Initial public release

backend/app/schemas/qa.py

@@ -0,0 +1,128 @@
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Any
+from uuid import UUID
+
+from pydantic import BaseModel, Field
+
+from app.schemas.common import GeoJsonFeatureCollection
+
+
+class QaProviderComparisonRequest(BaseModel):
+    candidate_dataset_id: UUID
+    reference_dataset_id: UUID
+    iou_threshold: float = Field(default=0.5, ge=0.0, le=1.0)
+    area_id: UUID | None = None
+
+
+class QaProviderComparisonResult(BaseModel):
+    status: str
+    warnings: list[str] = Field(default_factory=list)
+    # Counts of the population that was actually matched, so that
+    # ``matches + false_positives == candidate_feature_count`` holds even when
+    # an area filter or an unparseable geometry removed features. The ``_raw``
+    # fields keep the untouched dataset totals visible next to them.
+    candidate_feature_count: int
+    reference_feature_count: int
+    candidate_feature_count_raw: int | None = None
+    reference_feature_count_raw: int | None = None
+    matches: int
+    false_positives: int
+    false_negatives: int
+    precision: float | None
+    recall: float | None
+    f1_score: float | None
+    mean_iou: float | None
+    iou_threshold: float
+    unsupported_geometry: bool = False
+    unsupported_geometries: list[str] = Field(default_factory=list)
+    match_evidence: list[dict] = Field(default_factory=list)
+    false_positive_evidence: list[dict] = Field(default_factory=list)
+    false_negative_evidence: list[dict] = Field(default_factory=list)
+    generated_at: datetime
+
+
+class MetricRead(BaseModel):
+    id: UUID
+    quality_check_id: UUID | None = None
+    analysis_run_id: UUID | None = None
+    metric_key: str
+    metric_value: float | None = None
+    metric_unit: str | None = None
+    label: str | None = None
+    metadata_json: dict | None = None
+    created_at: datetime | None = None
+
+    model_config = {"from_attributes": True}
+
+
+class QualityCheckRead(BaseModel):
+    id: UUID
+    project_id: UUID
+    job_id: UUID | None = None
+    analysis_run_id: UUID | None = None
+    candidate_dataset_id: UUID | None = None
+    reference_dataset_id: UUID
+    check_type: str
+    status: str
+    score: float | None = None
+    parameters_json: dict | None = None
+    findings_json: dict | None = None
+    created_at: datetime | None = None
+    completed_at: datetime | None = None
+    metrics: list[MetricRead] = Field(default_factory=list)
+
+    model_config = {"from_attributes": True}
+
+
+class QualityCheckList(BaseModel):
+    items: list[QualityCheckRead]
+    total: int
+    limit: int
+    offset: int
+
+
+class QualityEvidenceResponse(BaseModel):
+    quality_check_id: UUID
+    project_id: UUID
+    candidate_dataset_id: UUID | None = None
+    reference_dataset_id: UUID
+    analysis_run_id: UUID | None = None
+    # The overlay is capped so a regional check stays reviewable; the counts in
+    # the quality check itself are always complete.
+    feature_count: int
+    total_feature_count: int | None = None
+    role_counts: dict[str, int] = Field(default_factory=dict)
+    truncated: bool = False
+    limit: int | None = None
+    warnings: list[str] = Field(default_factory=list)
+    geojson: GeoJsonFeatureCollection
+
+
+class AnalysisQaResponse(BaseModel):
+    status: str
+    quality_check_id: UUID
+    analysis_run_id: UUID
+    reference_dataset_id: UUID
+    candidate_feature_count: int
+    reference_feature_count: int
+    candidate_feature_count_raw: int | None = None
+    reference_feature_count_raw: int | None = None
+    matches: int
+    false_positives: int
+    false_negatives: int
+    precision: float | None = None
+    recall: float | None = None
+    f1_score: float | None = None
+    mean_iou: float | None = None
+    iou_threshold: float
+    warnings: list[str] = Field(default_factory=list)
+    coverage: dict[str, Any] | None = None
+    temporal_compatibility: dict[str, Any] | None = None
+    box_to_footprint_diagnostics: dict[str, Any] | None = None
+    precision_recall_curve: dict[str, Any] | None = None
+    calibration_sweep: list[dict[str, Any]] = Field(default_factory=list)
+    match_evidence: list[dict[str, Any]] = Field(default_factory=list)
+    false_positive_evidence: list[dict[str, Any]] = Field(default_factory=list)
+    false_negative_evidence: list[dict[str, Any]] = Field(default_factory=list)