tpcp.optimize.BaseOptimize#

class tpcp.optimize.BaseOptimize(*args, **kwds)[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.

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=.