from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, Normalizer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score

pipePre = Pipeline([
    ('simpleimputer', SimpleImputer(missing_values=np.nan, strategy='constant', fill_value=0)),
    ('standardscaler', StandardScaler()),
    ('normalizer', Normalizer())
     ])

df_train_x = pipePre.fit_transform(df_train_x)

X_train, X_test, y_train, y_test = train_test_split(df_train_x, df_train_y, test_size = 0.25, random_state=42)

lrg = LinearRegression().fit(X_train, y_train)

rig = Ridge().fit(X_train, y_train)

lreg = LogisticRegression().fit(X_train, y_train)

voting = VotingClassifier(estimators=[('lrg_v', lrg), ('rig_v', rig), 
                                      ('lreg_v', lreg)], voting='hard')
voting_fit = voting.fit(X_train, y_train)

y_pred = voting_fit.predict(X_test)
roc_auc_score(y_test, y_pred)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-50-506a80086b81> in <module>
----> 1 val_error(voting_fit, X_test, y_test)

<ipython-input-6-0fa46ec754f8> in val_error(model, tested, prediction)
     14         Data, prepaired as tested labels
     15     """
---> 16     y_pred = model.predict(tested)
     17     err = roc_auc_score(prediction, y_pred)
     18     return err

~\Anaconda3\lib\site-packages\sklearn\ensemble\voting.py in predict(self, X)
    302                 lambda x: np.argmax(
    303                     np.bincount(x, weights=self._weights_not_none)),
--> 304                 axis=1, arr=predictions)
    305 
    306         maj = self.le_.inverse_transform(maj)

~\Anaconda3\lib\site-packages\numpy\lib\shape_base.py in apply_along_axis(func1d, axis, arr, *args, **kwargs)
    378     except StopIteration:
    379         raise ValueError('Cannot apply_along_axis when any iteration dimensions are 0')
--> 380     res = asanyarray(func1d(inarr_view[ind0], *args, **kwargs))
    381 
    382     # build a buffer for storing evaluations of func1d.

~\Anaconda3\lib\site-packages\sklearn\ensemble\voting.py in <lambda>(x)
    301             maj = np.apply_along_axis(
    302                 lambda x: np.argmax(
--> 303                     np.bincount(x, weights=self._weights_not_none)),
    304                 axis=1, arr=predictions)
    305 

TypeError: Cannot cast array data from dtype('float64') to dtype('int64') according to the rule 'safe'