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.
New in version 0.17.
 Parameters
 copyboolean, optional, default is True
Set to False to perform inplace scaling and avoid a copy (if the input is already a numpy array).
 Attributes
 scale_ndarray, shape (n_features,)
Per feature relative scaling of the data.
New in version 0.17: scale_ attribute.
 max_abs_ndarray, shape (n_features,)
Per feature maximum absolute value.
 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.
For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.
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_params
([deep])Get parameters for this estimator.
Scale back the data to the original representation
partial_fit
(X[, y])Online computation of max absolute value of X for later scaling.
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{arraylike, sparse matrix}, shape [n_samples, n_features]
The data used to compute the perfeature minimum and maximum used for later scaling along the features axis.

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
 X{arraylike, sparse matrix, dataframe} of shape (n_samples, n_features)
 yndarray of shape (n_samples,), default=None
Target values.
 **fit_paramsdict
Additional fit parameters.
 Returns
 X_newndarray array of shape (n_samples, n_features_new)
Transformed array.

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
 paramsmapping of string to any
Parameter names mapped to their values.

inverse_transform
(X)[source]¶ Scale back the data to the original representation
 Parameters
 X{arraylike, sparse matrix}
The data that should be transformed back.

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 ofn_samples
or because X is read from a continuous stream. Parameters
 X{arraylike, sparse matrix}, 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
Transformer 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 pipelines). 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
 selfobject
Estimator instance.