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Note
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Pipeline Anova SVM¶
Simple usage of Pipeline that runs successively a univariate feature selection with anova and then a SVM of the selected features.
Using a sub-pipeline, the fitted coefficients can be mapped back into the original feature space.
Out:
precision recall f1-score support
0 0.75 0.50 0.60 6
1 0.67 1.00 0.80 6
2 0.67 0.80 0.73 5
3 1.00 0.75 0.86 8
accuracy 0.76 25
macro avg 0.77 0.76 0.75 25
weighted avg 0.79 0.76 0.76 25
[[-0.23912131 0. 0. 0. -0.3236911 0.
0. 0. 0. 0. 0. 0.
0.10836648 0. 0. 0. 0. 0.
0. 0. ]
[ 0.43878747 0. 0. 0. -0.51415652 0.
0. 0. 0. 0. 0. 0.
0.04845652 0. 0. 0. 0. 0.
0. 0. ]
[-0.65382998 0. 0. 0. 0.57962856 0.
0. 0. 0. 0. 0. 0.
-0.04736524 0. 0. 0. 0. 0.
0. 0. ]
[ 0.54403412 0. 0. 0. 0.58478491 0.
0. 0. 0. 0. 0. 0.
-0.11344659 0. 0. 0. 0. 0.
0. 0. ]]
from sklearn import svm
from sklearn.datasets import make_classification
from sklearn.feature_selection import SelectKBest, f_regression
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
print(__doc__)
# import some data to play with
X, y = make_classification(
n_features=20, n_informative=3, n_redundant=0, n_classes=4,
n_clusters_per_class=2)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
# ANOVA SVM-C
# 1) anova filter, take 3 best ranked features
anova_filter = SelectKBest(f_regression, k=3)
# 2) svm
clf = svm.LinearSVC()
anova_svm = make_pipeline(anova_filter, clf)
anova_svm.fit(X_train, y_train)
y_pred = anova_svm.predict(X_test)
print(classification_report(y_test, y_pred))
coef = anova_svm[:-1].inverse_transform(anova_svm['linearsvc'].coef_)
print(coef)
Total running time of the script: ( 0 minutes 0.341 seconds)
Estimated memory usage: 8 MB
