Iris Feature Selection

import matplotlib.pyplot as plt
from sklearn_genetic import GAFeatureSelectionCV
from sklearn_genetic.plots import plot_fitness_evolution
from sklearn.model_selection import train_test_split, StratifiedKFold
from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
import numpy as np

Import the data and split it in train and test sets

Random noise is added to simulate useless variables

data = load_iris()
X, y = data["data"], data["target"]

noise = np.random.uniform(0, 10, size=(X.shape[0], 10))

X = np.hstack((X, noise))
X.shape

(150, 14)

Split the training and test data

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0)

Define the GAFeatureSelectionCV options

clf = SVC(gamma='auto')

evolved_estimator = GAFeatureSelectionCV(
    estimator=clf,
    cv=3,
    scoring="accuracy",
    population_size=30,
    generations=20,
    n_jobs=-1,
    verbose=True,
    keep_top_k=2,
    elitism=True,
)

Fit the model and see some results

evolved_estimator.fit(X, y)
features = evolved_estimator.best_features_

# Predict only with the subset of selected features
y_predict_ga = evolved_estimator.predict(X_test[:, features])
accuracy = accuracy_score(y_test, y_predict_ga)

INSTANCE
True
gen     nevals  fitness         fitness_std     fitness_max     fitness_min
0       30      0.558444        0.155441        0.893333        0.253333
1       54      0.659333        0.132948        0.893333        0.333333
2       54      0.742667        0.0867111       0.893333        0.586667
3       55      0.805778        0.0740117       0.893333        0.653333
4       52      0.873333        0.0435125       0.906667        0.746667
5       53      0.896222        0.00659592      0.913333        0.893333
6       55      0.901111        0.0131186       0.953333        0.893333
7       54      0.911778        0.0206332       0.953333        0.893333
8       50      0.926444        0.0210455       0.953333        0.893333
9       51      0.941333        0.020177        0.966667        0.913333
10      49      0.955556        0.00978787      0.966667        0.913333
11      55      0.959111        0.00660714      0.966667        0.953333
12      57      0.965333        0.004           0.966667        0.953333
13      55      0.966444        0.00271257      0.973333        0.953333
14      58      0.966667        6.66134e-16     0.966667        0.966667
15      53      0.966889        0.0011967       0.973333        0.966667
16      56      0.967556        0.00226623      0.973333        0.966667
17      53      0.969556        0.00330357      0.973333        0.966667
18      51      0.971111        0.0031427       0.973333        0.966667
19      58      0.972889        0.00166296      0.973333        0.966667
20      54      0.973333        3.33067e-16     0.973333        0.973333

print(evolved_estimator.best_features_)
print("accuracy score: ", "{:.2f}".format(accuracy))

[ True  True  True  True False False False False False False False False
 False False]
accuracy score:  0.98

plot = plot_fitness_evolution(evolved_estimator, metric="fitness")
plt.show()