CODEWITHSUNDEEP
pythonscikit-learn
pythondumb
pythondumb

Reputation: 1207

Python: Scikit Learn MLPClassifier error when putting in Pipeline

I am using a OneVsRestClassifier on MLPClassifier. I am using this for classifying text data where X is set of questions in pd.DataFrame and Y is multi-label and multi-class strings. Please see below the code snippets

text_clf = Pipeline([('scale',StandardScaler(with_mean=False)),('clf',OneVsRestClassifier(MLPClassifier(learning_rate = 'adaptive', solver = 'lbfgs',random_state=9000)))])
parameters = {'clf__alpha':[10.0 ** ~ np.arange(1, 7).any()],'clf__hidden_layer_sizes': [(100,),(50,)],'clf__max_iter': [1000,500],'clf__activation':('relu','tanh')}
grid = GridSearchCV(text_clf, parameters, cv=3, n_jobs=-1, scoring= 'accuracy') 
with parallel_backend('threading'):
    grid.fit(X,Y)

I am getting the following error

ValueError: Invalid parameter activation for estimator OneVsRestClassifier(estimator=MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,
   beta_2=0.999, early_stopping=False, epsilon=1e-08,
   hidden_layer_sizes=(100,), learning_rate='adaptive',
   learning_rate_init=0.001, max_iter=200, momentum=0.9,
   n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,
   random_state=9000, shuffle=True, solver='lbfgs', tol=0.0001,
   validation_fraction=0.1, verbose=False, warm_start=False),
      n_jobs=None). Check the list of available parameters with `estimator.get_params().keys()`.

As per my understanding MLPClassifier supports Multi Label classification. Is it indicating that parameters need to be re-examined? If so, then can any body please give any clue on where to make changes in parameters?

Any help would really be appreciated.

Upvotes: 4

Views: 1297

Answers (1)

Chris
Chris

Reputation: 29742

Your MLPClassifier is nested into OneVsRestClassifier as its estimator.

In other words, parameters should specify that all of alpha, hidden_layer_sizes, ... are meant for nested estimator not OneVsRestClassifier.

Changing your parameters like following should do the job:

parameters = {'clf__estimator__alpha':[10.0 ** ~ np.arange(1,7).any()],
    'clf__estimator__hidden_layer_sizes': [(100,),(50,)],
    'clf__estimator__max_iter': [1000,500],
    'clf__estimator__activation':('relu','tanh')}

Upvotes: 3

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