Class Normalizer

sklearn/preprocessing/_data.py:2092–2225

Normalize samples individually to unit norm. Each sample (i.e. each row of the data matrix) with at least one non zero component is rescaled independently of other samples so that its norm (l1, l2 or inf) equals one. This transformer is able to work both with dense numpy arrays and

class Normalizer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator):
    """Normalize samples individually to unit norm.

    Each sample (i.e. each row of the data matrix) with at least one
    non zero component is rescaled independently of other samples so
    that its norm (l1, l2 or inf) equals one.

    This transformer is able to work both with dense numpy arrays and
    scipy.sparse matrix (use CSR format if you want to avoid the burden of
    a copy / conversion).

    Scaling inputs to unit norms is a common operation for text
    classification or clustering for instance. For instance the dot
    product of two l2-normalized TF-IDF vectors is the cosine similarity
    of the vectors and is the base similarity metric for the Vector
    Space Model commonly used by the Information Retrieval community.

    For an example visualization, refer to :ref:`Compare Normalizer with other
    scalers <plot_all_scaling_normalizer_section>`.

    Read more in the :ref:`User Guide <preprocessing_normalization>`.

    Parameters
    ----------
    norm : {'l1', 'l2', 'max'}, default='l2'
        The norm to use to normalize each non zero sample. If norm='max'
        is used, values will be rescaled by the maximum of the absolute
        values.

    copy : bool, default=True
        Set to False to perform inplace row normalization and avoid a
        copy (if the input is already a numpy array or a scipy.sparse
        CSR matrix).

    Attributes
    ----------
    n_features_in_ : int
        Number of features seen during :term:`fit`.

        .. versionadded:: 0.24

    feature_names_in_ : ndarray of shape (`n_features_in_`,)
        Names of features seen during :term:`fit`. Defined only when `X`
        has feature names that are all strings.

        .. versionadded:: 1.0

    See Also
    --------
    normalize : Equivalent function without the estimator API.

    Notes
    -----
    This estimator is :term:`stateless` and does not need to be fitted.
    However, we recommend to call :meth:`fit_transform` instead of
    :meth:`transform`, as parameter validation is only performed in
    :meth:`fit`.