⚡ AutoForge — Research-Grade AutoML Engine

Build, optimize, and deploy machine learning pipelines automatically.

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🧠 Self-Improving AutoML

AutoForge continuously learns from past experiments and adapts its search strategy.

• Prioritizes high-performing models automatically
• Avoids poor preprocessing choices
• Improves performance over time

This creates a feedback loop where AutoML becomes smarter with usage

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🚀 Features

• 🔍 Full Pipeline Search (Preprocessing + Model + Features)
• ⚡ Hyperparameter Optimization (Optuna powered)
• 🧠 Meta-Learning Engine (learns from past runs)
• 📊 Experiment Tracking System
• 🧩 Dynamic Feature Engineering
• 🤖 Multi-Model Ensembling (Stacking + Blending)
• 💻 CLI Interface for Production Use

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🧠 Architecture

flowchart LR
    A[Dataset] --> B[Feature Engineering]
    B --> C[Pipeline Search]
    C --> D[Model Optimization]
    D --> E[Ensembling]
    E --> F[Best Model]

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Data Flow Pipeline

flowchart TD
    A[Raw Input Data] --> B[Task Type Detection]
    B --> C[Dataset Profiling]
    C --> D[Meta-Learning Analysis]
    D --> E[Pipeline Search Space]
    E --> F[Hyperparameter Optimization]
    F --> G[Model Evaluation]
    G --> H[Ensemble Construction]
    H --> I[Best Pipeline Selection]
    I --> J[Model Serialization]
    
    J --> K[Predictions Output]
    J --> L[Pipeline Export]
    J --> M[Experiment Logs]

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⚙️ Installation

git clone https://github.com/YOUR_USERNAME/autoforge.git
cd autoforge
pip install -e .

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🚀 Quick Start

automl train data.csv --target price
automl predict model.pkl test.csv
automl logs

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📊 Example

from automl.api.automl import AutoML

automl = AutoML(n_trials=50)
automl.fit(X, y)

preds = automl.predict(X_test)

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🧠 What Makes AutoForge Special?

Unlike basic AutoML tools, AutoForge:

• Searches entire pipelines, not just models
• Learns from previous datasets (meta-learning)
• Combines models using stacking & blending
• Tracks experiments for reproducibility

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🔥 Roadmap

• Neural Architecture Search (NAS)
• Streamlit Dashboard
• Distributed AutoML (Ray)
• PyPI Package Release

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🤝 Contributing

Pull requests are welcome. For major changes, open an issue first.

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⭐ Support

If you like this project, give it a ⭐ on GitHub!

flowchart TD A[Raw Input Data] --> B[Task Type Detection] B --> C[Dataset Profiling] C --> D[Meta-Learning Analysis] D --> E[Pipeline Search Space] E --> F[Hyperparameter Optimization] F --> G[Model Evaluation] G --> H[Ensemble Construction] H --> I[Best Pipeline Selection] I --> J[Model Serialization]

J --> K[Predictions Output]
J --> L[Pipeline Export]
J --> M[Experiment Logs]