33 lines
1.2 KiB
Python
33 lines
1.2 KiB
Python
from __future__ import annotations
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import logging
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from functools import lru_cache
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from typing import Sequence
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from app.ml.feature_store import FeatureVector
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from app.ml.training import TrainingPipeline, TrainedArtifact
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logger = logging.getLogger(__name__)
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class Predictor:
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def __init__(self, pipeline: TrainingPipeline) -> None:
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self._pipeline = pipeline
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def predict(self, artifact_id: str, entity: FeatureVector) -> str:
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artifact = self._pipeline.export(artifact_id)
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if entity.sensor_id not in artifact.supported_sensors:
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raise ValueError(
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f"Sensor '{entity.sensor_id}' wird vom Modell '{artifact_id}' nicht unterstützt."
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)
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return f"{artifact_id}:{entity.sensor_id}:{entity.values}"
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def predict_batch(self, artifact_id: str, entities: Sequence[FeatureVector]) -> list[str]:
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return [self.predict(artifact_id, entity) for entity in entities]
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@staticmethod
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def default_artifact(pipeline: TrainingPipeline) -> TrainedArtifact:
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artifacts = list(pipeline._artifacts)
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if not artifacts:
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raise ValueError("Kein trainiertes Modell gefunden.")
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return pipeline.export(artifacts[-1]) |