optiflowx 0.0.4

A modular optimization and machine learning workflow framework for automatic model selection, hyperparameter tuning, and performance benchmarking across diverse datasets.

OptiFlowX

OptiFlowX is an open-source framework for hyperparameter optimization using combinatorial and metaheuristic algorithms.
It enables systematic exploration of search spaces for machine learning models using advanced stochastic and hybrid optimization techniques.

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🔍 Overview

Machine learning models rely on carefully tuned hyperparameters.
Conventional methods such as grid or random search are inefficient when dealing with discrete, mixed, or high-dimensional spaces.

OptiFlowX provides a modular ecosystem that leverages combinatorial optimization and metaheuristics (evolutionary, swarm-based, and probabilistic methods) to efficiently discover optimal configurations.

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⚙️ Key Features

• Unified interface for multiple optimization algorithms.
• Works with discrete, categorical, and continuous parameter spaces.
• Includes multiple strategies:
  • Genetic Algorithm (GA)
  • Particle Swarm Optimization (PSO)
  • Bayesian Optimization
  • Tree-structured Parzen Estimator (TPE)
  • Random Search
  • Simulated Annealing
• Scalable, parallel execution support.
• Compatible with any ML framework (scikit-learn, PyTorch, TensorFlow, etc.).

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⚗️ Installation

pip install optiflowx

or from source:

git clone https://github.com/Faycal214/optiflowx.git
cd optiflowx
pip install -e .

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🧠 Example Usage

from sklearn.datasets import make_classification
from optiflowx.optimizers.genetic import GeneticOptimizer
from optiflowx.optimizers.pso import PSOOptimizer
from optiflowx.optimizers.bayesian import BayesianOptimizer
from optiflowx.optimizers.tpe import TPEOptimizer
from optiflowx.optimizers.random_search import RandomSearchOptimizer
from optiflowx.optimizers.simulated_annealing import SimulatedAnnealingOptimizer
from optiflowx.models.configs.random_forest_config import RandomForestConfig


# 1. Generate sample dataset
X, y = make_classification(
    n_samples=100,
    n_features=10,
    n_informative=8,
    n_redundant=2,
    random_state=42,
)

# 2. Load model configuration and search space
cfg = RandomForestConfig()
search_space = cfg.build_search_space()
model_class = cfg.get_wrapper().model_class

# 3. Define optimizers to test
optimizers = [
    ("pso", PSOOptimizer, {"n_particles": 10, "w": 0.7, "c1": 1.4, "c2": 1.4}),
    ("genetic", GeneticOptimizer, {"population": 10, "mutation_prob": 0.3}),
    ("bayesian", BayesianOptimizer, {"n_initial_points": 5}),
    ("tpe", TPEOptimizer, {"population_size": 10}),
    ("random_search", RandomSearchOptimizer, {"n_samples": 20}),
    (
        "simulated_annealing",
        SimulatedAnnealingOptimizer,
        {
            "population_size": 10,
            "initial_temp": 1.0,
            "cooling_rate": 0.9,
            "mutation_rate": 0.3,
        },
    ),
]

# 4. Run each optimizer and print results
for opt_name, opt_class, opt_params in optimizers:
    print(f"\n{'='*30}\nTesting optimizer: {opt_name}\n{'='*30}")
    optimizer = opt_class(
        search_space=search_space,
        metric="accuracy",
        model_class=model_class,
        X=X,
        y=y,
        **opt_params,
    )

    best_params, best_score = optimizer.run(max_iters=5)
    print(f"Result for {opt_name} → score={best_score:.4f}, params={best_params}")

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🧪 Testing

pytest -v --maxfail=1 --disable-warnings

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📚 Citation

@software{optiflowx,
  author = {Faycal, Billel},
  title = {OptiFlowX: Combinatorial Hyperparameter Optimization Framework},
  year = {2025},
  url = {https://github.com/Faycal214/optiflowx}
}