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[MRG] EXA Improve example plot_svm_anova.py #11731

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Merged
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Jan 28, 2019
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[MRG] EXA Improve example plot_svm_anova.py #11731

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EXA Improve example plot_svm_anova.py
qinhanmin2014 Aug 1, 2018
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Merge branch 'master' into svm-anova-example
qinhanmin2014 Aug 25, 2018
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Merge branch 'master' into svm-anova-example
qinhanmin2014 Jan 26, 2019
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address review
qinhanmin2014 Jan 27, 2019
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address comment
qinhanmin2014 Jan 27, 2019
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more precise y_label
qinhanmin2014 Jan 27, 2019
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36 changes: 16 additions & 20 deletions examples/svm/plot_svm_anova.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,37 +4,35 @@
=================================================

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
Expand All @@ -44,17 +42,15 @@

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()