nos.transforms package

Submodules

nos.transforms.min_max_scale module

class nos.transforms.min_max_scale.MinMaxScale(min_value: Tensor, max_value: Tensor)

Bases: Transform

forward(tensor: Tensor) → Tensor

Applies the transformation.

Parameters:

tensor – Tensor that should be transformed.

Returns:

Transformed tensor.

undo(tensor: Tensor) → Tensor

Applies the inverse of the transformation (if it exists).

Parameters:

tensor – Transformed tensor.

Returns:

Tensor with the transformation undone.

Raises:

NotImplementedError – If the inverse of the transformation is not implemented.

nos.transforms.quantile_scaler module

class nos.transforms.quantile_scaler.QuantileScaler(src: Tensor, n_quantile_intervals: int = 1000, target_mean: float | Tensor = 0.0, target_std: float | Tensor = 1.0, eps: float = 0.001)

Bases: Transform

Quantile Scaler Class.

A transform for scaling input data to a specified target distribution using quantiles. This is particularly useful for normalizing data in a way that is more robust to outliers than standard z-score normalization.

The transformation maps the quantiles of the input data to the quantiles of the target distribution, effectively performing a non-linear scaling that preserves the relative distribution of the data.

Parameters:

• src – tensor from which the source distribution is drawn.

• n_quantile_intervals – Number of individual bins into which the data is categorized.

• target_mean – Mean of the target Gaussian distribution. Can be float (all dimensions use the same mean), or tensor (allows for different means along different dimensions).

• target_std – Std of the target Gaussian distribution. Can be float (all dimensions use the same std), or tensor (allows for different stds along different dimensions).

• eps – Small value to bound the target distribution to a finite interval.

forward(tensor: Tensor) → Tensor

Transforms the input tensor to match the target distribution using quantile scaling .

Parameters:

tensor – The input tensor to transform.

Returns:

The transformed tensor, scaled to the target distribution.

undo(tensor: Tensor) → Tensor

Reverses the transformation applied by the forward method, mapping the tensor back to its original distribution.

Parameters:

tensor – The tensor to reverse the transformation on.

Returns:

The tensor with the quantile scaling transformation reversed according to the src distribution.

Module contents

class nos.transforms.MinMaxScale(min_value: Tensor, max_value: Tensor)

Bases: Transform

forward(tensor: Tensor) → Tensor

Applies the transformation.

Parameters:

tensor – Tensor that should be transformed.

Returns:

Transformed tensor.

undo(tensor: Tensor) → Tensor

Applies the inverse of the transformation (if it exists).

Parameters:

tensor – Transformed tensor.

Returns:

Tensor with the transformation undone.

Raises:

NotImplementedError – If the inverse of the transformation is not implemented.

class nos.transforms.QuantileScaler(src: Tensor, n_quantile_intervals: int = 1000, target_mean: float | Tensor = 0.0, target_std: float | Tensor = 1.0, eps: float = 0.001)

Bases: Transform

Quantile Scaler Class.

A transform for scaling input data to a specified target distribution using quantiles. This is particularly useful for normalizing data in a way that is more robust to outliers than standard z-score normalization.

The transformation maps the quantiles of the input data to the quantiles of the target distribution, effectively performing a non-linear scaling that preserves the relative distribution of the data.

Parameters:

• src – tensor from which the source distribution is drawn.

• n_quantile_intervals – Number of individual bins into which the data is categorized.

• target_mean – Mean of the target Gaussian distribution. Can be float (all dimensions use the same mean), or tensor (allows for different means along different dimensions).

• target_std – Std of the target Gaussian distribution. Can be float (all dimensions use the same std), or tensor (allows for different stds along different dimensions).

• eps – Small value to bound the target distribution to a finite interval.

forward(tensor: Tensor) → Tensor

Transforms the input tensor to match the target distribution using quantile scaling .

Parameters:

tensor – The input tensor to transform.

Returns:

The transformed tensor, scaled to the target distribution.

undo(tensor: Tensor) → Tensor

Reverses the transformation applied by the forward method, mapping the tensor back to its original distribution.

Parameters:

tensor – The tensor to reverse the transformation on.

Returns:

The tensor with the quantile scaling transformation reversed according to the src distribution.