DummyOptimize#

class tpcp.optimize.DummyOptimize(pipeline: PipelineT)[source]#

Provide API compatibility for SimplePipelines in optimize wrappers.

This is a simple dummy Optimizer that will not optimize anything, but just provide the correct API so that pipelines that do not have the possibility to be optimized can be passed to wrappers like tpcp.validate.cross_validate.

Parameters:

pipeline: The pipeline to wrap. It will not be optimized in any way, but simply copied to self.optimized_pipeline_ if optimize is called.

Other Parameters:

dataset: The dataset used for optimization. As no optimization is performed, this will be ignored.

Attributes:

optimized_pipeline_: The optimized version of the pipeline. In case of this class, this is just an unmodified clone of the input pipeline.

Methods

`clone`()	Create a new instance of the class with all parameters copied over.
`get_params`([deep])	Get parameters for this algorithm.
`optimize`(dataset, **optimize_params)	Run the "dummy" optimization.
`run`(datapoint)	Run the optimized pipeline.
`safe_run`(datapoint)	Run the optimized pipeline.
`score`(datapoint)	Run score of the optimized pipeline.
`set_params`(**params)	Set the parameters of this Algorithm.

__init__(pipeline: PipelineT) → None[source]#

clone() → Self[source]#

Create a new instance of the class with all parameters copied over.

This will create a new instance of the class itself and all nested objects

get_params(deep: bool = True) → dict[str, Any][source]#

Get parameters for this algorithm.

Parameters:

deep: Only relevant if object contains nested algorithm objects. If this is the case and deep is True, the params of these nested objects are included in the output using a prefix like nested_object_name__ (Note the two “_” at the end)

Returns:

params: Parameter names mapped to their values.

optimize(dataset: DatasetT, **optimize_params: Any) → Self[source]#

Run the “dummy” optimization.

Parameters:

dataset: The parameter is ignored, as no real optimization is performed
optimize_params: The parameter is ignored, as no real optimization is performed

Returns:

self: The class instance with all result attributes populated

run(datapoint: DatasetT) → PipelineT[source]#

Run the optimized pipeline.

This is a wrapper to contain API compatibility with Pipeline.

safe_run(datapoint: DatasetT) → PipelineT[source]#

Run the optimized pipeline.

This is a wrapper to contain API compatibility with Pipeline.

score(datapoint: DatasetT) → float | dict[str, float][source]#

Run score of the optimized pipeline.

This is a wrapper to contain API compatibility with Pipeline.

set_params(**params: Any) → Self[source]#

Set the parameters of this Algorithm.

To set parameters of nested objects use nested_object_name__para_name=.