EXA Improve example plot_svm_anova.py
@@ -4,37 +4,35 @@ SVM-Anova: SVM with univariate feature selection
=================================================
This example shows how to perform univariate feature selection before running a
-SVC (support vector classifier) to improve the classification scores.
+SVC (support vector classifier) to improve the classification scores. We use
+the iris dataset (4 features) and add 36 non-informative features. We can find
+that our model achieves best performance when we select around 10% of features.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
-from sklearn.datasets import load_digits
+from sklearn.datasets import load_iris
from sklearn.feature_selection import SelectPercentile, chi2
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
# #############################################################################
# Import some data to play with
-X, y = load_digits(return_X_y=True)
-# Throw away data, to be in the curse of dimension settings
-X = X[:200]
-y = y[:200]
-n_samples = len(y)
-X = X.reshape((n_samples, -1))
-# add 200 non-informative features
-X = np.hstack((X, 2 * np.random.random((n_samples, 200))))
+X, y = load_iris(return_X_y=True)
+# Add non-informative features
+np.random.seed(0)
+X = np.hstack((X, 2 * np.random.random((X.shape[0], 36))))
# #############################################################################
-# Create a feature-selection transform and an instance of SVM that we
+# Create a feature-selection transform, a scaler and an instance of SVM that we
# combine together to have an full-blown estimator
-
-transform = SelectPercentile(chi2)
-
-clf = Pipeline([('anova', transform), ('svc', SVC(gamma="auto"))])
+clf = Pipeline([('anova', SelectPercentile(chi2)),
+ ('scaler', StandardScaler()),
+ ('svc', SVC(gamma="auto"))])
# #############################################################################
# Plot the cross-validation score as a function of percentile of features
@@ -44,17 +42,15 @@ percentiles = (1, 3, 6, 10, 15, 20, 30, 40, 60, 80, 100)
for percentile in percentiles:
clf.set_params(anova__percentile=percentile)
- # Compute cross-validation score using 1 CPU
- this_scores = cross_val_score(clf, X, y, cv=5, n_jobs=1)
+ this_scores = cross_val_score(clf, X, y, cv=5)
score_means.append(this_scores.mean())
score_stds.append(this_scores.std())
plt.errorbar(percentiles, score_means, np.array(score_stds))
-
plt.title(
'Performance of the SVM-Anova varying the percentile of features selected')
+plt.xticks(np.linspace(0, 100, 11, endpoint=True))
plt.xlabel('Percentile')
-plt.ylabel('Prediction rate')
-
+plt.ylabel('Accuracy Score')
plt.axis('tight')
plt.show()
