wildboar.transform.base#

Module Contents#

Classes#

BaseFeatureEngineerTransform

Base feature engineer transform
class wildboar.transform.base.BaseFeatureEngineerTransform(*, random_state=None, n_jobs=None)[source]#

Bases: sklearn.base.TransformerMixin, wildboar.base.BaseEstimator

Base feature engineer transform

embedding_[source]#

The underlying embedding.

Type:

Embedding

Parameters:

n_jobs (int, optional) – The number of jobs to run in parallel. None means 1 and -1 means using all processors.

fit(x, y=None)[source]#

Fit the transform.

Parameters:

  • x (array-like of shape [n_samples, n_timestep] or) –

  • [n_samples – The time series dataset.

  • n_dimensions – The time series dataset.

  • n_timestep] – The time series dataset.

  • y (None, optional) – For compatibility.

Returns:

self

Return type:

self

fit_transform(x, y=None)[source]#

Fit the embedding and return the transform of x.

Parameters:

  • x (array-like of shape [n_samples, n_timestep] or) –

  • [n_samples – The time series dataset.

  • n_dimensions – The time series dataset.

  • n_timestep] – The time series dataset.

  • y (None, optional) – For compatibility.

Returns:

x_embedding – The embedding.

Return type:

ndarray of shape [n_samples, n_outputs]

transform(x)[source]#

Transform the dataset.

Parameters:

  • x (array-like of shape [n_samples, n_timestep] or) –

  • [n_samples – The time series dataset.

  • n_dimensions – The time series dataset.

  • n_timestep] – The time series dataset.

Returns:

x_transform – The transformation.

Return type:

ndarray of shape [n_samples, n_outputs]