O1 ML Scientist Automation System

An automated machine learning system that leverages O1 and Claude to iteratively develop, improve, and optimize ML solutions.

Overview

This system automates the entire machine learning workflow by:

1. Generating ML code using O1
2. Fixing errors using Claude
3. Optimizing performance when needed
4. Tracking progress and improvements across iterations
5. Managing solution versions and submissions
6. NOTE: includes datasets for Spceship Titanic Kaggle challenge: https://www.kaggle.com/competitions/spaceship-titanic/overview

🏆 Proven Performance: This AI Data Scientist achieved remarkable success on Kaggle's Spaceship Titanic challenge, ranking 29th out of 2,400+ solutions (top 1%)! 🚀 The system autonomously developed, optimized, and fine-tuned its solution to reach this exceptional performance level. 🌟

⚠️ IMPORTANT SECURITY WARNING: This system automatically executes AI-generated code. running any auto-generated code carries inherent risks. Use with caution and creator is not responsible for code outputs and code execution!

🎥 Watch How It's Built!

Watch the complete build process on Patreon See exactly how this automation system was created step by step, with detailed explanations and insights into the development process.

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Key Features

🤖 AI Model Integration

• O1: Generates and improves ML solutions
• Claude: Handles error fixing and code repairs
• Both models maintain code quality and follow best practices

⚡ GPU Acceleration

• Automatic GPU detection and utilization
• Graceful fallback to CPU when GPU is unavailable
• Framework-specific GPU optimizations (PyTorch, TensorFlow, XGBoost, LightGBM)

⏱️ Performance Management

• Maximum runtime limit (default: 30 minutes)
• Automatic timeout detection
• Performance optimization suggestions when timeout occurs
• Maintains accuracy while improving efficiency

🔄 Iterative Improvement

• Tracks performance metrics across iterations
• Uses previous results to guide improvements
• Maintains history of all solutions and progress reports
• Automated versioning of solutions, reports, and submissions

📊 Progress Tracking

• Detailed progress reports in JSON format
• Cross-validation scores tracking
• Feature importance analysis
• Model performance metrics
• Execution logs with timestamps

🛠️ Error Handling

• Intelligent error vs. warning detection
• Automatic error fixing with Claude
• Missing package installation handling
• Clear error reporting and logging

File Structure

project/
├── o1_ml_scientist.py      # Main automation script
├── solution.py             # Current ML solution
├── progress_report.json    # Current progress metrics
├── submission.csv          # Current submission file
├── execution_outputs.txt   # Execution logs
├── older_solutions/        # Version history
│   ├── solution_1.py
│   ├── progress_report_1.json
│   ├── submission_1.csv
│   └── ...

Configuration

Key configurable parameters:

ITERATIONS = 50                # Maximum iterations
MAX_RUNTIME_MINUTES = 30      # Maximum runtime per solution
CLAUDE_MODEL = "claude-3-5-sonnet-20241022"
O1_MODEL = "o1"

Progress Report Format

{
    "cross_validation_scores": [...],
    "mean_cv_accuracy": float,
    "feature_importance": {
        "feature1": importance1,
        "feature2": importance2,
        ...
    },
    "model_parameters": {...},
    "execution_time": float
}

Error Handling Process

1. Code Generation: O1 generates ML solution
2. Execution: Code runs with timeout monitoring
3. Error Detection: System distinguishes between errors and warnings
4. Error Fixing: Claude fixes errors while maintaining core functionality
5. Performance Optimization: O1 optimizes slow-running solutions
6. Verification: System verifies fixes and optimizations

Best Practices Enforced

1. GPU utilization when available
2. Proper train/test splitting
3. Cross-validation for model evaluation
4. Feature importance analysis
5. Progress tracking and logging
6. Code efficiency and readability
7. UTF-8 encoding for file operations
8. Proper error handling and reporting

Limitations

1. Maximum runtime constraint
2. Model-specific GPU support
3. Dependent on API availability
4. Resource intensive for large datasets

Requirements

• Python 3.x
• OpenAI API access
• Anthropic API access
• Required Python packages:
  • openai
  • anthropic
  • pandas
  • numpy
  • scikit-learn
  • torch (optional for GPU)
  • termcolor
  • other ML frameworks as needed

Usage

1. Set up API keys as environment variables
2. Prepare your dataset (train.csv and test.csv)
3. Create additional_info.txt with problem description
4. Run the main script:

python o1_ml_scientist.py

Output

1. solution.py: Current ML solution
2. progress_report.json: Performance metrics
3. submission.csv: Predictions
4. execution_outputs.txt: Detailed logs
5. Version history in older_solutions/

Monitoring

• Real-time execution feedback
• Color-coded status messages
• Detailed error reporting
• Progress tracking across iterations
• Performance metrics logging