Error with Sklearn hyper parameter search with Multivariateclassifier

[timlac]

Error with Sklearn hyper parameter search with Multivariateclassifier

[johannfaouzi]

Hi,

I'm very sorry for the delayed response (vacations). Here is a stackoverflow post with a similar setting: a one-vs-rest classifier when one wants to try out several values for the hyperparameters of the base classifier. If you run rf.get_params(), you get the following dictionary:

>>> rf.get_params()
{'estimator__bootstrap': True,
 'estimator__ccp_alpha': 0.0,
 'estimator__class_weight': None,
 'estimator__criterion': 'entropy',
 'estimator__max_depth': None,
 'estimator__max_features': 'auto',
 'estimator__max_leaf_nodes': None,
 'estimator__max_samples': None,
 'estimator__min_impurity_decrease': 0.0,
 'estimator__min_samples_leaf': 1,
 'estimator__min_samples_split': 2,
 'estimator__min_weight_fraction_leaf': 0.0,
 'estimator__min_window_size': 1,
 'estimator__n_estimators': 500,
 'estimator__n_jobs': None,
 'estimator__n_windows': 1.0,
 'estimator__oob_score': False,
 'estimator__random_state': None,
 'estimator__verbose': 0,
 'estimator': TimeSeriesForest(),
 'weights': None}

So you can get and set the values of the base estimator by using the prefix estimator__. In your case, you have to replace

parameters = {'n_estimators': n_estimators_values}

with

parameters = {'estimator__n_estimators': n_estimators_values}

Hope that this answers your issue.

Best,
Johann