GeneticAlgorithmFeaturesSelection 0.0.3

Feature Selection with Genetic Algorithm

GeneticAlgorithmFeatureSelection

Feature Selection with Genetic Algorithm published in pypi.

Installation

pip install GeneticAlgorithmFeatureSelection

Example

The original example code can be found in test.py.

from sklearn.datasets import make_classification
import pandas as pd
from genetic_algoirthm.GA import GenticAlgorithmFeatureSelection

Define the sample classification dataset

x, y = make_classification(n_features=100, n_samples=2500)

input data must be pandas dataframe. we split target and features.

columns = [f'f_{i}' for i in range(1, 101)]
features = pd.DataFrame(x, columns=columns)
target = pd.DataFrame(y, columns=['target'])

run feature selection

 GA = GenticAlgorithmFeatureSelection(features=features, target=target, population_size=100, elite_rate=0.5,
                                      fitness_alpha=0.55, tourn_size=25, no_generation=50)
 
 GA.run()

see history

history = GA.history

for generation, detail in history.items():
    print(f'Generation :{generation}')
    print(f'best score: {detail["best_score"]}')
    print(f'features: {detail["selected_features"]}')

find best score and features in last generation

print(f'best score last generation :{GA.best_score}')
print(f'feature selected in last generation: {GA.selected_features}')