DummyOptimize#
- class tpcp.optimize.DummyOptimize(pipeline: PipelineT, *, ignore_potential_user_error_warning: bool = False)[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_
ifoptimize
is called.- ignore_potential_user_error_warning
If True, the warning about using a pipeline that implements
self_optimize
withDummyOptimize
will be ignored. Only use this, if you are sure about what you are doing.
- 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.
set_params
(**params)Set the parameters of this Algorithm.
- 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
.