sklearn.pipeline.Pipeline¶
- class sklearn.pipeline.Pipeline(steps)¶
Pipeline of transforms with a final estimator.
Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be ‘transforms’, that is, they must implements fit and transform methods. The final estimator needs only implements fit.
The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a ‘__’, as in the example below.
Parameters: steps: list :
List of (name, transform) tuples (implementing fit/transform) that are chained, in the order in which they are chained, with the last object an estimator.
Examples
>>> from sklearn import svm >>> from sklearn.datasets import samples_generator >>> from sklearn.feature_selection import SelectKBest >>> from sklearn.feature_selection import f_regression >>> from sklearn.pipeline import Pipeline >>> # generate some data to play with >>> X, y = samples_generator.make_classification( ... n_informative=5, n_redundant=0, random_state=42) >>> # ANOVA SVM-C >>> anova_filter = SelectKBest(f_regression, k=5) >>> clf = svm.SVC(kernel='linear') >>> anova_svm = Pipeline([('anova', anova_filter), ('svc', clf)]) >>> # You can set the parameters using the names issued >>> # For instance, fit using a k of 10 in the SelectKBest >>> # and a parameter 'C' of the svm >>> anova_svm.set_params(anova__k=10, svc__C=.1).fit(X, y) ... Pipeline(steps=[...]) >>> prediction = anova_svm.predict(X) >>> anova_svm.score(X, y) 0.77...
Methods
decision_function(X) Applies transforms to the data, and the decision_function method of the final estimator. fit(X[, y]) Fit all the transforms one after the other and transform the data, then fit the transformed data using the final estimator. fit_transform(X[, y]) Fit all the transforms one after the other and transform the data, then use fit_transform on transformed data using the final estimator. get_params([deep]) inverse_transform(X) predict(X) Applies transforms to the data, and the predict method of the final estimator. predict_log_proba(X) predict_proba(X) Applies transforms to the data, and the predict_proba method of the final estimator. score(X[, y]) Applies transforms to the data, and the score method of the final estimator. set_params(**params) Set the parameters of this estimator. transform(X) Applies transforms to the data, and the transform method of the final estimator. - __init__(steps)¶
- decision_function(X)¶
Applies transforms to the data, and the decision_function method of the final estimator. Valid only if the final estimator implements decision_function.
- fit(X, y=None, **fit_params)¶
Fit all the transforms one after the other and transform the data, then fit the transformed data using the final estimator.
- fit_transform(X, y=None, **fit_params)¶
Fit all the transforms one after the other and transform the data, then use fit_transform on transformed data using the final estimator.
- predict(X)¶
Applies transforms to the data, and the predict method of the final estimator. Valid only if the final estimator implements predict.
- predict_proba(X)¶
Applies transforms to the data, and the predict_proba method of the final estimator. Valid only if the final estimator implements predict_proba.
- score(X, y=None)¶
Applies transforms to the data, and the score method of the final estimator. Valid only if the final estimator implements score.
- set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns: self :
- transform(X)¶
Applies transforms to the data, and the transform method of the final estimator. Valid only if the final estimator implements transform.
