CODEWITHSUNDEEP
pythonscikit-learngrid-searchscikit-learn-pipeline
sudo_coffee
sudo_coffee

Reputation: 968

Invalid parameter for sklearn estimator pipeline

I am implementing an example from the O'Reilly book "Introduction to Machine Learning with Python", using Python 2.7 and sklearn 0.16.

The code I am using:

pipe = make_pipeline(TfidfVectorizer(), LogisticRegression())
param_grid = {"logisticregression_C": [0.001, 0.01, 0.1, 1, 10, 100], "tfidfvectorizer_ngram_range": [(1,1), (1,2), (1,3)]}
grid = GridSearchCV(pipe, param_grid, cv=5)
grid.fit(X_train, y_train)
print("Best cross-validation score: {:.2f}".format(grid.best_score_))

The error being returned boils down to:

ValueError: Invalid parameter logisticregression_C for estimator Pipeline

Is this an error related to using Make_pipeline from v.0.16? What is causing this error?

Upvotes: 50

Views: 79564

Answers (4)

Vivek Kumar
Vivek Kumar

Reputation: 36619

There should be two underscores between estimator name and it's parameters in a Pipeline logisticregression__C. Do the same for tfidfvectorizer

It is mentioned in the user guide here: https://scikit-learn.org/stable/modules/compose.html#nested-parameters.

See the example at https://scikit-learn.org/stable/auto_examples/compose/plot_compare_reduction.html#sphx-glr-auto-examples-compose-plot-compare-reduction-py

Upvotes: 74

Nishchal Nishant
Nishchal Nishant

Reputation: 54

You can always use the model.get_params().keys() [ in case you are using only model ] or pipeline.get_params().keys() [ in case you are using the pipeline] to get the keys to the parameters you can adjust.

Upvotes: 1

Bex T.
Bex T.

Reputation: 1806

For a more general answer to using Pipeline in a GridSearchCV, the parameter grid for the model should start with whatever name you gave when defining the pipeline. For example:

# Pay attention to the name of the second step, i. e. 'model'
pipeline = Pipeline(steps=[
     ('preprocess', preprocess),
     ('model', Lasso())
])

# Define the parameter grid to be used in GridSearch
param_grid = {'model__alpha': np.arange(0, 1, 0.05)}

search = GridSearchCV(pipeline, param_grid)
search.fit(X_train, y_train)

In the pipeline, we used the name model for the estimator step. So, in the grid search, any hyperparameter for Lasso regression should be given with the prefix model__. The parameters in the grid depends on what name you gave in the pipeline. In plain-old GridSearchCV without a pipeline, the grid would be given like this:

param_grid = {'alpha': np.arange(0, 1, 0.05)}
search = GridSearchCV(Lasso(), param_grid)

You can find out more about GridSearch from this post.

Upvotes: 26

Eric Wiener
Eric Wiener

Reputation: 5997

Note that if you are using a pipeline with a voting classifier and a column selector, you will need multiple layers of names:

pipe1 = make_pipeline(ColumnSelector(cols=(0, 1)),
                      LogisticRegression())
pipe2 = make_pipeline(ColumnSelector(cols=(1, 2, 3)),
                      SVC())
votingClassifier = VotingClassifier(estimators=[
        ('p1', pipe1), ('p2', pipe2)])

You will need a param grid that looks like the following:

param_grid = { 
        'p2__svc__kernel': ['rbf', 'poly'],
        'p2__svc__gamma': ['scale', 'auto'],
    }

p2 is the name of the pipe and svc is the default name of the classifier you create in that pipe. The third element is the parameter you want to modify.

Upvotes: 8

Related Questions