(Python - sklearn) How to pass parameters to the customize ModelTransformer class by gridsearchcv

Question

Below is my pipeline and it seems that I can't pass the parameters to my models by using the ModelTransformer class, which I take it from the link (http://zacstewart.com/2014/08/05/pipelines-of-featureunions-of-pipelines.html)

The error message makes sense to me, but I don't know how to fix this. Any idea how to fix this? Thanks.

# define a pipeline
pipeline = Pipeline([
('vect', DictVectorizer(sparse=False)),
('scale', preprocessing.MinMaxScaler()),
('ess', FeatureUnion(n_jobs=-1, 
                     transformer_list=[
     ('rfc', ModelTransformer(RandomForestClassifier(n_jobs=-1, random_state=1,  n_estimators=100))),
     ('svc', ModelTransformer(SVC(random_state=1))),],
                     transformer_weights=None)),
('es', EnsembleClassifier1()),
])

# define the parameters for the pipeline
parameters = {
'ess__rfc__n_estimators': (100, 200),
}

# ModelTransformer class. It takes it from the link
(http://zacstewart.com/2014/08/05/pipelines-of-featureunions-of-pipelines.html)
class ModelTransformer(TransformerMixin):
    def __init__(self, model):
        self.model = model
    def fit(self, *args, **kwargs):
        self.model.fit(*args, **kwargs)
        return self
    def transform(self, X, **transform_params):
        return DataFrame(self.model.predict(X))

grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1, refit=True)

Error Message: ValueError: Invalid parameter n_estimators for estimator ModelTransformer.

Answer 1

Essentially, GridSearchCV is additionally an expert, implementing fit() and predict() strategies, utilized by the pipeline.

So instead of:

grid = GridSearchCV(make_pipeline(StandardScaler(), LogisticRegression()), param_grid={'logisticregression__C': [0.1, 10.]}, cv=2, refit=False)

Do this:

clf = make_pipeline(StandardScaler(), GridSearchCV(LogisticRegression(),  param_grid={'logisticregression__C': [0.1, 10.]},  cv=2, refit=True)) 

clf.fit()

clf.predict()

it will do is, call the StandardScalar() only once, for one call to clf.fit() instead of multiple calls as you described.