lale.lib.rasl.metrics module¶
- class lale.lib.rasl.metrics.MetricMonoidFactory(*args, **kwargs)[source]¶
Bases:
MonoidFactory
[Tuple
[Union
[Series
,ndarray
],Union
[Series
,ndarray
],DataFrame
],float
,_M
],Protocol
Abstract base class for factories that create metrics with an associative monoid interface.
- score_data_batched(batches: Iterable[Tuple[Union[Series, ndarray], Union[Series, ndarray], DataFrame]]) float [source]¶
- abstract score_estimator(estimator: TrainedOperator, X: DataFrame, y: Series) float [source]¶
- lale.lib.rasl.metrics.accuracy_score(y_true: Series, y_pred: Series) float [source]¶
Replacement for sklearn’s accuracy_score function.
- lale.lib.rasl.metrics.balanced_accuracy_score(y_true: Series, y_pred: Series) float [source]¶
Replacement for sklearn’s balanced_accuracy_score function.
- lale.lib.rasl.metrics.f1_score(y_true: Series, y_pred: Series, pos_label: Union[int, float, str] = 1) float [source]¶
Replacement for sklearn’s f1_score function.
- lale.lib.rasl.metrics.get_scorer(scoring: str, **kwargs) MetricMonoidFactory [source]¶
Replacement for sklearn’s get_scorer function.