BaseOptimize#
- class tpcp.optimize.BaseOptimize[source]#
Base class for all optimizer.
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)Apply some form of optimization on the input parameters of 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__(*args, **kwargs)#
- 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]#
Apply some form of optimization on the input parameters of the pipeline.
- 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.