dtoolkit.pipeline.Pipeline

class dtoolkit.pipeline.Pipeline(steps, *, memory=None, verbose=False)

Pipeline of transforms with a final estimator.

Parameters

*args, **kwargs

See the documentation for sklearn.pipeline.Pipeline for complete details on the positional arguments and keyword arguments.

See also

make_pipeline

DToolKit’s version

sklearn.pipeline.make_pipeline

sklearn’s version

Notes

Different to sklearn.pipeline.Pipeline. This would let DataFrame in and DataFrame out.

Attributes

classes_

The classes labels.

feature_names_in_

Names of features seen during first step fit method.

n_features_in_

Number of features seen during first step fit method.

named_steps

Access the steps by name.

Methods

decision_function(X)	Transform the data, and apply decision_function with the final estimator.
fit(X[, y])	Fit the model.
fit_predict(X[, y])	Transform the data, and apply predict with the final estimator.
fit_transform(X[, y])	Fit the model and transform with the final estimator.
get_feature_names_out([input_features])	Get output feature names for transformation.
get_params([deep])	Get parameters for this estimator.
inverse_transform(Xt)	Apply inverse_transform for each step in a reverse order.
predict(X, **predict_params)	Transform the data, and apply predict with the final estimator.
predict_log_proba(X, **predict_log_proba_params)	Transform the data, and apply predict_log_proba with the final estimator.
predict_proba(X, **predict_proba_params)	Transform the data, and apply predict_proba with the final estimator.
score(X[, y, sample_weight])	Transform the data, and apply score with the final estimator.
score_samples(X)	Transform the data, and apply score_samples with the final estimator.
set_output(*[, transform])	Set the output container when "transform" and "fit_transform" are called.
set_params(**kwargs)	Set the parameters of this estimator.
transform(X)	Transform the data, and apply transform with the final estimator.