merge main

Signed-off-by: Adam Li <adam2392@gmail.com>

.github/workflows/build_wheels.yml

@@ -19,6 +19,17 @@ concurrency:
   cancel-in-progress: true
 
 jobs:
+  create_artifacts_folder:
+    name: Create Artifacts Folder
+    runs-on: ubuntu-latest
+    outputs:
+      artifacts_folder: ${{ steps.set_folder.outputs.folder_name }}
+
+    steps:
+      - name: Set Artifacts Folder
+        id: set_folder
+        run: echo "folder_name=artifacts/${{ github.run_id }}" >> $GITHUB_ENV
+
   build_wheels:
     name: Build wheels on ${{ matrix.os[1] }} - ${{ matrix.os[2] }} with Python ${{ matrix.python[0] }}
     runs-on: ${{ matrix.os[0] }}
@@ -60,7 +71,7 @@ jobs:
         if: ${{ runner.os == 'Windows' && env.IS_32_BIT == 'false' }}
 
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.16.1
+        uses: pypa/cibuildwheel@v2.16.2
         # Build all wheels here, apart from macosx_arm64, linux_aarch64
         # cibuildwheel is currently unable to pass configuration flags to
         # CIBW_BUILD_FRONTEND https://github.com/pypa/cibuildwheel/issues/1227
@@ -109,7 +120,7 @@ jobs:
           python spin setup-submodule
 
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.16.1
+        uses: pypa/cibuildwheel@v2.16.2
         env:
           CIBW_BUILD: ${{ matrix.python[0] }}-macosx_arm64
           CIBW_ARCHS_MACOS: arm64

.github/workflows/main.yml

@@ -249,52 +249,6 @@ jobs:
           name: sktree-build
           path: $PWD/build
 
-  # release is ran when a release is made on Github
-  release:
-    name: Release
-    runs-on: ubuntu-latest
-    needs: [build_and_test_slow]
-    if: startsWith(github.ref, 'refs/tags/')
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-      - name: Setup Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4.6.1
-        with:
-          python-version: 3.9
-          architecture: "x64"
-      - name: Install dependencies
-        run: |
-          python -m pip install --progress-bar off --upgrade pip setuptools wheel
-          python -m pip install --progress-bar off build twine
-      - name: Prepare environment
-        run: |
-          echo "RELEASE_VERSION=${GITHUB_REF#refs/tags/v}" >> $GITHUB_ENV
-          echo "TAG=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV
-      - name: Download package distribution files
-        uses: actions/download-artifact@v3
-        with:
-          name: package
-          path: dist
-      # TODO: refactor scripts to generate release notes from `whats_new.rst` file instead
-      # - name: Generate release notes
-      #   run: |
-      #     python scripts/release_notes.py > ${{ github.workspace }}-RELEASE_NOTES.md
-      - name: Publish package to PyPI
-        run: |
-          twine upload -u ${{ secrets.PYPI_USERNAME }} -p ${{ secrets.PYPI_PASSWORD }} dist/*
-      - name: Publish GitHub release
-        uses: softprops/action-gh-release@v1
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        with:
-          # body_path: ${{ github.workspace }}-RELEASE_NOTES.md
-          prerelease: ${{ contains(env.TAG, 'rc') }}
-          files: |
-            dist/*
-
-
-
     # build-windows:
     #   name: Meson build Windows
     #   runs-on: windows-2019

.github/workflows/release.yml

@@ -0,0 +1,61 @@
+name: "Release to PyPI"
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.number }}-${{ github.event.type }}
+  cancel-in-progress: true
+
+on:
+  workflow_run:
+    workflows: ["build_and_test_slow"]
+    types:
+      - completed
+  workflow_dispatch:
+
+env:
+  INSTALLDIR: "build-install"
+  CCACHE_DIR: "${{ github.workspace }}/.ccache"
+
+jobs:
+  # release is ran when a release is made on Github
+  release:
+    name: Release
+    runs-on: ubuntu-latest
+    needs: [build_and_test_slow]
+    if: startsWith(github.ref, 'refs/tags/')
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4.6.1
+        with:
+          python-version: 3.9
+          architecture: "x64"
+      - name: Install dependencies
+        run: |
+          python -m pip install --progress-bar off --upgrade pip setuptools wheel
+          python -m pip install --progress-bar off build twine
+      - name: Prepare environment
+        run: |
+          echo "RELEASE_VERSION=${GITHUB_REF#refs/tags/v}" >> $GITHUB_ENV
+          echo "TAG=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV
+      - name: Download package distribution files
+        uses: actions/download-artifact@v3
+        with:
+          name: package
+          path: dist
+      # TODO: refactor scripts to generate release notes from `whats_new.rst` file instead
+      # - name: Generate release notes
+      #   run: |
+      #     python scripts/release_notes.py > ${{ github.workspace }}-RELEASE_NOTES.md
+      - name: Publish package to PyPI
+        run: |
+          twine upload -u ${{ secrets.PYPI_USERNAME }} -p ${{ secrets.PYPI_PASSWORD }} dist/*
+      - name: Publish GitHub release
+        uses: softprops/action-gh-release@v1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+          # body_path: ${{ github.workspace }}-RELEASE_NOTES.md
+          prerelease: ${{ contains(env.TAG, 'rc') }}
+          files: |
+            dist/*

DEVELOPING.md

@@ -76,4 +76,8 @@ Verify that installations work as expected on your machine.
 
     twine upload dist/*
 
+or if you have two-factor authentication enabled: https://pypi.org/help/#apitoken
+
+    twine upload dist/* --repository scikit-tree
+
 4. Update version number on ``meson.build`` and ``_version.py`` to the relevant version.

doc/api.rst

@@ -65,6 +65,7 @@ how scikit-learn builds trees.
    PatchObliqueRandomForestClassifier
    PatchObliqueRandomForestRegressor
    HonestForestClassifier
+   MultiViewRandomForestClassifier
 
 .. currentmodule:: sktree.tree
 .. autosummary::
@@ -75,6 +76,7 @@ how scikit-learn builds trees.
    PatchObliqueDecisionTreeClassifier
    PatchObliqueDecisionTreeRegressor
    HonestTreeClassifier
+   MultiViewDecisionTreeClassifier
 
 Unsupervised
 ------------

doc/whats_new/v0.3.rst

@@ -14,6 +14,7 @@ Changelog
 ---------
 - |Fix| Fixes a bug in consistency of train/test samples when ``random_state`` is not set in FeatureImportanceForestClassifier and FeatureImportanceForestRegressor, by `Adam Li`_ (:pr:`135`)
 - |Fix| Fixes a bug where covariate indices were not shuffled by default when running FeatureImportanceForestClassifier and FeatureImportanceForestRegressor test methods, by `Sambit Panda`_ (:pr:`140`)
+- |Enhancement| Add multi-view splitter for axis-aligned decision trees, by `Adam Li`_ (:pr:`129`)
 
 Code and Documentation Contributors
 -----------------------------------

examples/README.txt

@@ -1,4 +1,4 @@
 Examples
---------
+========
 
 Examples demonstrating how to use scikit-tree algorithms.

examples/hypothesis_testing/README.txt

@@ -0,0 +1,6 @@
+.. _hyppo_examples:
+
+Hypothesis testing with decision trees
+--------------------------------------
+
+Examples demonstrating how to use decision-trees for statistical hypothesis testing.

examples/plot_might_auc.py → examples/hypothesis_testing/plot_might_auc.py

@@ -122,6 +122,9 @@
 # Extract the results from the model variables anytime. You can save the model with ``pickle``.
 #
 # ASH-90 / Partial AUC: ``est.observe_stat_``
+#
 # Observed Samples: ``est.observe_samples_``
+#
 # Tree Posteriors for the positive class: ``est.observe_posteriors_`` (n_trees, n_samples_test, 1)
+#
 # True Labels: ``est.y_true_final_``

examples/outlier_detection/README.txt

@@ -0,0 +1,6 @@
+.. _outlier_examples:
+
+Outlier-detection
+-----------------
+
+Examples concerning how to do outlier detection with decision trees.

examples/plot_extra_oblique_random_forest.py

@@ -58,6 +58,9 @@
 are not sorted and the split is determined by randomly drawing a threshold from the feature's
 range, hence the complexity is `O(n)`. This makes the algorithm more suitable for large datasets.
 
+To see how sample-sizes affect the performance of Extra Oblique Trees vs regular Oblique Trees,
+see :ref:`sphx_glr_auto_examples_plot_extra_orf_sample_size.py`
+
 References
 ----------
 .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees", Machine Learning, 63(1),
@@ -149,6 +152,7 @@ def get_scores(X, y, d_name, n_cv=5, n_repeats=1, **kwargs):
     "random_state": random_state,
     "n_cv": 10,
     "n_repeats": 1,
+    "n_jobs": -1,
 }
 
 for data_id in data_ids:

examples/plot_extra_orf_sample_size.py

@@ -122,6 +122,7 @@ def get_scores(X, y, d_name, n_cv=5, n_repeats=1, **kwargs):
     "random_state": random_state,
     "n_cv": 10,
     "n_repeats": 1,
+    "n_jobs": -1,
 }
 
 for data_id in data_ids:

examples/plot_oblique_random_forest.py

@@ -16,6 +16,9 @@
 will notice, of these three datasets, the oblique forest outperforms axis-aligned
 random forest on cnae-9 utilizing sparse random projection mechanism. All datasets
 are subsampled due to computational constraints.
+
+For an example of using extra-oblique trees/forests in practice on data, see the following
+example :ref:`sphx_glr_auto_examples_plot_extra_oblique_random_forest.py`.
 """
 
 from datetime import datetime

examples/splitters/README.txt

@@ -0,0 +1,6 @@
+.. _splitter_examples:
+
+Decision-tree splitters
+-----------------------
+
+Examples demonstrating different node-splitting strategies for decision trees.

examples/splitters/plot_multiview_axis_aligned_splitter.py

@@ -3,15 +3,15 @@
 Demonstrate and visualize a multi-view projection matrix for an axis-aligned tree
 =================================================================================
 
-This example shows how multi-view projection matrices are generated for an oblique tree,
-specifically the :class:`sktree.tree.ObliqueDecisionTreeClassifier`.
+This example shows how multi-view projection matrices are generated for a decision tree,
+specifically the :class:`sktree.tree.MultiViewDecisionTreeClassifier`.
 
 Multi-view projection matrices operate under the assumption that the input ``X`` array
 consists of multiple feature-sets that are groups of features important for predicting
 ``y``.
 
 For details on how to use the hyperparameters related to the multi-view, see
-:class:`sktree.tree.ObliqueDecisionTreeClassifier`.
+:class:`sktree.tree.MultiViewDecisionTreeClassifier`.
 """
 
 # import modules