`sklearn.preprocessing`.MaxAbsScaler¶

class sklearn.preprocessing.MaxAbsScaler(*, copy=True)[source]¶

Scale each feature by its maximum absolute value.

This estimator scales and translates each feature individually such that the maximal absolute value of each feature in the training set will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity.

This scaler can also be applied to sparse CSR or CSC matrices.

MaxAbsScaler doesn’t reduce the effect of outliers; it only linearily scales them down. For an example visualization, refer to Compare MaxAbsScaler with other scalers.

New in version 0.17.

Parameters:

copybool, default=True: Set to False to perform inplace scaling and avoid a copy (if the input is already a numpy array).

Attributes:

scale_ndarray of shape (n_features,): Per feature relative scaling of the data.

New in version 0.17: scale_ attribute.
max_abs_ndarray of shape (n_features,): Per feature maximum absolute value.
n_features_in_int: Number of features seen during fit.

New in version 0.24.
feature_names_in_ndarray of shape (n_features_in_,): Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.
n_samples_seen_int: The number of samples processed by the estimator. Will be reset on new calls to fit, but increments across partial_fit calls.

See also

maxabs_scale: Equivalent function without the estimator API.

Notes

NaNs are treated as missing values: disregarded in fit, and maintained in transform.

Examples

>>> from sklearn.preprocessing import MaxAbsScaler
>>> X = [[ 1., -1.,  2.],
...      [ 2.,  0.,  0.],
...      [ 0.,  1., -1.]]
>>> transformer = MaxAbsScaler().fit(X)
>>> transformer
MaxAbsScaler()
>>> transformer.transform(X)
array([[ 0.5, -1. ,  1. ],
       [ 1. ,  0. ,  0. ],
       [ 0. ,  1. , -0.5]])

Methods

`fit`(X[, y])	Compute the maximum absolute value to be used for later scaling.
`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`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`inverse_transform`(X)	Scale back the data to the original representation.
`partial_fit`(X[, y])	Online computation of max absolute value of X for later scaling.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Scale the data.

fit(X, y=None)[source]¶

Compute the maximum absolute value to be used for later scaling.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The data used to compute the per-feature minimum and maximum used for later scaling along the features axis.
yNone: Ignored.

Returns:

selfobject: Fitted scaler.

fit_transform(X, y=None, **fit_params)[source]¶

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:

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 is None, then feature_names_in_ is used as feature names in. If feature_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, then input_features must match feature_names_in_ if feature_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.

inverse_transform(X)[source]¶

Scale back the data to the original representation.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The data that should be transformed back.

Returns:

X_tr{ndarray, sparse matrix} of shape (n_samples, n_features): Transformed array.

partial_fit(X, y=None)[source]¶

Online computation of max absolute value of X for later scaling.

All of X is processed as a single batch. This is intended for cases when fit is not feasible due to very large number of n_samples or because X is read from a continuous stream.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The data used to compute the mean and standard deviation used for later scaling along the features axis.
yNone: Ignored.

Returns:

selfobject: Fitted scaler.

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 and fit_transform.

"default": Default output format of a transformer
"pandas": DataFrame output
None: 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.

transform(X)[source]¶

Scale the data.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The data that should be scaled.

Returns:

X_tr{ndarray, sparse matrix} of shape (n_samples, n_features): Transformed array.

Examples using `sklearn.preprocessing.MaxAbsScaler`¶

Compare the effect of different scalers on data with outliers

sklearn.preprocessing.MaxAbsScaler¶

Examples using sklearn.preprocessing.MaxAbsScaler¶

`sklearn.preprocessing`.MaxAbsScaler¶

Examples using `sklearn.preprocessing.MaxAbsScaler`¶