JensandClaude Opus 5
5278fcd361
evidence_geojson emitted one feature per false positive, one per false negative and two per match, with no limit. A regional check of 40k detections against 45k reference footprints produced well over a hundred thousand features in a single response, plus one warning string per unresolvable identifier. The endpoint the entire review workflow depends on therefore failed exactly where review matters most. What to draw is now decided before any geometry is fetched, so the query work is proportional to the result rather than to the size of the check — previously 130k geometries were resolved through an IN clause holding every identifier in the check, to then discard most of them. The budget is split between misses and false positives in proportion to their populations with at least one of each, rather than by strict priority, which would mean a check with 50.000 misses and three false positives never showed one. Confirmations fill what remains, and a match is kept or dropped as a pair because half a match is not reviewable evidence. limit_evidence and evidence_role_counts are removed: plan_evidence supersedes them, and helpers kept alive only by their own tests read like a contract. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
127 lines
4.1 KiB
Python
127 lines
4.1 KiB
Python
from __future__ import annotations
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from datetime import datetime
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from typing import Any
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from uuid import UUID
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from pydantic import BaseModel, Field
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from app.schemas.common import GeoJsonFeatureCollection
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class QaProviderComparisonRequest(BaseModel):
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candidate_dataset_id: UUID
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reference_dataset_id: UUID
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iou_threshold: float = Field(default=0.5, ge=0.0, le=1.0)
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area_id: UUID | None = None
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class QaProviderComparisonResult(BaseModel):
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status: str
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warnings: list[str] = Field(default_factory=list)
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# Counts of the population that was actually matched, so that
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# ``matches + false_positives == candidate_feature_count`` holds even when
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# an area filter or an unparseable geometry removed features. The ``_raw``
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# fields keep the untouched dataset totals visible next to them.
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candidate_feature_count: int
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reference_feature_count: int
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candidate_feature_count_raw: int | None = None
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reference_feature_count_raw: int | None = None
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matches: int
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false_positives: int
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false_negatives: int
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precision: float | None
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recall: float | None
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f1_score: float | None
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mean_iou: float | None
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iou_threshold: float
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unsupported_geometry: bool = False
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unsupported_geometries: list[str] = Field(default_factory=list)
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match_evidence: list[dict] = Field(default_factory=list)
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false_positive_evidence: list[dict] = Field(default_factory=list)
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false_negative_evidence: list[dict] = Field(default_factory=list)
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generated_at: datetime
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class MetricRead(BaseModel):
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id: UUID
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quality_check_id: UUID | None = None
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analysis_run_id: UUID | None = None
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metric_key: str
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metric_value: float | None = None
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metric_unit: str | None = None
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label: str | None = None
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metadata_json: dict | None = None
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created_at: datetime | None = None
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model_config = {"from_attributes": True}
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class QualityCheckRead(BaseModel):
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id: UUID
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project_id: UUID
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job_id: UUID | None = None
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analysis_run_id: UUID | None = None
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candidate_dataset_id: UUID | None = None
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reference_dataset_id: UUID
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check_type: str
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status: str
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score: float | None = None
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parameters_json: dict | None = None
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findings_json: dict | None = None
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created_at: datetime | None = None
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completed_at: datetime | None = None
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metrics: list[MetricRead] = Field(default_factory=list)
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model_config = {"from_attributes": True}
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class QualityCheckList(BaseModel):
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items: list[QualityCheckRead]
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total: int
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limit: int
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offset: int
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class QualityEvidenceResponse(BaseModel):
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quality_check_id: UUID
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project_id: UUID
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candidate_dataset_id: UUID | None = None
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reference_dataset_id: UUID
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analysis_run_id: UUID | None = None
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# The overlay is capped so a regional check stays reviewable; the counts in
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# the quality check itself are always complete.
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feature_count: int
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total_feature_count: int | None = None
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role_counts: dict[str, int] = Field(default_factory=dict)
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truncated: bool = False
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limit: int | None = None
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warnings: list[str] = Field(default_factory=list)
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geojson: GeoJsonFeatureCollection
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class AnalysisQaResponse(BaseModel):
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status: str
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quality_check_id: UUID
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analysis_run_id: UUID
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reference_dataset_id: UUID
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candidate_feature_count: int
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reference_feature_count: int
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candidate_feature_count_raw: int | None = None
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reference_feature_count_raw: int | None = None
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matches: int
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false_positives: int
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false_negatives: int
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precision: float | None = None
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recall: float | None = None
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f1_score: float | None = None
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mean_iou: float | None = None
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iou_threshold: float
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warnings: list[str] = Field(default_factory=list)
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coverage: dict[str, Any] | None = None
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temporal_compatibility: dict[str, Any] | None = None
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box_to_footprint_diagnostics: dict[str, Any] | None = None
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match_evidence: list[dict[str, Any]] = Field(default_factory=list)
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false_positive_evidence: list[dict[str, Any]] = Field(default_factory=list)
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false_negative_evidence: list[dict[str, Any]] = Field(default_factory=list)
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