skada.make_da_pipeline

skada.make_da_pipeline(*steps, memory: Memory | None = None, verbose: bool = False, default_selector: str | Callable[[BaseEstimator], BaseSelector] = 'shared') → Pipeline[source]

Construct a Pipeline from the given estimators.

This is a shorthand for the sklearn.pipeline.Pipeline constructor; it does not require, and does not permit, naming the estimators. Instead, their names will be set to the lowercase of their types automatically.

Parameters:

*stepslist of estimators or tuples of the form (name of step, estimator).: List of the scikit-learn estimators that are chained together.
memorystr or object with the joblib.Memory interface, default=None: Used to cache the fitted transformers of the pipeline. The last step will never be cached, even if it is a transformer. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute named_steps or steps to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming.
verbosebool, default=False: If True, the time elapsed while fitting each step will be printed as it is completed.
default_selectorstr or callable, default = 'shared': Specifies a domain selector to wrap the estimator, if it is not already wrapped. Refer to BaseSelector for an understanding of selector functionalities. The available options include 'shared' and 'per_domain'. For integrating a custom selector as the default, pass a callable that accepts BaseEstimator and returns the estimator encapsulated within a domain selector.

Returns:

pPipeline: Returns a scikit-learn Pipeline object.

Examples

>>> from sklearn.naive_bayes import GaussianNB
>>> from sklearn.preprocessing import StandardScaler
>>> from skada import make_da_pipeline
>>> make_da_pipeline(StandardScaler(), GaussianNB(priors=None))
Pipeline(steps=[('standardscaler',
                 Shared(base_estimator=StandardScaler(), copy=True,
                        with_mean=True, with_std=True)),
                ('gaussiannb',
                 Shared(base_estimator=GaussianNB(), priors=None,
                        var_smoothing=1e-09))])

Examples using `skada.make_da_pipeline`

How to use SKADA

Label Propagation methods

Optimal Transport Domain Adaptation (OTDA)

Using cross_val_score with skada

skada.make_da_pipeline

Examples using skada.make_da_pipeline

Examples using `skada.make_da_pipeline`