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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,
        *,
        test_data: datasets.Dataset,
        predictions: datasets.Dataset,
        past_data: datasets.Dataset,
        seasonality: int,
        quantile_levels: list[float],
        target_column: str = "target",
    ):
        y_test = self._get_y_test(test_data, target_column=target_column)
        y_pred = np.array(predictions[PREDICTIONS])
        return np.nanmean(np.abs(y_test - y_pred))

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,
        *,
        test_data: datasets.Dataset,
        predictions: datasets.Dataset,
        past_data: datasets.Dataset,
        seasonality: int,
        quantile_levels: list[float],
        target_column: str = "target",
    ):
        y_test = self._get_y_test(test_data, target_column=target_column)
        y_pred = np.array(predictions[PREDICTIONS])
        ratio = np.abs(y_test - y_pred) / np.abs(y_test)
        return self._safemean(ratio)

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,
        *,
        test_data: datasets.Dataset,
        predictions: datasets.Dataset,
        past_data: datasets.Dataset,
        seasonality: int,
        quantile_levels: list[float],
        target_column: str = "target",
    ):
        y_test = self._get_y_test(test_data, target_column=target_column)
        y_pred = np.array(predictions[PREDICTIONS])

        seasonal_error = _abs_seasonal_error_per_item(
            past_data=past_data, seasonality=seasonality, target_column=target_column
        )
        seasonal_error = np.clip(seasonal_error, self.epsilon, None)
        return self._safemean(np.abs(y_test - y_pred) / seasonal_error[:, None])

MQL

Bases: Metric

Mean quantile loss.

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

    needs_quantiles: bool = True

    def compute(
        self,
        *,
        test_data: datasets.Dataset,
        predictions: datasets.Dataset,
        past_data: datasets.Dataset,
        seasonality: int,
        quantile_levels: list[float],
        target_column: str = "target",
    ):
        if quantile_levels is None or len(quantile_levels) == 0:
            raise ValueError(f"{self.__class__.__name__} cannot be computed if quantile_levels is None")
        ql = _quantile_loss(
            test_data=test_data,
            predictions=predictions,
            quantile_levels=quantile_levels,
            target_column=target_column,
        )
        return np.nanmean(ql)

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,
        *,
        test_data: datasets.Dataset,
        predictions: datasets.Dataset,
        past_data: datasets.Dataset,
        seasonality: int,
        quantile_levels: list[float],
        target_column: str = "target",
    ):
        y_test = self._get_y_test(test_data, target_column=target_column)
        y_pred = np.array(predictions[PREDICTIONS])
        return np.nanmean((y_test - y_pred) ** 2)

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,
        *,
        test_data: datasets.Dataset,
        predictions: datasets.Dataset,
        past_data: datasets.Dataset,
        seasonality: int,
        quantile_levels: list[float],
        target_column: str = "target",
    ):
        y_test = self._get_y_test(test_data, target_column=target_column)
        y_pred = np.array(predictions[PREDICTIONS])
        return np.sqrt(np.nanmean((y_test - y_pred) ** 2))

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,
        *,
        test_data: datasets.Dataset,
        predictions: datasets.Dataset,
        past_data: datasets.Dataset,
        seasonality: int,
        quantile_levels: list[float],
        target_column: str = "target",
    ):
        y_test = self._get_y_test(test_data, target_column=target_column)
        y_pred = np.array(predictions[PREDICTIONS])
        return np.sqrt(np.nanmean((np.log1p(y_test) - np.log1p(y_pred)) ** 2))

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,
        *,
        test_data: datasets.Dataset,
        predictions: datasets.Dataset,
        past_data: datasets.Dataset,
        seasonality: int,
        quantile_levels: list[float],
        target_column: str = "target",
    ):
        y_test = self._get_y_test(test_data, target_column=target_column)
        y_pred = np.array(predictions[PREDICTIONS])
        seasonal_error = _squared_seasonal_error_per_item(
            past_data, seasonality=seasonality, target_column=target_column
        )
        seasonal_error = np.clip(seasonal_error, self.epsilon, None)
        return np.sqrt(self._safemean((y_test - y_pred) ** 2 / seasonal_error[:, None]))

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,
        *,
        test_data: datasets.Dataset,
        predictions: datasets.Dataset,
        past_data: datasets.Dataset,
        seasonality: int,
        quantile_levels: list[float],
        target_column: str = "target",
    ):
        y_test = self._get_y_test(test_data, target_column=target_column)
        y_pred = np.array(predictions[PREDICTIONS])
        return self._safemean(2 * np.abs(y_test - y_pred) / (np.abs(y_test) + np.abs(y_pred)))

SQL

Bases: Metric

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(Metric):
    """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.
    """

    needs_quantiles: bool = True

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

    def compute(
        self,
        *,
        test_data: datasets.Dataset,
        predictions: datasets.Dataset,
        past_data: datasets.Dataset,
        seasonality: int,
        quantile_levels: list[float],
        target_column: str = "target",
    ):
        ql = _quantile_loss(
            test_data=test_data,
            predictions=predictions,
            quantile_levels=quantile_levels,
            target_column=target_column,
        )
        ql_per_time_step = np.nanmean(ql, axis=2)  # [num_items, horizon]
        seasonal_error = _abs_seasonal_error_per_item(
            past_data=past_data, seasonality=seasonality, target_column=target_column
        )
        seasonal_error = np.clip(seasonal_error, self.epsilon, None)
        return self._safemean(ql_per_time_step / seasonal_error[:, None])

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,
        *,
        test_data: datasets.Dataset,
        predictions: datasets.Dataset,
        past_data: datasets.Dataset,
        seasonality: int,
        quantile_levels: list[float],
        target_column: str = "target",
    ):
        y_test = self._get_y_test(test_data, target_column=target_column)
        y_pred = np.array(predictions[PREDICTIONS])

        return np.nanmean(np.abs(y_test - y_pred)) / max(self.epsilon, np.nanmean(np.abs(y_test)))

WQL

Bases: Metric

Weighted quantile loss.

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

    needs_quantiles: bool = True

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

    def compute(
        self,
        *,
        test_data: datasets.Dataset,
        predictions: datasets.Dataset,
        past_data: datasets.Dataset,
        seasonality: int,
        quantile_levels: list[float],
        target_column: str = "target",
    ):
        ql = _quantile_loss(
            test_data=test_data,
            predictions=predictions,
            quantile_levels=quantile_levels,
            target_column=target_column,
        )
        return np.nanmean(ql) / max(self.epsilon, np.nanmean(np.abs(np.array(test_data[target_column]))))

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}")