example_pipeline.py

from sklearn import pipeline, grid_search, linear_model, feature_selection, decomposition, datasets, preprocessing

iris = datasets.load_iris()

clf = pipeline.Pipeline([
    ('scale', preprocessing.StandardScaler()),
    ('sel', feature_selection.SelectKBest()),
    ('clf', linear_model.ElasticNet(warm_start=False)),
])

param_grid = {'scale__with_std': [False, True],
              'sel__k': [1,2,3,4],
              'clf__alpha': [0.001, 0.01, 0.1, 1., 10.],
              'clf__l1_ratio': [0.5, 1.0]}

search = grid_search.GridSearchCV(clf, param_grid=param_grid)
search.fit(iris.data, iris.target)
print(search.best_params_, search.best_score_)