spectrochempy.LogTransformer

class LogTransformer(method='log1p', eps=1e-10)

Logarithmic transform.

This is a stateless transformer: fit() is a no-op and the same transform is applied regardless of the input data. It is provided for API uniformity so that all preprocessing steps can be expressed as transformer objects.

Parameters

• method (str, optional, default:’log1p’) – Transform to apply:

  • 'log1p' — compute log(1 + x) (stable for small or zero values).

  • 'log' — compute log(x). If the data contain values \(\le 0\), a small offset eps is added automatically.

• eps (float, optional, default:1e-10) – Offset added when method='log' and non-positive values are present.

Examples

>>> transformer = scp.LogTransformer(method="log1p")
>>> nd = transformer.fit_transform(dataset)

See also

log_transform

Procedural log transform function.

Methods Summary

fit(dataset)	Learn parameters from dataset.
fit_transform(dataset)	Fit to dataset, then transform it.
get_params([deep])	Get the constructor parameters of this transformer.
inverse_transform(dataset)	Reverse the learned transformation on dataset.
set_params(**params)	Set constructor parameters on this transformer.
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.

get_params(deep=True)

Get the constructor parameters of this transformer.

Parameters

deep (bool, optional, default:True) – Ignored. Present for compatibility with scikit-learn conventions.

Returns

dict – Mapping of parameter name -> current value.

Examples

>>> scaler = scp.AutoscaleTransformer(dim="y")
>>> scaler.get_params()
{'dim': 'y'}

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.

set_params(**params)

Set constructor parameters on this transformer.

Returns self so that calls can be chained.

Parameters

**params – Parameter names and values to update.

Returns

self

Raises

SpectroChemPyError – If a parameter name does not correspond to a constructor argument.

Examples

>>> scaler = scp.AutoscaleTransformer(dim="y")
>>> scaler.set_params(dim="x")
AutoscaleTransformer(dim='x')

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.LogTransformer