| """Calibrated Post-Processor for CompactTS-Agent-v2.
|
|
|
| Enforces physical validity, quantile monotonicity, and probabilistic
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| interval sharpness for state-of-the-art CRPS calibration.
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|
|
| Attribution: Daksh Bhalala
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| License: Business Source License 1.1 (BSL-1.1)
|
| """
|
|
|
| from typing import Optional
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| import numpy as np
|
|
|
|
|
| class CalibratedPostProcessor:
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| """Post-processing pipeline for probabilistic forecasts.
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|
|
| Ensures:
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| 1. Strict quantile non-crossing: q_0.1 <= q_0.2 <= ... <= q_0.9
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| 2. Physical support constraints: non-negativity for energy, volume, counts, solar
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| 3. Probabilistic quantile calibration via dispersion blending
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| """
|
|
|
| def __init__(self, timestral_quantile_weight: float = 0.0):
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| self.w_tms = timestral_quantile_weight
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|
|
| def process_quantiles(
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| self,
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| quantiles: np.ndarray,
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| aux_quantiles: Optional[np.ndarray] = None,
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| non_negative: bool = False,
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| ) -> np.ndarray:
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| """Process and calibrate a 9-quantile forecast array.
|
|
|
| Parameters
|
| ----------
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| quantiles : np.ndarray
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| Array of shape (9, H) representing quantile levels [0.1..0.9].
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| aux_quantiles : Optional[np.ndarray]
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| Secondary quantile array (e.g., Timestral-209M) for uncertainty calibration.
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| non_negative : bool
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| Whether to clip negative values to 0.0.
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|
|
| Returns
|
| -------
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| np.ndarray
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| Calibrated and monotonic quantile array of shape (9, H).
|
| """
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| out = quantiles.copy()
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|
|
|
|
| if aux_quantiles is not None and self.w_tms > 0:
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| median = out[4]
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| out = (1.0 - self.w_tms) * out + self.w_tms * aux_quantiles
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|
|
| out[4] = median
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|
|
|
|
| if non_negative:
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| out = np.maximum(out, 0.0)
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|
|
|
|
| out = np.sort(out, axis=0)
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|
|
| return out
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|
|
