watex.utils.naive_scaler#

watex.utils.naive_scaler(X, y=None, *, kind=<class 'sklearn.preprocessing._data.StandardScaler'>, copy=True, with_mean=True, with_std=True, feature_range=(0, 1), clip=False, norm='l2', **fit_params)[source]#

Quick data scaling using both strategies implemented in scikit-learn with StandardScaler and MinMaxScaler.

Function returns scaled frame if dataframe is passed or ndarray. For other scaling, call scikit-learn instead.

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.

  • y (None) – Ignored.

  • kind (str, default='StandardScaler') – Kind of data scaling. Can also be [‘MinMaxScaler’, ‘Normalizer’]. The default is ‘StandardScaler’

  • copy (bool, default=True) – If False, try to avoid a copy and do inplace scaling instead. This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned.

  • with_mean (bool, default=True) – If True, center the data before scaling. This does not work (and will raise an exception) when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory.

  • with_std (bool, default=True) – If True, scale the data to unit variance (or equivalently, unit standard deviation).

  • feature_range (tuple (min, max), default=(0, 1)) – Desired range of transformed data.

  • 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.

  • clip (bool, default=False) – Set to True to clip transformed values of held-out data to provided feature range.

  • fit_params (dict,) – keywords arguments passed to the scikit-learn fitting parameters More details on https://scikit-learn.org/stable/

Returns

X_sc – Transformed array.

Return type

{ndarray, sparse matrix} or dataframe of shape (n_samples, n_features)

Examples

>>> import numpy as np
>>> import pandas as pd
>>> from watex.utils.mlutils import naive_scaler
>>> X= np.random.randn (7 , 3 )
>>> X_std = naive_scaler (X )
... array([[ 0.17439644,  1.55683005,  0.24115109],
       [-0.59738672,  1.3166854 ,  1.23748004],
       [-1.6815365 , -1.19775838,  0.71381357],
       [-0.1518278 , -0.32063059, -0.47483155],
       [-0.41335886,  0.13880519,  0.69258621],
       [ 1.45221902, -1.03852015, -0.40157981],
       [ 1.21749443, -0.45541153, -2.00861955]])
>>> # use dataframe
>>> Xdf = pd.DataFrame (X, columns =['a', 'c', 'c'])
>>> naive_scaler (Xdf , kind='Normalizer') # return data frame
...           a         c         c
    0  0.252789  0.967481 -0.008858
    1 -0.265161  0.908862  0.321961
    2 -0.899863 -0.416231  0.130380
    3  0.178203  0.039443 -0.983203
    4 -0.418487  0.800306  0.429394
    5  0.933933 -0.309016 -0.179661
    6  0.795234 -0.051054 -0.604150