Merge pull request #10 from SpeysideHEP/load_efficiency

Initialisation efficiency improvement

README.md

@@ -2,9 +2,11 @@
 
 [![arxiv](https://img.shields.io/static/v1?style=plastic&label=arXiv&message=2307.06996&color=brightgreen&logo=arXiv)](https://arxiv.org/abs/2307.06996)
 [![DOI](https://img.shields.io/static/v1?style=plastic&label=DOI&message=TBA&color=blue)]()
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10527973.svg)](https://doi.org/10.5281/zenodo.10527973)
+[![DOI](https://zenodo.org/badge/617041391.svg)](https://zenodo.org/badge/latestdoi/617041391)
 [![doc](https://img.shields.io/static/v1?style=plastic&label&message=Documentation&logo=gitbook&logoColor=white&color=gray)](http://spey-pyhf.readthedocs.io/)
 
+<img src="https://raw.githubusercontent.com/SpeysideHEP/spey/main/docs/img/spey-logo.png" alt="Spey logo" style="float: right; margin-right: 20px" align="right" width=250px/>
+
 [![github](https://img.shields.io/static/v1?style=plastic&label&message=GitHub&logo=github&logoColor=black&color=white)](https://github.com/SpeysideHEP/spey-pyhf)
 [![PyPI - Version](https://img.shields.io/pypi/v/spey-pyhf?style=plastic)](https://pypi.org/project/spey-pyhf/)
 [![PyPI - Downloads](https://img.shields.io/pypi/dm/spey-pyhf?style=plastic&link=https%3A%2F%2Fpypi.org%2Fproject%2Fspey-pyhf)](https://pypi.org/project/spey-pyhf/)

docs/index.rst

@@ -16,7 +16,7 @@ Welcome to the documentation of the ``pyhf`` plugin for Spey
    :target: https://github.com/SpeysideHEP/spey-pyhf/issues
 
 .. image:: https://zenodo.org/badge/617041391.svg
-   :target: https://zenodo.org/doi/10.5281/zenodo.10527973
+   :target: https://zenodo.org/badge/latestdoi/617041391
 
 .. toctree::
    :maxdepth: 2

docs/releases/changelog-v0.1.md

@@ -1,6 +1,6 @@
 # Release notes v0.1
 
-## New features since last release
+## New features since the last release
 
 * A mapping functionality has been developed between ``pyhf``'s full
   statistical models and simplified likelihoods framework. Methodology
@@ -18,9 +18,12 @@
 * Update `pyhf` version.
   ([#8](https://github.com/SpeysideHEP/spey-pyhf/pull/8))
 
+* Model loading has been improved for prefit and postfit scenarios.
+  ([#10](https://github.com/SpeysideHEP/spey-pyhf/pull/10))
+
 ## Bug fixes
 
-* Bugfix in `simplify` module, where signal injector was not initiating properly.
+* Bugfix in `simplify` module, where signal injector was not initiated properly.
  ([#9](https://github.com/SpeysideHEP/spey-pyhf/pull/9))
 
 * Bugfix in apriori likelihood computation for full likelihoods mentioned in

setup.py

@@ -6,7 +6,7 @@
 with open("src/spey_pyhf/_version.py", mode="r", encoding="UTF-8") as f:
     version = f.readlines()[-1].split()[-1].strip("\"'")
 
-requirements = ["pyhf==0.7.6", "spey>=0.1.1"]
+requirements = ["pyhf==0.7.6", "spey>=0.1.5"]
 
 docs = [
     "sphinx==6.2.1",

src/spey_pyhf/_version.py

@@ -1,3 +1,3 @@
 """Version of the spey - pyhf plugin"""
 
-__version__ = "0.1.3"
+__version__ = "0.1.4"

src/spey_pyhf/data.py

@@ -191,21 +191,18 @@ def __post_init__(self):
             with open(self.background_only_model, "r", encoding="uft-8") as f:
                 self.background_only_model = json.load(f)
 
-        self.background_only_model_apriori = copy.deepcopy(self.background_only_model)
         interpreter = WorkspaceInterpreter(self.background_only_model)
         interpreter.add_patch(self.signal_patch)
 
         # set data as expected background events
-        self.background_only_model_apriori["observations"] = [
-            {"name": name, "data": data}
-            for name, data in interpreter.expected_background_yields.items()
-        ]
+        self.expected_background_yields = interpreter.expected_background_yields
 
-        self.workspace_apriori = manager.pyhf.Workspace(
-            self.background_only_model_apriori
-        )
         self.workspace = manager.pyhf.Workspace(self.background_only_model)
 
+        self._model = None
+        # Initialise config
+        model = self()[1]
+
         min_ratio = []
         for idc, channel in enumerate(self.background_only_model.get("channels", [])):
             current_signal = []
@@ -236,10 +233,6 @@ def __post_init__(self):
             -np.min(min_ratio).astype(np.float32) if len(min_ratio) > 0 else -np.inf
         )
 
-        self._models = {"post": None, "pre": None}
-        # Initialise config
-        model = self()[1]
-
         self._config = {
             "poi_index": model.config.poi_index,
             "minimum_poi": self._minimum_poi,
@@ -260,7 +253,11 @@ def channel_properties(self) -> Iterator[Tuple[int, Text, int]]:
         for idx, channel in enumerate(self.channels):
             yield idx, channel, self.workspace.channel_nbins[channel]
 
-    def __call__(self, expected: ExpectationType = ExpectationType.observed) -> Tuple:
+    def __call__(
+        self,
+        expected: ExpectationType = ExpectationType.observed,
+        include_aux: bool = True,
+    ) -> Tuple:
         """
         Retreive pyhf workspace
 
@@ -276,40 +273,35 @@ def __call__(self, expected: ExpectationType = ExpectationType.observed) -> Tupl
                 the truth.
               * :obj:`~spey.ExpectationType.apriori`: Computes the expected p-values with via pre-fit
                 prescription which means that the SM will be assumed to be the truth.
+            include_aux (`bool`, default `True`): Include auxiliary data.
 
         Returns:
             ``Tuple``:
             workspace, model and data
         """
-        if expected == ExpectationType.apriori:
-            if self._models["pre"] is None:
-                self._models["pre"] = self.workspace_apriori.model(
-                    patches=[self.signal_patch],
-                    modifier_settings={
-                        "normsys": {"interpcode": "code4"},
-                        "histosys": {"interpcode": "code4p"},
-                    },
-                )
-
-            return (
-                self.workspace_apriori,
-                self._models["pre"],
-                self.workspace_apriori.data(self._models["pre"]),
-            )
-
-        if self._models["post"] is None:
-            self._models["post"] = self.workspace.model(
+        if self._model is None:
+            self._model = self.workspace.model(
                 patches=[self.signal_patch],
                 modifier_settings={
                     "normsys": {"interpcode": "code4"},
                     "histosys": {"interpcode": "code4p"},
                 },
             )
-        return (
-            self.workspace,
-            self._models["post"],
-            self.workspace.data(self._models["post"]),
-        )
+
+        if expected == ExpectationType.apriori:
+            data = sum(
+                (
+                    self.expected_background_yields[ch]
+                    for ch in self._model.config.channels
+                ),
+                [],
+            )
+            if include_aux:
+                data += self._model.config.auxdata
+        else:
+            data = self.workspace.data(self._model, include_auxdata=include_aux)
+
+        return self.workspace, self._model, data
 
     def config(
         self, allow_negative_signal: bool = True, poi_upper_bound: Optional[float] = None

src/spey_pyhf/interface.py

@@ -73,7 +73,7 @@ class PyhfInterface(BackendBase):
     """Version of the backend"""
     author: Text = "SpeysideHEP"
     """Author of the backend"""
-    spey_requires: Text = ">=0.1.0"
+    spey_requires: Text = ">=0.1.5,<0.2.0"
     """Spey version required for the backend"""
     doi: List[Text] = ["10.5281/zenodo.1169739", "10.21105/joss.02823"]
     """Citable DOI for the backend"""

src/spey_pyhf/simplify.py

@@ -1,7 +1,7 @@
 """Interface to convert pyhf likelihoods to simplified likelihood framework"""
 import copy
 import warnings
-from typing import Callable, List, Optional, Text, Union
+from typing import Callable, List, Optional, Text, Union, Literal
 
 import numpy as np
 import spey
@@ -140,14 +140,16 @@ class Simplify(spey.ConverterBase):
     """Version of the backend"""
     author: Text = "SpeysideHEP"
     """Author of the backend"""
-    spey_requires: Text = ">=0.1.1"
+    spey_requires: Text = ">=0.1.5,<0.2.0"
     """Spey version required for the backend"""
 
     def __call__(
         self,
         statistical_model: spey.StatisticalModel,
-        fittype: Text = "postfit",
-        convert_to: Text = "default_pdf.correlated_background",
+        fittype: Literal["postfit", "prefit"] = "postfit",
+        convert_to: Literal[
+            "default_pdf.correlated_background", "default_pdf.third_moment_expansion"
+        ] = "default_pdf.correlated_background",
         number_of_samples: int = 1000,
         control_region_indices: Optional[Union[List[int], List[Text]]] = None,
         include_modifiers_in_control_model: bool = False,