Objective

Objective: the metric a problem ranks on, with a direction.

Metrics are pure functions (see emflow.problems.metrics); an Objective is the one a leaderboard sorts by — it adds direction (lower_is_better) and comparison helpers. This is what rebase-hillclimb’s val_score: line ultimately reports.

class emflow.problems.objective.Objective(metric: 'Metric', lower_is_better: 'bool' = True)

Bases: object

metric: Metric

lower_is_better: bool = True

property name: str

calculate(y_true, y_pred) → float

elementwise(y_true, y_pred)

is_better(a: float, b: float) → bool

True if score a beats score b.

class emflow.problems.objective.Metric

Bases: ABC

Base class for metrics. Stateless; safe to share across problems.

aggregate: str = 'mean'

How per-origin scores combine into an overall score: “mean” (pooled by scored timestamps — error metrics) or “sum” (revenue-type metrics).

property name: str

abstract elementwise(y_true, y_pred)

Per-timestamp scores (NaN where either side is missing).

calculate(y_true, y_pred) → float

class emflow.problems.objective.MeanAbsoluteError

Bases: Metric

elementwise(y_true, y_pred)

Per-timestamp scores (NaN where either side is missing).

aggregate: str = 'mean'

How per-origin scores combine into an overall score: “mean” (pooled by scored timestamps — error metrics) or “sum” (revenue-type metrics).

calculate(y_true, y_pred) → float

property name: str

class emflow.problems.objective.MeanSquaredError(squared: bool = True)

Bases: Metric

property name

elementwise(y_true, y_pred)

Per-timestamp scores (NaN where either side is missing).

calculate(y_true, y_pred) → float

aggregate: str = 'mean'

How per-origin scores combine into an overall score: “mean” (pooled by scored timestamps — error metrics) or “sum” (revenue-type metrics).

class emflow.problems.objective.RootMeanSquaredError

Bases: MeanSquaredError

aggregate: str = 'mean'

How per-origin scores combine into an overall score: “mean” (pooled by scored timestamps — error metrics) or “sum” (revenue-type metrics).

calculate(y_true, y_pred) → float

elementwise(y_true, y_pred)

Per-timestamp scores (NaN where either side is missing).

property name

class emflow.problems.objective.MeanAbsolutePercentageError

Bases: Metric

elementwise(y_true, y_pred)

Per-timestamp scores (NaN where either side is missing).

aggregate: str = 'mean'

How per-origin scores combine into an overall score: “mean” (pooled by scored timestamps — error metrics) or “sum” (revenue-type metrics).

calculate(y_true, y_pred) → float

property name: str

class emflow.problems.objective.PinballLoss(quantiles: Sequence[float] | None = None)

Bases: Metric

Average pinball (quantile) loss over the prediction’s quantile columns.

y_pred must carry quantile levels as float column names. If the metric was constructed with explicit quantiles, the prediction must provide exactly those columns (competition contract); otherwise whatever quantile columns are present are scored.

elementwise(y_true, y_pred)

Per-timestamp pinball loss, averaged across quantiles.

aggregate: str = 'mean'

How per-origin scores combine into an overall score: “mean” (pooled by scored timestamps — error metrics) or “sum” (revenue-type metrics).

calculate(y_true, y_pred) → float

property name: str

class emflow.problems.objective.PeakTimingError

Bases: Metric

BigDEAL-2022-style peak-timing score: per day, the absolute distance (in hours) between the true and predicted peak hour of y.

Both series are grouped by calendar day on their index; days missing from either side are skipped.

elementwise(y_true, y_pred)

Per-timestamp scores (NaN where either side is missing).

aggregate: str = 'mean'

How per-origin scores combine into an overall score: “mean” (pooled by scored timestamps — error metrics) or “sum” (revenue-type metrics).

calculate(y_true, y_pred) → float

property name: str