mlfastopt 0.0.8.1

ML Fast Opt - Advanced ensemble optimization system for LightGBM hyperparameter tuning

MLFastOpt

MLFastOpt is a comprehensive ensemble optimization system for Bayesian hyperparameter optimization of LightGBM models. It provides automated machine learning capabilities with a focus on speed, accuracy, and ease of use.

Features

• 🚀 Fast Optimization: Advanced Bayesian optimization algorithms
• 🎯 LightGBM Ensembles: Automated ensemble model creation and tuning
• 🌐 Web Interface: Interactive visualization and analysis tools
• ⚙️ Flexible Configuration: Environment-based configuration system
• 📊 Rich Analytics: Comprehensive performance analysis and visualization
• 🔧 Easy CLI: Simple command-line interface for all operations

Installation

pip install mlfastopt

For development installation:

git clone https://github.com/your-repo/mlfastopt
cd mlfastopt
pip install -e .[dev]

Quick Start

MLFastOpt is a framework that requires you to provide your own configuration files. Here's how to get started:

1. Create Directory Structure

mkdir -p config/hyperparameters
mkdir -p data
# Note: Output directories (outputs/, outputs/runs/, etc.) are created automatically

2. Create Hyperparameter Space

Create a hyperparameter space file (e.g., config/hyperparameters/my_space.py):

# config/hyperparameters/my_space.py
PARAMETERS = [
    {"name": "boosting_type", "type": "choice", "values": ["gbdt", "dart"], "value_type": "str"},
    {"name": "num_leaves", "type": "range", "bounds": [20, 200], "value_type": "int"},
    {"name": "learning_rate", "type": "range", "bounds": [0.01, 0.3], "value_type": "float", "log_scale": True},
    {"name": "n_estimators", "type": "range", "bounds": [100, 300], "value_type": "int"},
    {"name": "subsample", "type": "range", "bounds": [0.3, 1.0], "value_type": "float"},
    {"name": "colsample_bytree", "type": "range", "bounds": [0.3, 1.0], "value_type": "float"},
    {"name": "reg_alpha", "type": "range", "bounds": [1e-8, 0.5], "value_type": "float", "log_scale": True},
    {"name": "reg_lambda", "type": "range", "bounds": [1e-8, 0.5], "value_type": "float", "log_scale": True},
    {"name": "is_unbalance", "type": "choice", "values": [True, False], "value_type": "bool"},
]

def get_parameter_space():
    return PARAMETERS

3. Create Configuration File

Create your optimization configuration:

{
  "DATA_PATH": "data/your_dataset.csv",
  "HYPERPARAMETER_PATH": "config/hyperparameters/my_space.py",
  "TARGET_COLUMN": "target",
  "FEATURES": ["feature1", "feature2", "feature3"],
  "CATEGORICAL_FEATURES": ["feature1"],
  "CLASS_WEIGHT": {"0": 1, "1": 2},
  "N_ENSEMBLE_GROUP_NUMBER": 10,
  "AE_NUM_TRIALS": 20,
  "NUM_SOBOL_TRIALS": 5,
  "RANDOM_SEED": 42,
  "PARALLEL_TRAINING": true,
  "N_JOBS": -1,
  "SOFT_PREDICTION_THRESHOLD": 0.7,
  "F1_THRESHOLD": 0.7,
  "MIN_RECALL_THRESHOLD": 0.75,
  "UNDER_SAMPLE_MAJORITY_RATIO": 1
}

4. Run Optimization

# Set threading environment variable (important!)
export OMP_NUM_THREADS=1

# Run optimization
python -m mlfastopt.cli --config my_config.json

# Validate configuration first
python -m mlfastopt.cli --validate --config my_config.json

Architecture

MLFastOpt is organized into several key modules:

• mlfastopt.core: Core optimization engine and configuration management
• mlfastopt.cli: Command-line interface
• mlfastopt.web: Web-based visualization and analysis tools

Configuration System

MLFastOpt is a framework that requires user-provided configurations:

1. Configuration files: JSON files defining optimization parameters and data paths
2. Hyperparameter spaces: Python modules defining LightGBM parameter search spaces
3. Data files: Your datasets in CSV, Parquet, or other pandas-compatible formats

All output directories are created automatically by the framework.

Hyperparameter Tuning

MLFastOpt requires you to define custom hyperparameter spaces for your specific use case:

Creating Parameter Spaces

You must create your own hyperparameter space files. Here's the syntax:

Parameter Types

• Choice: {"name": "param", "type": "choice", "values": ["a", "b"], "value_type": "str"}
• Range (Int): {"name": "param", "type": "range", "bounds": [1, 100], "value_type": "int"}
• Range (Float): {"name": "param", "type": "range", "bounds": [0.1, 1.0], "value_type": "float"}
• Log Scale: Add "log_scale": True for logarithmic parameter exploration
• Boolean: {"name": "param", "type": "choice", "values": [True, False], "value_type": "bool"}

Example Parameter Space

# config/hyperparameters/my_space.py
PARAMETERS = [
    # Boosting algorithm
    {"name": "boosting_type", "type": "choice", "values": ["gbdt", "dart"], "value_type": "str"},
    
    # Tree structure
    {"name": "num_leaves", "type": "range", "bounds": [20, 200], "value_type": "int"},
    {"name": "max_depth", "type": "range", "bounds": [-1, 30], "value_type": "int"},
    
    # Learning parameters
    {"name": "learning_rate", "type": "range", "bounds": [0.01, 0.3], "value_type": "float", "log_scale": True},
    {"name": "n_estimators", "type": "range", "bounds": [100, 500], "value_type": "int"},
    
    # Regularization
    {"name": "reg_alpha", "type": "range", "bounds": [1e-8, 1.0], "value_type": "float", "log_scale": True},
    {"name": "reg_lambda", "type": "range", "bounds": [1e-8, 1.0], "value_type": "float", "log_scale": True},
    
    # Sampling
    {"name": "subsample", "type": "range", "bounds": [0.3, 1.0], "value_type": "float"},
    {"name": "colsample_bytree", "type": "range", "bounds": [0.3, 1.0], "value_type": "float"},
    
    # Class balance
    {"name": "is_unbalance", "type": "choice", "values": [True, False], "value_type": "bool"},
]

def get_parameter_space():
    """Required function that returns the parameter list"""
    return PARAMETERS

Configuration

Reference your parameter space in the config file:

{
  "HYPERPARAMETER_PATH": "config/hyperparameters/my_space.py",
  "DATA_PATH": "data/your_dataset.csv",
  "TARGET_COLUMN": "target",
  "AE_NUM_TRIALS": 50
}

Requirements

• Python 3.8+
• LightGBM 3.3.0+
• Pandas, NumPy, Scikit-learn
• Flask (for web interface)
• Plotly, Matplotlib (for visualization)

Performance Considerations

• Always set OMP_NUM_THREADS=1 for LightGBM to avoid thread conflicts
• Parallel training is controlled via configuration parameters
• Optimization algorithms benefit from multiple CPU cores

Examples

Development Run (Fast)

# 15 trials, 10 models (~15-20 minutes)
OMP_NUM_THREADS=1 python -m mlfastopt.cli --environment development

Production Run

# Full optimization with more trials
OMP_NUM_THREADS=1 python -m mlfastopt.cli --environment production

Validation

# Validate configuration without running optimization
python -m mlfastopt.cli --config config/environments/development.json --validate

Data Requirements

• Input data should be in Parquet, CSV, or other pandas-compatible formats
• Target column must be binary (0/1) for classification
• Features are automatically handled by LightGBM (nulls, categorical encoding)
• Categorical features should be specified in configuration

Output Structure

All outputs are organized under outputs/:

• outputs/runs/: Individual optimization run results
• outputs/best_trials/: Best performing configurations
• outputs/logs/: Execution logs
• outputs/visualizations/: Generated plots and analysis

CLI Commands

The package provides several command-line entry points:

• mlfastopt-optimize: Main optimization CLI
• mlfastopt-web: Web interface launcher
• mlfastopt-analyze: Analysis tools

Contributing

We welcome contributions! Please see our contributing guidelines for details.

License

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

Citation

If you use MLFastOpt in your research, please cite:

@software{mlfastopt,
  title={MLFastOpt: Fast Ensemble Optimization with Advanced Bayesian Methods},
  author={MLFastOpt Development Team},
  url={https://github.com/your-repo/mlfastopt},
  version={0.0.6},
  year={2025}
}