pipeline module¶

A pipeline to chain transformers.

The Pipeline class chains a sequence of tranformers, and provides global fit(), transform(), fit_transform() and inverse_transform() methods.

class gemseo.mlearning.transformers.pipeline.Pipeline(name='', transformers=None)[source]¶

BaseTransformer pipeline.

Parameters:

name (str) –
A name for this pipeline.

By default it is set to “”.
transformers (Sequence[BaseTransformer] | None) – A sequence of transformers to be chained. The transformers are chained in the order of appearance in the list, i.e. the first transformer is applied first. If transformers is an empty list or None, then the pipeline transformer behaves like an identity transformer.

compute_jacobian(data)[source]¶

Compute the Jacobian of the pipeline.transform().

Parameters:: data (ndarray) – The data where the Jacobian is to be computed.
Returns:: The Jacobian matrix.
Return type:: ndarray

compute_jacobian_inverse(data)[source]¶

Compute the Jacobian of the pipeline.inverse_transform().

Parameters:: data (ndarray) – The data where the Jacobian is to be computed.
Returns:: The Jacobian matrix.
Return type:: ndarray

duplicate()[source]¶

Duplicate the current object.

fit(data, *args)¶

Fit the transformer to the data.

Parameters:

data (ndarray) – The data to be fitted, shaped as (n_observations, n_features) or (n_observations, ).
args (float | int | str) –

Return type:

None

fit_transform(data, *args)¶

Fit the transformer to the data and transform the data.

Parameters:

data (ndarray) – The data to be transformed, shaped as (n_observations, n_features) or (n_observations, ).
args (float | int | str) –

Returns:

The transformed data, shaped as data.

Return type:

ndarray

inverse_transform(data)[source]¶

Perform an inverse transform on the data.

The data is inverse transformed sequentially, starting with the last transformer in the list.

transform(data)[source]¶

Transform the data.

The data is transformed sequentially, where the output of one transformer is the input of the next.

CROSSED: ClassVar[bool] = False¶: Whether the fit() method requires two data arrays.

property is_fitted: bool¶: Whether the transformer has been fitted from some data.

property parameters: dict[str, bool | int | float | ndarray | str | None]¶: The parameters of the transformer.

Examples using Pipeline¶