from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler
from sklearn.decomposition import PCA
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import GradientBoostingClassifier
from pipelinehelper.pipelinehelper import PipelineHelper

pipe = Pipeline([
    ('scaler', PipelineHelper([
        ('std', StandardScaler()),
        ('abs', MaxAbsScaler()),
        ('minmax', MinMaxScaler()),
        ('pca', PCA(svd_solver='full', whiten=True)),
    ])),
    ('classifier', PipelineHelper([
        ('knn', KNeighborsClassifier(weights='distance')),
        ('gbc', GradientBoostingClassifier())
    ])),
])
params = {
    'scaler__selected_model': pipe.named_steps['scaler'].generate({
        'std__with_mean': [True, False],
        'std__with_std': [True, False],
        'pca__n_components': [0.5, 0.75, 0.9, 0.99],
    }),
    'classifier__selected_model': pipe.named_steps['classifier'].generate({
        'knn__n_neighbors': [1, 3, 5, 7, 10],#, 30, 50, 70, 90, 110, 130, 150, 170, 190],
        'gbc__learning_rate': [0.1, 0.5, 1.0],
        'gbc__subsample': [0.5, 1.0],
    })
}

grid = GridSearchCV(pipe, params, scoring='roc_auc', n_jobs=1, verbose=1, cv=5)
grid.fit(X, y)

>>> X.shape
... (13885, 23)
>>> y.shape
... (13885,)
>>> X
... array([[ 0.        ,  0.        ,  0.        , ...,  7.14285714,
         0.9       , 35.4644354 ],
       [ 0.        ,  0.        ,  0.        , ...,  2.11442806,
         1.2       , 54.99027913],
       [ 1.        ,  0.        ,  0.        , ...,  2.64959194,
         0.7       , 70.07380534],
       ...,
       [ 1.        ,  0.        ,  0.        , ...,  4.375     ,
         0.5       , 91.85932945],
       [ 1.        ,  0.        ,  0.        , ...,  3.75      ,
         0.9       , 68.62436682],
       [ 0.        ,  0.        ,  1.        , ...,  3.01587302,
         4.1       , 57.25781074]])
>>> y
... array([0, 0, 0, ..., 0, 0, 1])
>>> y.mean()
... 0.11278357940223263
>>> sklearn.__version__
'0.20.3'

python3.7/site-packages/sklearn/metrics/ranking.py in roc_curve(y_true, y_score, pos_label, sample_weight, drop_intermediate)
    616     """
    617     fps, tps, thresholds = _binary_clf_curve(
--> 618         y_true, y_score, pos_label=pos_label, sample_weight=sample_weight)
    619
    620     # Attempt to drop thresholds corresponding to points in between and

python3.7/site-packages/sklearn/metrics/ranking.py in _binary_clf_curve(y_true, y_score, pos_label, sample_weight)
    399     check_consistent_length(y_true, y_score, sample_weight)
    400     y_true = column_or_1d(y_true)
--> 401     y_score = column_or_1d(y_score)
    402     assert_all_finite(y_true)
    403     assert_all_finite(y_score)

python3.7/site-packages/sklearn/utils/validation.py in column_or_1d(y, warn)
    795         return np.ravel(y)
    796
--> 797     raise ValueError("bad input shape {0}".format(shape))
    798
    799

ValueError: bad input shape ()

from sklearn.datasets import make_classification
X_test, y_test = make_classification(100, 23)

pipe = Pipeline([
    ('scaler', StandardScaler()),
    ('classifier', GradientBoostingClassifier()),
])
params = {
    'scaler__with_mean': [True, False],
    'scaler__with_std': [True, False],
    'classifier__learning_rate': [0.1, 0.5, 1.0],
    'classifier__subsample': [0.5, 1.0],
}