mlfastopt 0.0.9b3

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

MLFastOpt

MLFastOpt is a high-speed ensemble optimization system for Bayesian hyperparameter tuning of LightGBM, XGBoost, and Random Forest models.

Features

• 🚀 Fast Optimization: Advanced Bayesian optimization algorithms (Sobol + BoTorch).
• 🧩 Multi-Model Support: Tune LightGBM, XGBoost, or Random Forest ensembles.
• ⚙️ Simple Config: Hierarchical JSON configuration and YAML/Python search spaces.
• 📊 Rich Analytics: Built-in web dashboards and visualization tools.

Prerequisites

• Python 3.9+
• macOS Users: You must install openmp for LightGBM/XGBoost to work:

  brew install libomp
  

Installation

1. Activate Virtual Environment:

   source .venv/bin/activate
   # OR if you haven't created one yet:
   # python3.12 -m venv .venv && source .venv/bin/activate
   

2. Install Package:

   pip install -e .[dev]
   

Quick Start (End Users)

If you installed the package via pip install mlfastopt, follow these steps:

1. Create Configuration Files: You need a config.json and a hyperparameter space file (e.g., hyperparameters.yaml).

   config.json:

   {
     "data": { "path": "train.parquet", "label_column": "target", "features": "features.yaml" },
     "model": { "type": "xgboost", "hyperparameter_path": "config/hyperparameters/xgboost.yaml" },
     "training": { "metric": "f1", "total_trials": 20 },
     "output": { "dir": "outputs" }
   }
   

2. Run Optimization:

   export OMP_NUM_THREADS=1
   mlfastopt-optimize --config config.json
   

Quick Start (Developers)

Prerequisite: Input data must be preprocessed and numerical. Handle all categorical encoding (e.g., one-hot, label encoding) before using MLFastOpt (except for LightGBM/XGBoost which have some categorical support).

1. Setup

Create the required directory structure:

mkdir -p config/hyperparameters data

2. Define Parameter Space

We recommend using YAML for parameter spaces. Create config/hyperparameters/my_space.yaml:

parameters:
  - name: learning_rate
    type: range
    bounds: [0.01, 0.3]
    value_type: float
    log_scale: true

  - name: max_depth
    type: range
    bounds: [3, 10]
    value_type: int

3. Configure

Create my_config.json using the nested structure:

{
  "data": {
    "path": "data/your_dataset.parquet",
    "label_column": "target",
    "features": ["feature1", "feature2"],
    "class_weight": { "0": 1, "1": 5 },
    "under_sample_majority_ratio": 1.0
  },
  "model": {
    "type": "lightgbm",
    "hyperparameter_path": "config/hyperparameters/my_space.yaml",
    "ensemble_size": 5
  },
  "training": {
    "total_trials": 20,
    "sobol_trials": 5,
    "metric": "soft_recall",
    "parallel": true,
    "n_jobs": -1
  },
  "output": {
    "dir": "outputs/runs"
  }
}

4. Run

Execute optimization (ensure single-threading for LightGBM/XGBoost to avoid deadlocks):

OMP_NUM_THREADS=1 python -m mlfastopt.cli --config my_config.json

Configuration Reference

Data Section (data)

Parameter	Description	Default
path	Path to dataset (CSV/Parquet).	Required
label_column	Name of target column.	Required
features	List of features or path to YAML file.	Required
class_weight	Dictionary of class weights (e.g., {"0": 1, "1": 10}).	None

Model Section (model)

Parameter	Description	Default
type	Model type: lightgbm, xgboost, random_forest.	lightgbm
hyperparameter_path	Path to parameter space file.	Required
ensemble_size	Models per ensemble.	1

Training Section (training)

Parameter	Description	Default
total_trials	Total optimization trials.	20
metric	Metric to maximize (soft_recall, soft_f1_score, etc).	soft_recall
parallel	Enable parallel training of ensemble members.	false

Outputs

Results are saved to outputs/:

• runs/: Detailed logs and models for each run.
• best_trials/: JSON configurations of the best performing trials.
• visualizations/: Generated plots.