Adds X and y to the known training data for each learner and retrains learners with the augmented dataset. Args: X: The new samples for which the labels are supplied by the expert. y: Labels corresponding to the new instances in X. bootstrap: If T
(self, X: modALinput, y: modALinput, bootstrap: bool = False, only_new: bool = False, **fit_kwargs)
| 342 | self._fit_to_known(bootstrap=True, **fit_kwargs) |
| 343 | |
| 344 | def teach(self, X: modALinput, y: modALinput, bootstrap: bool = False, only_new: bool = False, **fit_kwargs) -> None: |
| 345 | """ |
| 346 | Adds X and y to the known training data for each learner and retrains learners with the augmented dataset. |
| 347 | Args: |
| 348 | X: The new samples for which the labels are supplied by the expert. |
| 349 | y: Labels corresponding to the new instances in X. |
| 350 | bootstrap: If True, trains each learner on a bootstrapped set. Useful when building the ensemble by bagging. |
| 351 | only_new: If True, the model is retrained using only X and y, ignoring the previously provided examples. |
| 352 | **fit_kwargs: Keyword arguments to be passed to the fit method of the predictor. |
| 353 | """ |
| 354 | self._add_training_data(X, y) |
| 355 | if not only_new: |
| 356 | self._fit_to_known(bootstrap=bootstrap, **fit_kwargs) |
| 357 | else: |
| 358 | self._fit_on_new(X, y, bootstrap=bootstrap, **fit_kwargs) |
| 359 | |
| 360 | @abc.abstractmethod |
| 361 | def predict(self, X: modALinput) -> Any: |
nothing calls this directly
no test coverage detected