CODEWITHSUNDEEP
pythonscikit-learnpipelinesvmgridsearchcv
Sagar
Sagar

Reputation: 191

How to go around the error "Invalid parameter estimator for estimator Pipeline" while using GridSearchCV with SVR?

I'm trying to GridSearch the best hyperparameters with this code:

search =GridSearchCV( 
    make_pipeline(RobustScaler(), 
                    SVR()#,
                    #cv=kf
                    #refit=True
                   ),
    param_grid = {
                'estimator__svr__kernel': ('linear', 'rbf','poly')#,
                #'estimator__svr__C':[ 10,20]
                #'estimator__svr__gamma': [1e-5, 3e-4 ],
                #'estimator__svr__epsilon':[0.001,0.002,0.006,0.008]#,
                # 'cv' : [10]
                 },
    refit=True)

search.fit(train, target)

I get this error : ValueError: Invalid parameter estimator for estimator Pipeline(steps=[('robustscaler', RobustScaler()), ('svr', SVR())]). Check the list of available parameters with estimator.get_params().keys()

The error doesn't pin-point any particular entry in the parameter grid. Moreover, estimator.get_params().keys() lists the prameters that I used:

dict_keys(['cv', 'error_score', 'estimator__memory', 'estimator__steps', 'estimator__verbose', 'estimator__robustscaler', 'estimator__svr', 'estimator__robustscaler__copy', 'estimator__robustscaler__quantile_range', 'estimator__robustscaler__unit_variance', 'estimator__robustscaler__with_centering', 'estimator__robustscaler__with_scaling', 'estimator__svr__C', 'estimator__svr__cache_size', 'estimator__svr__coef0', 'estimator__svr__degree', 'estimator__svr__epsilon', 'estimator__svr__gamma', 'estimator__svr__kernel', 'estimator__svr__max_iter', 'estimator__svr__shrinking', 'estimator__svr__tol', 'estimator__svr__verbose', 'estimator', 'n_jobs', 'param_grid', 'pre_dispatch', 'refit', 'return_train_score', 'scoring', 'verbose'])

No combination of param_grid seems to work.

Upvotes: 1

Views: 639

Answers (1)

Sheldon
Sheldon

Reputation: 4653

I think that you should use square brackets instead of parentheses for the estimator__svr__kernel:

'estimator__svr__kernel': ['linear', 'rbf','poly']

EDIT:

I was able to run your script against the iris dataset by using svr__kernel instead of estimator__svr__kernel in the paramter grid:

from sklearn.preprocessing import RobustScaler
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import make_pipeline
from sklearn.svm import SVR
from sklearn import datasets

iris = datasets.load_iris()
X = iris.data[:, :2]
y = iris.target

search =GridSearchCV( 
make_pipeline(RobustScaler(), 
                SVR()#,
                #cv=kf
                #refit=True
               ),
param_grid = {'svr__kernel': ('linear', 'rbf','poly')},
refit=True)

search.fit(X, y)

This returns:

GridSearchCV(estimator=Pipeline(steps=[('robustscaler', RobustScaler()),
                                       ('svr', SVR())]),
             param_grid={'svr__kernel': ('linear', 'rbf', 'poly')})

Upvotes: 1

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