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Release v0.1.5: stable release
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‎.github/workflows/release.yml‎

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@@ -7,7 +7,7 @@ on:
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workflow_dispatch:
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inputs:
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version:
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description: 'Version to release (e.g., 0.1.0)'
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description: 'Version to release (e.g., 0.1.5)'
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required: true
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type: string
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@@ -65,52 +65,61 @@ jobs:
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name: dist
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path: dist/
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# publish:
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# needs: build
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# runs-on: ubuntu-latest
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# permissions:
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# id-token: write # IMPORTANT: this permission is mandatory for trusted publishing
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# steps:
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# - name: Download build artifacts
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# uses: actions/download-artifact@v4
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# with:
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# name: dist
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# path: dist/
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#
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# - name: Publish to PyPI
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# uses: pypa/gh-action-pypi-publish@release/v1
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# # Uncomment when ready to publish to PyPI
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# # with:
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# # password: ${{ secrets.PYPI_API_TOKEN }}
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# # Or use trusted publishing (recommended):
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# # with:
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# # repository-url: https://upload.pypi.org/legacy/
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# create-release:
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# needs: build
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# runs-on: ubuntu-latest
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# permissions:
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# contents: write
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# steps:
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# - uses: actions/checkout@v4
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# with:
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# fetch-depth: 0
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# - name: Create GitHub Release
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# uses: ncipollo/release-action@v1
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# with:
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# tag: ${{ github.ref_name }}
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# name: Release ${{ github.ref_name }}
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# body: |
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# ## Changes
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# - See [CHANGELOG.md](CHANGELOG.md) for detailed changes
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# ## Installation
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# ```bash
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# pip install fastwoe==${{ github.ref_name }}
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# ```
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# draft: false
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# prerelease: false
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# token: ${{ secrets.GITHUB_TOKEN }}
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publish:
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needs: build
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runs-on: ubuntu-latest
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steps:
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- name: Download build artifacts
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uses: actions/download-artifact@v4
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with:
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name: dist
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path: dist/
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- name: Publish to PyPI
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uses: pypa/gh-action-pypi-publish@release/v1
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with:
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password: ${{ secrets.PYPI_API_TOKEN }}
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create-release:
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needs: build
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runs-on: ubuntu-latest
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permissions:
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contents: write
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steps:
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- uses: actions/checkout@v4
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with:
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fetch-depth: 0
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- name: Extract version from tag
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id: get_version
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run: |
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if [ "${{ github.event_name }}" = "workflow_dispatch" ]; then
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echo "version=${{ github.event.inputs.version }}" >> $GITHUB_OUTPUT
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echo "tag=v${{ github.event.inputs.version }}" >> $GITHUB_OUTPUT
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else
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echo "version=${GITHUB_REF#refs/tags/v}" >> $GITHUB_OUTPUT
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echo "tag=${GITHUB_REF#refs/tags/}" >> $GITHUB_OUTPUT
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fi
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- name: Create GitHub Release
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uses: ncipollo/release-action@v1
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with:
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tag: ${{ steps.get_version.outputs.tag }}
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name: Release ${{ steps.get_version.outputs.tag }}
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body: |
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## Changes
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See [CHANGELOG.md](https://github.com/xRiskLab/fastwoe/blob/main/CHANGELOG.md) for detailed changes.
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## Installation
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```bash
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pip install fastwoe==${{ steps.get_version.outputs.version }}
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```
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## What's New in v${{ steps.get_version.outputs.version }}
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Check the [CHANGELOG](https://github.com/xRiskLab/fastwoe/blob/main/CHANGELOG.md#version-${{ steps.get_version.outputs.version }}) for full details.
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draft: false
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prerelease: false
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token: ${{ secrets.GITHUB_TOKEN }}

‎CHANGELOG.md‎

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@@ -1,5 +1,29 @@
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# Changelog
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## Version 0.1.5 (2024-12-09)
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**Performance Fix & Code Cleanup**: Eliminated DataFrame fragmentation warning and removed debug statements
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- **Bug Fixes**:
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- **DataFrame Fragmentation Warning**: Fixed `PerformanceWarning: DataFrame is highly fragmented` in `transform()` method
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- Root cause: Iteratively adding columns to DataFrame with `woe_df[col] = woe_values` caused memory fragmentation
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- Solution: Collect all WOE columns in a dictionary first, then create DataFrame in one operation
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- Performance improvement: Eliminates repeated memory reallocation during transform
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- User impact: No more annoying performance warnings when transforming data
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- **Debug Print Statement**: Removed leftover debug `print("FAISS is available:", faiss)` statement in FAISS KMeans binning
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- Cleaned up console output when using `binning_method='faiss_kmeans'`
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- **Code Quality**: Improved transform method efficiency following pandas best practices
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- **Technical Details**:
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- Changed from: `for col in columns: woe_df[col] = values` (causes fragmentation)
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- Changed to: `woe_columns = {col: values for col in columns}; woe_df = pd.DataFrame(woe_columns)` (single allocation)
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- Follows pandas recommendation to use `pd.concat(axis=1)` or dict-based DataFrame construction
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- **Testing**:
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- All 102 tests passing successfully ✅
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- Verified no fragmentation warnings with multi-feature datasets
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- Backward compatible: transform output unchanged
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## Version 0.1.5rc1 (2025-10-26)
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**Clean API Refactoring & Pythonic Input Handling**: Release candidate with major UX improvements

‎examples/fastwoe_multiclass.ipynb‎

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"Author: https://www.github.com/xRiskLab\n",
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"\n",
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"This notebook demonstrates how to use FastWoe with a multiclass target in a row-level format. The target has three classes:\n",
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"\n",
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"- `0`: No Default\n",
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"- `1`: UTP Default\n",
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"- `2`: DPD Default"
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"- `2`: DPD Default\n"
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]
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},
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{
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],
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"metadata": {
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"kernelspec": {
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"display_name": "fastwoe",
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"display_name": ".venv (3.11.8)",
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"language": "python",
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"name": "python3"
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},

‎fastwoe/__init__.py‎

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from .fastwoe import FastWoe, WoePreprocessor
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from .interpret_fastwoe import WeightOfEvidence
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__version__ = "0.1.5rc1"
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__version__ = "0.1.5"
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__author__ = "xRiskLab"
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__email__ = "contact@xrisklab.ai"
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‎fastwoe/fastwoe.py‎

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@@ -1009,6 +1009,8 @@ def transform(self, X: Union[pd.DataFrame, np.ndarray, pd.Series]) -> pd.DataFra
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if mask_missing.any():
10101010
X_processed.loc[mask_missing, col] = "Missing"
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1012+
# Collect all WOE columns first to avoid DataFrame fragmentation
1013+
woe_columns = {}
10121014
for col in X_processed.columns:
10131015
if self.is_multiclass_target:
10141016
return self._transform_multiclass(X_processed)
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# Handle unseen categories - use default WOE or prior
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woe_values.append(0.0) # or np.log(self.odds_prior_) for prior
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1030-
woe_df[col] = woe_values
1032+
woe_columns[col] = woe_values
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# Create DataFrame from dict to avoid fragmentation warning
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if woe_columns:
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woe_df = pd.DataFrame(woe_columns, index=X.index)
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return woe_df
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"""Apply FAISS KMeans clustering to a numerical feature."""
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try:
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import faiss # noqa: F401
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1824-
print("FAISS is available:", faiss)
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except ImportError as e:
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raise ImportError(
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"FAISS is required for faiss_kmeans binning method. "

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