Scaler#

class mvpy.preprocessing.Scaler(with_mean: bool = True, with_std: bool = True, dims: list | tuple | int | None = None)[source]#

A standard scaler akin to sklearn.preprocessing.StandardScaler. See notes for some differences.

Parameters:

with_meanbool, default=True: If True, center the data before scaling.
with_stdbool, default=True: If True, scale the data to unit variance.
dimsint, list or tuple of ints, default=None: The dimensions over which to scale (None for first dimension).
copybool, default=False: If True, the data will be copied.

Attributes:

shape_tuple: The shape of the data.
mean_Union[np.ndarray, torch.Tensor]: The mean of the data.
var_Union[np.ndarray, torch.Tensor]: The variance of the data.
scale_Union[np.ndarray, torch.Tensor]: The scale of the data.

rac{x - mu}{sigma}

where \(\mu\) is the mean and \(\sigma\) is the standard deviation of the data.

Examples

>>> import torch
>>> from mvpy.estimators import Scaler
>>> X = torch.normal(5, 10, (1000, 5))
>>> print(X.std(0))
tensor([ 9.7033, 10.2510, 10.2483, 10.1274, 10.2013])
>>> scaler = Scaler().fit(X)
>>> X_s = scaler.transform(X)
>>> print(X_s.std(0))
tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000])
>>> X_i = scaler.inverse_transform(X_s)
>>> print(X_i.std(0))
tensor([ 9.7033, 10.2510, 10.2483, 10.1274, 10.2013])

clone()[source]#

Obtain a clone of this class.

Returns:

Scaler: The clone.

copy()[source]#

Obtain a copy of this class.

Returns:

Scaler: The copy.

fit(X: ndarray | Tensor, *args: Any, sample_weight: ndarray | Tensor | None = None) → Any[source]#

Fit the scaler.

Parameters:

XUnion[np.ndarray, torch.Tensor]: The data.
argsAny: Additional arguments.
sample_weightUnion[np.ndarray, torch.Tensor], default=None: The sample weights.

fit_transform(X: ndarray | Tensor, *args: Any, sample_weight: ndarray | Tensor | None = None) → ndarray | Tensor[source]#

Fit and transform the data in one step.

Parameters:

XUnion[np.ndarray, torch.Tensor]: The data.
argsAny: Additional arguments.
sample_weightUnion[np.ndarray, torch.Tensor], default=None: The sample weights.

Returns:

Union[np.ndarray, torch.Tensor]: The transformed data.

inverse_transform(X: ndarray | Tensor, *args: Any) → ndarray | Tensor[source]#

Invert the transform of the data.

Parameters:

XUnion[np.ndarray, torch.Tensor]: The data.
argsAny: Additional arguments.

Returns:

Union[np.ndarray, torch.Tensor]: The inverse transformed data.

to_numpy()[source]#

Selet the numpy scaler. Note that this cannot be called for conversion.

Returns:

_Scaler_numpy: The numpy scaler.

to_torch()[source]#

Selet the torch scaler. Note that this cannot be called for conversion.

Returns:

_Scaler_torch: The torch scaler.

transform(X: ndarray | Tensor, *args: Any) → ndarray | Tensor[source]#

Transform the data using scaler.

Parameters:

XUnion[np.ndarray, torch.Tensor]: The data.
argsAny: Additional arguments.

Returns:

Union[np.ndarray, torch.Tensor]: The transformed data.

Scaler#

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