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:
- 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.
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_feature_names_out
([input_features])Get output feature names for transformation.
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_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
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_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 ofn_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
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.
Examples using sklearn.preprocessing.MaxAbsScaler
¶
Compare the effect of different scalers on data with outliers