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_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.  
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_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_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_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.