chore: pdpbox deps

GilesStrong · Oct 29, 2024 · ba373f8 · ba373f8
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diff --git a/README.md b/README.md
@@ -112,16 +112,16 @@ And for a live tutorial, checkout my talk at PyHEP 2021: https://www.youtube.com
 
 Several examples are present in the form of Jupyter Notebooks in the `examples` folder. These can be run also on Google Colab to allow you to quickly try out the package.
 
-1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.0/examples/Simple_Binary_Classification_of_earnings.ipynb) `examples/Simple_Binary_Classification_of_earnings.ipynb`: Very basic binary-classification example
-1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.0/examples/Binary_Classification_Signal_versus_Background.ipynb) `examples/Binary_Classification_Signal_versus_Background.ipynb`: Binary-classification example in a high-energy physics context
-1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.0/examples/Multiclass_Classification_Signal_versus_Backgrounds.ipynb) `examples/Multiclass_Classification_Signal_versus_Backgrounds.ipynb`: Multiclass-classification example in a high-energy physics context
-1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.0/examples/Single_Target_Regression_Di-Higgs_mass_prediction.ipynb) `examples/Single_Target_Regression_Di-Higgs_mass_prediction.ipynb`: Single-target regression example in a high-energy physics context
-1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.0/examples/Multi_Target_Regression_Di-tau_momenta.ipynb) `examples/Multi_Target_Regression_Di-tau_momenta.ipynb`: Multi-target regression example in a high-energy physics context
-1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.0/examples/Feature_Selection.ipynb) `examples/Feature_Selection.ipynb`: In-depth walkthrough for automated feature-selection
-1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.0/examples/Advanced_Model_Building.ipynb) `examples/Advanced_Model_Building.ipynb`: In-depth look at building more complicated models and a few advanced interpretation techniques
-1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.0/examples/Model_Exporting.ipynb) `examples/Model_Exporting.ipynb`: Walkthough for exporting a trained model to ONNX and TensorFlow
-1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.0/examples/RNNs_CNNs_and_GNNs_for_matrix_data.ipynb) `examples/RNNs_CNNs_and_GNNs_for_matrix_data.ipynb`: Various examples of applying RNNs, CNNs, and GNNs to matrix data (top-tagging on jet constituents)
-1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.0/examples/Learning_To_Pivot.ipynb) `examples/Learning_To_Pivot.ipynb`: Example of adversarial training for parameter invariance
+1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.1/examples/Simple_Binary_Classification_of_earnings.ipynb) `examples/Simple_Binary_Classification_of_earnings.ipynb`: Very basic binary-classification example
+1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.1/examples/Binary_Classification_Signal_versus_Background.ipynb) `examples/Binary_Classification_Signal_versus_Background.ipynb`: Binary-classification example in a high-energy physics context
+1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.1/examples/Multiclass_Classification_Signal_versus_Backgrounds.ipynb) `examples/Multiclass_Classification_Signal_versus_Backgrounds.ipynb`: Multiclass-classification example in a high-energy physics context
+1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.1/examples/Single_Target_Regression_Di-Higgs_mass_prediction.ipynb) `examples/Single_Target_Regression_Di-Higgs_mass_prediction.ipynb`: Single-target regression example in a high-energy physics context
+1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.1/examples/Multi_Target_Regression_Di-tau_momenta.ipynb) `examples/Multi_Target_Regression_Di-tau_momenta.ipynb`: Multi-target regression example in a high-energy physics context
+1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.1/examples/Feature_Selection.ipynb) `examples/Feature_Selection.ipynb`: In-depth walkthrough for automated feature-selection
+1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.1/examples/Advanced_Model_Building.ipynb) `examples/Advanced_Model_Building.ipynb`: In-depth look at building more complicated models and a few advanced interpretation techniques
+1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.1/examples/Model_Exporting.ipynb) `examples/Model_Exporting.ipynb`: Walkthough for exporting a trained model to ONNX and TensorFlow
+1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.1/examples/RNNs_CNNs_and_GNNs_for_matrix_data.ipynb) `examples/RNNs_CNNs_and_GNNs_for_matrix_data.ipynb`: Various examples of applying RNNs, CNNs, and GNNs to matrix data (top-tagging on jet constituents)
+1. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/GilesStrong/lumin/blob/v0.9.1/examples/Learning_To_Pivot.ipynb) `examples/Learning_To_Pivot.ipynb`: Example of adversarial training for parameter invariance
 
 ## Installation
 
@@ -172,6 +172,8 @@ poetry run pre-commit install
 ### Optional requirements
 
 - sparse: enables loading on COO sparse-format tensors, install via e.g. `pip install sparse`
+- pdpbox: enables partial dependency plots, install via e.g. `pip install pdpbox`
+  - **Note**: `pdpbox` includes docs dependencies in its build environment, which can result in conflicts. A fork of `pdpbox` which removes these dependencies can be installed from [https://github.com/GilesStrong/PDPbox]
 
 ## Notes
 

diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -15,8 +15,6 @@
 import os
 import sys
 
-import pytorch_sphinx_theme
-
 sys.path.insert(0, os.path.abspath("../.."))
 
 

diff --git a/lumin/data_processing/hep_proc.py b/lumin/data_processing/hep_proc.py
@@ -1,4 +1,3 @@
-import warnings
 from typing import Dict, List, Optional, Set, Tuple, Union
 
 import numpy as np
@@ -404,15 +403,16 @@ def calc_pair_mass(
     df: pd.DataFrame, masses: Union[Tuple[float, float], Tuple[np.ndarray, np.ndarray]], feat_map: Dict[str, str]
 ) -> np.ndarray:
     r"""
-    Vectorised computation of invarient mass of pair of particles with given masses, using 3-momenta. Only works for vectors defined in Cartesian coordinates.
+        Vectorised computation of invarient mass o
+    f pair of particles with given masses, using 3-momenta. Only works for vectors defined in Cartesian coordinates.
 
-    Arguments:
-        df: DataFrame vector components
-        masses: tuple of masses of particles (either constant or different pair of masses per pair of particles)
-        feat_map: dictionary mapping of requested momentum components to the features in df
+        Arguments:
+            df: DataFrame vector components
+            masses: tuple of masses of particles (either constant or different pair of masses per pair of particles)
+            feat_map: dictionary mapping of requested momentum components to the features in df
 
-    Returns:
-        np.ndarray of invarient masses
+        Returns:
+            np.ndarray of invarient masses
     """
 
     # TODO: rewrite to not use a DataFrame for holding parent vector

diff --git a/lumin/nn/data/fold_yielder.py b/lumin/nn/data/fold_yielder.py
@@ -15,7 +15,6 @@
 from sklearn.model_selection import KFold
 from sklearn.pipeline import Pipeline
 from torch_geometric.data import Dataset as PyGDataset
-from torch_geometric.loader import DataLoader as PyGDataLoader
 
 from .batch_yielder import BatchYielder, TorchGeometricBatchYielder
 

diff --git a/lumin/optimisation/features.py b/lumin/optimisation/features.py
@@ -7,7 +7,6 @@
 import numpy as np
 import pandas as pd
 import pkg_resources
-import rfpimp
 from fastprogress import progress_bar
 from prettytable import PrettyTable
 from rfpimp import feature_dependence_matrix, importances, plot_dependence_heatmap

diff --git a/lumin/optimisation/threshold.py b/lumin/optimisation/threshold.py
@@ -1,4 +1,3 @@
-import warnings
 from typing import Tuple
 
 import matplotlib.pyplot as plt

diff --git a/lumin/plotting/interpretation.py b/lumin/plotting/interpretation.py
@@ -1,14 +1,13 @@
 from __future__ import annotations
 
 from collections import OrderedDict
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import seaborn as sns
 import torch
-from pdpbox import pdp
 from pdpbox.pdp import PDPInteract, PDPIsolate
 from sklearn.pipeline import Pipeline
 from torch import Tensor

diff --git a/poetry.lock b/poetry.lock
diff --git a/pyproject.toml b/pyproject.toml
@@ -1,7 +1,7 @@
 
 [tool.poetry]
 name = "lumin"
-version = "0.9.0"
+version = "0.9.1"
 description = "LUMIN Unifies Many Improvements for Networks: A PyTorch wrapper to make deep learning more accessable to scientists."
 license = "Apache Software License 2.0"
 authors = [
@@ -41,7 +41,6 @@ pandas = "<2.1.0"
 matplotlib = "^3.8.0"
 seaborn = "^0.13.0"
 poetry-plugin-export = "^1.8.0"
-pdpbox = { git = "https://github.com/GilesStrong/PDPbox.git", branch = "refactor_requirements" }
 torch-geometric = "^2.6.1"
 plotly = ">=5.9.0"
 
@@ -58,6 +57,7 @@ black = "^24.2.0"
 pandas-stubs = "^2.2.2"
 uproot = "^5.4.1"
 lxml-html-clean = "^0.3.1"
+pdpbox = { git = "https://github.com/GilesStrong/PDPbox.git" }
 
 [tool.poetry.group.docs.dependencies]
 sphinx = "<7.0.0"

diff --git a/requirements.txt b/requirements.txt
@@ -117,7 +117,7 @@ pandocfilters==1.5.1 ; python_version >= "3.10" and python_version < "4.0"
 parso==0.8.4 ; python_version >= "3.10" and python_version < "4.0"
 pathspec==0.12.1 ; python_version >= "3.10" and python_version < "4.0"
 patsy==0.5.6 ; python_version >= "3.10" and python_version < "4.0"
-pdpbox @ git+https://github.com/GilesStrong/PDPbox.git@519849d2613cf0111779b20b65d968fbc4d020c2 ; python_version >= "3.10" and python_version < "4.0"
+pdpbox @ git+https://github.com/GilesStrong/PDPbox.git@1ad2c9eb3da880339b831948b80b5378eeb5fc2b ; python_version >= "3.10" and python_version < "4.0"
 pexpect==4.9.0 ; python_version >= "3.10" and python_version < "4.0"
 pillow==11.0.0 ; python_version >= "3.10" and python_version < "4.0"
 pip==24.3.1 ; python_version >= "3.10" and python_version < "4.0"

diff --git a/setup.cfg b/setup.cfg
@@ -17,7 +17,7 @@ no_strict_optional = True
 disable_error_code = attr-defined, override, union-attr
 
 [flake8]
-ignore = E203, E266, E501, W503, F403, F401, E741, C901, W405, E402, E211
+ignore = E203, E266, E501, W503, E741, C901, W405, E402, E211
 max-line-length = 120
 max-complexity = 18
 select = B,C,E,F,W,T4,B9