Pipeline Anova SVM

Uso simple de Pipeline que ejecuta sucesivamente una selección de características univariantes con anova y luego una SVM de las características seleccionadas.

Usando un sub-pipeline, los coeficientes ajustados pueden ser mapeados de nuevo en el espacio original de características.

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

[[-0.23912051  0.          0.          0.         -0.32369992  0.
   0.          0.          0.          0.          0.          0.
   0.1083669   0.          0.          0.          0.          0.
   0.          0.        ]
 [ 0.43878897  0.          0.          0.         -0.514157    0.
   0.          0.          0.          0.          0.          0.
   0.04845592  0.          0.          0.          0.          0.
   0.          0.        ]
 [-0.65382765  0.          0.          0.          0.57962287  0.
   0.          0.          0.          0.          0.          0.
  -0.04736736  0.          0.          0.          0.          0.
   0.          0.        ]
 [ 0.544033    0.          0.          0.          0.58478674  0.
   0.          0.          0.          0.          0.          0.
  -0.11344771  0.          0.          0.          0.          0.
   0.          0.        ]]

from sklearn import svm
from sklearn.datasets import make_classification
from sklearn.feature_selection import SelectKBest, f_classif
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_classif, 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)