CODEWITHSUNDEEP
pythonmachine-learningscikit-learnpipeline
Alex
Alex

Reputation: 109

ValueError: Invalid parameter when fiting gridsearchcv

This is my code

param_grid = [{'kneighborsclassifier__n_neighbors': [3, 4, 5, 6, 7]}, 
              {'svc__C': [0.1, 1, 10, 100], 'svc__gamma': [0.001, 0.01, 0.1, 1, 10]},
              {'randomforestclassifier__n_estimators': [50, 100, 200, 300 ,400]},
              {'decisiontreeclassifier__max_depth': [12, 25, 50, 75, 100]},
              {'adaboostclassifier__n_estimators': [50, 100, 200, 300 ,400]},
              {'baggingclassifier__n_estimators': [50, 100, 200, 300, 400]}
             ]

inner_cv = StratifiedShuffleSplit(test_size=.8, train_size=.2, n_splits=5, random_state=0)
outer_cv = StratifiedShuffleSplit(test_size=.8, train_size=.2, n_splits=5, random_state=0)

models = [knn, svc, forest, dtc, ada, bag]
model_names = ['knn', 'svc','forest', 'dtc', 'ada', 'bag']

for m, mname in zip(models, model_names):
    pipe = make_pipeline(VarianceThreshold(threshold=1), 
                         MinMaxScaler(),
                         SelectKBest(f_classif, k=20),  
                         m)
    grid = GridSearchCV(pipe, param_grid=param_grid, cv=inner_cv)
    grid.fit(X_train_test, y_train_test)
    nested_score = cross_val_score(grid, X=X_train_test, y=y_train_test.values.ravel(), cv=outer_cv)
    print(mname)
    print(grid.best_params_)
    print(grid.best_score_)
    print('\n')

This is the error:

ValueError: Invalid parameter svc for estimator Pipeline(memory=None,
         steps=[('variancethreshold', VarianceThreshold(threshold=1)),
                ('minmaxscaler', MinMaxScaler(copy=True, feature_range=(0, 1))),
                ('selectkbest',
                 SelectKBest(k=20,
                             score_func=<function f_classif at 0x0000019E0A485AF8>)),
                ('kneighborsclassifier',
                 KNeighborsClassifier(algorithm='auto', leaf_size=30,
                                      metric='minkowski', metric_params=None,
                                      n_jobs=None, n_neighbors=5, p=2,
                                      weights='uniform'))],
         verbose=False). Check the list of available parameters with `estimator.get_params().keys()`.

I dont know whats wrong. I copied the parameter and model names from the named_steps of the pipeline. If I run it without a parameter grid it works so the problem is most likely there.

Upvotes: 0

Views: 322

Answers (1)

Alex
Alex

Reputation: 109

Seems to work like this, but I dont like it.

pg1 = {'kneighborsclassifier__n_neighbors': [3, 4, 5, 6, 7]}
pg2 = {'svc__C': [0.1, 1, 10, 100],
       'svc__gamma': [0.001, 0.01, 0.1, 1, 10]}
pg3 = {'randomforestclassifier__n_estimators': [50, 100, 200, 300 ,400]}
pg4 = {'decisiontreeclassifier__max_depth': [12, 25, 50, 75, 100]}
pg5 = {'adaboostclassifier__n_estimators': [50, 100, 200, 300 ,400]}
pg6 = {'baggingclassifier__n_estimators': [50, 100, 200, 300, 400]}

param_grid_list = [pg1, pg2, pg3, pg4, pg5, pg6]

And then the loop becomes:

for m, p, mname in zip(models, param_grid_list, model_names):
    pipe = make_pipeline(VarianceThreshold(threshold=1), 
                         MinMaxScaler(),
                         SelectKBest(f_classif, k=20),  
                         m)
    grid = GridSearchCV(pipe, param_grid=p, cv=inner_cv)
    grid.fit(X_train_test, y_train_test)
    nested_score = cross_val_score(grid, X=X_train_test, y=y_train_test.values.ravel(), cv=outer_cv)
    print(mname)
    print(grid.best_params_)
    print(grid.best_score_)
    print('\n')

Upvotes: 0

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