scikit-learn-contrib · mirand863 · Feb 12, 2025 · Jan 28, 2025 · Feb 12, 2025 · Feb 12, 2025
diff --git a/.github/workflows/test-pr.yml b/.github/workflows/test-pr.yml
@@ -15,7 +15,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
         os: [ubuntu-latest, macOS-latest, windows-latest]
     steps:
     - uses: actions/checkout@v2

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
@@ -3,7 +3,7 @@ version: 2
 build:
   os: "ubuntu-20.04"
   tools:
-    python: "3.8"
+    python: "3.12"
 
 # Build from the docs/ directory with Sphinx
 sphinx:

diff --git a/Pipfile b/Pipfile
@@ -24,14 +24,9 @@ sphinx_code_tabs = "0.5.3"
 sphinx-gallery = "0.10.1"
 matplotlib = "3.9.2"
 pandas = "1.4.2"
-bert-sklearn = {git = "https://github.com/charles9n/bert-sklearn.git@master", editable = true}
 black = {version = "24.3.0", extras = ["colorama"]}
 pre-commit = "2.20.0"
 pyfakefs = "*"
-shap = "0.44.1"
-xarray = "2023.1.0"
 
 [extras]
 ray = "*"
-shap = "0.44.1"
-xarray = "*"
diff --git a/Pipfile.lock b/Pipfile.lock
diff --git a/README.md b/README.md
@@ -16,7 +16,6 @@ HiClass is an open-source Python library for hierarchical classification compati
 - [Who is using HiClass?](#who-is-using-hiclass)
 - [Install](#install)
 - [Quick start](#quick-start)
-- [Explaining Hierarchical Classifiers](#explaining-hierarchical-classifiers)
 - [Step-by-step walk-through](#step-by-step-walk-through)
 - [API documentation](#api-documentation)
 - [FAQ](#faq)
@@ -34,8 +33,6 @@ HiClass is an open-source Python library for hierarchical classification compati
 - **[Build pipelines](https://hiclass.readthedocs.io/en/latest/auto_examples/plot_pipeline.html):** Since the hierarchical classifiers inherit from the BaseEstimator of scikit-learn, pipelines can be built to automate machine learning workflows.
 - **[Hierarchical metrics](https://hiclass.readthedocs.io/en/latest/api/utilities.html#hierarchical-metrics):** HiClass supports the computation of hierarchical precision, recall and f-score, which are more appropriate for hierarchical data than traditional metrics.
 - **[Compatible with pickle](https://hiclass.readthedocs.io/en/latest/auto_examples/plot_model_persistence.html):** Easily store trained models on disk for future use.
-- **[BERT sklearn](https://hiclass.readthedocs.io/en/latest/auto_examples/plot_bert.html):** Compatible with the library [BERT sklearn](https://github.com/charles9n/bert-sklearn).
-- **[Hierarchical Explanability](https://hiclass.readthedocs.io/en/latest/algorithms/explainer.html):**  HiClass allows explaining hierarchical models using the [SHAP](https://github.com/shap/shap) package.
 
 **Any feature missing on this list?** Search our [issue tracker](https://github.com/scikit-learn-contrib/hiclass/issues) to see if someone has already requested it and add a comment to it explaining your use-case. Otherwise, please open a new issue describing the requested feature and possible use-case scenario. We prioritize our roadmap based on user feedback, so we would love to hear from you.
 
@@ -115,7 +112,6 @@ pip install hiclass"[<extra_name>]"
 Replace <extra_name> with one of the following options:
 
 - ray: Installs the ray package, which is required for parallel processing support.
-- xai: Installs the shap and xarray packages, which are required for explaining Hiclass' predictions.
 
 ### Option 2: Conda
 
@@ -201,10 +197,6 @@ pipeline.fit(X_train, Y_train)
 predictions = pipeline.predict(X_test)
 ```
 
-## Explaining Hierarchical Classifiers
-
-Hierarchical classifiers can provide additional insights when combined with explainability methods. HiClass allows explaining hierarchical models using SHAP values. Different hierarchical models yield different insights. More information on explaining [Local classifier per parent node](https://colab.research.google.com/drive/1rVlYuRU_uO1jw5sD6qo2HoCpCz6E6z5J?usp=sharing), [Local classifier per node](https://colab.research.google.com/drive/1wqSl1t_Qn2f62WNZQ48mdB0mNeu1XSF1?usp=sharing), and [Local classifier per level](https://colab.research.google.com/drive/1VnGlJu-1wSG4wxHXL0Ijf2a7Pu3kklT-?usp=sharing) is available on [Read the Docs](https://hiclass.readthedocs.io/en/latest/algorithms/explainer.html).
-
 ## Step-by-step walk-through
 
 A step-by-step walk-through is available on our documentation hosted on [Read the Docs](https://hiclass.readthedocs.io/en/latest/index.html).

diff --git a/docs/examples/plot_bert.py b/docs/examples/plot_bert.py
diff --git a/docs/examples/plot_lcpl_explainer.py b/docs/examples/plot_lcpl_explainer.py
diff --git a/docs/examples/plot_lcpn_explainer.py b/docs/examples/plot_lcpn_explainer.py
diff --git a/docs/examples/plot_lcppn_explainer.py b/docs/examples/plot_lcppn_explainer.py
diff --git a/docs/examples/plot_multilabel.py b/docs/examples/plot_multilabel.py
diff --git a/docs/requirements.txt b/docs/requirements.txt
@@ -1,13 +1,8 @@
-# Defining the exact version will make sure things don't break
-sphinx==5.0.0
-sphinx_rtd_theme==1.0.0
-readthedocs-sphinx-search==0.1.2
-sphinx_code_tabs==0.5.3
-sphinx-gallery==0.10.1
-matplotlib==3.5.2
-pandas==1.4.2
+sphinx
+sphinx_rtd_theme
+readthedocs-sphinx-search
+sphinx_code_tabs
+sphinx-gallery
+matplotlib
 ray
 numpy
-git+https://github.com/charles9n/bert-sklearn.git@master
-shap==0.44.1
-xarray==2023.1.0
diff --git a/docs/source/algorithms/explainer-indexing.png b/docs/source/algorithms/explainer-indexing.png