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
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.support_

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

gen     nevals  fitness         fitness_std     fitness_max     fitness_min
0       30      0.550444        0.153446        0.86            0.293333
1       60      0.636889        0.119365        0.86            0.473333
2       60      0.698667        0.11242         0.873333        0.46
3       60      0.707556        0.103876        0.873333        0.486667
4       60      0.723556        0.144086        0.9             0.366667
5       60      0.745556        0.152637        0.913333        0.366667
6       60      0.792889        0.108402        0.873333        0.513333
7       60      0.749111        0.16456         0.873333        0.413333
8       60      0.728889        0.179747        0.966667        0.373333
9       60      0.728222        0.158994        0.893333        0.42
10      60      0.785556        0.134892        0.94            0.48
11      60      0.733556        0.175942        0.94            0.44
12      60      0.784889        0.150554        0.94            0.413333
13      60      0.818444        0.148101        0.966667        0.413333
14      60      0.871778        0.116272        0.966667        0.453333
15      60      0.801556        0.184163        0.966667        0.386667
16      60      0.810222        0.163994        0.966667        0.393333
17      60      0.814222        0.148949        0.966667        0.44
18      60      0.72            0.182525        0.966667        0.366667
19      60      0.783556        0.156269        0.966667        0.42
20      60      0.803778        0.146694        0.966667        0.486667

#Best features found
print(evolved_estimator.support_)
print("accuracy score: ", "{:.2f}".format(accuracy))

[False False  True  True False False False False False False False False
 False False]
accuracy score:  0.96

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

# Convert the original input to the selected input
evolved_estimator.transform(X_test)

array([[5.1, 2.4],
       [4. , 1. ],
       [1.4, 0.2],
       [6.3, 1.8],
       [1.5, 0.2],
       [6. , 2.5],
       [1.3, 0.3],
       [4.7, 1.5],
       [4.8, 1.4],
       [4. , 1.3],
       [5.6, 1.4],
       [4.5, 1.5],
       [4.7, 1.2],
       [4.6, 1.5],
       [4.7, 1.4],
       [1.4, 0.1],
       [4.5, 1.5],
       [4.4, 1.2],
       [1.4, 0.3],
       [1.3, 0.4],
       [4.9, 2. ],
       [4.5, 1.5],
       [1.9, 0.2],
       [1.4, 0.2],
       [4.8, 1.8],
       [1. , 0.2],
       [1.9, 0.4],
       [4.3, 1.3],
       [3.3, 1. ],
       [1.6, 0.4],
       [5.5, 1.8],
       [4.5, 1.5],
       [1.5, 0.2],
       [4.9, 1.8],
       [5.6, 2.2],
       [3.9, 1.4],
       [1.7, 0.3],
       [5.1, 1.6],
       [4.2, 1.5],
       [4. , 1.2],
       [5.5, 2.1],
       [1.3, 0.2],
       [5.1, 2.3],
       [1.6, 0.6],
       [1.5, 0.2],
       [3.5, 1. ],
       [5.5, 1.8],
       [5.7, 2.5],
       [5. , 1.5],
       [5.8, 1.8]])