from future import print_function
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.compose import ColumnTransformer
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
from sklearn.feature_selection import SelectKBest
iris = load_iris()
X, y = iris.data, iris.target
This dataset is way too high-dimensional. Better do PCA:
pca = PCA(n_components=2)
Maybe some original features where good, too?
selection = SelectKBest(k=1)
Build estimator from PCA and Univariate selection:
combined_features = FeatureUnion([("pca", pca), ("univ_select", selection)])
ct_f = ColumnTransformer([("pca", pca, [0,1,2,3]), ("univ_select", selection, [0,1,2,3])])
Use combined features to transform dataset:
X_features = combined_features.fit(X, y).transform(X)
print("Combined space has", X_features.shape[1], "features")
X_features2 = ct_f.fit(X, y).transform(X)
print("Combined space has", X_features2.shape[1], "features")
for i in range(20):
print(X_features[i],"--> ",X_features2[i],X_features[i]-X_features2[i])
