Estimator wrapper class

fit_and_save_model.py

@dataclass
class ModelMetaData:
    estimator: IsolationForest
    model_path: str


class AnomalyDetector:
    def __init__(self, estimator=None):
        self._estimator = estimator or IsolationForest(
            random_state=42,
            contamination=0.001,
        )
        self._features = ["feature1", "feature2"]
        self._models_directory = Path(__file__).parents[0] / "models"

    def fit_and_save_model(self, data: pd.DataFrame) -> ModelMetaData:
        features = data[self._features]
        fitted_estimator = self._estimator.fit(features)
        timestamp = int(datetime.now().timestamp())
        filename = f"model_{timestamp}.pkl"
        model_path = str(self._models_directory / filename)
        with open(model_path, "wb") as model_file:
            pickle.dump(fitted_estimator, model_file)
        return ModelMetaData(estimator=fitted_estimator, model_path=model_path)