featurecraft 0.1.1

Automatic feature engineering, insights, and sklearn pipelines for tabular ML with optional time-series support.

FeatureCraft

Automatic feature engineering, insights, and sklearn pipelines for tabular ML with optional time-series support.

Table of Contents

• About The Project
  • Built With
• Getting Started
  • Prerequisites
  • Installation
• Usage
  • CLI Usage
  • Python API
  • Estimator Families
  • Configuration
  • Output Artifacts
• Examples
• Documentation
• Roadmap
• Contributing
• License
• Contact
• Acknowledgments

About The Project

FeatureCraft is a comprehensive feature engineering library that automates the process of transforming raw tabular data into machine learning-ready features. It provides intelligent preprocessing, feature selection, encoding, and scaling tailored for different estimator families.

Key Features

✨ Automatic Feature Engineering: Intelligent preprocessing pipeline that handles missing values, outliers, categorical encoding, and feature scaling

🔍 Dataset Analysis: Comprehensive insights into your data including distributions, correlations, and data quality issues

🔮 Explainability & Transparency: Detailed explanations of why and how transformations are applied, with rich console output and export capabilities

📊 Multiple Estimator Support: Optimized preprocessing for tree-based models, linear models, SVMs, k-NN, and neural networks

🛠️ Sklearn Integration: Seamless integration with scikit-learn pipelines and ecosystem

📈 HTML Reports: Interactive visualizations and insights reports

⚡ CLI & Python API: Choose between command-line interface or programmatic usage

🕐 Time Series Support: Optional time-series aware preprocessing

Why FeatureCraft?

• Automated Workflow: No need to manually handle different data types and preprocessing steps
• Best Practices: Implements proven feature engineering techniques
• Performance Optimized: Different preprocessing strategies for different model types
• Production Ready: Exports sklearn-compatible pipelines for deployment
• Comprehensive Analysis: Deep insights into your dataset characteristics
• Explainable AI: Understand why transformations are applied and how they affect your data
• Transparency: Rich explanations help build trust and debug preprocessing decisions

Getting Started

Prerequisites

• Python 3.9 or higher
• pandas >= 1.5
• scikit-learn >= 1.3
• numpy >= 1.23

Installation

pip install featurecraft

All features including AI-powered planning, enhanced encoders, SHAP explainability, and schema validation are included by default.

Usage

CLI Usage

# Analyze dataset and generate comprehensive report
featurecraft analyze --input data.csv --target target_column --out artifacts/

# Fit preprocessing pipeline and transform data
featurecraft fit-transform --input data.csv --target target_column --out artifacts/ --estimator-family tree

# Open the generated HTML report
open artifacts/report.html

Python API

import pandas as pd
from featurecraft.pipeline import AutoFeatureEngineer

# Load your data
df = pd.read_csv("your_data.csv")

# Initialize the feature engineer
afe = AutoFeatureEngineer()

# Analyze dataset (optional but recommended)
summary = afe.analyze(df, target="target_column")
print(f"Detected task: {summary.task}")
print(f"Found {len(summary.issues)} data quality issues")

# Prepare features and target
X, y = df.drop(columns=["target_column"]), df["target_column"]

# Fit and transform with estimator-specific preprocessing
Xt = afe.fit_transform(X, y, estimator_family="tree")
print(f"Transformed {X.shape[1]} features into {Xt.shape[1]} features")

# Export pipeline for production use
afe.export("artifacts")

# Access detailed explanations (optional)
explanation = afe.get_explanation()
afe.print_explanation()  # Rich console output
afe.save_explanation("artifacts/explanation.md", format="markdown")

Estimator Families

Choose the preprocessing strategy based on your model type:

Family	Models	Scaling	Encoding	Best For
tree	XGBoost, LightGBM, Random Forest	None	Label Encoding	Tree-based models
linear	Linear/Logistic Regression	StandardScaler	One-hot + Target	Linear models
svm	SVM, SVC	StandardScaler	One-hot	Support Vector Machines
knn	k-Nearest Neighbors	MinMaxScaler	Label Encoding	Distance-based models
nn	Neural Networks	MinMaxScaler	Label Encoding	Deep learning

Configuration

Customize the preprocessing behavior:

from featurecraft.config import FeatureCraftConfig

# Custom configuration
config = FeatureCraftConfig(
    low_cardinality_max=15,        # Max unique values for low-cardinality features
    outlier_share_threshold=0.1,   # Threshold for outlier detection
    random_state=42,               # For reproducible results
    explain_transformations=True,  # Enable detailed explanations (default: True)
    explain_auto_print=True        # Auto-print explanations after fitting (default: True)
)

afe = AutoFeatureEngineer(config=config)

Output Artifacts

The export() method creates:

• pipeline.joblib: Fitted sklearn Pipeline ready for production
• metadata.json: Configuration and processing summary
• feature_names.txt: List of all output feature names
• explanation.md: Human-readable explanation of transformation decisions (when explanations enabled)
• explanation.json: Machine-readable explanation data (when explanations enabled)

The analyze() method generates:

• report.html: Interactive HTML report with plots, insights, and recommendations

Examples

Check out the examples directory for comprehensive usage examples:

• 01_quickstart.py: Basic usage with multiple datasets
• 02_kaggle_benchmark.py: Kaggle dataset benchmarking
• 03_complex_kaggle_benchmark.py: Advanced benchmarking with complex datasets
• 06_explainability_demo.py: Understanding transformation decisions and explanations

Run the quickstart example:

cd examples
python 01_quickstart.py --cases iris,wine,breast_cancer --artifacts ../artifacts

Documentation

Getting Started

• Getting Started: Quick start guide for new users
• API Reference: Complete Python API documentation
• CLI Reference: Command-line interface guide

Configuration & Optimization

• Configuration Guide: Comprehensive parameter reference
• Optimization Guide: Performance tuning and best practices

Advanced Topics

• Advanced Features: Drift detection, leakage prevention, SHAP
• Benchmarks: Real-world dataset performance results
• Troubleshooting: Common issues and solutions

Roadmap

See the open issues for a list of proposed features and known issues.

• Enhanced time series preprocessing
• Feature selection algorithms
• Integration with popular ML frameworks
• GPU acceleration support
• Advanced outlier detection methods
• Automated feature interaction detection

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".

Don't forget to give the project a star! Thanks again!

License

Distributed under the MIT License. See LICENSE for more information.

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