sklearn.preprocessing
.Normalizer¶
- class sklearn.preprocessing.Normalizer(norm='l2', *, copy=True)[source]¶
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 Compare Normalizer with other scalers.
Read more in the User Guide.
- 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.
- copybool, 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:
See also
normalize
Equivalent function without the estimator API.
Notes
This estimator is stateless and does not need to be fitted. However, we recommend to call
fit_transform
instead oftransform
, as parameter validation is only performed infit
.Examples
>>> from sklearn.preprocessing import Normalizer >>> X = [[4, 1, 2, 2], ... [1, 3, 9, 3], ... [5, 7, 5, 1]] >>> transformer = Normalizer().fit(X) # fit does nothing. >>> transformer Normalizer() >>> transformer.transform(X) array([[0.8, 0.2, 0.4, 0.4], [0.1, 0.3, 0.9, 0.3], [0.5, 0.7, 0.5, 0.1]])
Methods
fit
(X[, y])Only validates estimator's parameters.
fit_transform
(X[, y])Fit to data, then transform it.
get_feature_names_out
([input_features])Get output feature names for transformation.
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
set_output
(*[, transform])Set output container.
set_params
(**params)Set the parameters of this estimator.
set_transform_request
(*[, copy])Request metadata passed to the
transform
method.transform
(X[, copy])Scale each non zero row of X to unit norm.
- fit(X, y=None)[source]¶
Only validates estimator’s parameters.
This method allows to: (i) validate the estimator’s parameters and (ii) be consistent with the scikit-learn transformer API.
- Parameters:
- X{array-like, sparse matrix} of shape (n_samples, n_features)
The data to estimate the normalization parameters.
- yIgnored
Not used, present here for API consistency by convention.
- Returns:
- selfobject
Fitted transformer.
- fit_transform(X, y=None, **fit_params)[source]¶
Fit to data, then transform it.
Fits transformer to
X
andy
with optional parametersfit_params
and returns a transformed version ofX
.- Parameters:
- Xarray-like of shape (n_samples, n_features)
Input samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
- **fit_paramsdict
Additional fit parameters.
- Returns:
- X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
- get_feature_names_out(input_features=None)[source]¶
Get output feature names for transformation.
- Parameters:
- input_featuresarray-like of str or None, default=None
Input features.
If
input_features
isNone
, thenfeature_names_in_
is used as feature names in. Iffeature_names_in_
is not defined, then the following input feature names are generated:["x0", "x1", ..., "x(n_features_in_ - 1)"]
.If
input_features
is an array-like, theninput_features
must matchfeature_names_in_
iffeature_names_in_
is defined.
- Returns:
- feature_names_outndarray of str objects
Same as input features.
- get_metadata_routing()[source]¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
Parameter names mapped to their values.
- set_output(*, transform=None)[source]¶
Set output container.
See Introducing the set_output API for an example on how to use the API.
- Parameters:
- transform{“default”, “pandas”}, default=None
Configure output of
transform
andfit_transform
."default"
: Default output format of a transformer"pandas"
: DataFrame outputNone
: Transform configuration is unchanged
- Returns:
- selfestimator instance
Estimator instance.
- set_params(**params)[source]¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.
- set_transform_request(*, copy: Union[bool, None, str] = '$UNCHANGED$') Normalizer [source]¶
Request metadata passed to the
transform
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed totransform
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it totransform
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
- copystr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
copy
parameter intransform
.
- Returns:
- selfobject
The updated object.
- transform(X, copy=None)[source]¶
Scale each non zero row of X to unit norm.
- Parameters:
- X{array-like, sparse matrix} of 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.
- copybool, default=None
Copy the input X or not.
- Returns:
- X_tr{ndarray, sparse matrix} of shape (n_samples, n_features)
Transformed array.
Examples using sklearn.preprocessing.Normalizer
¶
Scalable learning with polynomial kernel approximation
Compare the effect of different scalers on data with outliers
Clustering text documents using k-means