× ⚠️ You are reading the development version documentation.
Read here the documentation of the last stable release.

spectrochempy.ParetoScaleTransformer

class ParetoScaleTransformer(dim='y')

Pareto scaling transformer.

Learns the mean and standard deviation along a dimension during fit() and applies \((x - \bar{x}) / \sqrt{s}\) during transform().

Parameters:

dim (str or int, optional, default:’y’) – Dimension along which the statistics are computed.

mean_

Learned mean.

Type:

ndarray

std_

Learned standard deviation.

Type:

ndarray

Examples

>>> scaler = scp.ParetoScaleTransformer(dim="y")
>>> scaler.fit(train)
>>> test_scaled = scaler.transform(test)

See also

pareto_scale

Procedural Pareto scaling function.

Methods Summary

fit(dataset)	Learn parameters from dataset.
fit_transform(dataset)	Fit to dataset, then transform it.
inverse_transform(dataset)	Reverse the learned transformation on dataset.
transform(dataset)	Apply the learned transformation to dataset.

Methods Documentation

fit(dataset)

Learn parameters from dataset.

Parameters:

dataset (NDDataset) – Training data.

Returns:

self – The fitted instance.

fit_transform(dataset)

Fit to dataset, then transform it.

Equivalent to self.fit(dataset).transform(dataset) but avoids an intermediate copy when possible.

Parameters:

dataset (NDDataset) – Training data.

Returns:

NDDataset – Transformed dataset.

inverse_transform(dataset)

Reverse the learned transformation on dataset.

Parameters:

dataset (NDDataset) – Data to invert.

Returns:

NDDataset – Dataset in the original space.

Raises:

SpectroChemPyError – If fit() has not been called first.

transform(dataset)

Apply the learned transformation to dataset.

Parameters:

dataset (NDDataset) – Data to transform.

Returns:

NDDataset – Transformed dataset.

Raises:

SpectroChemPyError – If fit() has not been called first.

Examples using spectrochempy.ParetoScaleTransformer