What causes "Please implement the `mean` method" in microsoft/qlib?

microsoft/qlib · error · NotImplementedError

Please implement the `mean` method

Error message

Please implement the `mean` method

What it means

BaseSingleMetric.mean() is the abstract per-stock average reduction, returning a float. The base class raises NotImplementedError; SingleMetric implements the NaN-aware mean. Calling .mean() on a bare BaseSingleMetric instance raises this error.

Source

Thrown at qlib/backtest/high_performance_ds.py:264

        raise NotImplementedError(f"Please implement the `__truediv__` method")

    def __eq__(self, other: object) -> BaseSingleMetric:
        raise NotImplementedError(f"Please implement the `__eq__` method")

    def __gt__(self, other: Union[BaseSingleMetric, int, float]) -> BaseSingleMetric:
        raise NotImplementedError(f"Please implement the `__gt__` method")

    def __lt__(self, other: Union[BaseSingleMetric, int, float]) -> BaseSingleMetric:
        raise NotImplementedError(f"Please implement the `__lt__` method")

    def __len__(self) -> int:
        raise NotImplementedError(f"Please implement the `__len__` method")

    def sum(self) -> float:
        raise NotImplementedError(f"Please implement the `sum` method")

    def mean(self) -> float:
        raise NotImplementedError(f"Please implement the `mean` method")

    def count(self) -> int:
        """Return the count of the single metric, NaN is not included."""

        raise NotImplementedError(f"Please implement the `count` method")

    def abs(self) -> BaseSingleMetric:
        raise NotImplementedError(f"Please implement the `abs` method")

    @property
    def empty(self) -> bool:
        """If metric is empty, return True."""

        raise NotImplementedError(f"Please implement the `empty` method")

    def add(self, other: BaseSingleMetric, fill_value: float = None) -> BaseSingleMetric:
        """Replace np.nan with fill_value in two metrics and add them."""

View on GitHub (pinned to 79633dd950)

Solutions

Use SingleMetric.mean()
Implement mean(self) in your subclass (NaN-aware, e.g. float(self.storage.mean()) if pandas-backed)
Reuse the base-class pattern: often mean can be implemented as self.sum() / self.count() once both are concrete

Example fix

# before
avg = BaseSingleMetric(values).mean()
# after
from qlib.backtest.high_performance_ds import SingleMetric
avg = SingleMetric(values).mean()

Defensive patterns

Strategy: type-guard

Validate before calling

from qlib.backtest.high_performance_ds import BaseSingleMetric
assert type(m).mean is not BaseSingleMetric.mean, "object does not implement mean()"

Type guard

def supports_mean(m) -> bool:
    from qlib.backtest.high_performance_ds import BaseSingleMetric
    return isinstance(m, BaseSingleMetric) and type(m).mean is not BaseSingleMetric.mean

Prevention

Use SingleMetric.mean() for cross-sectional averages
Remember mean is NaN-aware: pair it with count() to know the denominator
Implement all abstract reductions when subclassing, not just the one you need now

When it happens

Trigger: m.mean() where m is a raw BaseSingleMetric; cross-sectional statistics (average score, average weight) in evaluators; subclasses implementing sum but not mean.

Common situations: Reporting average per-stock metrics; factor evaluation code computing cross-sectional means; custom metric classes with partial reduction implementations.

Related errors

AI-assisted analysis of microsoft/qlib@79633dd950 (2026-08-15). Data as JSON: /api/errors/24470d140d44a933. Report an issue: GitHub.