torch_concepts.data.scalers.standard.StandardScaler

class StandardScaler(axis: int | Tuple = 0)

Z-score normalization scaler for PyTorch tensors.

Standardizes features by removing the mean and scaling to unit variance:

z = (x - μ) / σ

This scaler is useful for: - Normalizing input features before training - Ensuring all features are on the same scale - Improving gradient flow and training stability

Parameters:

axis (Union[int, Tuple], optional) – Axis or axes along which to compute mean and standard deviation. Typically 0 (across samples) for feature-wise normalization. Defaults to 0.

mean

Computed mean value(s) from fitted data.

Type:

Tensor

std

Computed standard deviation(s) from fitted data.

Type:

Tensor

Example

>>> # Normalize a batch of features
>>> scaler = StandardScaler(axis=0)
>>> X_train = torch.randn(1000, 50)  # 1000 samples, 50 features
>>> X_train_scaled = scaler.fit_transform(X_train)
>>>
>>> # Transform test data using training statistics
>>> X_test = torch.randn(200, 50)
>>> X_test_scaled = scaler.transform(X_test)
>>>
>>> # Inverse transform to original scale
>>> X_recovered = scaler.inverse_transform(X_test_scaled)

__init__(axis: int | Tuple = 0)

Initialize the StandardScaler. :param axis: Axis or axes along which to compute statistics (default: 0).

Methods

__init__([axis])	Initialize the StandardScaler.
fit(x)	Compute mean and standard deviation along specified dimension.
fit_transform(x[, dim])	Fit the scaler and transform the input data in one operation.
inverse_transform(x)	Reverse the standardization to recover original scale.
transform(x)	Standardize the input tensor using fitted statistics.