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) Union[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=.