=============================================
binlearn - Binning and Discretization Library
=============================================
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A modern, type-safe Python library for data binning and discretization with comprehensive error handling, sklearn compatibility, and DataFrame support.
🚀 **Key Features**
---------------------
✨ **Multiple Binning Methods**
* **EqualWidthBinning** - Equal-width intervals across data range
* **EqualFrequencyBinning** - Equal-frequency (quantile-based) bins
* **KMeansBinning** - K-means clustering-based discretization
* **GaussianMixtureBinning** - Gaussian mixture model clustering-based binning
* **DBSCANBinning** - Density-based clustering for natural groupings
* **EqualWidthMinimumWeightBinning** - Weight-constrained equal-width binning
* **TreeBinning** - Decision tree-based supervised binning for classification and regression
* **Chi2Binning** - Chi-square statistic-based supervised binning for optimal class separation
* **IsotonicBinning** - Isotonic regression-based supervised binning for monotonic relationships
* **ManualIntervalBinning** - Custom interval boundary specification
* **ManualFlexibleBinning** - Mixed interval and singleton bin definitions
* **SingletonBinning** - Creates one bin per unique numeric value
🔧 **Framework Integration**
* **Pandas DataFrames** - Native support with column name preservation
* **Polars DataFrames** - High-performance columnar data support (optional)
* **NumPy Arrays** - Efficient numerical array processing
* **Scikit-learn Pipelines** - Full transformer compatibility
⚡ **Modern Code Quality**
* **Type Safety** - 100% mypy compliance with comprehensive type annotations
* **Code Quality** - 100% ruff compliance with modern Python syntax
* **Error Handling** - Comprehensive validation with helpful error messages and suggestions
* **Test Coverage** - 100% code coverage with 841 comprehensive tests
* **Documentation** - Extensive examples and API documentation
📦 **Installation**
---------------------
.. code-block:: bash
pip install binlearn
🔥 **Quick Start**
--------------------
.. code-block:: python
import numpy as np
import pandas as pd
from binlearn import EqualWidthBinning, TreeBinning, SingletonBinning, Chi2Binning
# Create sample data
data = pd.DataFrame({
'age': np.random.normal(35, 10, 1000),
'income': np.random.lognormal(10, 0.5, 1000),
'score': np.random.uniform(0, 100, 1000)
})
# Equal-width binning with DataFrame preservation
binner = EqualWidthBinning(n_bins=5, preserve_dataframe=True)
data_binned = binner.fit_transform(data)
print(f"Original shape: {data.shape}")
print(f"Binned shape: {data_binned.shape}")
print(f"Bin edges for age: {binner.bin_edges_['age']}")
# SingletonBinning for numeric discrete values
numeric_discrete_data = pd.DataFrame({
'category_id': [1, 2, 1, 3, 2, 1],
'rating': [1, 2, 1, 3, 2, 1]
})
singleton_binner = SingletonBinning(preserve_dataframe=True)
numeric_binned = singleton_binner.fit_transform(numeric_discrete_data)
print(f"Numeric discrete binning: {numeric_binned.shape}")
🎯 **Supervised Binning Example**
-----------------------------------
.. code-block:: python
from binlearn import TreeBinning
import numpy as np
from sklearn.datasets import make_classification
# Create classification dataset
X, y = make_classification(n_samples=1000, n_features=4, n_classes=2, random_state=42)
# Method 1: Using guidance_columns (binlearn style)
# Combine features and target into single dataset
X_with_target = np.column_stack([X, y])
sup_binner1 = TreeBinning(
guidance_columns=[4], # Use the target column to guide binning
task_type='classification',
tree_params={'max_depth': 3, 'min_samples_leaf': 20}
)
X_binned1 = sup_binner1.fit_transform(X_with_target)
# Method 2: Using X and y parameters (sklearn style)
# Pass features and target separately like sklearn
sup_binner2 = TreeBinning(
task_type='classification',
tree_params={'max_depth': 3, 'min_samples_leaf': 20}
)
sup_binner2.fit(X, y) # y is automatically used as guidance
X_binned2 = sup_binner2.transform(X)
print(f"Method 1 - Input shape: {X_with_target.shape}, Output shape: {X_binned1.shape}")
print(f"Method 2 - Input shape: {X.shape}, Output shape: {X_binned2.shape}")
print(f"Both methods create same bins: {np.array_equal(X_binned1, X_binned2)}")
🛠️ **Scikit-learn Integration**
---------------------------------
.. code-block:: python
from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from binlearn import EqualFrequencyBinning
# Use the same classification dataset from previous example
X, y = make_classification(n_samples=1000, n_features=4, n_classes=2, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create ML pipeline with binning preprocessing
pipeline = Pipeline([
('binning', EqualFrequencyBinning(n_bins=5)),
('classifier', RandomForestClassifier(random_state=42))
])
# Train and evaluate
pipeline.fit(X_train, y_train)
accuracy = pipeline.score(X_test, y_test)
print(f"Pipeline accuracy: {accuracy:.3f}")
📚 **Available Methods**
--------------------------
**Interval-based Methods (Unsupervised):**
* ``EqualWidthBinning`` - Creates bins of equal width across the data range
* ``EqualFrequencyBinning`` - Creates bins with approximately equal number of samples
* ``KMeansBinning`` - Uses K-means clustering to determine bin boundaries
* ``GaussianMixtureBinning`` - Uses Gaussian mixture models for probabilistic clustering
* ``DBSCANBinning`` - Uses density-based clustering for natural groupings
* ``EqualWidthMinimumWeightBinning`` - Equal-width bins with weight constraints
* ``ManualIntervalBinning`` - Specify custom interval boundaries
**Supervised Methods:**
* ``TreeBinning`` - Decision tree-based binning optimized for target variables (classification and regression)
* ``Chi2Binning`` - Chi-square statistic-based binning for optimal feature-target association
* ``IsotonicBinning`` - Isotonic regression-based binning for monotonic relationships
**Flexible Methods:**
* ``ManualFlexibleBinning`` - Define mixed interval and singleton bins
* ``SingletonBinning`` - Creates one bin per unique numeric value
⚙️ **Requirements**
---------------------
**Python Versions**: 3.10, 3.11, 3.12, 3.13
**Core Dependencies**:
* NumPy >= 1.21.0
* SciPy >= 1.7.0
* Scikit-learn >= 1.0.0
* kmeans1d >= 0.3.0
**Optional Dependencies**:
* Pandas >= 1.3.0 (for DataFrame support)
* Polars >= 0.15.0 (for Polars DataFrame support)
**Development Dependencies**:
* pytest >= 6.0 (for testing)
* ruff >= 0.1.0 (for linting and formatting)
* mypy >= 1.0.0 (for type checking)
🧪 **Development Setup**
--------------------------
.. code-block:: bash
# Clone repository
git clone https://github.com/TheDAALab/binlearn.git
cd binlearn
# Install in development mode with all dependencies
pip install -e ".[tests,dev,pandas,polars]"
# Run all tests
pytest
# Run code quality checks
ruff check binlearn/
mypy binlearn/ --ignore-missing-imports
# Build documentation
cd docs && make html
🏆 **Code Quality Standards**
-------------------------------
* ✅ **100% Test Coverage** - Comprehensive test suite with 841 tests
* ✅ **100% Type Safety** - Complete mypy compliance with modern type annotations
* ✅ **100% Code Quality** - Full ruff compliance with modern Python standards
* ✅ **Comprehensive Documentation** - Detailed API docs and examples
* ✅ **Modern Python** - Uses latest Python features and best practices
* ✅ **Robust Error Handling** - Helpful error messages with actionable suggestions
🤝 **Contributing**
---------------------
We welcome contributions! Here's how to get started:
1. Fork the repository on GitHub
2. Create a feature branch: ``git checkout -b feature/your-feature``
3. Make your changes and add tests
4. Ensure all quality checks pass:
.. code-block:: bash
pytest # Run tests
ruff check binlearn/ # Check code quality
mypy binlearn/ --ignore-missing-imports # Check types
5. Submit a pull request
**Areas for Contribution**:
* 🐛 Bug reports and fixes
* ✨ New binning algorithms
* 📚 Documentation improvements
* 🧪 Additional test cases
* 🎯 Performance optimizations
🔗 **Links**
--------------
* **GitHub Repository**: https://github.com/TheDAALab/binlearn
* **Issue Tracker**: https://github.com/TheDAALab/binlearn/issues
* **Documentation**: https://binlearn.readthedocs.io/
📄 **License**
----------------
This project is licensed under the MIT License. See the `LICENSE <https://github.com/TheDAALab/binlearn/blob/main/LICENSE>`_ file for details.
**Developed by TheDAALab**
*A modern, type-safe binning framework for Python data science workflows.*
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:target: https://pandas.pydata.org/
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:alt: Integrates with Scikit-learn
:target: https://scikit-learn.org/
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:alt: Development Status
:target: https://pypi.org/project/binlearn/
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:alt: Contributors
:target: https://github.com/TheDAALab/binlearn/graphs/contributors
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"description": "=============================================\nbinlearn - Binning and Discretization Library\n=============================================\n\n.. image:: https://img.shields.io/pypi/v/binlearn\n :alt: PyPI Version\n :target: https://pypi.org/project/binlearn/\n\n.. image:: https://img.shields.io/pypi/pyversions/binlearn\n :alt: Python Versions\n :target: https://pypi.org/project/binlearn/\n\n.. image:: https://img.shields.io/github/actions/workflow/status/TheDAALab/binlearn/build.yml?branch=main\n :alt: Build Status\n :target: https://github.com/TheDAALab/binlearn/actions/workflows/build.yml\n\n.. image:: https://img.shields.io/codecov/c/github/TheDAALab/binlearn\n :alt: Code Coverage\n :target: https://codecov.io/gh/TheDAALab/binlearn\n\n.. image:: https://img.shields.io/github/license/TheDAALab/binlearn\n :alt: License\n :target: https://github.com/TheDAALab/binlearn/blob/main/LICENSE\n\n.. image:: https://img.shields.io/readthedocs/binlearn\n :alt: Documentation Status\n :target: https://binlearn.readthedocs.io/\n\n.. image:: https://img.shields.io/pypi/dm/binlearn\n :alt: Monthly Downloads\n :target: https://pypi.org/project/binlearn/\n\n.. image:: https://img.shields.io/github/stars/TheDAALab/binlearn?style=social\n :alt: GitHub Stars\n :target: https://github.com/TheDAALab/binlearn\n\n.. image:: https://img.shields.io/badge/code%20style-ruff-000000.svg\n :alt: Code Style - Ruff\n :target: https://github.com/astral-sh/ruff\n\n.. image:: https://img.shields.io/badge/typing-mypy-blue\n :alt: Type Checking - MyPy\n :target: https://mypy.readthedocs.io/\n\nA modern, type-safe Python library for data binning and discretization with comprehensive error handling, sklearn compatibility, and DataFrame support.\n\n\ud83d\ude80 **Key Features**\n---------------------\n\n\u2728 **Multiple Binning Methods**\n * **EqualWidthBinning** - Equal-width intervals across data range\n * **EqualFrequencyBinning** - Equal-frequency (quantile-based) bins \n * **KMeansBinning** - K-means clustering-based discretization\n * **GaussianMixtureBinning** - Gaussian mixture model clustering-based binning\n * **DBSCANBinning** - Density-based clustering for natural groupings\n * **EqualWidthMinimumWeightBinning** - Weight-constrained equal-width binning\n * **TreeBinning** - Decision tree-based supervised binning for classification and regression\n * **Chi2Binning** - Chi-square statistic-based supervised binning for optimal class separation\n * **IsotonicBinning** - Isotonic regression-based supervised binning for monotonic relationships\n * **ManualIntervalBinning** - Custom interval boundary specification\n * **ManualFlexibleBinning** - Mixed interval and singleton bin definitions\n * **SingletonBinning** - Creates one bin per unique numeric value\n\n\ud83d\udd27 **Framework Integration**\n * **Pandas DataFrames** - Native support with column name preservation\n * **Polars DataFrames** - High-performance columnar data support (optional)\n * **NumPy Arrays** - Efficient numerical array processing\n * **Scikit-learn Pipelines** - Full transformer compatibility\n\n\u26a1 **Modern Code Quality**\n * **Type Safety** - 100% mypy compliance with comprehensive type annotations\n * **Code Quality** - 100% ruff compliance with modern Python syntax\n * **Error Handling** - Comprehensive validation with helpful error messages and suggestions\n * **Test Coverage** - 100% code coverage with 841 comprehensive tests\n * **Documentation** - Extensive examples and API documentation\n\n\ud83d\udce6 **Installation**\n---------------------\n\n.. code-block:: bash\n\n pip install binlearn\n\n\ud83d\udd25 **Quick Start**\n--------------------\n\n.. code-block:: python\n\n import numpy as np\n import pandas as pd\n from binlearn import EqualWidthBinning, TreeBinning, SingletonBinning, Chi2Binning\n \n # Create sample data\n data = pd.DataFrame({\n 'age': np.random.normal(35, 10, 1000),\n 'income': np.random.lognormal(10, 0.5, 1000),\n 'score': np.random.uniform(0, 100, 1000)\n })\n \n # Equal-width binning with DataFrame preservation\n binner = EqualWidthBinning(n_bins=5, preserve_dataframe=True)\n data_binned = binner.fit_transform(data)\n \n print(f\"Original shape: {data.shape}\")\n print(f\"Binned shape: {data_binned.shape}\")\n print(f\"Bin edges for age: {binner.bin_edges_['age']}\")\n \n # SingletonBinning for numeric discrete values\n numeric_discrete_data = pd.DataFrame({\n 'category_id': [1, 2, 1, 3, 2, 1],\n 'rating': [1, 2, 1, 3, 2, 1]\n })\n \n singleton_binner = SingletonBinning(preserve_dataframe=True)\n numeric_binned = singleton_binner.fit_transform(numeric_discrete_data)\n print(f\"Numeric discrete binning: {numeric_binned.shape}\")\n\n\ud83c\udfaf **Supervised Binning Example**\n-----------------------------------\n\n.. code-block:: python\n\n from binlearn import TreeBinning\n import numpy as np\n from sklearn.datasets import make_classification\n \n # Create classification dataset\n X, y = make_classification(n_samples=1000, n_features=4, n_classes=2, random_state=42)\n \n # Method 1: Using guidance_columns (binlearn style)\n # Combine features and target into single dataset\n X_with_target = np.column_stack([X, y])\n \n sup_binner1 = TreeBinning(\n guidance_columns=[4], # Use the target column to guide binning\n task_type='classification',\n tree_params={'max_depth': 3, 'min_samples_leaf': 20}\n )\n X_binned1 = sup_binner1.fit_transform(X_with_target)\n \n # Method 2: Using X and y parameters (sklearn style)\n # Pass features and target separately like sklearn\n sup_binner2 = TreeBinning(\n task_type='classification',\n tree_params={'max_depth': 3, 'min_samples_leaf': 20}\n )\n sup_binner2.fit(X, y) # y is automatically used as guidance\n X_binned2 = sup_binner2.transform(X)\n \n print(f\"Method 1 - Input shape: {X_with_target.shape}, Output shape: {X_binned1.shape}\")\n print(f\"Method 2 - Input shape: {X.shape}, Output shape: {X_binned2.shape}\")\n print(f\"Both methods create same bins: {np.array_equal(X_binned1, X_binned2)}\")\n\n\ud83d\udee0\ufe0f **Scikit-learn Integration**\n---------------------------------\n\n.. code-block:: python\n\n from sklearn.pipeline import Pipeline\n from sklearn.ensemble import RandomForestClassifier\n from sklearn.model_selection import train_test_split\n from binlearn import EqualFrequencyBinning\n \n # Use the same classification dataset from previous example\n X, y = make_classification(n_samples=1000, n_features=4, n_classes=2, random_state=42)\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n \n # Create ML pipeline with binning preprocessing\n pipeline = Pipeline([\n ('binning', EqualFrequencyBinning(n_bins=5)),\n ('classifier', RandomForestClassifier(random_state=42))\n ])\n \n # Train and evaluate\n pipeline.fit(X_train, y_train)\n accuracy = pipeline.score(X_test, y_test)\n print(f\"Pipeline accuracy: {accuracy:.3f}\")\n\n\ud83d\udcda **Available Methods**\n--------------------------\n\n**Interval-based Methods (Unsupervised):**\n\n* ``EqualWidthBinning`` - Creates bins of equal width across the data range\n* ``EqualFrequencyBinning`` - Creates bins with approximately equal number of samples \n* ``KMeansBinning`` - Uses K-means clustering to determine bin boundaries\n* ``GaussianMixtureBinning`` - Uses Gaussian mixture models for probabilistic clustering\n* ``DBSCANBinning`` - Uses density-based clustering for natural groupings\n* ``EqualWidthMinimumWeightBinning`` - Equal-width bins with weight constraints\n* ``ManualIntervalBinning`` - Specify custom interval boundaries\n\n**Supervised Methods:**\n\n* ``TreeBinning`` - Decision tree-based binning optimized for target variables (classification and regression)\n* ``Chi2Binning`` - Chi-square statistic-based binning for optimal feature-target association\n* ``IsotonicBinning`` - Isotonic regression-based binning for monotonic relationships\n\n**Flexible Methods:**\n\n* ``ManualFlexibleBinning`` - Define mixed interval and singleton bins\n* ``SingletonBinning`` - Creates one bin per unique numeric value\n\n\u2699\ufe0f **Requirements**\n---------------------\n\n**Python Versions**: 3.10, 3.11, 3.12, 3.13\n\n**Core Dependencies**:\n * NumPy >= 1.21.0\n * SciPy >= 1.7.0\n * Scikit-learn >= 1.0.0\n * kmeans1d >= 0.3.0\n\n**Optional Dependencies**:\n * Pandas >= 1.3.0 (for DataFrame support)\n * Polars >= 0.15.0 (for Polars DataFrame support)\n\n**Development Dependencies**:\n * pytest >= 6.0 (for testing)\n * ruff >= 0.1.0 (for linting and formatting)\n * mypy >= 1.0.0 (for type checking)\n\n\ud83e\uddea **Development Setup**\n--------------------------\n\n.. code-block:: bash\n\n # Clone repository\n git clone https://github.com/TheDAALab/binlearn.git\n cd binlearn\n \n # Install in development mode with all dependencies\n pip install -e \".[tests,dev,pandas,polars]\"\n \n # Run all tests\n pytest\n \n # Run code quality checks\n ruff check binlearn/\n mypy binlearn/ --ignore-missing-imports\n \n # Build documentation\n cd docs && make html\n\n\ud83c\udfc6 **Code Quality Standards**\n-------------------------------\n\n* \u2705 **100% Test Coverage** - Comprehensive test suite with 841 tests\n* \u2705 **100% Type Safety** - Complete mypy compliance with modern type annotations\n* \u2705 **100% Code Quality** - Full ruff compliance with modern Python standards\n* \u2705 **Comprehensive Documentation** - Detailed API docs and examples\n* \u2705 **Modern Python** - Uses latest Python features and best practices\n* \u2705 **Robust Error Handling** - Helpful error messages with actionable suggestions\n\n\ud83e\udd1d **Contributing**\n---------------------\n\nWe welcome contributions! Here's how to get started:\n\n1. Fork the repository on GitHub\n2. Create a feature branch: ``git checkout -b feature/your-feature``\n3. Make your changes and add tests\n4. Ensure all quality checks pass:\n \n .. code-block:: bash\n \n pytest # Run tests\n ruff check binlearn/ # Check code quality \n mypy binlearn/ --ignore-missing-imports # Check types\n\n5. Submit a pull request\n\n**Areas for Contribution**:\n * \ud83d\udc1b Bug reports and fixes\n * \u2728 New binning algorithms\n * \ud83d\udcda Documentation improvements\n * \ud83e\uddea Additional test cases\n * \ud83c\udfaf Performance optimizations\n\n\ud83d\udd17 **Links**\n--------------\n\n* **GitHub Repository**: https://github.com/TheDAALab/binlearn\n* **Issue Tracker**: https://github.com/TheDAALab/binlearn/issues\n* **Documentation**: https://binlearn.readthedocs.io/\n\n\ud83d\udcc4 **License**\n----------------\n\nThis project is licensed under the MIT License. See the `LICENSE <https://github.com/TheDAALab/binlearn/blob/main/LICENSE>`_ file for details.\n\n\n\n**Developed by TheDAALab** \n\n*A modern, type-safe binning framework for Python data science workflows.*\n\n.. image:: https://img.shields.io/badge/Powered%20by-Python-blue.svg\n :alt: Powered by Python\n :target: https://www.python.org/\n\n.. image:: https://img.shields.io/badge/Built%20with-NumPy-orange.svg\n :alt: Built with NumPy\n :target: https://numpy.org/\n\n.. image:: https://img.shields.io/badge/Compatible%20with-Pandas-green.svg\n :alt: Compatible with Pandas\n :target: https://pandas.pydata.org/\n\n.. image:: https://img.shields.io/badge/Integrates%20with-Scikit--learn-red.svg\n :alt: Integrates with Scikit-learn\n :target: https://scikit-learn.org/\n\n.. image:: https://img.shields.io/pypi/status/binlearn\n :alt: Development Status\n :target: https://pypi.org/project/binlearn/\n\n.. image:: https://img.shields.io/github/contributors/TheDAALab/binlearn\n :alt: Contributors\n :target: https://github.com/TheDAALab/binlearn/graphs/contributors\n",
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