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68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
from __future__ import annotations
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from datetime import datetime
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from typing import Literal
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from uuid import UUID
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from pydantic import BaseModel, Field
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DetectionEvidenceRole = Literal["false_positive", "false_negative"]
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DetectionReviewDecision = Literal[
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"confirmed_model_false_positive",
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"confirmed_model_false_negative",
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"reference_gap_or_change",
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"qa_alignment_mismatch",
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"imagery_obscured_or_uncertain",
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"uncertain",
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"unreviewed",
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]
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class DetectionReviewUpsert(BaseModel):
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evidence_role: DetectionEvidenceRole
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evidence_feature_id: str = Field(min_length=1, max_length=255)
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decision: DetectionReviewDecision
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notes: str | None = Field(default=None, max_length=2000)
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reviewed_by: str = Field(default="operator", min_length=1, max_length=120)
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class DetectionReviewRead(BaseModel):
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id: UUID | None = None
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project_id: UUID
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quality_check_id: UUID
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analysis_run_id: UUID | None = None
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evidence_role: DetectionEvidenceRole
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evidence_feature_id: str
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detection_id: UUID | None = None
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reference_feature_id: UUID | None = None
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decision: DetectionReviewDecision = "unreviewed"
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notes: str | None = None
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reviewed_by: str | None = None
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confidence: float | None = None
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class_name: str | None = None
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source_tile_path: str | None = None
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created_at: datetime | None = None
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updated_at: datetime | None = None
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class DetectionReviewSummary(BaseModel):
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total: int
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reviewed: int
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remaining: int
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false_positive_total: int
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false_negative_total: int
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decision_counts: dict[str, int]
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# The score with the operator's verdicts applied, next to the raw one. A
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# finding adjudicated as a reference gap is not the model's error, and an
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# interval covers what the unreviewed remainder could still turn out to be.
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reviewed_metrics: dict | None = None
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class DetectionReviewList(BaseModel):
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items: list[DetectionReviewRead]
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total: int
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limit: int
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offset: int
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summary: DetectionReviewSummary
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