| """ |
| The shared contract every forecaster in this project implements, so |
| Moirai-MoE, AutoARIMA, and Chronos-2 are interchangeable to |
| decision_engine.py / backtest_engine.py / app.py without any of them |
| knowing about each other. Lives in its own module rather than inside |
| moirai_model.py on purpose: having arima_model.py or chronos_model.py |
| import a dataclass from a file named "moirai_model" would be exactly |
| the kind of cross-model coupling "each model on its own, don't mix |
| them" rules out, even for something as small as a shared type. |
| |
| Every model file's Forecaster class implements: |
| .load() -> None (raises ModelLoadError on failure) |
| .predict(context_df, target_col, past_covariate_cols, |
| prediction_length, freq) -> QuantileForecast |
| (raises ModelInputError for bad input, ModelInferenceError for |
| a failure during inference) |
| |
| None of the three models call each other, fall back to each other, or |
| blend/ensemble their outputs anywhere in this codebase — app.py's model |
| selector picks exactly one forecaster instance per request and that is |
| the only forecaster used for that request. |
| """ |
| from dataclasses import dataclass |
| from typing import List, Optional |
|
|
| import numpy as np |
|
|
|
|
| class ModelLoadError(Exception): |
| pass |
|
|
|
|
| class ModelInputError(Exception): |
| pass |
|
|
|
|
| class ModelInferenceError(Exception): |
| pass |
|
|
|
|
| @dataclass |
| class QuantileForecast: |
| horizon: int |
| quantile_levels: List[float] |
| quantiles: np.ndarray |
| samples: Optional[np.ndarray] |
| context_length_used: int |
| prediction_length: int |
|
|
