GeneticAlgorithmFeatureSelection

Feature Selection with Genetic Algorithm published in pypi.

Installation

from GeneticAlgorithmFeaturesSelection.GA import FeatureSelection

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=25, n_samples=1500, n_classes=4, n_clusters_per_class=3,
                           n_informative=4)

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

columns = [f'f_{i}' for i in range(1, 26)]
features = pd.DataFrame(x, columns=columns)
target = pd.DataFrame(y, columns=['target'])

run feature selection

 FS = FeatureSelection(features=features, target=target, population_size=1000, elite_rate=0.25,
                       mut_rate=0.2, k_folds=4, fitness_alpha=0.85, tourn_size=30,
                       no_generation=120, method='DesicionTree', scoring='accuracy')
 FS.run()

see history

history = FS.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}')