spectrochempy.AutoscaleTransformer

class AutoscaleTransformer(dim='y')[source]

Autoscaling (z-score) transformer.

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

Parameters: dim (str or int, optional, default:’y’) – Dimension along which the mean and standard deviation are computed.

mean_[source]

Learned mean.

Type: ndarray

std_[source]

Learned standard deviation.

Type: ndarray

Examples

>>> scaler = scp.AutoscaleTransformer(dim="y")
>>> scaler.fit(train)
>>> test_scaled = scaler.transform(test)
>>> train_restored = scaler.inverse_transform(test_scaled)

See also

autoscale: Procedural autoscaling 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)[source]

Learn parameters from dataset.

Parameters: dataset (NDDataset) – Training data.
Returns: self – The fitted instance.

fit_transform(dataset)[source]

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)[source]

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)[source]

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)[source]

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)[source]

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.AutoscaleTransformer

Preprocessing transformers and scikit-learn compatibility