46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
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from datetime import datetime
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from decimal import Decimal
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from typing import Iterable
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from app.domain.enums import NormalizationStatus
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from app.domain.models import AttributeStatement
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def statement_is_usable(statement: AttributeStatement | None, minimum_confidence: Decimal) -> bool:
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if statement is None:
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return False
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if statement.normalization_status != NormalizationStatus.NORMALIZED:
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return False
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if statement.normalized_value is None:
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return False
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return statement.confidence >= minimum_confidence
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def select_attribute_snapshot(
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statements: Iterable[AttributeStatement],
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*,
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as_of: datetime,
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snapshot_cutoff: datetime,
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) -> dict[str, AttributeStatement]:
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"""Pick the newest statement per key at or before the evaluation cutoff."""
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chosen: dict[str, AttributeStatement] = {}
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for statement in statements:
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if statement.observed_at > as_of or statement.ingested_at > snapshot_cutoff:
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continue
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current = chosen.get(statement.attribute)
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if current is None or _is_newer(statement, current):
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chosen[statement.attribute] = statement
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return chosen
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def _is_newer(candidate: AttributeStatement, current: AttributeStatement) -> bool:
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return (
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candidate.observed_at,
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candidate.ingested_at,
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candidate.id,
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) > (
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current.observed_at,
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current.ingested_at,
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current.id,
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)
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