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[MRG+2] EXA Get rid of warnings in plot_svm_anova.py #11588

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Jul 17, 2018
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[MRG+2] EXA Get rid of warnings in plot_svm_anova.py #11588

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14 changes: 8 additions & 6 deletions examples/svm/plot_svm_anova.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,17 +10,19 @@

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets, feature_selection
from sklearn.datasets import load_digits
from sklearn.feature_selection import SelectPercentile, chi2
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC


# #############################################################################
# Import some data to play with
digits = datasets.load_digits()
y = digits.target
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]
X = digits.data[:200]
n_samples = len(y)
X = X.reshape((n_samples, -1))
# add 200 non-informative features
Expand All @@ -30,9 +32,9 @@
# Create a feature-selection transform and an instance of SVM that we
# combine together to have an full-blown estimator

transform = feature_selection.SelectPercentile(feature_selection.f_classif)
transform = SelectPercentile(chi2)

clf = Pipeline([('anova', transform), ('svc', svm.SVC(C=1.0))])
clf = Pipeline([('anova', transform), ('svc', SVC(gamma="auto"))])

# #############################################################################
# Plot the cross-validation score as a function of percentile of features
Expand Down