FastWoe: Fast Weight of Evidence (WOE) encoding and inference

FastWoe is a Python library for efficient Weight of Evidence (WOE) encoding of categorical features and statistical inference. It's designed for machine learning practitioners seeking robust, interpretable feature engineering and likelihood-ratio-based inference for binary classification problems.

🌟 Key Features

• Fast WOE Encoding: Leverages scikit-learn's TargetEncoder for efficient computation
• Statistical Confidence Intervals: Provides standard errors and confidence intervals for WOE values
• Cardinality Control: Built-in preprocessing to handle high-cardinality categorical features
• Binning Summaries: Feature-level binning statistics including Gini score and Information Value (IV)
• Compatible with scikit-learn: Follows scikit-learn's preprocessing transformer interface
• Uncertainty Quantification: Combines Alan Turing's factor principle with Maximum Likelihood theory (see paper)

🎲 What is Weight of Evidence?

Weight of Evidence (WOE) is a statistical technique that:

• Transforms discrete features into logarithmic scores
• Measures the strength of relationship between feature categories and true labels
• Provides interpretable coefficients as weights in logistic regression models
• Handles missing values and rare categories gracefully

Mathematical Definition:

WOE = ln(P(Event|Category) / P(Non-Event|Category)) - ln(P(Event) / P(Non-Event))

Where WOE represents the log-odds difference between a category and the overall population.

🚀 Installation

Important

FastWoe requires Python 3.9+ and scikit-learn 1.3.0+ for TargetEncoder support.

From PyPI (Recommended)

pip install fastwoe

📦 View on PyPI: https://pypi.org/project/fastwoe/

From Source

git clone https://github.com/xRiskLab/fastwoe.git
cd fastwoe
pip install -e .

Development Installation

git clone https://github.com/xRiskLab/fastwoe.git
cd fastwoe
pip install -e ".[dev]"

Tip

For development work, we recommend using uv for faster package management:

uv sync --dev

📖 Quick Start

import pandas as pd
import numpy as np
from fastwoe import FastWoe, WoePreprocessor

# Create sample data
data = pd.DataFrame({
    'category': ['A', 'B', 'C'] * 100 + ['D'] * 50,
    'high_card_cat': [f'cat_{i}' for i in np.random.randint(0, 50, 350)],
    'target': np.random.binomial(1, 0.3, 350)
})

# Step 1: Preprocess high-cardinality features (optional)
preprocessor = WoePreprocessor(max_categories=10, min_count=5)
X_preprocessed = preprocessor.fit_transform(
    data[['category', 'high_card_cat']], 
    cat_features=['high_card_cat']  # Only preprocess this column
)

# Step 2: Apply WOE encoding
woe_encoder = FastWoe()
X_woe = woe_encoder.fit_transform(X_preprocessed, data['target'])

print("WOE-encoded features:")
print(X_woe.head())

# Step 3: Get detailed mappings with statistics
mapping = woe_encoder.get_mapping('category')
print("\nWOE Mapping for 'category':")
print(mapping[['category', 'count', 'event_rate', 'woe', 'woe_se']])

🔧 Advanced Usage

Caution

When we make inferences with predict_proba and predict_ci methods, we are making a (naive) assumption that pieces of evidence are independent. The sum of WOE scores can only produce meaningful probabilistic outputs if the data is not strongly correlated among features and does not contain very granular categories with very few observations.

Probability Predictions

# Get predictions with Naive Bayes classification
preds = woe_encoder.predict_proba(X_preprocessed)[:, 1]
print(preds.mean())

Confidence Intervals

Note

Statistical confidence intervals help assess the reliability of WOE estimates, especially for categories with small sample sizes.

# Get predictions with confidence intervals
ci_results = woe_encoder.predict_ci(X_preprocessed, alpha=0.05)
print(ci_results[['prediction', 'lower_ci', 'upper_ci']].head())

Feature Statistics

# Get comprehensive feature statistics
feature_stats = woe_encoder.get_feature_stats()
print(feature_stats)

Standardized WOE

# Get Wald scores (standardized log-odds) or use "woe" for raw WOE values
X_standardized = woe_encoder.transform_standardized(X_preprocessed, output='wald')

Pipeline Integration

from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

# Create a complete pipeline
pipeline = Pipeline([
    ('preprocessor', WoePreprocessor(top_p=0.95, min_count=10)),
    ('woe_encoder', FastWoe()),
    ('classifier', LogisticRegression())
])

# Fit the entire pipeline
pipeline.fit(data[['category', 'high_card_cat']], data['target'])

📋 API Reference

FastWoe Class

Parameters

• encoder_kwargs (dict): Additional parameters for sklearn's TargetEncoder
• random_state (int): Random state for reproducibility

Key Methods

• fit(X, y): Fit the WOE encoder
• transform(X): Transform features to WOE values
• fit_transform(X, y): Fit and transform in one step
• get_mapping(column): Get WOE mapping for specific column
• predict_proba(X): Get probability predictions
• predict_ci(X, alpha): Get predictions with confidence intervals

WoePreprocessor Class

The WoePreprocessor is a preprocessing step that reduces the cardinality of categorical features. It is used to handle high-cardinality categorical features.

Warning

High-cardinality features (>50 categories) can lead to overfitting and unreliable WOE estimates. Always use WoePreprocessor for such features if you plan to use in downstream tasks.

Parameters

• max_categories (int): Maximum categories to keep per feature
• top_p (float): Keep categories covering top_p% of frequency
• min_count (int): Minimum count required for category
• other_token (str): Token for grouping rare categories

Tip

The top_p parameter uses cumulative frequency to select categories. For example, top_p=0.95 keeps categories that together represent 95% of all observations, automatically grouping the long tail of rare categories into "__other__". This is more adaptive than fixed max_categories since it preserves the most important categories regardless of their absolute count.

Key Methods

• fit(X, cat_features): Fit preprocessor
• transform(X): Apply preprocessing
• get_reduction_summary(X): Get cardinality reduction statistics

Example: Using top_p parameter

# Dataset with 100 categories: 
# "A" (40%), "B" (30%), "C" (15%), "D" (10%), remaining 96 categories (5% total)

preprocessor = WoePreprocessor(top_p=0.95, min_count=5)
# Result: Keeps ["A", "B", "C", "D"] (95% coverage), groups rest as "__other__"
# Reduces 100 → 5 categories while preserving 95% of the categories

WeightOfEvidence Class

The WeightOfEvidence class provides interpretability for FastWoe classifiers with automatic parameter inference and uncertainty quantification through confidence intervals.

Parameters

• classifier (FastWoe, optional): FastWoe classifier to explain (auto-created if None)
• X_train (array-like, optional): Training features (auto-inferred if possible)
• y_train (array-like, optional): Training labels (auto-inferred if possible)
• feature_names (list, optional): Feature names (auto-inferred if possible)
• class_names (list, optional): Class names (auto-inferred if possible)
• auto_infer (bool): Enable automatic parameter inference (default=True)

Key Methods

• explain(x, sample_idx=None, class_to_explain=None, true_label=None, return_dict=True): Explain single sample or sample from dataset
• explain_ci(x, sample_idx=None, alpha=0.05, return_dict=True): Explain with confidence intervals for uncertainty quantification
• predict_ci(X, alpha=0.05): Batch predictions with confidence bounds
• summary(): Get explainer overview and statistics

Key Features

• Auto-Inference: Automatically detects parameters from FastWoe classifiers
• Dual Usage: Support both explain(sample) and explain(dataset, index) patterns
• Uncertainty Quantification: Confidence intervals for WOE scores and probabilities
• Rich Output: Human-readable interpretations with evidence strength levels

📊 Theoretical Background

This implementation is based on rigorous statistical theory:

1. WOE Standard Error: SE(WOE) = sqrt(1/good_count + 1/bad_count)
2. Confidence Intervals: Using normal approximation with calculated standard errors
3. Information Value: Measures predictive power of each feature
4. Gini Score: Derived from AUC to measure discriminatory power

For rare counts, we rely on the rule of three to calculate the standard error.

For technical details, see Weight of Evidence (WOE), Log Odds, and Standard Errors.

🧪 Testing

Run the test suite:

# Install test dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=fastwoe --cov-report=html

🛠️ Development

Development Setup

Clone the repository and install dependencies:

git clone https://github.com/xRiskLab/fastwoe.git
cd fastwoe
uv sync --dev

Running Tests

Run the main test suite:

uv run pytest

Run tests without slow compatibility tests:

uv run pytest -m "not slow"  

Run compatibility tests across Python/scikit-learn versions (requires uv):

uv run pytest -m compatibility

Run specific test categories:

# Only fast compatibility checks
uv run pytest -m "compatibility and not slow"

# Only slow cross-version tests  
uv run pytest -m "compatibility and slow"

Building the Package

Build wheel and source distribution:

uv build

Install from local build:

uv pip install dist/fastwoe-*.whl

Test installation in clean environment:

# Create temporary environment  
uv venv .test-env --python 3.9
uv pip install --python .test-env/bin/python dist/fastwoe-*.whl
.test-env/bin/python -c "import fastwoe; print(f'FastWoe {fastwoe.__version__} installed successfully!')"

Code Quality

Format code:

uv run black fastwoe/ tests/

Lint code:

uv run ruff check fastwoe/ tests/

📈 Performance Characteristics

• Memory Efficient: Uses pandas and numpy for vectorized operations
• Scalable: Handles datasets with millions of rows
• Fast: Leverages sklearn's optimized TargetEncoder implementation
• Robust: Handles edge cases like single categories and missing values

📋 Changelog

Version 0.1.1.post3 (Current)

• Fixed:
  • sklearn version compatibility: Fixed TypeError with quantile_method parameter in KBinsDiscretizer for older sklearn versions (< 1.3.0). The code now checks sklearn version and only uses quantile_method when supported.
  • API consistency: predict_ci() method now returns a numpy array instead of a DataFrame, consistent with predict_proba(). Returns shape (n_samples, 2) with columns [ci_lower, ci_upper].
• Improved:
  • Added comprehensive tests to verify compatibility across different sklearn versions
  • Updated WeightOfEvidence interpretability module to work with the new predict_ci format
• Notes:
  • All changes from 0.1.1.post2 are included in this release.
  • This release supersedes 0.1.1.post2.

Version 0.1.1.post2

• Fixed:
  • NumPy array input handling: FastWoe.fit and related methods now accept NumPy arrays as input, automatically converting them to pandas DataFrames/Series with a warning. This prevents AttributeError and improves user experience.
• Notes:
  • All changes from 0.1.1.post1 are included in this release.
  • This release supersedes 0.1.1.post1.

Version 0.1.1.post1

• Bug Fixes:
  • Fixed issues with pandas/numpy data type conversions
  • Improved handling of rare categories in WOE calculations
  • Better error messages for edge cases

Version 0.1.1

Enhanced Interpretability Module 🚀

✨ New Features

• WeightOfEvidence Interpretability: Explanation module for FastWoe classifiers
• Auto-Inference Capabilities: Automatically detect and infer feature names, class names, and training data
• Unified Explanation API: Single explain() method supporting both single samples and dataset+index patterns
• Enhanced Output Control: return_dict parameter for clean formatted output vs dictionary return

🔧 Usability Improvements

• Flexible Input Handling: Support for numpy arrays, pandas Series/DataFrames, and mixed data types
• Consistent Class Formatting: Unified formatting between true labels and predicted classes
• Enhanced Examples: Comprehensive examples showing FastWoe vs traditional classifiers

📊 Enhanced API

• WeightOfEvidence(): Auto-inference factory with intelligent parameter detection
• explain(sample) and explain(dataset, sample_idx): Dual usage patterns for maximum flexibility
• explain_ci(sample, alpha=0.05): Explain with confidence intervals for uncertainty quantification

Version 0.1.0

Initial Release 🎉

✨ Features

• Core WOE Implementation: Fast Weight of Evidence encoding using scikit-learn's TargetEncoder
• Statistical Rigor: MLE-based standard errors and confidence intervals for WOE estimates
• High-Cardinality Support: WoePreprocessor for handling features with many categories
• Comprehensive Statistics: Gini coefficient, Information Value (IV), and feature-level metrics
• Integration with scikit-learn: Full compatibility with sklearn pipelines and transformers
• Cross-Version Testing: Compatibility verified across Python 3.9-3.12 and sklearn 1.3.0+

📊 Supported Operations

• fit(), transform(), fit_transform(): Core WOE encoding
• get_mapping(): Detailed category-level WOE mappings
• predict_ci(): Predictions with confidence intervals
• get_feature_stats(): Feature-level discrimination metrics
• transform_standardized(): Wald scores and standardized outputs

Note

This is a beta release. The API is not considered stable for production use.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

📚 References

1. Alan M. Turing (1942). The Applications of Probability to Cryptography.
2. I. J. Good (1950). Probability and the Weighing of Evidence.
3. Daniele Micci-Barreca (2001). A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems.
4. Naeem Siddiqi (2006). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring.

🔗 Other Projects

• scikit-learn: Python Machine learning library providing TargetEncoder implementation
• category_encoders: Additional categorical encoding methods
• WoeBoost: Weight of Evidence (WOE) Gradient Boosting in Python

ℹ️ Additional Information

• Documentation: README.md and Theoretical Background
• Examples: See examples/ directory