spectrochempy.NormalizeTransformer

class NormalizeTransformer(method='max', dim='x')

Normalization transformer.

Learns the normalization factor along a dimension during fit() and applies it during transform().

Parameters

• method (str, optional, default:’max’) – Normalization method:

  • 'max' — divide by the maximum absolute value.

  • 'sum' — divide by the sum of absolute values.

  • 'vector' — divide by the Euclidean (L2) norm.

  • 'minmax' — scale linearly to the range [0, 1].

• dim (str or int, optional, default:’x’) – Dimension along which the normalization is computed.

norm_

Learned norm (for 'max', 'sum', 'vector').

Type

ndarray

dmin_

Learned minimum (for 'minmax').

Type

ndarray

dmax_

Learned maximum (for 'minmax').

Type

ndarray

range_

Learned range (for 'minmax').

Type

ndarray

Examples

>>> scaler = scp.NormalizeTransformer(method="max", dim="x")
>>> scaler.fit(train)
>>> test_norm = scaler.transform(test)

See also

normalize

Procedural normalization 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.NormalizeTransformer