Random Search

Introduction

The random search explores by choosing a new position at random after each iteration. Some random search implementations choose a new position within a large hypersphere around the current position. The implementation in hyperactive is purely random across the search space in each step.

Example

from hyperactive.opt.gfo import RandomSearch
from hyperactive.experiment.integrations import SklearnCvExperiment
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_digits

# Load dataset
X, y = load_digits(return_X_y=True)

# Define search space
param_grid = {
    "n_estimators": [50, 100, 150, 200],
    "max_depth": [5, 10, 15, 20, None],
    "min_samples_split": [2, 5, 10]
}

# Create experiment
experiment = SklearnCvExperiment(
    estimator=RandomForestClassifier(random_state=42),
    param_grid=param_grid,
    X=X, y=y,
    cv=3
)

# Create optimizer
optimizer = RandomSearch(experiment=experiment)

# Run optimization
best_params = optimizer.solve()
print("Best parameters:", best_params)

About the implementation

The random search is a very simple algorithm that has no parameters to change its behaviour. In each iteration the random position is selected via random.choice from a list of possible positions.