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File size: 2,291 Bytes
8d94f9b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | from __future__ import annotations
import uuid
from datetime import datetime
from typing import Optional
from sqlalchemy import String, Float, DateTime, ForeignKey, Integer, Index
from sqlalchemy.orm import Mapped, mapped_column
from sqlalchemy.sql import func
from sqlalchemy.dialects.postgresql import UUID, JSONB
from .base import Base
class MLOutput(Base):
__tablename__ = "ml_outputs"
id: Mapped[uuid.UUID] = mapped_column(
UUID(as_uuid=True),
primary_key=True,
default=uuid.uuid4,
)
input_id: Mapped[uuid.UUID] = mapped_column(
UUID(as_uuid=True),
ForeignKey("ml_inputs.id", ondelete="CASCADE"),
nullable=False,
index=True,
)
request_id: Mapped[Optional[str]] = mapped_column(
String(64),
nullable=True,
index=True,
)
model_name: Mapped[Optional[str]] = mapped_column(String(255), index=True)
model_version: Mapped[Optional[str]] = mapped_column(String(64), nullable=True)
created_at: Mapped[datetime] = mapped_column(
DateTime(timezone=True),
server_default=func.now(),
nullable=False,
index=True,
)
latency_ms: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
prediction: Mapped[str] = mapped_column(String(255), nullable=False)
prob: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
proba_defaut: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
proba_solvable: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
threshold: Mapped[Optional[float]] = mapped_column(Float, nullable=True)
classes: Mapped[Optional[dict]] = mapped_column(JSONB, nullable=True)
meta: Mapped[Optional[dict]] = mapped_column(JSONB, nullable=True)
error: Mapped[Optional[str]] = mapped_column(String(500), nullable=True)
__table_args__ = (
Index("ix_ml_outputs_model_created", "model_name", "created_at"),
Index("ix_ml_outputs_request_created", "request_id", "created_at"),
)
def __repr__(self) -> str:
return (
f"<MLOutput id={self.id} input_id={self.input_id} "
f"model={self.model_name}@{self.model_version} "
f"prediction={self.prediction} prob={self.prob}>"
)
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