()
-    }
-
-    jnius.protocol_map["org.terrier.structures.LexiconEntry"] = {
-        '__str__': lambda self: self.toString()
-    }
-
-    jnius.protocol_map["org.terrier.structures.Lexicon"] = {
-        '__getitem__': _lexicon_getitem,
-        '__contains__': lambda self, term: self.getLexiconEntry(term) is not None,
-        '__len__': lambda self: self.numberOfEntries()
-    }
-
-    jnius.protocol_map["org.terrier.querying.IndexRef"] = {
-        '__reduce__' : _index_ref_reduce,
-        '__getstate__' : lambda self : None,
-    }
-
-    jnius.protocol_map["org.terrier.matching.models.WeightingModel"] = {
-        '__reduce__' : _wmodel_reduce,
-        '__getstate__' : lambda self : None,
-    }
-
-    jnius.protocol_map["org.terrier.python.CallableWeightingModel"] = {
-        '__reduce__' : _callable_wmodel_reduce,
-        '__getstate__' : lambda self : None,
-    }
-
-    jnius.protocol_map["org.terrier.structures.Index"] = {
-        # this means that len(index) returns the number of documents in the index
-        '__len__': lambda self: self.getCollectionStatistics().getNumberOfDocuments(),
-
-        # document-wise composition of indices: adding more documents to an index, by merging two indices with 
-        # different numbers of documents. This implemented by the overloading the `+` Python operator
-        '__add__': _index_add,
-
-        # get_corpus_iter returns a yield generator that return {"docno": "d1", "toks" : {'a' : 1}}
-        'get_corpus_iter' : _index_corpusiter
-    }
-
-    pt.IndexRef = J.IndexRef
-    _properties = pt.java.J.Properties()
-    pt.ApplicationSetup = J.ApplicationSetup
-    J.ApplicationSetup.bootstrapInitialisation(_properties)
-
-    version_string = J.Version.VERSION
-    if "BUILD_DATE" in dir(J.Version):
-        version_string += f" (build: {J.Version.BUILD_USER} {J.Version.BUILD_DATE})"
-
-    res = f"version={version_string}, helper_version={configure['helper_version']}"
-    if configure['prf_version'] is not None:
-        res += f" prf_version={configure['prf_version']}"
-    return res
+class TerrierInit(pt.java.JavaInitializer):
+    def pre_init(self, jnius_config):
+        """
+        Download Terrier's jar file for the given version at the given file_path
+        Called by pt.init()
+
+        Args:
+            file_path(str): Where to download
+            terrier_version(str): Which version of Terrier - None is latest release; "snapshot" uses Jitpack to download a build of the current Github 5.x branch.
+            helper_version(str): Which version of the helper - None is latest
+        """
+        # If version is not specified, find newest and download it
+        if configure['terrier_version'] is None:
+            terrier_version = pt.java.mavenresolver.latest_version_num(TERRIER_PKG, "terrier-assemblies")
+        else:
+            terrier_version = str(configure['terrier_version']) # just in case its a float
+        configure['terrier_version'] = terrier_version # save this specific version
+
+        # obtain the fat jar from Maven
+        # "snapshot" means use Jitpack.io to get a build of the current
+        # 5.x branch from Github - see https://jitpack.io/#terrier-org/terrier-core/5.x-SNAPSHOT
+        if terrier_version == "snapshot":
+            trJar = pt.java.mavenresolver.get_package_jar("com.github.terrier-org.terrier-core", "terrier-assemblies", "5.x-SNAPSHOT", artifact="jar-with-dependencies", force_download=configure['force_download'])
+        else:
+            trJar = pt.java.mavenresolver.get_package_jar(TERRIER_PKG, "terrier-assemblies", terrier_version, artifact="jar-with-dependencies")
+        jnius_config.add_classpath(trJar)
+
+        # now the helper classes
+        if configure['helper_version'] is None or configure['helper_version'] == 'snapshot':
+            helper_version = pt.java.mavenresolver.latest_version_num(TERRIER_PKG, "terrier-python-helper")
+            configure['helper_version'] = helper_version # save this specific version
+        else:
+            helper_version = str(configure['helper_version']) # just in case its a float
+        helper_jar = pt.java.mavenresolver.get_package_jar(TERRIER_PKG, 'terrier-python-helper', helper_version)
+        jnius_config.add_classpath(helper_jar)
+
+        if configure['prf_version'] is not None:
+            prf_jar = pt.java.mavenresolver.get_package_jar('com.github.terrierteam', 'terrier-prf', configure['prf_version'])
+            jnius_config.add_classpath(prf_jar)
+
+
+    @pt.java.required_raise
+    def post_init(self, jnius):
+        global _properties
+
+        jnius.protocol_map["org.terrier.structures.postings.IterablePosting"] = {
+            '__iter__': lambda self: self,
+            '__next__': lambda self: _iterableposting_next(self),
+            '__str__': lambda self: self.toString()
+        }
+
+        jnius.protocol_map["org.terrier.structures.CollectionStatistics"] = {
+            '__str__': lambda self: self.toString()
+        }
+
+        jnius.protocol_map["org.terrier.structures.LexiconEntry"] = {
+            '__str__': lambda self: self.toString()
+        }
+
+        jnius.protocol_map["org.terrier.structures.Lexicon"] = {
+            '__getitem__': _lexicon_getitem,
+            '__contains__': lambda self, term: self.getLexiconEntry(term) is not None,
+            '__len__': lambda self: self.numberOfEntries()
+        }
+
+        jnius.protocol_map["org.terrier.querying.IndexRef"] = {
+            '__reduce__' : _index_ref_reduce,
+            '__getstate__' : lambda self : None,
+        }
+
+        jnius.protocol_map["org.terrier.matching.models.WeightingModel"] = {
+            '__reduce__' : _wmodel_reduce,
+            '__getstate__' : lambda self : None,
+        }
+
+        jnius.protocol_map["org.terrier.python.CallableWeightingModel"] = {
+            '__reduce__' : _callable_wmodel_reduce,
+            '__getstate__' : lambda self : None,
+        }
+
+        jnius.protocol_map["org.terrier.structures.Index"] = {
+            # this means that len(index) returns the number of documents in the index
+            '__len__': lambda self: self.getCollectionStatistics().getNumberOfDocuments(),
+
+            # document-wise composition of indices: adding more documents to an index, by merging two indices with 
+            # different numbers of documents. This implemented by the overloading the `+` Python operator
+            '__add__': _index_add,
+
+            # get_corpus_iter returns a yield generator that return {"docno": "d1", "toks" : {'a' : 1}}
+            'get_corpus_iter' : _index_corpusiter
+        }
+
+        self._post_init_index(jnius)
+
+        pt.IndexRef = J.IndexRef
+        _properties = pt.java.J.Properties()
+        pt.ApplicationSetup = J.ApplicationSetup
+        J.ApplicationSetup.bootstrapInitialisation(_properties)
+
+    def message(self):
+        version_string = J.Version.VERSION
+        if "BUILD_DATE" in dir(J.Version):
+            version_string += f" (build: {J.Version.BUILD_USER} {J.Version.BUILD_DATE})"
+
+        res = f"version={version_string}, helper_version={configure['helper_version']}"
+        if configure['prf_version'] is not None:
+            res += f" prf_version={configure['prf_version']}"
+
+        return res
+
+    def _post_init_index(self, jnius):
+        @pt.java.required
+        class DocListIterator(jnius.PythonJavaClass):
+            __javainterfaces__ = [
+                'java/util/Iterator',
+            ]
+
+            def __init__(self, pyiterator):
+                self.pyiterator = pyiterator
+                self.hasnext = True
+                self.lastdoc = None
+                self.tr57 = not pt.terrier.check_version("5.8")
+
+            @staticmethod 
+            def pyDictToMap(a_dict): #returns Map<String,String>
+                rtr = pt.java.J.HashMap()
+                for k,v in a_dict.items():
+                    rtr.put(k, v)
+                return rtr
+
+            def pyDictToMapEntry(self,doc_dict : Dict[str,Any]): #returns Map.Entry<Map<String,String>, DocumentPostingList>>
+                dpl = pt.terrier.J.DocumentPostingList()
+                # this works around a bug in the counting of doc lengths in Tr 5.7
+                if self.tr57:
+                    for t, tf in doc_dict["toks"].items():
+                        for i in range(int(tf)):
+                            dpl.insert(t)
+                else: # this code for 5.8 onwards
+                    for t, tf in doc_dict["toks"].items():
+                        dpl.insert(int(tf), t)
+                
+                # we cant make the toks column into the metaindex as it isnt a string. remove it.
+                del doc_dict["toks"]
+                return pt.terrier.J.MapEntry(DocListIterator.pyDictToMap(doc_dict), dpl)
+
+            @jnius.java_method('()Z')
+            def hasNext(self):
+                return self.hasnext
+
+            @jnius.java_method('()Ljava/lang/Object;')
+            def next(self):
+                try:
+                    doc_dict = next(self.pyiterator)
+                except StopIteration as se:
+                    self.hasnext = False
+                    # terrier will ignore a null return from an iterator
+                    return None
+                # keep this around to prevent being GCd before Java can read it
+                self.lastdoc = self.pyDictToMapEntry(doc_dict)
+                return self.lastdoc
+                
+        @pt.java.required
+        class TQDMCollection(jnius.PythonJavaClass):
+            __javainterfaces__ = ['org/terrier/indexing/Collection']
+
+            def __init__(self, collection):
+                super(TQDMCollection, self).__init__()
+                assert isinstance(collection, pt.terrier.J.MultiDocumentFileCollection)
+                self.collection = collection
+                size = self.collection.FilesToProcess.size()
+                self.pbar = pt.tqdm(total=size, unit="files")
+                self.last = -1
+            
+            @jnius.java_method('()Z')
+            def nextDocument(self):
+                rtr = self.collection.nextDocument()
+                filenum = self.collection.FileNumber
+                if filenum > self.last:
+                    self.pbar.update(filenum - self.last)
+                    self.last = filenum
+                return rtr
+
+            @jnius.java_method('()V')
+            def reset(self):
+                self.pbar.reset()
+                self.collection.reset()
+
+            @jnius.java_method('()V')
+            def close(self):
+                self.pbar.close()
+                self.collection.close()
+
+            @jnius.java_method('()Z')
+            def endOfCollection(self):
+                return self.collection.endOfCollection()
+
+            @jnius.java_method('()Lorg/terrier/indexing/Document;')
+            def getDocument(self):
+                global lastdoc
+                lastdoc = self.collection.getDocument()
+                return lastdoc
+
+        class PythonListIterator(jnius.PythonJavaClass):
+            __javainterfaces__ = ['java/util/Iterator']
+
+            def __init__(self, text, meta, convertFn, len=None, index=0):
+                super(PythonListIterator, self).__init__()
+                self.text = text
+                self.meta = meta
+                self.index = index
+                self.convertFn = convertFn
+                if len is None:
+                    self.len = len(self.text)
+                else:
+                    self.len = len
+
+            @jnius.java_method('()V')
+            def remove():
+                # 1
+                pass
+
+            @jnius.java_method('(Ljava/util/function/Consumer;)V')
+            def forEachRemaining(action):
+                # 1
+                pass
+
+            @jnius.java_method('()Z')
+            def hasNext(self):
+                return self.index < self.len
+
+            @jnius.java_method('()Ljava/lang/Object;')
+            def next(self):
+                text = self.text[self.index]
+                meta = self.meta.__next__()
+                self.index += 1
+                global lastdoc
+                if self.convertFn is not None:
+                    lastdoc = self.convertFn(text, meta)
+                else:
+                    lastdoc = [text, meta]
+                return lastdoc
+
+        @pt.java.required
+        class FlatJSONDocumentIterator(jnius.PythonJavaClass):
+            __javainterfaces__ = ['java/util/Iterator']
+
+            def __init__(self, it):
+                super(FlatJSONDocumentIterator, self).__init__()
+                self._it = it
+                # easiest way to support hasNext is just to start consuming right away, I think
+                self._next = next(self._it, StopIteration)
+
+            @jnius.java_method('()V')
+            def remove():
+                # 1
+                pass
+
+            @jnius.java_method('(Ljava/util/function/Consumer;)V')
+            def forEachRemaining(action):
+                # 1
+                pass
+
+            @jnius.java_method('()Z')
+            def hasNext(self):
+                return self._next is not StopIteration
+
+            @jnius.java_method('()Ljava/lang/Object;')
+            def next(self):
+                result = self._next
+                self._next = next(self._it, StopIteration)
+                if result is not StopIteration:
+                    global lastdoc
+                    lastdoc = pt.terrier.J.FlatJSONDocument(json.dumps(result))
+                    return lastdoc
+                return None
+
+        class TQDMSizeCollection(jnius.PythonJavaClass):
+            __javainterfaces__ = ['org/terrier/indexing/Collection']
+
+            def __init__(self, collection, total):
+                super(TQDMSizeCollection, self).__init__()
+                self.collection = collection
+                self.pbar = pt.tqdm(total=total, unit="documents")
+            
+            @jnius.java_method('()Z')
+            def nextDocument(self):
+                rtr = self.collection.nextDocument()
+                self.pbar.update()
+                return rtr
+
+            @jnius.java_method('()V')
+            def reset(self):
+                self.pbar.reset()
+                self.collection.reset()
+
+            @jnius.java_method('()V')
+            def close(self):
+                self.pbar.close()
+                self.collection.close()
+
+            @jnius.java_method('()Z')
+            def endOfCollection(self):
+                return self.collection.endOfCollection()
+
+            @jnius.java_method('()Lorg/terrier/indexing/Document;')
+            def getDocument(self):
+                global lastdoc
+                lastdoc = self.collection.getDocument()
+                return lastdoc
+
+        pt.terrier.index.DocListIterator = DocListIterator
+        pt.terrier.index.PythonListIterator = PythonListIterator
+        pt.terrier.index.FlatJSONDocumentIterator = FlatJSONDocumentIterator
+        pt.terrier.index.TQDMCollection = TQDMCollection
+        pt.terrier.index.TQDMSizeCollection = TQDMSizeCollection
 
 
 def _new_indexref(s):
diff --git a/setup.py b/setup.py
index 83e38858..045fffdf 100644
--- a/setup.py
+++ b/setup.py
@@ -71,15 +71,10 @@ def get_version(rel_path):
         "Operating System :: OS Independent",
     ],
     entry_points={
-        'pyterrier.java.pre_init': [
-            'pyterrier.terrier.java  = pyterrier.terrier.java:_pre_init',
-            'pyterrier.anserini.java = pyterrier.anserini.java:_pre_init',
-        ],
-        'pyterrier.java.post_init': [
-            'pyterrier.java          = pyterrier.java._init:_post_init',
-            'pyterrier.terrier.java  = pyterrier.terrier.java:_post_init',
-            'pyterrier.terrier.index = pyterrier.terrier.index:_java_post_init',
-            'pyterrier.anserini.java = pyterrier.anserini.java:_post_init',
+        'pyterrier.java.init': [
+            'pyterrier.java          = pyterrier.java._init:CoreInit',
+            'pyterrier.terrier.java  = pyterrier.terrier.java:TerrierInit',
+            'pyterrier.anserini.java = pyterrier.anserini.java:AnseriniInit',
         ],
     },
     install_requires=requirements,

From 865784d853a0667d833ca1de4abaddd807823616 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Wed, 31 Jul 2024 10:20:49 +0100
Subject: [PATCH 105/153] missing decorator

---
 pyterrier/terrier/java.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 4a4d3d96..4ec00eb0 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -146,6 +146,7 @@ def post_init(self, jnius):
         pt.ApplicationSetup = J.ApplicationSetup
         J.ApplicationSetup.bootstrapInitialisation(_properties)
 
+    @pt.java.required_raise
     def message(self):
         version_string = J.Version.VERSION
         if "BUILD_DATE" in dir(J.Version):

From dc3625a4f051bdb4ea790ded702cb381b0db821f Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Wed, 31 Jul 2024 10:31:37 +0100
Subject: [PATCH 106/153] misc fixes

---
 pyterrier/anserini/java.py      |  2 --
 pyterrier/java/_init.py         | 22 ++++++++++++++++++++++
 tests/anserini/test_anserini.py |  7 -------
 3 files changed, 22 insertions(+), 9 deletions(-)

diff --git a/pyterrier/anserini/java.py b/pyterrier/anserini/java.py
index a8bed7c4..cad1fc3e 100644
--- a/pyterrier/anserini/java.py
+++ b/pyterrier/anserini/java.py
@@ -1,8 +1,6 @@
 import os
-import inspect
 from glob import glob
 from typing import Callable, Optional, Union
-from functools import wraps
 import pyterrier as pt
 
 
diff --git a/pyterrier/java/_init.py b/pyterrier/java/_init.py
index 853e17a9..4dfe0eae 100644
--- a/pyterrier/java/_init.py
+++ b/pyterrier/java/_init.py
@@ -11,19 +11,41 @@
 
 
 class JavaInitializer:
+    """
+    A `JavaInitializer` manages the initilization of a module that uses java components. The two main methods are
+    `pre_init` and `post_init`, which perform configuration before and after the JVM has started, respectively.
+    """
+
     def priority(self) -> int:
+        """
+        Returns the priority of this initializer. A lower priority is executed first.
+        """
         return 0
 
     def condition(self) -> bool:
+        """
+        Returns True if the initializer should be run. Otherwise False.
+        """
         return True
 
     def pre_init(self, jnius_config) -> None:
+        """
+        Called before the JVM is started. `jnius_config` is the `jnius_config` module, whic can be used to configure
+        java, such as by adding jars to the classpath.
+        """
         pass
 
     def post_init(self, jnius) -> None:
+        """
+        Called after the JVM has started. `jnius` is the `jnius` module, which can be used to interact with java.
+        """
         pass
 
     def message(self) -> Optional[str]:
+        """
+        Returns a message to be displayed after the JVM has started alongside the name of the entry point. If None,
+        only the entry point name will be displayed.
+        """
         return None
 
 
diff --git a/tests/anserini/test_anserini.py b/tests/anserini/test_anserini.py
index 090d5d88..a48db50d 100644
--- a/tests/anserini/test_anserini.py
+++ b/tests/anserini/test_anserini.py
@@ -1,7 +1,6 @@
 import unittest
 import os
 import pyterrier as pt
-ANSERINI_VERSION="0.22.0"
 
 class AnseriniTestCase(unittest.TestCase):
 
@@ -15,12 +14,6 @@ def skip_pyserini(self):
     def __init__(self, *args, **kwargs):
         super(AnseriniTestCase, self).__init__(*args, **kwargs)
         if pt.anserini.is_installed():
-            anserini_version = os.environ.get("ANSERINI_VERSION", ANSERINI_VERSION)
-            terrier_version = os.environ.get("TERRIER_VERSION", None)
-            if terrier_version is not None:
-                print("Testing with Terrier version " + terrier_version)
-            if not pt.started():
-                pt.init(version=terrier_version, logging="DEBUG", boot_packages=["io.anserini:anserini:%s:fatjar" % anserini_version])
             self.here = os.path.dirname(os.path.realpath(__file__))
 
     def test_anserini_vaswani(self):

From 8b27581e0fc5167ea7185b65b834d49c5d95839e Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Wed, 31 Jul 2024 11:28:13 +0100
Subject: [PATCH 107/153] anserini messages

---
 pyterrier/anserini/java.py | 20 +++++++++++++++-----
 pyterrier/java/_init.py    |  2 ++
 2 files changed, 17 insertions(+), 5 deletions(-)

diff --git a/pyterrier/anserini/java.py b/pyterrier/anserini/java.py
index cad1fc3e..4ac3cf71 100644
--- a/pyterrier/anserini/java.py
+++ b/pyterrier/anserini/java.py
@@ -1,6 +1,7 @@
 import os
+from pathlib import Path
 from glob import glob
-from typing import Callable, Optional, Union
+from typing import Callable, Optional, Union, Tuple
 import pyterrier as pt
 
 
@@ -10,15 +11,20 @@
 
 
 class AnseriniInit(pt.java.JavaInitializer):
+    def __init__(self):
+        self._message = None
+
     def condition(self):
         return is_installed()
 
     def pre_init(self, jnius_config):
         if configure['version'] is None:
-            jar = _get_pyserini_jar()
+            jar, version = _get_pyserini_jar()
+            self._message = f"version={version} (from pyserini package)"
         else:
             # download and use the anserini version specified by the user
             jar = pt.java.mavenresolver.get_package_jar('io.anserini', "anserini", configure['version'], artifact='fatjar')
+            self._message = f"version={configure['version']} (local cache)"
 
         if jar is None:
             raise RuntimeError('Could not find anserini jar')
@@ -35,6 +41,9 @@ def post_init(self, jnius):
         finally:
             pyserini.setup.configure_classpath = _configure_classpath
 
+    def message(self):
+        return self._message
+
 
 def is_installed():
     try:
@@ -44,16 +53,17 @@ def is_installed():
     return True
 
 
-def _get_pyserini_jar() -> Optional[str]:
+def _get_pyserini_jar() -> Optional[Tuple[str, str]]:
     # find the anserini jar distributed with pyserini
     # Adapted from pyserini/setup.py and pyserini/pyclass.py
     import pyserini.setup
     jar_root = os.path.join(os.path.split(pyserini.setup.__file__)[0], 'resources/jars/')
     paths = glob(os.path.join(jar_root, 'anserini-*-fatjar.jar'))
     if not paths:
-        return None
+        return None, None
     latest_jar = max(paths, key=os.path.getctime)
-    return latest_jar
+    version = Path(latest_jar).name.split('-')[-2]
+    return latest_jar, version
 
 @pt.java.before_init
 def set_version(version: str):
diff --git a/pyterrier/java/_init.py b/pyterrier/java/_init.py
index 4dfe0eae..804efc72 100644
--- a/pyterrier/java/_init.py
+++ b/pyterrier/java/_init.py
@@ -156,6 +156,8 @@ def init() -> None:
     #     warnings.warn('pyterrier[java] not installed; no need to run pt.java.init()')
     #     return
 
+    # TODO: what about errors during init? What happens to _started? Etc.
+
     initalizers = []
     for entry_point in pt.utils.entry_points('pyterrier.java.init'):
         initalizer = entry_point.load()()

From 9869fe3e685975bfb99bd29d3282e6b5f0a6693b Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Wed, 31 Jul 2024 11:55:23 +0100
Subject: [PATCH 108/153] reorg pyterrier/java files to make more sense

---
 pyterrier/__init__.py      |   2 +-
 pyterrier/anserini/java.py |   2 +-
 pyterrier/java/__init__.py |   6 +-
 pyterrier/java/_init.py    | 289 ----------------------------
 pyterrier/java/config.py   |  52 -----
 pyterrier/java/core.py     | 197 +++++++++++++++++++
 pyterrier/java/utils.py    | 383 +++++++++++++++++++++++++------------
 pyterrier/terrier/java.py  |   2 +-
 setup.py                   |   2 +-
 9 files changed, 466 insertions(+), 469 deletions(-)
 delete mode 100644 pyterrier/java/_init.py
 delete mode 100644 pyterrier/java/config.py
 create mode 100644 pyterrier/java/core.py

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index dcbbf14e..8d611ee9 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -29,7 +29,7 @@
 # old name
 Utils = utils
 
-# will be set in terrier.java._post_init once java is loaded
+# will be set in terrier.terrier.java once java is loaded
 IndexRef = None
 
 
diff --git a/pyterrier/anserini/java.py b/pyterrier/anserini/java.py
index 4ac3cf71..298a8973 100644
--- a/pyterrier/anserini/java.py
+++ b/pyterrier/anserini/java.py
@@ -5,7 +5,7 @@
 import pyterrier as pt
 
 
-configure = pt.java.config.register('pyterrier.anserini', {
+configure = pt.java.register_config('pyterrier.anserini', {
     'version': None,
 })
 
diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index d47eeb51..08655365 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -1,8 +1,6 @@
 from pyterrier.java import mavenresolver
-from pyterrier.java._init import init, started, parallel_init, parallel_init_args, required, required_raise, before_init, autoclass, cast, legacy_init, JavaInitializer, CoreInit
-from pyterrier.java import config
-from pyterrier.java.config import configure
-from pyterrier.java.utils import redirect_stdouterr, bytebuffer_to_array, JavaClasses, J, add_jar, add_package, set_memory_limit, set_redirect_io, add_option, set_log_level
+from pyterrier.java.utils import JavaInitializer, init, started, parallel_init, parallel_init_args, required, required_raise, before_init, autoclass, cast, legacy_init, register_config, Configuration, JavaClasses
+from pyterrier.java.core import CoreInit, configure, redirect_stdouterr, bytebuffer_to_array, J, add_jar, add_package, set_memory_limit, set_redirect_io, add_option, set_log_level
 
 __all__ = [
     'add_jar', 'add_option', 'add_package', 'autoclass', 'before_init', 'bytebuffer_to_array', 'cast', 'config',
diff --git a/pyterrier/java/_init.py b/pyterrier/java/_init.py
deleted file mode 100644
index 804efc72..00000000
--- a/pyterrier/java/_init.py
+++ /dev/null
@@ -1,289 +0,0 @@
-import sys
-import os
-from warnings import warn
-from functools import wraps
-from typing import Dict, Any, Tuple, Callable, Optional, Union
-from copy import deepcopy
-import pyterrier as pt
-
-
-_started = False
-
-
-class JavaInitializer:
-    """
-    A `JavaInitializer` manages the initilization of a module that uses java components. The two main methods are
-    `pre_init` and `post_init`, which perform configuration before and after the JVM has started, respectively.
-    """
-
-    def priority(self) -> int:
-        """
-        Returns the priority of this initializer. A lower priority is executed first.
-        """
-        return 0
-
-    def condition(self) -> bool:
-        """
-        Returns True if the initializer should be run. Otherwise False.
-        """
-        return True
-
-    def pre_init(self, jnius_config) -> None:
-        """
-        Called before the JVM is started. `jnius_config` is the `jnius_config` module, whic can be used to configure
-        java, such as by adding jars to the classpath.
-        """
-        pass
-
-    def post_init(self, jnius) -> None:
-        """
-        Called after the JVM has started. `jnius` is the `jnius` module, which can be used to interact with java.
-        """
-        pass
-
-    def message(self) -> Optional[str]:
-        """
-        Returns a message to be displayed after the JVM has started alongside the name of the entry point. If None,
-        only the entry point name will be displayed.
-        """
-        return None
-
-
-def started() -> bool:
-    """
-    Returns True if pt.java.init() has been called. Otherwise False.
-    """
-    return _started
-
-
-@pt.utils.pre_invocation_decorator
-def required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
-    """
-    Requires the Java Virtual Machine to be started. If the JVM has not yet been started, it runs pt.java.init().
-
-    Can be used as either a standalone function or a function/class @decorator. When used as a class decorator, it
-    is applied to all methods defined by the class.
-    """
-    if not started():
-        init()
-
-
-def required_raise(fn: Optional[Callable] = None) -> Union[Callable, bool]:
-    """
-    Similar to `pt.java.required`, but raises an error if called before pt.java.init().
-    """
-    if fn is None:
-        return required_raise(pt.utils.noop)()
-
-    @wraps(fn)
-    def _wrapper(*args, **kwargs):
-        if not started():
-            raise RuntimeError(f'You need to call pt.java.init() required before you can call {fn}')
-        return fn(*args, **kwargs)
-    return _wrapper
-
-
-def before_init(fn: Optional[Callable] = None) -> Union[Callable, bool]:
-    """
-    If the JVM has already started, an error is raised.
-
-    Can be used as either a standalone function or a function decorator.
-    """
-    if fn is None:
-        return before_init(pt.utils.noop)()
-
-    @wraps(fn)
-    def _wrapper(*args, **kwargs):
-        if started():
-            raise RuntimeError(f'You can only call {fn} before either you start using java or call pt.java.init()')
-        return fn(*args, **kwargs)
-    return _wrapper
-
-
-class CoreInit(JavaInitializer):
-    def priority(self) -> int:
-        return -100 # run this initializer before anything else
-
-    def pre_init(self, jnius_config):
-        if pt.utils.is_windows():
-            if "JAVA_HOME" in os.environ:
-                java_home =  os.environ["JAVA_HOME"]
-                fix = f'{java_home}\\jre\\bin\\server\\;{java_home}\\jre\\bin\\client\\;{java_home}\\bin\\server\\'
-                os.environ["PATH"] = os.environ["PATH"] + ";" + fix
-
-        if pt.java.configure['mem'] is not None:
-            jnius_config.add_options('-Xmx' + str(pt.java.configure['mem']) + 'm')
-
-        for opt in pt.java.configure['options']:
-            jnius_config.add_options(opt)
-
-        for jar in pt.java.configure['jars']:
-            jnius_config.add_classpath(jar)
-
-    @required_raise
-    def post_init(self, jnius):
-        pt.java.set_log_level(pt.java.configure['log_level'])
-
-        if pt.java.configure['redirect_io']:
-            pt.java.redirect_stdouterr()
-
-        java_version = pt.java.J.System.getProperty("java.version")
-        if java_version.startswith("1.") or java_version.startswith("9."):
-            raise RuntimeError(f"Pyterrier requires Java 11 or newer, we only found Java version {java_version};"
-                + " install a more recent Java, or change os.environ['JAVA_HOME'] to point to the proper Java installation")
-
-        jnius.protocol_map['java.util.Map$Entry'] = {
-            '__getitem__' : _mapentry_getitem,
-            '__iter__' : lambda self: iter([self.getKey(), self.getValue()]),
-            '__len__' : lambda self: 2
-        }
-
-
-# Map$Entry can be decoded like a tuple
-def _mapentry_getitem(self, i):
-    if i == 0:
-        return self.getKey()
-    if i == 1:
-        return self.getValue()
-    raise IndexError()
-
-
-@pt.utils.once()
-def init() -> None:
-    global _started
-    # TODO: if we make java optional some day, should check here that it's installed. E.g.,
-    # if find_spec('jnius_config') is None:
-    #     warnings.warn('pyterrier[java] not installed; no need to run pt.java.init()')
-    #     return
-
-    # TODO: what about errors during init? What happens to _started? Etc.
-
-    initalizers = []
-    for entry_point in pt.utils.entry_points('pyterrier.java.init'):
-        initalizer = entry_point.load()()
-        if initalizer.condition():
-            initalizers.append((entry_point.name, initalizer))
-
-    # sort by priority
-    initalizers = sorted(initalizers, key=lambda i: i[1].priority())
-
-    import jnius_config
-
-    # run pre-initialization setup
-    for _, initializer in initalizers:
-        initializer.pre_init(jnius_config)
-
-    import jnius # noqa: PT100 
-    _started = True
-
-    # run post-initialization setup
-    for _, initializer in initalizers:
-        initializer.post_init(jnius)
-
-    # build "Java started" message
-    message = []
-    for name, initializer in initalizers:
-        msg = initializer.message()
-        if msg is None:
-            message.append(f' - {name}\n')
-        else:
-            message.append(f' - {name} [{msg}]\n')
-    sys.stderr.write('Java started and loaded:\n' + ''.join(message))
-
-
-def parallel_init(started: bool, configs: Dict[str, Dict[str, Any]]) -> None:
-    if started:
-        if not pt.java.started():
-            warn(f'Starting java parallel with configs {configs}')
-            pt.java.config._CONFIGS = configs
-            init()
-        else:
-            warn("Avoiding reinit of PyTerrier")
-
-
-def parallel_init_args() -> Tuple[bool, Dict[str, Dict[str, Any]]]:
-    return (
-        started(),
-        deepcopy(pt.java.config._CONFIGS),
-    )
-
-
-@required_raise
-def autoclass(*args, **kwargs):
-    """
-    Wraps jnius.autoclass once java has started. Raises an error if called before pt.java.init() is called.
-    """
-    import jnius # noqa: PT100
-    return jnius.autoclass(*args, **kwargs) # noqa: PT100
-
-
-@required_raise
-def cast(*args, **kwargs):
-    """
-    Wraps jnius.cast once java has started. Raises an error if called before pt.java.init() is called.
-    """
-    import jnius # noqa: PT100
-    return jnius.cast(*args, **kwargs) # noqa: PT100
-
-
-@before_init
-def legacy_init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, logging='WARN', home_dir=None, boot_packages=[], tqdm=None, no_download=False,helper_version = None):
-    """
-    Function that can be called before Terrier classes and methods can be used.
-    Loads the Terrier .jar file and imports classes. Also finds the correct version of Terrier to download if no version is specified.
-
-    Args:
-        version(str): Which version of Terrier to download. Default is `None`.
-
-         * If None, find the newest Terrier released version in MavenCentral and download it.
-         * If `"snapshot"`, will download the latest build from `Jitpack <https://jitpack.io/>`_.
-
-        mem(str): Maximum memory allocated for the Java virtual machine heap in MB. Corresponds to java `-Xmx` commandline argument. Default is 1/4 of physical memory.
-        boot_packages(list(str)): Extra maven package coordinates files to load before starting Java. Default=`[]`. There is more information about loading packages in the `Terrier documentation <https://github.com/terrier-org/terrier-core/blob/5.x/doc/terrier_develop.md>`_
-        packages(list(str)): Extra maven package coordinates files to load, using the Terrier classloader. Default=`[]`. See also `boot_packages` above.
-        jvm_opts(list(str)): Extra options to pass to the JVM. Default=`[]`. For instance, you may enable Java assertions by setting `jvm_opts=['-ea']`
-        redirect_io(boolean): If True, the Java `System.out` and `System.err` will be redirected to Pythons sys.out and sys.err. Default=True.
-        logging(str): the logging level to use:
-
-         * Can be one of `'INFO'`, `'DEBUG'`, `'TRACE'`, `'WARN'`, `'ERROR'`. The latter is the quietest.
-         * Default is `'WARN'`.
-
-        home_dir(str): the home directory to use. Default to PYTERRIER_HOME environment variable.
-        tqdm: The `tqdm <https://tqdm.github.io/>`_ instance to use for progress bars within PyTerrier. Defaults to tqdm.tqdm. Available options are `'tqdm'`, `'auto'` or `'notebook'`.
-        helper_version(str): Which version of the helper.
-
-   
-    **Locating the Terrier .jar file:** PyTerrier is not tied to a specific version of Terrier and will automatically locate and download a recent Terrier .jar file. However, inevitably, some functionalities will require more recent Terrier versions. 
-    
-     * If set, PyTerrier uses the `version` init kwarg to determine the .jar file to look for.
-     * If the `version` init kwarg is not set, Terrier will query MavenCentral to determine the latest Terrier release.
-     * If `version` is set to `"snapshot"`, the latest .jar file build derived from the `Terrier Github repository <https://github.com/terrier-org/terrier-core/>`_ will be downloaded from `Jitpack <https://jitpack.io/>`_.
-     * Otherwise the local (`~/.mvn`) and MavenCentral repositories are searched for the jar file at the given version.
-    In this way, the default setting is to download the latest release of Terrier from MavenCentral. The user is also able to use a locally installed copy in their private Maven repository, or track the latest build of Terrier from Jitpack.
-    
-    If you wish to run PyTerrier in an offline enviroment, you should ensure that the "terrier-assemblies-{your version}-jar-with-dependencies.jar" and "terrier-python-helper-{your helper version}.jar"
-    are in the  "~/.pyterrier" (if they are not present, they will be downloaded the first time). Then you should set their versions when calling ``init()`` function. For example:
-    ``pt.init(version = 5.5, helper_version = "0.0.6")``.
-    """
-
-    # Set the corresponding options
-    pt.java.set_memory_limit(mem)
-    pt.java.set_redirect_io(redirect_io)
-    pt.java.set_log_level(logging)
-    for package in boot_packages:
-        pt.java.add_package(*package.split(':')) # format: org:package:version:filetype (where version and filetype are optional)
-    for opt in jvm_opts:
-        pt.java.add_option(opt)
-    pt.terrier.set_version(version)
-    pt.terrier.set_helper_version(helper_version)
-    if tqdm is not None:
-        pt.utils.set_tqdm(tqdm)
-    if no_download:
-        pt.java.mavenresolver.offline()
-
-    pt.java.init()
-
-    # Import other java packages
-    if packages:
-        pkgs_string = ",".join(packages)
-        pt.terrier.set_property("terrier.mvn.coords", pkgs_string)
diff --git a/pyterrier/java/config.py b/pyterrier/java/config.py
deleted file mode 100644
index 778b9e2b..00000000
--- a/pyterrier/java/config.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from typing import Dict, Any
-from copy import deepcopy
-from pyterrier.java._init import before_init, started
-
-_CONFIGS = {}
-
-class Configuration:
-    def __init__(self, name):
-        self.name = name
-
-    def get(self, key):
-        return deepcopy(_CONFIGS[self.name][key])
-
-    @before_init
-    def set(self, key, value):
-        self(**{key: value})
-
-    def append(self, key, value):
-        res = self.get(key)
-        res.append(value)
-        self(**{key: res})
-
-    def __getitem__(self, key):
-        return self.get(key)
-
-    @before_init
-    def __setitem__(self, key, value):
-        self.set(key, value)
-
-    def __call__(self, **settings: Any):
-        if started() and any(settings):
-            raise RuntimeError('You cannot change java settings after java has started')
-        for key, value in settings.items():
-            if key not in _CONFIGS[self.name]:
-                raise AttributeError(f'{key!r} not defined as a java setting for {self.name!r}')
-            _CONFIGS[self.name][key] = value
-        return deepcopy(_CONFIGS[self.name])
-
-
-def register(name, config: Dict[str, Any]):
-    assert name not in _CONFIGS
-    _CONFIGS[name] = deepcopy(config)
-    return Configuration(name)
-
-
-configure = register('pyterrier.java', {
-    'jars': [],
-    'options': [],
-    'mem': None,
-    'log_level': 'WARN',
-    'redirect_io': True,
-})
diff --git a/pyterrier/java/core.py b/pyterrier/java/core.py
new file mode 100644
index 00000000..b5a2ae37
--- /dev/null
+++ b/pyterrier/java/core.py
@@ -0,0 +1,197 @@
+import os
+from pyterrier.java import required_raise, required, before_init, started, mavenresolver, JavaClasses, JavaInitializer, register_config
+from typing import Dict, Optional
+import pyterrier as pt
+
+
+stdout_ref = None
+stderr_ref = None
+
+
+
+configure = register_config('pyterrier.java', {
+    'jars': [],
+    'options': [],
+    'mem': None,
+    'log_level': 'WARN',
+    'redirect_io': True,
+})
+
+
+
+class CoreInit(JavaInitializer):
+    def priority(self) -> int:
+        return -100 # run this initializer before anything else
+
+    def pre_init(self, jnius_config):
+        if pt.utils.is_windows():
+            if "JAVA_HOME" in os.environ:
+                java_home =  os.environ["JAVA_HOME"]
+                fix = f'{java_home}\\jre\\bin\\server\\;{java_home}\\jre\\bin\\client\\;{java_home}\\bin\\server\\'
+                os.environ["PATH"] = os.environ["PATH"] + ";" + fix
+
+        if pt.java.configure['mem'] is not None:
+            jnius_config.add_options('-Xmx' + str(pt.java.configure['mem']) + 'm')
+
+        for opt in pt.java.configure['options']:
+            jnius_config.add_options(opt)
+
+        for jar in pt.java.configure['jars']:
+            jnius_config.add_classpath(jar)
+
+    @required_raise
+    def post_init(self, jnius):
+        pt.java.set_log_level(pt.java.configure['log_level'])
+
+        if pt.java.configure['redirect_io']:
+            pt.java.redirect_stdouterr()
+
+        java_version = pt.java.J.System.getProperty("java.version")
+        if java_version.startswith("1.") or java_version.startswith("9."):
+            raise RuntimeError(f"Pyterrier requires Java 11 or newer, we only found Java version {java_version};"
+                + " install a more recent Java, or change os.environ['JAVA_HOME'] to point to the proper Java installation")
+
+        jnius.protocol_map['java.util.Map$Entry'] = {
+            '__getitem__' : _mapentry_getitem,
+            '__iter__' : lambda self: iter([self.getKey(), self.getValue()]),
+            '__len__' : lambda self: 2
+        }
+
+
+# Map$Entry can be decoded like a tuple
+def _mapentry_getitem(self, i):
+    if i == 0:
+        return self.getKey()
+    if i == 1:
+        return self.getValue()
+    raise IndexError()
+
+
+def _is_binary(f):
+    import io
+    return isinstance(f, (io.RawIOBase, io.BufferedIOBase))
+
+
+@required
+def redirect_stdouterr():
+    from jnius import autoclass, PythonJavaClass, java_method
+
+    # TODO: encodings may be a probem here
+    class MyOut(PythonJavaClass):
+        __javainterfaces__ = ['org.terrier.python.OutputStreamable']
+
+        def __init__(self, pystream):
+            super(MyOut, self).__init__()
+            self.pystream = pystream
+            self.binary = _is_binary(pystream)
+
+        @java_method('()V')
+        def close(self):
+            self.pystream.close()
+
+        @java_method('()V')
+        def flush(self):
+            self.pystream.flush()
+
+        @java_method('([B)V', name='write')
+        def writeByteArray(self, byteArray):
+            # TODO probably this could be faster.
+            for c in byteArray:
+                self.writeChar(c)
+
+        @java_method('([BII)V', name='write')
+        def writeByteArrayIntInt(self, byteArray, offset, length):
+            # TODO probably this could be faster.
+            for i in range(offset, offset + length):
+                self.writeChar(byteArray[i])
+
+        @java_method('(I)V', name='write')
+        def writeChar(self, chara):
+            if self.binary:
+                return self.pystream.write(bytes([chara]))
+            return self.pystream.write(chr(chara))
+
+    # we need to hold lifetime references to stdout_ref/stderr_ref, to ensure
+    # they arent GCd. This prevents a crash when Java callsback to  GCd py obj
+
+    global stdout_ref
+    global stderr_ref
+    import sys
+    stdout_ref = MyOut(sys.stdout)
+    stderr_ref = MyOut(sys.stderr)
+    jls = autoclass("java.lang.System")
+    jls.setOut(
+        autoclass('java.io.PrintStream')(
+            autoclass('org.terrier.python.ProxyableOutputStream')(stdout_ref),
+            signature="(Ljava/io/OutputStream;)V"))
+    jls.setErr(
+        autoclass('java.io.PrintStream')(
+            autoclass('org.terrier.python.ProxyableOutputStream')(stderr_ref),
+            signature="(Ljava/io/OutputStream;)V"))
+
+
+def bytebuffer_to_array(buffer):
+    assert buffer is not None
+    def unsign(signed):
+        return signed + 256 if signed < 0 else signed
+    return bytearray([ unsign(buffer.get(offset)) for offset in range(buffer.capacity()) ])
+
+
+@before_init
+def add_jar(jar_path):
+    configure.append('jars', jar_path)
+
+
+@before_init
+def add_package(org_name: str = None, package_name: str = None, version: str = None, file_type='jar'):
+    if version is None or version == 'snapshot':
+        version = mavenresolver.latest_version_num(org_name, package_name)
+    file_name = mavenresolver.get_package_jar(org_name, package_name, version, artifact=file_type)
+    add_jar(file_name)
+
+
+@before_init
+def set_memory_limit(mem: Optional[float]):
+    configure['mem'] = mem
+
+
+@before_init
+def add_option(option: str):
+    configure.append('options', option)
+
+
+@before_init
+def set_redirect_io(redirect_io: bool):
+    configure['redirect_io'] = redirect_io
+
+
+def set_log_level(level):
+    """
+        Set the logging level. The following string values are allowed, corresponding
+        to Java logging levels:
+        
+         - `'ERROR'`: only show error messages
+         - `'WARN'`: only show warnings and error messages (default)
+         - `'INFO'`: show information, warnings and error messages
+         - `'DEBUG'`: show debugging, information, warnings and error messages
+        
+        Unlike other java settings, this can be changed either before or after init() has been called.
+    """
+    if not started():
+        configure['log_level'] = level
+    else:
+        J.PTUtils.setLogLevel(level, None) # noqa: PT100 handled by started() check above
+
+
+J = JavaClasses({
+    'ArrayList': 'java.util.ArrayList',
+    'Properties': 'java.util.Properties',
+    'PTUtils': 'org.terrier.python.PTUtils',
+    'System': 'java.lang.System',
+    'StringReader': 'java.io.StringReader',
+    'HashMap': 'java.util.HashMap',
+    'Arrays': 'java.util.Arrays',
+    'Array': 'java.lang.reflect.Array',
+    'String': 'java.lang.String',
+    'List': 'java.util.List',
+})
diff --git a/pyterrier/java/utils.py b/pyterrier/java/utils.py
index 223a5b1e..7ae05a30 100644
--- a/pyterrier/java/utils.py
+++ b/pyterrier/java/utils.py
@@ -1,157 +1,300 @@
+import sys
+from warnings import warn
+from functools import wraps
+from typing import Dict, Any, Tuple, Callable, Optional, Union
+from copy import deepcopy
 import pyterrier as pt
-from pyterrier.java import required_raise, required, before_init, started, configure, mavenresolver
-from typing import Dict, Optional
 
+_started = False
+_configs = {}
 
-stdout_ref = None
-stderr_ref = None
+class JavaInitializer:
+    """
+    A `JavaInitializer` manages the initilization of a module that uses java components. The two main methods are
+    `pre_init` and `post_init`, which perform configuration before and after the JVM has started, respectively.
+    """
 
+    def priority(self) -> int:
+        """
+        Returns the priority of this initializer. A lower priority is executed first.
+        """
+        return 0
+
+    def condition(self) -> bool:
+        """
+        Returns True if the initializer should be run. Otherwise False.
+        """
+        return True
+
+    def pre_init(self, jnius_config) -> None:
+        """
+        Called before the JVM is started. `jnius_config` is the `jnius_config` module, whic can be used to configure
+        java, such as by adding jars to the classpath.
+        """
+        pass
+
+    def post_init(self, jnius) -> None:
+        """
+        Called after the JVM has started. `jnius` is the `jnius` module, which can be used to interact with java.
+        """
+        pass
+
+    def message(self) -> Optional[str]:
+        """
+        Returns a message to be displayed after the JVM has started alongside the name of the entry point. If None,
+        only the entry point name will be displayed.
+        """
+        return None
+
+
+def started() -> bool:
+    """
+    Returns True if pt.java.init() has been called. Otherwise False.
+    """
+    return _started
 
-def _is_binary(f):
-    import io
-    return isinstance(f, (io.RawIOBase, io.BufferedIOBase))
 
+@pt.utils.pre_invocation_decorator
+def required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
+    """
+    Requires the Java Virtual Machine to be started. If the JVM has not yet been started, it runs pt.java.init().
 
-@required
-def redirect_stdouterr():
-    from jnius import autoclass, PythonJavaClass, java_method
+    Can be used as either a standalone function or a function/class @decorator. When used as a class decorator, it
+    is applied to all methods defined by the class.
+    """
+    if not started():
+        init()
 
-    # TODO: encodings may be a probem here
-    class MyOut(PythonJavaClass):
-        __javainterfaces__ = ['org.terrier.python.OutputStreamable']
 
-        def __init__(self, pystream):
-            super(MyOut, self).__init__()
-            self.pystream = pystream
-            self.binary = _is_binary(pystream)
+def required_raise(fn: Optional[Callable] = None) -> Union[Callable, bool]:
+    """
+    Similar to `pt.java.required`, but raises an error if called before pt.java.init().
+    """
+    if fn is None:
+        return required_raise(pt.utils.noop)()
 
-        @java_method('()V')
-        def close(self):
-            self.pystream.close()
+    @wraps(fn)
+    def _wrapper(*args, **kwargs):
+        if not started():
+            raise RuntimeError(f'You need to call pt.java.init() required before you can call {fn}')
+        return fn(*args, **kwargs)
+    return _wrapper
 
-        @java_method('()V')
-        def flush(self):
-            self.pystream.flush()
 
-        @java_method('([B)V', name='write')
-        def writeByteArray(self, byteArray):
-            # TODO probably this could be faster.
-            for c in byteArray:
-                self.writeChar(c)
+def before_init(fn: Optional[Callable] = None) -> Union[Callable, bool]:
+    """
+    If the JVM has already started, an error is raised.
 
-        @java_method('([BII)V', name='write')
-        def writeByteArrayIntInt(self, byteArray, offset, length):
-            # TODO probably this could be faster.
-            for i in range(offset, offset + length):
-                self.writeChar(byteArray[i])
+    Can be used as either a standalone function or a function decorator.
+    """
+    if fn is None:
+        return before_init(pt.utils.noop)()
 
-        @java_method('(I)V', name='write')
-        def writeChar(self, chara):
-            if self.binary:
-                return self.pystream.write(bytes([chara]))
-            return self.pystream.write(chr(chara))
+    @wraps(fn)
+    def _wrapper(*args, **kwargs):
+        if started():
+            raise RuntimeError(f'You can only call {fn} before either you start using java or call pt.java.init()')
+        return fn(*args, **kwargs)
+    return _wrapper
 
-    # we need to hold lifetime references to stdout_ref/stderr_ref, to ensure
-    # they arent GCd. This prevents a crash when Java callsback to  GCd py obj
+class JavaClasses:
+    def __init__(self, mapping: Dict[str, str]):
+        self._mapping = mapping
+        self._cache = {}
 
-    global stdout_ref
-    global stderr_ref
-    import sys
-    stdout_ref = MyOut(sys.stdout)
-    stderr_ref = MyOut(sys.stderr)
-    jls = autoclass("java.lang.System")
-    jls.setOut(
-        autoclass('java.io.PrintStream')(
-            autoclass('org.terrier.python.ProxyableOutputStream')(stdout_ref),
-            signature="(Ljava/io/OutputStream;)V"))
-    jls.setErr(
-        autoclass('java.io.PrintStream')(
-            autoclass('org.terrier.python.ProxyableOutputStream')(stderr_ref),
-            signature="(Ljava/io/OutputStream;)V"))
+    def __dir__(self):
+        return list(self._mapping.keys())
 
+    @required_raise
+    def __getattr__(self, key):
+        if key not in self._mapping:
+            return AttributeError(f'{self} has no attribute {key!r}')
+        if key not in self._cache:
+            clz = self._mapping[key]
+            if callable(clz):
+                clz = clz()
+            self._cache[key] = pt.java.autoclass(clz)
+        return self._cache[key]
 
-def bytebuffer_to_array(buffer):
-    assert buffer is not None
-    def unsign(signed):
-        return signed + 256 if signed < 0 else signed
-    return bytearray([ unsign(buffer.get(offset)) for offset in range(buffer.capacity()) ])
 
 
-@before_init
-def add_jar(jar_path):
-    configure.append('jars', jar_path)
 
+@pt.utils.once()
+def init() -> None:
+    global _started
+    # TODO: if we make java optional some day, should check here that it's installed. E.g.,
+    # if find_spec('jnius_config') is None:
+    #     warnings.warn('pyterrier[java] not installed; no need to run pt.java.init()')
+    #     return
 
-@before_init
-def add_package(org_name: str = None, package_name: str = None, version: str = None, file_type='jar'):
-    if version is None or version == 'snapshot':
-        version = mavenresolver.latest_version_num(org_name, package_name)
-    file_name = mavenresolver.get_package_jar(org_name, package_name, version, artifact=file_type)
-    add_jar(file_name)
+    # TODO: what about errors during init? What happens to _started? Etc.
 
+    initalizers = []
+    for entry_point in pt.utils.entry_points('pyterrier.java.init'):
+        initalizer = entry_point.load()()
+        if initalizer.condition():
+            initalizers.append((entry_point.name, initalizer))
 
-@before_init
-def set_memory_limit(mem: Optional[float]):
-    configure['mem'] = mem
+    # sort by priority
+    initalizers = sorted(initalizers, key=lambda i: i[1].priority())
 
+    import jnius_config
 
-@before_init
-def add_option(option: str):
-    configure.append('options', option)
+    # run pre-initialization setup
+    for _, initializer in initalizers:
+        initializer.pre_init(jnius_config)
 
+    import jnius # noqa: PT100 
+    _started = True
+
+    # run post-initialization setup
+    for _, initializer in initalizers:
+        initializer.post_init(jnius)
+
+    # build "Java started" message
+    message = []
+    for name, initializer in initalizers:
+        msg = initializer.message()
+        if msg is None:
+            message.append(f' - {name}\n')
+        else:
+            message.append(f' - {name} [{msg}]\n')
+    sys.stderr.write('Java started and loaded:\n' + ''.join(message))
 
-@before_init
-def set_redirect_io(redirect_io: bool):
-    configure['redirect_io'] = redirect_io
 
+def parallel_init(started: bool, configs: Dict[str, Dict[str, Any]]) -> None:
+    global _configs
+    if started:
+        if not pt.java.started():
+            warn(f'Starting java parallel with configs {configs}')
+            _configs = configs
+            init()
+        else:
+            warn("Avoiding reinit of PyTerrier")
 
-def set_log_level(level):
+
+def parallel_init_args() -> Tuple[bool, Dict[str, Dict[str, Any]]]:
+    return (
+        started(),
+        deepcopy(_configs),
+    )
+
+
+@required_raise
+def autoclass(*args, **kwargs):
     """
-        Set the logging level. The following string values are allowed, corresponding
-        to Java logging levels:
-        
-         - `'ERROR'`: only show error messages
-         - `'WARN'`: only show warnings and error messages (default)
-         - `'INFO'`: show information, warnings and error messages
-         - `'DEBUG'`: show debugging, information, warnings and error messages
-        
-        Unlike other java settings, this can be changed either before or after init() has been called.
+    Wraps jnius.autoclass once java has started. Raises an error if called before pt.java.init() is called.
     """
-    if not started():
-        configure['log_level'] = level
-    else:
-        J.PTUtils.setLogLevel(level, None) # noqa: PT100 handled by started() check above
+    import jnius # noqa: PT100
+    return jnius.autoclass(*args, **kwargs) # noqa: PT100
 
 
-class JavaClasses:
-    def __init__(self, mapping: Dict[str, str]):
-        self._mapping = mapping
-        self._cache = {}
-
-    def __dir__(self):
-        return list(self._mapping.keys())
+@required_raise
+def cast(*args, **kwargs):
+    """
+    Wraps jnius.cast once java has started. Raises an error if called before pt.java.init() is called.
+    """
+    import jnius # noqa: PT100
+    return jnius.cast(*args, **kwargs) # noqa: PT100
 
-    @required_raise
-    def __getattr__(self, key):
-        if key not in self._mapping:
-            return AttributeError(f'{self} has no attribute {key!r}')
-        if key not in self._cache:
-            clz = self._mapping[key]
-            if callable(clz):
-                clz = clz()
-            self._cache[key] = pt.java.autoclass(clz)
-        return self._cache[key]
 
+@before_init
+def legacy_init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, logging='WARN', home_dir=None, boot_packages=[], tqdm=None, no_download=False,helper_version = None):
+    """
+    Function that can be called before Terrier classes and methods can be used.
+    Loads the Terrier .jar file and imports classes. Also finds the correct version of Terrier to download if no version is specified.
+
+    Args:
+        version(str): Which version of Terrier to download. Default is `None`.
+
+         * If None, find the newest Terrier released version in MavenCentral and download it.
+         * If `"snapshot"`, will download the latest build from `Jitpack <https://jitpack.io/>`_.
+
+        mem(str): Maximum memory allocated for the Java virtual machine heap in MB. Corresponds to java `-Xmx` commandline argument. Default is 1/4 of physical memory.
+        boot_packages(list(str)): Extra maven package coordinates files to load before starting Java. Default=`[]`. There is more information about loading packages in the `Terrier documentation <https://github.com/terrier-org/terrier-core/blob/5.x/doc/terrier_develop.md>`_
+        packages(list(str)): Extra maven package coordinates files to load, using the Terrier classloader. Default=`[]`. See also `boot_packages` above.
+        jvm_opts(list(str)): Extra options to pass to the JVM. Default=`[]`. For instance, you may enable Java assertions by setting `jvm_opts=['-ea']`
+        redirect_io(boolean): If True, the Java `System.out` and `System.err` will be redirected to Pythons sys.out and sys.err. Default=True.
+        logging(str): the logging level to use:
+
+         * Can be one of `'INFO'`, `'DEBUG'`, `'TRACE'`, `'WARN'`, `'ERROR'`. The latter is the quietest.
+         * Default is `'WARN'`.
+
+        home_dir(str): the home directory to use. Default to PYTERRIER_HOME environment variable.
+        tqdm: The `tqdm <https://tqdm.github.io/>`_ instance to use for progress bars within PyTerrier. Defaults to tqdm.tqdm. Available options are `'tqdm'`, `'auto'` or `'notebook'`.
+        helper_version(str): Which version of the helper.
+
+   
+    **Locating the Terrier .jar file:** PyTerrier is not tied to a specific version of Terrier and will automatically locate and download a recent Terrier .jar file. However, inevitably, some functionalities will require more recent Terrier versions. 
+    
+     * If set, PyTerrier uses the `version` init kwarg to determine the .jar file to look for.
+     * If the `version` init kwarg is not set, Terrier will query MavenCentral to determine the latest Terrier release.
+     * If `version` is set to `"snapshot"`, the latest .jar file build derived from the `Terrier Github repository <https://github.com/terrier-org/terrier-core/>`_ will be downloaded from `Jitpack <https://jitpack.io/>`_.
+     * Otherwise the local (`~/.mvn`) and MavenCentral repositories are searched for the jar file at the given version.
+    In this way, the default setting is to download the latest release of Terrier from MavenCentral. The user is also able to use a locally installed copy in their private Maven repository, or track the latest build of Terrier from Jitpack.
+    
+    If you wish to run PyTerrier in an offline enviroment, you should ensure that the "terrier-assemblies-{your version}-jar-with-dependencies.jar" and "terrier-python-helper-{your helper version}.jar"
+    are in the  "~/.pyterrier" (if they are not present, they will be downloaded the first time). Then you should set their versions when calling ``init()`` function. For example:
+    ``pt.init(version = 5.5, helper_version = "0.0.6")``.
+    """
 
-J = JavaClasses({
-    'ArrayList': 'java.util.ArrayList',
-    'Properties': 'java.util.Properties',
-    'PTUtils': 'org.terrier.python.PTUtils',
-    'System': 'java.lang.System',
-    'StringReader': 'java.io.StringReader',
-    'HashMap': 'java.util.HashMap',
-    'Arrays': 'java.util.Arrays',
-    'Array': 'java.lang.reflect.Array',
-    'String': 'java.lang.String',
-    'List': 'java.util.List',
-})
+    # Set the corresponding options
+    pt.java.set_memory_limit(mem)
+    pt.java.set_redirect_io(redirect_io)
+    pt.java.set_log_level(logging)
+    for package in boot_packages:
+        pt.java.add_package(*package.split(':')) # format: org:package:version:filetype (where version and filetype are optional)
+    for opt in jvm_opts:
+        pt.java.add_option(opt)
+    pt.terrier.set_version(version)
+    pt.terrier.set_helper_version(helper_version)
+    if tqdm is not None:
+        pt.utils.set_tqdm(tqdm)
+    if no_download:
+        pt.java.mavenresolver.offline()
+
+    pt.java.init()
+
+    # Import other java packages
+    if packages:
+        pkgs_string = ",".join(packages)
+        pt.terrier.set_property("terrier.mvn.coords", pkgs_string)
+
+
+class Configuration:
+    def __init__(self, name):
+        self.name = name
+
+    def get(self, key):
+        return deepcopy(_configs[self.name][key])
+
+    @before_init
+    def set(self, key, value):
+        self(**{key: value})
+
+    def append(self, key, value):
+        res = self.get(key)
+        res.append(value)
+        self(**{key: res})
+
+    def __getitem__(self, key):
+        return self.get(key)
+
+    @before_init
+    def __setitem__(self, key, value):
+        self.set(key, value)
+
+    def __call__(self, **settings: Any):
+        if started() and any(settings):
+            raise RuntimeError('You cannot change java settings after java has started')
+        for key, value in settings.items():
+            if key not in _configs[self.name]:
+                raise AttributeError(f'{key!r} not defined as a java setting for {self.name!r}')
+            _configs[self.name][key] = value
+        return deepcopy(_configs[self.name])
+
+
+def register_config(name, config: Dict[str, Any]):
+    assert name not in _configs
+    _configs[name] = deepcopy(config)
+    return Configuration(name)
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 4ec00eb0..665ea905 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -11,7 +11,7 @@
 
 _properties = None
 
-configure = pt.java.config.register('pt.terrier.java', {
+configure = pt.java.register_config('pt.terrier.java', {
     'terrier_version': None,
     'helper_version': None,
     'boot_packages': [],
diff --git a/setup.py b/setup.py
index 045fffdf..938a698d 100644
--- a/setup.py
+++ b/setup.py
@@ -72,7 +72,7 @@ def get_version(rel_path):
     ],
     entry_points={
         'pyterrier.java.init': [
-            'pyterrier.java          = pyterrier.java._init:CoreInit',
+            'pyterrier.java          = pyterrier.java:CoreInit',
             'pyterrier.terrier.java  = pyterrier.terrier.java:TerrierInit',
             'pyterrier.anserini.java = pyterrier.anserini.java:AnseriniInit',
         ],

From 0d1acdd9bc633e4addc7e6cc58c49bb2ab3ec0f6 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Wed, 31 Jul 2024 12:25:51 +0100
Subject: [PATCH 109/153] organize java files

---
 pyterrier/java/core.py  |  50 ++++----
 pyterrier/java/utils.py | 246 ++++++++++++++++++++++++----------------
 2 files changed, 179 insertions(+), 117 deletions(-)

diff --git a/pyterrier/java/core.py b/pyterrier/java/core.py
index b5a2ae37..d1b54f81 100644
--- a/pyterrier/java/core.py
+++ b/pyterrier/java/core.py
@@ -1,23 +1,16 @@
 import os
 from pyterrier.java import required_raise, required, before_init, started, mavenresolver, JavaClasses, JavaInitializer, register_config
-from typing import Dict, Optional
+from typing import Optional
 import pyterrier as pt
 
 
-stdout_ref = None
-stderr_ref = None
-
-
-
-configure = register_config('pyterrier.java', {
-    'jars': [],
-    'options': [],
-    'mem': None,
-    'log_level': 'WARN',
-    'redirect_io': True,
-})
+_stdout_ref = None
+_stderr_ref = None
 
 
+# ----------------------------------------------------------
+# Java Initialization
+# ----------------------------------------------------------
 
 class CoreInit(JavaInitializer):
     def priority(self) -> int:
@@ -111,22 +104,22 @@ def writeChar(self, chara):
                 return self.pystream.write(bytes([chara]))
             return self.pystream.write(chr(chara))
 
-    # we need to hold lifetime references to stdout_ref/stderr_ref, to ensure
+    # we need to hold lifetime references to _stdout_ref/_stderr_ref, to ensure
     # they arent GCd. This prevents a crash when Java callsback to  GCd py obj
 
-    global stdout_ref
-    global stderr_ref
+    global _stdout_ref
+    global _stderr_ref
     import sys
-    stdout_ref = MyOut(sys.stdout)
-    stderr_ref = MyOut(sys.stderr)
+    _stdout_ref = MyOut(sys.stdout)
+    _stderr_ref = MyOut(sys.stderr)
     jls = autoclass("java.lang.System")
     jls.setOut(
         autoclass('java.io.PrintStream')(
-            autoclass('org.terrier.python.ProxyableOutputStream')(stdout_ref),
+            autoclass('org.terrier.python.ProxyableOutputStream')(_stdout_ref),
             signature="(Ljava/io/OutputStream;)V"))
     jls.setErr(
         autoclass('java.io.PrintStream')(
-            autoclass('org.terrier.python.ProxyableOutputStream')(stderr_ref),
+            autoclass('org.terrier.python.ProxyableOutputStream')(_stderr_ref),
             signature="(Ljava/io/OutputStream;)V"))
 
 
@@ -137,6 +130,19 @@ def unsign(signed):
     return bytearray([ unsign(buffer.get(offset)) for offset in range(buffer.capacity()) ])
 
 
+# ----------------------------------------------------------
+# Configuration
+# ----------------------------------------------------------
+
+configure = register_config('pyterrier.java', {
+    'jars': [],
+    'options': [],
+    'mem': None,
+    'log_level': 'WARN',
+    'redirect_io': True,
+})
+
+
 @before_init
 def add_jar(jar_path):
     configure.append('jars', jar_path)
@@ -183,6 +189,10 @@ def set_log_level(level):
         J.PTUtils.setLogLevel(level, None) # noqa: PT100 handled by started() check above
 
 
+# ----------------------------------------------------------
+# Common classes (accessible via pt.java.J.[ClassName])
+# ----------------------------------------------------------
+
 J = JavaClasses({
     'ArrayList': 'java.util.ArrayList',
     'Properties': 'java.util.Properties',
diff --git a/pyterrier/java/utils.py b/pyterrier/java/utils.py
index 7ae05a30..8678d45a 100644
--- a/pyterrier/java/utils.py
+++ b/pyterrier/java/utils.py
@@ -5,54 +5,19 @@
 from copy import deepcopy
 import pyterrier as pt
 
+
 _started = False
 _configs = {}
 
-class JavaInitializer:
-    """
-    A `JavaInitializer` manages the initilization of a module that uses java components. The two main methods are
-    `pre_init` and `post_init`, which perform configuration before and after the JVM has started, respectively.
-    """
-
-    def priority(self) -> int:
-        """
-        Returns the priority of this initializer. A lower priority is executed first.
-        """
-        return 0
-
-    def condition(self) -> bool:
-        """
-        Returns True if the initializer should be run. Otherwise False.
-        """
-        return True
-
-    def pre_init(self, jnius_config) -> None:
-        """
-        Called before the JVM is started. `jnius_config` is the `jnius_config` module, whic can be used to configure
-        java, such as by adding jars to the classpath.
-        """
-        pass
-
-    def post_init(self, jnius) -> None:
-        """
-        Called after the JVM has started. `jnius` is the `jnius` module, which can be used to interact with java.
-        """
-        pass
-
-    def message(self) -> Optional[str]:
-        """
-        Returns a message to be displayed after the JVM has started alongside the name of the entry point. If None,
-        only the entry point name will be displayed.
-        """
-        return None
-
-
-def started() -> bool:
-    """
-    Returns True if pt.java.init() has been called. Otherwise False.
-    """
-    return _started
 
+# ----------------------------------------------------------
+# Decorators
+# ----------------------------------------------------------
+# These functions wrap functions/classes to enforce certain
+# behavior regarding Java. For instance @pt.java.required
+# automatically starts java before it's invoked (if it's not
+# already started).
+# ----------------------------------------------------------
 
 @pt.utils.pre_invocation_decorator
 def required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
@@ -62,7 +27,7 @@ def required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
     Can be used as either a standalone function or a function/class @decorator. When used as a class decorator, it
     is applied to all methods defined by the class.
     """
-    if not started():
+    if not _started:
         init()
 
 
@@ -97,27 +62,40 @@ def _wrapper(*args, **kwargs):
         return fn(*args, **kwargs)
     return _wrapper
 
-class JavaClasses:
-    def __init__(self, mapping: Dict[str, str]):
-        self._mapping = mapping
-        self._cache = {}
 
-    def __dir__(self):
-        return list(self._mapping.keys())
+# ----------------------------------------------------------
+# Jnius Wrappers
+# ----------------------------------------------------------
+# These functions wrap jnius to make sure that java is
+# running before they're called. Doing it this way allows
+# functions to import them before java is loaded.
+# ----------------------------------------------------------
+
+@required_raise
+def autoclass(*args, **kwargs):
+    """
+    Wraps jnius.autoclass once java has started. Raises an error if called before pt.java.init() is called.
+    """
+    import jnius # noqa: PT100
+    return jnius.autoclass(*args, **kwargs) # noqa: PT100
 
-    @required_raise
-    def __getattr__(self, key):
-        if key not in self._mapping:
-            return AttributeError(f'{self} has no attribute {key!r}')
-        if key not in self._cache:
-            clz = self._mapping[key]
-            if callable(clz):
-                clz = clz()
-            self._cache[key] = pt.java.autoclass(clz)
-        return self._cache[key]
 
+@required_raise
+def cast(*args, **kwargs):
+    """
+    Wraps jnius.cast once java has started. Raises an error if called before pt.java.init() is called.
+    """
+    import jnius # noqa: PT100
+    return jnius.cast(*args, **kwargs) # noqa: PT100
 
 
+# ----------------------------------------------------------
+# Init
+# ----------------------------------------------------------
+# This function (along with legacy_init) loads all modules
+# registered via pyterrier.java.init entry points and starts
+# the JVM.
+# ----------------------------------------------------------
 
 @pt.utils.once()
 def init() -> None:
@@ -162,42 +140,6 @@ def init() -> None:
     sys.stderr.write('Java started and loaded:\n' + ''.join(message))
 
 
-def parallel_init(started: bool, configs: Dict[str, Dict[str, Any]]) -> None:
-    global _configs
-    if started:
-        if not pt.java.started():
-            warn(f'Starting java parallel with configs {configs}')
-            _configs = configs
-            init()
-        else:
-            warn("Avoiding reinit of PyTerrier")
-
-
-def parallel_init_args() -> Tuple[bool, Dict[str, Dict[str, Any]]]:
-    return (
-        started(),
-        deepcopy(_configs),
-    )
-
-
-@required_raise
-def autoclass(*args, **kwargs):
-    """
-    Wraps jnius.autoclass once java has started. Raises an error if called before pt.java.init() is called.
-    """
-    import jnius # noqa: PT100
-    return jnius.autoclass(*args, **kwargs) # noqa: PT100
-
-
-@required_raise
-def cast(*args, **kwargs):
-    """
-    Wraps jnius.cast once java has started. Raises an error if called before pt.java.init() is called.
-    """
-    import jnius # noqa: PT100
-    return jnius.cast(*args, **kwargs) # noqa: PT100
-
-
 @before_init
 def legacy_init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, logging='WARN', home_dir=None, boot_packages=[], tqdm=None, no_download=False,helper_version = None):
     """
@@ -261,6 +203,88 @@ def legacy_init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=Tr
         pt.terrier.set_property("terrier.mvn.coords", pkgs_string)
 
 
+def started() -> bool:
+    """
+    Returns True if pt.java.init() has been called. Otherwise False.
+    """
+    return _started
+
+
+class JavaInitializer:
+    """
+    A `JavaInitializer` manages the initilization of a module that uses java components. The two main methods are
+    `pre_init` and `post_init`, which perform configuration before and after the JVM has started, respectively.
+    """
+
+    def priority(self) -> int:
+        """
+        Returns the priority of this initializer. A lower priority is executed first.
+        """
+        return 0
+
+    def condition(self) -> bool:
+        """
+        Returns True if the initializer should be run. Otherwise False.
+        """
+        return True
+
+    def pre_init(self, jnius_config) -> None:
+        """
+        Called before the JVM is started. `jnius_config` is the `jnius_config` module, whic can be used to configure
+        java, such as by adding jars to the classpath.
+        """
+        pass
+
+    def post_init(self, jnius) -> None:
+        """
+        Called after the JVM has started. `jnius` is the `jnius` module, which can be used to interact with java.
+        """
+        pass
+
+    def message(self) -> Optional[str]:
+        """
+        Returns a message to be displayed after the JVM has started alongside the name of the entry point. If None,
+        only the entry point name will be displayed.
+        """
+        return None
+
+
+# ----------------------------------------------------------
+# Parallel
+# ----------------------------------------------------------
+# These functions are for working in parallel mode, e.g.,
+# with multiprocessing. They help restarting and configure
+# the JVM the same way it was when it was started in the
+# parent process
+# ----------------------------------------------------------
+
+def parallel_init(started: bool, configs: Dict[str, Dict[str, Any]]) -> None:
+    global _configs
+    if started:
+        if not pt.java.started():
+            warn(f'Starting java parallel with configs {configs}')
+            _configs = configs
+            init()
+        else:
+            warn("Avoiding reinit of PyTerrier")
+
+
+def parallel_init_args() -> Tuple[bool, Dict[str, Dict[str, Any]]]:
+    return (
+        started(),
+        deepcopy(_configs),
+    )
+
+
+# ----------------------------------------------------------
+# Configuration Utils
+# ----------------------------------------------------------
+# We need a global store of all java-related configurations
+# so that when running in paralle, we can set everying back
+# up the same way it started. These utils help manage this
+# gloabal configuration.
+# ----------------------------------------------------------
+
 class Configuration:
     def __init__(self, name):
         self.name = name
@@ -298,3 +322,31 @@ def register_config(name, config: Dict[str, Any]):
     assert name not in _configs
     _configs[name] = deepcopy(config)
     return Configuration(name)
+
+
+# ----------------------------------------------------------
+# Java Classes
+# ----------------------------------------------------------
+# This class enables the lazy loading of java classes. It
+# helps avod needing a ton of autclass() statements to
+# pre-load Java classes.
+# ----------------------------------------------------------
+
+class JavaClasses:
+    def __init__(self, mapping: Dict[str, str]):
+        self._mapping = mapping
+        self._cache = {}
+
+    def __dir__(self):
+        return list(self._mapping.keys())
+
+    @required_raise
+    def __getattr__(self, key):
+        if key not in self._mapping:
+            return AttributeError(f'{self} has no attribute {key!r}')
+        if key not in self._cache:
+            clz = self._mapping[key]
+            if callable(clz):
+                clz = clz()
+            self._cache[key] = pt.java.autoclass(clz)
+        return self._cache[key]

From c3a55b41d5177831ffe03af3fb525b5f54af0221 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Wed, 31 Jul 2024 13:43:37 +0100
Subject: [PATCH 110/153] remove deprecated calls in tests

---
 tests/test_br.py                    | 14 +++++++-------
 tests/test_dunder.py                |  6 +++---
 tests/test_fbr.py                   | 12 ++++++------
 tests/test_index_op.py              |  2 +-
 tests/test_index_with_background.py |  9 ++++-----
 tests/test_iterdictindex.py         | 10 +++++-----
 tests/test_iterdictindex_pretok.py  |  5 ++---
 tests/test_pickle.py                |  2 +-
 tests/test_scoring.py               |  2 +-
 9 files changed, 30 insertions(+), 32 deletions(-)

diff --git a/tests/test_br.py b/tests/test_br.py
index 094a1972..80f15636 100644
--- a/tests/test_br.py
+++ b/tests/test_br.py
@@ -89,7 +89,7 @@ def test_br_col_passthrough(self):
     def test_br_mem(self):
         indexloc = self.here + "/fixtures/index/data.properties"
         memindex = pt.IndexFactory.of(indexloc, memory=True)
-        pindex = pt.cast("org.terrier.structures.IndexOnDisk", memindex)
+        pindex = pt.java.cast("org.terrier.structures.IndexOnDisk", memindex)
         self.assertEqual("fileinmem", pindex.getIndexProperty("index.lexicon.data-source", "notfound"))
         retr = pt.BatchRetrieve(memindex)
         retr.search("chemical reactions")
@@ -149,7 +149,7 @@ def test_one_term_query_correct_docid_score_rank(self):
         jindex.close()
 
     def test_two_term_query_correct_qid_docid_score(self):
-        JIR = pt.autoclass('org.terrier.querying.IndexRef')
+        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
         retr = pt.BatchRetrieve(indexref)
         input = pd.DataFrame([["1", "Stability"], ["2", "Generator"]], columns=['qid', 'query'])
@@ -171,7 +171,7 @@ def test_two_term_query_correct_qid_docid_score(self):
             self.assertAlmostEqual(row['score'], exp_result[index][2])
 
     def test_num_results(self):
-        JIR = pt.autoclass('org.terrier.querying.IndexRef')
+        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here+"/fixtures/index/data.properties")
         retr = pt.BatchRetrieve(indexref, num_results=10)
         input=pd.DataFrame([["1", "Stability"]],columns=['qid','query'])
@@ -186,8 +186,8 @@ def test_num_results(self):
         self.assertEqual(len(result), 1001)
 
     def test_num_manual_wmodel(self):
-        JIR = pt.autoclass('org.terrier.querying.IndexRef')
-        Tf = pt.autoclass("org.terrier.matching.models.Tf")()
+        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
+        Tf = pt.java.autoclass("org.terrier.matching.models.Tf")()
         indexref = JIR.of(self.here+"/fixtures/index/data.properties")
         from jnius import JavaException
         try:
@@ -214,7 +214,7 @@ def test_threading_manualref(self):
         topics = pt.get_dataset("vaswani").get_topics().head(8)
 
         #this test ensures that we operate when the indexref is specified to be concurrent 
-        JIR = pt.autoclass('org.terrier.querying.IndexRef')
+        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of("concurrent:" + self.here+"/fixtures/index/data.properties")
         retr = pt.BatchRetrieve(indexref, threads=4)
         result = retr.transform(topics)
@@ -231,7 +231,7 @@ def test_threading_selfupgrade(self):
         topics = pt.get_dataset("vaswani").get_topics().head(10)
 
         #this test ensures we can upgrade the indexref to be concurrent
-        JIR = pt.autoclass('org.terrier.querying.IndexRef')
+        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here+"/fixtures/index/data.properties")
         retr = pt.BatchRetrieve(indexref, threads=5)
         result = retr.transform(topics)
diff --git a/tests/test_dunder.py b/tests/test_dunder.py
index 8c82b15f..d6fb5af9 100644
--- a/tests/test_dunder.py
+++ b/tests/test_dunder.py
@@ -6,7 +6,7 @@
 class TestDunder(TempDirTestCase):
 
     def test_callable_wmodel_dunders(self):
-        testPosting = pt.autoclass("org.terrier.structures.postings.BasicPostingImpl")(0,1)
+        testPosting = pt.java.autoclass("org.terrier.structures.postings.BasicPostingImpl")(0,1)
 
         lambdafn = lambda keyFreq, posting, entryStats, collStats: posting.getFrequency()
         callback, wmodel = pt.terrier.retriever._function2wmodel(lambdafn)
@@ -45,11 +45,11 @@ def test_callable_wmodel_dunders(self):
 
     def test_wmodel_dunders(self):
 
-        wmodel = pt.autoclass("org.terrier.matching.models.BM25")()
+        wmodel = pt.java.autoclass("org.terrier.matching.models.BM25")()
         wmodel.__reduce__()
         wmodel.__getstate__()
         rtr = wmodel.__reduce__()
-        pt.cast("org.terrier.matching.models.BM25", rtr[0](*rtr[1]))
+        pt.java.cast("org.terrier.matching.models.BM25", rtr[0](*rtr[1]))
         import pickle
         #import dill as pickle
         #check the byte array is picklable
diff --git a/tests/test_fbr.py b/tests/test_fbr.py
index 75b6bb5a..534242f0 100644
--- a/tests/test_fbr.py
+++ b/tests/test_fbr.py
@@ -27,7 +27,7 @@ def test_fbr_reranking(self):
         if not pt.terrier.check_version("5.4"):
             self.skipTest("Requires Terrier 5.4")
         # this test examines the use of ScoringMatchingWithFat 
-        JIR = pt.autoclass('org.terrier.querying.IndexRef')
+        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
         # we only want a candidate set of 2 documents
         firstpass = pt.BatchRetrieve(indexref, wmodel="BM25") % 2
@@ -70,7 +70,7 @@ def test_fbr_reranking2(self):
         if not pt.terrier.check_version("5.4"):
             self.skipTest("Requires Terrier 5.4")
         # this test examines the use of ScoringMatchingWithFat, using a particular case known to with Terrier 5.3 
-        JIR = pt.autoclass('org.terrier.querying.IndexRef')
+        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
         # we only want a candidate set of 3 documents
         firstpass = pt.BatchRetrieve(indexref, wmodel="BM25") % 3
@@ -104,7 +104,7 @@ def test_fbr_reranking2(self):
             self.assertAlmostEqual(result1F_map[docno], result2_map[docno], msg="feature score mismatch at rank %d for docno %s" % (rank, docno), places=4)
 
     def test_fbr_ltr(self):
-        JIR = pt.autoclass('org.terrier.querying.IndexRef')
+        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
         retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH")
         topics = pt.io.read_topics(self.here + "/fixtures/vaswani_npl/query-text.trec").head(3)
@@ -117,7 +117,7 @@ def test_fbr_ltr(self):
         RandomForestClassifier(n_estimators=10).fit(np.stack(res["features"]), res["label"])
 
     def test_fbr(self):
-        JIR = pt.autoclass('org.terrier.querying.IndexRef')
+        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
         retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH")
         input = pd.DataFrame([["1", "Stability"]], columns=['qid', 'query'])
@@ -138,7 +138,7 @@ def test_fbr(self):
             self.assertNotEqual(retrBasic.controls["matching"], "FatFeaturedScoringMatching,org.terrier.matching.daat.FatFull")
 
     def test_fbr_example(self):
-        JIR = pt.autoclass('org.terrier.querying.IndexRef')
+        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
         index = pt.IndexFactory.of(indexref)
         # this ranker will make the candidate set of documents for each query
@@ -167,7 +167,7 @@ def test_fbr_example(self):
         self.assertEqual(resultP.iloc[0].features[1], resultC.iloc[0].features[1])
 
     def test_fbr_empty(self):
-        JIR = pt.autoclass('org.terrier.querying.IndexRef')
+        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
         retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH")
         input = pd.DataFrame([["1", ""]], columns=['qid', 'query'])
diff --git a/tests/test_index_op.py b/tests/test_index_op.py
index a6f27538..87c86045 100644
--- a/tests/test_index_op.py
+++ b/tests/test_index_op.py
@@ -131,7 +131,7 @@ def test_index_add_write(self):
         self.assertEqual(len(index1) + len(index2), len(comb_index))
         
         # Instantiate writer object and write merged index to disk
-        writer = pt.autoclass("org.terrier.structures.indexing.DiskIndexWriter")(new_disk_index_loc, "data")
+        writer = pt.java.autoclass("org.terrier.structures.indexing.DiskIndexWriter")(new_disk_index_loc, "data")
         writer.write(comb_index)
 
         new_disk_index = pt.IndexFactory.of(new_disk_index_loc)
diff --git a/tests/test_index_with_background.py b/tests/test_index_with_background.py
index dd69e1f1..6e526fb5 100644
--- a/tests/test_index_with_background.py
+++ b/tests/test_index_with_background.py
@@ -46,9 +46,8 @@ def _test2_manual(self, type):
         indexref_big = pt.get_dataset("vaswani").get_index()
         index_big = pt.IndexFactory.of(indexref_big)
 
-        from pyterrier import autoclass
-        stopwords = autoclass("org.terrier.terms.Stopwords")(None)
-        stemmer = autoclass("org.terrier.terms.PorterStemmer")(None)
+        stopwords = pt.java.autoclass("org.terrier.terms.Stopwords")(None)
+        stemmer = pt.java.autoclass("org.terrier.terms.PorterStemmer")(None)
 
         q = "MATHEMATICAL ANALYSIS AND DESIGN DETAILS OF WAVEGUIDE FED MICROWAVE RADIATIONS"
         self.assertEqual("1048", index_big.getMetaIndex().getItem("docno", 1047))
@@ -86,7 +85,7 @@ def _check_index(index_small):
             self.assertTrue(np.array_equal(rtr1["score"].values, rtrall["score"].values))
         
         _check_index(index1)
-        _check_index( pt.autoclass("org.terrier.python.IndexWithBackground")(index1, index_big))
+        _check_index( pt.java.autoclass("org.terrier.python.IndexWithBackground")(index1, index_big))
 
     def test_itM(self):
         self._test_it(pt.index.IndexingType.MEMORY)
@@ -128,7 +127,7 @@ def _test_it(self, type):
             index2 = pt.IndexFactory.of(indexref2)
             self.assertEqual(1, index2.getCollectionStatistics().getNumberOfDocuments())
 
-            index_combined = pt.autoclass("org.terrier.python.IndexWithBackground")(index2, index1)
+            index_combined = pt.java.autoclass("org.terrier.python.IndexWithBackground")(index2, index1)
             self.assertEqual(3, index_combined.getCollectionStatistics().getNumberOfDocuments())
 
             self.assertEqual(1, index_combined.getLexicon()["test"].getFrequency())
diff --git a/tests/test_iterdictindex.py b/tests/test_iterdictindex.py
index ab0e9542..1d8c0458 100644
--- a/tests/test_iterdictindex.py
+++ b/tests/test_iterdictindex.py
@@ -184,7 +184,7 @@ def test_check_nostemmer_utf_toks(self):
         indexer = pt.IterDictIndexer(self.test_dir, stemmer=None, stopwords=None, tokeniser="UTFTokeniser")
         indexref = indexer.index(it)
         index = pt.IndexFactory.of(indexref)
-        index = pt.cast("org.terrier.structures.IndexOnDisk", index)
+        index = pt.java.cast("org.terrier.structures.IndexOnDisk", index)
         self.assertEqual("", index.getIndexProperty("termpipelines", "bla"))
         self.assertTrue("før" in index.getLexicon())
         # restore setting after test
@@ -201,7 +201,7 @@ def test_check_customstops(self):
         indexer = pt.IterDictIndexer(self.test_dir, tokeniser=pt.TerrierTokeniser.utf, stemmer=pt.TerrierStemmer.none, stopwords=['møney', 'crashing', ','], overwrite=True)
         indexref = indexer.index(it)
         index = pt.IndexFactory.of(indexref)
-        index = pt.cast("org.terrier.structures.IndexOnDisk", index)
+        index = pt.java.cast("org.terrier.structures.IndexOnDisk", index)
         self.assertIn(member="PyTerrierCustomStopwordList$Retrieval", container=index.getIndexProperty("termpipelines", "bla"))
         self.assertIsNotNone(index.getIndexProperty('pyterrier.stopwords', None))
 
@@ -210,7 +210,7 @@ def test_check_customstops(self):
         self.assertTrue("crashing" in index.getIndexProperty('pyterrier.stopwords', None))
 
         # lets validate the actual stopword list as parsed from the property
-        actual_stopwords = pt.autoclass("org.terrier.python.PyTerrierCustomStopwordList$Retrieval")()
+        actual_stopwords = pt.java.autoclass("org.terrier.python.PyTerrierCustomStopwordList$Retrieval")()
         actual_stopwords.setIndex(index)
         self.assertEqual(3, actual_stopwords.stopWords.size())
         self.assertTrue(actual_stopwords.stopWords.contains(","))
@@ -240,7 +240,7 @@ def test_check_nostemmer(self):
                 indexer = pt.IterDictIndexer(self.test_dir, stemmer=setting[0], stopwords=setting[1], overwrite=True)
                 indexref = indexer.index(it)
                 index = pt.IndexFactory.of(indexref)
-                index = pt.cast("org.terrier.structures.IndexOnDisk", index)
+                index = pt.java.cast("org.terrier.structures.IndexOnDisk", index)
                 self.assertEqual("", index.getIndexProperty("termpipelines", "bla"))
                 self.assertEqual(11, index.getDocumentIndex().getDocumentLength(0))
                 
@@ -265,7 +265,7 @@ def test_check_weakstemmer(self):
                 indexref = indexer.index(it)
                 index = pt.IndexFactory.of(indexref)
                 self.assertEqual(3, index.getDocumentIndex().getDocumentLength(0)) #ran money playing
-                index = pt.cast("org.terrier.structures.IndexOnDisk", index)
+                index = pt.java.cast("org.terrier.structures.IndexOnDisk", index)
                 # restore setting after test
                 pt.terrier.J.ApplicationSetup.setProperty("termpipelines", "Stopwords,PorterStemmer")
                 self.assertEqual("Stopwords,WeakPorterStemmer", index.getIndexProperty("termpipelines", "bla"))
diff --git a/tests/test_iterdictindex_pretok.py b/tests/test_iterdictindex_pretok.py
index 1523b47f..81645b15 100644
--- a/tests/test_iterdictindex_pretok.py
+++ b/tests/test_iterdictindex_pretok.py
@@ -63,12 +63,11 @@ def test_json_pretok_iterator(self):
         ]
 
         import json
-        from pyterrier import autoclass
         testfile = os.path.join(self.test_dir, "test.json")
         with open(testfile, 'wt') as file:
             for doc in it:
                 file.write(json.dumps(doc))
-        jparserCls = autoclass("org.terrier.python.JsonlPretokenisedIterator")
+        jparserCls = pt.java.autoclass("org.terrier.python.JsonlPretokenisedIterator")
         jparser = jparserCls(testfile)
         self.assertTrue(jparser.hasNext())
         nextRow = jparser.next()
@@ -120,7 +119,7 @@ def _make_pretok_index(self, n, index_type, meta=('docno', 'url')):
                 post.next()
                 self.assertEqual(1, post.frequency)
 
-                pindex = pt.cast("org.terrier.structures.PropertiesIndex", index)
+                pindex = pt.java.cast("org.terrier.structures.PropertiesIndex", index)
                 self.assertEqual("", pindex.getIndexProperty("termpipelines", "BLA"))
 
                 index.close()
diff --git a/tests/test_pickle.py b/tests/test_pickle.py
index 769b228d..32f96c74 100644
--- a/tests/test_pickle.py
+++ b/tests/test_pickle.py
@@ -83,7 +83,7 @@ def test_br_joblib_callback(self):
         self._br(joblib, wmodel=lambda keyFreq, posting, entryStats, collStats: posting.getFrequency())
 
     def test_br_pickle_straightwmodel(self):
-        self._br(pickle, wmodel=pt.autoclass("org.terrier.matching.models.BM25")())
+        self._br(pickle, wmodel=pt.java.autoclass("org.terrier.matching.models.BM25")())
 
     def test_br_joblib(self):
         import joblib
diff --git a/tests/test_scoring.py b/tests/test_scoring.py
index e7d62390..c30663bc 100644
--- a/tests/test_scoring.py
+++ b/tests/test_scoring.py
@@ -10,7 +10,7 @@
 class TestScoring(TempDirTestCase):
 
     def test_scoring_text(self):
-        pt.logging("DEBUG")
+        pt.java.set_log_level("DEBUG")
         dataset = pt.get_dataset("vaswani")
         indexer = pt.TRECCollectionIndexer(
             self.test_dir, 

From e1d7d3ef2b11a0c655416be86d845bd75fe3c71f Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Wed, 31 Jul 2024 15:47:48 +0100
Subject: [PATCH 111/153] documentation updates

---
 docs/conf.py                  |  1 -
 docs/datasets.rst             |  6 +--
 docs/installation.rst         | 92 ++++++++++++++++++++---------------
 docs/terrier-indexing.rst     | 20 ++++----
 docs/terrier-retrieval.rst    |  4 +-
 pyterrier/java/__init__.py    |  4 +-
 pyterrier/java/core.py        | 13 +++++
 pyterrier/terrier/__init__.py |  8 +--
 8 files changed, 86 insertions(+), 62 deletions(-)

diff --git a/docs/conf.py b/docs/conf.py
index b959a3ca..004130f5 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -21,7 +21,6 @@
 # -- Dataset table listing -----------------------------------------------------
 import pyterrier as pt
 import textwrap
-pt.init()
 
 from extras import generate_includes
 if not "QUICK" in os.environ:
diff --git a/docs/datasets.rst b/docs/datasets.rst
index 4a1e83e7..6490943b 100644
--- a/docs/datasets.rst
+++ b/docs/datasets.rst
@@ -11,11 +11,11 @@ each defined dataset can download and provide easy access to:
  - relevance assessments (aka, labels or qrels), as a dataframe, ready for evaluation
  - ready-made Terrier indices, where appropriate
 
-.. autofunction:: pyterrier.datasets.list_datasets()
+.. autofunction:: pyterrier.datasets.list_datasets
 
-.. autofunction:: pyterrier.datasets.find_datasets()
+.. autofunction:: pyterrier.datasets.find_datasets
 
-.. autofunction:: pyterrier.datasets.get_dataset()
+.. autofunction:: pyterrier.datasets.get_dataset
 
 .. autoclass:: pyterrier.datasets.Dataset
     :members:
diff --git a/docs/installation.rst b/docs/installation.rst
index 69abe482..8b9db2de 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -23,13 +23,13 @@ If you want the latest version of PyTerrier, you can install direct from the Git
 
 NB: There is no need to have a local installation of the Java component, Terrier. PyTerrier will download the latest release on startup.
 
-Installation Troubleshooting
-============
+Troubleshooting
+~~~~~~~~~~~~~~~
 
 We aim to ensure that there are pre-compiled binaries available for any dependencies with native components, for all supported Python versions and for all major platforms (Linux, macOS, Windows).
 One notable exception is Mac M1 etc., as there are no freely available GitHub Actions runners for M1. Mac M1 installs may require to compile some dependencies.
 
-If the installation failed due to `pyautocorpus` did not run successfully, you may need to install `pcre` to your machine.
+If the installation failed due to ``pyautocorpus`` did not run successfully, you may need to install ``pcre`` to your machine.
 
 macOS::
 
@@ -40,64 +40,76 @@ Linux::
     apt-get update -y
     apt-get install libpcre3-dev -y
 
+For users with an M1 Mac or later models, it is sometimes necessary to install the SSL certificates to avoid certificate errors. 
+To do this, locate the `Install Certificates.command` file within the `Application/Python[version]` directory. Once found, double-click on it to run the installation process.
 
-Configuration
-==============
+Runing PyTerrier
+================
 
-You must always start by importing PyTerrier and running init()::
+Once installed, you can get going with PyTerrier just by importing it. It's common to alias it as `pt` when importing::
 
     import pyterrier as pt
-    pt.init()
 
-PyTerrier uses `PyJnius <https://github.com/kivy/pyjnius>`_ as a "glue" layer in order to call Terrier's Java classes. PyJnius will search 
-the usual places on your machine for a Java installation. If you have problems, set the `JAVA_HOME` environment variable::
+Java Configuration
+==================
 
-    import os
-    os.environ["JAVA_HOME"] = "/path/to/my/jdk"
-    import pyterrier as pt
-    pt.init()
+PyTerrier integrates with several Java-based engines, such as Terrier. It uses `PyJnius <https://github.com/kivy/pyjnius>`_ as
+a "glue" layer in order to call these Java components. PyTerrier manages this for you automatically and uses reasonable defaults
+for the way it interacts with Java. However, sometimes you may need to modify the settings to work with your system. This section
+describes how to manage this configuration.
 
-`pt.init()` has a multitude of options, for instance that can make PyTerrier more notebook friendly, or to change the underlying version of Terrier, as described below.
+**Note:** Because these options affect the the JVM's settings, they need to be set before Java starts---for instance, at the top of
+a script/notebook before any Java components are loaded.
 
-For users with an M1 Mac or later models, it is necessary to install the SSL certificates to avoid certificate errors. 
-To do this, locate the `Install Certificates.command` file within the `Application/Python[version]` directory. Once found, double-click on it to run the installation process.
+**Starting Java.** PyTerrier will start java when you use a component that requires it, such as `BatchRetrieve`. However, sometimes
+you might want to start it early:
 
-API Reference
-=============
+.. autofunction:: pyterrier.java.init
 
-All usages of PyTerrier start by importing PyTerrier and starting it using the `init()` method::
+You can also check if Java has been started (either automatically or by `pt.java.init()`):
 
-    import pyterrier as pt
-    pt.init()
+.. autofunction:: pyterrier.java.started
+
+**Java Home Path.** PyJnius will search the usual places on your machine for a Java installation. If you have problems, you can
+overrirde the java home path:
 
-PyTerrier uses some of the functionality of the Java-based `Terrier <http://terrier.org>`_ IR platform
-for indexing and retrieval functionality. Calling `pt.init()` downloads, if necessary, the Terrier jar 
-file, and starts the Java Virtual Machine (JVM). It also configures the Terrier so that it can be more 
-easily used from Python, such as redirecting the stdout and stderr streams, logging level etc.
+.. autofunction:: pyterrier.java.set_java_home
 
-Below, there is more documentation about method related to starting Terrier using PyTerrier,
-and ways to change the configuration.
+**Other General Options.** The following are other options for configuring Java:
 
-Startup-related methods
-~~~~~~~~~~~~~~~~~~~~~~~
+.. autofunction:: pyterrier.java.add_jar
+.. autofunction:: pyterrier.java.add_package
+.. autofunction:: pyterrier.java.set_memory_limit
+.. autofunction:: pyterrier.java.set_redirect_io
+.. autofunction:: pyterrier.java.add_option
+.. autofunction:: pyterrier.java.set_log_level
 
-.. autofunction:: pyterrier.init()
+Terrier Configuration
+~~~~~~~~~~~~~~~~~~~~~
 
-.. autofunction:: pyterrier.started()
+These options adjust how the Terrier engine is loaded.
 
-.. autofunction:: pyterrier.run()
+.. autofunction:: pyterrier.terrier.set_version
+.. autofunction:: pyterrier.terrier.set_helper_version
+.. autofunction:: pyterrier.terrier.enable_prf
+.. autofunction:: pyterrier.terrier.set_property
+.. autofunction:: pyterrier.terrier.set_properties
+.. autofunction:: pyterrier.terrier.extend_classpath
 
-Methods to change PyTerrier configuration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Anserini Configuration
+~~~~~~~~~~~~~~~~~~~~~~
 
-.. autofunction:: pyterrier.extend_classpath()
+These options adjust how the Anserini engine is loaded. Note that the `pyserini` package needs to be
+installed to use PyTerrier's Anserini integration.
 
-.. autofunction:: pyterrier.logging()
+.. autofunction:: pyterrier.anserini.set_version
 
-.. autofunction:: pyterrier.redirect_stdouterr()
 
-.. autofunction:: pyterrier.set_property()
+Note on Deprecated Java Configuration
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autofunction:: pyterrier.set_properties()
+Previous versions of PyTerrier required you to run ``pt.init()`` (often in a ``if pt.started()`` block)
+to configure Java and start using PyTerrier. This function still exists (it calls ``pt.java.init()``
+and associated other configuration methods), but is no longer needed and deprecated.
 
-.. autofunction:: pyterrier.set_tqdm()
+Instead, PyTerrier now automatically loads Java when a function is called that needs it.
diff --git a/docs/terrier-indexing.rst b/docs/terrier-indexing.rst
index 3ea9128f..26022258 100644
--- a/docs/terrier-indexing.rst
+++ b/docs/terrier-indexing.rst
@@ -20,12 +20,12 @@ TerrierIndexer
 
 All indexer classes extend ``TerrierIndexer``. Common indexer contrustor arguments for all four indexers are shown below.
 
-.. autoclass:: pyterrier.index.TerrierIndexer
+.. autoclass:: pyterrier.terrier.TerrierIndexer
 
 TRECCollectionIndexer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pyterrier.TRECCollectionIndexer
+.. autoclass:: pyterrier.terrier.TRECCollectionIndexer
    :members: index
 
 Example indexing the TREC WT2G corpus::
@@ -47,13 +47,13 @@ Example indexing the TREC WT2G corpus::
 FilesIndexer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pyterrier.FilesIndexer
+.. autoclass:: pyterrier.terrier.FilesIndexer
    :members: index
 
 DFIndexer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pyterrier.DFIndexer
+.. autoclass:: pyterrier.terrier.DFIndexer
    :members: index
 
 Example indexing a dataframe::
@@ -79,7 +79,7 @@ Example indexing a dataframe::
 IterDictIndexer
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pyterrier.IterDictIndexer
+.. autoclass:: pyterrier.terrier.IterDictIndexer
    :members: index
 
 **Examples using IterDictIndexer**
@@ -130,7 +130,7 @@ NB: Use ``pt.io.autoopen()`` as a drop-in replacement for ``open()`` that suppor
 If you have TREC-formatted files that you wish to use with an IterDictIndexer-like indexer, ``pt.index.treccollection2textgen()`` can be used
 as a helper function to aid in parsing such files.
 
-.. autofunction:: pyterrier.index.treccollection2textgen
+.. autofunction:: pyterrier.terrier.treccollection2textgen
 
 Example using Indexing Pipelines::
 
@@ -165,7 +165,7 @@ indexing configurations, and how to apply them when indexing using PyTerrier, no
 Terrier has three different types of indexer. The choice of indexer is exposed using the ``type`` kwarg 
 to the indexer class. The indexer type can be set using the ``IndexingType`` enum.
 
-.. autoclass:: pyterrier.index.IndexingType
+.. autoclass:: pyterrier.terrier.IndexingType
    :inherited-members: CLASSIC, SINGLEPASS, MEMORY
 
 **Stemming configuation or stopwords**
@@ -174,7 +174,7 @@ The default Terrier indexing configuration is to apply an English stopword list,
 
     indexer = pt.IterDictIndexer(stemmer='SpanishSnowballStemmer', stopwords=None)
 
-.. autoclass:: pyterrier.index.TerrierStemmer
+.. autoclass:: pyterrier.terrier.TerrierStemmer
    :inherited-members: 
 
 See also the `org.terrier.terms <http://terrier.org/docs/current/javadoc/org/terrier/terms/package-summary.html>`_ package for a list of 
@@ -182,7 +182,7 @@ the available term pipeline objects provided by Terrier.
 
 Similarly the use of Terrier's English stopword list can be disabled using the ``stopwords`` kwarg.
 
-.. autoclass:: pyterrier.index.TerrierStopwords
+.. autoclass:: pyterrier.terrier.TerrierStopwords
    :inherited-members: 
 
 A custom stopword list can be set by setting the ``stopwords`` kwarg to a list of words::
@@ -198,7 +198,7 @@ package. For instance, its common to use `UTFTokeniser` when indexing non-Englis
 
     indexer = pt.IterDictIndexer(stemmer=None, stopwords=None, tokeniser="UTFTokeniser")
 
-.. autoclass:: pyterrier.index.TerrierTokeniser
+.. autoclass:: pyterrier.terrier.TerrierTokeniser
    :inherited-members: 
 
 
diff --git a/docs/terrier-retrieval.rst b/docs/terrier-retrieval.rst
index 8d437354..579938a2 100644
--- a/docs/terrier-retrieval.rst
+++ b/docs/terrier-retrieval.rst
@@ -30,7 +30,7 @@ For instance, to create a re-ranking pipeline that re-scores the top 100 BM25 do
 BatchRetrieve
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pyterrier.BatchRetrieve
+.. autoclass:: pyterrier.terrier.BatchRetrieve
     :members: transform, from_dataset
 
 
@@ -119,7 +119,7 @@ for the documents, and these are then scored.
 Optionally, an index-like object can be specified as the `background_index` kwarg, which will be used for
 the collection statistics (e.g. term frequencies, document lengths etc. 
 
-.. autoclass:: pyterrier.batchretrieve.TextScorer
+.. autoclass:: pyterrier.terrier.TextScorer
 
 Non-English Retrieval
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index 08655365..7f3a28c7 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -1,10 +1,10 @@
 from pyterrier.java import mavenresolver
 from pyterrier.java.utils import JavaInitializer, init, started, parallel_init, parallel_init_args, required, required_raise, before_init, autoclass, cast, legacy_init, register_config, Configuration, JavaClasses
-from pyterrier.java.core import CoreInit, configure, redirect_stdouterr, bytebuffer_to_array, J, add_jar, add_package, set_memory_limit, set_redirect_io, add_option, set_log_level
+from pyterrier.java.core import CoreInit, configure, redirect_stdouterr, bytebuffer_to_array, J, add_jar, add_package, set_memory_limit, set_redirect_io, add_option, set_log_level, set_java_home
 
 __all__ = [
     'add_jar', 'add_option', 'add_package', 'autoclass', 'before_init', 'bytebuffer_to_array', 'cast', 'config',
     'configure', 'init', 'J', 'JavaClasses', 'mavenresolver', 'parallel_init', 'parallel_init_args',
     'redirect_stdouterr', 'required', 'required_raise', 'set_log_level', 'set_memory_limit', 'set_redirect_io',
-    'started', 'legacy_init', 'JavaInitializer', 'CoreInit',
+    'started', 'legacy_init', 'JavaInitializer', 'CoreInit', 'set_java_home',
 ]
diff --git a/pyterrier/java/core.py b/pyterrier/java/core.py
index d1b54f81..c07063cf 100644
--- a/pyterrier/java/core.py
+++ b/pyterrier/java/core.py
@@ -17,6 +17,9 @@ def priority(self) -> int:
         return -100 # run this initializer before anything else
 
     def pre_init(self, jnius_config):
+        if configure['java_home']:
+            os.environ['JAVA_HOME'] = configure['java_home']
+
         if pt.utils.is_windows():
             if "JAVA_HOME" in os.environ:
                 java_home =  os.environ["JAVA_HOME"]
@@ -140,6 +143,7 @@ def unsign(signed):
     'mem': None,
     'log_level': 'WARN',
     'redirect_io': True,
+    'java_home': None,
 })
 
 
@@ -171,6 +175,15 @@ def set_redirect_io(redirect_io: bool):
     configure['redirect_io'] = redirect_io
 
 
+@before_init
+def set_java_home(java_home: str):
+    """
+    Sets the directory to search when loading Java.
+
+    Note that you can achieve the same outcome by setting the `JAVA_HOME` environment variable.
+    """
+    configure['java_home'] = java_home
+
 def set_log_level(level):
     """
         Set the logging level. The following string values are allowed, corresponding
diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
index 928ce72f..660b100b 100644
--- a/pyterrier/terrier/__init__.py
+++ b/pyterrier/terrier/__init__.py
@@ -1,13 +1,13 @@
 # java stuff
 from pyterrier.terrier import java
 from pyterrier.terrier.java import configure, set_version, set_helper_version, enable_prf, extend_classpath, J, set_property, set_properties, run, version, check_version, check_helper_version
-from pyterrier.terrier.retriever import BatchRetrieve, FeaturesBatchRetrieve
+from pyterrier.terrier.retriever import BatchRetrieve, FeaturesBatchRetrieve, TextScorer
 from pyterrier.terrier.index_factory import IndexFactory
 from pyterrier.terrier.stemmer import TerrierStemmer
 from pyterrier.terrier.tokeniser import TerrierTokeniser
 from pyterrier.terrier.stopwords import TerrierStopwords
 from pyterrier.terrier import index
-from pyterrier.terrier.index import TerrierIndexer, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, TerrierStemmer, TerrierStopwords, TerrierTokeniser
+from pyterrier.terrier.index import TerrierIndexer, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, treccollection2textgen
 from pyterrier.terrier import rewrite
 
 TerrierRetrieve = BatchRetrieve # BatchRetrieve is an alias to TerrierRetrieve
@@ -18,10 +18,10 @@
     'java', 'configure', 'set_version', 'set_helper_version', 'enable_prf', 'extend_classpath', 'J', 'version', 'check_version', 'check_helper_version',
 
     # retrieval
-    'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve',
+    'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve', 'TextScorer',
 
     # indexing
-    'index', 'TerrierIndexer', 'FilesIndexer', 'TRECCollectionIndexer', 'DFIndexer', 'DFIndexUtils', 'IterDictIndexer', 'IndexingType',
+    'index', 'TerrierIndexer', 'FilesIndexer', 'TRECCollectionIndexer', 'DFIndexer', 'DFIndexUtils', 'IterDictIndexer', 'IndexingType', 'treccollection2textgen',
 
     # rewriting
     'rewrite',

From b88ef8ebb8172e4f40f3e01c0e000b7d51e3e5df Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Wed, 31 Jul 2024 18:04:10 +0100
Subject: [PATCH 112/153] private modules

---
 pyterrier/java/__init__.py             | 6 +++---
 pyterrier/java/{core.py => _core.py}   | 0
 pyterrier/java/{utils.py => _utils.py} | 0
 3 files changed, 3 insertions(+), 3 deletions(-)
 rename pyterrier/java/{core.py => _core.py} (100%)
 rename pyterrier/java/{utils.py => _utils.py} (100%)

diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index 7f3a28c7..9838a378 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -1,10 +1,10 @@
 from pyterrier.java import mavenresolver
-from pyterrier.java.utils import JavaInitializer, init, started, parallel_init, parallel_init_args, required, required_raise, before_init, autoclass, cast, legacy_init, register_config, Configuration, JavaClasses
-from pyterrier.java.core import CoreInit, configure, redirect_stdouterr, bytebuffer_to_array, J, add_jar, add_package, set_memory_limit, set_redirect_io, add_option, set_log_level, set_java_home
+from pyterrier.java._utils import JavaInitializer, init, started, parallel_init, parallel_init_args, required, required_raise, before_init, autoclass, cast, legacy_init, register_config, Configuration, JavaClasses
+from pyterrier.java._core import CoreInit, configure, redirect_stdouterr, bytebuffer_to_array, J, add_jar, add_package, set_memory_limit, set_redirect_io, add_option, set_log_level, set_java_home
 
 __all__ = [
     'add_jar', 'add_option', 'add_package', 'autoclass', 'before_init', 'bytebuffer_to_array', 'cast', 'config',
     'configure', 'init', 'J', 'JavaClasses', 'mavenresolver', 'parallel_init', 'parallel_init_args',
     'redirect_stdouterr', 'required', 'required_raise', 'set_log_level', 'set_memory_limit', 'set_redirect_io',
-    'started', 'legacy_init', 'JavaInitializer', 'CoreInit', 'set_java_home',
+    'started', 'legacy_init', 'JavaInitializer', 'CoreInit', 'set_java_home', 'register_config', 'Configuration',
 ]
diff --git a/pyterrier/java/core.py b/pyterrier/java/_core.py
similarity index 100%
rename from pyterrier/java/core.py
rename to pyterrier/java/_core.py
diff --git a/pyterrier/java/utils.py b/pyterrier/java/_utils.py
similarity index 100%
rename from pyterrier/java/utils.py
rename to pyterrier/java/_utils.py

From 1e03262b9d6c8aec88ea8d8f057ee4e733a01b2c Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Wed, 31 Jul 2024 18:17:45 +0100
Subject: [PATCH 113/153] note about what triggered init in java started
 message

---
 pyterrier/anserini/java.py |  4 ++--
 pyterrier/java/_utils.py   | 22 +++++++++++++++-------
 pyterrier/terrier/java.py  |  4 ++--
 pyterrier/utils.py         | 11 ++++-------
 4 files changed, 23 insertions(+), 18 deletions(-)

diff --git a/pyterrier/anserini/java.py b/pyterrier/anserini/java.py
index 298a8973..696ba7a4 100644
--- a/pyterrier/anserini/java.py
+++ b/pyterrier/anserini/java.py
@@ -71,12 +71,12 @@ def set_version(version: str):
 
 
 @pt.utils.pre_invocation_decorator
-def required() -> Union[Callable, bool]:
+def required(fn: Callable):
     """
     Requires pyserini to be installed (raises error if not installed). If the JVM has not yet been started, it runs
     pt.java.init(), too, similar to pt.java.required().
 
-    Can be used as either a standalone function or a function/class @decorator. When used as a class decorator, it
+    Can be used as a function/class @decorator. When used as a class decorator, it
     is applied to all methods defined by the class.
     """
     if not is_installed():
diff --git a/pyterrier/java/_utils.py b/pyterrier/java/_utils.py
index 8678d45a..d1d93a07 100644
--- a/pyterrier/java/_utils.py
+++ b/pyterrier/java/_utils.py
@@ -20,15 +20,16 @@
 # ----------------------------------------------------------
 
 @pt.utils.pre_invocation_decorator
-def required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
+def required(fn: Callable):
     """
     Requires the Java Virtual Machine to be started. If the JVM has not yet been started, it runs pt.java.init().
 
-    Can be used as either a standalone function or a function/class @decorator. When used as a class decorator, it
+    Can be used as a function/class @decorator. When used as a class decorator, it
     is applied to all methods defined by the class.
     """
     if not _started:
-        init()
+        trigger = fn.__qualname__ if hasattr(fn, '__qualname__') else fn.__name__
+        _init(trigger=trigger)
 
 
 def required_raise(fn: Optional[Callable] = None) -> Union[Callable, bool]:
@@ -97,8 +98,12 @@ def cast(*args, **kwargs):
 # the JVM.
 # ----------------------------------------------------------
 
-@pt.utils.once()
 def init() -> None:
+    _init()
+
+
+@pt.utils.once()
+def _init(trigger=None):
     global _started
     # TODO: if we make java optional some day, should check here that it's installed. E.g.,
     # if find_spec('jnius_config') is None:
@@ -131,13 +136,17 @@ def init() -> None:
 
     # build "Java started" message
     message = []
+    if trigger:
+        message.append(f'Java started (triggered by {trigger}) and loaded:\n')
+    else:
+        message.append('Java started and loaded:\n')
     for name, initializer in initalizers:
         msg = initializer.message()
         if msg is None:
             message.append(f' - {name}\n')
         else:
             message.append(f' - {name} [{msg}]\n')
-    sys.stderr.write('Java started and loaded:\n' + ''.join(message))
+    sys.stderr.write(''.join(message))
 
 
 @before_init
@@ -262,9 +271,8 @@ def parallel_init(started: bool, configs: Dict[str, Dict[str, Any]]) -> None:
     global _configs
     if started:
         if not pt.java.started():
-            warn(f'Starting java parallel with configs {configs}')
             _configs = configs
-            init()
+            _init(trigger='parallel_init')
         else:
             warn("Avoiding reinit of PyTerrier")
 
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 665ea905..147dafce 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -36,11 +36,11 @@ def enable_prf(version: Optional[str] = '-SNAPSHOT'):
 
 
 @pt.utils.pre_invocation_decorator
-def prf_required(fn: Optional[Callable] = None) -> Union[Callable, bool]:
+def prf_required(fn: Callable):
     """
     Requires prf to be enabled (raises error if not enabled).
 
-    Can be used as either a standalone function or a function/class @decorator. When used as a class decorator, it
+    Can be used as a function/class @decorator. When used as a class decorator, it
     is applied to all methods defined by the class.
     """
     if not configure['prf_version']:
diff --git a/pyterrier/utils.py b/pyterrier/utils.py
index f471189f..67828b09 100644
--- a/pyterrier/utils.py
+++ b/pyterrier/utils.py
@@ -189,15 +189,12 @@ def get_class_methods(cls) -> List[Tuple[str, Callable]]:
 
 def pre_invocation_decorator(decorator):
     """
-    Builds a decorator function that funs the decoraded code before running the wrapped function. It can run as a
-    standalone function, a function @decorator, or a class @decorator. When used as a class decorator, it is applied to
-    all functions defined by the class.
+    Builds a decorator function that runs the decoraded code before running the wrapped function. It can run as a
+    function @decorator, or a class @decorator. When used as a class decorator, it is applied to all functions defined
+    by the class.
     """
     @wraps(decorator)
     def _decorator_wrapper(fn):
-        if fn is None: # standalone call
-            return _decorator_wrapper(pt.utils.noop)()
-
         if isinstance(fn, type): # wrapping a class
             for name, value in pt.utils.get_class_methods(fn):
                 setattr(fn, name, _decorator_wrapper(value))
@@ -206,7 +203,7 @@ def _decorator_wrapper(fn):
         else: # wrapping a function
             @wraps(fn)
             def _wrapper(*args, **kwargs):
-                decorator()
+                decorator(fn)
                 return fn(*args, **kwargs)
             return _wrapper
     return _decorator_wrapper

From 6fb88f7b14a83bf6791d3d9534faee0a754faffe Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Wed, 31 Jul 2024 20:19:17 +0100
Subject: [PATCH 114/153] more concise java started message

---
 pyterrier/java/_utils.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/pyterrier/java/_utils.py b/pyterrier/java/_utils.py
index d1d93a07..c2b5e479 100644
--- a/pyterrier/java/_utils.py
+++ b/pyterrier/java/_utils.py
@@ -137,16 +137,16 @@ def _init(trigger=None):
     # build "Java started" message
     message = []
     if trigger:
-        message.append(f'Java started (triggered by {trigger}) and loaded:\n')
+        message.append(f'Java started (triggered by {trigger}) and loaded: ')
     else:
-        message.append('Java started and loaded:\n')
+        message.append('Java started and loaded: ')
     for name, initializer in initalizers:
         msg = initializer.message()
         if msg is None:
-            message.append(f' - {name}\n')
+            message.append(name)
         else:
-            message.append(f' - {name} [{msg}]\n')
-    sys.stderr.write(''.join(message))
+            message.append(f'{name} [{msg}]')
+    sys.stderr.write(message[0] + ', '.join(message[1:]) + '\n')
 
 
 @before_init

From e721392574bb40fab860cff2b132b332a57b677b Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Wed, 31 Jul 2024 20:37:08 +0100
Subject: [PATCH 115/153] error when no java initializers found (likely an
 issue with the editable/develop installation)

---
 pyterrier/java/_utils.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/pyterrier/java/_utils.py b/pyterrier/java/_utils.py
index c2b5e479..557dcdaa 100644
--- a/pyterrier/java/_utils.py
+++ b/pyterrier/java/_utils.py
@@ -112,26 +112,30 @@ def _init(trigger=None):
 
     # TODO: what about errors during init? What happens to _started? Etc.
 
-    initalizers = []
+    initializers = []
     for entry_point in pt.utils.entry_points('pyterrier.java.init'):
         initalizer = entry_point.load()()
         if initalizer.condition():
-            initalizers.append((entry_point.name, initalizer))
+            initializers.append((entry_point.name, initalizer))
+
+    if len(initializers) == 0:
+        raise RuntimeError('No Java initializers found. This is likely a configuration issue with the package. '
+                           'If installed using `pip install -e .` or `python setup.py develop`, try reinstalling.')
 
     # sort by priority
-    initalizers = sorted(initalizers, key=lambda i: i[1].priority())
+    initializers = sorted(initializers, key=lambda i: i[1].priority())
 
     import jnius_config
 
     # run pre-initialization setup
-    for _, initializer in initalizers:
+    for _, initializer in initializers:
         initializer.pre_init(jnius_config)
 
     import jnius # noqa: PT100 
     _started = True
 
     # run post-initialization setup
-    for _, initializer in initalizers:
+    for _, initializer in initializers:
         initializer.post_init(jnius)
 
     # build "Java started" message
@@ -140,7 +144,7 @@ def _init(trigger=None):
         message.append(f'Java started (triggered by {trigger}) and loaded: ')
     else:
         message.append('Java started and loaded: ')
-    for name, initializer in initalizers:
+    for name, initializer in initializers:
         msg = initializer.message()
         if msg is None:
             message.append(name)

From 2f6a094c14743f24e99af75c92491dcfcdc000ab Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Wed, 31 Jul 2024 20:44:49 +0100
Subject: [PATCH 116/153] avoid the annoying "bootstrap configuration" message

---
 pyterrier/terrier/java.py | 47 ++++++++++++++++++++++++++++++++++++++-
 1 file changed, 46 insertions(+), 1 deletion(-)

diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 147dafce..7481a9df 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -1,6 +1,7 @@
+import os
 import sys
 import json
-from functools import wraps
+from pathlib import Path
 from packaging.version import Version
 from typing import Optional, Union, List, Callable, Dict, Any
 import pyterrier as pt
@@ -58,6 +59,13 @@ def pre_init(self, jnius_config):
             terrier_version(str): Which version of Terrier - None is latest release; "snapshot" uses Jitpack to download a build of the current Github 5.x branch.
             helper_version(str): Which version of the helper - None is latest
         """
+        properties_file = Path(pt.io.pyterrier_home()) / 'etc' / 'terrier.default.properties'
+        if not properties_file.exists():
+            properties_file.parent.mkdir(parents=True, exist_ok=True)
+            with properties_file.open('wt') as fout:
+                fout.write(_DEFAULT_PROPERTIES)
+        jnius_config.add_options(f'-Dterrier.setup={properties_file}')
+
         # If version is not specified, find newest and download it
         if configure['terrier_version'] is None:
             terrier_version = pt.java.mavenresolver.latest_version_num(TERRIER_PKG, "terrier-assemblies")
@@ -641,3 +649,40 @@ def check_helper_version(min):
     'RM3': 'org.terrier.querying.RM3',
     'AxiomaticQE': 'org.terrier.querying.AxiomaticQE',
 })
+
+
+# Pulled from <https://github.com/terrier-org/terrier-core/blob/5.x/modules/core/src/main/resources/terrier.default.properties>
+_DEFAULT_PROPERTIES = """
+#default controls for manager
+querying.processes=terrierql:TerrierQLParser,parsecontrols:TerrierQLToControls,parseql:TerrierQLToMatchingQueryTerms,matchopql:MatchingOpQLParser,applypipeline:ApplyTermPipeline,localmatching:LocalManager$ApplyLocalMatching,qe:QueryExpansion,labels:org.terrier.learning.LabelDecorator,filters:LocalManager$PostFilterProcess,decorate:SimpleDecorateProcess
+#default controls for the web-based interface. SimpleDecorate
+#is the simplest metadata decorator. For more control, see Decorate.
+querying.postfilters=decorate:SimpleDecorate,site:SiteFilter,scope:Scope
+
+#default and allowed controls
+querying.default.controls=wmodel:DPH,parsecontrols:on,parseql:on,applypipeline:on,terrierql:on,localmatching:on,filters:on,decorate:on,decorate_batch:on
+querying.allowed.controls=scope,qe,qemodel,start,end,site,scope
+
+#the processing stages a term goes through
+termpipelines=Stopwords,PorterStemmer
+
+#document tags specification
+#for processing the contents of
+#the documents, ignoring DOCHDR
+TrecDocTags.doctag=DOC
+TrecDocTags.idtag=DOCNO
+TrecDocTags.skip=DOCHDR
+#set to true if the tags can be of various case
+TrecDocTags.casesensitive=false
+
+
+#starting from Terrier 5.3, we assume that documents are in UTF-8
+trec.encoding=UTF-8
+
+
+#query tags specification
+TrecQueryTags.doctag=TOP
+TrecQueryTags.idtag=NUM
+TrecQueryTags.process=TOP,NUM,TITLE
+TrecQueryTags.skip=DESC,NARR
+"""

From 783f39ae523560790d9c664448ff7a0360200e1b Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Wed, 31 Jul 2024 20:44:49 +0100
Subject: [PATCH 117/153] avoid the annoying "bootstrap configuration" message

---
 pyterrier/terrier/java.py | 50 ++++++++++++++++++++++++++++++++++++++-
 1 file changed, 49 insertions(+), 1 deletion(-)

diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 147dafce..d79e3a0d 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -1,6 +1,7 @@
+import os
 import sys
 import json
-from functools import wraps
+from pathlib import Path
 from packaging.version import Version
 from typing import Optional, Union, List, Callable, Dict, Any
 import pyterrier as pt
@@ -58,6 +59,16 @@ def pre_init(self, jnius_config):
             terrier_version(str): Which version of Terrier - None is latest release; "snapshot" uses Jitpack to download a build of the current Github 5.x branch.
             helper_version(str): Which version of the helper - None is latest
         """
+
+        # Make sure the terrier.default.properties file exists and is registered as an option, which avoids an annoying
+        # "No etc/terrier.properties, using terrier.default.properties for bootstrap configuration." message.
+        properties_file = Path(pt.io.pyterrier_home()) / 'etc' / 'terrier.default.properties'
+        if not properties_file.exists():
+            properties_file.parent.mkdir(parents=True, exist_ok=True)
+            with properties_file.open('wt') as fout:
+                fout.write(_DEFAULT_PROPERTIES)
+        jnius_config.add_options(f'-Dterrier.setup={properties_file}')
+
         # If version is not specified, find newest and download it
         if configure['terrier_version'] is None:
             terrier_version = pt.java.mavenresolver.latest_version_num(TERRIER_PKG, "terrier-assemblies")
@@ -641,3 +652,40 @@ def check_helper_version(min):
     'RM3': 'org.terrier.querying.RM3',
     'AxiomaticQE': 'org.terrier.querying.AxiomaticQE',
 })
+
+
+# Pulled from <https://github.com/terrier-org/terrier-core/blob/5.x/modules/core/src/main/resources/terrier.default.properties>
+_DEFAULT_PROPERTIES = """
+#default controls for manager
+querying.processes=terrierql:TerrierQLParser,parsecontrols:TerrierQLToControls,parseql:TerrierQLToMatchingQueryTerms,matchopql:MatchingOpQLParser,applypipeline:ApplyTermPipeline,localmatching:LocalManager$ApplyLocalMatching,qe:QueryExpansion,labels:org.terrier.learning.LabelDecorator,filters:LocalManager$PostFilterProcess,decorate:SimpleDecorateProcess
+#default controls for the web-based interface. SimpleDecorate
+#is the simplest metadata decorator. For more control, see Decorate.
+querying.postfilters=decorate:SimpleDecorate,site:SiteFilter,scope:Scope
+
+#default and allowed controls
+querying.default.controls=wmodel:DPH,parsecontrols:on,parseql:on,applypipeline:on,terrierql:on,localmatching:on,filters:on,decorate:on,decorate_batch:on
+querying.allowed.controls=scope,qe,qemodel,start,end,site,scope
+
+#the processing stages a term goes through
+termpipelines=Stopwords,PorterStemmer
+
+#document tags specification
+#for processing the contents of
+#the documents, ignoring DOCHDR
+TrecDocTags.doctag=DOC
+TrecDocTags.idtag=DOCNO
+TrecDocTags.skip=DOCHDR
+#set to true if the tags can be of various case
+TrecDocTags.casesensitive=false
+
+
+#starting from Terrier 5.3, we assume that documents are in UTF-8
+trec.encoding=UTF-8
+
+
+#query tags specification
+TrecQueryTags.doctag=TOP
+TrecQueryTags.idtag=NUM
+TrecQueryTags.process=TOP,NUM,TITLE
+TrecQueryTags.skip=DESC,NARR
+"""

From dace9babaae03b40797e1dbe619aae4033f63ede Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 3 Aug 2024 09:05:17 +0100
Subject: [PATCH 118/153] updated documentation about init

---
 docs/pipeline_examples.md    |  5 +++--
 docs/terrier-indexing.rst    |  1 -
 pyterrier/terrier/java.py    | 10 ----------
 pyterrier/terrier/rewrite.py |  8 ++++----
 4 files changed, 7 insertions(+), 17 deletions(-)

diff --git a/docs/pipeline_examples.md b/docs/pipeline_examples.md
index d6ea455d..2dcab7d7 100644
--- a/docs/pipeline_examples.md
+++ b/docs/pipeline_examples.md
@@ -46,10 +46,11 @@ pipe = (pt.BatchRetrieve(wikipedia_index, wmodel="BM25") >>
 
 ### RM3 Query Expansion
 
-We also provide RM3 query expansion, by virtue of an external plugin to Terrier called [terrier-prf](https://github.com/terrierteam/terrier-prf). This needs to be load at initialisation time.
+We also provide RM3 query expansion, using an external plugin to Terrier called [terrier-prf](https://github.com/terrierteam/terrier-prf).
+You need to call `pt.terrier.enable_prf()` to download and load this plugin.
 
 ```python
-pt.init(boot_packages=["com.github.terrierteam:terrier-prf:-SNAPSHOT"])
+pt.terrier.enable_prf() # This is required before Java starts
 pipe = (pt.BatchRetrieve(indexref, wmodel="BM25") >> 
     pt.rewrite.RM3(indexref) >> 
     pt.BatchRetrieve(indexref, wmodel="BM25")
diff --git a/docs/terrier-indexing.rst b/docs/terrier-indexing.rst
index 26022258..e5771a13 100644
--- a/docs/terrier-indexing.rst
+++ b/docs/terrier-indexing.rst
@@ -31,7 +31,6 @@ TRECCollectionIndexer
 Example indexing the TREC WT2G corpus::
 
     import pyterrier as pt
-    pt.init()
     # list of filenames to index
     files = pt.io.find_files("/path/to/WT2G/wt2g-corpus/")
 
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index d79e3a0d..3f5c2f60 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -50,16 +50,6 @@ def prf_required(fn: Callable):
 
 class TerrierInit(pt.java.JavaInitializer):
     def pre_init(self, jnius_config):
-        """
-        Download Terrier's jar file for the given version at the given file_path
-        Called by pt.init()
-
-        Args:
-            file_path(str): Where to download
-            terrier_version(str): Which version of Terrier - None is latest release; "snapshot" uses Jitpack to download a build of the current Github 5.x branch.
-            helper_version(str): Which version of the helper - None is latest
-        """
-
         # Make sure the terrier.default.properties file exists and is registered as an option, which avoids an annoying
         # "No etc/terrier.properties, using terrier.default.properties for bootstrap configuration." message.
         properties_file = Path(pt.io.pyterrier_home()) / 'etc' / 'terrier.default.properties'
diff --git a/pyterrier/terrier/rewrite.py b/pyterrier/terrier/rewrite.py
index f8beefb9..3440ce60 100644
--- a/pyterrier/terrier/rewrite.py
+++ b/pyterrier/terrier/rewrite.py
@@ -354,8 +354,8 @@ def __init__(self, *args, **kwargs):
 class RM3(QueryExpansion):
     '''
         Performs query expansion using RM3 relevance models. RM3 relies on an external Terrier plugin, 
-        `terrier-prf <https://github.com/terrierteam/terrier-prf/>`_. You should start PyTerrier with 
-        `pt.init(boot_packages=["com.github.terrierteam:terrier-prf:-SNAPSHOT"])`.
+        `terrier-prf <https://github.com/terrierteam/terrier-prf/>`_. You need to call ``pt.terrier.enable_prf()``
+        before RM3 is available.
 
         This transformer must be followed by a Terrier Retrieve() transformer.
         The original query is saved in the `"query_0"` column, which can be restored using `pt.rewrite.reset()`.
@@ -412,8 +412,8 @@ def transform(self, queries_and_docs):
 class AxiomaticQE(QueryExpansion):
     '''
         Performs query expansion using axiomatic query expansion. This class relies on an external Terrier plugin, 
-        `terrier-prf <https://github.com/terrierteam/terrier-prf/>`_. You should start PyTerrier with 
-        `pt.init(boot_packages=["com.github.terrierteam:terrier-prf:-SNAPSHOT"])`.
+        `terrier-prf <https://github.com/terrierteam/terrier-prf/>`_. You need to call ``pt.terrier.enable_prf()``
+        before AxiomaticQE is available.
 
         This transformer must be followed by a Terrier Retrieve() transformer.
         The original query is saved in the `"query_0"` column, which can be restored using `pt.rewrite.reset()`.

From efc3279712db85b674457beef96a654294dff95a Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 3 Aug 2024 09:09:35 +0100
Subject: [PATCH 119/153] renamed java Java Initializers to better clarify
 their purpose

---
 pyterrier/anserini/java.py | 2 +-
 pyterrier/java/__init__.py | 4 ++--
 pyterrier/java/_core.py    | 2 +-
 pyterrier/terrier/java.py  | 2 +-
 setup.py                   | 6 +++---
 5 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/pyterrier/anserini/java.py b/pyterrier/anserini/java.py
index 696ba7a4..be8411c6 100644
--- a/pyterrier/anserini/java.py
+++ b/pyterrier/anserini/java.py
@@ -10,7 +10,7 @@
 })
 
 
-class AnseriniInit(pt.java.JavaInitializer):
+class AnseriniJavaInit(pt.java.JavaInitializer):
     def __init__(self):
         self._message = None
 
diff --git a/pyterrier/java/__init__.py b/pyterrier/java/__init__.py
index 9838a378..30c7e9ae 100644
--- a/pyterrier/java/__init__.py
+++ b/pyterrier/java/__init__.py
@@ -1,10 +1,10 @@
 from pyterrier.java import mavenresolver
 from pyterrier.java._utils import JavaInitializer, init, started, parallel_init, parallel_init_args, required, required_raise, before_init, autoclass, cast, legacy_init, register_config, Configuration, JavaClasses
-from pyterrier.java._core import CoreInit, configure, redirect_stdouterr, bytebuffer_to_array, J, add_jar, add_package, set_memory_limit, set_redirect_io, add_option, set_log_level, set_java_home
+from pyterrier.java._core import CoreJavaInit, configure, redirect_stdouterr, bytebuffer_to_array, J, add_jar, add_package, set_memory_limit, set_redirect_io, add_option, set_log_level, set_java_home
 
 __all__ = [
     'add_jar', 'add_option', 'add_package', 'autoclass', 'before_init', 'bytebuffer_to_array', 'cast', 'config',
     'configure', 'init', 'J', 'JavaClasses', 'mavenresolver', 'parallel_init', 'parallel_init_args',
     'redirect_stdouterr', 'required', 'required_raise', 'set_log_level', 'set_memory_limit', 'set_redirect_io',
-    'started', 'legacy_init', 'JavaInitializer', 'CoreInit', 'set_java_home', 'register_config', 'Configuration',
+    'started', 'legacy_init', 'JavaInitializer', 'CoreJavaInit', 'set_java_home', 'register_config', 'Configuration',
 ]
diff --git a/pyterrier/java/_core.py b/pyterrier/java/_core.py
index c07063cf..8afcf98d 100644
--- a/pyterrier/java/_core.py
+++ b/pyterrier/java/_core.py
@@ -12,7 +12,7 @@
 # Java Initialization
 # ----------------------------------------------------------
 
-class CoreInit(JavaInitializer):
+class CoreJavaInit(JavaInitializer):
     def priority(self) -> int:
         return -100 # run this initializer before anything else
 
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 3f5c2f60..b6fd7c54 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -48,7 +48,7 @@ def prf_required(fn: Callable):
         raise RuntimeError('you need to call pt.terrier.enable_prf() before java is loaded to use this function.')
 
 
-class TerrierInit(pt.java.JavaInitializer):
+class TerrierJavaInit(pt.java.JavaInitializer):
     def pre_init(self, jnius_config):
         # Make sure the terrier.default.properties file exists and is registered as an option, which avoids an annoying
         # "No etc/terrier.properties, using terrier.default.properties for bootstrap configuration." message.
diff --git a/setup.py b/setup.py
index 938a698d..0bc4798b 100644
--- a/setup.py
+++ b/setup.py
@@ -72,9 +72,9 @@ def get_version(rel_path):
     ],
     entry_points={
         'pyterrier.java.init': [
-            'pyterrier.java          = pyterrier.java:CoreInit',
-            'pyterrier.terrier.java  = pyterrier.terrier.java:TerrierInit',
-            'pyterrier.anserini.java = pyterrier.anserini.java:AnseriniInit',
+            'pyterrier.java          = pyterrier.java:CoreJavaInit',
+            'pyterrier.terrier.java  = pyterrier.terrier.java:TerrierJavaInit',
+            'pyterrier.anserini.java = pyterrier.anserini.java:AnseriniJavaInit',
         ],
     },
     install_requires=requirements,

From c8206814e3c1df56505a0ba64a78bcb9d6f836cb Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 3 Aug 2024 09:18:03 +0100
Subject: [PATCH 120/153] I think I prefer the kwargs style for JavaClasses

---
 pyterrier/anserini/java.py | 12 +++---
 pyterrier/java/_core.py    | 24 +++++------
 pyterrier/java/_utils.py   |  2 +-
 pyterrier/terrier/java.py  | 88 +++++++++++++++++++-------------------
 4 files changed, 63 insertions(+), 63 deletions(-)

diff --git a/pyterrier/anserini/java.py b/pyterrier/anserini/java.py
index be8411c6..bbb04d2c 100644
--- a/pyterrier/anserini/java.py
+++ b/pyterrier/anserini/java.py
@@ -85,9 +85,9 @@ def required(fn: Callable):
         pt.java.init()
 
 
-J = pt.java.JavaClasses({
-    'ClassicSimilarity': 'org.apache.lucene.search.similarities.ClassicSimilarity',
-    'BM25Similarity': 'org.apache.lucene.search.similarities.BM25Similarity',
-    'LMDirichletSimilarity': 'org.apache.lucene.search.similarities.LMDirichletSimilarity',
-    'IndexReaderUtils': 'io.anserini.index.IndexReaderUtils',
-})
+J = pt.java.JavaClasses(
+    ClassicSimilarity = 'org.apache.lucene.search.similarities.ClassicSimilarity',
+    BM25Similarity = 'org.apache.lucene.search.similarities.BM25Similarity',
+    LMDirichletSimilarity = 'org.apache.lucene.search.similarities.LMDirichletSimilarity',
+    IndexReaderUtils = 'io.anserini.index.IndexReaderUtils',
+)
diff --git a/pyterrier/java/_core.py b/pyterrier/java/_core.py
index 8afcf98d..c3490c0e 100644
--- a/pyterrier/java/_core.py
+++ b/pyterrier/java/_core.py
@@ -206,15 +206,15 @@ def set_log_level(level):
 # Common classes (accessible via pt.java.J.[ClassName])
 # ----------------------------------------------------------
 
-J = JavaClasses({
-    'ArrayList': 'java.util.ArrayList',
-    'Properties': 'java.util.Properties',
-    'PTUtils': 'org.terrier.python.PTUtils',
-    'System': 'java.lang.System',
-    'StringReader': 'java.io.StringReader',
-    'HashMap': 'java.util.HashMap',
-    'Arrays': 'java.util.Arrays',
-    'Array': 'java.lang.reflect.Array',
-    'String': 'java.lang.String',
-    'List': 'java.util.List',
-})
+J = JavaClasses(
+    ArrayList = 'java.util.ArrayList',
+    Properties = 'java.util.Properties',
+    PTUtils = 'org.terrier.python.PTUtils',
+    System = 'java.lang.System',
+    StringReader = 'java.io.StringReader',
+    HashMap = 'java.util.HashMap',
+    Arrays = 'java.util.Arrays',
+    Array = 'java.lang.reflect.Array',
+    String = 'java.lang.String',
+    List = 'java.util.List',
+)
diff --git a/pyterrier/java/_utils.py b/pyterrier/java/_utils.py
index 557dcdaa..14dbfa1f 100644
--- a/pyterrier/java/_utils.py
+++ b/pyterrier/java/_utils.py
@@ -345,7 +345,7 @@ def register_config(name, config: Dict[str, Any]):
 # ----------------------------------------------------------
 
 class JavaClasses:
-    def __init__(self, mapping: Dict[str, str]):
+    def __init__(self, **mapping: Union[str, Callable[[], str]]):
         self._mapping = mapping
         self._cache = {}
 
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index b6fd7c54..326eaaa5 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -594,54 +594,54 @@ def check_helper_version(min):
 
 
 # Terrier-specific classes
-J = pt.java.JavaClasses({
-    'ApplicationSetup': 'org.terrier.utility.ApplicationSetup',
-    'IndexRef': 'org.terrier.querying.IndexRef',
-    'Version': 'org.terrier.Version',
-    'Tokenizer': 'org.terrier.indexing.tokenisation.Tokeniser',
-    'Serialization': 'org.terrier.python.Serialization',
-    'IndexOnDisk': 'org.terrier.structures.IndexOnDisk',
-    'IndexFactory': 'org.terrier.structures.IndexFactory',
-    'MultiIndex': 'org.terrier.realtime.multi.MultiIndex',
-    'CLITool': 'org.terrier.applications.CLITool',
-    'ApplyTermPipeline': 'org.terrier.querying.ApplyTermPipeline',
-    'ManagerFactory': 'org.terrier.querying.ManagerFactory',
-    'Request': 'org.terrier.querying.Request',
+J = pt.java.JavaClasses(
+    ApplicationSetup = 'org.terrier.utility.ApplicationSetup',
+    IndexRef = 'org.terrier.querying.IndexRef',
+    Version = 'org.terrier.Version',
+    Tokenizer = 'org.terrier.indexing.tokenisation.Tokeniser',
+    Serialization = 'org.terrier.python.Serialization',
+    IndexOnDisk = 'org.terrier.structures.IndexOnDisk',
+    IndexFactory = 'org.terrier.structures.IndexFactory',
+    MultiIndex = 'org.terrier.realtime.multi.MultiIndex',
+    CLITool = 'org.terrier.applications.CLITool',
+    ApplyTermPipeline = 'org.terrier.querying.ApplyTermPipeline',
+    ManagerFactory = 'org.terrier.querying.ManagerFactory',
+    Request = 'org.terrier.querying.Request',
 
     # Indexing
-    'TaggedDocument': 'org.terrier.indexing.TaggedDocument',
-    'FlatJSONDocument': 'org.terrier.indexing.FlatJSONDocument',
-    'Tokeniser': 'org.terrier.indexing.tokenisation.Tokeniser',
-    'TRECCollection': 'org.terrier.indexing.TRECCollection',
-    'SimpleFileCollection': 'org.terrier.indexing.SimpleFileCollection',
-    'BasicIndexer': 'org.terrier.structures.indexing.classical.BasicIndexer',
-    'BlockIndexer': 'org.terrier.structures.indexing.classical.BlockIndexer',
-    'BasicSinglePassIndexer': 'org.terrier.structures.indexing.singlepass.BasicSinglePassIndexer',
-    'BlockSinglePassIndexer': 'org.terrier.structures.indexing.singlepass.BlockSinglePassIndexer',
-    'BasicMemoryIndexer': lambda: 'org.terrier.realtime.memory.MemoryIndexer' if check_version("5.7") else 'org.terrier.python.MemoryIndexer',
-    'Collection': 'org.terrier.indexing.Collection',
-    'StructureMerger': 'org.terrier.structures.merging.StructureMerger',
-    'BlockStructureMerger': 'org.terrier.structures.merging.BlockStructureMerger',
-    'DocumentPostingList': 'org.terrier.structures.indexing.DocumentPostingList',
-    'MapEntry': 'org.terrier.structures.collections.MapEntry',
-    'MultiDocumentFileCollection': 'org.terrier.indexing.MultiDocumentFileCollection',
-    'CollectionFactory': 'org.terrier.indexing.CollectionFactory',
-    'TagSet': 'org.terrier.utility.TagSet',
-    'CollectionFromDocumentIterator': 'org.terrier.python.CollectionFromDocumentIterator',
-    'PTUtils': 'org.terrier.python.PTUtils',
-    'Index': 'org.terrier.structures.Index',
-    'JsonlDocumentIterator': 'org.terrier.python.JsonlDocumentIterator',
-    'JsonlPretokenisedIterator': 'org.terrier.python.JsonlPretokenisedIterator',
-    'ParallelIndexer': 'org.terrier.python.ParallelIndexer',
+    TaggedDocument = 'org.terrier.indexing.TaggedDocument',
+    FlatJSONDocument = 'org.terrier.indexing.FlatJSONDocument',
+    Tokeniser = 'org.terrier.indexing.tokenisation.Tokeniser',
+    TRECCollection = 'org.terrier.indexing.TRECCollection',
+    SimpleFileCollection = 'org.terrier.indexing.SimpleFileCollection',
+    BasicIndexer = 'org.terrier.structures.indexing.classical.BasicIndexer',
+    BlockIndexer = 'org.terrier.structures.indexing.classical.BlockIndexer',
+    BasicSinglePassIndexer = 'org.terrier.structures.indexing.singlepass.BasicSinglePassIndexer',
+    BlockSinglePassIndexer = 'org.terrier.structures.indexing.singlepass.BlockSinglePassIndexer',
+    BasicMemoryIndexer = lambda: 'org.terrier.realtime.memory.MemoryIndexer' if check_version("5.7") else 'org.terrier.python.MemoryIndexer',
+    Collection = 'org.terrier.indexing.Collection',
+    StructureMerger = 'org.terrier.structures.merging.StructureMerger',
+    BlockStructureMerger = 'org.terrier.structures.merging.BlockStructureMerger',
+    DocumentPostingList = 'org.terrier.structures.indexing.DocumentPostingList',
+    MapEntry = 'org.terrier.structures.collections.MapEntry',
+    MultiDocumentFileCollection = 'org.terrier.indexing.MultiDocumentFileCollection',
+    CollectionFactory = 'org.terrier.indexing.CollectionFactory',
+    TagSet = 'org.terrier.utility.TagSet',
+    CollectionFromDocumentIterator = 'org.terrier.python.CollectionFromDocumentIterator',
+    PTUtils = 'org.terrier.python.PTUtils',
+    Index = 'org.terrier.structures.Index',
+    JsonlDocumentIterator = 'org.terrier.python.JsonlDocumentIterator',
+    JsonlPretokenisedIterator = 'org.terrier.python.JsonlPretokenisedIterator',
+    ParallelIndexer = 'org.terrier.python.ParallelIndexer',
 
     # PRF
-    'TerrierQLParser': 'org.terrier.querying.TerrierQLParser',
-    'TerrierQLToMatchingQueryTerms': 'org.terrier.querying.TerrierQLToMatchingQueryTerms',
-    'QueryResultSet': 'org.terrier.matching.QueryResultSet',
-    'DependenceModelPreProcess': 'org.terrier.querying.DependenceModelPreProcess',
-    'RM3': 'org.terrier.querying.RM3',
-    'AxiomaticQE': 'org.terrier.querying.AxiomaticQE',
-})
+    TerrierQLParser = 'org.terrier.querying.TerrierQLParser',
+    TerrierQLToMatchingQueryTerms = 'org.terrier.querying.TerrierQLToMatchingQueryTerms',
+    QueryResultSet = 'org.terrier.matching.QueryResultSet',
+    DependenceModelPreProcess = 'org.terrier.querying.DependenceModelPreProcess',
+    RM3 = 'org.terrier.querying.RM3',
+    AxiomaticQE = 'org.terrier.querying.AxiomaticQE',
+)
 
 
 # Pulled from <https://github.com/terrier-org/terrier-core/blob/5.x/modules/core/src/main/resources/terrier.default.properties>

From 7d857e45535e6fb79c4500052a07bafa3dc6a5b5 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 3 Aug 2024 09:22:07 +0100
Subject: [PATCH 121/153] normalize how the extra requirement decorators are
 done

---
 pyterrier/anserini/__init__.py  | 4 ++--
 pyterrier/anserini/java.py      | 7 ++-----
 pyterrier/anserini/retriever.py | 5 +++--
 pyterrier/terrier/rewrite.py    | 4 ++--
 4 files changed, 9 insertions(+), 11 deletions(-)

diff --git a/pyterrier/anserini/__init__.py b/pyterrier/anserini/__init__.py
index 919d9f25..a5da02b1 100644
--- a/pyterrier/anserini/__init__.py
+++ b/pyterrier/anserini/__init__.py
@@ -1,4 +1,4 @@
-from pyterrier.anserini.java import is_installed, set_version, required, J
+from pyterrier.anserini.java import is_installed, set_version, pyserini_required, J
 from pyterrier.anserini.retriever import AnseriniBatchRetrieve
 
-__all__ = ['is_installed', 'set_version', 'required', 'AnseriniBatchRetrieve', 'J']
+__all__ = ['is_installed', 'set_version', 'pyserini_required', 'AnseriniBatchRetrieve', 'J']
diff --git a/pyterrier/anserini/java.py b/pyterrier/anserini/java.py
index bbb04d2c..ee460175 100644
--- a/pyterrier/anserini/java.py
+++ b/pyterrier/anserini/java.py
@@ -71,18 +71,15 @@ def set_version(version: str):
 
 
 @pt.utils.pre_invocation_decorator
-def required(fn: Callable):
+def pyserini_required(fn: Callable):
     """
-    Requires pyserini to be installed (raises error if not installed). If the JVM has not yet been started, it runs
-    pt.java.init(), too, similar to pt.java.required().
+    Requires pyserini to be installed (raises error if not installed).
 
     Can be used as a function/class @decorator. When used as a class decorator, it
     is applied to all methods defined by the class.
     """
     if not is_installed():
         raise RuntimeError('pyserini required to use pyterrier.anserini. `pip install pyserini` and try again.')
-    if not pt.java.started():
-        pt.java.init()
 
 
 J = pt.java.JavaClasses(
diff --git a/pyterrier/anserini/retriever.py b/pyterrier/anserini/retriever.py
index a7b1b3ee..d0cec6ad 100644
--- a/pyterrier/anserini/retriever.py
+++ b/pyterrier/anserini/retriever.py
@@ -1,10 +1,11 @@
 from warnings import warn
 import pandas as pd
 import pyterrier as pt
-from pyterrier.anserini.java import required
+from pyterrier.anserini.java import pyserini_required
 
 
-@required
+@pyserini_required
+@pt.java.required
 class AnseriniBatchRetrieve(pt.Transformer):
     """
         Allows retrieval from an Anserini index. To use this class, you must first enable anserini using `pt.anserini.enable()`.
diff --git a/pyterrier/terrier/rewrite.py b/pyterrier/terrier/rewrite.py
index 3440ce60..bdf41665 100644
--- a/pyterrier/terrier/rewrite.py
+++ b/pyterrier/terrier/rewrite.py
@@ -350,6 +350,7 @@ def __init__(self, *args, **kwargs):
         kwargs["qemodel"] = "KL"
         super().__init__(*args, **kwargs)
 
+@prf_required
 @pt.java.required
 class RM3(QueryExpansion):
     '''
@@ -376,7 +377,6 @@ class RM3(QueryExpansion):
                         )
  
     '''
-    @prf_required
     def __init__(self, *args, fb_terms=10, fb_docs=3, fb_lambda=0.6, **kwargs):
         """
         Args:
@@ -408,6 +408,7 @@ def transform(self, queries_and_docs):
         self.qe.fbDocs = self.fb_docs
         return super().transform(queries_and_docs)
 
+@prf_required
 @pt.java.required
 class AxiomaticQE(QueryExpansion):
     '''
@@ -422,7 +423,6 @@ class AxiomaticQE(QueryExpansion):
          - fb_terms(int): number of feedback terms. Defaults to 10
          - fb_docs(int): number of feedback documents. Defaults to 3
     '''
-    @prf_required
     def __init__(self, *args, fb_terms=10, fb_docs=3, **kwargs):
         """
         Args:

From 8fdca57803514ecb4b921e4f6b5875d4010db5f5 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 3 Aug 2024 09:39:56 +0100
Subject: [PATCH 122/153] whoops

---
 deleteme/x.pkl | Bin 6835025 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 deleteme/x.pkl

diff --git a/deleteme/x.pkl b/deleteme/x.pkl
deleted file mode 100644
index 285bee1c4062624cfe02a732d22ee55327510725..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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From 864dbe6f753e3bab3d251c2454413112d51d58e8 Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Tue, 6 Aug 2024 13:49:26 +0100
Subject: [PATCH 123/153] fix comment typos

---
 pyterrier/java/_utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyterrier/java/_utils.py b/pyterrier/java/_utils.py
index 14dbfa1f..ebfa00d0 100644
--- a/pyterrier/java/_utils.py
+++ b/pyterrier/java/_utils.py
@@ -292,7 +292,7 @@ def parallel_init_args() -> Tuple[bool, Dict[str, Dict[str, Any]]]:
 # Configuration Utils
 # ----------------------------------------------------------
 # We need a global store of all java-related configurations
-# so that when running in paralle, we can set everying back
+# so that when running in parallel, we can set everying back
 # up the same way it started. These utils help manage this
 # gloabal configuration.
 # ----------------------------------------------------------
@@ -340,7 +340,7 @@ def register_config(name, config: Dict[str, Any]):
 # Java Classes
 # ----------------------------------------------------------
 # This class enables the lazy loading of java classes. It
-# helps avod needing a ton of autclass() statements to
+# helps avoid needing a ton of autclass() statements to
 # pre-load Java classes.
 # ----------------------------------------------------------
 

From 28aca381d8948f5ea00ab44b24645dc7243550e3 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 6 Aug 2024 19:16:07 +0100
Subject: [PATCH 124/153] more expressive deprecated warning for pt.init()

from https://github.com/terrier-org/pyterrier/compare/java...java_init_warn
---
 pyterrier/__init__.py    |  2 +-
 pyterrier/java/_utils.py | 53 ++++++++++++++++++++++++++++++++++------
 2 files changed, 46 insertions(+), 9 deletions(-)

diff --git a/pyterrier/__init__.py b/pyterrier/__init__.py
index 8d611ee9..40267b49 100644
--- a/pyterrier/__init__.py
+++ b/pyterrier/__init__.py
@@ -34,7 +34,7 @@
 
 
 # deprecated functions explored to the main namespace, which will be removed in a future version
-init = deprecated(version='0.11.0', reason='No longer needed, java is started automatically. To force initialization early, use pt.java.init() instead.')(java.legacy_init)
+init = java.legacy_init # java.legacy_init raises a deprecated warning internally
 started = deprecated(version='0.11.0', reason="use pt.java.started() instead")(java.started)
 logging = deprecated(version='0.11.0', reason="use pt.java.set_log_level(...) instead")(java.set_log_level)
 version = deprecated(version='0.11.0', reason="use pt.terrier.version() instead")(terrier.version)
diff --git a/pyterrier/java/_utils.py b/pyterrier/java/_utils.py
index ebfa00d0..2df029e5 100644
--- a/pyterrier/java/_utils.py
+++ b/pyterrier/java/_utils.py
@@ -1,5 +1,5 @@
 import sys
-from warnings import warn
+import warnings
 from functools import wraps
 from typing import Dict, Any, Tuple, Callable, Optional, Union
 from copy import deepcopy
@@ -154,7 +154,7 @@ def _init(trigger=None):
 
 
 @before_init
-def legacy_init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, logging='WARN', home_dir=None, boot_packages=[], tqdm=None, no_download=False,helper_version = None):
+def legacy_init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=True, logging='WARN', home_dir=None, boot_packages=[], tqdm=None, no_download=False, helper_version = None):
     """
     Function that can be called before Terrier classes and methods can be used.
     Loads the Terrier .jar file and imports classes. Also finds the correct version of Terrier to download if no version is specified.
@@ -193,27 +193,64 @@ def legacy_init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=Tr
     ``pt.init(version = 5.5, helper_version = "0.0.6")``.
     """
 
+    deprecated_calls = []
+
     # Set the corresponding options
-    pt.java.set_memory_limit(mem)
-    pt.java.set_redirect_io(redirect_io)
-    pt.java.set_log_level(logging)
+    if mem is not None:
+        pt.java.set_memory_limit(mem)
+        deprecated_calls.append(f'pt.java.set_memory_limit({mem!r})')
+
+    if not redirect_io:
+        pt.java.set_redirect_io(redirect_io)
+        deprecated_calls.append(f'pt.java.set_redirect_io({redirect_io!r})')
+
+    if logging != 'WARN':
+        pt.java.set_log_level(logging)
+        deprecated_calls.append(f'pt.java.set_log_level({logging!r})')
+
     for package in boot_packages:
         pt.java.add_package(*package.split(':')) # format: org:package:version:filetype (where version and filetype are optional)
+        deprecated_calls.append(f'pt.java.add_package({package!r})')
+
     for opt in jvm_opts:
         pt.java.add_option(opt)
-    pt.terrier.set_version(version)
-    pt.terrier.set_helper_version(helper_version)
+        deprecated_calls.append(f'pt.java.add_option({opt!r})')
+
+    if version is not None:
+        pt.terrier.set_version(version)
+        deprecated_calls.append(f'pt.terrier.set_version({version!r})')
+
+    if helper_version is not None:
+        pt.terrier.set_helper_version(helper_version)
+        deprecated_calls.append(f'pt.terrier.set_helper_version({helper_version!r})')
+
     if tqdm is not None:
         pt.utils.set_tqdm(tqdm)
+        deprecated_calls.append(f'pt.utils.set_tqdm({tqdm!r})')
+
     if no_download:
         pt.java.mavenresolver.offline()
+        deprecated_calls.append('pt.java.mavenresolver.offline()')
 
     pt.java.init()
+    deprecated_calls.append('pt.java.init()')
 
     # Import other java packages
     if packages:
         pkgs_string = ",".join(packages)
         pt.terrier.set_property("terrier.mvn.coords", pkgs_string)
+        deprecated_calls.append(f'pt.terrier.set_property("terrier.mvn.coords", {pkgs_string!r})')
+
+    # Warning to give new initialization
+    deprecated_message = 'Call to deprecated method pt.init(). Deprecated since version 0.11.0.'
+    if len(deprecated_calls) > 1: # called setup other than pt.java.init()
+        deprecated_message = deprecated_message + '\nThe following code will have the same effect:'
+    else: # only called pt.java.init()
+        deprecated_message = deprecated_message + '\njava is now started automatically with default settings. To force initialization early, run:'
+    deprecated_message = '\n'.join([deprecated_message] + deprecated_calls)
+    with warnings.catch_warnings():
+        warnings.simplefilter('once', DeprecationWarning) # DeprecationWarning hidden by default, @deprecated does this to show the messages
+        warnings.warn(deprecated_message, category=DeprecationWarning, stacklevel=3) # stacklevel=3 prints wherever this call comes from, rather than here, see @deprecated source
 
 
 def started() -> bool:
@@ -278,7 +315,7 @@ def parallel_init(started: bool, configs: Dict[str, Dict[str, Any]]) -> None:
             _configs = configs
             _init(trigger='parallel_init')
         else:
-            warn("Avoiding reinit of PyTerrier")
+            warnings.warn("Avoiding reinit of PyTerrier")
 
 
 def parallel_init_args() -> Tuple[bool, Dict[str, Dict[str, Any]]]:

From d7b49f97efa7977daaab7daa772e908344b46b5a Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Wed, 7 Aug 2024 10:58:48 +0100
Subject: [PATCH 125/153] add comment to warning

---
 pyterrier/java/_utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/pyterrier/java/_utils.py b/pyterrier/java/_utils.py
index 2df029e5..618bd7d0 100644
--- a/pyterrier/java/_utils.py
+++ b/pyterrier/java/_utils.py
@@ -233,7 +233,7 @@ def legacy_init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=Tr
         deprecated_calls.append('pt.java.mavenresolver.offline()')
 
     pt.java.init()
-    deprecated_calls.append('pt.java.init()')
+    deprecated_calls.append('pt.java.init() # optional, forces java initialisation')
 
     # Import other java packages
     if packages:
@@ -246,7 +246,7 @@ def legacy_init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=Tr
     if len(deprecated_calls) > 1: # called setup other than pt.java.init()
         deprecated_message = deprecated_message + '\nThe following code will have the same effect:'
     else: # only called pt.java.init()
-        deprecated_message = deprecated_message + '\njava is now started automatically with default settings. To force initialization early, run:'
+        deprecated_message = deprecated_message + '\njava is now started automatically with default settings. To force initialisation early, run:'
     deprecated_message = '\n'.join([deprecated_message] + deprecated_calls)
     with warnings.catch_warnings():
         warnings.simplefilter('once', DeprecationWarning) # DeprecationWarning hidden by default, @deprecated does this to show the messages

From 434bebdcb535fa02755341035808b69690597e39 Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Wed, 7 Aug 2024 13:18:39 +0100
Subject: [PATCH 126/153] move to pt.terrier.Retrieve etc by default

---
 docs/experiments/Robust04.md                  |    8 +-
 docs/ltr.rst                                  |    8 +-
 docs/terrier-retrieval.rst                    |   52 +-
 examples/notebooks/experiment.ipynb           |  392 +-
 examples/notebooks/index_api.ipynb            |   70 +-
 examples/notebooks/indexing.ipynb             |  373 +-
 examples/notebooks/ltr.ipynb                  |   31 +-
 examples/notebooks/non_en_retrieval.ipynb     |    2 -
 .../notebooks/retrieval_and_evaluation.ipynb  |   25 +-
 .../notebooks/sentence_transformers.ipynb     | 4969 ++++++++---------
 pyterrier/terrier/__init__.py                 |    9 +-
 pyterrier/terrier/java.py                     |    2 +-
 .../terrier/{retriever.py => retrieve.py}     |   66 +-
 pyterrier/terrier/rewrite.py                  |    2 +-
 pyterrier/text.py                             |    2 +-
 tests/test_dunder.py                          |    2 +-
 tests/test_operators.py                       |    2 +-
 tests/test_scoring.py                         |   10 +-
 18 files changed, 3057 insertions(+), 2968 deletions(-)
 rename pyterrier/terrier/{retriever.py => retrieve.py} (94%)

diff --git a/docs/experiments/Robust04.md b/docs/experiments/Robust04.md
index 773464b7..1a33dd4f 100644
--- a/docs/experiments/Robust04.md
+++ b/docs/experiments/Robust04.md
@@ -34,10 +34,10 @@ Here we define and evaluate standard weighting models.
 
 ```python
 
-BM25 = pt.BatchRetrieve(index, wmodel="BM25")
-DPH  = pt.BatchRetrieve(index, wmodel="DPH")
-PL2  = pt.BatchRetrieve(index, wmodel="PL2")
-DLM  = pt.BatchRetrieve(index, wmodel="DirichletLM")
+BM25 = pt.terrier.Retrieve(index, wmodel="BM25")
+DPH  = pt.terrier.Retrieve(index, wmodel="DPH")
+PL2  = pt.terrier.Retrieve(index, wmodel="PL2")
+DLM  = pt.terrier.Retrieve(index, wmodel="DirichletLM")
 
 pt.Experiment(
     [BM25, DPH, PL2, DLM],
diff --git a/docs/ltr.rst b/docs/ltr.rst
index e8bdab35..28b23a9c 100644
--- a/docs/ltr.rst
+++ b/docs/ltr.rst
@@ -64,17 +64,17 @@ the `"features"` column, as follows:
 FeaturesBatchRetrieve
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-When executing the pipeline above, the re-ranking of the documents again can be slow, as each separate BatchRetrieve
-object has to re-access the inverted index. For this reason, PyTerrier provides a class called FeaturesBatchRetrieve,
+When executing the pipeline above, the re-ranking of the documents again can be slow, as each separate Retrieve
+object has to re-access the inverted index. For this reason, PyTerrier provides a class called FeaturesRetrieve,
 which allows multiple query dependent features to be calculated at once, by virtue of Terrier's Fat framework.
 
-.. autoclass:: pyterrier.FeaturesBatchRetrieve
+.. autoclass:: pyterrier.terrier.FeaturesRetrieve
     :members: transform 
 
 An equivalent pipeline to the example above would be::
 
     #pipeline = bm25 >> (tf ** pl2)
-    pipeline = pt.FeaturesBatchRetrieve(index, wmodel="BM25", features=["WMODEL:Tf", "WMODEL:PL2"])
+    pipeline = pyterrier.terrier.FeaturesRetrieve(index, wmodel="BM25", features=["WMODEL:Tf", "WMODEL:PL2"])
 
 
 Apply Functions for Custom Features
diff --git a/docs/terrier-retrieval.rst b/docs/terrier-retrieval.rst
index 579938a2..55510a59 100644
--- a/docs/terrier-retrieval.rst
+++ b/docs/terrier-retrieval.rst
@@ -1,7 +1,7 @@
 Terrier Retrieval
 -----------------
 
-BatchRetrieve is one of the most commonly used PyTerrier objects. It represents a retrieval transformation, 
+terrier.Retrieve is one of the most commonly used PyTerrier objects. It represents a retrieval transformation, 
 in which queries are mapped to retrieved documents. BatchRetrieve uses a pre-existing Terrier index data
 structure, typically saved on disk.
 
@@ -9,28 +9,28 @@ structure, typically saved on disk.
 Typical usage::
 
     index = pt.IndexFactory.of("/path/to/data.properties")
-    tf_idf = pt.BatchRetrieve(index, wmodel="TF_IDF")
-    bm25 = pt.BatchRetrieve(index, wmodel="BM25")
-    pl2 = pt.BatchRetrieve(index, wmodel="PL2")
+    tf_idf = pt.terrier.Retrieve(index, wmodel="TF_IDF")
+    bm25 = pt.terrier.Retrieve(index, wmodel="BM25")
+    pl2 = pt.terrier.Retrieve(index, wmodel="PL2")
 
     pt.Experiment([tf_idf, bm25, pl2], topic, qrels, eval_metrics=["map"])
 
-As BatchRetrieve is a retrieval transformation, it takes as input dataframes with columns `["qid", "query"]`,
+As Retrieve is a retrieval transformation, it takes as input dataframes with columns `["qid", "query"]`,
 and returns dataframes with columns `["qid", "query", "docno", "score", "rank"]`.
 
-However, BatchRetrieve can also act as a re-ranker. In this scenario, it takes as input dataframes with 
+However, Retrieve can also act as a re-ranker. In this scenario, it takes as input dataframes with 
 columns `["qid", "query", "docno"]`, and returns dataframes with columns `["qid", "query", "docno", "score", "rank"]`.
 
 For instance, to create a re-ranking pipeline that re-scores the top 100 BM25 documents using PL2::
 
-    bm25 = pt.BatchRetrieve(index, wmodel="BM25")
-    pl2 = pt.BatchRetrieve(index, wmodel="PL2")
+    bm25 = pt.terrier.Retrieve(index, wmodel="BM25")
+    pl2 = pt.terrier.Retrieve(index, wmodel="PL2")
     pipeline = (bm25 % 100) >> pl2
 
-BatchRetrieve
+Retrieve
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pyterrier.terrier.BatchRetrieve
+.. autoclass:: pyterrier.terrier.Retrieve
     :members: transform, from_dataset
 
 
@@ -42,7 +42,7 @@ When using PyTerrier, we have to be aware of the underlying Terrier configuratio
 namely *properties* and *controls*. Properties are global configuration and were 
 traditionally configured by editing a `terrier.properties` file; In contrast, 
 controls are per-query configuration. In PyTerrier, we specify both when we construct
-the BatchRetrieve object:
+the Retrieve object:
 
 Common controls:
  - `"wmodel"` - the name of the weighting model. (This can also be specified using the wmodel kwarg).
@@ -59,24 +59,24 @@ Common properties:
 
 **Examples**::
 
-    # these two BatchRetrieve instances are identical, using the same weighting model
-    bm25a = pt.BatchRetrieve(index, wmodel="BM25")
-    bm25b = pt.BatchRetrieve(index, controls={"wmodel":"BM25"})
+    # these two Retrieve instances are identical, using the same weighting model
+    bm25a = pt.terrier.Retrieve(index, wmodel="BM25")
+    bm25b = pt.terrier.Retrieve(index, controls={"wmodel":"BM25"})
 
     # this one also applies query expansion inside Terrier
-    bm25_qe = pt.BatchRetrieve(index, wmodel="BM25", controls={"qe":"on", "qemodel" : "Bo1"})
+    bm25_qe = pt.terrier.Retrieve(index, wmodel="BM25", controls={"qe":"on", "qemodel" : "Bo1"})
 
     # when we introduce an unstemmed BatchRetrieve, we ensure to explicitly set the termpipelines
     # for the other BatchRetrieve as well
-    bm25s_unstemmed = pt.BatchRetrieve(indexUS, wmodel="BM25", properties={"termpipelines" : ""})
-    bm25s_stemmed = pt.BatchRetrieve(indexSS, wmodel="BM25", properties={"termpipelines" : "Stopwords,PorterStemmer"})
+    bm25s_unstemmed = pt.terrier.Retrieve(indexUS, wmodel="BM25", properties={"termpipelines" : ""})
+    bm25s_stemmed = pt.terrier.Retrieve(indexSS, wmodel="BM25", properties={"termpipelines" : "Stopwords,PorterStemmer"})
     
 
 
 Index-Like Objects
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-When working with Terrier indices, BatchRetrieve allows can make use of:
+When working with Terrier indices, Retrieve allows can make use of:
 
  - a string representing an index, such as "/path/to/data.properties"
  - a Terrier `IndexRef <http://terrier.org/docs/current/javadoc/org/terrier/querying/IndexRef.html>`_ object, constructed from a string, but which may also hold a reference to the existing index.
@@ -87,26 +87,26 @@ Where possible, for faster reuse, load the actual Index.
 
 Bad Practice::
 
-    bm25 = pt.BatchRetrieve("/path/to/data.properties", wmodel="BM25")
-    pl2 = pt.BatchRetrieve("/path/to/data.properties", wmodel="PL2")
+    bm25 = pt.terrier.Retrieve("/path/to/data.properties", wmodel="BM25")
+    pl2 = pt.terrier.Retrieve("/path/to/data.properties", wmodel="PL2")
     # here, the same index must be loaded twice
 
 Good Practice::
 
     index = pt.IndexFactory.of("/path/to/data.properties")
-    bm25 = pt.BatchRetrieve(index, wmodel="BM25")
-    pl2 = pt.BatchRetrieve(index, wmodel="PL2")
+    bm25 = pt.terrier.Retrieve(index, wmodel="BM25")
+    pl2 = pt.terrier.Retrieve(index, wmodel="PL2")
     # here, we share the index between two instances of BatchRetrieve
 
 You can use the IndexFactory to specify that the index data structures to be loaded into memory::
 
     # load all structures into memory
     inmemindex = pt.IndexFactory.of("/path/to/data.properties", memory=True)
-    bm25_fast = pt.BatchRetrieve(inmemindex, wmodel="BM25")
+    bm25_fast = pt.terrier.Retrieve(inmemindex, wmodel="BM25")
 
     # load just inverted and lexicon into memory
     inmem_inverted_index = pt.IndexFactory.of("/path/to/data.properties", memory=['inverted', 'lexicon'])
-    bm25_fast = pt.BatchRetrieve(inmem_inverted_index, wmodel="BM25")
+    bm25_fast = pt.terrier.Retrieve(inmem_inverted_index, wmodel="BM25")
 
 
 TextScorer
@@ -141,13 +141,13 @@ If you have your own Java weighting model instance (which extends the
 you can load it and pass it directly to BatchRetrieve::
 
     mymodel = pt.autoclass("com.example.MyTF")()
-    retr = pt.BatchRetrieve(indexref, wmodel=mymodel)
+    retr = pt.terrier.Retrieve(indexref, wmodel=mymodel)
 
 More usefully, it is possible to express a weighting model entirely in Python, as a function or a lambda expression, that can be
 used by Terrier for scoring. In this example, we create a Terrier BatchRetrieve instance that scores based solely on term frequency::
 
     Tf = lambda keyFreq, posting, entryStats, collStats: posting.getFrequency()
-    retr = pt.BatchRetrieve(indexref, wmodel=Tf)
+    retr = pt.terrier.Retrieve(indexref, wmodel=Tf)
 
 All functions passed must accept 4 arguments, as follows:
 
diff --git a/examples/notebooks/experiment.ipynb b/examples/notebooks/experiment.ipynb
index 2c6aa164..c1afd0d6 100644
--- a/examples/notebooks/experiment.ipynb
+++ b/examples/notebooks/experiment.ipynb
@@ -3,8 +3,8 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "P5kzUfb36WMd",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "P5kzUfb36WMd"
       },
       "source": [
         "# Pyterrier - Example Experiment\n",
@@ -14,21 +14,19 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 1,
       "metadata": {
-        "id": "TejY4gN66TCc",
-        "colab_type": "code",
-        "outputId": "262dc29e-aa9b-4de6-9014-2887d107417a",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 765
-        }
+        },
+        "colab_type": "code",
+        "id": "TejY4gN66TCc",
+        "outputId": "262dc29e-aa9b-4de6-9014-2887d107417a"
       },
-      "source": [
-        "!pip install python-terrier"
-      ],
-      "execution_count": 1,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Collecting python-terrier\n",
@@ -75,84 +73,50 @@
             "Successfully built python-terrier wget pytrec-eval hopcroftkarp\n",
             "Installing collected packages: pyjnius, wget, pytrec-eval, multiset, hopcroftkarp, matchpy, deprecation, python-terrier\n",
             "Successfully installed deprecation-2.1.0 hopcroftkarp-1.2.5 matchpy-0.5.1 multiset-2.1.1 pyjnius-1.3.0 python-terrier-0.1.3 pytrec-eval-0.4 wget-3.2\n"
-          ],
-          "name": "stdout"
+          ]
         }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "nt9K3eSWc_Zk",
-        "colab_type": "text"
-      },
+      ],
       "source": [
-        "# Init \n",
-        "\n",
-        "You must run `pt.init()` before other pyterrier functions and classes\n",
-        "\n",
-        "Arguments:\n",
-        " - `version` - Terrier platform version e.g. `\"5.2\"`    \n",
-        " - `mem` - megabytes allocated to Java e.g. `4096`      \n"
+        "%pip install -q python-terrier"
       ]
     },
     {
       "cell_type": "code",
-      "metadata": {
-        "id": "4nt1cgv-6dzf",
-        "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 85
-        },
-        "outputId": "7b801c7d-3964-4980-fc3d-8b0dc23748bb"
-      },
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
       "source": [
-        "import pyterrier as pt\n",
-        "if not pt.started():\n",
-        "  pt.init()"
-      ],
-      "execution_count": 1,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "/Users/craigmacdonald/opt/anaconda3/lib/python3.8/site-packages/tqdm/std.py:697: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
-            "  from pandas import Panel\n",
-            "PyTerrier 0.4.0 has loaded Terrier 5.4 (built by craigm on 2021-01-16 14:17)\n"
-          ]
-        }
+        "import pyterrier as pt"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "ZfFfllxN6Vnv",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "ZfFfllxN6Vnv"
       },
       "source": [
-        "Again, we're using the Dataset interface to quickly access a test collection."
+        "we're using the Dataset interface to quickly access a test collection."
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 2,
       "metadata": {
-        "id": "LVboB-IY5Joj",
+        "colab": {},
         "colab_type": "code",
-        "colab": {}
+        "id": "LVboB-IY5Joj"
       },
+      "outputs": [],
       "source": [
         "vaswani = pt.datasets.get_dataset(\"vaswani\")"
-      ],
-      "execution_count": 2,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "Vxgbi63k64pj",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "Vxgbi63k64pj"
       },
       "source": [
         "# Experiment\n",
@@ -163,8 +127,8 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "OVmeqAX-67Am",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "OVmeqAX-67Am"
       },
       "source": [
         "First create the BatchRetrieve objects with the configurartion that you wish to use"
@@ -172,28 +136,28 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 3,
       "metadata": {
-        "id": "9bMNAVT06j6E",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
         },
+        "colab_type": "code",
+        "id": "9bMNAVT06j6E",
         "outputId": "93e2dd5e-f986-48f4-ed17-e8264341f3c4"
       },
+      "outputs": [],
       "source": [
         "TF_IDF = pt.BatchRetrieve(vaswani.get_index(), wmodel=\"TF_IDF\")\n",
         "BM25 = pt.BatchRetrieve(vaswani.get_index(), wmodel=\"BM25\")\n",
         "PL2 = pt.BatchRetrieve(vaswani.get_index(), wmodel=\"PL2\")"
-      ],
-      "execution_count": 3,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "ozgSUBhD7I6L",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "ozgSUBhD7I6L"
       },
       "source": [
         "Call `pt.Experiment` with the list of retrieval objects, topics, qrels and list of metrics\n",
@@ -207,41 +171,89 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 4,
       "metadata": {
-        "id": "8LCmxPtY6j3Q",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 142
         },
+        "colab_type": "code",
+        "id": "8LCmxPtY6j3Q",
         "outputId": "d8c64099-b122-47f2-a5be-e86768241b8e"
       },
-      "source": [
-        "pt.Experiment(\n",
-        "    [TF_IDF,BM25,PL2],\n",
-        "    vaswani.get_topics(),\n",
-        "    vaswani.get_qrels(),\n",
-        "    ['map','ndcg'])"
-      ],
-      "execution_count": 4,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>name</th>\n",
+              "      <th>map</th>\n",
+              "      <th>ndcg</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>BR(TF_IDF)</td>\n",
+              "      <td>0.290905</td>\n",
+              "      <td>0.615367</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>BR(BM25)</td>\n",
+              "      <td>0.296517</td>\n",
+              "      <td>0.621197</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>BR(PL2)</td>\n",
+              "      <td>0.276264</td>\n",
+              "      <td>0.601225</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
             "text/plain": [
               "         name       map      ndcg\n",
               "0  BR(TF_IDF)  0.290905  0.615367\n",
               "1    BR(BM25)  0.296517  0.621197\n",
               "2     BR(PL2)  0.276264  0.601225"
-            ],
-            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>map</th>\n      <th>ndcg</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>BR(TF_IDF)</td>\n      <td>0.290905</td>\n      <td>0.615367</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>BR(BM25)</td>\n      <td>0.296517</td>\n      <td>0.621197</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>BR(PL2)</td>\n      <td>0.276264</td>\n      <td>0.601225</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+            ]
           },
+          "execution_count": 4,
           "metadata": {},
-          "execution_count": 4
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "pt.Experiment(\n",
+        "    [TF_IDF,BM25,PL2],\n",
+        "    vaswani.get_topics(),\n",
+        "    vaswani.get_qrels(),\n",
+        "    ['map','ndcg'])"
       ]
     },
     {
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "## Declaring a baseline\n",
         "\n",
@@ -249,9 +261,7 @@
         "This will enable significance testing (paired t-test), as well as showing the number of queries improved and degraded \n",
         "wrt. the baseline.\n",
         "\n"
-      ],
-      "cell_type": "markdown",
-      "metadata": {}
+      ]
     },
     {
       "cell_type": "code",
@@ -259,8 +269,78 @@
       "metadata": {},
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>name</th>\n",
+              "      <th>map</th>\n",
+              "      <th>ndcg</th>\n",
+              "      <th>map +</th>\n",
+              "      <th>map -</th>\n",
+              "      <th>map p-value</th>\n",
+              "      <th>ndcg +</th>\n",
+              "      <th>ndcg -</th>\n",
+              "      <th>ndcg p-value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>BR(TF_IDF)</td>\n",
+              "      <td>0.290905</td>\n",
+              "      <td>0.615367</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>BR(BM25)</td>\n",
+              "      <td>0.296517</td>\n",
+              "      <td>0.621197</td>\n",
+              "      <td>46.0</td>\n",
+              "      <td>45.0</td>\n",
+              "      <td>0.237317</td>\n",
+              "      <td>45.0</td>\n",
+              "      <td>46.0</td>\n",
+              "      <td>0.143493</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>BR(PL2)</td>\n",
+              "      <td>0.276264</td>\n",
+              "      <td>0.601225</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>0.008827</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>73.0</td>\n",
+              "      <td>0.002583</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
             "text/plain": [
               "         name       map      ndcg  map +  map -  map p-value  ndcg +  ndcg -  \\\n",
               "0  BR(TF_IDF)  0.290905  0.615367    NaN    NaN          NaN     NaN     NaN   \n",
@@ -271,11 +351,11 @@
               "0           NaN  \n",
               "1      0.143493  \n",
               "2      0.002583  "
-            ],
-            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>map</th>\n      <th>ndcg</th>\n      <th>map +</th>\n      <th>map -</th>\n      <th>map p-value</th>\n      <th>ndcg +</th>\n      <th>ndcg -</th>\n      <th>ndcg p-value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>BR(TF_IDF)</td>\n      <td>0.290905</td>\n      <td>0.615367</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>BR(BM25)</td>\n      <td>0.296517</td>\n      <td>0.621197</td>\n      <td>46.0</td>\n      <td>45.0</td>\n      <td>0.237317</td>\n      <td>45.0</td>\n      <td>46.0</td>\n      <td>0.143493</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>BR(PL2)</td>\n      <td>0.276264</td>\n      <td>0.601225</td>\n      <td>13.0</td>\n      <td>77.0</td>\n      <td>0.008827</td>\n      <td>17.0</td>\n      <td>73.0</td>\n      <td>0.002583</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+            ]
           },
+          "execution_count": 5,
           "metadata": {},
-          "execution_count": 5
+          "output_type": "execute_result"
         }
       ],
       "source": [
@@ -288,31 +368,108 @@
       ]
     },
     {
+      "cell_type": "markdown",
+      "metadata": {},
       "source": [
         "We can also apply correcion for multiple testing. Use the `correction=` kwarg - 'b' is a short hand for 'bonferroni'. The additional columns\n",
         "\"map p-value corrected\" \"map reject\" are added. The former shows what the new, adjusted, p-value is, and the latter shows if the \n",
         "null hypothesis can rejected at $\\alpha=0.05$."
-      ],
-      "cell_type": "markdown",
-      "metadata": {}
+      ]
     },
     {
-      "source": [
-        "pt.Experiment(\n",
-        "    [TF_IDF,BM25,PL2],\n",
-        "    vaswani.get_topics(),\n",
-        "    vaswani.get_qrels(),\n",
-        "    ['map','ndcg'],\n",
-        "    baseline=0,\n",
-        "    correction='b')"
-      ],
       "cell_type": "code",
-      "metadata": {},
       "execution_count": 6,
+      "metadata": {},
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>name</th>\n",
+              "      <th>map</th>\n",
+              "      <th>ndcg</th>\n",
+              "      <th>map +</th>\n",
+              "      <th>map -</th>\n",
+              "      <th>map p-value</th>\n",
+              "      <th>map reject</th>\n",
+              "      <th>map p-value corrected</th>\n",
+              "      <th>ndcg +</th>\n",
+              "      <th>ndcg -</th>\n",
+              "      <th>ndcg p-value</th>\n",
+              "      <th>ndcg reject</th>\n",
+              "      <th>ndcg p-value corrected</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>BR(TF_IDF)</td>\n",
+              "      <td>0.290905</td>\n",
+              "      <td>0.615367</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>False</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>False</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>BR(BM25)</td>\n",
+              "      <td>0.296517</td>\n",
+              "      <td>0.621197</td>\n",
+              "      <td>46.0</td>\n",
+              "      <td>45.0</td>\n",
+              "      <td>0.237317</td>\n",
+              "      <td>False</td>\n",
+              "      <td>0.711951</td>\n",
+              "      <td>45.0</td>\n",
+              "      <td>46.0</td>\n",
+              "      <td>0.143493</td>\n",
+              "      <td>False</td>\n",
+              "      <td>0.43048</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>BR(PL2)</td>\n",
+              "      <td>0.276264</td>\n",
+              "      <td>0.601225</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>0.008827</td>\n",
+              "      <td>True</td>\n",
+              "      <td>0.026482</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>73.0</td>\n",
+              "      <td>0.002583</td>\n",
+              "      <td>True</td>\n",
+              "      <td>0.00775</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
             "text/plain": [
               "         name       map      ndcg  map +  map -  map p-value  map reject  \\\n",
               "0  BR(TF_IDF)  0.290905  0.615367    NaN    NaN          NaN       False   \n",
@@ -328,27 +485,36 @@
               "0                     NaN  \n",
               "1                 0.43048  \n",
               "2                 0.00775  "
-            ],
-            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>map</th>\n      <th>ndcg</th>\n      <th>map +</th>\n      <th>map -</th>\n      <th>map p-value</th>\n      <th>map reject</th>\n      <th>map p-value corrected</th>\n      <th>ndcg +</th>\n      <th>ndcg -</th>\n      <th>ndcg p-value</th>\n      <th>ndcg reject</th>\n      <th>ndcg p-value corrected</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>BR(TF_IDF)</td>\n      <td>0.290905</td>\n      <td>0.615367</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>False</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>BR(BM25)</td>\n      <td>0.296517</td>\n      <td>0.621197</td>\n      <td>46.0</td>\n      <td>45.0</td>\n      <td>0.237317</td>\n      <td>False</td>\n      <td>0.711951</td>\n      <td>45.0</td>\n      <td>46.0</td>\n      <td>0.143493</td>\n      <td>False</td>\n      <td>0.43048</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>BR(PL2)</td>\n      <td>0.276264</td>\n      <td>0.601225</td>\n      <td>13.0</td>\n      <td>77.0</td>\n      <td>0.008827</td>\n      <td>True</td>\n      <td>0.026482</td>\n      <td>17.0</td>\n      <td>73.0</td>\n      <td>0.002583</td>\n      <td>True</td>\n      <td>0.00775</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+            ]
           },
+          "execution_count": 6,
           "metadata": {},
-          "execution_count": 6
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "pt.Experiment(\n",
+        "    [TF_IDF,BM25,PL2],\n",
+        "    vaswani.get_topics(),\n",
+        "    vaswani.get_qrels(),\n",
+        "    ['map','ndcg'],\n",
+        "    baseline=0,\n",
+        "    correction='b')"
       ]
     }
   ],
   "metadata": {
     "colab": {
+      "collapsed_sections": [],
       "name": "Experiment Example.ipynb",
-      "provenance": [],
-      "collapsed_sections": []
+      "provenance": []
     },
     "kernelspec": {
-      "name": "python3",
       "display_name": "Python 3",
-      "language": "python"
+      "language": "python",
+      "name": "python3"
     }
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/examples/notebooks/index_api.ipynb b/examples/notebooks/index_api.ipynb
index 77804728..217eaa82 100644
--- a/examples/notebooks/index_api.ipynb
+++ b/examples/notebooks/index_api.ipynb
@@ -34,10 +34,7 @@
         },
         "colab_type": "code",
         "id": "JWLqWXBHeBRc",
-        "outputId": "618fe9cb-2bd3-47ff-fbdf-816d4b1cadce",
-        "vscode": {
-          "languageId": "python"
-        }
+        "outputId": "618fe9cb-2bd3-47ff-fbdf-816d4b1cadce"
       },
       "outputs": [
         {
@@ -92,7 +89,7 @@
         }
       ],
       "source": [
-        "%pip install -q python-terrier\n"
+        "%pip install -q python-terrier"
       ]
     },
     {
@@ -102,15 +99,7 @@
         "id": "ixzDjvtTOQbB"
       },
       "source": [
-        "## Init \n",
-        "\n",
-        "You must run `pt.init()` before other pyterrier functions and classes\n",
-        "\n",
-        "Optional Arguments:    \n",
-        " - `version` - terrier IR version e.g. \"5.2\"    \n",
-        " - `mem` - megabytes allocated to java e.g. \"4096\"      \n",
-        " - `packages` - external java packages for Terrier to load e.g. [\"org.terrier:terrier.prf\"]\n",
-        " - `logging` - logging level for Terrier. Defaults to \"WARN\", use \"INFO\" or \"DEBUG\" for more output.\n",
+        "## Initialisation\n",
         "\n",
         "NB: Pyterrier needs Java 11 installed. If it cannot find your Java installation, you can set the `JAVA_HOME` environment variable."
       ]
@@ -125,10 +114,7 @@
         },
         "colab_type": "code",
         "id": "k3ltUZ8PgWmz",
-        "outputId": "28681afb-8132-4cde-a0eb-6dc4c53da1b4",
-        "vscode": {
-          "languageId": "python"
-        }
+        "outputId": "28681afb-8132-4cde-a0eb-6dc4c53da1b4"
       },
       "outputs": [
         {
@@ -143,9 +129,7 @@
         }
       ],
       "source": [
-        "import pyterrier as pt\n",
-        "if not pt.started():\n",
-        "  pt.init()"
+        "import pyterrier as pt\n"
       ]
     },
     {
@@ -170,10 +154,7 @@
         },
         "colab_type": "code",
         "id": "eRx5kIL9nmsB",
-        "outputId": "b6774593-d847-4d23-bd77-c266b8a32d2f",
-        "vscode": {
-          "languageId": "python"
-        }
+        "outputId": "b6774593-d847-4d23-bd77-c266b8a32d2f"
       },
       "outputs": [
         {
@@ -210,10 +191,7 @@
         },
         "colab_type": "code",
         "id": "YxW1gSJh-MLr",
-        "outputId": "d0ef80f7-de1a-4e1f-ddb3-dd8d98d634e7",
-        "vscode": {
-          "languageId": "python"
-        }
+        "outputId": "d0ef80f7-de1a-4e1f-ddb3-dd8d98d634e7"
       },
       "outputs": [
         {
@@ -267,10 +245,7 @@
         },
         "colab_type": "code",
         "id": "r1en_8ga-Y0d",
-        "outputId": "8feff0b9-53ab-4acd-be51-150359b9c3ff",
-        "vscode": {
-          "languageId": "python"
-        }
+        "outputId": "8feff0b9-53ab-4acd-be51-150359b9c3ff"
       },
       "outputs": [
         {
@@ -314,10 +289,7 @@
         },
         "colab_type": "code",
         "id": "7jEPzyru-tCF",
-        "outputId": "b0f0b461-be41-4382-cfa2-22de5cac3f1c",
-        "vscode": {
-          "languageId": "python"
-        }
+        "outputId": "b0f0b461-be41-4382-cfa2-22de5cac3f1c"
       },
       "outputs": [
         {
@@ -357,10 +329,7 @@
         },
         "colab_type": "code",
         "id": "DS2aAdbE_OqA",
-        "outputId": "1c0abbad-0664-423a-8c51-b41d0f22124b",
-        "vscode": {
-          "languageId": "python"
-        }
+        "outputId": "1c0abbad-0664-423a-8c51-b41d0f22124b"
       },
       "outputs": [
         {
@@ -422,10 +391,7 @@
         },
         "colab_type": "code",
         "id": "CfDga790AZ26",
-        "outputId": "ab52b562-cc5b-4733-a9fc-934ca7fc71f5",
-        "vscode": {
-          "languageId": "python"
-        }
+        "outputId": "ab52b562-cc5b-4733-a9fc-934ca7fc71f5"
       },
       "outputs": [
         {
@@ -506,10 +472,7 @@
         },
         "colab_type": "code",
         "id": "JVO3BjLTBBXb",
-        "outputId": "c5d8e8d9-266a-4480-bc28-e08164af2bcc",
-        "vscode": {
-          "languageId": "python"
-        }
+        "outputId": "c5d8e8d9-266a-4480-bc28-e08164af2bcc"
       },
       "outputs": [
         {
@@ -561,10 +524,7 @@
       "metadata": {
         "colab": {},
         "colab_type": "code",
-        "id": "j7HZ9-cPCkEU",
-        "vscode": {
-          "languageId": "python"
-        }
+        "id": "j7HZ9-cPCkEU"
       },
       "outputs": [],
       "source": []
@@ -580,6 +540,10 @@
     "kernelspec": {
       "display_name": "Python 3",
       "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.9.13"
     }
   },
   "nbformat": 4,
diff --git a/examples/notebooks/indexing.ipynb b/examples/notebooks/indexing.ipynb
index 4b0baa76..8d9dc3e3 100644
--- a/examples/notebooks/indexing.ipynb
+++ b/examples/notebooks/indexing.ipynb
@@ -1,24 +1,10 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "Indexing Demo.ipynb",
-      "provenance": [],
-      "collapsed_sections": [],
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    }
-  },
   "cells": [
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "TNdMHD8LS2Bm",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "TNdMHD8LS2Bm"
       },
       "source": [
         "# PyTerrier Indexing Demo\n",
@@ -29,8 +15,8 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "pkMrNonZrpEg",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "pkMrNonZrpEg"
       },
       "source": [
         "## Prerequisites\n",
@@ -40,22 +26,19 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 1,
       "metadata": {
-        "id": "JWLqWXBHeBRc",
-        "colab_type": "code",
-        "outputId": "04d8b4fa-7e60-4ccc-ad6b-3b95f0445437",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 765
-        }
+        },
+        "colab_type": "code",
+        "id": "JWLqWXBHeBRc",
+        "outputId": "04d8b4fa-7e60-4ccc-ad6b-3b95f0445437"
       },
-      "source": [
-        "!pip install python-terrier\n",
-        "#!pip install --upgrade git+https://github.com/terrier-org/pyterrier.git#egg=python-terrier"
-      ],
-      "execution_count": 1,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Collecting python-terrier\n",
@@ -102,66 +85,60 @@
             "Successfully built python-terrier wget pytrec-eval hopcroftkarp\n",
             "Installing collected packages: pyjnius, wget, pytrec-eval, multiset, hopcroftkarp, matchpy, deprecation, python-terrier\n",
             "Successfully installed deprecation-2.1.0 hopcroftkarp-1.2.5 matchpy-0.5.1 multiset-2.1.1 pyjnius-1.3.0 python-terrier-0.1.3 pytrec-eval-0.4 wget-3.2\n"
-          ],
-          "name": "stdout"
+          ]
         }
+      ],
+      "source": [
+        "%pip install -q python-terrier"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "ixzDjvtTOQbB",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "ixzDjvtTOQbB"
       },
       "source": [
-        "## Init \n",
+        "## Initialisation\n",
         "\n",
-        "You must run `pt.init()` before other pyterrier functions and classes\n",
-        "\n",
-        "Optional Arguments:    \n",
-        " - `version` - terrier IR version e.g. \"5.2\"    \n",
-        " - `mem` - megabytes allocated to java e.g. \"4096\"      \n",
-        " - `packages` - external java packages for Terrier to load e.g. [\"org.terrier:terrier.prf\"]\n",
-        " - `logging` - logging level for Terrier. Defaults to \"WARN\", use \"INFO\" or \"DEBUG\" for more output.\n",
-        "\n",
-        "NB: PyTerrier needs Java 11 installed. If it cannot find your Java installation, you can set the `JAVA_HOME` environment variable."
+        "PyTerrier needs Java 11 installed. If it cannot find your Java installation, you can set the `JAVA_HOME` environment variable."
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 2,
       "metadata": {
-        "id": "k3ltUZ8PgWmz",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 85
         },
+        "colab_type": "code",
+        "id": "k3ltUZ8PgWmz",
         "outputId": "647fe3a9-6171-4ad0-8537-17c90aa03943"
       },
-      "source": [
-        "import pyterrier as pt\n",
-        "if not pt.started():\n",
-        "  pt.init()"
-      ],
-      "execution_count": 2,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "terrier-assemblies 5.2  jar-with-dependencies not found, downloading to /root/.pyterrier...\n",
             "Done\n",
             "terrier-python-helper 0.0.2  jar not found, downloading to /root/.pyterrier...\n",
             "Done\n"
-          ],
-          "name": "stdout"
+          ]
         }
+      ],
+      "source": [
+        "import pyterrier as pt\n",
+        "if not pt.started():\n",
+        "  pt.init()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "By5UFYnRLgD0",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "By5UFYnRLgD0"
       },
       "source": [
         "## TREC Indexing\n",
@@ -171,49 +148,49 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 5,
       "metadata": {
-        "id": "eRx5kIL9nmsB",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
         },
+        "colab_type": "code",
+        "id": "eRx5kIL9nmsB",
         "outputId": "c63da1f0-8d73-4c36-bc0e-ab8366779840"
       },
-      "source": [
-        "dataset = pt.datasets.get_dataset(\"vaswani\")\n",
-        "\n",
-        "print(\"Files in vaswani corpus: %s \" % dataset.get_corpus())"
-      ],
-      "execution_count": 5,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Files in vaswani corpus: ['/root/.pyterrier/corpora/vaswani/corpus/doc-text.trec'] \n"
-          ],
-          "name": "stdout"
+          ]
         }
+      ],
+      "source": [
+        "dataset = pt.datasets.get_dataset(\"vaswani\")\n",
+        "\n",
+        "print(\"Files in vaswani corpus: %s \" % dataset.get_corpus())"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
-        "id": "iXq3z6MdgWi4",
+        "colab": {},
         "colab_type": "code",
-        "colab": {}
+        "id": "iXq3z6MdgWi4"
       },
+      "outputs": [],
       "source": [
         "index_path = \"./index\""
-      ],
-      "execution_count": 0,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "gjsYdSDvOTi_",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "gjsYdSDvOTi_"
       },
       "source": [
         "Create `pt.TRECCollectionIndexer` object    \n",
@@ -222,36 +199,36 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 8,
       "metadata": {
-        "id": "-Va70HfBN7s7",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 51
         },
+        "colab_type": "code",
+        "id": "-Va70HfBN7s7",
         "outputId": "c74112c1-acca-488c-bd86-07f278265507"
       },
-      "source": [
-        "!rm -rf ./index\n",
-        "indexer = pt.TRECCollectionIndexer(index_path, blocks=True)"
-      ],
-      "execution_count": 8,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "IndexingType.CLASSIC\n",
             "IndexingType.CLASSIC\n"
-          ],
-          "name": "stdout"
+          ]
         }
+      ],
+      "source": [
+        "!rm -rf ./index\n",
+        "indexer = pt.TRECCollectionIndexer(index_path, blocks=True)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "ak_VGdxfQm0p",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "ak_VGdxfQm0p"
       },
       "source": [
         "Index the files by calling the index method on the TRECCollectionIndexer object"
@@ -259,26 +236,26 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
-        "id": "l3C3NLUwQq0p",
+        "colab": {},
         "colab_type": "code",
-        "colab": {}
+        "id": "l3C3NLUwQq0p"
       },
+      "outputs": [],
       "source": [
         "indexref = indexer.index(dataset.get_corpus())\n",
         "\n",
         "# indexer method takes either a string or a list of strings with the files names\n",
         "# indexer.index([\"/vaswani_corpus/doc-text.trec\",])\n",
         "# indexer.index(\"/vaswani_corpus/doc-text.trec\")\n"
-      ],
-      "execution_count": 0,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "cYDrBpM6ofW-",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "cYDrBpM6ofW-"
       },
       "source": [
         "Lets see what we got from the indexer.\n",
@@ -288,39 +265,39 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 12,
       "metadata": {
-        "id": "hI9iNQdpoegm",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
         },
+        "colab_type": "code",
+        "id": "hI9iNQdpoegm",
         "outputId": "a34fa4e1-a8de-4843-ab47-4db649c88f5c"
       },
-      "source": [
-        "indexref.toString()"
-      ],
-      "execution_count": 12,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "'./index/data.properties'"
             ]
           },
+          "execution_count": 12,
           "metadata": {
             "tags": []
           },
-          "execution_count": 12
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "indexref.toString()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "Hf7hX8xXpN6q",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "Hf7hX8xXpN6q"
       },
       "source": [
         "We can use that to get more information about the index. For instance, to see the statistics of the index, lets use `index.getCollectionStatistics().toString()`. You can see that we have indexed 11429 documents, containing a total of 7756 unique words."
@@ -328,22 +305,19 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 13,
       "metadata": {
-        "id": "_wbIUBzeoqkZ",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 119
         },
+        "colab_type": "code",
+        "id": "_wbIUBzeoqkZ",
         "outputId": "180e7910-274e-4b08-d974-da47033d4e87"
       },
-      "source": [
-        "index = pt.IndexFactory.of(indexref)\n",
-        "print(index.getCollectionStatistics().toString())"
-      ],
-      "execution_count": 13,
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Number of documents: 11429\n",
@@ -352,16 +326,19 @@
             "Field names: []\n",
             "Number of tokens: 271581\n",
             "\n"
-          ],
-          "name": "stdout"
+          ]
         }
+      ],
+      "source": [
+        "index = pt.IndexFactory.of(indexref)\n",
+        "print(index.getCollectionStatistics().toString())"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "yVjVoDYfTN2z",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "yVjVoDYfTN2z"
       },
       "source": [
         "To index TXT, PDF, Microsoft Word, etc files use pt.FilesIndexer instead of pt.TRECCollectionIndexer"
@@ -370,8 +347,8 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "wGV18JV1P129",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "wGV18JV1P129"
       },
       "source": [
         "## Indexing a Pandas dataframe\n",
@@ -383,15 +360,25 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 16,
       "metadata": {
-        "id": "vw-4NIjlUY16",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
         },
+        "colab_type": "code",
+        "id": "vw-4NIjlUY16",
         "outputId": "ebc0b75e-3449-4e38-c6e2-01ddd77f5de9"
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "IndexingType.CLASSIC\n"
+          ]
+        }
+      ],
       "source": [
         "import pandas as pd\n",
         "!rm -rf ./pd_index\n",
@@ -400,25 +387,17 @@
         "# optionally modify properties\n",
         "# index_properies = {\"block.indexing\":\"true\", \"invertedfile.lexiconscanner\":\"pointers\"}\n",
         "# indexer.setProperties(**index_properies)"
-      ],
-      "execution_count": 16,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "IndexingType.CLASSIC\n"
-          ],
-          "name": "stdout"
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
-        "id": "NNkn2SdsPzBQ",
+        "colab": {},
         "colab_type": "code",
-        "colab": {}
+        "id": "NNkn2SdsPzBQ"
       },
+      "outputs": [],
       "source": [
         "df = pd.DataFrame({ \n",
         "'docno':\n",
@@ -430,28 +409,22 @@
         "'The waves were crashing on the shore; it was a',\n",
         "'The body may perhaps compensates for the loss']\n",
         "})"
-      ],
-      "execution_count": 0,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": 18,
       "metadata": {
-        "id": "Vn-2M9iFyW9K",
-        "colab_type": "code",
-        "outputId": "7375299a-c13d-42fc-d2cd-34aecdbbee4a",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 142
-        }
+        },
+        "colab_type": "code",
+        "id": "Vn-2M9iFyW9K",
+        "outputId": "7375299a-c13d-42fc-d2cd-34aecdbbee4a"
       },
-      "source": [
-        "df"
-      ],
-      "execution_count": 18,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/html": [
               "<div>\n",
@@ -507,18 +480,22 @@
               "2     3  url3   The body may perhaps compensates for the loss"
             ]
           },
+          "execution_count": 18,
           "metadata": {
             "tags": []
           },
-          "execution_count": 18
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "df"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "K-idBQ5OUV9c",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "K-idBQ5OUV9c"
       },
       "source": [
         "Then there are a number of options to index the dataframe:    \n",
@@ -528,11 +505,13 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {
-        "id": "rBXHWO4yUJT7",
+        "colab": {},
         "colab_type": "code",
-        "colab": {}
+        "id": "rBXHWO4yUJT7"
       },
+      "outputs": [],
       "source": [
         "# no metadata\n",
         "# pd_indexer.index(df[\"text\"])\n",
@@ -550,15 +529,13 @@
         "\n",
         "# Add the entire dataframe as metadata\n",
         "# pd_indexer.index(df[\"text\"], df)"
-      ],
-      "execution_count": 0,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "zjSUuBcou_L2",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "zjSUuBcou_L2"
       },
       "source": [
         "## Indexing a iterable, generator, etc.\n",
@@ -570,15 +547,29 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 3,
       "metadata": {
-        "id": "CBYEKxK4u_uV",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000
         },
+        "colab_type": "code",
+        "id": "CBYEKxK4u_uV",
         "outputId": "f1880b9e-495e-44f2-a094-dbbf5ae7d1d9"
       },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "processing document 0\n",
+            "processing document 100000\n",
+            "processing document 200000\n",
+            "processing document 300000\n",
+            "processing document 400000\n"
+          ]
+        }
+      ],
       "source": [
         "# As an example, we will stream the ANTIQUE collection.\n",
         "# It is formatted as \"[docno] \\t [text] \\n\"\n",
@@ -603,27 +594,13 @@
         "# indexed and which are used as metadata with the fields and meta parameters.\n",
         "# yield {'docno': docno, 'title': title, 'text': text, 'url': url}\n",
         "# iter_indexer.index(doc_iter, fields=['text', 'title'], meta=['docno', 'url'])"
-      ],
-      "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "processing document 0\n",
-            "processing document 100000\n",
-            "processing document 200000\n",
-            "processing document 300000\n",
-            "processing document 400000\n"
-          ],
-          "name": "stdout"
-        }
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "IKrZNyaUXRMg",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "IKrZNyaUXRMg"
       },
       "source": [
         "## Retrieval\n",
@@ -635,22 +612,18 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 21,
       "metadata": {
-        "id": "zWj6YnN4Wnsz",
-        "colab_type": "code",
-        "outputId": "11b3b274-2921-4663-f91b-9ef711376582",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 419
-        }
+        },
+        "colab_type": "code",
+        "id": "zWj6YnN4Wnsz",
+        "outputId": "11b3b274-2921-4663-f91b-9ef711376582"
       },
-      "source": [
-        "pt.BatchRetrieve(indexref).search(\"mathematical\")"
-      ],
-      "execution_count": 21,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/html": [
               "<div>\n",
@@ -801,18 +774,22 @@
               "[152 rows x 6 columns]"
             ]
           },
+          "execution_count": 21,
           "metadata": {
             "tags": []
           },
-          "execution_count": 21
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "pt.BatchRetrieve(indexref).search(\"mathematical\")"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "oK_wOzQrqi8t",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "oK_wOzQrqi8t"
       },
       "source": [
         "However, most IR experiments, will use a set of queries. You can pass such a set using a data frame for input."
@@ -820,24 +797,18 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 20,
       "metadata": {
-        "id": "dgtauIOAXGFY",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 419
         },
+        "colab_type": "code",
+        "id": "dgtauIOAXGFY",
         "outputId": "6a4e7e45-eaa4-48a0-d069-b271e3554826"
       },
-      "source": [
-        "import pandas as pd\n",
-        "topics = pd.DataFrame([[\"2\", \"mathematical\"]],columns=['qid','query'])\n",
-        "pt.BatchRetrieve(indexref).transform(topics)"
-      ],
-      "execution_count": 20,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/html": [
               "<div>\n",
@@ -988,22 +959,42 @@
               "[152 rows x 6 columns]"
             ]
           },
+          "execution_count": 20,
           "metadata": {
             "tags": []
           },
-          "execution_count": 20
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "topics = pd.DataFrame([[\"2\", \"mathematical\"]],columns=['qid','query'])\n",
+        "pt.BatchRetrieve(indexref).transform(topics)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "XLcNP0XHqwQs",
-        "colab_type": "text"
+        "colab_type": "text",
+        "id": "XLcNP0XHqwQs"
       },
       "source": [
         "Thats the end of the indexing tutorial - you can continue with other example tutorials."
       ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "Indexing Demo.ipynb",
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/examples/notebooks/ltr.ipynb b/examples/notebooks/ltr.ipynb
index 5cc117b7..1de9ad09 100644
--- a/examples/notebooks/ltr.ipynb
+++ b/examples/notebooks/ltr.ipynb
@@ -26,23 +26,6 @@
         "%pip install -q python-terrier"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5thmTselkuBv"
-      },
-      "source": [
-        "## Init\n",
-        "\n",
-        "You must run `pt.init()` before other PyTerrier functions and classes.\n",
-        "\n",
-        "`pt.init()` takes arguments such as:    \n",
-        "- `version` - Terrier version e.g. \"5.2\"    \n",
-        "- `mem` - megabytes allocated to JVM e.g. 4096\n",
-        "\n",
-        "See also: https://pyterrier.readthedocs.io/en/latest/installation.html"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": 2,
@@ -75,9 +58,7 @@
       "source": [
         "import numpy as np\n",
         "import pandas as pd\n",
-        "import pyterrier as pt\n",
-        "if not pt.started():\n",
-        "  pt.init(version='snapshot')"
+        "import pyterrier as pt"
       ]
     },
     {
@@ -161,11 +142,11 @@
       "outputs": [],
       "source": [
         "#this ranker will make the candidate set of documents for each query\n",
-        "BM25 = pt.BatchRetrieve(indexref, wmodel=\"BM25\")\n",
+        "BM25 = pt.terrier.Retrieve(indexref, wmodel=\"BM25\")\n",
         "\n",
         "#these rankers we will use to re-rank the BM25 results\n",
-        "TF_IDF =  pt.BatchRetrieve(indexref, wmodel=\"TF_IDF\")\n",
-        "PL2 =  pt.BatchRetrieve(indexref, wmodel=\"PL2\")"
+        "TF_IDF =  pt.terrier.Retrieve(indexref, wmodel=\"TF_IDF\")\n",
+        "PL2 =  pt.terrier.Retrieve(indexref, wmodel=\"PL2\")"
       ]
     },
     {
@@ -388,7 +369,7 @@
         }
       ],
       "source": [
-        "fbr = pt.FeaturesBatchRetrieve(indexref, wmodel=\"BM25\", features=[\"WMODEL:TF_IDF\", \"WMODEL:PL2\"])\n",
+        "fbr = pt.terrier.FeaturesRetrieve(indexref, wmodel=\"BM25\", features=[\"WMODEL:TF_IDF\", \"WMODEL:PL2\"])\n",
         "#lets look at the top 2 results\n",
         "(fbr %2).search(\"chemical\")"
       ]
@@ -592,7 +573,7 @@
         }
       ],
       "source": [
-        "fbr3f = pt.FeaturesBatchRetrieve(indexref, wmodel=\"BM25\", features=[\"SAMPLE\", \"WMODEL:TF_IDF\", \"WMODEL:PL2\"])\n",
+        "fbr3f = pt.terrier.FeaturesRetrieve(indexref, wmodel=\"BM25\", features=[\"SAMPLE\", \"WMODEL:TF_IDF\", \"WMODEL:PL2\"])\n",
         "(fbr3f %2).search(\"chemical\")"
       ]
     },
diff --git a/examples/notebooks/non_en_retrieval.ipynb b/examples/notebooks/non_en_retrieval.ipynb
index 33678392..5a5a3237 100644
--- a/examples/notebooks/non_en_retrieval.ipynb
+++ b/examples/notebooks/non_en_retrieval.ipynb
@@ -66,8 +66,6 @@
         "from nltk import word_tokenize\n",
         "from nltk.corpus import stopwords\n",
         "import pyterrier as pt\n",
-        "if not pt.started():\n",
-        "    pt.init()\n",
         "from pyterrier.measures import *"
       ]
     },
diff --git a/examples/notebooks/retrieval_and_evaluation.ipynb b/examples/notebooks/retrieval_and_evaluation.ipynb
index b7279fc0..94674b16 100644
--- a/examples/notebooks/retrieval_and_evaluation.ipynb
+++ b/examples/notebooks/retrieval_and_evaluation.ipynb
@@ -75,24 +75,7 @@
         }
       ],
       "source": [
-        "#!pip install --upgrade git+https://github.com/terrier-org/pyterrier.git#egg=python-terrier\n",
-        "!pip install python-terrier"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "colab_type": "text",
-        "id": "E8avEGFTczuu"
-      },
-      "source": [
-        "# Init \n",
-        "\n",
-        "You must run `pt.init()` before other pyterrier functions and classes\n",
-        "\n",
-        "Arguments:    \n",
-        " - `version` - terrier IR version e.g. \"5.2\"    \n",
-        " - `mem` - megabytes allocated to java e.g. \"4096\"      \n"
+        "%pip install -q python-terrier"
       ]
     },
     {
@@ -120,9 +103,7 @@
         }
       ],
       "source": [
-        "import pyterrier as pt\n",
-        "if not pt.started():\n",
-        "  pt.init()"
+        "import pyterrier as pt"
       ]
     },
     {
@@ -343,7 +324,7 @@
       },
       "outputs": [],
       "source": [
-        "retr = pt.BatchRetrieve(index, controls = {\"wmodel\": \"TF_IDF\"})\n",
+        "retr = pt.terrier.Retrieve(index, controls = {\"wmodel\": \"TF_IDF\"})\n",
         "\n",
         "retr.setControl(\"wmodel\", \"TF_IDF\")\n",
         "retr.setControls({\"wmodel\": \"TF_IDF\"})\n",
diff --git a/examples/notebooks/sentence_transformers.ipynb b/examples/notebooks/sentence_transformers.ipynb
index 47a2038b..ed625c15 100644
--- a/examples/notebooks/sentence_transformers.ipynb
+++ b/examples/notebooks/sentence_transformers.ipynb
@@ -1,694 +1,724 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
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+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NqDE3-xBkzsC"
+      },
+      "source": [
+        "# MSMARCO Passage Reranking using PyTerrier and Sentence Transformers\n",
+        "\n",
+        "This notebook demonstrates the easy application of Sentence Transformers using PyTerrier."
+      ]
     },
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+        "id": "ks_OLOPtja1O",
+        "outputId": "f112fc12-e2bf-4bab-d0db-523f8fd52ffa"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[K     |████████████████████████████████| 102 kB 39.6 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 85 kB 4.9 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 1.3 MB 50.2 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 69 kB 8.2 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 311 kB 50.9 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 46 kB 3.9 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 286 kB 45.4 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 45 kB 3.4 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 72 kB 1.8 MB/s \n",
+            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[K     |████████████████████████████████| 294 kB 61.5 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 1.2 MB 56.1 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 126 kB 72.2 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 5.5 MB 51.8 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 1.3 MB 59.1 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 163 kB 67.8 MB/s \n",
+            "\u001b[K     |████████████████████████████████| 7.6 MB 13.3 MB/s \n",
+            "\u001b[?25h  Building wheel for python-terrier (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for ir-measures (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for cwl-eval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for cbor (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for unlzw3 (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for warc3-wet-clueweb09 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for chest (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for sklearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
+          ]
+        }
+      ],
+      "source": [
+        "%pip install -q python-terrier sentence-transformers"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
         },
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+        "id": "__lKfgQnjeqt",
+        "outputId": "666bbb26-345d-4dc0-a6c2-366adfe225fb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "terrier-assemblies 5.7 jar-with-dependencies not found, downloading to /root/.pyterrier...\n",
+            "Done\n",
+            "terrier-python-helper 0.0.7 jar not found, downloading to /root/.pyterrier...\n",
+            "Done\n"
+          ]
         },
-        "46c4c41634c349a6b5e81fc1d19ee169": {
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+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "PyTerrier 0.9.1 has loaded Terrier 5.7 (built by craigm on 2022-11-10 18:30) and terrier-helper 0.0.7\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "import pyterrier as pt\n",
+        "from pyterrier.measures import *"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hO_ItLzOk8dI"
+      },
+      "source": [
+        "## BM25\n",
+        "\n",
+        "We use a pre-built index for MSMARCO v1 passage corpus, obtained direct from the [Terrier data repository](http://data.terrier.org/). On Colab, this [particular index](http://data.terrier.org/msmarco_passage.dataset.html#terrier_stemmed_text) takes around 4 minutes to download."
+      ]
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+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "18:37:34.127 [main] WARN org.terrier.structures.BaseCompressingMetaIndex - Structure meta reading data file directly from disk (SLOW) - try index.meta.data-source=fileinmem in the index properties file. 1.9 GiB of memory would be required.\n"
+          ]
+        }
+      ],
+      "source": [
+        "bm25 = pt.terrier.Retrieve.from_dataset('msmarco_passage', 'terrier_stemmed_text', wmodel='BM25', metadata=['docno', 'text'])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ip8AyuBOlStP"
+      },
+      "source": [
+        "This is the dataset we are will use for evaluation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "na5rhnAbks26"
+      },
+      "outputs": [],
+      "source": [
+        "dataset = pt.get_dataset(\"trec-deep-learning-passages\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IoKKA2EblY20"
+      },
+      "source": [
+        "We now instantiate the SentenceTransformers models. Both of these model files download the necessary files from HuggingFace. \n",
+        "\n",
+        "We `pt.apply.doc_score()` transformers for applying these models. The use of a `batch_size` kwarg allows the models to be applied on multiple query-document pairs at once.\n",
+        "\n",
+        "Finally, we use a `pt.Experiment` for evaluation. We combine the cross-encoder and bi-encoders to re-rank the output of BM25. We"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
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+              "    <div class=\"colab-df-container\">\n",
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+              "<style scoped>\n",
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+              "        vertical-align: top;\n",
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+              "</style>\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>name</th>\n",
+              "      <th>RR(rel=2)</th>\n",
+              "      <th>nDCG@10</th>\n",
+              "      <th>nDCG@100</th>\n",
+              "      <th>AP(rel=2)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
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+              "      <th>0</th>\n",
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+              "      <td>0.641565</td>\n",
+              "      <td>0.479540</td>\n",
+              "      <td>0.487416</td>\n",
+              "      <td>0.286448</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>BM25 &gt;&gt; BiEncoder</td>\n",
+              "      <td>0.793350</td>\n",
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+              "      <td>BM25 &gt;&gt; CrossEncoder</td>\n",
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+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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+              "                   name  RR(rel=2)   nDCG@10  nDCG@100  AP(rel=2)\n",
+              "0                  BM25   0.641565  0.479540  0.487416   0.286448\n",
+              "1     BM25 >> BiEncoder   0.793350  0.605352  0.549940   0.362597\n",
+              "2  BM25 >> CrossEncoder   0.914729  0.738378  0.669271   0.488546"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "from sentence_transformers import CrossEncoder, SentenceTransformer\n",
+        "crossmodel = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-12-v2', max_length=512)\n",
+        "bimodel = SentenceTransformer('paraphrase-MiniLM-L6-v2')\n",
+        "\n",
+        "def _crossencoder_apply(df : pd.DataFrame):\n",
+        "    return crossmodel.predict(list(zip(df['query'].values, df['text'].values)))\n",
+        "\n",
+        "cross_encT = pt.apply.doc_score(_crossencoder_apply, batch_size=128)\n",
+        "\n",
+        "def _biencoder_apply(df : pd.DataFrame):\n",
+        "    from sentence_transformers.util import cos_sim\n",
+        "    query_embs = bimodel.encode(df['query'].values)\n",
+        "    doc_embs = bimodel.encode(df['text'].values)\n",
+        "    scores =  cos_sim(query_embs, doc_embs)\n",
+        "    return scores[0]\n",
+        "\n",
+        "bi_encT = pt.apply.doc_score(_biencoder_apply, batch_size=128)\n",
+        "\n",
+        "pt.Experiment(\n",
+        "    [ bm25, bm25 >> bi_encT, bm25 >> cross_encT ],\n",
+        "    dataset.get_topics(\"test-2019\"),\n",
+        "    dataset.get_qrels(\"test-2019\"),\n",
+        "    [RR(rel=2), nDCG@10, nDCG@100, AP(rel=2)],\n",
+        "    names=[\"BM25\", \"BM25 >> BiEncoder\", \"BM25 >> CrossEncoder\"],\n",
+        "    filter_by_qrels=True\n",
+        ")"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "provenance": []
+    },
+    "gpuClass": "standard",
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-      "source": [
-        "# MSMARCO Passage Reranking using PyTerrier and Sentence Transformers\n",
-        "\n",
-        "This notebook demonstrates the easy application of Sentence Transformers using PyTerrier."
-      ],
-      "metadata": {
-        "id": "NqDE3-xBkzsC"
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-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "\u001b[K     |████████████████████████████████| 102 kB 39.6 MB/s \n",
-            "\u001b[K     |████████████████████████████████| 85 kB 4.9 MB/s \n",
-            "\u001b[K     |████████████████████████████████| 1.3 MB 50.2 MB/s \n",
-            "\u001b[K     |████████████████████████████████| 69 kB 8.2 MB/s \n",
-            "\u001b[K     |████████████████████████████████| 311 kB 50.9 MB/s \n",
-            "\u001b[K     |████████████████████████████████| 46 kB 3.9 MB/s \n",
-            "\u001b[K     |████████████████████████████████| 286 kB 45.4 MB/s \n",
-            "\u001b[K     |████████████████████████████████| 45 kB 3.4 MB/s \n",
-            "\u001b[K     |████████████████████████████████| 72 kB 1.8 MB/s \n",
-            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
-            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
-            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
-            "\u001b[K     |████████████████████████████████| 294 kB 61.5 MB/s \n",
-            "\u001b[K     |████████████████████████████████| 1.2 MB 56.1 MB/s \n",
-            "\u001b[K     |████████████████████████████████| 126 kB 72.2 MB/s \n",
-            "\u001b[K     |████████████████████████████████| 5.5 MB 51.8 MB/s \n",
-            "\u001b[K     |████████████████████████████████| 1.3 MB 59.1 MB/s \n",
-            "\u001b[K     |████████████████████████████████| 163 kB 67.8 MB/s \n",
-            "\u001b[K     |████████████████████████████████| 7.6 MB 13.3 MB/s \n",
-            "\u001b[?25h  Building wheel for python-terrier (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for ir-measures (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for cwl-eval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for cbor (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for unlzw3 (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for warc3-wet-clueweb09 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for chest (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for sklearn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
-          ]
-        }
-      ],
-      "source": [
-        "%pip install -q python-terrier sentence-transformers"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import pyterrier as pt\n",
-        "pt.init()\n",
-        "from pyterrier.measures import *"
-      ],
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
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-        "id": "__lKfgQnjeqt",
-        "outputId": "666bbb26-345d-4dc0-a6c2-366adfe225fb"
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-      "execution_count": 2,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "terrier-assemblies 5.7 jar-with-dependencies not found, downloading to /root/.pyterrier...\n",
-            "Done\n",
-            "terrier-python-helper 0.0.7 jar not found, downloading to /root/.pyterrier...\n",
-            "Done\n"
-          ]
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+            "style": "IPY_MODEL_28199016470f4bc0afed1b9e203642ea",
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-        {
-          "output_type": "stream",
-          "name": "stderr",
-          "text": [
-            "PyTerrier 0.9.1 has loaded Terrier 5.7 (built by craigm on 2022-11-10 18:30) and terrier-helper 0.0.7\n",
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-        "## BM25\n",
-        "\n",
-        "We use a pre-built index for MSMARCO v1 passage corpus, obtained direct from the [Terrier data repository](http://data.terrier.org/). On Colab, this [particular index](http://data.terrier.org/msmarco_passage.dataset.html#terrier_stemmed_text) takes around 4 minutes to download."
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-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "18:37:34.127 [main] WARN org.terrier.structures.BaseCompressingMetaIndex - Structure meta reading data file directly from disk (SLOW) - try index.meta.data-source=fileinmem in the index properties file. 1.9 GiB of memory would be required.\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "This is the dataset we are will use for evaluation."
-      ],
-      "metadata": {
-        "id": "Ip8AyuBOlStP"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "dataset = pt.get_dataset(\"trec-deep-learning-passages\")"
-      ],
-      "metadata": {
-        "id": "na5rhnAbks26"
-      },
-      "execution_count": 5,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": [
-        "We now instantiate the SentenceTransformers models. Both of these model files download the necessary files from HuggingFace. \n",
-        "\n",
-        "We `pt.apply.doc_score()` transformers for applying these models. The use of a `batch_size` kwarg allows the models to be applied on multiple query-document pairs at once.\n",
-        "\n",
-        "Finally, we use a `pt.Experiment` for evaluation. We combine the cross-encoder and bi-encoders to re-rank the output of BM25. We"
-      ],
-      "metadata": {
-        "id": "IoKKA2EblY20"
-      }
-    },
-    {
-      "cell_type": "code",
-      "source": [
-        "import pandas as pd\n",
-        "from sentence_transformers import CrossEncoder, SentenceTransformer\n",
-        "crossmodel = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-12-v2', max_length=512)\n",
-        "bimodel = SentenceTransformer('paraphrase-MiniLM-L6-v2')\n",
-        "\n",
-        "def _crossencoder_apply(df : pd.DataFrame):\n",
-        "    return crossmodel.predict(list(zip(df['query'].values, df['text'].values)))\n",
-        "\n",
-        "cross_encT = pt.apply.doc_score(_crossencoder_apply, batch_size=128)\n",
-        "\n",
-        "def _biencoder_apply(df : pd.DataFrame):\n",
-        "    from sentence_transformers.util import cos_sim\n",
-        "    query_embs = bimodel.encode(df['query'].values)\n",
-        "    doc_embs = bimodel.encode(df['text'].values)\n",
-        "    scores =  cos_sim(query_embs, doc_embs)\n",
-        "    return scores[0]\n",
-        "\n",
-        "bi_encT = pt.apply.doc_score(_biencoder_apply, batch_size=128)\n",
-        "\n",
-        "pt.Experiment(\n",
-        "    [ bm25, bm25 >> bi_encT, bm25 >> cross_encT ],\n",
-        "    dataset.get_topics(\"test-2019\"),\n",
-        "    dataset.get_qrels(\"test-2019\"),\n",
-        "    [RR(rel=2), nDCG@10, nDCG@100, AP(rel=2)],\n",
-        "    names=[\"BM25\", \"BM25 >> BiEncoder\", \"BM25 >> CrossEncoder\"],\n",
-        "    filter_by_qrels=True\n",
-        ")"
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-        "colab": {
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+            "value": " 43/43 [02:08&lt;00:00,  2.95s/row]"
+          }
         },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "                   name  RR(rel=2)   nDCG@10  nDCG@100  AP(rel=2)\n",
-              "0                  BM25   0.641565  0.479540  0.487416   0.286448\n",
-              "1     BM25 >> BiEncoder   0.793350  0.605352  0.549940   0.362597\n",
-              "2  BM25 >> CrossEncoder   0.914729  0.738378  0.669271   0.488546"
-            ],
-            "text/html": [
-              "\n",
-              "  <div id=\"df-88014de0-93cf-4f30-bcd7-1e8b861fe50d\">\n",
-              "    <div class=\"colab-df-container\">\n",
-              "      <div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
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-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>name</th>\n",
-              "      <th>RR(rel=2)</th>\n",
-              "      <th>nDCG@10</th>\n",
-              "      <th>nDCG@100</th>\n",
-              "      <th>AP(rel=2)</th>\n",
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-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>BM25</td>\n",
-              "      <td>0.641565</td>\n",
-              "      <td>0.479540</td>\n",
-              "      <td>0.487416</td>\n",
-              "      <td>0.286448</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>BM25 &gt;&gt; BiEncoder</td>\n",
-              "      <td>0.793350</td>\n",
-              "      <td>0.605352</td>\n",
-              "      <td>0.549940</td>\n",
-              "      <td>0.362597</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>BM25 &gt;&gt; CrossEncoder</td>\n",
-              "      <td>0.914729</td>\n",
-              "      <td>0.738378</td>\n",
-              "      <td>0.669271</td>\n",
-              "      <td>0.488546</td>\n",
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-              "  </tbody>\n",
-              "</table>\n",
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-              "      display:flex;\n",
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-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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-              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                     [key], {});\n",
-              "          if (!dataTable) return;\n",
-              "\n",
-              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "            + ' to learn more about interactive tables.';\n",
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-              "  </div>\n",
-              "  "
-            ]
-          },
-          "metadata": {},
-          "execution_count": 7
+        "ff97475aa91b4648a5c3be86c063bf6a": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_module_version": "1.5.0",
+          "model_name": "DescriptionStyleModel",
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+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
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+          }
         }
-      ]
+      }
     }
-  ]
-}
\ No newline at end of file
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
index 660b100b..44474f28 100644
--- a/pyterrier/terrier/__init__.py
+++ b/pyterrier/terrier/__init__.py
@@ -1,7 +1,7 @@
 # java stuff
 from pyterrier.terrier import java
 from pyterrier.terrier.java import configure, set_version, set_helper_version, enable_prf, extend_classpath, J, set_property, set_properties, run, version, check_version, check_helper_version
-from pyterrier.terrier.retriever import BatchRetrieve, FeaturesBatchRetrieve, TextScorer
+from pyterrier.terrier.retrieve import Retrieve, FeaturesRetrieve, TextScorer
 from pyterrier.terrier.index_factory import IndexFactory
 from pyterrier.terrier.stemmer import TerrierStemmer
 from pyterrier.terrier.tokeniser import TerrierTokeniser
@@ -9,16 +9,19 @@
 from pyterrier.terrier import index
 from pyterrier.terrier.index import TerrierIndexer, FilesIndexer, TRECCollectionIndexer, DFIndexer, DFIndexUtils, IterDictIndexer, IndexingType, treccollection2textgen
 from pyterrier.terrier import rewrite
+from deprecated import deprecated
 
-TerrierRetrieve = BatchRetrieve # BatchRetrieve is an alias to TerrierRetrieve
 
+TerrierRetrieve = deprecated(version='0.11.0', reason="use pt.terrier.Retrieve() instead")(Retrieve)
+BatchRetrieve = deprecated(version='0.11.0', reason="use pt.terrier.Retrieve() instead")(Retrieve)
+FeaturesBatchRetrieve = deprecated(version='0.11.0', reason="use pt.terrier.FeaturesRetrieve() instead")(FeaturesRetrieve)
 
 __all__ = [
     # java stuff
     'java', 'configure', 'set_version', 'set_helper_version', 'enable_prf', 'extend_classpath', 'J', 'version', 'check_version', 'check_helper_version',
 
     # retrieval
-    'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve', 'TextScorer',
+    'Retrieve', 'BatchRetrieve', 'TerrierRetrieve', 'FeaturesRetrieve', 'FeaturesBatchRetrieve', 'TerrierRetrieve', 'TextScorer',
 
     # indexing
     'index', 'TerrierIndexer', 'FilesIndexer', 'TRECCollectionIndexer', 'DFIndexer', 'DFIndexUtils', 'IterDictIndexer', 'IndexingType', 'treccollection2textgen',
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 326eaaa5..ce200242 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -384,7 +384,7 @@ def _new_callable_wmodel(byterep):
     #we need to prevent these functions from being GCd.
     global _SAVED_FNS
     _SAVED_FNS.append(fn)
-    callback, wmodel = pt.terrier.retriever._function2wmodel(fn)
+    callback, wmodel = pt.terrier.retrieve._function2wmodel(fn)
     _SAVED_FNS.append(callback)
     #print("Stored lambda fn  %s and callback in SAVED_FNS, now %d stored" % (str(fn), len(SAVED_FNS)))
     return wmodel
diff --git a/pyterrier/terrier/retriever.py b/pyterrier/terrier/retrieve.py
similarity index 94%
rename from pyterrier/terrier/retriever.py
rename to pyterrier/terrier/retrieve.py
index 51351c23..1f0bd6fd 100644
--- a/pyterrier/terrier/retriever.py
+++ b/pyterrier/terrier/retrieve.py
@@ -1,6 +1,7 @@
 from typing import Union
 import pandas as pd
 import numpy as np
+from deprecated import deprecated
 from warnings import warn
 from pyterrier.datasets import Dataset
 from pyterrier.transformer import Symbol
@@ -104,19 +105,25 @@ def _from_dataset(dataset : Union[str,Dataset],
     indexref = dataset.get_index(variant)
 
     classname = clz.__name__
-    # now look for, e.g., BatchRetrieve.args.json file, which will define the args for BatchRetrieve, e.g. stemming
-    indexdir = indexref #os.path.dirname(indexref.toString())
-    argsfile = os.path.join(indexdir, classname + ".args.json")
-    if os.path.exists(argsfile):
-        with autoopen(argsfile, "rt") as f:
-            args = json.load(f)
-            # anything specified in kwargs of this methods overrides the .args.json file
-            args.update(kwargs)
-            kwargs = args
-    return clz(indexref, **kwargs)   
+    classnames = [classname]
+    if classname == 'Retrieve':
+        classnames.append('BatchRetrieve')
+    # we need to look for BatchRetrievel.args.json for legacy support
+    for c in classnames:
+        # now look for, e.g., BatchRetrieve.args.json file, which will define the args for Retrieve, e.g. stemming
+        indexdir = indexref #os.path.dirname(indexref.toString())
+        argsfile = os.path.join(indexdir, classname + ".args.json")
+        if os.path.exists(argsfile):
+            with autoopen(argsfile, "rt") as f:
+                args = json.load(f)
+                # anything specified in kwargs of this methods overrides the .args.json file
+                args.update(kwargs)
+                kwargs = args
+        return clz(indexref, **kwargs)
+    raise ValueError("No .args.json files found for %s" % str(classnames))
                 
 @pt.java.required
-class BatchRetrieve(BatchRetrieveBase):
+class Retrieve(BatchRetrieveBase):
     """
     Use this class for retrieval by Terrier
     """
@@ -143,15 +150,15 @@ def from_dataset(dataset : Union[str,Dataset],
             version='latest',            
             **kwargs):
         """
-        Instantiates a BatchRetrieve object from a pre-built index access via a dataset.
+        Instantiates a Retrieve object from a pre-built index access via a dataset.
         Pre-built indices are ofen provided via the `Terrier Data Repository <http://data.terrier.org/>`_.
 
         Examples::
 
             dataset = pt.get_dataset("vaswani")
-            bm25 = pt.BatchRetrieve.from_dataset(dataset, "terrier_stemmed", wmodel="BM25")
+            bm25 = pt.terrier.Retrieve.from_dataset(dataset, "terrier_stemmed", wmodel="BM25")
             #or
-            bm25 = pt.BatchRetrieve.from_dataset("vaswani", "terrier_stemmed", wmodel="BM25")
+            bm25 = pt.terrier.Retrieve.from_dataset("vaswani", "terrier_stemmed", wmodel="BM25")
 
         **Index Variants**:
 
@@ -163,7 +170,7 @@ def from_dataset(dataset : Union[str,Dataset],
          - `terrier_unstemmed_text` - as per `terrier_stemmed`, but also containing the raw text of the documents
 
         """
-        return _from_dataset(dataset, variant=variant, version=version, clz=BatchRetrieve, **kwargs)
+        return _from_dataset(dataset, variant=variant, version=version, clz=Retrieve, **kwargs)
 
     #: default_controls(dict): stores the default controls
     default_controls = {
@@ -200,7 +207,7 @@ def __init__(self, index_location, controls=None, properties=None, metadata=["do
         """
         super().__init__(kwargs)
         self.indexref = _parse_index_like(index_location)
-        self.properties = _mergeDicts(BatchRetrieve.default_properties, properties)
+        self.properties = _mergeDicts(Retrieve.default_properties, properties)
         self.concurrentIL = pt.java.autoclass("org.terrier.structures.ConcurrentIndexLoader")
         if pt.terrier.check_version(5.5) and "SimpleDecorateProcess" not in self.properties["querying.processes"]:
             self.properties["querying.processes"] += ",decorate:SimpleDecorateProcess"
@@ -212,7 +219,7 @@ def __init__(self, index_location, controls=None, properties=None, metadata=["do
         for key, value in self.properties.items():
             pt.terrier.J.ApplicationSetup.setProperty(str(key), str(value))
         
-        self.controls = _mergeDicts(BatchRetrieve.default_controls, controls)
+        self.controls = _mergeDicts(Retrieve.default_controls, controls)
         if wmodel is not None:
             from pyterrier.transformer import is_lambda, is_function
             if isinstance(wmodel, str):
@@ -456,7 +463,6 @@ def setControls(self, controls):
     def setControl(self, control, value):
         self.controls[str(control)] = str(value)
 
-
 @pt.java.required
 class TextIndexProcessor(pt.Transformer):
     '''
@@ -576,21 +582,21 @@ class TextScorer(TextIndexProcessor):
     """
 
     def __init__(self, takes="docs", **kwargs):
-        super().__init__(BatchRetrieve, takes=takes, **kwargs)
+        super().__init__(Retrieve, takes=takes, **kwargs)
 
 
 @pt.java.required
-class FeaturesBatchRetrieve(BatchRetrieve):
+class FeaturesRetrieve(Retrieve):
     """
     Use this class for retrieval with multiple features
     """
 
     #: FBR_default_controls(dict): stores the default properties for a FBR
-    FBR_default_controls = BatchRetrieve.default_controls.copy()
+    FBR_default_controls = Retrieve.default_controls.copy()
     FBR_default_controls["matching"] = "FatFeaturedScoringMatching,org.terrier.matching.daat.FatFull"
     del FBR_default_controls["wmodel"]
     #: FBR_default_properties(dict): stores the default properties
-    FBR_default_properties = BatchRetrieve.default_properties.copy()
+    FBR_default_properties = Retrieve.default_properties.copy()
 
     def __init__(self, index_location, features, controls=None, properties=None, threads=1, **kwargs):
         """
@@ -604,8 +610,8 @@ def __init__(self, index_location, features, controls=None, properties=None, thr
                 verbose(bool): If True transform method will display progress
                 num_results(int): Number of results to retrieve. 
         """
-        controls = _mergeDicts(FeaturesBatchRetrieve.FBR_default_controls, controls)
-        properties = _mergeDicts(FeaturesBatchRetrieve.FBR_default_properties, properties)
+        controls = _mergeDicts(FeaturesRetrieve.FBR_default_controls, controls)
+        properties = _mergeDicts(FeaturesRetrieve.FBR_default_properties, properties)
         self.features = features
         properties["fat.featured.scoring.matching.features"] = ";".join(features)
 
@@ -658,14 +664,14 @@ def from_dataset(dataset : Union[str,Dataset],
             variant : str = None, 
             version='latest',            
             **kwargs):
-        return _from_dataset(dataset, variant=variant, version=version, clz=FeaturesBatchRetrieve, **kwargs)
+        return _from_dataset(dataset, variant=variant, version=version, clz=FeaturesRetrieve, **kwargs)
 
     @staticmethod 
     def from_dataset(dataset : Union[str,Dataset], 
             variant : str = None, 
             version='latest',            
             **kwargs):
-        return _from_dataset(dataset, variant=variant, version=version, clz=FeaturesBatchRetrieve, **kwargs)
+        return _from_dataset(dataset, variant=variant, version=version, clz=FeaturesRetrieve, **kwargs)
 
     def transform(self, queries):
         """
@@ -803,9 +809,9 @@ def setup_rewrites():
     y = Wildcard.dot('y')
     z = Wildcard.dot('z')
     # two different match retrives
-    _br1 = Wildcard.symbol('_br1', BatchRetrieve)
-    _br2 = Wildcard.symbol('_br2', BatchRetrieve)
-    _fbr = Wildcard.symbol('_fbr', FeaturesBatchRetrieve)
+    _br1 = Wildcard.symbol('_br1', Retrieve)
+    _br2 = Wildcard.symbol('_br2', Retrieve)
+    _fbr = Wildcard.symbol('_fbr', FeaturesRetrieve)
     
     # batch retrieves for the same index
     BR_index_matches = CustomConstraint(lambda _br1, _br2: _br1.indexref == _br2.indexref)
@@ -842,7 +848,7 @@ def setup_rewrites():
     # rewrite batch a feature union of BRs into an FBR
     rewrite_rules.append(ReplacementRule(
         Pattern(FeatureUnionPipeline(_br1, _br2), BR_index_matches),
-        lambda _br1, _br2: FeaturesBatchRetrieve(_br1.indexref, ["WMODEL:" + _br1.controls["wmodel"], "WMODEL:" + _br2.controls["wmodel"]])
+        lambda _br1, _br2: FeaturesRetrieve(_br1.indexref, ["WMODEL:" + _br1.controls["wmodel"], "WMODEL:" + _br2.controls["wmodel"]])
     ))
 
     def push_fbr_earlier(_br1, _fbr):
diff --git a/pyterrier/terrier/rewrite.py b/pyterrier/terrier/rewrite.py
index bdf41665..ebf3275e 100644
--- a/pyterrier/terrier/rewrite.py
+++ b/pyterrier/terrier/rewrite.py
@@ -178,7 +178,7 @@ def __init__(self, index_like, fb_terms=10, fb_docs=3, qeclass="org.terrier.quer
             self.qe = pt.java.autoclass(qeclass)()
         else:
             self.qe = qeclass
-        self.indexref = pt.terrier.retriever._parse_index_like(index_like)
+        self.indexref = pt.terrier.retrieve._parse_index_like(index_like)
         self.properties = properties
         for k,v in properties.items():
             pt.terrier.J.ApplicationSetup.setProperty(k, str(v))
diff --git a/pyterrier/text.py b/pyterrier/text.py
index 9cb730f0..1d622b06 100644
--- a/pyterrier/text.py
+++ b/pyterrier/text.py
@@ -160,7 +160,7 @@ def scorer(*args, **kwargs) -> pt.Transformer:
         textscorerTfIdf = pt.text.scorer(body_attr="text", wmodel="TF_IDF", background_index=index)
 
     """
-    return pt.terrier.retriever.TextScorer(*args, **kwargs)
+    return pt.terrier.retrieve.TextScorer(*args, **kwargs)
 
 def sliding( text_attr='body', length=150, stride=75, join=' ', prepend_attr='title', tokenizer=None, **kwargs) -> pt.Transformer:
     r"""
diff --git a/tests/test_dunder.py b/tests/test_dunder.py
index d6fb5af9..8419561f 100644
--- a/tests/test_dunder.py
+++ b/tests/test_dunder.py
@@ -9,7 +9,7 @@ def test_callable_wmodel_dunders(self):
         testPosting = pt.java.autoclass("org.terrier.structures.postings.BasicPostingImpl")(0,1)
 
         lambdafn = lambda keyFreq, posting, entryStats, collStats: posting.getFrequency()
-        callback, wmodel = pt.terrier.retriever._function2wmodel(lambdafn)
+        callback, wmodel = pt.terrier.retrieve._function2wmodel(lambdafn)
         
         byterep = pt.java.bytebuffer_to_array(wmodel.scoringClass.serializeFn())
         import dill as pickle
diff --git a/tests/test_operators.py b/tests/test_operators.py
index 810fd8b0..d25c5de2 100644
--- a/tests/test_operators.py
+++ b/tests/test_operators.py
@@ -337,7 +337,7 @@ def test_feature_union_multi(self):
         
         self.assertEqual(2, len(mock12a.models))
         self.assertEqual(2, len(mock12a.models))
-        pt.terrier.retriever.setup_rewrites()
+        pt.terrier.retrieve.setup_rewrites()
 
         mock123_simple = mock123a.compile()
         self.assertIsNotNone(mock123_simple)
diff --git a/tests/test_scoring.py b/tests/test_scoring.py
index c30663bc..23647e5d 100644
--- a/tests/test_scoring.py
+++ b/tests/test_scoring.py
@@ -39,7 +39,7 @@ def _test_scoring_text(self, dataset, index, wmodel):
         output1 = br1(input)
         self.assertTrue( "body" in output1.columns )
         input2 = output1[["qid", "query", "docno", "body"]]
-        br2 = pt.terrier.retriever.TextScorer(background_index=index, wmodel=wmodel, verbose=True)
+        br2 = pt.terrier.TextScorer(background_index=index, wmodel=wmodel, verbose=True)
         output2 = br2(input2)
         self.assertTrue( "score" in output2.columns )
 
@@ -52,7 +52,7 @@ def _test_scoring_text(self, dataset, index, wmodel):
 
     def test_scoring_manual_empty(self):
         input = pd.DataFrame([["q1", "fox", "d1", ""]], columns=["qid", "query", "docno", "body"])
-        from pyterrier.terrier.retriever import TextScorer
+        from pyterrier.terrier.retrieve import TextScorer
         scorer = TextScorer(wmodel="Tf")
         rtr = scorer(input)
         self.assertEqual(1, len(rtr))
@@ -62,7 +62,7 @@ def test_scoring_manual_empty(self):
 
     def test_scoring_manual(self):
         input = pd.DataFrame([["q1", "fox", "d1", "all the fox were fox"]], columns=["qid", "query", "docno", "body"])
-        from pyterrier.terrier.retriever import TextScorer
+        from pyterrier.terrier.retrieve import TextScorer
         scorer = TextScorer(wmodel="Tf")
         rtr = scorer(input)
         self.assertEqual(1, len(rtr))
@@ -78,7 +78,7 @@ def test_scoring_manual(self):
 
     def test_scoring_manual_background(self):
         input = pd.DataFrame([["q1", "fox", "d1", "all the fox were fox"]], columns=["qid", "query", "docno", "body"])
-        from pyterrier.terrier.retriever import TextScorer
+        from pyterrier.terrier.retrieve import TextScorer
         scorer = TextScorer(wmodel="Tf", background_index=pt.get_dataset("vaswani").get_index())
         rtr = scorer(input)
         self.assertEqual(1, len(rtr))
@@ -94,7 +94,7 @@ def test_scoring_manual_background(self):
         
     def test_scoring_qe(self):
         input = pd.DataFrame([["q1", "fox", "d1", "all the fox were fox"]], columns=["qid", "query", "docno", "body"])
-        scorer = pt.terrier.retriever.TextIndexProcessor(pt.rewrite.Bo1QueryExpansion, takes="docs", returns="queries")
+        scorer = pt.terrier.retrieve.TextIndexProcessor(pt.rewrite.Bo1QueryExpansion, takes="docs", returns="queries")
         rtr = scorer(input)
         self.assertTrue("qid" in rtr.columns)
         self.assertTrue("query" in rtr.columns)

From 71c374e788dc48504c131df34aca0bfc093cb5cf Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Wed, 7 Aug 2024 13:19:32 +0100
Subject: [PATCH 127/153] update README examples

---
 README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index fe00370b..a33fd144 100644
--- a/README.md
+++ b/README.md
@@ -36,7 +36,7 @@ See the [indexing documentation](https://pyterrier.readthedocs.io/en/latest/terr
 ```python
 topics = pt.io.read_topics(topicsFile)
 qrels = pt.io.read_qrels(qrelsFile)
-BM25_br = pt.BatchRetrieve(index, wmodel="BM25")
+BM25_br = pt.terrier.Retrieve(index, wmodel="BM25")
 res = BM25_br.transform(topics)
 pt.Evaluate(res, qrels, metrics = ['map'])
 ```
@@ -56,7 +56,7 @@ There is a worked example in the [experiment notebook](examples/notebooks/experi
 
 PyTerrier makes it easy to develop complex retrieval pipelines using Python operators such as `>>` to chain different retrieval components. Each retrieval approach is a [transformer](https://pyterrier.readthedocs.io/en/latest/transformer.html), having one key method, `transform()`, which takes a single Pandas dataframe as input, and returns another dataframe. Two examples might encapsulate applying the sequential dependence model, or a query expansion process:
 ```python
-sdm_bm25 = pt.rewrite.SDM() >> pt.BatchRetrieve(indexref, wmodel="BM25")
+sdm_bm25 = pt.rewrite.SDM() >> pt.terrier.Retrieve(indexref, wmodel="BM25")
 bo1_qe = BM25_br >> pt.rewrite.Bo1QueryExpansion() >> BM25_br
 ```
 
@@ -83,8 +83,8 @@ You can see examples of how to use these, including notebooks that run on Google
 Complex learning to rank pipelines, including for learning-to-rank, can be constructed using PyTerrier's operator language. For example, to combine two features and make them available for learning, we can use the `**` operator.
 ```python
 two_features = BM25_br >> ( 
-  pt.BatchRetrieve(indexref, wmodel="DirichletLM") ** 
-  pt.BatchRetrieve(indexref, wmodel="PL2") 
+  pt.terrier.Retrieve(indexref, wmodel="DirichletLM") ** 
+  pt.terrier.Retrieve(indexref, wmodel="PL2") 
 )
 ```
 

From 81ba34f217dd599111bc0a758357bcb52ec3cab9 Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Wed, 7 Aug 2024 13:27:52 +0100
Subject: [PATCH 128/153] udpate all references to BatchRetrieve in the
 documentation

---
 docs/anserini.rst                         |  4 +-
 docs/apply.rst                            |  4 +-
 docs/datamodel.md                         |  2 +-
 docs/datasets.rst                         | 12 +++---
 docs/experiments.rst                      |  4 +-
 docs/extras/generate_includes.py          |  4 +-
 docs/ltr.rst                              |  8 ++--
 docs/operators.rst                        |  2 +-
 docs/parallel.rst                         | 16 +++----
 docs/pipeline_examples.md                 | 36 ++++++++--------
 docs/rewrite.rst                          | 14 +++---
 docs/terrier-index-api.rst                |  2 +-
 docs/terrier-indexing.rst                 |  4 +-
 docs/text.rst                             |  6 +--
 docs/transformer.rst                      |  2 +-
 docs/tuning.rst                           |  8 ++--
 examples/experiments/Robust04.ipynb       | 52 +++++++++++------------
 examples/notebooks/experiment.ipynb       |  6 +--
 examples/notebooks/indexing.ipynb         |  4 +-
 examples/notebooks/non_en_retrieval.ipynb |  6 +--
 pyterrier/apply.py                        | 14 +++---
 pyterrier/apply_base.py                   | 12 +++---
 pyterrier/cache.py                        |  2 +-
 pyterrier/ltr.py                          |  6 +--
 pyterrier/ops.py                          |  6 +--
 pyterrier/pipelines.py                    |  4 +-
 pyterrier/terrier/rewrite.py              | 16 +++----
 pyterrier/text.py                         |  8 ++--
 pyterrier/transformer.py                  |  6 +--
 29 files changed, 135 insertions(+), 135 deletions(-)

diff --git a/docs/anserini.rst b/docs/anserini.rst
index 41fecfa0..f65c12b4 100644
--- a/docs/anserini.rst
+++ b/docs/anserini.rst
@@ -16,7 +16,7 @@ Comparative retrieval from Anserini and Terrier::
     trIndex = "/path/to/data.properties"
     luceneIndex "/path/to/lucene-index-dir"
 
-    BM25_tr = pt.BatchRetrieve(trIndex, wmodel="BM25")
+    BM25_tr = pt.terrier.Retrieve(trIndex, wmodel="BM25")
     BM25_ai = pt.anserini.AnseriniBatchRetrieve(luceneIndex, wmodel="BM25")
 
     pt.Experiment([BM25_tr, BM25_ai], topics, qrels, eval_metrics=["map"])
@@ -24,7 +24,7 @@ Comparative retrieval from Anserini and Terrier::
 
 AnseriniBatchRetrieve can also be used as a re-ranker::
 
-    BM25_tr = pt.BatchRetrieve(trIndex, wmodel="BM25")
+    BM25_tr = pt.terrrier.Retrieve(trIndex, wmodel="BM25")
     QLD_ai = pt.anserini.AnseriniBatchRetrieve(luceneIndex, wmodel="QLD")
 
     pipe = BM25_tr >> QLD_ai
diff --git a/docs/apply.rst b/docs/apply.rst
index 6880b2bf..937ce10a 100644
--- a/docs/apply.rst
+++ b/docs/apply.rst
@@ -85,9 +85,9 @@ Its also possible to construct a transformer that makes a new column on a row-wi
 
 For instance, if the column you are creating is called rank_2, it might be created as follows::
 
-    pipe = pt.BatchRetrieve(index) >> pt.apply.rank_2(lambda row: row["rank"] * 2)
+    pipe = pt.terrrier.Retrieve(index) >> pt.apply.rank_2(lambda row: row["rank"] * 2)
 
 To create a transformer that drops a column, you can instead pass `drop=True` as a kwarg::
 
-    pipe = pt.BatchRetrieve(index, metadata=["docno", "text"] >> pt.text.scorer() >> pt.apply.text(drop=True)
+    pipe = pt.terrrier.Retrieve(index, metadata=["docno", "text"] >> pt.text.scorer() >> pt.apply.text(drop=True)
 
diff --git a/docs/datamodel.md b/docs/datamodel.md
index d74a7c21..275c7324 100644
--- a/docs/datamodel.md
+++ b/docs/datamodel.md
@@ -51,7 +51,7 @@ A dataframe representing which documents are retrieved and scored for a given qu
 
 Note that rank is computed by sorting by qid ascending, then score descending. The first rank for each query is 0. The `pyterrier.model.add_rank()` function is used for adding the rank column. 
 
-Optional columns might support additional transformers, such as text (for the contents of the documents), url or title columns. Their presence can facilitate more advanced transformers, such as BERT-based transformers which operate on the raw text of the documents. For instance, if the Terrier index has additional metadata attributes, these can be included by BatchRetrieve using the `metadata` kwarg, i.e. `pt.BatchRetrieve(index, metadata=["docno", "title", "body"])`. 
+Optional columns might support additional transformers, such as text (for the contents of the documents), url or title columns. Their presence can facilitate more advanced transformers, such as BERT-based transformers which operate on the raw text of the documents. For instance, if the Terrier index has additional metadata attributes, these can be included by BatchRetrieve using the `metadata` kwarg, i.e. `pt.terrier.Retrieve(index, metadata=["docno", "title", "body"])`. 
 
 Note that the retrieved documents is a subset of the cartesian product of documents and queries; it is important that the query (text) attribute is present for at least ONE document rather than all documents for a given query.
 
diff --git a/docs/datasets.rst b/docs/datasets.rst
index 6490943b..249f4e71 100644
--- a/docs/datasets.rst
+++ b/docs/datasets.rst
@@ -27,8 +27,8 @@ Many of the PyTerrier unit tests are based on the `Vaswani NPL test collection <
 PyTerrier provides a ready-made index on the `Terrier Data Repository <http://data.terrier.org/>`_. This allows experiments to be easily conducted::
 
     dataset = pt.get_dataset("vaswani")
-    bm25 = pt.BatchRetrieve.from_dataset(dataset, "terrier_stemmed", wmodel="BM25")
-    dph = pt.BatchRetrieve.from_dataset(dataset, "terrier_stemmed", wmodel="DPH")
+    bm25 = pt.terrier.Retrieve.from_dataset(dataset, "terrier_stemmed", wmodel="BM25")
+    dph = pt.terrier.Retrieve.from_dataset(dataset, "terrier_stemmed", wmodel="DPH")
     pt.Experiment(
         [bm25, dph],
         dataset.get_topics(),
@@ -44,8 +44,8 @@ Indexing and then retrieval of documents from the `MSMARCO document corpus <http
     indexref = indexer.index(dataset.get_corpus())
     index = pt.IndexFactory.of(indexref)
 
-    DPH_br = pt.BatchRetrieve(index, wmodel="DPH") % 100
-    BM25_br = pt.BatchRetrieve(index, wmodel="BM25") % 100
+    DPH_br = pt.terrier.Retrieve(index, wmodel="DPH") % 100
+    BM25_br = pt.terrier.Retrieve(index, wmodel="BM25") % 100
     # this runs an experiment to obtain results on the TREC 2019 Deep Learning track queries and qrels
     pt.Experiment(
         [DPH_br, BM25_br], 
@@ -62,8 +62,8 @@ You can also index datasets that include a corpus using IterDictIndexer and get_
     indexref = indexer.index(dataset.get_corpus_iter(), fields=('title', 'abstract'))
     index = pt.IndexFactory.of(indexref)
 
-    DPH_br = pt.BatchRetrieve(index, wmodel="DPH") % 100
-    BM25_br = pt.BatchRetrieve(index, wmodel="BM25") % 100
+    DPH_br = pt.terrier.Retrieve(index, wmodel="DPH") % 100
+    BM25_br = pt.terrier.Retrieve(index, wmodel="BM25") % 100
     # this runs an experiment to obtain results on the TREC COVID queries and qrels
     pt.Experiment(
         [DPH_br, BM25_br], 
diff --git a/docs/experiments.rst b/docs/experiments.rst
index 982cca16..a8e21d7d 100644
--- a/docs/experiments.rst
+++ b/docs/experiments.rst
@@ -27,8 +27,8 @@ Getting average effectiveness over a set of topics::
     # vaswani dataset provides an index, topics and qrels
 
     # lets generate two BRs to compare
-    tfidf = pt.BatchRetrieve(dataset.get_index(), wmodel="TF_IDF")
-    bm25 = pt.BatchRetrieve(dataset.get_index(), wmodel="BM25")
+    tfidf = pt.terrier.Retrieve(dataset.get_index(), wmodel="TF_IDF")
+    bm25 = pt.terrier.Retrieve(dataset.get_index(), wmodel="BM25")
 
     pt.Experiment(
         [tfidf, bm25],
diff --git a/docs/extras/generate_includes.py b/docs/extras/generate_includes.py
index 27e6ea56..cc2b01e8 100644
--- a/docs/extras/generate_includes.py
+++ b/docs/extras/generate_includes.py
@@ -49,8 +49,8 @@ def experiment_includes():
                 os.path.join(tempfile.gettempdir(), "vaswani_index")
             ).index(pt.get_dataset('vaswani').get_corpus_iter())
 
-    tfidf = pt.BatchRetrieve(indexref, wmodel="TF_IDF")
-    bm25 = pt.BatchRetrieve(indexref, wmodel="BM25")
+    tfidf = pt.terrier.Retrieve(indexref, wmodel="TF_IDF")
+    bm25 = pt.terrier.Retrieve(indexref, wmodel="BM25")
 
     table = pt.Experiment(
         [tfidf, bm25],
diff --git a/docs/ltr.rst b/docs/ltr.rst
index 28b23a9c..91cb2057 100644
--- a/docs/ltr.rst
+++ b/docs/ltr.rst
@@ -14,7 +14,7 @@ PyTerrier makes it easy to formulate learning to rank pipelines. Conceptually, l
 
 PyTerrier allows each of these phases to be expressed as transformers, and for them to be composed into a full pipeline.  
 
-In particular, conventional retrieval transformers (such as `pt.BatchRetrieve`) can be used for the first phase.
+In particular, conventional retrieval transformers (such as `pt.terrier.Retrieve`) can be used for the first phase.
 To permit the second phase, PyTerrier data model allows for a `"features"` column to be associated to each retrieved document. 
 Such features can be generated using specialised transformers, or by combining other re-ranking transformers using the  `**`
 feature-union operator; Lastly, to facilitate the final phase, we provide easy ways to integrate PyTerrier pipelines with standard learning libraries
@@ -34,9 +34,9 @@ PyTerrier's main way to faciliate calculating and intgrating extra features is t
 the candidate set should be identified using the BM25 weighting model, and then additional features computed using the
 Tf and PL2 models::
 
-    bm25 = pt.BatchRetrieve(index, wmodel="BM25")
-    tf = pt.BatchRetrieve(index, wmodel="Tf")
-    pl2 = pt.BatchRetrieve(index, wmodel="PL2")
+    bm25 = pt.terrier.Retrieve(index, wmodel="BM25")
+    tf = pt.terrier.Retrieve(index, wmodel="Tf")
+    pl2 = pt.terrier.Retrieve(index, wmodel="PL2")
     pipeline = bm25 >> (tf ** pl2)
 
 The output of the bm25 ranker would look like:
diff --git a/docs/operators.rst b/docs/operators.rst
index 164aefde..7d128259 100644
--- a/docs/operators.rst
+++ b/docs/operators.rst
@@ -187,7 +187,7 @@ Rank Cutoff (`%`)
 
 The `%` operator is called rank cutoff, and limits the number of results for each query::
 
-    pipe1 = pt.BatchRetrieve(index, "BM25") % 2
+    pipe1 = pt.terrier.Retrieve(index, "BM25") % 2
 
 **Example:**
 
diff --git a/docs/parallel.rst b/docs/parallel.rst
index 0fd32782..62e3b11e 100644
--- a/docs/parallel.rst
+++ b/docs/parallel.rst
@@ -26,7 +26,7 @@ Parallelisation using Joblib
 
 A transformer pipeline can be parallelised by using the .parallel() transformer method::
 
-    dph = pt.BatchRetrieve(index, wmodel='DPH')
+    dph = pt.terrier.Retrieve(index, wmodel='DPH')
     dph_fast = dph.parallel(2)
 
 In this way, any set of queries passed to dph_fast will be separated into two partitions, based on qid, and executed on dph.
@@ -40,7 +40,7 @@ a similar way as for joblib::
 
     import ray
     ray.init() #configure Ray as per your cluster setup
-    dph = pt.BatchRetrieve(index, wmodel='DPH')
+    dph = pt.terrier.Retrieve(index, wmodel='DPH')
     dph_fast = dph.parallel(2, backend='ray')
 
 In particular, `ray.init()` must have been called before calling `.parallel()`.
@@ -51,8 +51,8 @@ What to Parallelise
 Only transformers that can be `pickled <https://docs.python.org/3/library/pickle.html>`_. Transformers that use native code
 may not be possible to pickle. Some standard PyTerrier transformers have additional support for parallelisation:
 
- - Terrier retrieval: pt.BatchRetrieve(), pt.FeaturesBatchRetrieve()
- - Anserini retrieval: pt.AnseriniBatchRetrieve()
+ - Terrier retrieval: pt.terrier.Retrieve(), pt.FeaturesBatchRetrieve()
+ - Anserini retrieval: pt.anserini.AnseriniBatchRetrieve()
 
 Pure python transformers, such as `pt.text.sliding()` are picklable. However, parallelising only `pt.text.sliding()` may not produce
 efficiency gains, due to the overheads of shuffling data back and forward. 
@@ -61,14 +61,14 @@ Entire transformer pipelines (i.e. combined using operators) can be pickled and
 the most inefficient component of your process, while also minimising the amount of data being transferred between processes. For instance,
 consider the following pipeline::
 
-    pipe = pt.BatchRetrieve(index, metadata=["docno", "text"] >> pt.text.sliding() >> pt.text.scorer() >> pt.text.max_passage()
+    pipe = pt.terrier.Retrieve(index, metadata=["docno", "text"] >> pt.text.sliding() >> pt.text.scorer() >> pt.text.max_passage()
 
 While BatchRetrieve might represent the slowest component of the pipeline, it might make sense to parallelise pipe as a whole,
 rather than just BatchRetrieve, as then only the queries and final results  need to be passed betwene processes. Indeed among the
 following semantically equivalent pipelines, we expect `parallel_pipe0`  and `parallel_pipe2`  to be faster than `parallel_pipe1`::
 
-    parallel_pipe0 = pt.BatchRetrieve(index, metadata=["docno", "text"]).parallel() >> pt.text.sliding() >> pt.text.scorer() >> pt.text.max_passage()
-    parallel_pipe1 = ( pt.BatchRetrieve(index, metadata=["docno", "text"]).parallel() >> pt.text.sliding() ).parallel(2)  >> pt.text.max_passage()
+    parallel_pipe0 = pt.terrier.Retrieve(index, metadata=["docno", "text"]).parallel() >> pt.text.sliding() >> pt.text.scorer() >> pt.text.max_passage()
+    parallel_pipe1 = ( pt.terrier.Retrieve(index, metadata=["docno", "text"]).parallel() >> pt.text.sliding() ).parallel(2)  >> pt.text.max_passage()
     parallel_pipe2 = pipe.parallel(2)
 
 
@@ -84,5 +84,5 @@ Outlook
 =======
 
 We expect to integate parallelisation at different parts of the PyTerrier platform, such as for conducting a gridsearch. Moreover, we hope 
-that proper integration of multi-threaded retrieval in pt.BatchRetrieve() (while requires upstream improvements in the underlying Terrier platform) 
+that proper integration of multi-threaded retrieval in pt.terrier.Retrieve() (while requires upstream improvements in the underlying Terrier platform) 
 will reduce the need for this form of parallelisation.
diff --git a/docs/pipeline_examples.md b/docs/pipeline_examples.md
index 2dcab7d7..4d28d1a9 100644
--- a/docs/pipeline_examples.md
+++ b/docs/pipeline_examples.md
@@ -5,7 +5,7 @@
 ### Sequential Dependence Model
 
 ```python
-pipe = pt.rewrite.SDM() >> pt.BatchRetrieve(indexref, wmodel="BM25")
+pipe = pt.rewrite.SDM() >> pt.terrier.Retrieve(indexref, wmodel="BM25")
 ```
 
 Note that the SDM() rewriter has a number of constructor parameters:
@@ -17,16 +17,16 @@ Note that the SDM() rewriter has a number of constructor parameters:
 
 A simple QE transformer can be achieved using
 ```python
-qe = pt.BatchRetrieve(indexref, wmodel="BM25", controls={"qe" : "on"})
+qe = pt.terrier.Retrieve(indexref, wmodel="BM25", controls={"qe" : "on"})
 ```
 
 As this is pseudo-relevance feedback in nature, it identifies a set of documents, extracts informative term in the top-ranked documents, and re-exectutes the query.
 
 However, more control can be achieved by using the QueryExpansion transformer separately, as thus:
 ```python
-qe = (pt.BatchRetrieve(indexref, wmodel="BM25") >> 
+qe = (pt.terrier.Retrieve(indexref, wmodel="BM25") >> 
     pt.rewrite.QueryExpansion(indexref) >> 
-    pt.BatchRetrieve(indexref, wmodel="BM25")
+    pt.terrier.Retrieve(indexref, wmodel="BM25")
 )
 ```
 
@@ -38,9 +38,9 @@ The QueryExpansion() object has the following constructor parameters:
 Note that different indexes can be used to achieve query expansion using an external collection (sometimes called collection enrichment or external feedback).  For example, to expand queries using Wikipedia as an external resource, in order to get higher quality query re-weighted queries, would look like this:
 
 ```python
-pipe = (pt.BatchRetrieve(wikipedia_index, wmodel="BM25") >> 
+pipe = (pt.terrier.Retrieve(wikipedia_index, wmodel="BM25") >> 
     pt.rewrite.QueryExpansion(wikipedia_index) >> 
-    pt.BatchRetrieve(local_index, wmodel="BM25")
+    pt.terrier.Retrieve(local_index, wmodel="BM25")
 )
 ```
 
@@ -51,32 +51,32 @@ You need to call `pt.terrier.enable_prf()` to download and load this plugin.
 
 ```python
 pt.terrier.enable_prf() # This is required before Java starts
-pipe = (pt.BatchRetrieve(indexref, wmodel="BM25") >> 
+pipe = (pt.terrier.Retrieve(indexref, wmodel="BM25") >> 
     pt.rewrite.RM3(indexref) >> 
-    pt.BatchRetrieve(indexref, wmodel="BM25")
+    pt.terrier.Retrieve(indexref, wmodel="BM25")
 )
 ```
 ## Combining Rankings
 
 Sometimes we have good retrieval approaches and we wish to combine these in a unsupervised manner. We can do that using the linear combination operator:
 ```python
-bm25 = pt.BatchRetrieve(indexref, wmodel="BM25")
-dph = pt.BatchRetrieve(indexref, wmodel="DPH")
+bm25 = pt.terrier.Retrieve(indexref, wmodel="BM25")
+dph = pt.terrier.Retrieve(indexref, wmodel="DPH")
 linear = bm25 + dph
 ```
 
 Of course, some weighting can help:
 ```python
-bm25 = pt.BatchRetrieve(indexref, wmodel="BM25")
-dph = pt.BatchRetrieve(indexref, wmodel="DPH")
+bm25 = pt.terrier.Retrieve(indexref, wmodel="BM25")
+dph = pt.terrier.Retrieve(indexref, wmodel="DPH")
 linear = bm25 + 2* dph
 ```
 
 However, if the score distributions are not similar, finding a good weight can be tricky. Normalisation of retrieval scores can be advantagous in this case. We provide PerQueryMaxMinScoreTransformer() to make easy normalisation.
 
 ```python
-bm25 = pt.BatchRetrieve(indexref, wmodel="BM25") >> pt.pipelines.PerQueryMaxMinScoreTransformer()
-dph = pt.BatchRetrieve(indexref, wmodel="DPH" >> pt.pipelines.PerQueryMaxMinScoreTransformer()
+bm25 = pt.terrier.Retrieve(indexref, wmodel="BM25") >> pt.pipelines.PerQueryMaxMinScoreTransformer()
+dph = pt.terrier.Retrieve(indexref, wmodel="DPH" >> pt.pipelines.PerQueryMaxMinScoreTransformer()
 linear = 0.75 * bm25 + 0.25 * dph
 ```
 
@@ -88,13 +88,13 @@ Having shown some of the main formulations, lets show how to build different for
  - We then score each of the retrieved documents 
 
 ```python
-bm25_cands = pt.BatchRetrieve(indexref, wmodel="BM25")
-dph_cands = pt.BatchRetrieve(indexref, wmodel="DPH")
+bm25_cands = pt.terrier.Retrieve(indexref, wmodel="BM25")
+dph_cands = pt.terrier.Retrieve(indexref, wmodel="DPH")
 all_cands = bm25_cands | dph_cands
 
 all_features = all_cands >> (  
-    pt.BatchRetrieve(indexref, wmodel="BM25F") **
-    pt.rewrite.SDM() >> pt.BatchRetrieve(indexref, wmodel="BM25")
+    pt.terrier.Retrieve(indexref, wmodel="BM25F") **
+    pt.rewrite.SDM() >> pt.terrier.Retrieve(indexref, wmodel="BM25")
     )
 
 import xgboost as xgb
diff --git a/docs/rewrite.rst b/docs/rewrite.rst
index 450b6950..b5df332c 100644
--- a/docs/rewrite.rst
+++ b/docs/rewrite.rst
@@ -40,7 +40,7 @@ This transfomer is only compatible with BatchRetrieve, as Terrier supports the `
 Example::
 
     sdm = pt.rewrite.SequentialDependence()
-    dph = pt.BatchRetrieve(index, wmodel="DPH")
+    dph = pt.terrier.Retrieve(index, wmodel="DPH")
     pipeline = sdm >> dph
 
 References:
@@ -61,7 +61,7 @@ and returns a dataframe with  `["qid", "query"]`.
 Example::
 
     bo1 = pt.rewrite.Bo1QueryExpansion(index)
-    dph = pt.BatchRetrieve(index, wmodel="DPH")
+    dph = pt.terrier.Retrieve(index, wmodel="DPH")
     pipelineQE = dph >> bo1 >> dph
 
 View the expansion terms::
@@ -77,7 +77,7 @@ View the expansion terms::
 Note that it is also possible to configure BatchRetrieve to perform QE directly using controls,
 which will result in identical retrieval effectiveness::
 
-    pipelineQE = pt.BatchRetrieve(index, wmodel="DPH", controls={"qemodel" : "Bo1", "qe" : "on"})
+    pipelineQE = pt.terrier.Retrieve(index, wmodel="DPH", controls={"qemodel" : "Bo1", "qe" : "on"})
 
 However, using `pt.rewrite.Bo1QueryExpansion` is preferable as:
 
@@ -161,7 +161,7 @@ This can be attained in PyTerrier through use of `stash_results()` and `reset_re
 
     # index: the corpus you are ranking
 
-    dph = pt.BatchRetrieve(index)
+    dph = pt.terrier.Retrieve(index)
     Pipe = dph 
         >> pt.rewrite.stash_results(clear=False)
         >> pt.rewrite.RM3(index)
@@ -193,17 +193,17 @@ Example: Collection Enrichment as a re-ranker::
     # index: the corpus you are ranking
     # wiki_index: index of Wikipedia, used for enrichment
 
-    dph = pt.BatchRetrieve(index)
+    dph = pt.terrier.Retrieve(index)
     Pipe = dph 
         >> pt.rewrite.stash_results()          
-        >> pt.BatchRetrieve(wiki_index)
+        >> pt.terrier.Retrieve(wiki_index)
         >> pt.rewrite.RM3(wiki_index)
         >> pt.rewrite.reset_results()
         >> dph
 
 In general, collection enrichment describes conducting a PRF query expansion process on an external corpus (often Wikipedia), 
 before applying the reformulated query to the main corpus. Collection enrichment can be used for improving a first pass 
-retrieval (`pt.BatchRetrieve(wiki_index) >> pt.rewrite.RM3(wiki_index) >> pt.BatchRetrieve(main_index)`). Instead, the particular 
+retrieval (`pt.terrier.Retrieve(wiki_index) >> pt.rewrite.RM3(wiki_index) >> pt.terrier.Retrieve(main_index)`). Instead, the particular 
 example shown above applies collection enrichment as a re-ranker.
 
 
diff --git a/docs/terrier-index-api.rst b/docs/terrier-index-api.rst
index 438053ab..e8eab989 100644
--- a/docs/terrier-index-api.rst
+++ b/docs/terrier-index-api.rst
@@ -28,7 +28,7 @@ IndexRefs can also be obtained from a PyTerrier dataset::
 
 IndexRef objects can be directly passed to BatchRetrieve::
 
-    pt.BatchRetrieve(indexref).search("chemical reactions")
+    pt.terrier.Retrieve(indexref).search("chemical reactions")
 
 If you want to access the underlying data structures, you need to use IndexFactory, using the indexref, or the string location:: 
     
diff --git a/docs/terrier-indexing.rst b/docs/terrier-indexing.rst
index e5771a13..6133a76a 100644
--- a/docs/terrier-indexing.rst
+++ b/docs/terrier-indexing.rst
@@ -255,10 +255,10 @@ This allows tokenisation using, for instance, the `HuggingFace tokenizers <https
     ])
 
 At retrieval time, WordPieces that contain special characters (e.g. `'##w'` `'[SEP]'`) need to be encoded so as to avoid Terrier's tokeniser. 
-We use ``pt.rewrite.tokenise()`` to apply a tokeniser to the query, setting ``matchop`` to True, such that ``pt.BatchRetrieve.matchop()`` 
+We use ``pt.rewrite.tokenise()`` to apply a tokeniser to the query, setting ``matchop`` to True, such that ``pt.terrier.Retrieve.matchop()`` 
 is called to ensure that rewritten query terms are properly encoded::
 
-    br = pt.BatchRetrieve(indexref)
+    br = pt.terrier.Retrieve(indexref)
     tok = AutoTokenizer.from_pretrained("bert-base-uncased")
     query_toks = pt.rewrite.tokenise(tok.tokenize, matchop=True)
     retr_pipe = query_toks >> br
diff --git a/docs/text.rst b/docs/text.rst
index f6c0fd15..174d0aa3 100644
--- a/docs/text.rst
+++ b/docs/text.rst
@@ -53,12 +53,12 @@ all document fields, not just those that were included as meta fields when index
 Examples::
 
     # the following pipelines are equivalent
-    pipe1 = pt.BatchRetrieve(index, metadata=["docno", "body"])
+    pipe1 = pt.terrier.Retrieve(index, metadata=["docno", "body"])
 
-    pipe2 = pt.BatchRetrieve(index) >> pt.text.get_text(index, "body")
+    pipe2 = pt.terrier.Retrieve(index) >> pt.text.get_text(index, "body")
 
     dataset = pt.get_dataset('irds:vaswani')
-    pipe3 = pt.BatchRetrieve(index) >> pt.text.get_text(dataset, "text")
+    pipe3 = pt.terrier.Retrieve(index) >> pt.text.get_text(dataset, "text")
 
 
 .. autofunction:: pyterrier.text.get_text()
diff --git a/docs/transformer.rst b/docs/transformer.rst
index a05c532b..5f2a382b 100644
--- a/docs/transformer.rst
+++ b/docs/transformer.rst
@@ -17,7 +17,7 @@ before returning it.
 +=======+=========+=============+==================+==============================+
 |   Q   |    Q    |   1 to 1    | Query rewriting  | `pt.rewrite.SDM()`           |
 +-------+---------+-------------+------------------+------------------------------+
-|   Q   |  Q x D  |   1 to N    | Retrieval        | `pt.BatchRetrieve()`         |
+|   Q   |  Q x D  |   1 to N    | Retrieval        | `pt.terrier.Retrieve()`      |
 +-------+---------+-------------+------------------+------------------------------+
 | Q x D |    Q    |   N to 1    | Query expansion  | `pt.rewrite.RM3()`           |
 +-------+---------+-------------+------------------+------------------------------+
diff --git a/docs/tuning.rst b/docs/tuning.rst
index 8655b71d..b188770c 100644
--- a/docs/tuning.rst
+++ b/docs/tuning.rst
@@ -39,7 +39,7 @@ When using BatchRetrieve, the `b` parameter of the BM25 weighting model can be c
 We must give this control an initial value when contructing the BatchRetrieve instance. Thereafter, the GridSearch
 parameter dictionary can be constructed by refering to the instance of transformer that has that parameter::
 
-    BM25 = pt.BatchRetrieve(index, wmodel="BM25", controls={"bm25.b" : 0.75})
+    BM25 = pt.terrier.Retrieve(index, wmodel="BM25", controls={"bm25.b" : 0.75})
     pt.GridSearch(
         BM25,  
         {BM25 : {"c" : [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 ]}}
@@ -49,7 +49,7 @@ parameter dictionary can be constructed by refering to the instance of transform
 
 Terrier's BM25 also responds to controls named `"bm25.k_1"` and  `"bm25.k_3"`, such that all three controls can be tuned concurrently::
 
-    BM25 = pt.BatchRetrieve(index, wmodel="BM25", controls={"bm25.b" : 0.75, "bm25.k_1": 0.75, "bm25.k_3": 0.75})
+    BM25 = pt.terrier.Retrieve(index, wmodel="BM25", controls={"bm25.b" : 0.75, "bm25.k_1": 0.75, "bm25.k_3": 0.75})
     pt.GridSearch(
         BM25,  
         {BM25: {"bm25.b"  : [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 ],
@@ -72,7 +72,7 @@ of feedback documents and expansion terms, namely:
 
 A full tuning of BM25 and RM3 can be achieved as thus::
 
-    bm25_for_qe = pt.BatchRetrieve(index, wmodel="BM25", controls={"bm25.b" : 0.75})
+    bm25_for_qe = pt.terrier.Retrieve(index, wmodel="BM25", controls={"bm25.b" : 0.75})
     rm3 = pt.rewrite.RM3(index, fb_terms=10, fb_docs=3)
     pipe_qe = bm25_for_qe >> rm3 >> bm25_for_qe
 
@@ -121,7 +121,7 @@ The following is an example of scanning the parameters of BM25F for an index wit
     assert 2 == index.getCollectionStatistics().getNumberOfFields()
 
     # instantiate BatchRetrieve for BM25F
-    bm25f = pt.BatchRetrieve(
+    bm25f = pt.terrier.Retrieve(
         index, 
         wmodel = 'BM25F', 
         controls = {'w.0' : 1, 'w.1' : 1, 'c.0' : 0.4, 'c.1' : 0.4}
diff --git a/examples/experiments/Robust04.ipynb b/examples/experiments/Robust04.ipynb
index 8dd9e389..2921ba22 100644
--- a/examples/experiments/Robust04.ipynb
+++ b/examples/experiments/Robust04.ipynb
@@ -1,20 +1,20 @@
 {
  "cells": [
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## TREC Robust 04\n",
     "\n",
     "This notebook demonstrates baseline experiments on the TREC Robust04 test collection. More information is provided [in the PyTerrier documenatation](https://pyterrier.readthedocs.io/en/latest/experiments/Robust04.html)\n"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Install PyTerrier - this installs the latest version from the GitHub repository."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -34,11 +34,11 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Start PyTerrier. By using `version='snapshot'`, we use the latest version of Terrier from its own [GitHub repository](http://github.com/terrier-org/terrier-core/)."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -71,13 +71,13 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Update this configuration, to detail:\n",
     " - where your copy of the TREC Disk 4 and 5 corpus is\n",
     " - where you wish to store your index."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -98,13 +98,13 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Indexing\n",
     "\n",
     "This indexes the corpus; it took around 8 minutes using a single thread."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -176,11 +176,11 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Retrieval - Simple Weighting Models"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -197,10 +197,10 @@
    "outputs": [],
    "source": [
     "\n",
-    "BM25 = pt.BatchRetrieve(index, wmodel=\"BM25\")\n",
-    "DPH  = pt.BatchRetrieve(index, wmodel=\"DPH\")\n",
-    "PL2  = pt.BatchRetrieve(index, wmodel=\"PL2\")\n",
-    "DLM  = pt.BatchRetrieve(index, wmodel=\"DirichletLM\")\n"
+    "BM25 = pt.terrier.Retrieve(index, wmodel=\"BM25\")\n",
+    "DPH  = pt.terrier.Retrieve(index, wmodel=\"DPH\")\n",
+    "PL2  = pt.terrier.Retrieve(index, wmodel=\"PL2\")\n",
+    "DLM  = pt.terrier.Retrieve(index, wmodel=\"DirichletLM\")\n"
    ]
   },
   {
@@ -312,11 +312,11 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "## Retrieval - Query Expansion"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
@@ -585,4 +585,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/examples/notebooks/experiment.ipynb b/examples/notebooks/experiment.ipynb
index c1afd0d6..bc1d9ea9 100644
--- a/examples/notebooks/experiment.ipynb
+++ b/examples/notebooks/experiment.ipynb
@@ -148,9 +148,9 @@
       },
       "outputs": [],
       "source": [
-        "TF_IDF = pt.BatchRetrieve(vaswani.get_index(), wmodel=\"TF_IDF\")\n",
-        "BM25 = pt.BatchRetrieve(vaswani.get_index(), wmodel=\"BM25\")\n",
-        "PL2 = pt.BatchRetrieve(vaswani.get_index(), wmodel=\"PL2\")"
+        "TF_IDF = pt.terrier.Retrieve(vaswani.get_index(), wmodel=\"TF_IDF\")\n",
+        "BM25 = pt.terrier.Retrieve(vaswani.get_index(), wmodel=\"BM25\")\n",
+        "PL2 = pt.terrier.Retrieve(vaswani.get_index(), wmodel=\"PL2\")"
       ]
     },
     {
diff --git a/examples/notebooks/indexing.ipynb b/examples/notebooks/indexing.ipynb
index 8d9dc3e3..7e69b81a 100644
--- a/examples/notebooks/indexing.ipynb
+++ b/examples/notebooks/indexing.ipynb
@@ -782,7 +782,7 @@
         }
       ],
       "source": [
-        "pt.BatchRetrieve(indexref).search(\"mathematical\")"
+        "pt.terrier.Retrieve(indexref).search(\"mathematical\")"
       ]
     },
     {
@@ -969,7 +969,7 @@
       "source": [
         "import pandas as pd\n",
         "topics = pd.DataFrame([[\"2\", \"mathematical\"]],columns=['qid','query'])\n",
-        "pt.BatchRetrieve(indexref).transform(topics)"
+        "pt.terrier.Retrieve(indexref).transform(topics)"
       ]
     },
     {
diff --git a/examples/notebooks/non_en_retrieval.ipynb b/examples/notebooks/non_en_retrieval.ipynb
index 5a5a3237..59471252 100644
--- a/examples/notebooks/non_en_retrieval.ipynb
+++ b/examples/notebooks/non_en_retrieval.ipynb
@@ -481,14 +481,14 @@
         }
       ],
       "source": [
-        "bm25_nostem = pt.BatchRetrieve(index_nostem, wmodel='BM25')\n",
+        "bm25_nostem = pt.terrier.Retrieve(index_nostem, wmodel='BM25')\n",
         "bm25_nostem.search('El Centro') \n",
         "\n",
-        "bm25_stem = pt.BatchRetrieve(index_stem, wmodel='BM25')\n",
+        "bm25_stem = pt.terrier.Retrieve(index_stem, wmodel='BM25')\n",
         "bm25_stem.search('El Centro') \n",
         "\n",
         "# to apply the es_preprocess function to the query text, use a pt.apply.query transformer\n",
-        "bm25_custom = pt.apply.query(lambda row: es_preprocess(row.query)) >> pt.BatchRetrieve(index_custom, wmodel='BM25')\n",
+        "bm25_custom = pt.apply.query(lambda row: es_preprocess(row.query)) >> pt.terrier.Retrieve(index_custom, wmodel='BM25')\n",
         "bm25_custom.search('El Centro')"
       ]
     },
diff --git a/pyterrier/apply.py b/pyterrier/apply.py
index 115ad138..a4b3c5f7 100644
--- a/pyterrier/apply.py
+++ b/pyterrier/apply.py
@@ -43,12 +43,12 @@ def _remove_stops(q):
                 return " ".join(terms)
 
             # a query rewriting transformer that applies the _remove_stops to each row of an input dataframe
-            p1 = pt.apply.query(_remove_stops) >> pt.BatchRetrieve(index, wmodel="DPH")
+            p1 = pt.apply.query(_remove_stops) >> pt.terrier.Retrieve(index, wmodel="DPH")
 
             # an equivalent query rewriting transformer using an anonymous lambda function
             p2 = pt.apply.query(
                     lambda q :  " ".join([t for t in q["query"].split(" ") if t not in stops ])
-                ) >> pt.BatchRetrieve(index, wmodel="DPH")
+                ) >> pt.terrier.Retrieve(index, wmodel="DPH")
 
         In both of the example pipelines above (`p1` and `p2`), the exact topics are not known until the pipeline is invoked, e.g.
         by using `p1.transform(topics)` on a topics dataframe, or within a `pt.Experiment()`. When the pipeline 
@@ -75,7 +75,7 @@ def doc_score(fn : Union[Callable[[pd.Series], float], Callable[[pd.DataFrame],
         Example (Row-wise)::
 
             # this transformer will subtract 5 from the score of each document
-            p = pt.BatchRetrieve(index, wmodel="DPH") >> 
+            p = pt.terrier.Retrieve(index, wmodel="DPH") >> 
                 pt.apply.doc_score(lambda doc : doc["score"] -5)
 
         Can be used in batch-wise manner, which is particularly useful for appling neural models. In this case,
@@ -85,7 +85,7 @@ def _doclen(df):
                 # returns series of lengths
                 return df.text.str.len()
             
-            pipe = pt.BatchRetrieve(index) >> pt.apply.doc_score(_doclen, batch_size=128)
+            pipe = pt.terrier.Retrieve(index) >> pt.apply.doc_score(_doclen, batch_size=128)
 
     """
     return ApplyDocumentScoringTransformer(fn, *args, batch_size=batch_size, **kwargs)
@@ -113,7 +113,7 @@ def _features(row):
                 f2 = len(content.split(" "))
                 return np.array([f1, f2])
 
-            p = pt.BatchRetrieve(index, wmodel="BM25") >> 
+            p = pt.terrier.Retrieve(index, wmodel="BM25") >> 
                 pt.apply.doc_features(_features )
 
     """
@@ -129,7 +129,7 @@ def rename(columns : Dict[str,str], *args, errors='raise', **kwargs) -> pt.Trans
 
         Example::
             
-            pipe = pt.BatchRetrieve(index, metadata=["docno", "body"]) >> pt.apply.rename({'body':'text'})
+            pipe = pt.terrier.Retrieve(index, metadata=["docno", "body"]) >> pt.apply.rename({'body':'text'})
     """
     return ApplyGenericTransformer(lambda df: df.rename(columns=columns, errors=errors), *args, **kwargs)
 
@@ -151,7 +151,7 @@ def generic(fn : Callable[[pd.DataFrame], pd.DataFrame], *args, batch_size=None,
             # this transformer will remove all documents at rank greater than 2.
 
             # this pipeline would remove all but the first two documents from a result set
-            pipe = pt.BatchRetrieve(index) >> pt.apply.generic(lambda res : res[res["rank"] < 2])
+            pipe = pt.terrier.Retrieve(index) >> pt.apply.generic(lambda res : res[res["rank"] < 2])
 
     """
     return ApplyGenericTransformer(fn, *args, batch_size=batch_size, **kwargs)
diff --git a/pyterrier/apply_base.py b/pyterrier/apply_base.py
index 8a30c403..e9ec6661 100644
--- a/pyterrier/apply_base.py
+++ b/pyterrier/apply_base.py
@@ -78,7 +78,7 @@ class ApplyDocumentScoringTransformer(ApplyTransformerBase):
             def _score_fn(row):
                 return float(row["url".count("/")])
             
-            pipe = pt.BatchRetrieve(index) >> pt.apply.doc_score(_score_fn)
+            pipe = pt.terrier.Retrieve(index) >> pt.apply.doc_score(_score_fn)
 
         Can be used in batching manner, which is particularly useful for appling neural models. In this case,
         the scoring function receives a dataframe, rather than a single row::
@@ -86,7 +86,7 @@ def _score_fn(row):
             def _doclen(df):
                 return df.text.str.len()
             
-            pipe = pt.BatchRetrieve(index) >> pt.apply.doc_score(_doclen)
+            pipe = pt.terrier.Retrieve(index) >> pt.apply.doc_score(_doclen)
 
     """
     def __init__(self, fn,  *args, batch_size=None, **kwargs):
@@ -148,7 +148,7 @@ class ApplyDocFeatureTransformer(ApplyTransformerBase):
             def _feature_fn(row):
                 return numpy.array([len(row["url"], row["url".count("/")])
             
-            pipe = pt.BatchRetrieve(index) >> pt.apply.doc_features(_feature_fn) >> pt.LTRpipeline(xgBoost())
+            pipe = pt.terrier.Retrieve(index) >> pt.apply.doc_features(_feature_fn) >> pt.LTRpipeline(xgBoost())
     """
     def __init__(self, fn,  *args, **kwargs):
         """
@@ -180,11 +180,11 @@ class ApplyQueryTransformer(ApplyTransformerBase):
             def _rewriting_fn(row):
                 return row["query"] + " extra words"
             
-            pipe = pt.apply.query(_rewriting_fn) >> pt.BatchRetrieve(index)
+            pipe = pt.apply.query(_rewriting_fn) >> pt.terrier.Retrieve(index)
 
         Similarly, a lambda function can also be used::
 
-            pipe = pt.apply.query(lambda row: row["query"] + " extra words") >> pt.BatchRetrieve(index)
+            pipe = pt.apply.query(lambda row: row["query"] + " extra words") >> pt.terrier.Retrieve(index)
 
         In the resulting dataframe, the previous query for each row can be found in the query_0 column.
 
@@ -238,7 +238,7 @@ class ApplyGenericTransformer(ApplyTransformerBase):
         # this pipeline would remove all but the first two documents from a result set
         lp = ApplyGenericTransformer(lambda res : res[res["rank"] < 2])
 
-        pipe = pt.BatchRetrieve(index) >> lp
+        pipe = pt.terrier.Retrieve(index) >> lp
 
     """
 
diff --git a/pyterrier/cache.py b/pyterrier/cache.py
index e0d46486..f317e692 100644
--- a/pyterrier/cache.py
+++ b/pyterrier/cache.py
@@ -71,7 +71,7 @@ class ChestCacheTransformer(Transformer):
 
             dataset = pt.get_dataset("trec-robust-2004")
             # use for first pass and 2nd pass
-            BM25 = pt.BatchRetrieve(index, wmodel="BM25")
+            BM25 = pt.terrier.Retrieve(index, wmodel="BM25")
 
             # used for query expansion
             RM3 = pt.rewrite.RM3(index)
diff --git a/pyterrier/ltr.py b/pyterrier/ltr.py
index f8b92502..f4af190f 100644
--- a/pyterrier/ltr.py
+++ b/pyterrier/ltr.py
@@ -276,9 +276,9 @@ def score_to_feature() -> Transformer:
 
         Example::
 
-            cands = pt.BatchRetrieve(index, wmodel="BM25")
-            bm25f = pt.BatchRetrieve(index, wmodel="BM25F")
-            pl2f = pt.BatchRetrieve(index, wmodel="PL2F")
+            cands = pt.terrier.Retrieve(index, wmodel="BM25")
+            bm25f = pt.terrier.Retrieve(index, wmodel="BM25F")
+            pl2f = pt.terrier.Retrieve(index, wmodel="PL2F")
             
             two_features = cands >> (bm25f  **  pl2f)
             three_features = cands >> (bm25f  **  pl2f ** pt.ltr.score_to_feature())  
diff --git a/pyterrier/ops.py b/pyterrier/ops.py
index 1d8dead2..df8b9e06 100644
--- a/pyterrier/ops.py
+++ b/pyterrier/ops.py
@@ -200,9 +200,9 @@ class FeatureUnionPipeline(NAryTransformerBase):
         Implements the feature union operator.
 
         Example::
-            cands = pt.BatchRetrieve(index wmodel="BM25")
-            pl2f = pt.BatchRetrieve(index wmodel="PL2F")
-            bm25f = pt.BatchRetrieve(index wmodel="BM25F")
+            cands = pt.terrier.Retrieve(index wmodel="BM25")
+            pl2f = pt.terrier.Retrieve(index wmodel="PL2F")
+            bm25f = pt.terrier.Retrieve(index wmodel="BM25F")
             pipe = cands >> (pl2f ** bm25f)
     """
     name = "FUnion"
diff --git a/pyterrier/pipelines.py b/pyterrier/pipelines.py
index 07621708..d17971e8 100644
--- a/pyterrier/pipelines.py
+++ b/pyterrier/pipelines.py
@@ -653,7 +653,7 @@ def KFoldGridSearch(
 
     Consider tuning PL2 where folds of queries are pre-determined::
 
-        pl2 = pt.BatchRetrieve(index, wmodel="PL2", controls={'c' : 1})
+        pl2 = pt.terrier.Retrieve(index, wmodel="PL2", controls={'c' : 1})
         tuned_pl2, _ = pt.KFoldGridSearch(
             pl2, 
             {pl2 : {'c' : [0.1, 1, 5, 10, 20, 100]}}, 
@@ -827,7 +827,7 @@ def GridScan(
     Example::
 
         # graph how PL2's c parameter affects MAP
-        pl2 = pt.BatchRetrieve(index, wmodel="PL2", controls={'c' : 1})
+        pl2 = pt.terrier.Retrieve(index, wmodel="PL2", controls={'c' : 1})
         rtr = pt.GridScan(
             pl2, 
             {pl2 : {'c' : [0.1, 1, 5, 10, 20, 100]}}, 
diff --git a/pyterrier/terrier/rewrite.py b/pyterrier/terrier/rewrite.py
index ebf3275e..2829b297 100644
--- a/pyterrier/terrier/rewrite.py
+++ b/pyterrier/terrier/rewrite.py
@@ -19,22 +19,22 @@ def tokenise(tokeniser : Union[str,TerrierTokeniser,FunctionType] = 'english', m
     
     Example - use default tokeniser::
 
-        pipe = pt.rewrite.tokenise() >> pt.BatchRetrieve()
+        pipe = pt.rewrite.tokenise() >> pt.terrier.Retrieve()
         pipe.search("Question with 'capitals' and other stuff?")
     
     Example - roll your own tokeniser::
 
-        poortokenisation = pt.rewrite.tokenise(lambda query: query.split(" ")) >> pt.BatchRetrieve()
+        poortokenisation = pt.rewrite.tokenise(lambda query: query.split(" ")) >> pt.terrier.Retrieve()
 
     Example - for non-English languages, tokenise on standard UTF non-alphanumeric characters::
 
-        utftokenised = pt.rewrite.tokenise(pt.TerrierTokeniser.utf)) >> pt.BatchRetrieve()
-        utftokenised = pt.rewrite.tokenise("utf")) >> pt.BatchRetrieve()
+        utftokenised = pt.rewrite.tokenise(pt.TerrierTokeniser.utf)) >> pt.terrier.Retrieve()
+        utftokenised = pt.rewrite.tokenise("utf")) >> pt.terrier.Retrieve()
 
     Example - tokenising queries using a `HuggingFace tokenizer <https://huggingface.co/docs/transformers/fast_tokenizers>`_ ::
 
         # this assumes the index was created in a pretokenised manner
-        br = pt.BatchRetrieve(indexref)
+        br = pt.terrier.Retrieve(indexref)
         tok = AutoTokenizer.from_pretrained("bert-base-uncased")
         query_toks = pt.rewrite.tokenise(tok.tokenize, matchop=True)
         retr_pipe = query_toks >> br
@@ -58,7 +58,7 @@ def _join_str(input : Union[str,List[str]]):
 
     def _join_str_matchop(input : List[str]):
         assert not isinstance(input, str), "Expected a list of strings"
-        return ' '.join(map(pt.BatchRetrieve.matchop, input))
+        return ' '.join(map(pt.terrier.Retrieve.matchop, input))
     
     if matchop:
         return pt.apply.query(lambda r: _join_str_matchop(_query_fn(r.query)))
@@ -76,7 +76,7 @@ def reset() -> pt.Transformer:
 
         Example::
 
-            firststage = pt.rewrite.SDM() >> pt.BatchRetrieve(index, wmodel="DPH")
+            firststage = pt.rewrite.SDM() >> pt.terrier.Retrieve(index, wmodel="DPH")
             secondstage = pyterrier_bert.cedr.CEDRPipeline()
             fullranker = firststage >> pt.rewrite.reset() >> secondstage
 
@@ -368,7 +368,7 @@ class RM3(QueryExpansion):
 
         Example:: 
 
-            bm25 = pt.BatchRetrieve(index, wmodel="BM25")
+            bm25 = pt.terrier.Retrieve(index, wmodel="BM25")
             rm3_pipe = bm25 >> pt.rewrite.RM3(index) >> bm25
             pt.Experiment([bm25, rm3_pipe],
                         dataset.get_topics(),
diff --git a/pyterrier/text.py b/pyterrier/text.py
index 1d622b06..5fc90ae5 100644
--- a/pyterrier/text.py
+++ b/pyterrier/text.py
@@ -28,7 +28,7 @@ def get_text(
 
     Example::
 
-        pipe = ( pt.BatchRetrieve(index, wmodel="DPH")
+        pipe = ( pt.terrier.Retrieve(index, wmodel="DPH")
             >> pt.text.get_text(index)
             >> pt.text.scorer(wmodel="DPH") )
 
@@ -188,7 +188,7 @@ def sliding( text_attr='body', length=150, stride=75, join=' ', prepend_attr='ti
     
     Example::
     
-        pipe = ( pt.BatchRetrieve(index, wmodel="DPH", metadata=["docno", "body"]) 
+        pipe = ( pt.terrier.Retrieve(index, wmodel="DPH", metadata=["docno", "body"]) 
             >> pt.text.sliding(length=128, stride=64, prepend_attr=None) 
             >> pt.text.scorer(wmodel="DPH") 
             >> pt.text.max_passage() )
@@ -196,7 +196,7 @@ def sliding( text_attr='body', length=150, stride=75, join=' ', prepend_attr='ti
         # tokenizer model 
         from transformers import AutoTokenizer
         tok = AutoTokenizer.from_pretrained("bert-base-uncased")
-        pipe = (pt.BatchRetrieve(index, wmodel="DPH", metadata=["docno", "body"])
+        pipe = (pt.terrier.Retrieve(index, wmodel="DPH", metadata=["docno", "body"])
             >> pt.text.sliding(length=128, stride=64, prepend_attr=None, tokenizer=tok)
             >> pt.text.scorer(wmodel="DPH")
             >> pt.text.max_passage() )
@@ -289,7 +289,7 @@ def snippets(
     Example::
 
         # retrieve documents with text
-        br = pt.BatchRetrieve(index, metadata=['docno', 'text'])
+        br = pt.terrier.Retrieve(index, metadata=['docno', 'text'])
 
         # use Tf as a passage scorer on sliding window passages 
         psg_scorer = ( 
diff --git a/pyterrier/transformer.py b/pyterrier/transformer.py
index 7d1c55e3..7103eff6 100644
--- a/pyterrier/transformer.py
+++ b/pyterrier/transformer.py
@@ -64,8 +64,8 @@ def identity() -> 'Transformer':
         This can be useful for adding the candidate ranking score
         as a feature in for learning-to-rank::
 
-            bm25 = pt.BatchRetrieve(index, wmodel="BM25")
-            two_feat_pipe = bm25 >> pt.Transformer.identify() ** pt.BatchRetrieve(index, wmodel="PL2")
+            bm25 = pt.terrier.Retrieve(index, wmodel="BM25")
+            two_feat_pipe = bm25 >> pt.Transformer.identify() ** pt.terrier.Retrieve(index, wmodel="PL2")
         
         This will return a pipeline that produces a score column (BM25), but also has a features column containing
         BM25 and PL2 scores.
@@ -155,7 +155,7 @@ def search(self, query : str, qid : str = "1", sort=True) -> pd.DataFrame:
 
             Example::
 
-                bm25 = pt.BatchRetrieve(index, wmodel="BM25")
+                bm25 = pt.terrier.Retrieve(index, wmodel="BM25")
                 res = bm25.search("example query")
 
                 # is equivalent to

From 94fd5ae9ba9bdd467ed7aa973d84fe49f5956fd7 Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Wed, 7 Aug 2024 14:01:40 +0100
Subject: [PATCH 129/153] fix typo comment

---
 pyterrier/java/_utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyterrier/java/_utils.py b/pyterrier/java/_utils.py
index 618bd7d0..338be36e 100644
--- a/pyterrier/java/_utils.py
+++ b/pyterrier/java/_utils.py
@@ -331,7 +331,7 @@ def parallel_init_args() -> Tuple[bool, Dict[str, Dict[str, Any]]]:
 # We need a global store of all java-related configurations
 # so that when running in parallel, we can set everying back
 # up the same way it started. These utils help manage this
-# gloabal configuration.
+# global configuration.
 # ----------------------------------------------------------
 
 class Configuration:

From 3a3b74ef8fc414b204c48c088382fe3493f16e2e Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Fri, 9 Aug 2024 15:21:40 +0100
Subject: [PATCH 130/153] always load prf package

---
 docs/installation.rst         |  2 +-
 docs/pipeline_examples.md     |  2 --
 pyterrier/terrier/__init__.py |  4 ++--
 pyterrier/terrier/java.py     | 21 ++++-----------------
 pyterrier/terrier/rewrite.py  | 11 ++---------
 tests/base.py                 |  1 -
 6 files changed, 9 insertions(+), 32 deletions(-)

diff --git a/docs/installation.rst b/docs/installation.rst
index 8b9db2de..aa7fcdee 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -91,7 +91,7 @@ These options adjust how the Terrier engine is loaded.
 
 .. autofunction:: pyterrier.terrier.set_version
 .. autofunction:: pyterrier.terrier.set_helper_version
-.. autofunction:: pyterrier.terrier.enable_prf
+.. autofunction:: pyterrier.terrier.set_prf_version
 .. autofunction:: pyterrier.terrier.set_property
 .. autofunction:: pyterrier.terrier.set_properties
 .. autofunction:: pyterrier.terrier.extend_classpath
diff --git a/docs/pipeline_examples.md b/docs/pipeline_examples.md
index 2dcab7d7..f3e86d8c 100644
--- a/docs/pipeline_examples.md
+++ b/docs/pipeline_examples.md
@@ -47,10 +47,8 @@ pipe = (pt.BatchRetrieve(wikipedia_index, wmodel="BM25") >>
 ### RM3 Query Expansion
 
 We also provide RM3 query expansion, using an external plugin to Terrier called [terrier-prf](https://github.com/terrierteam/terrier-prf).
-You need to call `pt.terrier.enable_prf()` to download and load this plugin.
 
 ```python
-pt.terrier.enable_prf() # This is required before Java starts
 pipe = (pt.BatchRetrieve(indexref, wmodel="BM25") >> 
     pt.rewrite.RM3(indexref) >> 
     pt.BatchRetrieve(indexref, wmodel="BM25")
diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
index 660b100b..33dc726f 100644
--- a/pyterrier/terrier/__init__.py
+++ b/pyterrier/terrier/__init__.py
@@ -1,6 +1,6 @@
 # java stuff
 from pyterrier.terrier import java
-from pyterrier.terrier.java import configure, set_version, set_helper_version, enable_prf, extend_classpath, J, set_property, set_properties, run, version, check_version, check_helper_version
+from pyterrier.terrier.java import configure, set_version, set_helper_version, set_prf_version, extend_classpath, J, set_property, set_properties, run, version, check_version, check_helper_version
 from pyterrier.terrier.retriever import BatchRetrieve, FeaturesBatchRetrieve, TextScorer
 from pyterrier.terrier.index_factory import IndexFactory
 from pyterrier.terrier.stemmer import TerrierStemmer
@@ -15,7 +15,7 @@
 
 __all__ = [
     # java stuff
-    'java', 'configure', 'set_version', 'set_helper_version', 'enable_prf', 'extend_classpath', 'J', 'version', 'check_version', 'check_helper_version',
+    'java', 'configure', 'set_version', 'set_helper_version', 'set_prf_version', 'extend_classpath', 'J', 'version', 'check_version', 'check_helper_version',
 
     # retrieval
     'BatchRetrieve', 'TerrierRetrieve', 'FeaturesBatchRetrieve', 'TextScorer',
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 326eaaa5..422121d3 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -17,7 +17,7 @@
     'helper_version': None,
     'boot_packages': [],
     'force_download': True,
-    'prf_version': None,
+    'prf_version': '-SNAPSHOT',
 })
 
 
@@ -32,22 +32,10 @@ def set_helper_version(version: Optional[str] = None):
 
 
 @pt.java.before_init
-def enable_prf(version: Optional[str] = '-SNAPSHOT'):
+def set_prf_version(version: Optional[str] = None):
     configure['prf_version'] = version
 
 
-@pt.utils.pre_invocation_decorator
-def prf_required(fn: Callable):
-    """
-    Requires prf to be enabled (raises error if not enabled).
-
-    Can be used as a function/class @decorator. When used as a class decorator, it
-    is applied to all methods defined by the class.
-    """
-    if not configure['prf_version']:
-        raise RuntimeError('you need to call pt.terrier.enable_prf() before java is loaded to use this function.')
-
-
 class TerrierJavaInit(pt.java.JavaInitializer):
     def pre_init(self, jnius_config):
         # Make sure the terrier.default.properties file exists and is registered as an option, which avoids an annoying
@@ -84,9 +72,8 @@ def pre_init(self, jnius_config):
         helper_jar = pt.java.mavenresolver.get_package_jar(TERRIER_PKG, 'terrier-python-helper', helper_version)
         jnius_config.add_classpath(helper_jar)
 
-        if configure['prf_version'] is not None:
-            prf_jar = pt.java.mavenresolver.get_package_jar('com.github.terrierteam', 'terrier-prf', configure['prf_version'])
-            jnius_config.add_classpath(prf_jar)
+        prf_jar = pt.java.mavenresolver.get_package_jar('com.github.terrierteam', 'terrier-prf', configure['prf_version'])
+        jnius_config.add_classpath(prf_jar)
 
 
     @pt.java.required_raise
diff --git a/pyterrier/terrier/rewrite.py b/pyterrier/terrier/rewrite.py
index bdf41665..002de10a 100644
--- a/pyterrier/terrier/rewrite.py
+++ b/pyterrier/terrier/rewrite.py
@@ -4,7 +4,6 @@
 from types import FunctionType
 import pyterrier as pt
 from pyterrier.terrier.index import TerrierTokeniser
-from pyterrier.terrier.java import prf_required
 
 @pt.java.required
 def tokenise(tokeniser : Union[str,TerrierTokeniser,FunctionType] = 'english', matchop=False) -> pt.Transformer:
@@ -350,13 +349,10 @@ def __init__(self, *args, **kwargs):
         kwargs["qemodel"] = "KL"
         super().__init__(*args, **kwargs)
 
-@prf_required
 @pt.java.required
 class RM3(QueryExpansion):
     '''
-        Performs query expansion using RM3 relevance models. RM3 relies on an external Terrier plugin, 
-        `terrier-prf <https://github.com/terrierteam/terrier-prf/>`_. You need to call ``pt.terrier.enable_prf()``
-        before RM3 is available.
+        Performs query expansion using RM3 relevance models.
 
         This transformer must be followed by a Terrier Retrieve() transformer.
         The original query is saved in the `"query_0"` column, which can be restored using `pt.rewrite.reset()`.
@@ -408,13 +404,10 @@ def transform(self, queries_and_docs):
         self.qe.fbDocs = self.fb_docs
         return super().transform(queries_and_docs)
 
-@prf_required
 @pt.java.required
 class AxiomaticQE(QueryExpansion):
     '''
-        Performs query expansion using axiomatic query expansion. This class relies on an external Terrier plugin, 
-        `terrier-prf <https://github.com/terrierteam/terrier-prf/>`_. You need to call ``pt.terrier.enable_prf()``
-        before AxiomaticQE is available.
+        Performs query expansion using axiomatic query expansion.
 
         This transformer must be followed by a Terrier Retrieve() transformer.
         The original query is saved in the `"query_0"` column, which can be restored using `pt.rewrite.reset()`.
diff --git a/tests/base.py b/tests/base.py
index 5d18dec6..71dfa6c7 100644
--- a/tests/base.py
+++ b/tests/base.py
@@ -20,7 +20,6 @@ def __init__(self, *args, **kwargs):
             
             pt.terrier.set_version(terrier_version)
             pt.terrier.set_helper_version(terrier_helper_version)
-            pt.terrier.enable_prf()
             pt.java.set_log_level("DEBUG")
             # pt.java.add_option('-ea') can be added here to ensure that all Java assertions are met
             pt.java.init()

From 34ef71c147200365d445eba12161e998d3dcc2cf Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Fri, 9 Aug 2024 16:44:46 +0100
Subject: [PATCH 131/153] split out parallel tests into a separate gh action

---
 .../{anserini.yml => test-anserini.yml}       |  0
 .github/workflows/test-parallel.yaml          | 72 +++++++++++++++++++
 tests/base.py                                 |  1 +
 tests/test_grid.py                            |  3 +-
 tests/test_parallel.py                        |  5 +-
 tests/test_pool.py                            |  3 +-
 6 files changed, 81 insertions(+), 3 deletions(-)
 rename .github/workflows/{anserini.yml => test-anserini.yml} (100%)
 create mode 100644 .github/workflows/test-parallel.yaml

diff --git a/.github/workflows/anserini.yml b/.github/workflows/test-anserini.yml
similarity index 100%
rename from .github/workflows/anserini.yml
rename to .github/workflows/test-anserini.yml
diff --git a/.github/workflows/test-parallel.yaml b/.github/workflows/test-parallel.yaml
new file mode 100644
index 00000000..fd98beb2
--- /dev/null
+++ b/.github/workflows/test-parallel.yaml
@@ -0,0 +1,72 @@
+name: Continuous Testing of Parallel Components
+
+on:
+  push:
+    branches: [ master ]
+  pull_request:
+    branches: [ master ]
+
+jobs:
+  build:
+    
+    strategy:
+      matrix:
+        python-version: ['3.8', '3.11']
+        java: [11, 13]
+        os: ['ubuntu-latest']
+        terrier: ['snapshot']
+
+    runs-on: ${{ matrix.os }}
+    steps:
+
+    - name: Setup dependencies for xgBoost on macOs-latest
+      if: matrix.os == 'macOs-latest'
+      run: |
+        brew install libomp
+
+    - uses: actions/checkout@v4
+    
+    - name: Set up Python ${{ matrix.python-version }}
+      if: matrix.os != 'self-hosted'
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+    
+    - name: Setup java
+      if: matrix.os != 'self-hosted'
+      uses: actions/setup-java@v4
+      with:
+        java-version: ${{ matrix.java }}
+        distribution: 'zulu'
+    
+    - name: Install Terrier snapshot
+      if: matrix.terrier == '5.4-SNAPSHOT'
+      run: |
+        git clone https://github.com/terrier-org/terrier-core.git
+        cd terrier-core
+        mvn -B -DskipTests install
+
+    # follows https://medium.com/ai2-blog/python-caching-in-github-actions-e9452698e98d
+    - name: Loading Python & dependencies from cache
+      if: matrix.os != 'self-hosted'
+      uses: actions/cache@v4
+      with:
+        path: ${{ env.pythonLocation }}
+        key: ${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('requirements.txt') }}-${{ hashFiles('requirements-test.txt') }}
+
+    - name: Install Python dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install --upgrade --upgrade-strategy eager -r requirements.txt
+        pip install --upgrade --upgrade-strategy eager -r requirements-test.txt
+        #install this software
+        pip install --timeout=120 .
+        pip install pytest
+
+    - name: All unit tests
+      env:
+        TERRIER_VERSION: ${{ matrix.terrier }}
+        PARALLEL_TESTING: '1'
+      run: |
+        pytest --durations=20 -p no:faulthandler tests/test_grid.py tests/test_grid.py tests/test_parallel.py tests/test_pool.py
+
diff --git a/tests/base.py b/tests/base.py
index 71dfa6c7..13ec23f9 100644
--- a/tests/base.py
+++ b/tests/base.py
@@ -40,3 +40,4 @@ def tearDown(self):
             pass
 
     
+parallel_test = unittest.skipIf(os.environ.get("PARALLEL_TESTING") is None, "Parallel test disabled, enable with PARALLEL_TESTING=1")
diff --git a/tests/test_grid.py b/tests/test_grid.py
index 233796d8..80dcc654 100644
--- a/tests/test_grid.py
+++ b/tests/test_grid.py
@@ -1,6 +1,6 @@
 import pandas as pd
 import pyterrier as pt
-from .base import BaseTestCase
+from .base import BaseTestCase, parallel_test
 
 
 class TestGrid(BaseTestCase):
@@ -68,6 +68,7 @@ def test_gridscan_1param_df(self):
         self.assertTrue(isinstance(rtr, pd.DataFrame))
         #print(rtr)
 
+    @parallel_test
     def test_gridscan_joblib2(self):
         self.skip_windows()
         dataset = pt.get_dataset("vaswani")
diff --git a/tests/test_parallel.py b/tests/test_parallel.py
index 2ce82124..c5a2fff5 100644
--- a/tests/test_parallel.py
+++ b/tests/test_parallel.py
@@ -1,7 +1,8 @@
-from .base import BaseTestCase
+from .base import BaseTestCase, parallel_test
 import pyterrier as pt
 class TestParallel(BaseTestCase):
 
+    @parallel_test
     def test_parallel_joblib_experiment(self):
         self.skip_windows()
         dataset = pt.get_dataset("vaswani")
@@ -14,6 +15,7 @@ def test_parallel_joblib_experiment(self):
         )
         self.assertEqual(df.iloc[0]["map"], df.iloc[1]["map"])
 
+    @parallel_test
     def test_parallel_joblib_experiment_br_callback(self):
         self.skip_windows()
         dataset = pt.get_dataset("vaswani")
@@ -27,6 +29,7 @@ def test_parallel_joblib_experiment_br_callback(self):
         )
         self.assertEqual(df.iloc[0]["map"], df.iloc[1]["map"])
 
+    @parallel_test
     def test_parallel_joblib_ops(self):
         self.skip_windows()
         dataset = pt.get_dataset("vaswani")
diff --git a/tests/test_pool.py b/tests/test_pool.py
index 3c955499..6c18fcd9 100644
--- a/tests/test_pool.py
+++ b/tests/test_pool.py
@@ -1,10 +1,11 @@
-from .base import BaseTestCase
+from .base import BaseTestCase, parallel_test
 import pandas as pd
 import pickle
 import pyterrier as pt
 import datetime
 class TestPool(BaseTestCase):
 
+    @parallel_test
     def test_br_parallel(self):
         self.skip_windows()
         vaswani = pt.datasets.get_dataset("vaswani")

From 5e8f5b41ab6f9d06b4fbe12ccf5b61545f8fe0ed Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 10 Aug 2024 13:35:15 +0100
Subject: [PATCH 132/153] move retrieve.py back to retriever.py

---
 pyterrier/terrier/{retrieve.py => retriever.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename pyterrier/terrier/{retrieve.py => retriever.py} (100%)

diff --git a/pyterrier/terrier/retrieve.py b/pyterrier/terrier/retriever.py
similarity index 100%
rename from pyterrier/terrier/retrieve.py
rename to pyterrier/terrier/retriever.py

From 12fef4958ca384f34c02c97010109e210e365ec8 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 10 Aug 2024 13:41:16 +0100
Subject: [PATCH 133/153] Retrieve -> Retriever

---
 README.md                                     |  8 ++--
 docs/anserini.rst                             |  4 +-
 docs/apply.rst                                |  4 +-
 docs/datamodel.md                             |  2 +-
 docs/datasets.rst                             | 12 +++---
 docs/experiments.rst                          |  4 +-
 docs/experiments/Robust04.md                  |  8 ++--
 docs/extras/generate_includes.py              |  4 +-
 docs/ltr.rst                                  | 14 +++----
 docs/operators.rst                            |  2 +-
 docs/parallel.rst                             | 14 +++----
 docs/pipeline_examples.md                     | 36 ++++++++---------
 docs/rewrite.rst                              | 14 +++----
 docs/terrier-index-api.rst                    |  2 +-
 docs/terrier-indexing.rst                     |  4 +-
 docs/terrier-retrieval.rst                    | 40 +++++++++----------
 docs/text.rst                                 |  6 +--
 docs/transformer.rst                          |  2 +-
 docs/tuning.rst                               |  8 ++--
 examples/experiments/Robust04.ipynb           |  8 ++--
 examples/notebooks/experiment.ipynb           |  6 +--
 examples/notebooks/indexing.ipynb             |  4 +-
 examples/notebooks/ltr.ipynb                  | 10 ++---
 examples/notebooks/non_en_retrieval.ipynb     |  6 +--
 .../notebooks/retrieval_and_evaluation.ipynb  |  2 +-
 .../notebooks/sentence_transformers.ipynb     |  2 +-
 pyterrier/apply.py                            | 14 +++----
 pyterrier/apply_base.py                       | 12 +++---
 pyterrier/cache.py                            |  2 +-
 pyterrier/ltr.py                              |  6 +--
 pyterrier/ops.py                              |  6 +--
 pyterrier/pipelines.py                        |  4 +-
 pyterrier/terrier/__init__.py                 | 10 ++---
 pyterrier/terrier/java.py                     |  2 +-
 pyterrier/terrier/retriever.py                | 18 ++++-----
 pyterrier/terrier/rewrite.py                  | 18 ++++-----
 pyterrier/text.py                             | 10 ++---
 pyterrier/transformer.py                      |  6 +--
 tests/test_dunder.py                          |  2 +-
 tests/test_operators.py                       |  2 +-
 tests/test_scoring.py                         |  8 ++--
 41 files changed, 173 insertions(+), 173 deletions(-)

diff --git a/README.md b/README.md
index a33fd144..6fcf522b 100644
--- a/README.md
+++ b/README.md
@@ -36,7 +36,7 @@ See the [indexing documentation](https://pyterrier.readthedocs.io/en/latest/terr
 ```python
 topics = pt.io.read_topics(topicsFile)
 qrels = pt.io.read_qrels(qrelsFile)
-BM25_br = pt.terrier.Retrieve(index, wmodel="BM25")
+BM25_br = pt.terrier.Retriever(index, wmodel="BM25")
 res = BM25_br.transform(topics)
 pt.Evaluate(res, qrels, metrics = ['map'])
 ```
@@ -56,7 +56,7 @@ There is a worked example in the [experiment notebook](examples/notebooks/experi
 
 PyTerrier makes it easy to develop complex retrieval pipelines using Python operators such as `>>` to chain different retrieval components. Each retrieval approach is a [transformer](https://pyterrier.readthedocs.io/en/latest/transformer.html), having one key method, `transform()`, which takes a single Pandas dataframe as input, and returns another dataframe. Two examples might encapsulate applying the sequential dependence model, or a query expansion process:
 ```python
-sdm_bm25 = pt.rewrite.SDM() >> pt.terrier.Retrieve(indexref, wmodel="BM25")
+sdm_bm25 = pt.rewrite.SDM() >> pt.terrier.Retriever(indexref, wmodel="BM25")
 bo1_qe = BM25_br >> pt.rewrite.Bo1QueryExpansion() >> BM25_br
 ```
 
@@ -83,8 +83,8 @@ You can see examples of how to use these, including notebooks that run on Google
 Complex learning to rank pipelines, including for learning-to-rank, can be constructed using PyTerrier's operator language. For example, to combine two features and make them available for learning, we can use the `**` operator.
 ```python
 two_features = BM25_br >> ( 
-  pt.terrier.Retrieve(indexref, wmodel="DirichletLM") ** 
-  pt.terrier.Retrieve(indexref, wmodel="PL2") 
+  pt.terrier.Retriever(indexref, wmodel="DirichletLM") ** 
+  pt.terrier.Retriever(indexref, wmodel="PL2") 
 )
 ```
 
diff --git a/docs/anserini.rst b/docs/anserini.rst
index f65c12b4..f9ae5407 100644
--- a/docs/anserini.rst
+++ b/docs/anserini.rst
@@ -16,7 +16,7 @@ Comparative retrieval from Anserini and Terrier::
     trIndex = "/path/to/data.properties"
     luceneIndex "/path/to/lucene-index-dir"
 
-    BM25_tr = pt.terrier.Retrieve(trIndex, wmodel="BM25")
+    BM25_tr = pt.terrier.Retriever(trIndex, wmodel="BM25")
     BM25_ai = pt.anserini.AnseriniBatchRetrieve(luceneIndex, wmodel="BM25")
 
     pt.Experiment([BM25_tr, BM25_ai], topics, qrels, eval_metrics=["map"])
@@ -24,7 +24,7 @@ Comparative retrieval from Anserini and Terrier::
 
 AnseriniBatchRetrieve can also be used as a re-ranker::
 
-    BM25_tr = pt.terrrier.Retrieve(trIndex, wmodel="BM25")
+    BM25_tr = pt.terrier.Retrieverr(trIndex, wmodel="BM25")
     QLD_ai = pt.anserini.AnseriniBatchRetrieve(luceneIndex, wmodel="QLD")
 
     pipe = BM25_tr >> QLD_ai
diff --git a/docs/apply.rst b/docs/apply.rst
index 937ce10a..61f9f732 100644
--- a/docs/apply.rst
+++ b/docs/apply.rst
@@ -85,9 +85,9 @@ Its also possible to construct a transformer that makes a new column on a row-wi
 
 For instance, if the column you are creating is called rank_2, it might be created as follows::
 
-    pipe = pt.terrrier.Retrieve(index) >> pt.apply.rank_2(lambda row: row["rank"] * 2)
+    pipe = pt.terrier.Retrieverr(index) >> pt.apply.rank_2(lambda row: row["rank"] * 2)
 
 To create a transformer that drops a column, you can instead pass `drop=True` as a kwarg::
 
-    pipe = pt.terrrier.Retrieve(index, metadata=["docno", "text"] >> pt.text.scorer() >> pt.apply.text(drop=True)
+    pipe = pt.terrier.Retrieverr(index, metadata=["docno", "text"] >> pt.text.scorer() >> pt.apply.text(drop=True)
 
diff --git a/docs/datamodel.md b/docs/datamodel.md
index 275c7324..eeaf42ee 100644
--- a/docs/datamodel.md
+++ b/docs/datamodel.md
@@ -51,7 +51,7 @@ A dataframe representing which documents are retrieved and scored for a given qu
 
 Note that rank is computed by sorting by qid ascending, then score descending. The first rank for each query is 0. The `pyterrier.model.add_rank()` function is used for adding the rank column. 
 
-Optional columns might support additional transformers, such as text (for the contents of the documents), url or title columns. Their presence can facilitate more advanced transformers, such as BERT-based transformers which operate on the raw text of the documents. For instance, if the Terrier index has additional metadata attributes, these can be included by BatchRetrieve using the `metadata` kwarg, i.e. `pt.terrier.Retrieve(index, metadata=["docno", "title", "body"])`. 
+Optional columns might support additional transformers, such as text (for the contents of the documents), url or title columns. Their presence can facilitate more advanced transformers, such as BERT-based transformers which operate on the raw text of the documents. For instance, if the Terrier index has additional metadata attributes, these can be included by BatchRetrieve using the `metadata` kwarg, i.e. `pt.terrier.Retriever(index, metadata=["docno", "title", "body"])`. 
 
 Note that the retrieved documents is a subset of the cartesian product of documents and queries; it is important that the query (text) attribute is present for at least ONE document rather than all documents for a given query.
 
diff --git a/docs/datasets.rst b/docs/datasets.rst
index 249f4e71..bc23718d 100644
--- a/docs/datasets.rst
+++ b/docs/datasets.rst
@@ -27,8 +27,8 @@ Many of the PyTerrier unit tests are based on the `Vaswani NPL test collection <
 PyTerrier provides a ready-made index on the `Terrier Data Repository <http://data.terrier.org/>`_. This allows experiments to be easily conducted::
 
     dataset = pt.get_dataset("vaswani")
-    bm25 = pt.terrier.Retrieve.from_dataset(dataset, "terrier_stemmed", wmodel="BM25")
-    dph = pt.terrier.Retrieve.from_dataset(dataset, "terrier_stemmed", wmodel="DPH")
+    bm25 = pt.terrier.Retriever.from_dataset(dataset, "terrier_stemmed", wmodel="BM25")
+    dph = pt.terrier.Retriever.from_dataset(dataset, "terrier_stemmed", wmodel="DPH")
     pt.Experiment(
         [bm25, dph],
         dataset.get_topics(),
@@ -44,8 +44,8 @@ Indexing and then retrieval of documents from the `MSMARCO document corpus <http
     indexref = indexer.index(dataset.get_corpus())
     index = pt.IndexFactory.of(indexref)
 
-    DPH_br = pt.terrier.Retrieve(index, wmodel="DPH") % 100
-    BM25_br = pt.terrier.Retrieve(index, wmodel="BM25") % 100
+    DPH_br = pt.terrier.Retriever(index, wmodel="DPH") % 100
+    BM25_br = pt.terrier.Retriever(index, wmodel="BM25") % 100
     # this runs an experiment to obtain results on the TREC 2019 Deep Learning track queries and qrels
     pt.Experiment(
         [DPH_br, BM25_br], 
@@ -62,8 +62,8 @@ You can also index datasets that include a corpus using IterDictIndexer and get_
     indexref = indexer.index(dataset.get_corpus_iter(), fields=('title', 'abstract'))
     index = pt.IndexFactory.of(indexref)
 
-    DPH_br = pt.terrier.Retrieve(index, wmodel="DPH") % 100
-    BM25_br = pt.terrier.Retrieve(index, wmodel="BM25") % 100
+    DPH_br = pt.terrier.Retriever(index, wmodel="DPH") % 100
+    BM25_br = pt.terrier.Retriever(index, wmodel="BM25") % 100
     # this runs an experiment to obtain results on the TREC COVID queries and qrels
     pt.Experiment(
         [DPH_br, BM25_br], 
diff --git a/docs/experiments.rst b/docs/experiments.rst
index a8e21d7d..c8c22749 100644
--- a/docs/experiments.rst
+++ b/docs/experiments.rst
@@ -27,8 +27,8 @@ Getting average effectiveness over a set of topics::
     # vaswani dataset provides an index, topics and qrels
 
     # lets generate two BRs to compare
-    tfidf = pt.terrier.Retrieve(dataset.get_index(), wmodel="TF_IDF")
-    bm25 = pt.terrier.Retrieve(dataset.get_index(), wmodel="BM25")
+    tfidf = pt.terrier.Retriever(dataset.get_index(), wmodel="TF_IDF")
+    bm25 = pt.terrier.Retriever(dataset.get_index(), wmodel="BM25")
 
     pt.Experiment(
         [tfidf, bm25],
diff --git a/docs/experiments/Robust04.md b/docs/experiments/Robust04.md
index 1a33dd4f..52571116 100644
--- a/docs/experiments/Robust04.md
+++ b/docs/experiments/Robust04.md
@@ -34,10 +34,10 @@ Here we define and evaluate standard weighting models.
 
 ```python
 
-BM25 = pt.terrier.Retrieve(index, wmodel="BM25")
-DPH  = pt.terrier.Retrieve(index, wmodel="DPH")
-PL2  = pt.terrier.Retrieve(index, wmodel="PL2")
-DLM  = pt.terrier.Retrieve(index, wmodel="DirichletLM")
+BM25 = pt.terrier.Retriever(index, wmodel="BM25")
+DPH  = pt.terrier.Retriever(index, wmodel="DPH")
+PL2  = pt.terrier.Retriever(index, wmodel="PL2")
+DLM  = pt.terrier.Retriever(index, wmodel="DirichletLM")
 
 pt.Experiment(
     [BM25, DPH, PL2, DLM],
diff --git a/docs/extras/generate_includes.py b/docs/extras/generate_includes.py
index cc2b01e8..42dd4ddf 100644
--- a/docs/extras/generate_includes.py
+++ b/docs/extras/generate_includes.py
@@ -49,8 +49,8 @@ def experiment_includes():
                 os.path.join(tempfile.gettempdir(), "vaswani_index")
             ).index(pt.get_dataset('vaswani').get_corpus_iter())
 
-    tfidf = pt.terrier.Retrieve(indexref, wmodel="TF_IDF")
-    bm25 = pt.terrier.Retrieve(indexref, wmodel="BM25")
+    tfidf = pt.terrier.Retriever(indexref, wmodel="TF_IDF")
+    bm25 = pt.terrier.Retriever(indexref, wmodel="BM25")
 
     table = pt.Experiment(
         [tfidf, bm25],
diff --git a/docs/ltr.rst b/docs/ltr.rst
index 91cb2057..238e8198 100644
--- a/docs/ltr.rst
+++ b/docs/ltr.rst
@@ -14,7 +14,7 @@ PyTerrier makes it easy to formulate learning to rank pipelines. Conceptually, l
 
 PyTerrier allows each of these phases to be expressed as transformers, and for them to be composed into a full pipeline.  
 
-In particular, conventional retrieval transformers (such as `pt.terrier.Retrieve`) can be used for the first phase.
+In particular, conventional retrieval transformers (such as `pt.terrier.Retriever`) can be used for the first phase.
 To permit the second phase, PyTerrier data model allows for a `"features"` column to be associated to each retrieved document. 
 Such features can be generated using specialised transformers, or by combining other re-ranking transformers using the  `**`
 feature-union operator; Lastly, to facilitate the final phase, we provide easy ways to integrate PyTerrier pipelines with standard learning libraries
@@ -34,9 +34,9 @@ PyTerrier's main way to faciliate calculating and intgrating extra features is t
 the candidate set should be identified using the BM25 weighting model, and then additional features computed using the
 Tf and PL2 models::
 
-    bm25 = pt.terrier.Retrieve(index, wmodel="BM25")
-    tf = pt.terrier.Retrieve(index, wmodel="Tf")
-    pl2 = pt.terrier.Retrieve(index, wmodel="PL2")
+    bm25 = pt.terrier.Retriever(index, wmodel="BM25")
+    tf = pt.terrier.Retriever(index, wmodel="Tf")
+    pl2 = pt.terrier.Retriever(index, wmodel="PL2")
     pipeline = bm25 >> (tf ** pl2)
 
 The output of the bm25 ranker would look like:
@@ -65,16 +65,16 @@ FeaturesBatchRetrieve
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 When executing the pipeline above, the re-ranking of the documents again can be slow, as each separate Retrieve
-object has to re-access the inverted index. For this reason, PyTerrier provides a class called FeaturesRetrieve,
+object has to re-access the inverted index. For this reason, PyTerrier provides a class called FeaturesRetriever,
 which allows multiple query dependent features to be calculated at once, by virtue of Terrier's Fat framework.
 
-.. autoclass:: pyterrier.terrier.FeaturesRetrieve
+.. autoclass:: pyterrier.terrier.FeaturesRetriever
     :members: transform 
 
 An equivalent pipeline to the example above would be::
 
     #pipeline = bm25 >> (tf ** pl2)
-    pipeline = pyterrier.terrier.FeaturesRetrieve(index, wmodel="BM25", features=["WMODEL:Tf", "WMODEL:PL2"])
+    pipeline = pyterrier.terrier.FeaturesRetriever(index, wmodel="BM25", features=["WMODEL:Tf", "WMODEL:PL2"])
 
 
 Apply Functions for Custom Features
diff --git a/docs/operators.rst b/docs/operators.rst
index 7d128259..5a553c35 100644
--- a/docs/operators.rst
+++ b/docs/operators.rst
@@ -187,7 +187,7 @@ Rank Cutoff (`%`)
 
 The `%` operator is called rank cutoff, and limits the number of results for each query::
 
-    pipe1 = pt.terrier.Retrieve(index, "BM25") % 2
+    pipe1 = pt.terrier.Retriever(index, "BM25") % 2
 
 **Example:**
 
diff --git a/docs/parallel.rst b/docs/parallel.rst
index 62e3b11e..f13745ba 100644
--- a/docs/parallel.rst
+++ b/docs/parallel.rst
@@ -26,7 +26,7 @@ Parallelisation using Joblib
 
 A transformer pipeline can be parallelised by using the .parallel() transformer method::
 
-    dph = pt.terrier.Retrieve(index, wmodel='DPH')
+    dph = pt.terrier.Retriever(index, wmodel='DPH')
     dph_fast = dph.parallel(2)
 
 In this way, any set of queries passed to dph_fast will be separated into two partitions, based on qid, and executed on dph.
@@ -40,7 +40,7 @@ a similar way as for joblib::
 
     import ray
     ray.init() #configure Ray as per your cluster setup
-    dph = pt.terrier.Retrieve(index, wmodel='DPH')
+    dph = pt.terrier.Retriever(index, wmodel='DPH')
     dph_fast = dph.parallel(2, backend='ray')
 
 In particular, `ray.init()` must have been called before calling `.parallel()`.
@@ -51,7 +51,7 @@ What to Parallelise
 Only transformers that can be `pickled <https://docs.python.org/3/library/pickle.html>`_. Transformers that use native code
 may not be possible to pickle. Some standard PyTerrier transformers have additional support for parallelisation:
 
- - Terrier retrieval: pt.terrier.Retrieve(), pt.FeaturesBatchRetrieve()
+ - Terrier retrieval: pt.terrier.Retriever(), pt.FeaturesBatchRetrieve()
  - Anserini retrieval: pt.anserini.AnseriniBatchRetrieve()
 
 Pure python transformers, such as `pt.text.sliding()` are picklable. However, parallelising only `pt.text.sliding()` may not produce
@@ -61,14 +61,14 @@ Entire transformer pipelines (i.e. combined using operators) can be pickled and
 the most inefficient component of your process, while also minimising the amount of data being transferred between processes. For instance,
 consider the following pipeline::
 
-    pipe = pt.terrier.Retrieve(index, metadata=["docno", "text"] >> pt.text.sliding() >> pt.text.scorer() >> pt.text.max_passage()
+    pipe = pt.terrier.Retriever(index, metadata=["docno", "text"] >> pt.text.sliding() >> pt.text.scorer() >> pt.text.max_passage()
 
 While BatchRetrieve might represent the slowest component of the pipeline, it might make sense to parallelise pipe as a whole,
 rather than just BatchRetrieve, as then only the queries and final results  need to be passed betwene processes. Indeed among the
 following semantically equivalent pipelines, we expect `parallel_pipe0`  and `parallel_pipe2`  to be faster than `parallel_pipe1`::
 
-    parallel_pipe0 = pt.terrier.Retrieve(index, metadata=["docno", "text"]).parallel() >> pt.text.sliding() >> pt.text.scorer() >> pt.text.max_passage()
-    parallel_pipe1 = ( pt.terrier.Retrieve(index, metadata=["docno", "text"]).parallel() >> pt.text.sliding() ).parallel(2)  >> pt.text.max_passage()
+    parallel_pipe0 = pt.terrier.Retriever(index, metadata=["docno", "text"]).parallel() >> pt.text.sliding() >> pt.text.scorer() >> pt.text.max_passage()
+    parallel_pipe1 = ( pt.terrier.Retriever(index, metadata=["docno", "text"]).parallel() >> pt.text.sliding() ).parallel(2)  >> pt.text.max_passage()
     parallel_pipe2 = pipe.parallel(2)
 
 
@@ -84,5 +84,5 @@ Outlook
 =======
 
 We expect to integate parallelisation at different parts of the PyTerrier platform, such as for conducting a gridsearch. Moreover, we hope 
-that proper integration of multi-threaded retrieval in pt.terrier.Retrieve() (while requires upstream improvements in the underlying Terrier platform) 
+that proper integration of multi-threaded retrieval in pt.terrier.Retriever() (while requires upstream improvements in the underlying Terrier platform) 
 will reduce the need for this form of parallelisation.
diff --git a/docs/pipeline_examples.md b/docs/pipeline_examples.md
index 4d28d1a9..07a5ea44 100644
--- a/docs/pipeline_examples.md
+++ b/docs/pipeline_examples.md
@@ -5,7 +5,7 @@
 ### Sequential Dependence Model
 
 ```python
-pipe = pt.rewrite.SDM() >> pt.terrier.Retrieve(indexref, wmodel="BM25")
+pipe = pt.rewrite.SDM() >> pt.terrier.Retriever(indexref, wmodel="BM25")
 ```
 
 Note that the SDM() rewriter has a number of constructor parameters:
@@ -17,16 +17,16 @@ Note that the SDM() rewriter has a number of constructor parameters:
 
 A simple QE transformer can be achieved using
 ```python
-qe = pt.terrier.Retrieve(indexref, wmodel="BM25", controls={"qe" : "on"})
+qe = pt.terrier.Retriever(indexref, wmodel="BM25", controls={"qe" : "on"})
 ```
 
 As this is pseudo-relevance feedback in nature, it identifies a set of documents, extracts informative term in the top-ranked documents, and re-exectutes the query.
 
 However, more control can be achieved by using the QueryExpansion transformer separately, as thus:
 ```python
-qe = (pt.terrier.Retrieve(indexref, wmodel="BM25") >> 
+qe = (pt.terrier.Retriever(indexref, wmodel="BM25") >> 
     pt.rewrite.QueryExpansion(indexref) >> 
-    pt.terrier.Retrieve(indexref, wmodel="BM25")
+    pt.terrier.Retriever(indexref, wmodel="BM25")
 )
 ```
 
@@ -38,9 +38,9 @@ The QueryExpansion() object has the following constructor parameters:
 Note that different indexes can be used to achieve query expansion using an external collection (sometimes called collection enrichment or external feedback).  For example, to expand queries using Wikipedia as an external resource, in order to get higher quality query re-weighted queries, would look like this:
 
 ```python
-pipe = (pt.terrier.Retrieve(wikipedia_index, wmodel="BM25") >> 
+pipe = (pt.terrier.Retriever(wikipedia_index, wmodel="BM25") >> 
     pt.rewrite.QueryExpansion(wikipedia_index) >> 
-    pt.terrier.Retrieve(local_index, wmodel="BM25")
+    pt.terrier.Retriever(local_index, wmodel="BM25")
 )
 ```
 
@@ -51,32 +51,32 @@ You need to call `pt.terrier.enable_prf()` to download and load this plugin.
 
 ```python
 pt.terrier.enable_prf() # This is required before Java starts
-pipe = (pt.terrier.Retrieve(indexref, wmodel="BM25") >> 
+pipe = (pt.terrier.Retriever(indexref, wmodel="BM25") >> 
     pt.rewrite.RM3(indexref) >> 
-    pt.terrier.Retrieve(indexref, wmodel="BM25")
+    pt.terrier.Retriever(indexref, wmodel="BM25")
 )
 ```
 ## Combining Rankings
 
 Sometimes we have good retrieval approaches and we wish to combine these in a unsupervised manner. We can do that using the linear combination operator:
 ```python
-bm25 = pt.terrier.Retrieve(indexref, wmodel="BM25")
-dph = pt.terrier.Retrieve(indexref, wmodel="DPH")
+bm25 = pt.terrier.Retriever(indexref, wmodel="BM25")
+dph = pt.terrier.Retriever(indexref, wmodel="DPH")
 linear = bm25 + dph
 ```
 
 Of course, some weighting can help:
 ```python
-bm25 = pt.terrier.Retrieve(indexref, wmodel="BM25")
-dph = pt.terrier.Retrieve(indexref, wmodel="DPH")
+bm25 = pt.terrier.Retriever(indexref, wmodel="BM25")
+dph = pt.terrier.Retriever(indexref, wmodel="DPH")
 linear = bm25 + 2* dph
 ```
 
 However, if the score distributions are not similar, finding a good weight can be tricky. Normalisation of retrieval scores can be advantagous in this case. We provide PerQueryMaxMinScoreTransformer() to make easy normalisation.
 
 ```python
-bm25 = pt.terrier.Retrieve(indexref, wmodel="BM25") >> pt.pipelines.PerQueryMaxMinScoreTransformer()
-dph = pt.terrier.Retrieve(indexref, wmodel="DPH" >> pt.pipelines.PerQueryMaxMinScoreTransformer()
+bm25 = pt.terrier.Retriever(indexref, wmodel="BM25") >> pt.pipelines.PerQueryMaxMinScoreTransformer()
+dph = pt.terrier.Retriever(indexref, wmodel="DPH" >> pt.pipelines.PerQueryMaxMinScoreTransformer()
 linear = 0.75 * bm25 + 0.25 * dph
 ```
 
@@ -88,13 +88,13 @@ Having shown some of the main formulations, lets show how to build different for
  - We then score each of the retrieved documents 
 
 ```python
-bm25_cands = pt.terrier.Retrieve(indexref, wmodel="BM25")
-dph_cands = pt.terrier.Retrieve(indexref, wmodel="DPH")
+bm25_cands = pt.terrier.Retriever(indexref, wmodel="BM25")
+dph_cands = pt.terrier.Retriever(indexref, wmodel="DPH")
 all_cands = bm25_cands | dph_cands
 
 all_features = all_cands >> (  
-    pt.terrier.Retrieve(indexref, wmodel="BM25F") **
-    pt.rewrite.SDM() >> pt.terrier.Retrieve(indexref, wmodel="BM25")
+    pt.terrier.Retriever(indexref, wmodel="BM25F") **
+    pt.rewrite.SDM() >> pt.terrier.Retriever(indexref, wmodel="BM25")
     )
 
 import xgboost as xgb
diff --git a/docs/rewrite.rst b/docs/rewrite.rst
index b5df332c..0a1729bd 100644
--- a/docs/rewrite.rst
+++ b/docs/rewrite.rst
@@ -40,7 +40,7 @@ This transfomer is only compatible with BatchRetrieve, as Terrier supports the `
 Example::
 
     sdm = pt.rewrite.SequentialDependence()
-    dph = pt.terrier.Retrieve(index, wmodel="DPH")
+    dph = pt.terrier.Retriever(index, wmodel="DPH")
     pipeline = sdm >> dph
 
 References:
@@ -61,7 +61,7 @@ and returns a dataframe with  `["qid", "query"]`.
 Example::
 
     bo1 = pt.rewrite.Bo1QueryExpansion(index)
-    dph = pt.terrier.Retrieve(index, wmodel="DPH")
+    dph = pt.terrier.Retriever(index, wmodel="DPH")
     pipelineQE = dph >> bo1 >> dph
 
 View the expansion terms::
@@ -77,7 +77,7 @@ View the expansion terms::
 Note that it is also possible to configure BatchRetrieve to perform QE directly using controls,
 which will result in identical retrieval effectiveness::
 
-    pipelineQE = pt.terrier.Retrieve(index, wmodel="DPH", controls={"qemodel" : "Bo1", "qe" : "on"})
+    pipelineQE = pt.terrier.Retriever(index, wmodel="DPH", controls={"qemodel" : "Bo1", "qe" : "on"})
 
 However, using `pt.rewrite.Bo1QueryExpansion` is preferable as:
 
@@ -161,7 +161,7 @@ This can be attained in PyTerrier through use of `stash_results()` and `reset_re
 
     # index: the corpus you are ranking
 
-    dph = pt.terrier.Retrieve(index)
+    dph = pt.terrier.Retriever(index)
     Pipe = dph 
         >> pt.rewrite.stash_results(clear=False)
         >> pt.rewrite.RM3(index)
@@ -193,17 +193,17 @@ Example: Collection Enrichment as a re-ranker::
     # index: the corpus you are ranking
     # wiki_index: index of Wikipedia, used for enrichment
 
-    dph = pt.terrier.Retrieve(index)
+    dph = pt.terrier.Retriever(index)
     Pipe = dph 
         >> pt.rewrite.stash_results()          
-        >> pt.terrier.Retrieve(wiki_index)
+        >> pt.terrier.Retriever(wiki_index)
         >> pt.rewrite.RM3(wiki_index)
         >> pt.rewrite.reset_results()
         >> dph
 
 In general, collection enrichment describes conducting a PRF query expansion process on an external corpus (often Wikipedia), 
 before applying the reformulated query to the main corpus. Collection enrichment can be used for improving a first pass 
-retrieval (`pt.terrier.Retrieve(wiki_index) >> pt.rewrite.RM3(wiki_index) >> pt.terrier.Retrieve(main_index)`). Instead, the particular 
+retrieval (`pt.terrier.Retriever(wiki_index) >> pt.rewrite.RM3(wiki_index) >> pt.terrier.Retriever(main_index)`). Instead, the particular 
 example shown above applies collection enrichment as a re-ranker.
 
 
diff --git a/docs/terrier-index-api.rst b/docs/terrier-index-api.rst
index e8eab989..eec8b817 100644
--- a/docs/terrier-index-api.rst
+++ b/docs/terrier-index-api.rst
@@ -28,7 +28,7 @@ IndexRefs can also be obtained from a PyTerrier dataset::
 
 IndexRef objects can be directly passed to BatchRetrieve::
 
-    pt.terrier.Retrieve(indexref).search("chemical reactions")
+    pt.terrier.Retriever(indexref).search("chemical reactions")
 
 If you want to access the underlying data structures, you need to use IndexFactory, using the indexref, or the string location:: 
     
diff --git a/docs/terrier-indexing.rst b/docs/terrier-indexing.rst
index 6133a76a..b5885290 100644
--- a/docs/terrier-indexing.rst
+++ b/docs/terrier-indexing.rst
@@ -255,10 +255,10 @@ This allows tokenisation using, for instance, the `HuggingFace tokenizers <https
     ])
 
 At retrieval time, WordPieces that contain special characters (e.g. `'##w'` `'[SEP]'`) need to be encoded so as to avoid Terrier's tokeniser. 
-We use ``pt.rewrite.tokenise()`` to apply a tokeniser to the query, setting ``matchop`` to True, such that ``pt.terrier.Retrieve.matchop()`` 
+We use ``pt.rewrite.tokenise()`` to apply a tokeniser to the query, setting ``matchop`` to True, such that ``pt.terrier.Retriever.matchop()`` 
 is called to ensure that rewritten query terms are properly encoded::
 
-    br = pt.terrier.Retrieve(indexref)
+    br = pt.terrier.Retriever(indexref)
     tok = AutoTokenizer.from_pretrained("bert-base-uncased")
     query_toks = pt.rewrite.tokenise(tok.tokenize, matchop=True)
     retr_pipe = query_toks >> br
diff --git a/docs/terrier-retrieval.rst b/docs/terrier-retrieval.rst
index 55510a59..ad779882 100644
--- a/docs/terrier-retrieval.rst
+++ b/docs/terrier-retrieval.rst
@@ -1,7 +1,7 @@
 Terrier Retrieval
 -----------------
 
-terrier.Retrieve is one of the most commonly used PyTerrier objects. It represents a retrieval transformation, 
+terrier.Retriever is one of the most commonly used PyTerrier objects. It represents a retrieval transformation, 
 in which queries are mapped to retrieved documents. BatchRetrieve uses a pre-existing Terrier index data
 structure, typically saved on disk.
 
@@ -9,9 +9,9 @@ structure, typically saved on disk.
 Typical usage::
 
     index = pt.IndexFactory.of("/path/to/data.properties")
-    tf_idf = pt.terrier.Retrieve(index, wmodel="TF_IDF")
-    bm25 = pt.terrier.Retrieve(index, wmodel="BM25")
-    pl2 = pt.terrier.Retrieve(index, wmodel="PL2")
+    tf_idf = pt.terrier.Retriever(index, wmodel="TF_IDF")
+    bm25 = pt.terrier.Retriever(index, wmodel="BM25")
+    pl2 = pt.terrier.Retriever(index, wmodel="PL2")
 
     pt.Experiment([tf_idf, bm25, pl2], topic, qrels, eval_metrics=["map"])
 
@@ -23,14 +23,14 @@ columns `["qid", "query", "docno"]`, and returns dataframes with columns `["qid"
 
 For instance, to create a re-ranking pipeline that re-scores the top 100 BM25 documents using PL2::
 
-    bm25 = pt.terrier.Retrieve(index, wmodel="BM25")
-    pl2 = pt.terrier.Retrieve(index, wmodel="PL2")
+    bm25 = pt.terrier.Retriever(index, wmodel="BM25")
+    pl2 = pt.terrier.Retriever(index, wmodel="PL2")
     pipeline = (bm25 % 100) >> pl2
 
 Retrieve
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. autoclass:: pyterrier.terrier.Retrieve
+.. autoclass:: pyterrier.terrier.Retriever
     :members: transform, from_dataset
 
 
@@ -60,16 +60,16 @@ Common properties:
 **Examples**::
 
     # these two Retrieve instances are identical, using the same weighting model
-    bm25a = pt.terrier.Retrieve(index, wmodel="BM25")
-    bm25b = pt.terrier.Retrieve(index, controls={"wmodel":"BM25"})
+    bm25a = pt.terrier.Retriever(index, wmodel="BM25")
+    bm25b = pt.terrier.Retriever(index, controls={"wmodel":"BM25"})
 
     # this one also applies query expansion inside Terrier
-    bm25_qe = pt.terrier.Retrieve(index, wmodel="BM25", controls={"qe":"on", "qemodel" : "Bo1"})
+    bm25_qe = pt.terrier.Retriever(index, wmodel="BM25", controls={"qe":"on", "qemodel" : "Bo1"})
 
     # when we introduce an unstemmed BatchRetrieve, we ensure to explicitly set the termpipelines
     # for the other BatchRetrieve as well
-    bm25s_unstemmed = pt.terrier.Retrieve(indexUS, wmodel="BM25", properties={"termpipelines" : ""})
-    bm25s_stemmed = pt.terrier.Retrieve(indexSS, wmodel="BM25", properties={"termpipelines" : "Stopwords,PorterStemmer"})
+    bm25s_unstemmed = pt.terrier.Retriever(indexUS, wmodel="BM25", properties={"termpipelines" : ""})
+    bm25s_stemmed = pt.terrier.Retriever(indexSS, wmodel="BM25", properties={"termpipelines" : "Stopwords,PorterStemmer"})
     
 
 
@@ -87,26 +87,26 @@ Where possible, for faster reuse, load the actual Index.
 
 Bad Practice::
 
-    bm25 = pt.terrier.Retrieve("/path/to/data.properties", wmodel="BM25")
-    pl2 = pt.terrier.Retrieve("/path/to/data.properties", wmodel="PL2")
+    bm25 = pt.terrier.Retriever("/path/to/data.properties", wmodel="BM25")
+    pl2 = pt.terrier.Retriever("/path/to/data.properties", wmodel="PL2")
     # here, the same index must be loaded twice
 
 Good Practice::
 
     index = pt.IndexFactory.of("/path/to/data.properties")
-    bm25 = pt.terrier.Retrieve(index, wmodel="BM25")
-    pl2 = pt.terrier.Retrieve(index, wmodel="PL2")
+    bm25 = pt.terrier.Retriever(index, wmodel="BM25")
+    pl2 = pt.terrier.Retriever(index, wmodel="PL2")
     # here, we share the index between two instances of BatchRetrieve
 
 You can use the IndexFactory to specify that the index data structures to be loaded into memory::
 
     # load all structures into memory
     inmemindex = pt.IndexFactory.of("/path/to/data.properties", memory=True)
-    bm25_fast = pt.terrier.Retrieve(inmemindex, wmodel="BM25")
+    bm25_fast = pt.terrier.Retriever(inmemindex, wmodel="BM25")
 
     # load just inverted and lexicon into memory
     inmem_inverted_index = pt.IndexFactory.of("/path/to/data.properties", memory=['inverted', 'lexicon'])
-    bm25_fast = pt.terrier.Retrieve(inmem_inverted_index, wmodel="BM25")
+    bm25_fast = pt.terrier.Retriever(inmem_inverted_index, wmodel="BM25")
 
 
 TextScorer
@@ -141,13 +141,13 @@ If you have your own Java weighting model instance (which extends the
 you can load it and pass it directly to BatchRetrieve::
 
     mymodel = pt.autoclass("com.example.MyTF")()
-    retr = pt.terrier.Retrieve(indexref, wmodel=mymodel)
+    retr = pt.terrier.Retriever(indexref, wmodel=mymodel)
 
 More usefully, it is possible to express a weighting model entirely in Python, as a function or a lambda expression, that can be
 used by Terrier for scoring. In this example, we create a Terrier BatchRetrieve instance that scores based solely on term frequency::
 
     Tf = lambda keyFreq, posting, entryStats, collStats: posting.getFrequency()
-    retr = pt.terrier.Retrieve(indexref, wmodel=Tf)
+    retr = pt.terrier.Retriever(indexref, wmodel=Tf)
 
 All functions passed must accept 4 arguments, as follows:
 
diff --git a/docs/text.rst b/docs/text.rst
index 174d0aa3..6859d886 100644
--- a/docs/text.rst
+++ b/docs/text.rst
@@ -53,12 +53,12 @@ all document fields, not just those that were included as meta fields when index
 Examples::
 
     # the following pipelines are equivalent
-    pipe1 = pt.terrier.Retrieve(index, metadata=["docno", "body"])
+    pipe1 = pt.terrier.Retriever(index, metadata=["docno", "body"])
 
-    pipe2 = pt.terrier.Retrieve(index) >> pt.text.get_text(index, "body")
+    pipe2 = pt.terrier.Retriever(index) >> pt.text.get_text(index, "body")
 
     dataset = pt.get_dataset('irds:vaswani')
-    pipe3 = pt.terrier.Retrieve(index) >> pt.text.get_text(dataset, "text")
+    pipe3 = pt.terrier.Retriever(index) >> pt.text.get_text(dataset, "text")
 
 
 .. autofunction:: pyterrier.text.get_text()
diff --git a/docs/transformer.rst b/docs/transformer.rst
index 5f2a382b..90910a90 100644
--- a/docs/transformer.rst
+++ b/docs/transformer.rst
@@ -17,7 +17,7 @@ before returning it.
 +=======+=========+=============+==================+==============================+
 |   Q   |    Q    |   1 to 1    | Query rewriting  | `pt.rewrite.SDM()`           |
 +-------+---------+-------------+------------------+------------------------------+
-|   Q   |  Q x D  |   1 to N    | Retrieval        | `pt.terrier.Retrieve()`      |
+|   Q   |  Q x D  |   1 to N    | Retrieval        | `pt.terrier.Retriever()`      |
 +-------+---------+-------------+------------------+------------------------------+
 | Q x D |    Q    |   N to 1    | Query expansion  | `pt.rewrite.RM3()`           |
 +-------+---------+-------------+------------------+------------------------------+
diff --git a/docs/tuning.rst b/docs/tuning.rst
index b188770c..1c542b04 100644
--- a/docs/tuning.rst
+++ b/docs/tuning.rst
@@ -39,7 +39,7 @@ When using BatchRetrieve, the `b` parameter of the BM25 weighting model can be c
 We must give this control an initial value when contructing the BatchRetrieve instance. Thereafter, the GridSearch
 parameter dictionary can be constructed by refering to the instance of transformer that has that parameter::
 
-    BM25 = pt.terrier.Retrieve(index, wmodel="BM25", controls={"bm25.b" : 0.75})
+    BM25 = pt.terrier.Retriever(index, wmodel="BM25", controls={"bm25.b" : 0.75})
     pt.GridSearch(
         BM25,  
         {BM25 : {"c" : [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 ]}}
@@ -49,7 +49,7 @@ parameter dictionary can be constructed by refering to the instance of transform
 
 Terrier's BM25 also responds to controls named `"bm25.k_1"` and  `"bm25.k_3"`, such that all three controls can be tuned concurrently::
 
-    BM25 = pt.terrier.Retrieve(index, wmodel="BM25", controls={"bm25.b" : 0.75, "bm25.k_1": 0.75, "bm25.k_3": 0.75})
+    BM25 = pt.terrier.Retriever(index, wmodel="BM25", controls={"bm25.b" : 0.75, "bm25.k_1": 0.75, "bm25.k_3": 0.75})
     pt.GridSearch(
         BM25,  
         {BM25: {"bm25.b"  : [0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1 ],
@@ -72,7 +72,7 @@ of feedback documents and expansion terms, namely:
 
 A full tuning of BM25 and RM3 can be achieved as thus::
 
-    bm25_for_qe = pt.terrier.Retrieve(index, wmodel="BM25", controls={"bm25.b" : 0.75})
+    bm25_for_qe = pt.terrier.Retriever(index, wmodel="BM25", controls={"bm25.b" : 0.75})
     rm3 = pt.rewrite.RM3(index, fb_terms=10, fb_docs=3)
     pipe_qe = bm25_for_qe >> rm3 >> bm25_for_qe
 
@@ -121,7 +121,7 @@ The following is an example of scanning the parameters of BM25F for an index wit
     assert 2 == index.getCollectionStatistics().getNumberOfFields()
 
     # instantiate BatchRetrieve for BM25F
-    bm25f = pt.terrier.Retrieve(
+    bm25f = pt.terrier.Retriever(
         index, 
         wmodel = 'BM25F', 
         controls = {'w.0' : 1, 'w.1' : 1, 'c.0' : 0.4, 'c.1' : 0.4}
diff --git a/examples/experiments/Robust04.ipynb b/examples/experiments/Robust04.ipynb
index 2921ba22..e4e8ea1e 100644
--- a/examples/experiments/Robust04.ipynb
+++ b/examples/experiments/Robust04.ipynb
@@ -197,10 +197,10 @@
    "outputs": [],
    "source": [
     "\n",
-    "BM25 = pt.terrier.Retrieve(index, wmodel=\"BM25\")\n",
-    "DPH  = pt.terrier.Retrieve(index, wmodel=\"DPH\")\n",
-    "PL2  = pt.terrier.Retrieve(index, wmodel=\"PL2\")\n",
-    "DLM  = pt.terrier.Retrieve(index, wmodel=\"DirichletLM\")\n"
+    "BM25 = pt.terrier.Retriever(index, wmodel=\"BM25\")\n",
+    "DPH  = pt.terrier.Retriever(index, wmodel=\"DPH\")\n",
+    "PL2  = pt.terrier.Retriever(index, wmodel=\"PL2\")\n",
+    "DLM  = pt.terrier.Retriever(index, wmodel=\"DirichletLM\")\n"
    ]
   },
   {
diff --git a/examples/notebooks/experiment.ipynb b/examples/notebooks/experiment.ipynb
index bc1d9ea9..d9a4392a 100644
--- a/examples/notebooks/experiment.ipynb
+++ b/examples/notebooks/experiment.ipynb
@@ -148,9 +148,9 @@
       },
       "outputs": [],
       "source": [
-        "TF_IDF = pt.terrier.Retrieve(vaswani.get_index(), wmodel=\"TF_IDF\")\n",
-        "BM25 = pt.terrier.Retrieve(vaswani.get_index(), wmodel=\"BM25\")\n",
-        "PL2 = pt.terrier.Retrieve(vaswani.get_index(), wmodel=\"PL2\")"
+        "TF_IDF = pt.terrier.Retriever(vaswani.get_index(), wmodel=\"TF_IDF\")\n",
+        "BM25 = pt.terrier.Retriever(vaswani.get_index(), wmodel=\"BM25\")\n",
+        "PL2 = pt.terrier.Retriever(vaswani.get_index(), wmodel=\"PL2\")"
       ]
     },
     {
diff --git a/examples/notebooks/indexing.ipynb b/examples/notebooks/indexing.ipynb
index 7e69b81a..a7e257c1 100644
--- a/examples/notebooks/indexing.ipynb
+++ b/examples/notebooks/indexing.ipynb
@@ -782,7 +782,7 @@
         }
       ],
       "source": [
-        "pt.terrier.Retrieve(indexref).search(\"mathematical\")"
+        "pt.terrier.Retriever(indexref).search(\"mathematical\")"
       ]
     },
     {
@@ -969,7 +969,7 @@
       "source": [
         "import pandas as pd\n",
         "topics = pd.DataFrame([[\"2\", \"mathematical\"]],columns=['qid','query'])\n",
-        "pt.terrier.Retrieve(indexref).transform(topics)"
+        "pt.terrier.Retriever(indexref).transform(topics)"
       ]
     },
     {
diff --git a/examples/notebooks/ltr.ipynb b/examples/notebooks/ltr.ipynb
index 1de9ad09..94a9eaef 100644
--- a/examples/notebooks/ltr.ipynb
+++ b/examples/notebooks/ltr.ipynb
@@ -142,11 +142,11 @@
       "outputs": [],
       "source": [
         "#this ranker will make the candidate set of documents for each query\n",
-        "BM25 = pt.terrier.Retrieve(indexref, wmodel=\"BM25\")\n",
+        "BM25 = pt.terrier.Retriever(indexref, wmodel=\"BM25\")\n",
         "\n",
         "#these rankers we will use to re-rank the BM25 results\n",
-        "TF_IDF =  pt.terrier.Retrieve(indexref, wmodel=\"TF_IDF\")\n",
-        "PL2 =  pt.terrier.Retrieve(indexref, wmodel=\"PL2\")"
+        "TF_IDF =  pt.terrier.Retriever(indexref, wmodel=\"TF_IDF\")\n",
+        "PL2 =  pt.terrier.Retriever(indexref, wmodel=\"PL2\")"
       ]
     },
     {
@@ -369,7 +369,7 @@
         }
       ],
       "source": [
-        "fbr = pt.terrier.FeaturesRetrieve(indexref, wmodel=\"BM25\", features=[\"WMODEL:TF_IDF\", \"WMODEL:PL2\"])\n",
+        "fbr = pt.terrier.FeaturesRetriever(indexref, wmodel=\"BM25\", features=[\"WMODEL:TF_IDF\", \"WMODEL:PL2\"])\n",
         "#lets look at the top 2 results\n",
         "(fbr %2).search(\"chemical\")"
       ]
@@ -573,7 +573,7 @@
         }
       ],
       "source": [
-        "fbr3f = pt.terrier.FeaturesRetrieve(indexref, wmodel=\"BM25\", features=[\"SAMPLE\", \"WMODEL:TF_IDF\", \"WMODEL:PL2\"])\n",
+        "fbr3f = pt.terrier.FeaturesRetriever(indexref, wmodel=\"BM25\", features=[\"SAMPLE\", \"WMODEL:TF_IDF\", \"WMODEL:PL2\"])\n",
         "(fbr3f %2).search(\"chemical\")"
       ]
     },
diff --git a/examples/notebooks/non_en_retrieval.ipynb b/examples/notebooks/non_en_retrieval.ipynb
index 59471252..4c3f7a1e 100644
--- a/examples/notebooks/non_en_retrieval.ipynb
+++ b/examples/notebooks/non_en_retrieval.ipynb
@@ -481,14 +481,14 @@
         }
       ],
       "source": [
-        "bm25_nostem = pt.terrier.Retrieve(index_nostem, wmodel='BM25')\n",
+        "bm25_nostem = pt.terrier.Retriever(index_nostem, wmodel='BM25')\n",
         "bm25_nostem.search('El Centro') \n",
         "\n",
-        "bm25_stem = pt.terrier.Retrieve(index_stem, wmodel='BM25')\n",
+        "bm25_stem = pt.terrier.Retriever(index_stem, wmodel='BM25')\n",
         "bm25_stem.search('El Centro') \n",
         "\n",
         "# to apply the es_preprocess function to the query text, use a pt.apply.query transformer\n",
-        "bm25_custom = pt.apply.query(lambda row: es_preprocess(row.query)) >> pt.terrier.Retrieve(index_custom, wmodel='BM25')\n",
+        "bm25_custom = pt.apply.query(lambda row: es_preprocess(row.query)) >> pt.terrier.Retriever(index_custom, wmodel='BM25')\n",
         "bm25_custom.search('El Centro')"
       ]
     },
diff --git a/examples/notebooks/retrieval_and_evaluation.ipynb b/examples/notebooks/retrieval_and_evaluation.ipynb
index 94674b16..51686c42 100644
--- a/examples/notebooks/retrieval_and_evaluation.ipynb
+++ b/examples/notebooks/retrieval_and_evaluation.ipynb
@@ -324,7 +324,7 @@
       },
       "outputs": [],
       "source": [
-        "retr = pt.terrier.Retrieve(index, controls = {\"wmodel\": \"TF_IDF\"})\n",
+        "retr = pt.terrier.Retriever(index, controls = {\"wmodel\": \"TF_IDF\"})\n",
         "\n",
         "retr.setControl(\"wmodel\", \"TF_IDF\")\n",
         "retr.setControls({\"wmodel\": \"TF_IDF\"})\n",
diff --git a/examples/notebooks/sentence_transformers.ipynb b/examples/notebooks/sentence_transformers.ipynb
index ed625c15..bcdb4a02 100644
--- a/examples/notebooks/sentence_transformers.ipynb
+++ b/examples/notebooks/sentence_transformers.ipynb
@@ -421,7 +421,7 @@
         }
       ],
       "source": [
-        "bm25 = pt.terrier.Retrieve.from_dataset('msmarco_passage', 'terrier_stemmed_text', wmodel='BM25', metadata=['docno', 'text'])"
+        "bm25 = pt.terrier.Retriever.from_dataset('msmarco_passage', 'terrier_stemmed_text', wmodel='BM25', metadata=['docno', 'text'])"
       ]
     },
     {
diff --git a/pyterrier/apply.py b/pyterrier/apply.py
index a4b3c5f7..bdce4c1d 100644
--- a/pyterrier/apply.py
+++ b/pyterrier/apply.py
@@ -43,12 +43,12 @@ def _remove_stops(q):
                 return " ".join(terms)
 
             # a query rewriting transformer that applies the _remove_stops to each row of an input dataframe
-            p1 = pt.apply.query(_remove_stops) >> pt.terrier.Retrieve(index, wmodel="DPH")
+            p1 = pt.apply.query(_remove_stops) >> pt.terrier.Retriever(index, wmodel="DPH")
 
             # an equivalent query rewriting transformer using an anonymous lambda function
             p2 = pt.apply.query(
                     lambda q :  " ".join([t for t in q["query"].split(" ") if t not in stops ])
-                ) >> pt.terrier.Retrieve(index, wmodel="DPH")
+                ) >> pt.terrier.Retriever(index, wmodel="DPH")
 
         In both of the example pipelines above (`p1` and `p2`), the exact topics are not known until the pipeline is invoked, e.g.
         by using `p1.transform(topics)` on a topics dataframe, or within a `pt.Experiment()`. When the pipeline 
@@ -75,7 +75,7 @@ def doc_score(fn : Union[Callable[[pd.Series], float], Callable[[pd.DataFrame],
         Example (Row-wise)::
 
             # this transformer will subtract 5 from the score of each document
-            p = pt.terrier.Retrieve(index, wmodel="DPH") >> 
+            p = pt.terrier.Retriever(index, wmodel="DPH") >> 
                 pt.apply.doc_score(lambda doc : doc["score"] -5)
 
         Can be used in batch-wise manner, which is particularly useful for appling neural models. In this case,
@@ -85,7 +85,7 @@ def _doclen(df):
                 # returns series of lengths
                 return df.text.str.len()
             
-            pipe = pt.terrier.Retrieve(index) >> pt.apply.doc_score(_doclen, batch_size=128)
+            pipe = pt.terrier.Retriever(index) >> pt.apply.doc_score(_doclen, batch_size=128)
 
     """
     return ApplyDocumentScoringTransformer(fn, *args, batch_size=batch_size, **kwargs)
@@ -113,7 +113,7 @@ def _features(row):
                 f2 = len(content.split(" "))
                 return np.array([f1, f2])
 
-            p = pt.terrier.Retrieve(index, wmodel="BM25") >> 
+            p = pt.terrier.Retriever(index, wmodel="BM25") >> 
                 pt.apply.doc_features(_features )
 
     """
@@ -129,7 +129,7 @@ def rename(columns : Dict[str,str], *args, errors='raise', **kwargs) -> pt.Trans
 
         Example::
             
-            pipe = pt.terrier.Retrieve(index, metadata=["docno", "body"]) >> pt.apply.rename({'body':'text'})
+            pipe = pt.terrier.Retriever(index, metadata=["docno", "body"]) >> pt.apply.rename({'body':'text'})
     """
     return ApplyGenericTransformer(lambda df: df.rename(columns=columns, errors=errors), *args, **kwargs)
 
@@ -151,7 +151,7 @@ def generic(fn : Callable[[pd.DataFrame], pd.DataFrame], *args, batch_size=None,
             # this transformer will remove all documents at rank greater than 2.
 
             # this pipeline would remove all but the first two documents from a result set
-            pipe = pt.terrier.Retrieve(index) >> pt.apply.generic(lambda res : res[res["rank"] < 2])
+            pipe = pt.terrier.Retriever(index) >> pt.apply.generic(lambda res : res[res["rank"] < 2])
 
     """
     return ApplyGenericTransformer(fn, *args, batch_size=batch_size, **kwargs)
diff --git a/pyterrier/apply_base.py b/pyterrier/apply_base.py
index e9ec6661..d138d68b 100644
--- a/pyterrier/apply_base.py
+++ b/pyterrier/apply_base.py
@@ -78,7 +78,7 @@ class ApplyDocumentScoringTransformer(ApplyTransformerBase):
             def _score_fn(row):
                 return float(row["url".count("/")])
             
-            pipe = pt.terrier.Retrieve(index) >> pt.apply.doc_score(_score_fn)
+            pipe = pt.terrier.Retriever(index) >> pt.apply.doc_score(_score_fn)
 
         Can be used in batching manner, which is particularly useful for appling neural models. In this case,
         the scoring function receives a dataframe, rather than a single row::
@@ -86,7 +86,7 @@ def _score_fn(row):
             def _doclen(df):
                 return df.text.str.len()
             
-            pipe = pt.terrier.Retrieve(index) >> pt.apply.doc_score(_doclen)
+            pipe = pt.terrier.Retriever(index) >> pt.apply.doc_score(_doclen)
 
     """
     def __init__(self, fn,  *args, batch_size=None, **kwargs):
@@ -148,7 +148,7 @@ class ApplyDocFeatureTransformer(ApplyTransformerBase):
             def _feature_fn(row):
                 return numpy.array([len(row["url"], row["url".count("/")])
             
-            pipe = pt.terrier.Retrieve(index) >> pt.apply.doc_features(_feature_fn) >> pt.LTRpipeline(xgBoost())
+            pipe = pt.terrier.Retriever(index) >> pt.apply.doc_features(_feature_fn) >> pt.LTRpipeline(xgBoost())
     """
     def __init__(self, fn,  *args, **kwargs):
         """
@@ -180,11 +180,11 @@ class ApplyQueryTransformer(ApplyTransformerBase):
             def _rewriting_fn(row):
                 return row["query"] + " extra words"
             
-            pipe = pt.apply.query(_rewriting_fn) >> pt.terrier.Retrieve(index)
+            pipe = pt.apply.query(_rewriting_fn) >> pt.terrier.Retriever(index)
 
         Similarly, a lambda function can also be used::
 
-            pipe = pt.apply.query(lambda row: row["query"] + " extra words") >> pt.terrier.Retrieve(index)
+            pipe = pt.apply.query(lambda row: row["query"] + " extra words") >> pt.terrier.Retriever(index)
 
         In the resulting dataframe, the previous query for each row can be found in the query_0 column.
 
@@ -238,7 +238,7 @@ class ApplyGenericTransformer(ApplyTransformerBase):
         # this pipeline would remove all but the first two documents from a result set
         lp = ApplyGenericTransformer(lambda res : res[res["rank"] < 2])
 
-        pipe = pt.terrier.Retrieve(index) >> lp
+        pipe = pt.terrier.Retriever(index) >> lp
 
     """
 
diff --git a/pyterrier/cache.py b/pyterrier/cache.py
index f317e692..dd9bc609 100644
--- a/pyterrier/cache.py
+++ b/pyterrier/cache.py
@@ -71,7 +71,7 @@ class ChestCacheTransformer(Transformer):
 
             dataset = pt.get_dataset("trec-robust-2004")
             # use for first pass and 2nd pass
-            BM25 = pt.terrier.Retrieve(index, wmodel="BM25")
+            BM25 = pt.terrier.Retriever(index, wmodel="BM25")
 
             # used for query expansion
             RM3 = pt.rewrite.RM3(index)
diff --git a/pyterrier/ltr.py b/pyterrier/ltr.py
index f4af190f..e3a3d02e 100644
--- a/pyterrier/ltr.py
+++ b/pyterrier/ltr.py
@@ -276,9 +276,9 @@ def score_to_feature() -> Transformer:
 
         Example::
 
-            cands = pt.terrier.Retrieve(index, wmodel="BM25")
-            bm25f = pt.terrier.Retrieve(index, wmodel="BM25F")
-            pl2f = pt.terrier.Retrieve(index, wmodel="PL2F")
+            cands = pt.terrier.Retriever(index, wmodel="BM25")
+            bm25f = pt.terrier.Retriever(index, wmodel="BM25F")
+            pl2f = pt.terrier.Retriever(index, wmodel="PL2F")
             
             two_features = cands >> (bm25f  **  pl2f)
             three_features = cands >> (bm25f  **  pl2f ** pt.ltr.score_to_feature())  
diff --git a/pyterrier/ops.py b/pyterrier/ops.py
index df8b9e06..1de6a445 100644
--- a/pyterrier/ops.py
+++ b/pyterrier/ops.py
@@ -200,9 +200,9 @@ class FeatureUnionPipeline(NAryTransformerBase):
         Implements the feature union operator.
 
         Example::
-            cands = pt.terrier.Retrieve(index wmodel="BM25")
-            pl2f = pt.terrier.Retrieve(index wmodel="PL2F")
-            bm25f = pt.terrier.Retrieve(index wmodel="BM25F")
+            cands = pt.terrier.Retriever(index wmodel="BM25")
+            pl2f = pt.terrier.Retriever(index wmodel="PL2F")
+            bm25f = pt.terrier.Retriever(index wmodel="BM25F")
             pipe = cands >> (pl2f ** bm25f)
     """
     name = "FUnion"
diff --git a/pyterrier/pipelines.py b/pyterrier/pipelines.py
index d17971e8..332b4251 100644
--- a/pyterrier/pipelines.py
+++ b/pyterrier/pipelines.py
@@ -653,7 +653,7 @@ def KFoldGridSearch(
 
     Consider tuning PL2 where folds of queries are pre-determined::
 
-        pl2 = pt.terrier.Retrieve(index, wmodel="PL2", controls={'c' : 1})
+        pl2 = pt.terrier.Retriever(index, wmodel="PL2", controls={'c' : 1})
         tuned_pl2, _ = pt.KFoldGridSearch(
             pl2, 
             {pl2 : {'c' : [0.1, 1, 5, 10, 20, 100]}}, 
@@ -827,7 +827,7 @@ def GridScan(
     Example::
 
         # graph how PL2's c parameter affects MAP
-        pl2 = pt.terrier.Retrieve(index, wmodel="PL2", controls={'c' : 1})
+        pl2 = pt.terrier.Retriever(index, wmodel="PL2", controls={'c' : 1})
         rtr = pt.GridScan(
             pl2, 
             {pl2 : {'c' : [0.1, 1, 5, 10, 20, 100]}}, 
diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
index 44474f28..7578ba87 100644
--- a/pyterrier/terrier/__init__.py
+++ b/pyterrier/terrier/__init__.py
@@ -1,7 +1,7 @@
 # java stuff
 from pyterrier.terrier import java
 from pyterrier.terrier.java import configure, set_version, set_helper_version, enable_prf, extend_classpath, J, set_property, set_properties, run, version, check_version, check_helper_version
-from pyterrier.terrier.retrieve import Retrieve, FeaturesRetrieve, TextScorer
+from pyterrier.terrier.retriever import Retrieve, FeaturesRetriever, TextScorer
 from pyterrier.terrier.index_factory import IndexFactory
 from pyterrier.terrier.stemmer import TerrierStemmer
 from pyterrier.terrier.tokeniser import TerrierTokeniser
@@ -12,16 +12,16 @@
 from deprecated import deprecated
 
 
-TerrierRetrieve = deprecated(version='0.11.0', reason="use pt.terrier.Retrieve() instead")(Retrieve)
-BatchRetrieve = deprecated(version='0.11.0', reason="use pt.terrier.Retrieve() instead")(Retrieve)
-FeaturesBatchRetrieve = deprecated(version='0.11.0', reason="use pt.terrier.FeaturesRetrieve() instead")(FeaturesRetrieve)
+TerrierRetrieve = deprecated(version='0.11.0', reason="use pt.terrier.Retriever() instead")(Retrieve)
+BatchRetrieve = deprecated(version='0.11.0', reason="use pt.terrier.Retriever() instead")(Retrieve)
+FeaturesBatchRetrieve = deprecated(version='0.11.0', reason="use pt.terrier.FeaturesRetriever() instead")(FeaturesRetriever)
 
 __all__ = [
     # java stuff
     'java', 'configure', 'set_version', 'set_helper_version', 'enable_prf', 'extend_classpath', 'J', 'version', 'check_version', 'check_helper_version',
 
     # retrieval
-    'Retrieve', 'BatchRetrieve', 'TerrierRetrieve', 'FeaturesRetrieve', 'FeaturesBatchRetrieve', 'TerrierRetrieve', 'TextScorer',
+    'Retrieve', 'BatchRetrieve', 'TerrierRetrieve', 'FeaturesRetriever', 'FeaturesBatchRetrieve', 'TerrierRetrieve', 'TextScorer',
 
     # indexing
     'index', 'TerrierIndexer', 'FilesIndexer', 'TRECCollectionIndexer', 'DFIndexer', 'DFIndexUtils', 'IterDictIndexer', 'IndexingType', 'treccollection2textgen',
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index ce200242..326eaaa5 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -384,7 +384,7 @@ def _new_callable_wmodel(byterep):
     #we need to prevent these functions from being GCd.
     global _SAVED_FNS
     _SAVED_FNS.append(fn)
-    callback, wmodel = pt.terrier.retrieve._function2wmodel(fn)
+    callback, wmodel = pt.terrier.retriever._function2wmodel(fn)
     _SAVED_FNS.append(callback)
     #print("Stored lambda fn  %s and callback in SAVED_FNS, now %d stored" % (str(fn), len(SAVED_FNS)))
     return wmodel
diff --git a/pyterrier/terrier/retriever.py b/pyterrier/terrier/retriever.py
index 1f0bd6fd..b07cce09 100644
--- a/pyterrier/terrier/retriever.py
+++ b/pyterrier/terrier/retriever.py
@@ -156,9 +156,9 @@ def from_dataset(dataset : Union[str,Dataset],
         Examples::
 
             dataset = pt.get_dataset("vaswani")
-            bm25 = pt.terrier.Retrieve.from_dataset(dataset, "terrier_stemmed", wmodel="BM25")
+            bm25 = pt.terrier.Retriever.from_dataset(dataset, "terrier_stemmed", wmodel="BM25")
             #or
-            bm25 = pt.terrier.Retrieve.from_dataset("vaswani", "terrier_stemmed", wmodel="BM25")
+            bm25 = pt.terrier.Retriever.from_dataset("vaswani", "terrier_stemmed", wmodel="BM25")
 
         **Index Variants**:
 
@@ -586,7 +586,7 @@ def __init__(self, takes="docs", **kwargs):
 
 
 @pt.java.required
-class FeaturesRetrieve(Retrieve):
+class FeaturesRetriever(Retrieve):
     """
     Use this class for retrieval with multiple features
     """
@@ -610,8 +610,8 @@ def __init__(self, index_location, features, controls=None, properties=None, thr
                 verbose(bool): If True transform method will display progress
                 num_results(int): Number of results to retrieve. 
         """
-        controls = _mergeDicts(FeaturesRetrieve.FBR_default_controls, controls)
-        properties = _mergeDicts(FeaturesRetrieve.FBR_default_properties, properties)
+        controls = _mergeDicts(FeaturesRetriever.FBR_default_controls, controls)
+        properties = _mergeDicts(FeaturesRetriever.FBR_default_properties, properties)
         self.features = features
         properties["fat.featured.scoring.matching.features"] = ";".join(features)
 
@@ -664,14 +664,14 @@ def from_dataset(dataset : Union[str,Dataset],
             variant : str = None, 
             version='latest',            
             **kwargs):
-        return _from_dataset(dataset, variant=variant, version=version, clz=FeaturesRetrieve, **kwargs)
+        return _from_dataset(dataset, variant=variant, version=version, clz=FeaturesRetriever, **kwargs)
 
     @staticmethod 
     def from_dataset(dataset : Union[str,Dataset], 
             variant : str = None, 
             version='latest',            
             **kwargs):
-        return _from_dataset(dataset, variant=variant, version=version, clz=FeaturesRetrieve, **kwargs)
+        return _from_dataset(dataset, variant=variant, version=version, clz=FeaturesRetriever, **kwargs)
 
     def transform(self, queries):
         """
@@ -811,7 +811,7 @@ def setup_rewrites():
     # two different match retrives
     _br1 = Wildcard.symbol('_br1', Retrieve)
     _br2 = Wildcard.symbol('_br2', Retrieve)
-    _fbr = Wildcard.symbol('_fbr', FeaturesRetrieve)
+    _fbr = Wildcard.symbol('_fbr', FeaturesRetriever)
     
     # batch retrieves for the same index
     BR_index_matches = CustomConstraint(lambda _br1, _br2: _br1.indexref == _br2.indexref)
@@ -848,7 +848,7 @@ def setup_rewrites():
     # rewrite batch a feature union of BRs into an FBR
     rewrite_rules.append(ReplacementRule(
         Pattern(FeatureUnionPipeline(_br1, _br2), BR_index_matches),
-        lambda _br1, _br2: FeaturesRetrieve(_br1.indexref, ["WMODEL:" + _br1.controls["wmodel"], "WMODEL:" + _br2.controls["wmodel"]])
+        lambda _br1, _br2: FeaturesRetriever(_br1.indexref, ["WMODEL:" + _br1.controls["wmodel"], "WMODEL:" + _br2.controls["wmodel"]])
     ))
 
     def push_fbr_earlier(_br1, _fbr):
diff --git a/pyterrier/terrier/rewrite.py b/pyterrier/terrier/rewrite.py
index 2829b297..8ccf0196 100644
--- a/pyterrier/terrier/rewrite.py
+++ b/pyterrier/terrier/rewrite.py
@@ -19,22 +19,22 @@ def tokenise(tokeniser : Union[str,TerrierTokeniser,FunctionType] = 'english', m
     
     Example - use default tokeniser::
 
-        pipe = pt.rewrite.tokenise() >> pt.terrier.Retrieve()
+        pipe = pt.rewrite.tokenise() >> pt.terrier.Retriever()
         pipe.search("Question with 'capitals' and other stuff?")
     
     Example - roll your own tokeniser::
 
-        poortokenisation = pt.rewrite.tokenise(lambda query: query.split(" ")) >> pt.terrier.Retrieve()
+        poortokenisation = pt.rewrite.tokenise(lambda query: query.split(" ")) >> pt.terrier.Retriever()
 
     Example - for non-English languages, tokenise on standard UTF non-alphanumeric characters::
 
-        utftokenised = pt.rewrite.tokenise(pt.TerrierTokeniser.utf)) >> pt.terrier.Retrieve()
-        utftokenised = pt.rewrite.tokenise("utf")) >> pt.terrier.Retrieve()
+        utftokenised = pt.rewrite.tokenise(pt.TerrierTokeniser.utf)) >> pt.terrier.Retriever()
+        utftokenised = pt.rewrite.tokenise("utf")) >> pt.terrier.Retriever()
 
     Example - tokenising queries using a `HuggingFace tokenizer <https://huggingface.co/docs/transformers/fast_tokenizers>`_ ::
 
         # this assumes the index was created in a pretokenised manner
-        br = pt.terrier.Retrieve(indexref)
+        br = pt.terrier.Retriever(indexref)
         tok = AutoTokenizer.from_pretrained("bert-base-uncased")
         query_toks = pt.rewrite.tokenise(tok.tokenize, matchop=True)
         retr_pipe = query_toks >> br
@@ -58,7 +58,7 @@ def _join_str(input : Union[str,List[str]]):
 
     def _join_str_matchop(input : List[str]):
         assert not isinstance(input, str), "Expected a list of strings"
-        return ' '.join(map(pt.terrier.Retrieve.matchop, input))
+        return ' '.join(map(pt.terrier.Retriever.matchop, input))
     
     if matchop:
         return pt.apply.query(lambda r: _join_str_matchop(_query_fn(r.query)))
@@ -76,7 +76,7 @@ def reset() -> pt.Transformer:
 
         Example::
 
-            firststage = pt.rewrite.SDM() >> pt.terrier.Retrieve(index, wmodel="DPH")
+            firststage = pt.rewrite.SDM() >> pt.terrier.Retriever(index, wmodel="DPH")
             secondstage = pyterrier_bert.cedr.CEDRPipeline()
             fullranker = firststage >> pt.rewrite.reset() >> secondstage
 
@@ -178,7 +178,7 @@ def __init__(self, index_like, fb_terms=10, fb_docs=3, qeclass="org.terrier.quer
             self.qe = pt.java.autoclass(qeclass)()
         else:
             self.qe = qeclass
-        self.indexref = pt.terrier.retrieve._parse_index_like(index_like)
+        self.indexref = pt.terrier.retriever._parse_index_like(index_like)
         self.properties = properties
         for k,v in properties.items():
             pt.terrier.J.ApplicationSetup.setProperty(k, str(v))
@@ -368,7 +368,7 @@ class RM3(QueryExpansion):
 
         Example:: 
 
-            bm25 = pt.terrier.Retrieve(index, wmodel="BM25")
+            bm25 = pt.terrier.Retriever(index, wmodel="BM25")
             rm3_pipe = bm25 >> pt.rewrite.RM3(index) >> bm25
             pt.Experiment([bm25, rm3_pipe],
                         dataset.get_topics(),
diff --git a/pyterrier/text.py b/pyterrier/text.py
index 5fc90ae5..d294693f 100644
--- a/pyterrier/text.py
+++ b/pyterrier/text.py
@@ -28,7 +28,7 @@ def get_text(
 
     Example::
 
-        pipe = ( pt.terrier.Retrieve(index, wmodel="DPH")
+        pipe = ( pt.terrier.Retriever(index, wmodel="DPH")
             >> pt.text.get_text(index)
             >> pt.text.scorer(wmodel="DPH") )
 
@@ -160,7 +160,7 @@ def scorer(*args, **kwargs) -> pt.Transformer:
         textscorerTfIdf = pt.text.scorer(body_attr="text", wmodel="TF_IDF", background_index=index)
 
     """
-    return pt.terrier.retrieve.TextScorer(*args, **kwargs)
+    return pt.terrier.retriever.TextScorer(*args, **kwargs)
 
 def sliding( text_attr='body', length=150, stride=75, join=' ', prepend_attr='title', tokenizer=None, **kwargs) -> pt.Transformer:
     r"""
@@ -188,7 +188,7 @@ def sliding( text_attr='body', length=150, stride=75, join=' ', prepend_attr='ti
     
     Example::
     
-        pipe = ( pt.terrier.Retrieve(index, wmodel="DPH", metadata=["docno", "body"]) 
+        pipe = ( pt.terrier.Retriever(index, wmodel="DPH", metadata=["docno", "body"]) 
             >> pt.text.sliding(length=128, stride=64, prepend_attr=None) 
             >> pt.text.scorer(wmodel="DPH") 
             >> pt.text.max_passage() )
@@ -196,7 +196,7 @@ def sliding( text_attr='body', length=150, stride=75, join=' ', prepend_attr='ti
         # tokenizer model 
         from transformers import AutoTokenizer
         tok = AutoTokenizer.from_pretrained("bert-base-uncased")
-        pipe = (pt.terrier.Retrieve(index, wmodel="DPH", metadata=["docno", "body"])
+        pipe = (pt.terrier.Retriever(index, wmodel="DPH", metadata=["docno", "body"])
             >> pt.text.sliding(length=128, stride=64, prepend_attr=None, tokenizer=tok)
             >> pt.text.scorer(wmodel="DPH")
             >> pt.text.max_passage() )
@@ -289,7 +289,7 @@ def snippets(
     Example::
 
         # retrieve documents with text
-        br = pt.terrier.Retrieve(index, metadata=['docno', 'text'])
+        br = pt.terrier.Retriever(index, metadata=['docno', 'text'])
 
         # use Tf as a passage scorer on sliding window passages 
         psg_scorer = ( 
diff --git a/pyterrier/transformer.py b/pyterrier/transformer.py
index 7103eff6..56b34dde 100644
--- a/pyterrier/transformer.py
+++ b/pyterrier/transformer.py
@@ -64,8 +64,8 @@ def identity() -> 'Transformer':
         This can be useful for adding the candidate ranking score
         as a feature in for learning-to-rank::
 
-            bm25 = pt.terrier.Retrieve(index, wmodel="BM25")
-            two_feat_pipe = bm25 >> pt.Transformer.identify() ** pt.terrier.Retrieve(index, wmodel="PL2")
+            bm25 = pt.terrier.Retriever(index, wmodel="BM25")
+            two_feat_pipe = bm25 >> pt.Transformer.identify() ** pt.terrier.Retriever(index, wmodel="PL2")
         
         This will return a pipeline that produces a score column (BM25), but also has a features column containing
         BM25 and PL2 scores.
@@ -155,7 +155,7 @@ def search(self, query : str, qid : str = "1", sort=True) -> pd.DataFrame:
 
             Example::
 
-                bm25 = pt.terrier.Retrieve(index, wmodel="BM25")
+                bm25 = pt.terrier.Retriever(index, wmodel="BM25")
                 res = bm25.search("example query")
 
                 # is equivalent to
diff --git a/tests/test_dunder.py b/tests/test_dunder.py
index 8419561f..d6fb5af9 100644
--- a/tests/test_dunder.py
+++ b/tests/test_dunder.py
@@ -9,7 +9,7 @@ def test_callable_wmodel_dunders(self):
         testPosting = pt.java.autoclass("org.terrier.structures.postings.BasicPostingImpl")(0,1)
 
         lambdafn = lambda keyFreq, posting, entryStats, collStats: posting.getFrequency()
-        callback, wmodel = pt.terrier.retrieve._function2wmodel(lambdafn)
+        callback, wmodel = pt.terrier.retriever._function2wmodel(lambdafn)
         
         byterep = pt.java.bytebuffer_to_array(wmodel.scoringClass.serializeFn())
         import dill as pickle
diff --git a/tests/test_operators.py b/tests/test_operators.py
index d25c5de2..810fd8b0 100644
--- a/tests/test_operators.py
+++ b/tests/test_operators.py
@@ -337,7 +337,7 @@ def test_feature_union_multi(self):
         
         self.assertEqual(2, len(mock12a.models))
         self.assertEqual(2, len(mock12a.models))
-        pt.terrier.retrieve.setup_rewrites()
+        pt.terrier.retriever.setup_rewrites()
 
         mock123_simple = mock123a.compile()
         self.assertIsNotNone(mock123_simple)
diff --git a/tests/test_scoring.py b/tests/test_scoring.py
index 23647e5d..21019963 100644
--- a/tests/test_scoring.py
+++ b/tests/test_scoring.py
@@ -52,7 +52,7 @@ def _test_scoring_text(self, dataset, index, wmodel):
 
     def test_scoring_manual_empty(self):
         input = pd.DataFrame([["q1", "fox", "d1", ""]], columns=["qid", "query", "docno", "body"])
-        from pyterrier.terrier.retrieve import TextScorer
+        from pyterrier.terrier.retriever import TextScorer
         scorer = TextScorer(wmodel="Tf")
         rtr = scorer(input)
         self.assertEqual(1, len(rtr))
@@ -62,7 +62,7 @@ def test_scoring_manual_empty(self):
 
     def test_scoring_manual(self):
         input = pd.DataFrame([["q1", "fox", "d1", "all the fox were fox"]], columns=["qid", "query", "docno", "body"])
-        from pyterrier.terrier.retrieve import TextScorer
+        from pyterrier.terrier.retriever import TextScorer
         scorer = TextScorer(wmodel="Tf")
         rtr = scorer(input)
         self.assertEqual(1, len(rtr))
@@ -78,7 +78,7 @@ def test_scoring_manual(self):
 
     def test_scoring_manual_background(self):
         input = pd.DataFrame([["q1", "fox", "d1", "all the fox were fox"]], columns=["qid", "query", "docno", "body"])
-        from pyterrier.terrier.retrieve import TextScorer
+        from pyterrier.terrier.retriever import TextScorer
         scorer = TextScorer(wmodel="Tf", background_index=pt.get_dataset("vaswani").get_index())
         rtr = scorer(input)
         self.assertEqual(1, len(rtr))
@@ -94,7 +94,7 @@ def test_scoring_manual_background(self):
         
     def test_scoring_qe(self):
         input = pd.DataFrame([["q1", "fox", "d1", "all the fox were fox"]], columns=["qid", "query", "docno", "body"])
-        scorer = pt.terrier.retrieve.TextIndexProcessor(pt.rewrite.Bo1QueryExpansion, takes="docs", returns="queries")
+        scorer = pt.terrier.retriever.TextIndexProcessor(pt.rewrite.Bo1QueryExpansion, takes="docs", returns="queries")
         rtr = scorer(input)
         self.assertTrue("qid" in rtr.columns)
         self.assertTrue("query" in rtr.columns)

From 8347a3be944e7a2bca45b05025f620eed6c87de5 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 10 Aug 2024 13:48:36 +0100
Subject: [PATCH 134/153] fix typo

---
 docs/anserini.rst | 2 +-
 docs/apply.rst    | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/anserini.rst b/docs/anserini.rst
index f9ae5407..aa48540e 100644
--- a/docs/anserini.rst
+++ b/docs/anserini.rst
@@ -24,7 +24,7 @@ Comparative retrieval from Anserini and Terrier::
 
 AnseriniBatchRetrieve can also be used as a re-ranker::
 
-    BM25_tr = pt.terrier.Retrieverr(trIndex, wmodel="BM25")
+    BM25_tr = pt.terrier.Retriever(trIndex, wmodel="BM25")
     QLD_ai = pt.anserini.AnseriniBatchRetrieve(luceneIndex, wmodel="QLD")
 
     pipe = BM25_tr >> QLD_ai
diff --git a/docs/apply.rst b/docs/apply.rst
index 61f9f732..a211094b 100644
--- a/docs/apply.rst
+++ b/docs/apply.rst
@@ -85,9 +85,9 @@ Its also possible to construct a transformer that makes a new column on a row-wi
 
 For instance, if the column you are creating is called rank_2, it might be created as follows::
 
-    pipe = pt.terrier.Retrieverr(index) >> pt.apply.rank_2(lambda row: row["rank"] * 2)
+    pipe = pt.terrier.Retriever(index) >> pt.apply.rank_2(lambda row: row["rank"] * 2)
 
 To create a transformer that drops a column, you can instead pass `drop=True` as a kwarg::
 
-    pipe = pt.terrier.Retrieverr(index, metadata=["docno", "text"] >> pt.text.scorer() >> pt.apply.text(drop=True)
+    pipe = pt.terrier.Retriever(index, metadata=["docno", "text"] >> pt.text.scorer() >> pt.apply.text(drop=True)
 

From e6da26b75ca193f65c8c6890f09f6f064dfb7761 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 10 Aug 2024 14:07:49 +0100
Subject: [PATCH 135/153] a bunch of BatchRetrieve -> Retriever (mostly in
 docs)

---
 docs/installation.rst                         |  2 +-
 docs/ltr.rst                                  |  4 +--
 docs/operators.rst                            | 18 ++++++------
 docs/parallel.rst                             |  6 ++--
 docs/pipeline_examples.md                     |  2 +-
 docs/rewrite.rst                              | 10 +++----
 docs/terrier-index-api.rst                    |  6 ++--
 docs/terrier-retrieval.rst                    | 26 ++++++++---------
 docs/text.rst                                 |  4 +--
 docs/transformer.rst                          | 28 +++++++++----------
 docs/tuning.rst                               |  6 ++--
 examples/notebooks/experiment.ipynb           |  2 +-
 examples/notebooks/indexing.ipynb             |  2 +-
 examples/notebooks/ltr.ipynb                  |  4 +--
 .../notebooks/retrieval_and_evaluation.ipynb  |  2 +-
 pyterrier/pipelines.py                        |  6 ++--
 pyterrier/terrier/retriever.py                |  4 +--
 pyterrier/terrier/rewrite.py                  |  2 +-
 pyterrier/utils.py                            |  2 +-
 19 files changed, 68 insertions(+), 68 deletions(-)

diff --git a/docs/installation.rst b/docs/installation.rst
index aa7fcdee..628917ed 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -61,7 +61,7 @@ describes how to manage this configuration.
 **Note:** Because these options affect the the JVM's settings, they need to be set before Java starts---for instance, at the top of
 a script/notebook before any Java components are loaded.
 
-**Starting Java.** PyTerrier will start java when you use a component that requires it, such as `BatchRetrieve`. However, sometimes
+**Starting Java.** PyTerrier will start java when you use a component that requires it, such as ``pt.terrier.Retriever``. However, sometimes
 you might want to start it early:
 
 .. autofunction:: pyterrier.java.init
diff --git a/docs/ltr.rst b/docs/ltr.rst
index 238e8198..e132b951 100644
--- a/docs/ltr.rst
+++ b/docs/ltr.rst
@@ -61,10 +61,10 @@ the `"features"` column, as follows:
 
 
 
-FeaturesBatchRetrieve
+FeaturesRetriever
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-When executing the pipeline above, the re-ranking of the documents again can be slow, as each separate Retrieve
+When executing the pipeline above, the re-ranking of the documents again can be slow, as each separate Retriever
 object has to re-access the inverted index. For this reason, PyTerrier provides a class called FeaturesRetriever,
 which allows multiple query dependent features to be calculated at once, by virtue of Terrier's Fat framework.
 
diff --git a/docs/operators.rst b/docs/operators.rst
index 5a553c35..cffc5772 100644
--- a/docs/operators.rst
+++ b/docs/operators.rst
@@ -29,7 +29,7 @@ Apply one transformation followed by another::
 
     #rewrites topics to include #1 etc
     sdm = pt.rewrite.SDM()
-    br = BatchRetrieve(index, "DPH")
+    br = pt.terrier.Retriever(index, "DPH")
 
     res = br.transform( sdm.transform(topics))
 
@@ -58,7 +58,7 @@ q1  test query #1(test query) #uw8(test query)
 
 NB: In practice the query reformulation generated by SDM() is more complex, due to the presence of weights etc in the resulting query.
 
-Then the final res dataframe would contain the results of applying BatchRetrieve on the rewritten queries, as follows:
+Then the final res dataframe would contain the results of applying a Retriever on the rewritten queries, as follows:
 
 === ============================================= ===== ===== ====
 qid query                                         docno score rank
@@ -69,7 +69,7 @@ q1  test query #1(test query) #uw8(test query)    d04   3.8   1
 
 NB: Then can also be used for retrieval and re-ranking pipelines, such as:: 
 
-    pipeline = BatchRetrieve(index, "DPH") >> BatchRetrieve(index, "BM25")
+    pipeline = pt.terrier.Retriever(index, "DPH") >> pt.terrier.Retriever(index, "BM25")
 
 
 Linear Combine and Scalar Factor (`+`, `*`)
@@ -80,8 +80,8 @@ different retrieval systems to be linearly combined (with weights).
 
 Instead of the following Python::
 
-    br_DPH = BatchRetrieve(index, "DPH")
-    br_BM25 = BatchRetrieve(index, "BM25")
+    br_DPH = pt.terrier.Retriever(index, "DPH")
+    br_BM25 = pt.terrier.Retriever(index, "BM25")
 
     res1 = br_DPH.transform(topics)
     res2 = br_BM25.transform(topics)
@@ -90,8 +90,8 @@ Instead of the following Python::
 
 We use binary `+` and `*` operators. This is natural, as it is intuitive to combine weighted retrieval functions using `+` and `*` ::
 
-    br_DPH = BatchRetrieve(index, "DPH")
-    br_BM25 = BatchRetrieve(index, "BM25")
+    br_DPH = pt.terrier.Retriever(index, "DPH")
+    br_BM25 = pt.terrier.Retriever(index, "BM25")
     res = (2* br_DPH + br_BM25).transform(topics)
 
 
@@ -136,8 +136,8 @@ The set that only includes documents that occur in the intersection (`&`)
 and union (`|`) of both retrieval sets. Scores and ranks are not returned - hence,
 the rankings documents would normally be re-scored::
 
-    BM25_br = BatchRetrieve(index, "BM25")
-    PL2_br = BatchRetrieve(index, "PL2")
+    BM25_br = pt.terrier.Retriever(index, "BM25")
+    PL2_br = pt.terrier.Retriever(index, "PL2")
 
     res_intersection = (BM25_br & PL2_br).transform(topics)
     res_union = (BM25_br | PL2_br).transform(topics)
diff --git a/docs/parallel.rst b/docs/parallel.rst
index f13745ba..0dca2c21 100644
--- a/docs/parallel.rst
+++ b/docs/parallel.rst
@@ -51,7 +51,7 @@ What to Parallelise
 Only transformers that can be `pickled <https://docs.python.org/3/library/pickle.html>`_. Transformers that use native code
 may not be possible to pickle. Some standard PyTerrier transformers have additional support for parallelisation:
 
- - Terrier retrieval: pt.terrier.Retriever(), pt.FeaturesBatchRetrieve()
+ - Terrier retrieval: pt.terrier.Retriever(), pt.terrier.FeaturesRetriever()
  - Anserini retrieval: pt.anserini.AnseriniBatchRetrieve()
 
 Pure python transformers, such as `pt.text.sliding()` are picklable. However, parallelising only `pt.text.sliding()` may not produce
@@ -63,8 +63,8 @@ consider the following pipeline::
 
     pipe = pt.terrier.Retriever(index, metadata=["docno", "text"] >> pt.text.sliding() >> pt.text.scorer() >> pt.text.max_passage()
 
-While BatchRetrieve might represent the slowest component of the pipeline, it might make sense to parallelise pipe as a whole,
-rather than just BatchRetrieve, as then only the queries and final results  need to be passed betwene processes. Indeed among the
+While ``Retriever`` might represent the slowest component of the pipeline, it might make sense to parallelise pipe as a whole,
+rather than just ``Retriever``, as then only the queries and final results  need to be passed betwene processes. Indeed among the
 following semantically equivalent pipelines, we expect `parallel_pipe0`  and `parallel_pipe2`  to be faster than `parallel_pipe1`::
 
     parallel_pipe0 = pt.terrier.Retriever(index, metadata=["docno", "text"]).parallel() >> pt.text.sliding() >> pt.text.scorer() >> pt.text.max_passage()
diff --git a/docs/pipeline_examples.md b/docs/pipeline_examples.md
index c68ed561..1b1eba11 100644
--- a/docs/pipeline_examples.md
+++ b/docs/pipeline_examples.md
@@ -31,7 +31,7 @@ qe = (pt.terrier.Retriever(indexref, wmodel="BM25") >>
 ```
 
 The QueryExpansion() object has the following constructor parameters:
- - `index_like` - which index you are using to obtain the contents of the documents. This should match the preceeding BatchRetrieve. 
+ - `index_like` - which index you are using to obtain the contents of the documents. This should match the preceeding Retriever. 
  - `fb_docs` - number of feedback documents to examine
  - `fb_terms` - number of feedback terms to add to the query
 
diff --git a/docs/rewrite.rst b/docs/rewrite.rst
index 0a1729bd..f1588234 100644
--- a/docs/rewrite.rst
+++ b/docs/rewrite.rst
@@ -2,7 +2,7 @@ Query Rewriting & Expansion
 ---------------------------
 
 Query rewriting refers to changing the formulation of the query in order to improve the effectiveness of the
-search ranking. PyTerrier supplies a number of query rewriting transformers designed to work with BatchRetrieve.
+search ranking. PyTerrier supplies a number of query rewriting transformers designed to work with Retriever.
 
 Firstly, we differentiate between two forms of query rewriting:
 
@@ -32,7 +32,7 @@ For example, the query `pyterrier IR platform` would become `pyterrier IR platfo
 NB: Acutally, we have simplified the rewritten query - in practice, we also (a) set the weight of the proximity terms to be low using a `#combine()`
 operator and (b) set a proximity term weighting model.
 
-This transfomer is only compatible with BatchRetrieve, as Terrier supports the `#1` and `#uwN` complex query terms operators. The Terrier index must have blocks (positional information) recorded in the index.
+This transfomer is only compatible with Retriever, as Terrier supports the `#1` and `#uwN` complex query terms operators. The Terrier index must have blocks (positional information) recorded in the index.
 
 .. autoclass:: pyterrier.rewrite.SequentialDependence
     :members: transform
@@ -74,7 +74,7 @@ View the expansion terms::
 
 **Alternative Formulations**
 
-Note that it is also possible to configure BatchRetrieve to perform QE directly using controls,
+Note that it is also possible to configure Retriever to perform QE directly using controls,
 which will result in identical retrieval effectiveness::
 
     pipelineQE = pt.terrier.Retriever(index, wmodel="DPH", controls={"qemodel" : "Bo1", "qe" : "on"})
@@ -216,7 +216,7 @@ Summary of dataframe types:
 +--------------+-----------------------+-------------------------------------------+
 |stash_results |Q + "stashed_results_0"|qid, query, saved_docs_0                   |
 +--------------+-----------------------+-------------------------------------------+
-|BatchRetrieve |R + "stashed_results_0"|qid, query, docno, score, stashed_results_0|
+|Retriever     |R + "stashed_results_0"|qid, query, docno, score, stashed_results_0|
 +--------------+-----------------------+-------------------------------------------+
 |RM3           |Q + "stashed_results_0"|qid, query, query_0, stashed_results_0     |
 +--------------+-----------------------+-------------------------------------------+
@@ -225,5 +225,5 @@ Summary of dataframe types:
 |dph           |R                      |qid, query, docno, score, query_0          |
 +--------------+-----------------------+-------------------------------------------+
 
-In this example, we have a BatchRetrieve instance executed on the wiki_index before RM3, so we clear the
+In this example, we have a Retriever instance executed on the wiki_index before RM3, so we clear the
 document ranking columns when using `stash_results()`.
diff --git a/docs/terrier-index-api.rst b/docs/terrier-index-api.rst
index eec8b817..296671cb 100644
--- a/docs/terrier-index-api.rst
+++ b/docs/terrier-index-api.rst
@@ -26,7 +26,7 @@ IndexRefs can also be obtained from a PyTerrier dataset::
 
     indexref = dataset.get_index()
 
-IndexRef objects can be directly passed to BatchRetrieve::
+IndexRef objects can be directly passed to Retriever::
 
     pt.terrier.Retriever(indexref).search("chemical reactions")
 
@@ -36,7 +36,7 @@ If you want to access the underlying data structures, you need to use IndexFacto
     #or
     index = pt.IndexFactory.of("/path/to/data.properties")
 
-NB: BatchRetrieve will accept anything "index-like", i.e. a string location of an index, an IndexRef or an Index.
+NB: Retriever will accept anything "index-like", i.e. a string location of an index, an IndexRef or an Index.
 
 We can also ask for the index to be loaded into memory::
 
@@ -170,7 +170,7 @@ of the term (obtained from the Lexicon, in the form of the LexiconEntry), as wel
         score = wmodel.score(posting)
         print("%s with score %0.4f"  % (docno, score))
 
-Note that using BatchRetrieve or similar is probably an easier prospect for such a use case.
+Note that using Retriever or similar is probably an easier prospect for such a use case.
 
 Can I get the index as a corpus_iter()?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/docs/terrier-retrieval.rst b/docs/terrier-retrieval.rst
index ad779882..68d897bb 100644
--- a/docs/terrier-retrieval.rst
+++ b/docs/terrier-retrieval.rst
@@ -2,7 +2,7 @@ Terrier Retrieval
 -----------------
 
 terrier.Retriever is one of the most commonly used PyTerrier objects. It represents a retrieval transformation, 
-in which queries are mapped to retrieved documents. BatchRetrieve uses a pre-existing Terrier index data
+in which queries are mapped to retrieved documents. Retriever uses a pre-existing Terrier index data
 structure, typically saved on disk.
 
 
@@ -15,10 +15,10 @@ Typical usage::
 
     pt.Experiment([tf_idf, bm25, pl2], topic, qrels, eval_metrics=["map"])
 
-As Retrieve is a retrieval transformation, it takes as input dataframes with columns `["qid", "query"]`,
+As Retriever is a retrieval transformation, it takes as input dataframes with columns `["qid", "query"]`,
 and returns dataframes with columns `["qid", "query", "docno", "score", "rank"]`.
 
-However, Retrieve can also act as a re-ranker. In this scenario, it takes as input dataframes with 
+However, Retriever can also act as a re-ranker. In this scenario, it takes as input dataframes with 
 columns `["qid", "query", "docno"]`, and returns dataframes with columns `["qid", "query", "docno", "score", "rank"]`.
 
 For instance, to create a re-ranking pipeline that re-scores the top 100 BM25 documents using PL2::
@@ -27,7 +27,7 @@ For instance, to create a re-ranking pipeline that re-scores the top 100 BM25 do
     pl2 = pt.terrier.Retriever(index, wmodel="PL2")
     pipeline = (bm25 % 100) >> pl2
 
-Retrieve
+Retriever
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: pyterrier.terrier.Retriever
@@ -42,7 +42,7 @@ When using PyTerrier, we have to be aware of the underlying Terrier configuratio
 namely *properties* and *controls*. Properties are global configuration and were 
 traditionally configured by editing a `terrier.properties` file; In contrast, 
 controls are per-query configuration. In PyTerrier, we specify both when we construct
-the Retrieve object:
+the Retriever object:
 
 Common controls:
  - `"wmodel"` - the name of the weighting model. (This can also be specified using the wmodel kwarg).
@@ -55,19 +55,19 @@ Common controls:
 Common properties:
  - `"termpipelines"` - the default Terrier term pipeline configuration is `"Stopwords,PorterStemmer"`.
    If you have created an index with a different configuration, you will need to set the  `"termpipelines"`
-   property for *each* BatchRetrieve constructed.
+   property for *each* Retriever constructed.
 
 **Examples**::
 
-    # these two Retrieve instances are identical, using the same weighting model
+    # these two Retriever instances are identical, using the same weighting model
     bm25a = pt.terrier.Retriever(index, wmodel="BM25")
     bm25b = pt.terrier.Retriever(index, controls={"wmodel":"BM25"})
 
     # this one also applies query expansion inside Terrier
     bm25_qe = pt.terrier.Retriever(index, wmodel="BM25", controls={"qe":"on", "qemodel" : "Bo1"})
 
-    # when we introduce an unstemmed BatchRetrieve, we ensure to explicitly set the termpipelines
-    # for the other BatchRetrieve as well
+    # when we introduce an unstemmed Retriever, we ensure to explicitly set the termpipelines
+    # for the other Retriever as well
     bm25s_unstemmed = pt.terrier.Retriever(indexUS, wmodel="BM25", properties={"termpipelines" : ""})
     bm25s_stemmed = pt.terrier.Retriever(indexSS, wmodel="BM25", properties={"termpipelines" : "Stopwords,PorterStemmer"})
     
@@ -76,7 +76,7 @@ Common properties:
 Index-Like Objects
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-When working with Terrier indices, Retrieve allows can make use of:
+When working with Terrier indices, Retriever allows can make use of:
 
  - a string representing an index, such as "/path/to/data.properties"
  - a Terrier `IndexRef <http://terrier.org/docs/current/javadoc/org/terrier/querying/IndexRef.html>`_ object, constructed from a string, but which may also hold a reference to the existing index.
@@ -96,7 +96,7 @@ Good Practice::
     index = pt.IndexFactory.of("/path/to/data.properties")
     bm25 = pt.terrier.Retriever(index, wmodel="BM25")
     pl2 = pt.terrier.Retriever(index, wmodel="PL2")
-    # here, we share the index between two instances of BatchRetrieve
+    # here, we share the index between two instances of Retriever
 
 You can use the IndexFactory to specify that the index data structures to be loaded into memory::
 
@@ -138,13 +138,13 @@ unless the class name is fully qualified (e.g. `"com.example.MyTF"`).
 
 If you have your own Java weighting model instance (which extends the 
 `WeightingModel abstract class <http://terrier.org/docs/current/javadoc/org/terrier/matching/models/WeightingModel.html>`_, 
-you can load it and pass it directly to BatchRetrieve::
+you can load it and pass it directly to Retriever::
 
     mymodel = pt.autoclass("com.example.MyTF")()
     retr = pt.terrier.Retriever(indexref, wmodel=mymodel)
 
 More usefully, it is possible to express a weighting model entirely in Python, as a function or a lambda expression, that can be
-used by Terrier for scoring. In this example, we create a Terrier BatchRetrieve instance that scores based solely on term frequency::
+used by Terrier for scoring. In this example, we create a Terrier Retriever instance that scores based solely on term frequency::
 
     Tf = lambda keyFreq, posting, entryStats, collStats: posting.getFrequency()
     retr = pt.terrier.Retriever(indexref, wmodel=Tf)
diff --git a/docs/text.rst b/docs/text.rst
index 6859d886..20020b3e 100644
--- a/docs/text.rst
+++ b/docs/text.rst
@@ -41,10 +41,10 @@ using IterDictIndexer::
 During retrieval you will need to have the text stored as an attribute in your dataframes.
 
 This can be achieved in one of several ways:
- - requesting document metadata when using `BatchRetrieve`
+ - requesting document metadata when using `Retriever`
  - adding document metadata later using `get_text()`
 
-BatchRetrieve accepts a `metadata` keyword-argument which allows for additional metadata attributes to be retrieved.
+Retriever accepts a `metadata` keyword-argument which allows for additional metadata attributes to be retrieved.
 
 Alternatively, the `pt.text.get_text()` transformer can be used, which can extract metadata from a Terrier index
 or IRDSDataset for documents already retrieved. The main advantage of using IRDSDataset is that it supports
diff --git a/docs/transformer.rst b/docs/transformer.rst
index 90910a90..05ef93b4 100644
--- a/docs/transformer.rst
+++ b/docs/transformer.rst
@@ -12,19 +12,19 @@ PyTerrier's retrieval architecture is based on three concepts:
 In essence, a PyTerrier transformer is a class with a ``transform()`` method, which takes as input a dataframe, and changes it,
 before returning it. 
 
-+-------+---------+-------------+------------------+------------------------------+
-+ Input | Output  | Cardinality | Example          | Concrete Transformer Example |
-+=======+=========+=============+==================+==============================+
-|   Q   |    Q    |   1 to 1    | Query rewriting  | `pt.rewrite.SDM()`           |
-+-------+---------+-------------+------------------+------------------------------+
-|   Q   |  Q x D  |   1 to N    | Retrieval        | `pt.terrier.Retriever()`      |
-+-------+---------+-------------+------------------+------------------------------+
-| Q x D |    Q    |   N to 1    | Query expansion  | `pt.rewrite.RM3()`           |
-+-------+---------+-------------+------------------+------------------------------+
-| Q x D |  Q x D  |   1 to 1    | Re-ranking       | `pt.apply.doc_score()`       |
-+-------+---------+-------------+------------------+------------------------------+
-| Q x D |  Q x Df |   1 to 1    | Feature scoring  | `pt.FeaturesBatchRetrieve()` |
-+-------+---------+-------------+------------------+------------------------------+
++-------+---------+-------------+------------------+----------------------------------+
++ Input | Output  | Cardinality | Example          | Concrete Transformer Example     |
++=======+=========+=============+==================+==================================+
+|   Q   |    Q    |   1 to 1    | Query rewriting  | `pt.rewrite.SDM()`               |
++-------+---------+-------------+------------------+----------------------------------+
+|   Q   |  Q x D  |   1 to N    | Retrieval        | `pt.terrier.Retriever()`         |
++-------+---------+-------------+------------------+----------------------------------+
+| Q x D |    Q    |   N to 1    | Query expansion  | `pt.rewrite.RM3()`               |
++-------+---------+-------------+------------------+----------------------------------+
+| Q x D |  Q x D  |   1 to 1    | Re-ranking       | `pt.apply.doc_score()`           |
++-------+---------+-------------+------------------+----------------------------------+
+| Q x D |  Q x Df |   1 to 1    | Feature scoring  | `pt.terrier.FeaturesRetriever()` |
++-------+---------+-------------+------------------+----------------------------------+
 
 Optimisation
 ============
@@ -32,7 +32,7 @@ Optimisation
 Some operators applied to transformer can be optimised by the underlying search engine - for instance, cutting a ranking 
 earlier. So while the following two pipelines are semantically equivalent, the latter might be more efficient::
 
-    pipe1 = BatchRetrieve(index, "BM25") % 10
+    pipe1 = pt.terrier.Retrieve(index, "BM25") % 10
     pipe2 = pipe1.compile()
 
 Fitting
diff --git a/docs/tuning.rst b/docs/tuning.rst
index 1c542b04..0bc57a28 100644
--- a/docs/tuning.rst
+++ b/docs/tuning.rst
@@ -35,8 +35,8 @@ Examples
 Tuning BM25
 ~~~~~~~~~~~
 
-When using BatchRetrieve, the `b` parameter of the BM25 weighting model can be controled using the "bm25.b" control. 
-We must give this control an initial value when contructing the BatchRetrieve instance. Thereafter, the GridSearch
+When using Retriever, the `b` parameter of the BM25 weighting model can be controled using the "bm25.b" control. 
+We must give this control an initial value when contructing the Retriever instance. Thereafter, the GridSearch
 parameter dictionary can be constructed by refering to the instance of transformer that has that parameter::
 
     BM25 = pt.terrier.Retriever(index, wmodel="BM25", controls={"bm25.b" : 0.75})
@@ -120,7 +120,7 @@ The following is an example of scanning the parameters of BM25F for an index wit
     # check your index has exactly 2 fields
     assert 2 == index.getCollectionStatistics().getNumberOfFields()
 
-    # instantiate BatchRetrieve for BM25F
+    # instantiate Retriever for BM25F
     bm25f = pt.terrier.Retriever(
         index, 
         wmodel = 'BM25F', 
diff --git a/examples/notebooks/experiment.ipynb b/examples/notebooks/experiment.ipynb
index d9a4392a..84a80243 100644
--- a/examples/notebooks/experiment.ipynb
+++ b/examples/notebooks/experiment.ipynb
@@ -131,7 +131,7 @@
         "id": "OVmeqAX-67Am"
       },
       "source": [
-        "First create the BatchRetrieve objects with the configurartion that you wish to use"
+        "First create the Retriever objects with the configurartion that you wish to use"
       ]
     },
     {
diff --git a/examples/notebooks/indexing.ipynb b/examples/notebooks/indexing.ipynb
index a7e257c1..8f0ffe05 100644
--- a/examples/notebooks/indexing.ipynb
+++ b/examples/notebooks/indexing.ipynb
@@ -605,7 +605,7 @@
       "source": [
         "## Retrieval\n",
         "\n",
-        "Lets see how we can use one of these for retrieval. Retrieval takes place using the `BatchRetrieve` object, by invoking `transform()` method for one or more queries. For a quick test, you can give just pass your query to `transform()`. \n",
+        "Lets see how we can use one of these for retrieval. Retrieval takes place using the `Retriever` object, by invoking `transform()` method for one or more queries. For a quick test, you can give just pass your query to `transform()`. \n",
         "\n",
         "BatchRetrieve will return the results as a Pandas dataframe.\n"
       ]
diff --git a/examples/notebooks/ltr.ipynb b/examples/notebooks/ltr.ipynb
index 94a9eaef..89434f27 100644
--- a/examples/notebooks/ltr.ipynb
+++ b/examples/notebooks/ltr.ipynb
@@ -283,7 +283,7 @@
         "id": "Ye6ZpcZaMBjT"
       },
       "source": [
-        "*A note about efficiency*: doing retrieval, then re-ranking the documents again can be slow. For this reason, Terrier has a FeaturesBatchRetrieve. Lets try this:"
+        "*A note about efficiency*: doing retrieval, then re-ranking the documents again can be slow. For this reason, Terrier has a FeaturesRetriever. Lets try this:"
       ]
     },
     {
@@ -484,7 +484,7 @@
       },
       "source": [
         "Finally, often we want our initial retrieval score to be a feature also. We can do this in one of two ways:\n",
-        " - by adding a `SAMPLE` feature to FeaturesBatchRetrieve\n",
+        " - by adding a `SAMPLE` feature to FeaturesRetriever\n",
         " - or in the original feature-union definition, including an identity Transformer\n",
         "\n",
         "In doing so, the BM25 score (13.47 andf 12.51) are now copied in as the first position of the features column."
diff --git a/examples/notebooks/retrieval_and_evaluation.ipynb b/examples/notebooks/retrieval_and_evaluation.ipynb
index 51686c42..bb3caa5b 100644
--- a/examples/notebooks/retrieval_and_evaluation.ipynb
+++ b/examples/notebooks/retrieval_and_evaluation.ipynb
@@ -306,7 +306,7 @@
         "id": "4-fwnGg-lIzM"
       },
       "source": [
-        "Create BatchRetrieve object\n",
+        "Create Retriever object\n",
         "\n",
         "You can optionally set the controls and the properties by passing a dictionary to the 'controls' and 'properties' arguments\n",
         "or by calling setControl or setControls methods on a created object, or use the default controls.\n",
diff --git a/pyterrier/pipelines.py b/pyterrier/pipelines.py
index 332b4251..5d4fe3c8 100644
--- a/pyterrier/pipelines.py
+++ b/pyterrier/pipelines.py
@@ -803,7 +803,7 @@ def GridScan(
     must be specified. The trec_eval measure names can be optionally specified.
     The transformers being tuned, and their respective parameters are named in the param_dict. The parameter being
     varied must be changable using the :func:`set_parameter()` method. This means instance variables,
-    as well as controls in the case of BatchRetrieve.
+    as well as controls in the case of Retriever.
 
     Args:
         pipeline(Transformer): a transformer or pipeline
@@ -852,7 +852,7 @@ def GridScan(
         metrics = [metrics]
 
     # Store the all parameter names and candidate values into a dictionary, keyed by a tuple of the transformer and the parameter name
-    # such as {(BatchRetrieve, 'wmodel'): ['BM25', 'PL2'], (BatchRetrieve, 'c'): [0.1, 0.2, 0.3], (Bla, 'lr'): [0.001, 0.01, 0.1]}
+    # such as {(Retriever, 'wmodel'): ['BM25', 'PL2'], (Retriever, 'c'): [0.1, 0.2, 0.3], (Bla, 'lr'): [0.001, 0.01, 0.1]}
     candi_dict={}
     for tran, param_set in params.items():
         for param_name, values in param_set.items():
@@ -878,7 +878,7 @@ def _evaluate_one_setting(keys, values):
         # Set the parameter value in the corresponding transformer of the pipeline
         for (tran, param_name), value in params.items():
             tran.set_parameter(param_name, value)
-            # such as (BatchRetrieve, 'wmodel', 'BM25')
+            # such as (Retriever, 'wmodel', 'BM25')
             parameter_list.append( (tran, param_name, value) )
             
         time, eval_scores = _run_and_evaluate(pipeline, topics, qrels, metrics, perquery=False, batch_size=batch_size)
diff --git a/pyterrier/terrier/retriever.py b/pyterrier/terrier/retriever.py
index b07cce09..f6851fb3 100644
--- a/pyterrier/terrier/retriever.py
+++ b/pyterrier/terrier/retriever.py
@@ -123,7 +123,7 @@ def _from_dataset(dataset : Union[str,Dataset],
     raise ValueError("No .args.json files found for %s" % str(classnames))
                 
 @pt.java.required
-class Retrieve(BatchRetrieveBase):
+class Retriever(BatchRetrieveBase):
     """
     Use this class for retrieval by Terrier
     """
@@ -546,7 +546,7 @@ def transform(self, topics_and_res):
 class TextScorer(TextIndexProcessor):
     """
         A re-ranker class, which takes the queries and the contents of documents, indexes the contents of the documents using a MemoryIndex, and performs ranking of those documents with respect to the queries.
-        Unknown kwargs are passed to BatchRetrieve.
+        Unknown kwargs are passed to Retriever.
 
         Arguments:
             takes(str): configuration - what is needed as input: `"queries"`, or `"docs"`. Default is `"docs"` since v0.8.
diff --git a/pyterrier/terrier/rewrite.py b/pyterrier/terrier/rewrite.py
index 9f6ccf45..6152d5d8 100644
--- a/pyterrier/terrier/rewrite.py
+++ b/pyterrier/terrier/rewrite.py
@@ -14,7 +14,7 @@ def tokenise(tokeniser : Union[str,TerrierTokeniser,FunctionType] = 'english', m
 
     Args:
         tokeniser(Union[str,TerrierTokeniser,FunctionType]): Defines what tokeniser should be used - either a Java tokeniser name in Terrier, a TerrierTokeniser instance, or a function that takes a str as input and returns a list of str.
-        matchop(bool): Whether query terms should be wrapped in matchops, to ensure they can be parsed by a Terrier BatchRetrieve transformer.
+        matchop(bool): Whether query terms should be wrapped in matchops, to ensure they can be parsed by a Terrier Retriever transformer.
     
     Example - use default tokeniser::
 
diff --git a/pyterrier/utils.py b/pyterrier/utils.py
index 67828b09..2ee631e8 100644
--- a/pyterrier/utils.py
+++ b/pyterrier/utils.py
@@ -133,7 +133,7 @@ def set_tqdm(type=None):
     """
         Set the tqdm progress bar type that Pyterrier will use internally.
         Many PyTerrier transformations can be expensive to apply in some settings - users can
-        view progress by using the verbose=True kwarg to many classes, such as BatchRetrieve.
+        view progress by using the verbose=True kwarg to many classes, such as terrier.Retriever.
 
         The `tqdm <https://tqdm.github.io/>`_ progress bar can be made prettier when using appropriately configured Jupyter notebook setups.
         We use this automatically when Google Colab is detected.

From bdcfa884f4ef19dffa02776cfab1b7316fe19a58 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Sat, 10 Aug 2024 14:09:40 +0100
Subject: [PATCH 136/153] merge error

---
 pyterrier/terrier/__init__.py  |  2 +-
 pyterrier/terrier/retriever.py | 30 +++++++++++++++---------------
 2 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
index 8cba694b..e1f0ff92 100644
--- a/pyterrier/terrier/__init__.py
+++ b/pyterrier/terrier/__init__.py
@@ -1,6 +1,6 @@
 # java stuff
 from pyterrier.terrier import java
-from pyterrier.terrier.java import configure, set_version, set_helper_version, enable_prf, extend_classpath, J, set_property, set_properties, run, version, check_version, check_helper_version
+from pyterrier.terrier.java import configure, set_version, set_helper_version, set_prf_version, extend_classpath, J, set_property, set_properties, run, version, check_version, check_helper_version
 from pyterrier.terrier.retriever import Retriever, FeaturesRetriever, TextScorer
 from pyterrier.terrier.index_factory import IndexFactory
 from pyterrier.terrier.stemmer import TerrierStemmer
diff --git a/pyterrier/terrier/retriever.py b/pyterrier/terrier/retriever.py
index f6851fb3..eeedb629 100644
--- a/pyterrier/terrier/retriever.py
+++ b/pyterrier/terrier/retriever.py
@@ -106,11 +106,11 @@ def _from_dataset(dataset : Union[str,Dataset],
 
     classname = clz.__name__
     classnames = [classname]
-    if classname == 'Retrieve':
+    if classname == 'Retriever':
         classnames.append('BatchRetrieve')
     # we need to look for BatchRetrievel.args.json for legacy support
     for c in classnames:
-        # now look for, e.g., BatchRetrieve.args.json file, which will define the args for Retrieve, e.g. stemming
+        # now look for, e.g., BatchRetrieve.args.json file, which will define the args for Retriever, e.g. stemming
         indexdir = indexref #os.path.dirname(indexref.toString())
         argsfile = os.path.join(indexdir, classname + ".args.json")
         if os.path.exists(argsfile):
@@ -150,7 +150,7 @@ def from_dataset(dataset : Union[str,Dataset],
             version='latest',            
             **kwargs):
         """
-        Instantiates a Retrieve object from a pre-built index access via a dataset.
+        Instantiates a Retriever object from a pre-built index access via a dataset.
         Pre-built indices are ofen provided via the `Terrier Data Repository <http://data.terrier.org/>`_.
 
         Examples::
@@ -170,7 +170,7 @@ def from_dataset(dataset : Union[str,Dataset],
          - `terrier_unstemmed_text` - as per `terrier_stemmed`, but also containing the raw text of the documents
 
         """
-        return _from_dataset(dataset, variant=variant, version=version, clz=Retrieve, **kwargs)
+        return _from_dataset(dataset, variant=variant, version=version, clz=Retriever, **kwargs)
 
     #: default_controls(dict): stores the default controls
     default_controls = {
@@ -207,7 +207,7 @@ def __init__(self, index_location, controls=None, properties=None, metadata=["do
         """
         super().__init__(kwargs)
         self.indexref = _parse_index_like(index_location)
-        self.properties = _mergeDicts(Retrieve.default_properties, properties)
+        self.properties = _mergeDicts(Retriever.default_properties, properties)
         self.concurrentIL = pt.java.autoclass("org.terrier.structures.ConcurrentIndexLoader")
         if pt.terrier.check_version(5.5) and "SimpleDecorateProcess" not in self.properties["querying.processes"]:
             self.properties["querying.processes"] += ",decorate:SimpleDecorateProcess"
@@ -219,7 +219,7 @@ def __init__(self, index_location, controls=None, properties=None, metadata=["do
         for key, value in self.properties.items():
             pt.terrier.J.ApplicationSetup.setProperty(str(key), str(value))
         
-        self.controls = _mergeDicts(Retrieve.default_controls, controls)
+        self.controls = _mergeDicts(Retriever.default_controls, controls)
         if wmodel is not None:
             from pyterrier.transformer import is_lambda, is_function
             if isinstance(wmodel, str):
@@ -582,21 +582,21 @@ class TextScorer(TextIndexProcessor):
     """
 
     def __init__(self, takes="docs", **kwargs):
-        super().__init__(Retrieve, takes=takes, **kwargs)
+        super().__init__(Retriever, takes=takes, **kwargs)
 
 
 @pt.java.required
-class FeaturesRetriever(Retrieve):
+class FeaturesRetriever(Retriever):
     """
     Use this class for retrieval with multiple features
     """
 
     #: FBR_default_controls(dict): stores the default properties for a FBR
-    FBR_default_controls = Retrieve.default_controls.copy()
+    FBR_default_controls = Retriever.default_controls.copy()
     FBR_default_controls["matching"] = "FatFeaturedScoringMatching,org.terrier.matching.daat.FatFull"
     del FBR_default_controls["wmodel"]
     #: FBR_default_properties(dict): stores the default properties
-    FBR_default_properties = Retrieve.default_properties.copy()
+    FBR_default_properties = Retriever.default_properties.copy()
 
     def __init__(self, index_location, features, controls=None, properties=None, threads=1, **kwargs):
         """
@@ -628,7 +628,7 @@ def __init__(self, index_location, features, controls=None, properties=None, thr
             assert pt.terrier.check_version(5.9), "Terrier 5.9 is required for this functionality, see https://github.com/terrier-org/terrier-core/pull/246"
             
         if threads > 1:
-            raise ValueError("Multi-threaded retrieval not yet supported by FeaturesBatchRetrieve")
+            raise ValueError("Multi-threaded retrieval not yet supported by FeaturesRetriever")
         
         super().__init__(index_location, controls, properties, **kwargs)
 
@@ -709,8 +709,8 @@ def transform(self, queries):
             assert not scores_provided
 
             if self.wmodel is None:
-                raise ValueError("We're in retrieval mode (input columns were "+str(queries.columns)+"), but wmodel is None. FeaturesBatchRetrieve requires a wmodel be set for identifying the candidate set. "
-                    +" Hint: wmodel argument for FeaturesBatchRetrieve, e.g. FeaturesBatchRetrieve(index, features, wmodel=\"DPH\")")
+                raise ValueError("We're in retrieval mode (input columns were "+str(queries.columns)+"), but wmodel is None. FeaturesRetriever requires a wmodel be set for identifying the candidate set. "
+                    +" Hint: wmodel argument for FeaturesRetriever, e.g. FeaturesRetriever(index, features, wmodel=\"DPH\")")
 
         if queries["qid"].dtype == np.int64:
             queries['qid'] = queries['qid'].astype(str)
@@ -809,8 +809,8 @@ def setup_rewrites():
     y = Wildcard.dot('y')
     z = Wildcard.dot('z')
     # two different match retrives
-    _br1 = Wildcard.symbol('_br1', Retrieve)
-    _br2 = Wildcard.symbol('_br2', Retrieve)
+    _br1 = Wildcard.symbol('_br1', Retriever)
+    _br2 = Wildcard.symbol('_br2', Retriever)
     _fbr = Wildcard.symbol('_fbr', FeaturesRetriever)
     
     # batch retrieves for the same index

From bf785eea6bccdb5cb61cd0e787b6362207c09023 Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Tue, 13 Aug 2024 13:18:06 +0100
Subject: [PATCH 137/153] fix warning message to correctly split out pkg defn

---
 pyterrier/java/_utils.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/pyterrier/java/_utils.py b/pyterrier/java/_utils.py
index 338be36e..191a4b5d 100644
--- a/pyterrier/java/_utils.py
+++ b/pyterrier/java/_utils.py
@@ -209,8 +209,10 @@ def legacy_init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=Tr
         deprecated_calls.append(f'pt.java.set_log_level({logging!r})')
 
     for package in boot_packages:
-        pt.java.add_package(*package.split(':')) # format: org:package:version:filetype (where version and filetype are optional)
-        deprecated_calls.append(f'pt.java.add_package({package!r})')
+        pkg_split = package.split(':')
+        pkg_string = ", ".join(f'"{w}"' for w in pkg_split)
+        pt.java.add_package(*pkg_split) # format: org:package:version:filetype (where version and filetype are optional)
+        deprecated_calls.append(f'pt.java.add_package({pkg_string})')
 
     for opt in jvm_opts:
         pt.java.add_option(opt)

From 4be5ee8a1fe9d9db7601f70d6ae7527f405d59cf Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Tue, 13 Aug 2024 13:18:20 +0100
Subject: [PATCH 138/153] upgrade for java branch

---
 tests/test_rewrite_rm3.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/tests/test_rewrite_rm3.py b/tests/test_rewrite_rm3.py
index 68e371a8..1ba811a8 100644
--- a/tests/test_rewrite_rm3.py
+++ b/tests/test_rewrite_rm3.py
@@ -5,10 +5,13 @@
 from .base import TempDirTestCase
 import pytest
 
-if not pt.started():
+if not pt.java.started():
     terrier_version = os.environ.get("TERRIER_VERSION", None)
     terrier_helper_version = os.environ.get("TERRIER_HELPER_VERSION", None)
-    pt.init(version=terrier_version, logging="DEBUG", helper_version=terrier_helper_version, boot_packages=["com.github.terrierteam:terrier-prf:-SNAPSHOT"])
+    pt.java.set_log_level('DEBUG')
+    pt.terrier.set_version(terrier_version)
+    pt.terrier.set_helper_version(terrier_helper_version)
+    pt.java.init() # optional, forces java initialisation
     TERRIER_PRF_ON_CLASSPATH = True
 else:
     TERRIER_PRF_ON_CLASSPATH = False
@@ -153,7 +156,7 @@ def test_rm3_end_to_end(self):
     def test_scoring_rm3_qe(self):
         expected = 'applypipeline:off fox^0.600000024'
         input = pd.DataFrame([["q1", "fox", "d1", "all the fox were fox", 3], ["q1", "fox", "d2", "brown fox jumps", 2]], columns=["qid", "query", "docno", "body", "score"])
-        scorer = pt.batchretrieve.TextIndexProcessor(pt.rewrite.RM3, takes="docs", returns="queries")
+        scorer = pt.terrier.retriever.TextIndexProcessor(pt.rewrite.RM3, takes="docs", returns="queries")
         rtr = scorer(input)
         self.assertTrue("qid" in rtr.columns)
         self.assertTrue("query" in rtr.columns)

From 87ccb05f120c121194eff647aa275bd886483138 Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Tue, 13 Aug 2024 13:18:33 +0100
Subject: [PATCH 139/153] make it easier to take env variables

---
 pyterrier/terrier/java.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 422121d3..13821af8 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -23,17 +23,20 @@
 
 @pt.java.before_init
 def set_version(version: Optional[str] = None):
-    configure['terrier_version'] = version
+    if version is not None:
+        configure['terrier_version'] = version
 
 
 @pt.java.before_init
 def set_helper_version(version: Optional[str] = None):
-    configure['helper_version'] = version
+    if version is not None:
+        configure['helper_version'] = version
 
 
 @pt.java.before_init
 def set_prf_version(version: Optional[str] = None):
-    configure['prf_version'] = version
+    if version is not None:
+        configure['prf_version'] = version
 
 
 class TerrierJavaInit(pt.java.JavaInitializer):

From 59e2adc18f5214ba306ce8ec5121131a20d78621 Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Tue, 13 Aug 2024 13:42:28 +0100
Subject: [PATCH 140/153] use !r repr for strings

---
 pyterrier/java/_utils.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/pyterrier/java/_utils.py b/pyterrier/java/_utils.py
index 191a4b5d..ee1107f9 100644
--- a/pyterrier/java/_utils.py
+++ b/pyterrier/java/_utils.py
@@ -209,9 +209,10 @@ def legacy_init(version=None, mem=None, packages=[], jvm_opts=[], redirect_io=Tr
         deprecated_calls.append(f'pt.java.set_log_level({logging!r})')
 
     for package in boot_packages:
+        # format: org:package:version:filetype (where version and filetype are optional)
         pkg_split = package.split(':')
-        pkg_string = ", ".join(f'"{w}"' for w in pkg_split)
-        pt.java.add_package(*pkg_split) # format: org:package:version:filetype (where version and filetype are optional)
+        pkg_string = ", ".join(f'{w!r}' for w in pkg_split)
+        pt.java.add_package(*pkg_split) 
         deprecated_calls.append(f'pt.java.add_package({pkg_string})')
 
     for opt in jvm_opts:

From 488bd038aeec5ad0acedf8f538740a49192a18a4 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Tue, 13 Aug 2024 16:20:08 +0100
Subject: [PATCH 141/153] Update push.yml

---
 .github/workflows/push.yml | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml
index 3875c5c1..3766e449 100644
--- a/.github/workflows/push.yml
+++ b/.github/workflows/push.yml
@@ -6,8 +6,7 @@ name: Continuous Testing
 on:
   push:
     branches: [ master ]
-  pull_request:
-    branches: [ master ]
+  pull_request: {}
 
 jobs:
   build:

From 813453669591f3615c5cc02ab76ce968e65d9ed2 Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Wed, 14 Aug 2024 10:56:58 +0100
Subject: [PATCH 142/153] deprecated modifies the class so need stubs for
 deprecated transformers

---
 pyterrier/terrier/__init__.py | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
index e1f0ff92..9389e728 100644
--- a/pyterrier/terrier/__init__.py
+++ b/pyterrier/terrier/__init__.py
@@ -12,9 +12,20 @@
 from deprecated import deprecated
 
 
-TerrierRetrieve = deprecated(version='0.11.0', reason="use pt.terrier.Retriever() instead")(Retriever)
-BatchRetrieve = deprecated(version='0.11.0', reason="use pt.terrier.Retriever() instead")(Retriever)
-FeaturesBatchRetrieve = deprecated(version='0.11.0', reason="use pt.terrier.FeaturesRetriever() instead")(FeaturesRetriever)
+@deprecated(version='0.11.0', reason="use pt.terrier.Retriever() instead")
+class TerrierRetrieve(Retriever):
+    pass
+
+
+@deprecated(version='0.11.0', reason="use pt.terrier.Retriever() instead")
+class BatchRetrieve(Retriever):
+    pass
+
+
+@deprecated(version='0.11.0', reason="use pt.terrier.FeaturesRetriever() instead")
+class FeaturesBatchRetrieve(FeaturesRetriever):
+    pass
+
 
 __all__ = [
     # java stuff

From 4eb0799ae7e327546e4adfdcc6157f70d5e85591 Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Wed, 14 Aug 2024 12:43:47 +0100
Subject: [PATCH 143/153] use tiebreak results for RM3

---
 tests/test_rewrite_rm3.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tests/test_rewrite_rm3.py b/tests/test_rewrite_rm3.py
index 1ba811a8..5dbe7316 100644
--- a/tests/test_rewrite_rm3.py
+++ b/tests/test_rewrite_rm3.py
@@ -11,6 +11,7 @@
     pt.java.set_log_level('DEBUG')
     pt.terrier.set_version(terrier_version)
     pt.terrier.set_helper_version(terrier_helper_version)
+    pt.terrier.set_prf_version('rm_tiebreak-SNAPSHOT')
     pt.java.init() # optional, forces java initialisation
     TERRIER_PRF_ON_CLASSPATH = True
 else:
@@ -25,7 +26,7 @@ class TestRewriteRm3(TempDirTestCase):
     @pytest.mark.skipif(not TERRIER_PRF_ON_CLASSPATH, reason="This test only works in isolation when terrier-prf is on the jnius classpath.")
     def test_rm3_expansion_for_query_compact_on_tf_idf(self):
         # top-retrieval results of TF-IDF and BM25 below change, so the RM3 weights differ
-        expected = 'applypipeline:off equip^0.037346367 sideband^0.027371584 ferrit^0.027371584 modul^0.027371584 suppli^0.037346367 design^0.056739070 unit^0.037346367 anod^0.037346367 compact^0.674414337 stabil^0.037346367'
+        expected = 'applypipeline:off equip^0.037346367 ferrit^0.027371584 modul^0.027371584 suppli^0.037346367 design^0.056739070 microwav^0.027371584 anod^0.037346367 unit^0.037346367 compact^0.674414337 stabil^0.037346367'
         
         indexref = pt.datasets.get_dataset("vaswani").get_index()
         queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
@@ -41,7 +42,7 @@ def test_rm3_expansion_for_query_compact_on_tf_idf(self):
     @pytest.mark.skipif(not TERRIER_PRF_ON_CLASSPATH, reason="This test only works in isolation when terrier-prf is on the jnius classpath.")
     def test_rm3_expansion_for_query_compact_on_bm25(self):
         # top-retrieval results of BM25 and TF-IDF above change, so the RM3 weights differ
-        expected = 'applypipeline:off equip^0.040264640 sideband^0.025508020 ferrit^0.025508020 modul^0.025508020 suppli^0.040264640 design^0.051008232 unit^0.040264640 anod^0.040264640 compact^0.671144485 stabil^0.040264640'
+        expected = 'applypipeline:off equip^0.040264644 ferrit^0.025508024 modul^0.025508024 suppli^0.040264644 design^0.051008239 microwav^0.025508024 anod^0.040264644 unit^0.040264644 compact^0.671144485 stabil^0.040264644'
         
         indexref = pt.datasets.get_dataset("vaswani").get_index()
         queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])

From d2491166057a5027ab42e3312390c189fcacaf9d Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Wed, 14 Aug 2024 14:01:08 +0100
Subject: [PATCH 144/153] reduce digits for term weights to avoid rounding
 errors on different platforms

---
 tests/test_rewrite_rm3.py | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/tests/test_rewrite_rm3.py b/tests/test_rewrite_rm3.py
index 5dbe7316..21f0a208 100644
--- a/tests/test_rewrite_rm3.py
+++ b/tests/test_rewrite_rm3.py
@@ -18,6 +18,15 @@
     TERRIER_PRF_ON_CLASSPATH = False
 
 
+def normalize_term_weights(term_weights, digits=7):
+    ret = ''
+    for i in term_weights.split():
+        if '^' in i:
+            i = i.split('^')
+            i = i[0] + '^' + i[1][:digits]
+        ret += ' ' + i
+    return ret.strip()
+
 class TestRewriteRm3(TempDirTestCase):
     """This is a set of unit tests for RM3 that can currently not run in the complete test suite, as the "com.github.terrierteam:terrier-prf:-SNAPSHOT" would have to be added to the boot classpath.
     As workaround, the RM3 tests that can not be executed within the complete test suite are added to this dedicated file, so that they can be executed in isolation by running pytest tests/test_rewrite_rm3.py.
@@ -37,7 +46,7 @@ def test_rm3_expansion_for_query_compact_on_tf_idf(self):
         actual = qe.transform(br.transform(queriesIn))
 
         self.assertEqual(len(actual), 1)
-        self.assertEqual(expected, actual.iloc[0]["query"])
+        self.assertEqual(normalize_term_weights(expected), normalize_term_weights(actual.iloc[0]["query"]))
 
     @pytest.mark.skipif(not TERRIER_PRF_ON_CLASSPATH, reason="This test only works in isolation when terrier-prf is on the jnius classpath.")
     def test_rm3_expansion_for_query_compact_on_bm25(self):
@@ -53,7 +62,7 @@ def test_rm3_expansion_for_query_compact_on_bm25(self):
         actual = qe.transform(br.transform(queriesIn))
 
         self.assertEqual(len(actual), 1)
-        self.assertEqual(expected, actual.iloc[0]["query"])
+        self.assertEqual(normalize_term_weights(expected), normalize_term_weights(actual.iloc[0]["query"]))
 
     @pytest.mark.skipif(not TERRIER_PRF_ON_CLASSPATH, reason="This test only works in isolation when terrier-prf is on the jnius classpath.")
     def test_axiomatic_qe_expansion_for_query_compact_on_bm25(self):

From 608419ad0b7d87c6ebf0a1c535b077aa2312ecc5 Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Wed, 14 Aug 2024 15:55:45 +0100
Subject: [PATCH 145/153] wip - docvectors support in FeaturesRetrieve

---
 pyterrier/terrier/retriever.py |  33 ++++++-
 tests/test_fbr_dv.py           | 170 +++++++++++++++++++++++++++++++++
 2 files changed, 198 insertions(+), 5 deletions(-)
 create mode 100644 tests/test_fbr_dv.py

diff --git a/pyterrier/terrier/retriever.py b/pyterrier/terrier/retriever.py
index eeedb629..1b1456e7 100644
--- a/pyterrier/terrier/retriever.py
+++ b/pyterrier/terrier/retriever.py
@@ -588,17 +588,24 @@ def __init__(self, takes="docs", **kwargs):
 @pt.java.required
 class FeaturesRetriever(Retriever):
     """
-    Use this class for retrieval with multiple features
+    Use this class for retrieval with multiple features. Features, typically weighting model, are specificed in the ``features`` list specified in the constructor. An index must always be specified.
+
+    This class can behave in two ways:
+      - if a ``wmodel`` kwarg is supplied to the constructor, it acts as a retriever. The expected input columns are ['qid', 'query']. The output columns are ['qid', 'query', 'docno', 'score', 'rank', 'features']
+      - if a ``wmodel`` kwarg is not supplied, it acts as feature extractor. The expected input columns are ['qid', 'query', 'docno']. The output columns are ['qid', 'query', 'docno', 'features']
+
+    Two underlying retrieval methods are supported: 'fat' and 'docvectors', which can be selected using the ``method`` kwarg of the constructor.
+     - 'fat' is more efficient for doing retrieval directly followed by adding more features. It relies only on a single access to the inverted index. 
+     - 'docvectors' is more efficient for doing calculating features on an existing PyTerrier result set. It uses the direct index.
     """
 
     #: FBR_default_controls(dict): stores the default properties for a FBR
     FBR_default_controls = Retriever.default_controls.copy()
-    FBR_default_controls["matching"] = "FatFeaturedScoringMatching,org.terrier.matching.daat.FatFull"
     del FBR_default_controls["wmodel"]
     #: FBR_default_properties(dict): stores the default properties
     FBR_default_properties = Retriever.default_properties.copy()
 
-    def __init__(self, index_location, features, controls=None, properties=None, threads=1, **kwargs):
+    def __init__(self, index_location, features, controls=None, properties=None, threads=1, method='fat', **kwargs):
         """
             Init method
 
@@ -607,6 +614,7 @@ def __init__(self, index_location, features, controls=None, properties=None, thr
                 features(list): List of features to use
                 controls(dict): A dictionary with the control names and values
                 properties(dict): A dictionary with the property keys and values
+                method(str): A string containing either 'fat' or 'docvectors'/'dv'
                 verbose(bool): If True transform method will display progress
                 num_results(int): Number of results to retrieve. 
         """
@@ -622,6 +630,16 @@ def __init__(self, index_location, features, controls=None, properties=None, thr
             self.wmodel = kwargs["wmodel"]
         if "wmodel" in controls:
             self.wmodel = controls["wmodel"]
+
+        assert method in ['fat', 'docvectors', 'dv']
+        self.method = method
+
+        if self.method == 'fat':
+            controls["matching"] = "FatFeaturedScoringMatching,org.terrier.matching.daat.FatFull"
+        elif self.method in ['docvectors', 'dv']:
+            assert pt.terrier.check_version(5.10), "Terrier 5.10 is required for this docvectors-based feature retrieval"
+            # TODO check for direct index?
+            controls["matching"] = "DVFeaturedScoringMatching,org.terrier.matching.daat.Full"
         
         # check for terrier-core#246 bug usiung FatFull
         if self.wmodel is not None:    
@@ -645,7 +663,8 @@ def __getstate__(self):
                 'properties' : self.properties, 
                 'metadata' : self.metadata,
                 'features' : self.features,
-                'wmodel' : self.wmodel
+                'wmodel' : self.wmodel,
+                'method' : self.method
                 #TODO consider the context state?
                 }
 
@@ -654,6 +673,7 @@ def __setstate__(self, d):
         self.metadata = d["metadata"]
         self.features = d["features"]
         self.wmodel = d["wmodel"]
+        self.method = d['method']
         self.properties.update(d["properties"])
         for key,value in d["properties"].items():
             pt.terrier.J.ApplicationSetup.setProperty(key, str(value))
@@ -758,7 +778,10 @@ def transform(self, queries):
                     else:
                         srq.setControl("wmodel", self.wmodel)
                 matching_config_factory.build()
-                srq.setControl("matching", ",".join(["FatFeaturedScoringMatching","ScoringMatchingWithFat", srq.getControl("matching")]))
+                if self.method == 'fat':
+                    srq.setControl("matching", ",".join(["FatFeaturedScoringMatching","ScoringMatchingWithFat", srq.getControl("matching")]))
+                elif self.method in ['docvectors', 'dv']:
+                    srq.setControl("matching", ",".join(["DVFeaturedScoringMatching", srq.getControl("matching")]))
             
             self.manager.runSearchRequest(srq)
             srq = pt.java.cast('org.terrier.querying.Request', srq)
diff --git a/tests/test_fbr_dv.py b/tests/test_fbr_dv.py
new file mode 100644
index 00000000..9d61ef69
--- /dev/null
+++ b/tests/test_fbr_dv.py
@@ -0,0 +1,170 @@
+import pandas as pd
+import unittest
+import os
+import pyterrier as pt
+from .base import BaseTestCase
+import warnings
+
+class TestFeaturesBatchRetrieve(BaseTestCase):
+
+    def test_fbr_reranking(self):
+        if not pt.terrier.check_version("5.4"):
+            self.skipTest("Requires Terrier 5.4")
+        # this test examines the use of ScoringMatchingWithFat 
+        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
+        indexref = JIR.of(self.here + "/fixtures/index/data.properties")
+        # we only want a candidate set of 2 documents
+        firstpass = pt.BatchRetrieve(indexref, wmodel="BM25") % 2
+        pipe = firstpass >> pt.FeaturesBatchRetrieve(indexref, features=["WMODEL:DPH", "WMODEL:PL2"], method='docvectors')
+        input = pd.DataFrame([["1", "Stability"]], columns=['qid', 'query'])
+
+        result0 = firstpass(input)
+        result0_map = { row.docno : row.score for row in result0.itertuples() }
+
+        result = pipe.transform(input)
+        self.assertTrue("qid" in result.columns)
+        self.assertTrue("docno" in result.columns)
+        self.assertTrue("score" in result.columns)
+        self.assertTrue("features" in result.columns)
+        self.assertEqual(2, len(result))
+        self.assertEqual(result.iloc[0]["features"].size, 2)
+        
+        result1S_map = { row.docno : row.score for row in result.itertuples() }
+        self.assertEqual(result0_map, result1S_map)
+
+        pipe_simple = firstpass >> (pt.BatchRetrieve(indexref, wmodel="DPH") ** pt.BatchRetrieve(indexref, wmodel="PL2"))
+        result2 = pipe.transform(input)
+        import numpy as np
+        f1 = np.stack(result["features"].values)
+        f2 = np.stack(result2["features"].values)
+        self.assertTrue( np.array_equal(f1, f2))
+
+        result2S_map = { row.docno : row.score for row in result2.itertuples() }
+        self.assertEqual(result0_map, result2S_map)
+
+        result1F0_map = { row.docno : row.features[0] for row in result.itertuples() }
+        result2F0_map = { row.docno : row.features[0] for row in result2.itertuples() }
+        result1F1_map = { row.docno : row.features[1] for row in result.itertuples() }
+        result2F1_map = { row.docno : row.features[1] for row in result2.itertuples() }
+
+        self.assertEqual(result1F0_map, result2F0_map)
+        self.assertEqual(result1F1_map, result2F1_map)
+
+    def test_fbr_reranking2(self):
+        if not pt.terrier.check_version("5.4"):
+            self.skipTest("Requires Terrier 5.4")
+        # this test examines the use of ScoringMatchingWithFat, using a particular case known to with Terrier 5.3 
+        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
+        indexref = JIR.of(self.here + "/fixtures/index/data.properties")
+        # we only want a candidate set of 3 documents
+        firstpass = pt.BatchRetrieve(indexref, wmodel="BM25") % 3
+        pipe1 = firstpass >> pt.FeaturesBatchRetrieve(indexref, features=["WMODEL:PL2", "WMODEL:Tf"], method='docvectors')
+        pipe2 = firstpass >> pt.BatchRetrieve(indexref, wmodel="PL2")
+        pipe3 = firstpass >> pt.BatchRetrieve(indexref, wmodel="Tf")
+        
+        input = pd.DataFrame([["1", "Stability"]], columns=['qid', 'query'])
+        result0 = firstpass.transform(input)
+        result1 = pipe1.transform(input)
+        result2 = pipe2.transform(input)
+        result3 = pipe3.transform(input)
+
+        print(result1)
+        print(result3)
+        #BM25 score
+        result0_map = { row.docno : row.score for row in result0.itertuples() }
+        result1S_map = { row.docno : row.score for row in result1.itertuples() }
+        #Tf score
+        result12F_map = { row.docno : row.features[1] for row in result1.itertuples() }
+        result3_map = { row.docno : row.score for row in result3.itertuples() }
+        #PL2 score
+        result1F_map = { row.docno : row.features[0] for row in result1.itertuples() }
+        result2_map = { row.docno : row.score for row in result2.itertuples() }
+
+        
+
+        # check features scores
+        # NB: places can go no less than 4, as two documents have similar PL2 scores
+        for rank, row in enumerate(result0.itertuples()):
+            docno = row.docno
+            # check that score is unchanged
+            self.assertAlmostEqual(result1S_map[docno], result0_map[docno], msg="input score mismatch at rank %d for docno %s" % (rank, docno), places=4)
+            #  check that Tf feature score is correct
+            self.assertAlmostEqual(result12F_map[docno], result3_map[docno], msg="Tf feature score mismatch at rank %d for docno %s" % (rank, docno))
+            #  check that feature score is correct
+            self.assertAlmostEqual(result1F_map[docno], result2_map[docno], msg="PL2 feature score mismatch at rank %d for docno %s" % (rank, docno), places=4)
+
+    def test_fbr_ltr(self):
+        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
+        indexref = JIR.of(self.here + "/fixtures/index/data.properties")
+        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH", method='docvectors')
+        topics = pt.io.read_topics(self.here + "/fixtures/vaswani_npl/query-text.trec").head(3)
+        qrels = pt.io.read_qrels(self.here + "/fixtures/vaswani_npl/qrels")
+        res = retr.transform(topics)
+        res = res.merge(qrels, on=['qid', 'docno'], how='left').fillna(0)
+        from sklearn.ensemble import RandomForestClassifier
+        import numpy as np
+        #print(res.dtypes)
+        RandomForestClassifier(n_estimators=10).fit(np.stack(res["features"]), res["label"])
+
+    def test_fbr(self):
+        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
+        indexref = JIR.of(self.here + "/fixtures/index/data.properties")
+        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH", method='docvectors')
+        input = pd.DataFrame([["1", "Stability"]], columns=['qid', 'query'])
+        result = retr.transform(input)
+        self.assertTrue("qid" in result.columns)
+        self.assertTrue("docno" in result.columns)
+        self.assertTrue("score" in result.columns)
+        self.assertTrue("features" in result.columns)
+        self.assertTrue(len(result) > 0)
+        self.assertEqual(result.iloc[0]["features"].size, 1)
+
+        input = pd.DataFrame([["1", "Stability", "u1"]], columns=['qid', 'query', 'username'])
+        result = retr.transform(input)
+        self.assertIn("username", result.columns)
+
+        retrBasic = pt.BatchRetrieve(indexref)
+        if "matching" in retrBasic.controls:
+            self.assertNotEqual(retrBasic.controls["matching"], "FatFeaturedScoringMatching,org.terrier.matching.daat.FatFull")
+
+    def test_fbr_example(self):
+        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
+        indexref = JIR.of(self.here + "/fixtures/index/data.properties")
+        index = pt.IndexFactory.of(indexref)
+        # this ranker will make the candidate set of documents for each query
+        BM25 = pt.BatchRetrieve(index, wmodel="BM25")
+
+        # these rankers we will use to re-rank the BM25 results
+        TF_IDF =  pt.BatchRetrieve(index, wmodel="Dl")
+        PL2 =  pt.BatchRetrieve(index, wmodel="PL2")
+
+        pipe =  (BM25 %2) >> (TF_IDF ** PL2)
+        fbr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:Dl", "WMODEL:PL2"], wmodel="BM25", method='docvectors') % 2
+        resultP = pipe.search("chemical")
+        resultF = fbr.search("chemical")
+        pd.set_option('display.max_columns', None)
+
+        self.assertEqual(resultP.iloc[0].docno, resultF.iloc[0].docno)
+        self.assertEqual(resultP.iloc[0].score, resultF.iloc[0].score)
+        self.assertEqual(resultP.iloc[0].features[0], resultF.iloc[0].features[0])
+        self.assertEqual(resultP.iloc[0].features[1], resultF.iloc[0].features[1])
+
+        pipeCompiled = pipe.compile()
+        resultC = pipeCompiled.search("chemical")
+        self.assertEqual(resultP.iloc[0].docno, resultC.iloc[0].docno)
+        self.assertEqual(resultP.iloc[0].score, resultC.iloc[0].score)
+        self.assertEqual(resultP.iloc[0].features[0], resultC.iloc[0].features[0])
+        self.assertEqual(resultP.iloc[0].features[1], resultC.iloc[0].features[1])
+
+    def test_fbr_empty(self):
+        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
+        indexref = JIR.of(self.here + "/fixtures/index/data.properties")
+        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH", method='docvectors')
+        input = pd.DataFrame([["1", ""]], columns=['qid', 'query'])
+        with warnings.catch_warnings(record=True) as w:
+            result = retr.transform(input)
+            assert "Skipping empty query" in str(w[-1].message)
+        self.assertTrue(len(result) == 0)
+
+if __name__ == "__main__":
+    unittest.main()

From f903c9044e09b187d50d050c784a1097f1572406 Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Thu, 15 Aug 2024 17:38:36 +0100
Subject: [PATCH 146/153] improved test cases

---
 tests/test_fbr.py    | 120 +++++++++++++++++--------------
 tests/test_fbr_dv.py | 164 ++-----------------------------------------
 2 files changed, 73 insertions(+), 211 deletions(-)

diff --git a/tests/test_fbr.py b/tests/test_fbr.py
index 534242f0..d788f8f3 100644
--- a/tests/test_fbr.py
+++ b/tests/test_fbr.py
@@ -7,6 +7,10 @@
 
 class TestFeaturesBatchRetrieve(BaseTestCase):
 
+    def __init__(self, *args):
+        super().__init__(*args)
+        self.method = 'fat'
+
     def test_compile_to_fbr(self):
         indexref = pt.IndexRef.of(self.here + "/fixtures/index/data.properties")
         # we only want a candidate set of 2 documents
@@ -23,48 +27,48 @@ def test_compile_to_fbr(self):
         res4 = compiled(input)
         
 
-    def test_fbr_reranking(self):
+    def test_fbr_reranking2(self):
         if not pt.terrier.check_version("5.4"):
             self.skipTest("Requires Terrier 5.4")
-        # this test examines the use of ScoringMatchingWithFat 
+        # this test examines the use of ScoringMatchingWithFat, using a particular case known to with Terrier 5.3 
         JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
-        # we only want a candidate set of 2 documents
-        firstpass = pt.BatchRetrieve(indexref, wmodel="BM25") % 2
-        pipe = firstpass >> pt.FeaturesBatchRetrieve(indexref, features=["WMODEL:DPH", "WMODEL:PL2"])
+        # we only want a candidate set of 3 documents
+        firstpass = pt.BatchRetrieve(indexref, wmodel="BM25") % 3
+        pipe1 = firstpass >> pt.FeaturesBatchRetrieve(indexref, features=["WMODEL:PL2", "WMODEL:Tf"], method=self.method)
+        pipe2 = firstpass >> pt.BatchRetrieve(indexref, wmodel="PL2")
+        pipe3 = firstpass >> pt.BatchRetrieve(indexref, wmodel="Tf")
+        
         input = pd.DataFrame([["1", "Stability"]], columns=['qid', 'query'])
+        result0 = firstpass.transform(input)
+        result1 = pipe1.transform(input)
+        result2 = pipe2.transform(input)
+        result3 = pipe3.transform(input)
 
-        result0 = firstpass(input)
+        print(result1)
+        print(result3)
+        #BM25 score
         result0_map = { row.docno : row.score for row in result0.itertuples() }
+        result1S_map = { row.docno : row.score for row in result1.itertuples() }
+        #Tf score
+        result12F_map = { row.docno : row.features[1] for row in result1.itertuples() }
+        result3_map = { row.docno : row.score for row in result3.itertuples() }
+        #PL2 score
+        result1F_map = { row.docno : row.features[0] for row in result1.itertuples() }
+        result2_map = { row.docno : row.score for row in result2.itertuples() }
 
-        result = pipe.transform(input)
-        self.assertTrue("qid" in result.columns)
-        self.assertTrue("docno" in result.columns)
-        self.assertTrue("score" in result.columns)
-        self.assertTrue("features" in result.columns)
-        self.assertEqual(2, len(result))
-        self.assertEqual(result.iloc[0]["features"].size, 2)
         
-        result1S_map = { row.docno : row.score for row in result.itertuples() }
-        self.assertEqual(result0_map, result1S_map)
-
-        pipe_simple = firstpass >> (pt.BatchRetrieve(indexref, wmodel="DPH") ** pt.BatchRetrieve(indexref, wmodel="PL2"))
-        result2 = pipe.transform(input)
-        import numpy as np
-        f1 = np.stack(result["features"].values)
-        f2 = np.stack(result2["features"].values)
-        self.assertTrue( np.array_equal(f1, f2))
 
-        result2S_map = { row.docno : row.score for row in result2.itertuples() }
-        self.assertEqual(result0_map, result2S_map)
-
-        result1F0_map = { row.docno : row.features[0] for row in result.itertuples() }
-        result2F0_map = { row.docno : row.features[0] for row in result2.itertuples() }
-        result1F1_map = { row.docno : row.features[1] for row in result.itertuples() }
-        result2F1_map = { row.docno : row.features[1] for row in result2.itertuples() }
-
-        self.assertEqual(result1F0_map, result2F0_map)
-        self.assertEqual(result1F1_map, result2F1_map)
+        # check features scores
+        # NB: places can go no less than 4, as two documents have similar PL2 scores
+        for rank, row in enumerate(result0.itertuples()):
+            docno = row.docno
+            # check that score is unchanged
+            self.assertAlmostEqual(result1S_map[docno], result0_map[docno], msg="input score mismatch at rank %d for docno %s" % (rank, docno), places=4)
+            #  check that Tf feature score is correct
+            self.assertAlmostEqual(result12F_map[docno], result3_map[docno], msg="Tf feature score mismatch at rank %d for docno %s" % (rank, docno))
+            #  check that feature score is correct
+            self.assertAlmostEqual(result1F_map[docno], result2_map[docno], msg="PL2 feature score mismatch at rank %d for docno %s" % (rank, docno), places=4)
 
     def test_fbr_reranking2(self):
         if not pt.terrier.check_version("5.4"):
@@ -74,7 +78,7 @@ def test_fbr_reranking2(self):
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
         # we only want a candidate set of 3 documents
         firstpass = pt.BatchRetrieve(indexref, wmodel="BM25") % 3
-        pipe1 = firstpass >> pt.FeaturesBatchRetrieve(indexref, features=["WMODEL:PL2"])
+        pipe1 = firstpass >> pt.FeaturesBatchRetrieve(indexref, features=["WMODEL:PL2"], method=self.method)
         pipe2 = firstpass >> pt.BatchRetrieve(indexref, wmodel="PL2")
         
         input = pd.DataFrame([["1", "Stability"]], columns=['qid', 'query'])
@@ -106,7 +110,7 @@ def test_fbr_reranking2(self):
     def test_fbr_ltr(self):
         JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
-        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH")
+        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH", method=self.method)
         topics = pt.io.read_topics(self.here + "/fixtures/vaswani_npl/query-text.trec").head(3)
         qrels = pt.io.read_qrels(self.here + "/fixtures/vaswani_npl/qrels")
         res = retr.transform(topics)
@@ -119,7 +123,7 @@ def test_fbr_ltr(self):
     def test_fbr(self):
         JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
-        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH")
+        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH", method=self.method)
         input = pd.DataFrame([["1", "Stability"]], columns=['qid', 'query'])
         result = retr.transform(input)
         self.assertTrue("qid" in result.columns)
@@ -145,26 +149,38 @@ def test_fbr_example(self):
         BM25 = pt.BatchRetrieve(index, wmodel="BM25")
 
         # these rankers we will use to re-rank the BM25 results
-        TF_IDF =  pt.BatchRetrieve(index, wmodel="Dl")
+        Tf =  pt.BatchRetrieve(index, wmodel="Tf")
+        Dl =  pt.BatchRetrieve(index, wmodel="Dl")
         PL2 =  pt.BatchRetrieve(index, wmodel="PL2")
 
-        pipe =  (BM25 %2) >> (TF_IDF ** PL2)
-        fbr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:Dl", "WMODEL:PL2"], wmodel="BM25") % 2
+        pipe =  (BM25 %2) >> (Tf ** Dl ** PL2)
         resultP = pipe.search("chemical")
-        resultF = fbr.search("chemical")
-        pd.set_option('display.max_columns', None)
-
-        self.assertEqual(resultP.iloc[0].docno, resultF.iloc[0].docno)
-        self.assertEqual(resultP.iloc[0].score, resultF.iloc[0].score)
-        self.assertEqual(resultP.iloc[0].features[0], resultF.iloc[0].features[0])
-        self.assertEqual(resultP.iloc[0].features[1], resultF.iloc[0].features[1])
-
-        pipeCompiled = pipe.compile()
-        resultC = pipeCompiled.search("chemical")
-        self.assertEqual(resultP.iloc[0].docno, resultC.iloc[0].docno)
-        self.assertEqual(resultP.iloc[0].score, resultC.iloc[0].score)
-        self.assertEqual(resultP.iloc[0].features[0], resultC.iloc[0].features[0])
-        self.assertEqual(resultP.iloc[0].features[1], resultC.iloc[0].features[1])
+        
+        variants = [
+            pt.FeaturesBatchRetrieve(indexref, ["WMODEL:Tf", "WMODEL:Dl", "WMODEL:PL2"], wmodel="BM25", method=self.method) % 2,
+            BM25 %2 >> pt.FeaturesBatchRetrieve(indexref, ["WMODEL:Tf", "WMODEL:Dl", "WMODEL:PL2"], method=self.method) 
+        ]
+
+        for fbr in variants:
+            resultF = fbr.search("chemical")
+            print(resultP)
+            print(resultF)
+            
+            pd.set_option('display.max_columns', None)
+
+            self.assertEqual(resultP.iloc[0].docno, resultF.iloc[0].docno)
+            self.assertEqual(resultP.iloc[0].score, resultF.iloc[0].score)
+            self.assertEqual(resultP.iloc[0].features[0], resultF.iloc[0].features[0])
+            self.assertEqual(resultP.iloc[0].features[1], resultF.iloc[0].features[1])
+            self.assertEqual(resultP.iloc[0].features[2], resultF.iloc[0].features[2])
+
+            pipeCompiled = pipe.compile()
+            resultC = pipeCompiled.search("chemical")
+            self.assertEqual(resultP.iloc[0].docno, resultC.iloc[0].docno)
+            self.assertEqual(resultP.iloc[0].score, resultC.iloc[0].score)
+            self.assertEqual(resultP.iloc[0].features[0], resultC.iloc[0].features[0])
+            self.assertEqual(resultP.iloc[0].features[1], resultC.iloc[0].features[1])
+            self.assertEqual(resultP.iloc[0].features[2], resultC.iloc[0].features[2])
 
     def test_fbr_empty(self):
         JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
diff --git a/tests/test_fbr_dv.py b/tests/test_fbr_dv.py
index 9d61ef69..a2126c31 100644
--- a/tests/test_fbr_dv.py
+++ b/tests/test_fbr_dv.py
@@ -3,168 +3,14 @@
 import os
 import pyterrier as pt
 from .base import BaseTestCase
+from .test_fbr import TestFeaturesBatchRetrieve
 import warnings
 
-class TestFeaturesBatchRetrieve(BaseTestCase):
+class TestDVFeaturesBatchRetrieve(TestFeaturesBatchRetrieve):
 
-    def test_fbr_reranking(self):
-        if not pt.terrier.check_version("5.4"):
-            self.skipTest("Requires Terrier 5.4")
-        # this test examines the use of ScoringMatchingWithFat 
-        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
-        indexref = JIR.of(self.here + "/fixtures/index/data.properties")
-        # we only want a candidate set of 2 documents
-        firstpass = pt.BatchRetrieve(indexref, wmodel="BM25") % 2
-        pipe = firstpass >> pt.FeaturesBatchRetrieve(indexref, features=["WMODEL:DPH", "WMODEL:PL2"], method='docvectors')
-        input = pd.DataFrame([["1", "Stability"]], columns=['qid', 'query'])
-
-        result0 = firstpass(input)
-        result0_map = { row.docno : row.score for row in result0.itertuples() }
-
-        result = pipe.transform(input)
-        self.assertTrue("qid" in result.columns)
-        self.assertTrue("docno" in result.columns)
-        self.assertTrue("score" in result.columns)
-        self.assertTrue("features" in result.columns)
-        self.assertEqual(2, len(result))
-        self.assertEqual(result.iloc[0]["features"].size, 2)
-        
-        result1S_map = { row.docno : row.score for row in result.itertuples() }
-        self.assertEqual(result0_map, result1S_map)
-
-        pipe_simple = firstpass >> (pt.BatchRetrieve(indexref, wmodel="DPH") ** pt.BatchRetrieve(indexref, wmodel="PL2"))
-        result2 = pipe.transform(input)
-        import numpy as np
-        f1 = np.stack(result["features"].values)
-        f2 = np.stack(result2["features"].values)
-        self.assertTrue( np.array_equal(f1, f2))
-
-        result2S_map = { row.docno : row.score for row in result2.itertuples() }
-        self.assertEqual(result0_map, result2S_map)
-
-        result1F0_map = { row.docno : row.features[0] for row in result.itertuples() }
-        result2F0_map = { row.docno : row.features[0] for row in result2.itertuples() }
-        result1F1_map = { row.docno : row.features[1] for row in result.itertuples() }
-        result2F1_map = { row.docno : row.features[1] for row in result2.itertuples() }
-
-        self.assertEqual(result1F0_map, result2F0_map)
-        self.assertEqual(result1F1_map, result2F1_map)
-
-    def test_fbr_reranking2(self):
-        if not pt.terrier.check_version("5.4"):
-            self.skipTest("Requires Terrier 5.4")
-        # this test examines the use of ScoringMatchingWithFat, using a particular case known to with Terrier 5.3 
-        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
-        indexref = JIR.of(self.here + "/fixtures/index/data.properties")
-        # we only want a candidate set of 3 documents
-        firstpass = pt.BatchRetrieve(indexref, wmodel="BM25") % 3
-        pipe1 = firstpass >> pt.FeaturesBatchRetrieve(indexref, features=["WMODEL:PL2", "WMODEL:Tf"], method='docvectors')
-        pipe2 = firstpass >> pt.BatchRetrieve(indexref, wmodel="PL2")
-        pipe3 = firstpass >> pt.BatchRetrieve(indexref, wmodel="Tf")
-        
-        input = pd.DataFrame([["1", "Stability"]], columns=['qid', 'query'])
-        result0 = firstpass.transform(input)
-        result1 = pipe1.transform(input)
-        result2 = pipe2.transform(input)
-        result3 = pipe3.transform(input)
-
-        print(result1)
-        print(result3)
-        #BM25 score
-        result0_map = { row.docno : row.score for row in result0.itertuples() }
-        result1S_map = { row.docno : row.score for row in result1.itertuples() }
-        #Tf score
-        result12F_map = { row.docno : row.features[1] for row in result1.itertuples() }
-        result3_map = { row.docno : row.score for row in result3.itertuples() }
-        #PL2 score
-        result1F_map = { row.docno : row.features[0] for row in result1.itertuples() }
-        result2_map = { row.docno : row.score for row in result2.itertuples() }
-
-        
-
-        # check features scores
-        # NB: places can go no less than 4, as two documents have similar PL2 scores
-        for rank, row in enumerate(result0.itertuples()):
-            docno = row.docno
-            # check that score is unchanged
-            self.assertAlmostEqual(result1S_map[docno], result0_map[docno], msg="input score mismatch at rank %d for docno %s" % (rank, docno), places=4)
-            #  check that Tf feature score is correct
-            self.assertAlmostEqual(result12F_map[docno], result3_map[docno], msg="Tf feature score mismatch at rank %d for docno %s" % (rank, docno))
-            #  check that feature score is correct
-            self.assertAlmostEqual(result1F_map[docno], result2_map[docno], msg="PL2 feature score mismatch at rank %d for docno %s" % (rank, docno), places=4)
-
-    def test_fbr_ltr(self):
-        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
-        indexref = JIR.of(self.here + "/fixtures/index/data.properties")
-        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH", method='docvectors')
-        topics = pt.io.read_topics(self.here + "/fixtures/vaswani_npl/query-text.trec").head(3)
-        qrels = pt.io.read_qrels(self.here + "/fixtures/vaswani_npl/qrels")
-        res = retr.transform(topics)
-        res = res.merge(qrels, on=['qid', 'docno'], how='left').fillna(0)
-        from sklearn.ensemble import RandomForestClassifier
-        import numpy as np
-        #print(res.dtypes)
-        RandomForestClassifier(n_estimators=10).fit(np.stack(res["features"]), res["label"])
-
-    def test_fbr(self):
-        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
-        indexref = JIR.of(self.here + "/fixtures/index/data.properties")
-        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH", method='docvectors')
-        input = pd.DataFrame([["1", "Stability"]], columns=['qid', 'query'])
-        result = retr.transform(input)
-        self.assertTrue("qid" in result.columns)
-        self.assertTrue("docno" in result.columns)
-        self.assertTrue("score" in result.columns)
-        self.assertTrue("features" in result.columns)
-        self.assertTrue(len(result) > 0)
-        self.assertEqual(result.iloc[0]["features"].size, 1)
-
-        input = pd.DataFrame([["1", "Stability", "u1"]], columns=['qid', 'query', 'username'])
-        result = retr.transform(input)
-        self.assertIn("username", result.columns)
-
-        retrBasic = pt.BatchRetrieve(indexref)
-        if "matching" in retrBasic.controls:
-            self.assertNotEqual(retrBasic.controls["matching"], "FatFeaturedScoringMatching,org.terrier.matching.daat.FatFull")
-
-    def test_fbr_example(self):
-        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
-        indexref = JIR.of(self.here + "/fixtures/index/data.properties")
-        index = pt.IndexFactory.of(indexref)
-        # this ranker will make the candidate set of documents for each query
-        BM25 = pt.BatchRetrieve(index, wmodel="BM25")
-
-        # these rankers we will use to re-rank the BM25 results
-        TF_IDF =  pt.BatchRetrieve(index, wmodel="Dl")
-        PL2 =  pt.BatchRetrieve(index, wmodel="PL2")
-
-        pipe =  (BM25 %2) >> (TF_IDF ** PL2)
-        fbr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:Dl", "WMODEL:PL2"], wmodel="BM25", method='docvectors') % 2
-        resultP = pipe.search("chemical")
-        resultF = fbr.search("chemical")
-        pd.set_option('display.max_columns', None)
-
-        self.assertEqual(resultP.iloc[0].docno, resultF.iloc[0].docno)
-        self.assertEqual(resultP.iloc[0].score, resultF.iloc[0].score)
-        self.assertEqual(resultP.iloc[0].features[0], resultF.iloc[0].features[0])
-        self.assertEqual(resultP.iloc[0].features[1], resultF.iloc[0].features[1])
-
-        pipeCompiled = pipe.compile()
-        resultC = pipeCompiled.search("chemical")
-        self.assertEqual(resultP.iloc[0].docno, resultC.iloc[0].docno)
-        self.assertEqual(resultP.iloc[0].score, resultC.iloc[0].score)
-        self.assertEqual(resultP.iloc[0].features[0], resultC.iloc[0].features[0])
-        self.assertEqual(resultP.iloc[0].features[1], resultC.iloc[0].features[1])
-
-    def test_fbr_empty(self):
-        JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
-        indexref = JIR.of(self.here + "/fixtures/index/data.properties")
-        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH", method='docvectors')
-        input = pd.DataFrame([["1", ""]], columns=['qid', 'query'])
-        with warnings.catch_warnings(record=True) as w:
-            result = retr.transform(input)
-            assert "Skipping empty query" in str(w[-1].message)
-        self.assertTrue(len(result) == 0)
+    def __init__(self, *args):
+        super().__init__(*args)
+        self.method = 'dv'
 
 if __name__ == "__main__":
     unittest.main()

From b5b5b87840e66ecc41de23916ae27036b9994678 Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Thu, 15 Aug 2024 17:45:51 +0100
Subject: [PATCH 147/153] configurable DV matching implementation

---
 pyterrier/terrier/retriever.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/pyterrier/terrier/retriever.py b/pyterrier/terrier/retriever.py
index 1b1456e7..633b4026 100644
--- a/pyterrier/terrier/retriever.py
+++ b/pyterrier/terrier/retriever.py
@@ -584,6 +584,8 @@ class TextScorer(TextIndexProcessor):
     def __init__(self, takes="docs", **kwargs):
         super().__init__(Retriever, takes=takes, **kwargs)
 
+#_DVFeaturesMatching = "DVFeaturedScoringMatching2"
+_DVFeaturesMatching = "DVFeaturedScoringMatching"
 
 @pt.java.required
 class FeaturesRetriever(Retriever):
@@ -639,7 +641,7 @@ def __init__(self, index_location, features, controls=None, properties=None, thr
         elif self.method in ['docvectors', 'dv']:
             assert pt.terrier.check_version(5.10), "Terrier 5.10 is required for this docvectors-based feature retrieval"
             # TODO check for direct index?
-            controls["matching"] = "DVFeaturedScoringMatching,org.terrier.matching.daat.Full"
+            controls["matching"] = _DVFeaturesMatching + ",org.terrier.matching.daat.Full"
         
         # check for terrier-core#246 bug usiung FatFull
         if self.wmodel is not None:    
@@ -781,7 +783,7 @@ def transform(self, queries):
                 if self.method == 'fat':
                     srq.setControl("matching", ",".join(["FatFeaturedScoringMatching","ScoringMatchingWithFat", srq.getControl("matching")]))
                 elif self.method in ['docvectors', 'dv']:
-                    srq.setControl("matching", ",".join(["DVFeaturedScoringMatching", srq.getControl("matching")]))
+                    srq.setControl("matching", ",".join([_DVFeaturesMatching, srq.getControl("matching")]))
             
             self.manager.runSearchRequest(srq)
             srq = pt.java.cast('org.terrier.querying.Request', srq)

From 7c868e91c6c3b7565d6d736c847aa78bc30e7276 Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Thu, 15 Aug 2024 17:51:40 +0100
Subject: [PATCH 148/153] ensure 5.10 is there before tests will run

---
 tests/test_fbr.py    | 12 +++++++++++-
 tests/test_fbr_dv.py |  4 ++++
 2 files changed, 15 insertions(+), 1 deletion(-)

diff --git a/tests/test_fbr.py b/tests/test_fbr.py
index d788f8f3..f41ecd72 100644
--- a/tests/test_fbr.py
+++ b/tests/test_fbr.py
@@ -11,7 +11,11 @@ def __init__(self, *args):
         super().__init__(*args)
         self.method = 'fat'
 
+    def check_version(self):
+        pass
+
     def test_compile_to_fbr(self):
+        self.check_version()
         indexref = pt.IndexRef.of(self.here + "/fixtures/index/data.properties")
         # we only want a candidate set of 2 documents
         firstpass = pt.BatchRetrieve(indexref, wmodel="BM25")
@@ -30,6 +34,7 @@ def test_compile_to_fbr(self):
     def test_fbr_reranking2(self):
         if not pt.terrier.check_version("5.4"):
             self.skipTest("Requires Terrier 5.4")
+        self.check_version()
         # this test examines the use of ScoringMatchingWithFat, using a particular case known to with Terrier 5.3 
         JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
@@ -73,6 +78,7 @@ def test_fbr_reranking2(self):
     def test_fbr_reranking2(self):
         if not pt.terrier.check_version("5.4"):
             self.skipTest("Requires Terrier 5.4")
+        self.check_version()
         # this test examines the use of ScoringMatchingWithFat, using a particular case known to with Terrier 5.3 
         JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
@@ -108,6 +114,7 @@ def test_fbr_reranking2(self):
             self.assertAlmostEqual(result1F_map[docno], result2_map[docno], msg="feature score mismatch at rank %d for docno %s" % (rank, docno), places=4)
 
     def test_fbr_ltr(self):
+        self.check_version()
         JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
         retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH", method=self.method)
@@ -121,6 +128,7 @@ def test_fbr_ltr(self):
         RandomForestClassifier(n_estimators=10).fit(np.stack(res["features"]), res["label"])
 
     def test_fbr(self):
+        self.check_version()
         JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
         retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH", method=self.method)
@@ -142,6 +150,7 @@ def test_fbr(self):
             self.assertNotEqual(retrBasic.controls["matching"], "FatFeaturedScoringMatching,org.terrier.matching.daat.FatFull")
 
     def test_fbr_example(self):
+        self.check_version()
         JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
         index = pt.IndexFactory.of(indexref)
@@ -183,9 +192,10 @@ def test_fbr_example(self):
             self.assertEqual(resultP.iloc[0].features[2], resultC.iloc[0].features[2])
 
     def test_fbr_empty(self):
+        self.check_version()
         JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
-        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH")
+        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH", method=self.method)
         input = pd.DataFrame([["1", ""]], columns=['qid', 'query'])
         with warnings.catch_warnings(record=True) as w:
             result = retr.transform(input)
diff --git a/tests/test_fbr_dv.py b/tests/test_fbr_dv.py
index a2126c31..66b6607c 100644
--- a/tests/test_fbr_dv.py
+++ b/tests/test_fbr_dv.py
@@ -12,5 +12,9 @@ def __init__(self, *args):
         super().__init__(*args)
         self.method = 'dv'
 
+    def check_version(self):
+        if not pt.terrier.check_version("5.10"):
+            self.skipTest("Terrier 5.10 is required")
+
 if __name__ == "__main__":
     unittest.main()

From bc6cb518e09fd89a854d5d15947dd915bd3a6c9b Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Fri, 16 Aug 2024 09:22:39 +0100
Subject: [PATCH 149/153] Revert "Merge pull request #455 from terrier-org/dv"

This reverts commit 087c29d55b3066be8fd9a2fc92782482fad8237b, reversing
changes made to 813453669591f3615c5cc02ab76ce968e65d9ed2.

We'll make this as a seprate PR to main later
---
 pyterrier/terrier/retriever.py |  35 ++-------
 tests/test_fbr.py              | 132 +++++++++++++--------------------
 tests/test_fbr_dv.py           |  20 -----
 3 files changed, 58 insertions(+), 129 deletions(-)
 delete mode 100644 tests/test_fbr_dv.py

diff --git a/pyterrier/terrier/retriever.py b/pyterrier/terrier/retriever.py
index 633b4026..eeedb629 100644
--- a/pyterrier/terrier/retriever.py
+++ b/pyterrier/terrier/retriever.py
@@ -584,30 +584,21 @@ class TextScorer(TextIndexProcessor):
     def __init__(self, takes="docs", **kwargs):
         super().__init__(Retriever, takes=takes, **kwargs)
 
-#_DVFeaturesMatching = "DVFeaturedScoringMatching2"
-_DVFeaturesMatching = "DVFeaturedScoringMatching"
 
 @pt.java.required
 class FeaturesRetriever(Retriever):
     """
-    Use this class for retrieval with multiple features. Features, typically weighting model, are specificed in the ``features`` list specified in the constructor. An index must always be specified.
-
-    This class can behave in two ways:
-      - if a ``wmodel`` kwarg is supplied to the constructor, it acts as a retriever. The expected input columns are ['qid', 'query']. The output columns are ['qid', 'query', 'docno', 'score', 'rank', 'features']
-      - if a ``wmodel`` kwarg is not supplied, it acts as feature extractor. The expected input columns are ['qid', 'query', 'docno']. The output columns are ['qid', 'query', 'docno', 'features']
-
-    Two underlying retrieval methods are supported: 'fat' and 'docvectors', which can be selected using the ``method`` kwarg of the constructor.
-     - 'fat' is more efficient for doing retrieval directly followed by adding more features. It relies only on a single access to the inverted index. 
-     - 'docvectors' is more efficient for doing calculating features on an existing PyTerrier result set. It uses the direct index.
+    Use this class for retrieval with multiple features
     """
 
     #: FBR_default_controls(dict): stores the default properties for a FBR
     FBR_default_controls = Retriever.default_controls.copy()
+    FBR_default_controls["matching"] = "FatFeaturedScoringMatching,org.terrier.matching.daat.FatFull"
     del FBR_default_controls["wmodel"]
     #: FBR_default_properties(dict): stores the default properties
     FBR_default_properties = Retriever.default_properties.copy()
 
-    def __init__(self, index_location, features, controls=None, properties=None, threads=1, method='fat', **kwargs):
+    def __init__(self, index_location, features, controls=None, properties=None, threads=1, **kwargs):
         """
             Init method
 
@@ -616,7 +607,6 @@ def __init__(self, index_location, features, controls=None, properties=None, thr
                 features(list): List of features to use
                 controls(dict): A dictionary with the control names and values
                 properties(dict): A dictionary with the property keys and values
-                method(str): A string containing either 'fat' or 'docvectors'/'dv'
                 verbose(bool): If True transform method will display progress
                 num_results(int): Number of results to retrieve. 
         """
@@ -632,16 +622,6 @@ def __init__(self, index_location, features, controls=None, properties=None, thr
             self.wmodel = kwargs["wmodel"]
         if "wmodel" in controls:
             self.wmodel = controls["wmodel"]
-
-        assert method in ['fat', 'docvectors', 'dv']
-        self.method = method
-
-        if self.method == 'fat':
-            controls["matching"] = "FatFeaturedScoringMatching,org.terrier.matching.daat.FatFull"
-        elif self.method in ['docvectors', 'dv']:
-            assert pt.terrier.check_version(5.10), "Terrier 5.10 is required for this docvectors-based feature retrieval"
-            # TODO check for direct index?
-            controls["matching"] = _DVFeaturesMatching + ",org.terrier.matching.daat.Full"
         
         # check for terrier-core#246 bug usiung FatFull
         if self.wmodel is not None:    
@@ -665,8 +645,7 @@ def __getstate__(self):
                 'properties' : self.properties, 
                 'metadata' : self.metadata,
                 'features' : self.features,
-                'wmodel' : self.wmodel,
-                'method' : self.method
+                'wmodel' : self.wmodel
                 #TODO consider the context state?
                 }
 
@@ -675,7 +654,6 @@ def __setstate__(self, d):
         self.metadata = d["metadata"]
         self.features = d["features"]
         self.wmodel = d["wmodel"]
-        self.method = d['method']
         self.properties.update(d["properties"])
         for key,value in d["properties"].items():
             pt.terrier.J.ApplicationSetup.setProperty(key, str(value))
@@ -780,10 +758,7 @@ def transform(self, queries):
                     else:
                         srq.setControl("wmodel", self.wmodel)
                 matching_config_factory.build()
-                if self.method == 'fat':
-                    srq.setControl("matching", ",".join(["FatFeaturedScoringMatching","ScoringMatchingWithFat", srq.getControl("matching")]))
-                elif self.method in ['docvectors', 'dv']:
-                    srq.setControl("matching", ",".join([_DVFeaturesMatching, srq.getControl("matching")]))
+                srq.setControl("matching", ",".join(["FatFeaturedScoringMatching","ScoringMatchingWithFat", srq.getControl("matching")]))
             
             self.manager.runSearchRequest(srq)
             srq = pt.java.cast('org.terrier.querying.Request', srq)
diff --git a/tests/test_fbr.py b/tests/test_fbr.py
index f41ecd72..534242f0 100644
--- a/tests/test_fbr.py
+++ b/tests/test_fbr.py
@@ -7,15 +7,7 @@
 
 class TestFeaturesBatchRetrieve(BaseTestCase):
 
-    def __init__(self, *args):
-        super().__init__(*args)
-        self.method = 'fat'
-
-    def check_version(self):
-        pass
-
     def test_compile_to_fbr(self):
-        self.check_version()
         indexref = pt.IndexRef.of(self.here + "/fixtures/index/data.properties")
         # we only want a candidate set of 2 documents
         firstpass = pt.BatchRetrieve(indexref, wmodel="BM25")
@@ -31,60 +23,58 @@ def test_compile_to_fbr(self):
         res4 = compiled(input)
         
 
-    def test_fbr_reranking2(self):
+    def test_fbr_reranking(self):
         if not pt.terrier.check_version("5.4"):
             self.skipTest("Requires Terrier 5.4")
-        self.check_version()
-        # this test examines the use of ScoringMatchingWithFat, using a particular case known to with Terrier 5.3 
+        # this test examines the use of ScoringMatchingWithFat 
         JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
-        # we only want a candidate set of 3 documents
-        firstpass = pt.BatchRetrieve(indexref, wmodel="BM25") % 3
-        pipe1 = firstpass >> pt.FeaturesBatchRetrieve(indexref, features=["WMODEL:PL2", "WMODEL:Tf"], method=self.method)
-        pipe2 = firstpass >> pt.BatchRetrieve(indexref, wmodel="PL2")
-        pipe3 = firstpass >> pt.BatchRetrieve(indexref, wmodel="Tf")
-        
+        # we only want a candidate set of 2 documents
+        firstpass = pt.BatchRetrieve(indexref, wmodel="BM25") % 2
+        pipe = firstpass >> pt.FeaturesBatchRetrieve(indexref, features=["WMODEL:DPH", "WMODEL:PL2"])
         input = pd.DataFrame([["1", "Stability"]], columns=['qid', 'query'])
-        result0 = firstpass.transform(input)
-        result1 = pipe1.transform(input)
-        result2 = pipe2.transform(input)
-        result3 = pipe3.transform(input)
 
-        print(result1)
-        print(result3)
-        #BM25 score
+        result0 = firstpass(input)
         result0_map = { row.docno : row.score for row in result0.itertuples() }
-        result1S_map = { row.docno : row.score for row in result1.itertuples() }
-        #Tf score
-        result12F_map = { row.docno : row.features[1] for row in result1.itertuples() }
-        result3_map = { row.docno : row.score for row in result3.itertuples() }
-        #PL2 score
-        result1F_map = { row.docno : row.features[0] for row in result1.itertuples() }
-        result2_map = { row.docno : row.score for row in result2.itertuples() }
 
+        result = pipe.transform(input)
+        self.assertTrue("qid" in result.columns)
+        self.assertTrue("docno" in result.columns)
+        self.assertTrue("score" in result.columns)
+        self.assertTrue("features" in result.columns)
+        self.assertEqual(2, len(result))
+        self.assertEqual(result.iloc[0]["features"].size, 2)
         
+        result1S_map = { row.docno : row.score for row in result.itertuples() }
+        self.assertEqual(result0_map, result1S_map)
 
-        # check features scores
-        # NB: places can go no less than 4, as two documents have similar PL2 scores
-        for rank, row in enumerate(result0.itertuples()):
-            docno = row.docno
-            # check that score is unchanged
-            self.assertAlmostEqual(result1S_map[docno], result0_map[docno], msg="input score mismatch at rank %d for docno %s" % (rank, docno), places=4)
-            #  check that Tf feature score is correct
-            self.assertAlmostEqual(result12F_map[docno], result3_map[docno], msg="Tf feature score mismatch at rank %d for docno %s" % (rank, docno))
-            #  check that feature score is correct
-            self.assertAlmostEqual(result1F_map[docno], result2_map[docno], msg="PL2 feature score mismatch at rank %d for docno %s" % (rank, docno), places=4)
+        pipe_simple = firstpass >> (pt.BatchRetrieve(indexref, wmodel="DPH") ** pt.BatchRetrieve(indexref, wmodel="PL2"))
+        result2 = pipe.transform(input)
+        import numpy as np
+        f1 = np.stack(result["features"].values)
+        f2 = np.stack(result2["features"].values)
+        self.assertTrue( np.array_equal(f1, f2))
+
+        result2S_map = { row.docno : row.score for row in result2.itertuples() }
+        self.assertEqual(result0_map, result2S_map)
+
+        result1F0_map = { row.docno : row.features[0] for row in result.itertuples() }
+        result2F0_map = { row.docno : row.features[0] for row in result2.itertuples() }
+        result1F1_map = { row.docno : row.features[1] for row in result.itertuples() }
+        result2F1_map = { row.docno : row.features[1] for row in result2.itertuples() }
+
+        self.assertEqual(result1F0_map, result2F0_map)
+        self.assertEqual(result1F1_map, result2F1_map)
 
     def test_fbr_reranking2(self):
         if not pt.terrier.check_version("5.4"):
             self.skipTest("Requires Terrier 5.4")
-        self.check_version()
         # this test examines the use of ScoringMatchingWithFat, using a particular case known to with Terrier 5.3 
         JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
         # we only want a candidate set of 3 documents
         firstpass = pt.BatchRetrieve(indexref, wmodel="BM25") % 3
-        pipe1 = firstpass >> pt.FeaturesBatchRetrieve(indexref, features=["WMODEL:PL2"], method=self.method)
+        pipe1 = firstpass >> pt.FeaturesBatchRetrieve(indexref, features=["WMODEL:PL2"])
         pipe2 = firstpass >> pt.BatchRetrieve(indexref, wmodel="PL2")
         
         input = pd.DataFrame([["1", "Stability"]], columns=['qid', 'query'])
@@ -114,10 +104,9 @@ def test_fbr_reranking2(self):
             self.assertAlmostEqual(result1F_map[docno], result2_map[docno], msg="feature score mismatch at rank %d for docno %s" % (rank, docno), places=4)
 
     def test_fbr_ltr(self):
-        self.check_version()
         JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
-        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH", method=self.method)
+        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH")
         topics = pt.io.read_topics(self.here + "/fixtures/vaswani_npl/query-text.trec").head(3)
         qrels = pt.io.read_qrels(self.here + "/fixtures/vaswani_npl/qrels")
         res = retr.transform(topics)
@@ -128,10 +117,9 @@ def test_fbr_ltr(self):
         RandomForestClassifier(n_estimators=10).fit(np.stack(res["features"]), res["label"])
 
     def test_fbr(self):
-        self.check_version()
         JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
-        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH", method=self.method)
+        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH")
         input = pd.DataFrame([["1", "Stability"]], columns=['qid', 'query'])
         result = retr.transform(input)
         self.assertTrue("qid" in result.columns)
@@ -150,7 +138,6 @@ def test_fbr(self):
             self.assertNotEqual(retrBasic.controls["matching"], "FatFeaturedScoringMatching,org.terrier.matching.daat.FatFull")
 
     def test_fbr_example(self):
-        self.check_version()
         JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
         index = pt.IndexFactory.of(indexref)
@@ -158,44 +145,31 @@ def test_fbr_example(self):
         BM25 = pt.BatchRetrieve(index, wmodel="BM25")
 
         # these rankers we will use to re-rank the BM25 results
-        Tf =  pt.BatchRetrieve(index, wmodel="Tf")
-        Dl =  pt.BatchRetrieve(index, wmodel="Dl")
+        TF_IDF =  pt.BatchRetrieve(index, wmodel="Dl")
         PL2 =  pt.BatchRetrieve(index, wmodel="PL2")
 
-        pipe =  (BM25 %2) >> (Tf ** Dl ** PL2)
+        pipe =  (BM25 %2) >> (TF_IDF ** PL2)
+        fbr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:Dl", "WMODEL:PL2"], wmodel="BM25") % 2
         resultP = pipe.search("chemical")
-        
-        variants = [
-            pt.FeaturesBatchRetrieve(indexref, ["WMODEL:Tf", "WMODEL:Dl", "WMODEL:PL2"], wmodel="BM25", method=self.method) % 2,
-            BM25 %2 >> pt.FeaturesBatchRetrieve(indexref, ["WMODEL:Tf", "WMODEL:Dl", "WMODEL:PL2"], method=self.method) 
-        ]
-
-        for fbr in variants:
-            resultF = fbr.search("chemical")
-            print(resultP)
-            print(resultF)
-            
-            pd.set_option('display.max_columns', None)
-
-            self.assertEqual(resultP.iloc[0].docno, resultF.iloc[0].docno)
-            self.assertEqual(resultP.iloc[0].score, resultF.iloc[0].score)
-            self.assertEqual(resultP.iloc[0].features[0], resultF.iloc[0].features[0])
-            self.assertEqual(resultP.iloc[0].features[1], resultF.iloc[0].features[1])
-            self.assertEqual(resultP.iloc[0].features[2], resultF.iloc[0].features[2])
-
-            pipeCompiled = pipe.compile()
-            resultC = pipeCompiled.search("chemical")
-            self.assertEqual(resultP.iloc[0].docno, resultC.iloc[0].docno)
-            self.assertEqual(resultP.iloc[0].score, resultC.iloc[0].score)
-            self.assertEqual(resultP.iloc[0].features[0], resultC.iloc[0].features[0])
-            self.assertEqual(resultP.iloc[0].features[1], resultC.iloc[0].features[1])
-            self.assertEqual(resultP.iloc[0].features[2], resultC.iloc[0].features[2])
+        resultF = fbr.search("chemical")
+        pd.set_option('display.max_columns', None)
+
+        self.assertEqual(resultP.iloc[0].docno, resultF.iloc[0].docno)
+        self.assertEqual(resultP.iloc[0].score, resultF.iloc[0].score)
+        self.assertEqual(resultP.iloc[0].features[0], resultF.iloc[0].features[0])
+        self.assertEqual(resultP.iloc[0].features[1], resultF.iloc[0].features[1])
+
+        pipeCompiled = pipe.compile()
+        resultC = pipeCompiled.search("chemical")
+        self.assertEqual(resultP.iloc[0].docno, resultC.iloc[0].docno)
+        self.assertEqual(resultP.iloc[0].score, resultC.iloc[0].score)
+        self.assertEqual(resultP.iloc[0].features[0], resultC.iloc[0].features[0])
+        self.assertEqual(resultP.iloc[0].features[1], resultC.iloc[0].features[1])
 
     def test_fbr_empty(self):
-        self.check_version()
         JIR = pt.java.autoclass('org.terrier.querying.IndexRef')
         indexref = JIR.of(self.here + "/fixtures/index/data.properties")
-        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH", method=self.method)
+        retr = pt.FeaturesBatchRetrieve(indexref, ["WMODEL:PL2"], wmodel="DPH")
         input = pd.DataFrame([["1", ""]], columns=['qid', 'query'])
         with warnings.catch_warnings(record=True) as w:
             result = retr.transform(input)
diff --git a/tests/test_fbr_dv.py b/tests/test_fbr_dv.py
deleted file mode 100644
index 66b6607c..00000000
--- a/tests/test_fbr_dv.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import pandas as pd
-import unittest
-import os
-import pyterrier as pt
-from .base import BaseTestCase
-from .test_fbr import TestFeaturesBatchRetrieve
-import warnings
-
-class TestDVFeaturesBatchRetrieve(TestFeaturesBatchRetrieve):
-
-    def __init__(self, *args):
-        super().__init__(*args)
-        self.method = 'dv'
-
-    def check_version(self):
-        if not pt.terrier.check_version("5.10"):
-            self.skipTest("Terrier 5.10 is required")
-
-if __name__ == "__main__":
-    unittest.main()

From cad18c1310afdf4d48e1a78c9b262f2fd12965cd Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Fri, 16 Aug 2024 10:09:50 +0100
Subject: [PATCH 150/153] flash before RM3

---
 .github/workflows/push.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml
index 3766e449..826ca2f2 100644
--- a/.github/workflows/push.yml
+++ b/.github/workflows/push.yml
@@ -78,18 +78,18 @@ jobs:
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
         #flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
 
-    - name: RM3 unit tests
+    - name: Flash unit tests
       env:
         TERRIER_VERSION: ${{ matrix.terrier }}
       run: |
-        pytest -p no:faulthandler tests/test_rewrite_rm3.py
+        pytest -p no:faulthandler tests/test_flash.py
         # Hide underlying Jnius problem by disabling faulthandler: https://github.com/pytest-dev/pytest/issues/7634
 
-    - name: Flash unit tests
+    - name: RM3 unit tests
       env:
         TERRIER_VERSION: ${{ matrix.terrier }}
       run: |
-        pytest -p no:faulthandler tests/test_flash.py
+        pytest -p no:faulthandler tests/test_rewrite_rm3.py
         # Hide underlying Jnius problem by disabling faulthandler: https://github.com/pytest-dev/pytest/issues/7634
 
     - name: All unit tests

From d726e4a10315fd6bbb5e19134e432d19fd17a82f Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Fri, 16 Aug 2024 10:33:50 +0100
Subject: [PATCH 151/153] mergeing rm3 tests back into the main rewrite test
 file

---
 .github/workflows/push.yml |   7 --
 pyterrier/terrier/java.py  |  17 +---
 tests/base.py              |   3 +-
 tests/test_rewrite.py      | 163 ++++++++++++++++++++++++++++------
 tests/test_rewrite_rm3.py  | 174 -------------------------------------
 5 files changed, 141 insertions(+), 223 deletions(-)
 delete mode 100644 tests/test_rewrite_rm3.py

diff --git a/.github/workflows/push.yml b/.github/workflows/push.yml
index 826ca2f2..03b0ff84 100644
--- a/.github/workflows/push.yml
+++ b/.github/workflows/push.yml
@@ -85,13 +85,6 @@ jobs:
         pytest -p no:faulthandler tests/test_flash.py
         # Hide underlying Jnius problem by disabling faulthandler: https://github.com/pytest-dev/pytest/issues/7634
 
-    - name: RM3 unit tests
-      env:
-        TERRIER_VERSION: ${{ matrix.terrier }}
-      run: |
-        pytest -p no:faulthandler tests/test_rewrite_rm3.py
-        # Hide underlying Jnius problem by disabling faulthandler: https://github.com/pytest-dev/pytest/issues/7634
-
     - name: All unit tests
       env:
         TERRIER_VERSION: ${{ matrix.terrier }}
diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index 13821af8..f060192c 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -13,30 +13,21 @@
 _properties = None
 
 configure = pt.java.register_config('pt.terrier.java', {
-    'terrier_version': None,
-    'helper_version': None,
+    'terrier_version': os.environ.get("TERRIER_VERSION") or None,
+    'helper_version': os.environ.get("TERRIER_HELPER_VERSION") or None,
     'boot_packages': [],
     'force_download': True,
     'prf_version': '-SNAPSHOT',
 })
 
-
 @pt.java.before_init
 def set_version(version: Optional[str] = None):
-    if version is not None:
-        configure['terrier_version'] = version
+    configure['terrier_version'] = version
 
 
 @pt.java.before_init
 def set_helper_version(version: Optional[str] = None):
-    if version is not None:
-        configure['helper_version'] = version
-
-
-@pt.java.before_init
-def set_prf_version(version: Optional[str] = None):
-    if version is not None:
-        configure['prf_version'] = version
+    configure['helper_version'] = version
 
 
 class TerrierJavaInit(pt.java.JavaInitializer):
diff --git a/tests/base.py b/tests/base.py
index 13ec23f9..6691c73d 100644
--- a/tests/base.py
+++ b/tests/base.py
@@ -18,8 +18,7 @@ def __init__(self, *args, **kwargs):
             if terrier_helper_version is not None:
                 print("Testing with Terrier Helper version " + terrier_helper_version)
             
-            pt.terrier.set_version(terrier_version)
-            pt.terrier.set_helper_version(terrier_helper_version)
+            pt.terrier.set_prf_version('rm_tiebreak-SNAPSHOT')
             pt.java.set_log_level("DEBUG")
             # pt.java.add_option('-ea') can be added here to ensure that all Java assertions are met
             pt.java.init()
diff --git a/tests/test_rewrite.py b/tests/test_rewrite.py
index 90493e13..c4342dcd 100644
--- a/tests/test_rewrite.py
+++ b/tests/test_rewrite.py
@@ -8,6 +8,15 @@
 from matchpy import *
 from .base import TempDirTestCase
 
+def normalize_term_weights(term_weights, digits=7):
+    ret = ''
+    for i in term_weights.split():
+        if '^' in i:
+            i = i.split('^')
+            i = i[0] + '^' + i[1][:digits]
+        ret += ' ' + i
+    return ret.strip()
+
 class TestRewrite(TempDirTestCase):
 
     def test_stash_results_noclear(self):
@@ -221,41 +230,141 @@ def _sdm(self, freq):
             pt.Evaluate(br_sdm.transform(t), dataset.get_qrels(), metrics=["map"])["map"], 
             places=4)
 
-    # RM3 cannot be tested with current jnius, as it must be placed into the boot classpath
-    # As workaround for the moment, those RM3 tests are implemented in a single file tests/test_rewrite_rm3.py that is skipped when executing the complete pipeline, but are executed when run in isolation.
-    # def test_rm3(self):
-    #     dataset = pt.datasets.get_dataset("vaswani")
-    #     indexref = dataset.get_index()
+    def test_rm3_expansion_for_query_compact_on_tf_idf(self):
+        # top-retrieval results of TF-IDF and BM25 below change, so the RM3 weights differ
+        expected = 'applypipeline:off equip^0.037346367 ferrit^0.027371584 modul^0.027371584 suppli^0.037346367 design^0.056739070 microwav^0.027371584 anod^0.037346367 unit^0.037346367 compact^0.674414337 stabil^0.037346367'
+        
+        indexref = pt.datasets.get_dataset("vaswani").get_index()
+        queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
+
+        qe = pt.rewrite.RM3(indexref)
+        br = pt.BatchRetrieve(indexref, wmodel='TF_IDF')
+
+        actual = qe.transform(br.transform(queriesIn))
+
+        self.assertEqual(len(actual), 1)
+        self.assertEqual(normalize_term_weights(expected), normalize_term_weights(actual.iloc[0]["query"]))
 
-        # qe = pt.rewrite.RM3(indexref)
-        # br = pt.BatchRetrieve(indexref)
+    def test_rm3_expansion_for_query_compact_on_bm25(self):
+        # top-retrieval results of BM25 and TF-IDF above change, so the RM3 weights differ
+        expected = 'applypipeline:off equip^0.040264644 ferrit^0.025508024 modul^0.025508024 suppli^0.040264644 design^0.051008239 microwav^0.025508024 anod^0.040264644 unit^0.040264644 compact^0.671144485 stabil^0.040264644'
+        
+        indexref = pt.datasets.get_dataset("vaswani").get_index()
+        queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
+
+        qe = pt.rewrite.RM3(indexref)
+        br = pt.BatchRetrieve(indexref, wmodel='BM25')
+
+        actual = qe.transform(br.transform(queriesIn))
 
-        # queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
-        # res = br.transform(queriesIn)
+        self.assertEqual(len(actual), 1)
+        self.assertEqual(normalize_term_weights(expected), normalize_term_weights(actual.iloc[0]["query"]))
 
-        # queriesOut = qe.transform(res)
-        # self.assertEqual(len(queriesOut), 1)
-        # query = queriesOut.iloc[0]["query"]
-        # #self.assertTrue("compact^1.82230972" in query)
-        # self.assertTrue("applypipeline:off " in query)
+    def test_axiomatic_qe_expansion_for_query_compact_on_bm25(self):
+        # just ensure that AxiomaticQE results do not change
+        expected = 'applypipeline:off compact^1.000000000'
         
-        # pipe = br >> qe >> br
+        indexref = pt.datasets.get_dataset("vaswani").get_index()
+        queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
 
-        # # lets go faster, we only need 18 topics. qid 16 had a tricky case
-        # t = dataset.get_topics().head(18)
+        qe = pt.rewrite.AxiomaticQE(indexref)
+        br = pt.BatchRetrieve(indexref, wmodel='BM25')
 
-        # all_qe_res = pipe.transform(t)
-        # map_pipe = pt.Evaluate(all_qe_res, dataset.get_qrels(), metrics=["map"])["map"]
+        actual = qe.transform(br.transform(queriesIn))
 
-        # br_qe = pt.BatchRetrieve(indexref, 
-        #     controls={"qe":"on"},
-        #     properties={"querying.processes" : "terrierql:TerrierQLParser,parsecontrols:TerrierQLToControls,"\
-        #             +"parseql:TerrierQLToMatchingQueryTerms,matchopql:MatchingOpQLParser,applypipeline:ApplyTermPipeline,"\
-        #             +"sd:DependenceModelPreProcess,localmatching:LocalManager$ApplyLocalMatching,qe:RM3,"\
-        #             +"labels:org.terrier.learning.LabelDecorator,filters:LocalManager$PostFilterProcess"})
-        # map_qe = pt.Evaluate(br_qe.transform(t), dataset.get_qrels(), metrics=["map"])["map"]
+        self.assertEqual(len(actual), 1)
+        self.assertEqual(expected, actual.iloc[0]["query"])
+
+    def test_kl_qe_expansion_for_query_compact_on_bm25(self):
+        # just ensure that KLQueryExpansion results do not change
+        expected = 'applypipeline:off compact^1.840895333 design^0.348370740 equip^0.000000000 purpos^0.000000000 instrument^0.000000000 ferrit^0.000000000 anod^0.000000000 aircraft^0.000000000 microwav^0.000000000 sideband^0.000000000'
+        
+        indexref = pt.datasets.get_dataset("vaswani").get_index()
+        queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
 
-        # self.assertAlmostEqual(map_qe, map_pipe, places=2)
+        qe = pt.rewrite.KLQueryExpansion(indexref)
+        br = pt.BatchRetrieve(indexref, wmodel='BM25')
+
+        actual = qe.transform(br.transform(queriesIn))
+
+        self.assertEqual(len(actual), 1)
+        self.assertEqual(expected, actual.iloc[0]["query"])
+
+    def test_bo1_qe_expansion_for_query_compact_on_bm25(self):
+        # just ensure that Bo1QueryExpansion results do not change
+        expected = 'applypipeline:off compact^1.822309726 design^0.287992096 equip^0.000000000 purpos^0.000000000 instrument^0.000000000 ferrit^0.000000000 anod^0.000000000 aircraft^0.000000000 microwav^0.000000000 sideband^0.000000000'
+        
+        indexref = pt.datasets.get_dataset("vaswani").get_index()
+        queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
+
+        qe = pt.rewrite.Bo1QueryExpansion(indexref)
+        br = pt.BatchRetrieve(indexref, wmodel='BM25')
+
+        actual = qe.transform(br.transform(queriesIn))
+
+        self.assertEqual(len(actual), 1)
+        self.assertEqual(expected, actual.iloc[0]["query"])
+
+    def test_dfr_qe_expansion_for_query_compact_on_bm25(self):
+        # just ensure that DFRQueryExpansion results do not change
+        expected = 'applypipeline:off compact^1.822309726 design^0.287992096 equip^0.000000000 purpos^0.000000000 instrument^0.000000000 ferrit^0.000000000 anod^0.000000000 aircraft^0.000000000 microwav^0.000000000 sideband^0.000000000'
+        
+        indexref = pt.datasets.get_dataset("vaswani").get_index()
+        queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
+
+        qe = pt.rewrite.DFRQueryExpansion(indexref)
+        br = pt.BatchRetrieve(indexref, wmodel='BM25')
+
+        actual = qe.transform(br.transform(queriesIn))
+
+        self.assertEqual(len(actual), 1)
+        self.assertEqual(expected, actual.iloc[0]["query"])
+
+    def test_rm3_end_to_end(self):
+        """An end-to-end test, contrasting the smaller tests (that fail faster) from above.
+        """
+        dataset = pt.datasets.get_dataset("vaswani")
+        indexref = dataset.get_index()
+
+        qe = pt.rewrite.RM3(indexref)
+        br = pt.BatchRetrieve(indexref)
+
+        queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
+        res = br.transform(queriesIn)
+
+        queriesOut = qe.transform(res)
+        self.assertEqual(len(queriesOut), 1)
+        query = queriesOut.iloc[0]["query"]
+        #self.assertTrue("compact^1.82230972" in query)
+        self.assertTrue("applypipeline:off " in query)
+        
+        pipe = br >> qe >> br
+
+        # lets go faster, we only need 18 topics. qid 16 had a tricky case
+        t = dataset.get_topics().head(18)
+
+        all_qe_res = pipe.transform(t)
+        map_pipe = pt.Evaluate(all_qe_res, dataset.get_qrels(), metrics=["map"])["map"]
+
+        br_qe = pt.BatchRetrieve(indexref, 
+            controls={"qe":"on"},
+            properties={"querying.processes" : "terrierql:TerrierQLParser,parsecontrols:TerrierQLToControls,"\
+                    +"parseql:TerrierQLToMatchingQueryTerms,matchopql:MatchingOpQLParser,applypipeline:ApplyTermPipeline,"\
+                    +"sd:DependenceModelPreProcess,localmatching:LocalManager$ApplyLocalMatching,qe:RM3,"\
+                    +"labels:org.terrier.learning.LabelDecorator,filters:LocalManager$PostFilterProcess"})
+        map_qe = pt.Evaluate(br_qe.transform(t), dataset.get_qrels(), metrics=["map"])["map"]
+
+        self.assertAlmostEqual(map_qe, map_pipe, places=2)
+
+    def test_scoring_rm3_qe(self):
+        expected = 'applypipeline:off fox^0.600000024'
+        input = pd.DataFrame([["q1", "fox", "d1", "all the fox were fox", 3], ["q1", "fox", "d2", "brown fox jumps", 2]], columns=["qid", "query", "docno", "body", "score"])
+        scorer = pt.terrier.retriever.TextIndexProcessor(pt.rewrite.RM3, takes="docs", returns="queries")
+        rtr = scorer(input)
+        self.assertTrue("qid" in rtr.columns)
+        self.assertTrue("query" in rtr.columns)
+        self.assertTrue("docno" not in rtr.columns)
+        self.assertTrue(expected, rtr.iloc[0]["query"])
 
     def test_linear_terrierql(self):
         pipe = pt.apply.query(lambda row: "az") >> pt.rewrite.linear(0.75, 0.25)
diff --git a/tests/test_rewrite_rm3.py b/tests/test_rewrite_rm3.py
deleted file mode 100644
index 21f0a208..00000000
--- a/tests/test_rewrite_rm3.py
+++ /dev/null
@@ -1,174 +0,0 @@
-import pandas as pd
-import pyterrier as pt
-import os
-from matchpy import *
-from .base import TempDirTestCase
-import pytest
-
-if not pt.java.started():
-    terrier_version = os.environ.get("TERRIER_VERSION", None)
-    terrier_helper_version = os.environ.get("TERRIER_HELPER_VERSION", None)
-    pt.java.set_log_level('DEBUG')
-    pt.terrier.set_version(terrier_version)
-    pt.terrier.set_helper_version(terrier_helper_version)
-    pt.terrier.set_prf_version('rm_tiebreak-SNAPSHOT')
-    pt.java.init() # optional, forces java initialisation
-    TERRIER_PRF_ON_CLASSPATH = True
-else:
-    TERRIER_PRF_ON_CLASSPATH = False
-
-
-def normalize_term_weights(term_weights, digits=7):
-    ret = ''
-    for i in term_weights.split():
-        if '^' in i:
-            i = i.split('^')
-            i = i[0] + '^' + i[1][:digits]
-        ret += ' ' + i
-    return ret.strip()
-
-class TestRewriteRm3(TempDirTestCase):
-    """This is a set of unit tests for RM3 that can currently not run in the complete test suite, as the "com.github.terrierteam:terrier-prf:-SNAPSHOT" would have to be added to the boot classpath.
-    As workaround, the RM3 tests that can not be executed within the complete test suite are added to this dedicated file, so that they can be executed in isolation by running pytest tests/test_rewrite_rm3.py.
-    """
-
-    @pytest.mark.skipif(not TERRIER_PRF_ON_CLASSPATH, reason="This test only works in isolation when terrier-prf is on the jnius classpath.")
-    def test_rm3_expansion_for_query_compact_on_tf_idf(self):
-        # top-retrieval results of TF-IDF and BM25 below change, so the RM3 weights differ
-        expected = 'applypipeline:off equip^0.037346367 ferrit^0.027371584 modul^0.027371584 suppli^0.037346367 design^0.056739070 microwav^0.027371584 anod^0.037346367 unit^0.037346367 compact^0.674414337 stabil^0.037346367'
-        
-        indexref = pt.datasets.get_dataset("vaswani").get_index()
-        queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
-
-        qe = pt.rewrite.RM3(indexref)
-        br = pt.BatchRetrieve(indexref, wmodel='TF_IDF')
-
-        actual = qe.transform(br.transform(queriesIn))
-
-        self.assertEqual(len(actual), 1)
-        self.assertEqual(normalize_term_weights(expected), normalize_term_weights(actual.iloc[0]["query"]))
-
-    @pytest.mark.skipif(not TERRIER_PRF_ON_CLASSPATH, reason="This test only works in isolation when terrier-prf is on the jnius classpath.")
-    def test_rm3_expansion_for_query_compact_on_bm25(self):
-        # top-retrieval results of BM25 and TF-IDF above change, so the RM3 weights differ
-        expected = 'applypipeline:off equip^0.040264644 ferrit^0.025508024 modul^0.025508024 suppli^0.040264644 design^0.051008239 microwav^0.025508024 anod^0.040264644 unit^0.040264644 compact^0.671144485 stabil^0.040264644'
-        
-        indexref = pt.datasets.get_dataset("vaswani").get_index()
-        queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
-
-        qe = pt.rewrite.RM3(indexref)
-        br = pt.BatchRetrieve(indexref, wmodel='BM25')
-
-        actual = qe.transform(br.transform(queriesIn))
-
-        self.assertEqual(len(actual), 1)
-        self.assertEqual(normalize_term_weights(expected), normalize_term_weights(actual.iloc[0]["query"]))
-
-    @pytest.mark.skipif(not TERRIER_PRF_ON_CLASSPATH, reason="This test only works in isolation when terrier-prf is on the jnius classpath.")
-    def test_axiomatic_qe_expansion_for_query_compact_on_bm25(self):
-        # just ensure that AxiomaticQE results do not change
-        expected = 'applypipeline:off compact^1.000000000'
-        
-        indexref = pt.datasets.get_dataset("vaswani").get_index()
-        queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
-
-        qe = pt.rewrite.AxiomaticQE(indexref)
-        br = pt.BatchRetrieve(indexref, wmodel='BM25')
-
-        actual = qe.transform(br.transform(queriesIn))
-
-        self.assertEqual(len(actual), 1)
-        self.assertEqual(expected, actual.iloc[0]["query"])
-
-    def test_kl_qe_expansion_for_query_compact_on_bm25(self):
-        # just ensure that KLQueryExpansion results do not change
-        expected = 'applypipeline:off compact^1.840895333 design^0.348370740 equip^0.000000000 purpos^0.000000000 instrument^0.000000000 ferrit^0.000000000 anod^0.000000000 aircraft^0.000000000 microwav^0.000000000 sideband^0.000000000'
-        
-        indexref = pt.datasets.get_dataset("vaswani").get_index()
-        queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
-
-        qe = pt.rewrite.KLQueryExpansion(indexref)
-        br = pt.BatchRetrieve(indexref, wmodel='BM25')
-
-        actual = qe.transform(br.transform(queriesIn))
-
-        self.assertEqual(len(actual), 1)
-        self.assertEqual(expected, actual.iloc[0]["query"])
-
-    def test_bo1_qe_expansion_for_query_compact_on_bm25(self):
-        # just ensure that Bo1QueryExpansion results do not change
-        expected = 'applypipeline:off compact^1.822309726 design^0.287992096 equip^0.000000000 purpos^0.000000000 instrument^0.000000000 ferrit^0.000000000 anod^0.000000000 aircraft^0.000000000 microwav^0.000000000 sideband^0.000000000'
-        
-        indexref = pt.datasets.get_dataset("vaswani").get_index()
-        queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
-
-        qe = pt.rewrite.Bo1QueryExpansion(indexref)
-        br = pt.BatchRetrieve(indexref, wmodel='BM25')
-
-        actual = qe.transform(br.transform(queriesIn))
-
-        self.assertEqual(len(actual), 1)
-        self.assertEqual(expected, actual.iloc[0]["query"])
-
-    def test_dfr_qe_expansion_for_query_compact_on_bm25(self):
-        # just ensure that DFRQueryExpansion results do not change
-        expected = 'applypipeline:off compact^1.822309726 design^0.287992096 equip^0.000000000 purpos^0.000000000 instrument^0.000000000 ferrit^0.000000000 anod^0.000000000 aircraft^0.000000000 microwav^0.000000000 sideband^0.000000000'
-        
-        indexref = pt.datasets.get_dataset("vaswani").get_index()
-        queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
-
-        qe = pt.rewrite.DFRQueryExpansion(indexref)
-        br = pt.BatchRetrieve(indexref, wmodel='BM25')
-
-        actual = qe.transform(br.transform(queriesIn))
-
-        self.assertEqual(len(actual), 1)
-        self.assertEqual(expected, actual.iloc[0]["query"])
-
-    @pytest.mark.skipif(not TERRIER_PRF_ON_CLASSPATH, reason="This test only works in isolation when terrier-prf is on the jnius classpath.")
-    def test_rm3_end_to_end(self):
-        """An end-to-end test, contrasting the smaller tests (that fail faster) from above.
-        """
-        dataset = pt.datasets.get_dataset("vaswani")
-        indexref = dataset.get_index()
-
-        qe = pt.rewrite.RM3(indexref)
-        br = pt.BatchRetrieve(indexref)
-
-        queriesIn = pd.DataFrame([["1", "compact"]], columns=["qid", "query"])
-        res = br.transform(queriesIn)
-
-        queriesOut = qe.transform(res)
-        self.assertEqual(len(queriesOut), 1)
-        query = queriesOut.iloc[0]["query"]
-        #self.assertTrue("compact^1.82230972" in query)
-        self.assertTrue("applypipeline:off " in query)
-        
-        pipe = br >> qe >> br
-
-        # lets go faster, we only need 18 topics. qid 16 had a tricky case
-        t = dataset.get_topics().head(18)
-
-        all_qe_res = pipe.transform(t)
-        map_pipe = pt.Evaluate(all_qe_res, dataset.get_qrels(), metrics=["map"])["map"]
-
-        br_qe = pt.BatchRetrieve(indexref, 
-            controls={"qe":"on"},
-            properties={"querying.processes" : "terrierql:TerrierQLParser,parsecontrols:TerrierQLToControls,"\
-                    +"parseql:TerrierQLToMatchingQueryTerms,matchopql:MatchingOpQLParser,applypipeline:ApplyTermPipeline,"\
-                    +"sd:DependenceModelPreProcess,localmatching:LocalManager$ApplyLocalMatching,qe:RM3,"\
-                    +"labels:org.terrier.learning.LabelDecorator,filters:LocalManager$PostFilterProcess"})
-        map_qe = pt.Evaluate(br_qe.transform(t), dataset.get_qrels(), metrics=["map"])["map"]
-
-        self.assertAlmostEqual(map_qe, map_pipe, places=2)
-
-    @pytest.mark.skipif(not TERRIER_PRF_ON_CLASSPATH, reason="This test only works in isolation when terrier-prf is on the jnius classpath.")
-    def test_scoring_rm3_qe(self):
-        expected = 'applypipeline:off fox^0.600000024'
-        input = pd.DataFrame([["q1", "fox", "d1", "all the fox were fox", 3], ["q1", "fox", "d2", "brown fox jumps", 2]], columns=["qid", "query", "docno", "body", "score"])
-        scorer = pt.terrier.retriever.TextIndexProcessor(pt.rewrite.RM3, takes="docs", returns="queries")
-        rtr = scorer(input)
-        self.assertTrue("qid" in rtr.columns)
-        self.assertTrue("query" in rtr.columns)
-        self.assertTrue("docno" not in rtr.columns)
-        self.assertTrue(expected, rtr.iloc[0]["query"])

From 57fe30d5ec82ddf7025917a726e3911f8a1eba9f Mon Sep 17 00:00:00 2001
From: Sean MacAvaney <sean.macavaney@gmail.com>
Date: Fri, 16 Aug 2024 10:37:04 +0100
Subject: [PATCH 152/153] in a short interim

---
 pyterrier/terrier/java.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/pyterrier/terrier/java.py b/pyterrier/terrier/java.py
index f060192c..83904d41 100644
--- a/pyterrier/terrier/java.py
+++ b/pyterrier/terrier/java.py
@@ -30,6 +30,11 @@ def set_helper_version(version: Optional[str] = None):
     configure['helper_version'] = version
 
 
+@pt.java.before_init
+def set_prf_version(version: Optional[str] = None):
+    configure['prf_version'] = version
+
+
 class TerrierJavaInit(pt.java.JavaInitializer):
     def pre_init(self, jnius_config):
         # Make sure the terrier.default.properties file exists and is registered as an option, which avoids an annoying

From 8159417b909383c5e8d9534489b5cdfa711b656b Mon Sep 17 00:00:00 2001
From: Craig Macdonald <craig.macdonald@glasgow.ac.uk>
Date: Fri, 16 Aug 2024 10:37:12 +0100
Subject: [PATCH 153/153] add deprecation on (F)BR.from_dataset

---
 pyterrier/terrier/__init__.py | 18 +++++++++++++++---
 1 file changed, 15 insertions(+), 3 deletions(-)

diff --git a/pyterrier/terrier/__init__.py b/pyterrier/terrier/__init__.py
index 9389e728..db47b773 100644
--- a/pyterrier/terrier/__init__.py
+++ b/pyterrier/terrier/__init__.py
@@ -14,17 +14,29 @@
 
 @deprecated(version='0.11.0', reason="use pt.terrier.Retriever() instead")
 class TerrierRetrieve(Retriever):
-    pass
+    
+    @staticmethod
+    @deprecated(version='0.11.0', reason="use pt.terrier.Retriever.from_dataset() instead")
+    def from_dataset(*args, **kwargs):
+        return Retriever.from_dataset(*args, **kwargs)
 
 
 @deprecated(version='0.11.0', reason="use pt.terrier.Retriever() instead")
 class BatchRetrieve(Retriever):
-    pass
+
+    @staticmethod
+    @deprecated(version='0.11.0', reason="use pt.terrier.Retriever.from_dataset() instead")
+    def from_dataset(*args, **kwargs):
+        return Retriever.from_dataset(*args, **kwargs)
 
 
 @deprecated(version='0.11.0', reason="use pt.terrier.FeaturesRetriever() instead")
 class FeaturesBatchRetrieve(FeaturesRetriever):
-    pass
+    
+    @staticmethod
+    @deprecated(version='0.11.0', reason="use pt.terrier.FeaturesRetriever.from_dataset() instead")
+    def from_dataset(*args, **kwargs):
+        return FeaturesRetriever.from_dataset(*args, **kwargs)
 
 
 __all__ = [