Application
- apply(*table: str | Path | DataFrame, folder: str | Path | None = None, model_id=None, explain=None, check_ood=None, out: str | Path | None = None, jobs: int | None = None, batch_size: int | None = None, from_invocation: str | Path | dict | None = None, print_results: bool | str = False)[source]
Apply an existing CaTabRa object (prediction model, OOD-detector, …) to given data.
- Parameters:
*table (str | Path | DataFrame) – The table(s) to apply the CaTabRa object to. If multiple are given, their columns are merged into a single table. Must have the same format as the table(s) initially passed to function analyze().
folder (str | Path) – The folder containing the CaTabRa object to apply.
model_id (str, optional) – ID of the prediction model to apply. If None or “__ensemble__”, the sole trained model or the entire ensemble are applied.
explain ("all" | Iterable[str], optional) – Explain prediction model(s) on the given data. If “__all__”, all models specified by model_id are explained; otherwise, must be a list of the model ID(s) to explain, which must be a subset of the models that are applied.
check_ood (bool, optional) – Whether to apply the OOD-detector to the given data. If True, the results of OOD-detection are added to the table containing the model predictions.
out (str | Path, optional) – Directory where to save all generated artifacts. Defaults to a directory located in folder, with a name following a fixed naming pattern. If out already exists, the user is prompted to specify whether it should be replaced; otherwise, it is automatically created.
jobs (int, optional) – Number of jobs to use. Overwrites the “jobs” config param.
batch_size (int, optional) – Batch size used for applying the prediction model.
from_invocation (dict | str | Path, optional) – Dict or path to an invocation.json file. All arguments of this function not explicitly specified are taken from this dict; this also includes the table to apply the CaTabRa object to.
print_results (bool | str, optional) – Whether to print prediction results. If “auto”, results are only printed if the number of samples does not exceed 30.