(*steps, **kwargs)
return Pipeline(pipeline._name_estimators(steps), memory=memory)
Construct a Pipeline from the given estimators.

    This is a shorthand for the 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
    ----------
    *steps : list of estimators.

    memory : None, str or object with the joblib.Memory interface, optional
        Used to cache the fitted transformers of the pipeline. 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.

    Returns
    -------
    p : Pipeline

    See also
    --------
    imblearn.pipeline.Pipeline : Class for creating a pipeline of
        transforms with a final estimator.

    Examples
    --------
    >>> from sklearn.naive_bayes import GaussianNB
    >>> from sklearn.preprocessing import StandardScaler
    >>> make_pipeline(StandardScaler(), GaussianNB(priors=None))
    ... # doctest: +NORMALIZE_WHITESPACE
    Pipeline(memory=None,
             steps=[('standardscaler',
                     StandardScaler(copy=True, with_mean=True, with_std=True)),
                    ('gaussiannb',
                     GaussianNB(priors=None, var_smoothing=1e-09))],
             verbose=False)
Construct a Pipeline from the given estimators.
Construct a Pipeline from the given estimators .
def make_pipeline(*steps, **kwargs):
    """Construct a Pipeline from the given estimators.

    This is a shorthand for the 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
    ----------
    *steps : list of estimators.

    memory : None, str or object with the joblib.Memory interface, optional
        Used to cache the fitted transformers of the pipeline. 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.

    Returns
    -------
    p : Pipeline

    See also
    --------
    imblearn.pipeline.Pipeline : Class for creating a pipeline of
        transforms with a final estimator.

    Examples
    --------
    >>> from sklearn.naive_bayes import GaussianNB
    >>> from sklearn.preprocessing import StandardScaler
    >>> make_pipeline(StandardScaler(), GaussianNB(priors=None))
    ... # doctest: +NORMALIZE_WHITESPACE
    Pipeline(memory=None,
             steps=[('standardscaler',
                     StandardScaler(copy=True, with_mean=True, with_std=True)),
                    ('gaussiannb',
                     GaussianNB(priors=None, var_smoothing=1e-09))],
             verbose=False)
    """
    memory = kwargs.pop('memory', None)
    if kwargs:
        raise TypeError(
            f'Unknown keyword arguments: "{list(kwargs.keys())[0]}"')
    return Pipeline(pipeline._name_estimators(steps), memory=memory)
def make_pipeline ( * steps , * * kwargs ) : memory = kwargs . pop ( 'memory' , None ) if kwargs : raise TypeError ( f'Unknown keyword arguments: "{list(kwargs.keys())[0]}"' ) return Pipeline ( pipeline . _name_estimators ( steps ) , memory = memory )