Prediction queries

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`PredictiveQuery`

Entities and anchor time to score with a fitted predictor.

PredictiveQuery(
    *,
    entities: EntitySelector,
    at_timestamp: str | pd.Timestamp,
)

Type aliases

EntitySelector = Union[str, Sequence[int]]

Fields

Name	Type	Default	Description
`entities`	`EntitySelector`	Required	`"all"` or a sequence of original entity ids.
`at_timestamp`	`str \| pd.Timestamp`	Required	Anchor time, or `"test_timestamp"` for the context’s test cutoff.

A training task and database specification.

PredictiveQuerySpec(
    database: DatabaseSpec,
    task: PredictiveTaskSpec,
)

Fields

Name	Type	Default	Description
`database`	`DatabaseSpec`	Required	—
`task`	`PredictiveTaskSpec`	Required	—

Load a spec from a task YAML file.

PredictiveQuerySpec.from_yaml(
    path: str | Path,
    *,
    data_dir: str | Path | None = None,
) -> PredictiveQuerySpec

Parameters

Parameter	Type	Default	Description
`path`	`str \| Path`	Required	Task YAML with the label `query`, task fields, split cutoffs and a `database` path, relative to this file unless absolute.
`data_dir`	`str \| Path \| None`	`None`	Base directory for the tables’ data files. `None` uses the database YAML’s directory.

Returns

Type	Description
`PredictiveQuerySpec`	—