Pipeline Anova SVMΒΆ

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 samples_generator
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 = samples_generator.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 0.546 seconds)

Estimated memory usage: 8 MB

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