Jakub Famulski 2
8981ae0a00
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87 lines
2.7 KiB
Python
87 lines
2.7 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 typing import Protocol
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import pandas as pd
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from final_pipeline import (
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DiagnosticModels,
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predict_test,
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train_models,
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validate_submission,
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)
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from .errors import InferenceError
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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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"""Thin adapter around the frozen, validated competition pipeline."""
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def __init__(self, models: DiagnosticModels) -> None:
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self._models = models
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@classmethod
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def train(
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cls,
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reference_frame: pd.DataFrame,
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*,
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random_state: int = 42,
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n_jobs: int = -1,
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) -> "SklearnPredictionModel":
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try:
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models = train_models(
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reference_frame,
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random_state=random_state,
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n_jobs=n_jobs,
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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 przygotować modelu referencyjnego.",
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hint="Sprawdź kompletność val.csv oraz zgodność wersji zależności.",
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) from exc
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return cls(models)
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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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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 val.csv i uruchom ponownie aplikację, aby diagnozować własne pliki.",
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)
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return PredictionBundle(
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self._submission.copy(),
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self._diagnostics.copy(),
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)
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