validate#

tpcp.validate.validate(pipeline: PipelineT, dataset: DatasetT, *, scoring: Callable[[PipelineT, DatasetT], float | Aggregator[Any] | dict[str, float | Aggregator[Any]]] | Scorer[PipelineT, DatasetT], n_jobs: int | None = None, verbose: int = 0, pre_dispatch: str | int = '2*n_jobs', progress_bar: bool = True) → dict[str, Any][source]#

Evaluate a pipeline on a dataset without any optimization.

Parameters:

pipeline: A Pipeline to evaluate on the given data.
dataset: A Dataset containing all information.
scoring: A callable that can score a single data point given a pipeline. This function should return either a single score or a dictionary of scores.
n_jobs: Number of jobs to run in parallel. One job is created per datapoint. The default (None) means 1 job at the time, hence, no parallel computing.
verbose: The verbosity level (larger number -> higher verbosity). At the moment this only effects Parallel.
pre_dispatch: The number of jobs that should be pre dispatched. For an explanation see the documentation of Parallel.
progress_bar: True/False to enable/disable a tqdm progress bar.

Examples using `tpcp.validate.validate`#