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| """Inference math matching notebook 7's rank-averaging and calibration.""" | |
| import numpy as np | |
| from scipy.stats import rankdata | |
| def normalize_scores(values): | |
| values = np.clip(np.asarray(values, dtype=float), 1e-12, None) | |
| return values / values.sum(axis=-1, keepdims=True) | |
| def calibrate(values, temperature): | |
| temperature = float(temperature) | |
| if not np.isfinite(temperature) or temperature <= 0: | |
| raise ValueError('Calibration temperature must be positive') | |
| logits = np.log(normalize_scores(values)) / temperature | |
| logits -= logits.max(axis=-1, keepdims=True) | |
| return normalize_scores(np.exp(logits)) | |
| def rank_average(probabilities): | |
| stack = np.asarray(probabilities, dtype=float) | |
| if stack.ndim != 3 or not np.isfinite(stack).all(): | |
| raise ValueError('Expected finite model-by-phrase-by-class probabilities') | |
| ranks = np.stack([np.apply_along_axis(rankdata, 1, values) for values in stack]) | |
| return normalize_scores(np.mean(ranks, axis=0)) | |