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Metrics

For the precise mathematical definitions of metrics, see AutoGluon documentation.

Note: Currently, multivariate metrics are computed by first computing the univariate metric on each target column and then averaging the results, similar to the following: 

metric_value = np.mean(
    [metric.compute_metric(test_data[col], predictions[col])
    for col in task.target_columns]
)
For some metrics like WAPE, this leads to results that are different from first concatenating all target columns into a single array and computing the metric on it.

metrics

Classes

MAE

Bases: Metric

Mean absolute error.

Source code in src/fev/metrics.py 
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class MAE(Metric):
    """Mean absolute error."""

    def compute(
        self,
        *,
        y_true: np.ndarray,
        y_pred: np.ndarray,
        y_past: np.ndarray,
        y_past_lengths: np.ndarray,
        q_pred: np.ndarray,
        seasonality: int,
        quantile_levels: list[float],
    ) -> float:
        per_dim = np.nanmean(np.abs(y_true - y_pred), axis=(0, 1))  # [D]
        return float(np.mean(per_dim))

MAPE

Bases: Metric

Mean absolute percentage error.

Source code in src/fev/metrics.py 
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class MAPE(Metric):
    """Mean absolute percentage error."""

    def compute(
        self,
        *,
        y_true: np.ndarray,
        y_pred: np.ndarray,
        y_past: np.ndarray,
        y_past_lengths: np.ndarray,
        q_pred: np.ndarray,
        seasonality: int,
        quantile_levels: list[float],
    ) -> float:
        ratio = np.abs(y_true - y_pred) / np.abs(y_true)  # [N, H, D]
        return float(np.mean(self._safemean(ratio, axis=(0, 1))))

MASE

Bases: Metric

Mean absolute scaled error.

Warning: Items with undefined in-sample seasonal error (e.g., history shorter than seasonality, all-NaN history, or zero seasonal error) are excluded from aggregation.

Source code in src/fev/metrics.py 
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class MASE(Metric):
    """Mean absolute scaled error.

    Warning:
        Items with undefined in-sample seasonal error (e.g., history shorter than `seasonality`,
        all-NaN history, or zero seasonal error) are excluded from aggregation.
    """

    def __init__(self, epsilon: float = 0.0) -> None:
        self.epsilon = epsilon

    def compute(
        self,
        *,
        y_true: np.ndarray,
        y_pred: np.ndarray,
        y_past: np.ndarray,
        y_past_lengths: np.ndarray,
        q_pred: np.ndarray,
        seasonality: int,
        quantile_levels: list[float],
    ) -> float:
        seasonal_error = _abs_seasonal_error_per_item(
            y_past=y_past, y_past_lengths=y_past_lengths, seasonality=seasonality
        )  # [N, D]
        seasonal_error = np.clip(seasonal_error, self.epsilon, None)
        scaled = np.abs(y_true - y_pred) / seasonal_error[:, None, :]  # [N, H, D]
        return float(np.mean(self._safemean(scaled, axis=(0, 1))))

MQL

Bases: QuantileMetric

Mean quantile loss.

Source code in src/fev/metrics.py 
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class MQL(QuantileMetric):
    """Mean quantile loss."""

    def _per_quantile_level(
        self,
        *,
        y_true: np.ndarray,
        y_pred: np.ndarray,
        y_past: np.ndarray,
        y_past_lengths: np.ndarray,
        q_pred: np.ndarray,
        seasonality: int,
        quantile_levels: list[float],
    ) -> np.ndarray:
        ql = _quantile_loss(y_true=y_true, q_pred=q_pred, quantile_levels=quantile_levels)  # [N, H, D, Q]
        per_dim = np.nanmean(ql, axis=(0, 1))  # [D, Q]
        return np.mean(per_dim, axis=0)  # [Q]

MSE

Bases: Metric

Mean squared error.

Source code in src/fev/metrics.py 
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class MSE(Metric):
    """Mean squared error."""

    def compute(
        self,
        *,
        y_true: np.ndarray,
        y_pred: np.ndarray,
        y_past: np.ndarray,
        y_past_lengths: np.ndarray,
        q_pred: np.ndarray,
        seasonality: int,
        quantile_levels: list[float],
    ) -> float:
        per_dim = np.nanmean((y_true - y_pred) ** 2, axis=(0, 1))  # [D]
        return float(np.mean(per_dim))

RMSE

Bases: Metric

Root mean squared error.

Source code in src/fev/metrics.py 
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class RMSE(Metric):
    """Root mean squared error."""

    def compute(
        self,
        *,
        y_true: np.ndarray,
        y_pred: np.ndarray,
        y_past: np.ndarray,
        y_past_lengths: np.ndarray,
        q_pred: np.ndarray,
        seasonality: int,
        quantile_levels: list[float],
    ) -> float:
        per_dim = np.sqrt(np.nanmean((y_true - y_pred) ** 2, axis=(0, 1)))  # [D]
        return float(np.mean(per_dim))

RMSLE

Bases: Metric

Root mean squared logarithmic error.

Source code in src/fev/metrics.py 
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class RMSLE(Metric):
    """Root mean squared logarithmic error."""

    def compute(
        self,
        *,
        y_true: np.ndarray,
        y_pred: np.ndarray,
        y_past: np.ndarray,
        y_past_lengths: np.ndarray,
        q_pred: np.ndarray,
        seasonality: int,
        quantile_levels: list[float],
    ) -> float:
        per_dim = np.sqrt(np.nanmean((np.log1p(y_true) - np.log1p(y_pred)) ** 2, axis=(0, 1)))  # [D]
        return float(np.mean(per_dim))

RMSSE

Bases: Metric

Root mean squared scaled error.

Warning: Items with undefined in-sample seasonal error (e.g., history shorter than seasonality, all-NaN history, or zero seasonal error) are excluded from aggregation.

Source code in src/fev/metrics.py 
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class RMSSE(Metric):
    """Root mean squared scaled error.

    Warning:
        Items with undefined in-sample seasonal error (e.g., history shorter than `seasonality`,
        all-NaN history, or zero seasonal error) are excluded from aggregation.
    """

    def __init__(self, epsilon: float = 0.0) -> None:
        self.epsilon = epsilon

    def compute(
        self,
        *,
        y_true: np.ndarray,
        y_pred: np.ndarray,
        y_past: np.ndarray,
        y_past_lengths: np.ndarray,
        q_pred: np.ndarray,
        seasonality: int,
        quantile_levels: list[float],
    ) -> float:
        seasonal_error = _squared_seasonal_error_per_item(
            y_past=y_past, y_past_lengths=y_past_lengths, seasonality=seasonality
        )  # [N, D]
        seasonal_error = np.clip(seasonal_error, self.epsilon, None)
        scaled = (y_true - y_pred) ** 2 / seasonal_error[:, None, :]  # [N, H, D]
        return float(np.mean(np.sqrt(self._safemean(scaled, axis=(0, 1)))))

SMAPE

Bases: Metric

Symmetric mean absolute percentage error.

Source code in src/fev/metrics.py 
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class SMAPE(Metric):
    """Symmetric mean absolute percentage error."""

    def compute(
        self,
        *,
        y_true: np.ndarray,
        y_pred: np.ndarray,
        y_past: np.ndarray,
        y_past_lengths: np.ndarray,
        q_pred: np.ndarray,
        seasonality: int,
        quantile_levels: list[float],
    ) -> float:
        val = 2 * np.abs(y_true - y_pred) / (np.abs(y_true) + np.abs(y_pred))  # [N, H, D]
        return float(np.mean(self._safemean(val, axis=(0, 1))))

SQL

Bases: QuantileMetric

Scaled quantile loss.

Warning: Items with undefined in-sample seasonal error (e.g., history shorter than seasonality, all-NaN history, or zero seasonal error) are excluded from aggregation.

Source code in src/fev/metrics.py 
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class SQL(QuantileMetric):
    """Scaled quantile loss.

    Warning:
        Items with undefined in-sample seasonal error (e.g., history shorter than `seasonality`,
        all-NaN history, or zero seasonal error) are excluded from aggregation.
    """

    def __init__(self, epsilon: float = 0.0) -> None:
        self.epsilon = epsilon

    def _per_quantile_level(
        self,
        *,
        y_true: np.ndarray,
        y_pred: np.ndarray,
        y_past: np.ndarray,
        y_past_lengths: np.ndarray,
        q_pred: np.ndarray,
        seasonality: int,
        quantile_levels: list[float],
    ) -> np.ndarray:
        ql = _quantile_loss(y_true=y_true, q_pred=q_pred, quantile_levels=quantile_levels)  # [N, H, D, Q]
        seasonal_error = _abs_seasonal_error_per_item(
            y_past=y_past, y_past_lengths=y_past_lengths, seasonality=seasonality
        )  # [N, D]
        seasonal_error = np.clip(seasonal_error, self.epsilon, None)
        scaled = ql / seasonal_error[:, None, :, None]  # [N, H, D, Q]
        per_dim = self._safemean(scaled, axis=(0, 1))  # [D, Q]
        return np.mean(per_dim, axis=0)  # [Q]

WAPE

Bases: Metric

Weighted absolute percentage error.

Source code in src/fev/metrics.py 
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class WAPE(Metric):
    """Weighted absolute percentage error."""

    def __init__(self, epsilon: float = 0.0) -> None:
        self.epsilon = epsilon

    def compute(
        self,
        *,
        y_true: np.ndarray,
        y_pred: np.ndarray,
        y_past: np.ndarray,
        y_past_lengths: np.ndarray,
        q_pred: np.ndarray,
        seasonality: int,
        quantile_levels: list[float],
    ) -> float:
        abs_err_per_dim = np.nanmean(np.abs(y_true - y_pred), axis=(0, 1))  # [D]
        abs_true_per_dim = np.nanmean(np.abs(y_true), axis=(0, 1))  # [D]
        per_dim = abs_err_per_dim / np.maximum(abs_true_per_dim, self.epsilon)
        return float(np.mean(per_dim))

WQL

Bases: QuantileMetric

Weighted quantile loss.

Source code in src/fev/metrics.py 
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class WQL(QuantileMetric):
    """Weighted quantile loss."""

    def __init__(self, epsilon: float = 0.0) -> None:
        self.epsilon = epsilon

    def _per_quantile_level(
        self,
        *,
        y_true: np.ndarray,
        y_pred: np.ndarray,
        y_past: np.ndarray,
        y_past_lengths: np.ndarray,
        q_pred: np.ndarray,
        seasonality: int,
        quantile_levels: list[float],
    ) -> np.ndarray:
        ql = _quantile_loss(y_true=y_true, q_pred=q_pred, quantile_levels=quantile_levels)  # [N, H, D, Q]
        ql_per_dim = np.nanmean(ql, axis=(0, 1))  # [D, Q]
        abs_true_per_dim = np.nanmean(np.abs(y_true), axis=(0, 1))  # [D]
        per_dim = ql_per_dim / np.maximum(abs_true_per_dim, self.epsilon)[:, None]  # [D, Q]
        return np.mean(per_dim, axis=0)  # [Q]

Functions:

get_metric(metric: MetricConfig) -> Metric

Get a metric class by name or configuration.

Source code in src/fev/metrics.py 
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def get_metric(metric: MetricConfig) -> Metric:
    """Get a metric class by name or configuration."""
    metric_name = metric if isinstance(metric, str) else metric["name"]
    try:
        metric_type = AVAILABLE_METRICS[metric_name.upper()]
    except KeyError:
        raise ValueError(
            f"Evaluation metric '{metric_name}' is not available. Available metrics: {sorted(AVAILABLE_METRICS)}"
        )

    if isinstance(metric, str):
        return metric_type()
    elif isinstance(metric, dict):
        return metric_type(**{k: v for k, v in metric.items() if k != "name"})
    else:
        raise ValueError(f"Invalid metric configuration: {metric}")