title: sklearn.model_selection.cross_val_score returns mean_absolute_percentage_error regardless what I choose in the estimator
body: <p>Please tell me if it's possible to change scoring for <code>sklearn.model_selection.cross_val_score</code> ? It returns the negative value of the <code>mean_absolute_percentage_error</code> regardless of my choice in the estimator. For example if I select <code>metrics=['mean_absolute_error']</code> it would be changed during ANN training but <code>cross_val_score</code> will still return the negative value of the <code>mean_absolute_percentage</code>.</p>
  
  <p>My code is:</p>
  
  <pre><code>def create_network():
      model = models.Sequential()
      model.add(layers.Dense(64, activation='relu', input_shape=(X.shape[1],)))
      model.add(layers.Dense(64, activation='relu'))
      model.add(layers.Dense(1))
      model.compile(optimizer='rmsprop',
                    loss='mse',
                    metrics=['mean_absolute_error'])
      return model
  
  
  from keras.wrappers.scikit_learn import KerasRegressor
  neural_network = KerasRegressor(build_fn=create_network, 
                                   epochs=20, 
                                   batch_size=10, 
                                   verbose=1)
  
  X=feature_normalization(X)[0]
  
  from sklearn.model_selection import cross_val_score
  scores = cross_val_score(neural_network, X, y, cv=4)
  print ('Scores:',scores)
  print ('Average score:',np.average(scores))
  </code></pre>
  
  <p>Results:</p>
  
  <pre><code>Epoch 20/20
  380/380 [==============================] - 0s 82us/step - loss: 18.5750 - mean_absolute_error: 2.9808
  126/126 [==============================] - 0s 124us/step
  Scores: [-27.60875144 -15.73322312 -35.57359647 -21.53566427]
  Average score: -25.112808823950544
  </code></pre>