evolutune 0.0.3

A Genetic Algorithm-based hyperparameter tuner for machine learning models.

Evolutune

A Genetic Algorithm-based hyperparameter tuner for machine learning models.

Introduction

Evolutune, implements a hyperparameter tuner based on the principles of a genetic algorithm. The genetic algorithm evolves a population of hyperparameter sets over several generations, aiming to find the set that optimizes a given scoring metric. This tuner is designed to work with various machine learning models.

Dependencies

Make sure you have the following dependencies installed:

• numpy
• joblib.Parallel and joblib.delayed
• sklearn.metrics.get_scorer

Installation

pip install evolutune

Usage

from evolutune import GeneticTuner

# Define your machine learning model
# model = ...

# Define the hyperparameter search space
param_grid = {
    'param1': [value1, value2, ...],
    'param2': [value3, value4, ...],
    # Add more hyperparameters as needed
}

# Define the scoring metric to optimize
scoring_metric = 'accuracy'  # Replace with your preferred metric

# Instantiate the GeneticTuner
genetic_tuner = GeneticTuner(
    model=model,
    param_grid=param_grid,
    scoring=scoring_metric,
    population_size=10,
    generations=100,
    mutation_rate=0.1,
    random_state=None,
    n_jobs=None
)

Fitting the Tuner

# Define your training and evaluation sets
train_set = [X_train, y_train]
eval_set = [X_eval, y_eval]  # Set to None to use the training set for evaluation

# Specify the optimization direction ('maximize' or 'minimize')
direction = 'maximize'

# Fit the tuner on the training set
genetic_tuner.fit(train_set, eval_set, direction)

Accessing Results

# Access the best score and corresponding hyperparameters
best_score = genetic_tuner.best_score_
best_params = genetic_tuner.best_params_

print(f"Best Score: {best_score}")
print("Best Hyperparameters:")
for param, value in best_params.items():
    print(f"{param}: {value}")

Methods

Method	Description
initialize_population(population_size: int) -> list	Initialize a population of individuals with random hyperparameters.
crossover(parent1: dict, parent2: dict) -> tuple	Perform crossover between two parents to generate two children.
mutate(individual: dict, mutation_rate: float) -> dict	Introduce random mutations to an individual's hyperparameters.
calculate_fitness(train_set: list, eval_set: list, parameters: dict) -> float	Evaluate the fitness (scoring metric) of a set of hyperparameters.
fit(train_set: list, eval_set: list = None, direction: str = "maximize")	Fit the GeneticTuner on the training set and optional evaluation set.

Example

An example script demonstrating the usage of the GeneticTuner class is provided in the example.py file.