mojolearn.MaxAbsScaler#

class mojolearn.MaxAbsScaler(*, copy=True)#

Scale each feature by its maximum absolute value.

Follows scikit-learn’s MaxAbsScaler: each column is divided by the largest absolute value among its non-NaN entries, so the result lies in [-1, 1]. NaN values are kept as NaN by transform. A column of zeros is scaled by one. Input and output are float32.

Parameters:

copy (bool, default True) – Accepted for scikit-learn compatibility; transform always returns a new array.

Variables:

  • max_abs_ (array of shape (n_features,)) – Per-column maximum absolute value.

  • scale_ (array of shape (n_features,)) – Per-column divisor (max_abs_, with zeros replaced by one).

  • n_features_in_ (int) – Number of columns seen in fit.

  • n_samples_seen_ (int) – Number of rows seen in fit.

  • numeric_mode_ (str) – Numeric mode the estimator was fitted in; transform runs in the same mode.

Examples

>>> import numpy as np, mojolearn
>>> X = np.random.default_rng(0).standard_normal((1000, 8)).astype(np.float32)
>>> Xt = mojolearn.MaxAbsScaler().fit_transform(X)

See also

StandardScaler, MinMaxScaler, RobustScaler

User guide: Preprocessing

Choosing an estimator

Methods

fit(X[, y])

fit_transform(X[, y])

Fit to X, then transform X.

get_params([deep])

Return the constructor parameters as a dict.

set_params(**params)

Set constructor parameters.

transform(X)

fit_transform(X, y=None, **fit_params)#

Fit to X, then transform X.

Returns:

array – The transformed data.

get_params(deep=True)#

Return the constructor parameters as a dict.

Parameters:

deep (bool, default True) – Accepted for scikit-learn compatibility.

Returns:

dict – Parameter names mapped to their values.

set_params(**params)#

Set constructor parameters.

An unknown parameter name raises ValueError.

Returns:

self