8.24.2. sklearn.preprocessing.Normalizer

class sklearn.preprocessing.Normalizer(norm='l2', copy=True)

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 or l2) 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.

Parameters :

norm : ‘l1’ or ‘l2’, optional (‘l2’ by default)

The norm to use to normalize each non zero sample.

copy : boolean, optional, default is 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).

See also

sklearn.preprocessing.normalize, without

Notes

This estimator is stateless (besides constructor parameters), the fit method does nothing but is useful when used in a pipeline.

Methods

fit(X[, y])	Do nothing and return the estimator unchanged
fit_transform(X[, y])	Fit to data, then transform it
get_params([deep])	Get parameters for the estimator
set_params(**params)	Set the parameters of the estimator.
transform(X[, y, copy])	Scale each non zero row of X to unit norm

__init__(norm='l2', copy=True)

fit(X, y=None)

Do nothing and return the estimator unchanged

This method is just there to implement the usual API and hence work in pipelines.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters :

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

Returns :

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

Notes

This method just calls fit and transform consecutively, i.e., it is not an optimized implementation of fit_transform, unlike other transformers such as PCA.

get_params(deep=True)

Get parameters for the estimator

Parameters :

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

set_params(**params)

Set the parameters of the 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, y=None, copy=None)

Scale each non zero row of X to unit norm

Parameters :

X : array or scipy.sparse matrix with shape [n_samples, n_features]

The data to normalize, row by row. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy.