75 lines
2.0 KiB
Python
75 lines
2.0 KiB
Python
from __future__ import annotations
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from datetime import datetime
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from uuid import UUID
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from pydantic import BaseModel, Field
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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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candidate_feature_count: int
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reference_feature_count: int
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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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