Error when using scikit-learn to use pipelines

Question

I am trying to perform scaling using StandardScaler and define a KNeighborsClassifier(Create pipeline of scaler and estimator)

Finally, I want to create a Grid Search cross validator for the above where param_grid will be a dictionary containing n_neighbors as hyperparameter and k_vals as values.

def kNearest(k_vals):

    skf = StratifiedKFold(n_splits=5, random_state=23)

    svp = Pipeline([('ss', StandardScaler()),
                ('knc', neighbors.KNeighborsClassifier())])

    parameters = {'n_neighbors': k_vals}

    clf = GridSearchCV(estimator=svp, param_grid=parameters, cv=skf)

    return clf

But doing this will give me an error saying that

Invalid parameter n_neighbors for estimator Pipeline. Check the list of available parameters with `estimator.get_params().keys()`.

I've read the documentation, but still don't quite get what the error indicates and how to fix it.

Could you try your code with parameters = {'knn__n_neighbors': k_vals} . Note the double _ after knn. This is not misspelled, this is the right syntax. — KRKirov
– KRKirov, Commented Feb 11, 2018 at 1:29

Brad Solomon · Accepted Answer · 2018-02-11 08:27:45Z

You are right, this is not exactly well-documented by scikit-learn. (Zero reference to it in the class docstring.)

If you use a pipeline as the estimator in a grid search, you need to use a special syntax when specifying the parameter grid. Specifically, you need to use the step name followed by a double underscore, followed by the parameter name as you would pass it to the estimator. I.e.

'<named_step>__<parameter>': value

In your case:

parameters = {'knc__n_neighbors': k_vals}

should do the trick.

Here knc is a named step in your pipeline. There is an attribute that shows these steps as a dictionary:

svp.named_steps

{'knc': KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
            metric_params=None, n_jobs=1, n_neighbors=5, p=2,
            weights='uniform'),
 'ss': StandardScaler(copy=True, with_mean=True, with_std=True)}

And as your traceback alludes to:

svp.get_params().keys()
dict_keys(['memory', 'steps', 'ss', 'knc', 'ss__copy', 'ss__with_mean', 'ss__with_std', 'knc__algorithm', 'knc__leaf_size', 'knc__metric', 'knc__metric_params', 'knc__n_jobs', 'knc__n_neighbors', 'knc__p', 'knc__weights'])

Some official references to this:

The user guide on pipelines
Sample pipeline for text feature extraction and evaluation

Collectives™ on Stack Overflow

Error when using scikit-learn to use pipelines

1 Answer 1

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

Comments

Your Answer

Sign up or log in

Post as a guest

Related