A R R R Associates
79 lines
4.1 KiB
Python
79 lines
4.1 KiB
Python
from __future__ import annotations
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from datetime import datetime, timezone
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from sqlalchemy import Boolean, DateTime, Float, ForeignKey, Integer, String, Text
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from sqlalchemy.orm import Mapped, mapped_column
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from app.core.db.common import CommonBase
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class AccountingInternalModel(CommonBase):
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__tablename__ = "accounting_internal_models"
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id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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tenant_id: Mapped[int] = mapped_column(ForeignKey("tenants.id", ondelete="CASCADE"), nullable=False, index=True)
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version_label: Mapped[str] = mapped_column(String(80), nullable=False, index=True)
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algorithm: Mapped[str] = mapped_column(String(80), nullable=False, default="multinomial_nb_v1")
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status: Mapped[str] = mapped_column(String(30), nullable=False, default="shadow", index=True)
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is_active: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False, index=True)
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training_examples: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
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validation_examples: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
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class_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
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vocabulary_size: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
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validation_accuracy: Mapped[float] = mapped_column(Float, nullable=False, default=0)
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validation_macro_recall: Mapped[float] = mapped_column(Float, nullable=False, default=0)
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validation_top2_accuracy: Mapped[float] = mapped_column(Float, nullable=False, default=0)
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model_json: Mapped[str] = mapped_column(Text, nullable=False)
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metrics_json: Mapped[str | None] = mapped_column(Text, nullable=True)
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training_summary_json: Mapped[str | None] = mapped_column(Text, nullable=True)
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trained_by_user_id: Mapped[int | None] = mapped_column(
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ForeignKey("users.id", ondelete="SET NULL"), nullable=True
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)
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trained_at_utc: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), nullable=False, default=lambda: datetime.now(timezone.utc), index=True
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)
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class AccountingInternalPrediction(CommonBase):
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__tablename__ = "accounting_internal_predictions"
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id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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tenant_id: Mapped[int] = mapped_column(ForeignKey("tenants.id", ondelete="CASCADE"), nullable=False, index=True)
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client_id: Mapped[int] = mapped_column(ForeignKey("clients.id", ondelete="CASCADE"), nullable=False, index=True)
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model_id: Mapped[int] = mapped_column(
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ForeignKey("accounting_internal_models.id", ondelete="CASCADE"), nullable=False, index=True
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)
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source_type: Mapped[str] = mapped_column(String(30), nullable=False, index=True)
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source_record_id: Mapped[int] = mapped_column(Integer, nullable=False, index=True)
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source_fingerprint: Mapped[str] = mapped_column(String(80), nullable=False, default="", index=True)
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predicted_nature_id: Mapped[int | None] = mapped_column(
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ForeignKey("accounting_natures.id", ondelete="SET NULL"), nullable=True, index=True
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)
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predicted_probability: Mapped[float] = mapped_column(Float, nullable=False, default=0)
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top2_json: Mapped[str | None] = mapped_column(Text, nullable=True)
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explanation_json: Mapped[str | None] = mapped_column(Text, nullable=True)
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shadow_mode: Mapped[bool] = mapped_column(Boolean, nullable=False, default=True, index=True)
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applied_to_source: Mapped[bool] = mapped_column(Boolean, nullable=False, default=False, index=True)
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final_nature_id: Mapped[int | None] = mapped_column(
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ForeignKey("accounting_natures.id", ondelete="SET NULL"), nullable=True, index=True
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)
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prediction_correct: Mapped[bool | None] = mapped_column(Boolean, nullable=True, index=True)
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reviewed_by_user_id: Mapped[int | None] = mapped_column(
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ForeignKey("users.id", ondelete="SET NULL"), nullable=True
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)
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reviewed_at_utc: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
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created_at_utc: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), nullable=False, default=lambda: datetime.now(timezone.utc), index=True
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)
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