tpcp
.OptimizablePipeline#
- class tpcp.OptimizablePipeline(*args, **kwds)[source]#
Pipeline with custom ways to optimize and/or train input parameters.
OptimizablePipelines are expected to implement a concrete way to train internal models or optimize parameters. This should not be a reimplementation of GridSearch or similar methods. For this
tpcp.pipelines.GridSearch
should be used directly.It is important that
self_optimize
only modifies input parameters of the pipeline that are marked asOptimizableParameter
. This means, if a parameter is optimized, byself_optimize
it should be named in the__init__
, should be exportable when callingpipeline.get_params
and should be annotated using theOptimizableParameter
type hint on class level. For the sake of documentation (and potential automatic checks) in the future, it also makes sense to add theHyperParameter
type annotation to all parameters that act as hyper parameters for the optimization performed inself_optimize
. To learn more about parameter annotations check this example and this ` guide <optimization>`_ in the docs.It is also possible to optimize nested parameters. For example, if the input of the pipeline is an algorithm or another pipeline on its own, all parameters of these objects can be modified as well. In any case, you should make sure that all optimized parameters are still there if you call
.clone()
on the optimized pipeline.Methods
clone
()Create a new instance of the class with all parameters copied over.
get_params
([deep])Get parameters for this algorithm.
run
(datapoint)Run the pipeline.
safe_run
(datapoint)Run the pipeline with some additional checks.
score
(datapoint)Calculate performance of the pipeline on a datapoint with reference information.
self_optimize
(dataset, **kwargs)Optimize the input parameters of the pipeline or algorithm using any logic.
self_optimize_with_info
(dataset, **kwargs)Optimize the input parameters of the pipeline or algorithm using any logic.
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.
- run(datapoint: DatasetT) Self [source]#
Run the pipeline.
Note
It is usually preferred to use
safe_run
on custom pipelines instead ofrun
, assafe_run
can catch certain implementation errors of the run method.- Parameters:
- datapoint
An instance of a
tpcp.Dataset
containing only a single datapoint. The structure of the data will depend on the dataset.
- Returns:
- self
The class instance with all result attributes populated
- safe_run(datapoint: DatasetT) Self [source]#
Run the pipeline with some additional checks.
It is preferred to use this method over
run
, as it can catch some simple implementation errors of custom pipelines.The following things are checked:
The run method must return
self
(or at least an instance of the pipeline)The run method must set result attributes on the pipeline
All result attributes must have a trailing
_
in their nameThe run method must not modify the input parameters of the pipeline
- Parameters:
- datapoint
An instance of a
tpcp.Dataset
containing only a single datapoint. The structure of the data will depend on the dataset.
- Returns:
- self
The class instance with all result attributes populated
- score(datapoint: DatasetT) Union[float, Dict[str, float]] [source]#
Calculate performance of the pipeline on a datapoint with reference information.
This is an optional method and does not need to be implemented in many cases. Usually stand-a-lone functions are better suited as scorer.
A typical score method will call
self.run(datapoint)
and then compare the results with reference values also available on the dataset.- Parameters:
- datapoint
An instance of a
tpcp.Dataset
containing only a single datapoint. The structure of the data and the available reference information will depend on the dataset.
- Returns:
- score
A float or dict of float quantifying the quality of the pipeline on the provided data. A higher score is always better.
- self_optimize(dataset: DatasetT, **kwargs) Self [source]#
Optimize the input parameters of the pipeline or algorithm using any logic.
This method can be used to adapt the input parameters (values provided in the init) based on any data driven heuristic.
Note
The optimizations must only modify the input parameters (aka
self.clone
should retain the optimization results). If you need to return further information, implementself_optimize_with_info
instead.- Parameters:
- dataset
An instance of a
tpcp.Dataset
containing one or multiple data points that can be used for training. The structure of the data and the available reference information will depend on the dataset.- kwargs
Additional parameters required for the optimization process.
- Returns:
- self
The class instance with optimized input parameters.
- self_optimize_with_info(dataset: DatasetT, **kwargs) Tuple[Self, Any] [source]#
Optimize the input parameters of the pipeline or algorithm using any logic.
This is equivalent to
self_optimize
, but allows you to return additional information as a second return value. If you implement this method, there is no need to implementself_optimize
as well.- Parameters:
- dataset
An instance of a
tpcp.Dataset
containing one or multiple data points that can be used for training. The structure of the data and the available reference information will depend on the dataset.- kwargs
Additional parameters required for the optimization process.
- Returns:
- self
The class instance with optimized input parameters.
- info
An arbitrary piece of information