tpcp.optimize
.Optimize#
- class tpcp.optimize.Optimize(pipeline: tpcp._pipeline.OptimizablePipelineT, *, safe_optimize: bool = True)[source]#
Run a generic self-optimization on the pipeline.
This is a simple wrapper for pipelines that already implement a
self_optimize
method. This wrapper can be used to ensure that these algorithms can be optimized with the same interface as other optimization methods and can hence be used in methods liketpcp.validate.cross_validate
.Optimize will never modify the original pipeline, but will store a copy of the optimized pipeline as
optimized_pipeline_
.If
safe_optimize
is True, the wrapper applies the same runtime checks as provided bymake_optimize_safe
.- Parameters
- pipeline
The pipeline to optimize. The pipeline must implement
self_optimize
to optimize its own input parameters.- safe_optimize
If True, we add additional checks to make sure the
self_optimize
method of the pipeline is correctly implemented. Seemake_optimize_safe
for more info.
- Other Parameters
- dataset
The dataset used for optimization.
- Attributes
- optimized_pipeline_
The optimized version of the pipeline. That is a copy of the input pipeline with modified params.
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 self-optimization defined by the pipeline.
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: tpcp._pipeline.OptimizablePipelineT, *, safe_optimize: bool = True) None [source]#
- clone() typing_extensions.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: tpcp._dataset.DatasetT, **optimize_params: Any) typing_extensions.Self [source]#
Run the self-optimization defined by the pipeline.
The optimized version of the pipeline is stored as
self.optimized_pipeline_
.- Parameters
- dataset
An instance of a
Dataset
containing one or multiple data points that can be used for optimization. The structure of the data and the available reference information will depend on the dataset.- optimize_params
Additional parameter for the optimization process. They are forwarded to
pipeline.self_optimize
.
- Returns
- self
The class instance with all result attributes populated
- run(datapoint: tpcp._dataset.DatasetT) tpcp._pipeline.PipelineT [source]#
Run the optimized pipeline.
This is a wrapper to contain API compatibility with
Pipeline
.
- safe_run(datapoint: tpcp._dataset.DatasetT) tpcp._pipeline.PipelineT [source]#
Run the optimized pipeline.
This is a wrapper to contain API compatibility with
Pipeline
.