Jakub Famulski 2
8e6dcb750d
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90 lines
3.3 KiB
Python
90 lines
3.3 KiB
Python
"""Prediction model adapters used through a small dependency-injection boundary."""
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from __future__ import annotations
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Protocol
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import pandas as pd
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from .artifact import ArtifactMetadata, load_model_artifact
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from .errors import InferenceError
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from .inference import DiagnosticModels, predict_test, validate_submission
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@dataclass(frozen=True)
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class PredictionBundle:
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submission: pd.DataFrame
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diagnostics: pd.DataFrame
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class PredictionModel(Protocol):
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def predict(self, frame: pd.DataFrame) -> PredictionBundle: ...
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class SklearnPredictionModel:
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"""Runtime adapter around a verified, pre-trained model artifact."""
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def __init__(
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self, models: DiagnosticModels, metadata: ArtifactMetadata | None = None
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) -> None:
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self._models = models
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self.metadata = metadata
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@classmethod
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def from_artifact(
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cls,
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directory: Path,
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*,
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expected_model_version: str | None = None,
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) -> "SklearnPredictionModel":
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try:
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artifact = load_model_artifact(
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directory, expected_model_version=expected_model_version
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)
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except Exception as exc:
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raise InferenceError(
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"Nie udało się załadować zweryfikowanego artefaktu modelu.",
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hint="Sprawdź manifest, checksumę oraz zgodność wersji zależności.",
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) from exc
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return cls(artifact.models, artifact.metadata)
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def predict(self, frame: pd.DataFrame) -> PredictionBundle:
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try:
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submission, diagnostics = predict_test(self._models, frame)
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validate_submission(submission, frame)
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except Exception as exc:
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raise InferenceError(
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"Model odrzucił pomiary podczas predykcji.",
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hint="Zweryfikuj kompletność silników oraz brak nietypowych wartości w widmie.",
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) from exc
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diagnostics = diagnostics.copy()
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diagnostics["model_version"] = (
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self.metadata.model_version if self.metadata else "unversioned"
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)
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diagnostics["model_artifact_sha256"] = (
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self.metadata.model_sha256 if self.metadata else "unavailable"
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)
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return PredictionBundle(submission, diagnostics)
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class PrecomputedPredictionModel:
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"""Read-only demo fallback; accepts only the exact precomputed key set."""
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def __init__(self, submission: pd.DataFrame, diagnostics: pd.DataFrame) -> None:
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self._submission = submission.reset_index(drop=True).copy()
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self._diagnostics = diagnostics.reset_index(drop=True).copy()
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def predict(self, frame: pd.DataFrame) -> PredictionBundle:
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keys = ["engine_id", "cylinder"]
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if not frame[keys].reset_index(drop=True).equals(self._submission[keys]):
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raise InferenceError(
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"Tryb awaryjny obsługuje wyłącznie dołączony zestaw demonstracyjny.",
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hint="Przywróć artefakt modelu i uruchom ponownie aplikację, aby diagnozować własne pliki.",
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)
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diagnostics = self._diagnostics.copy()
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diagnostics["model_version"] = "demo-precomputed"
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diagnostics["model_artifact_sha256"] = "unavailable"
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return PredictionBundle(self._submission.copy(), diagnostics)
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