ROCAuC¶

class ROCAuC(BaseRankingScorer):
    r"""ROC AUC from class-probability forecasts.

    Computes the Area Under the Receiver Operating Characteristic curve
    using a one-vs-rest (OvR) strategy for multiclass problems.

    Parameters
    ----------
    average : str, default="macro"
        Class averaging strategy: ``"macro"`` (unweighted mean across
        classes) or ``"weighted"`` (support-weighted mean).
    aggregation_method : list of str or str, default="all"
        Dimensions to aggregate over.
    groups : list of str, dict of str to float, or None, default=None
        Panel group filter or filter with weights.
    components : list of str, dict of str to float, or None, default=None
        Component filter or filter with weights.
    time_weighter : BaseWeighter or None, default=None
        Weighter applied along the time axis (observed timestamps). If None,
        all timestamps contribute equally.
    step_weighter : BaseWeighter or None, default=None
        Weighter applied along the forecasting-step axis. If None, all
        forecasting steps contribute equally.
    vintage_weighter : BaseWeighter or None, default=None
        Weighter applied along the vintage-time axis. If None, all vintages
        contribute equally.

    Attributes
    ----------
    lower_is_better : bool
        Always False (higher AUC is better).

    Examples
    --------
    >>> import polars as pl
    >>> from datetime import datetime
    >>> from yohou.metrics.classification import ROCAuC
    >>> y_true = pl.DataFrame({
    ...     "time": [datetime(2020, 1, i) for i in range(1, 6)],
    ...     "weather": ["sunny", "rainy", "cloudy", "sunny", "rainy"],
    ... })
    >>> y_pred = pl.DataFrame({
    ...     "vintage_time": [datetime(2019, 12, 31)] * 5,
    ...     "time": [datetime(2020, 1, i) for i in range(1, 6)],
    ...     "weather_proba_sunny": [0.7, 0.1, 0.2, 0.6, 0.1],
    ...     "weather_proba_rainy": [0.2, 0.8, 0.1, 0.3, 0.8],
    ...     "weather_proba_cloudy": [0.1, 0.1, 0.7, 0.1, 0.1],
    ... })
    >>> scorer = ROCAuC()
    >>> _ = scorer.fit(y_true)
    >>> scorer.score(y_true, y_pred)
    1.0

    See Also
    --------
    - [`PRAuC`][yohou.metrics.classification.PRAuC] : Precision-Recall AUC.

    """

    _metric_name = "roc_auc"
    _lower_is_better = False

    def _compute_ranking_metric(
        self,
        y_true_binary: np.ndarray,
        y_proba: np.ndarray,
        sample_weight: np.ndarray | None = None,
    ) -> float:
        """Compute ROC AUC via sklearn."""
        return float(roc_auc_score(y_true_binary, y_proba, sample_weight=sample_weight))