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| """Metrics and reports for paired height arrays.""" | |
| import numpy as np | |
| from .calibration import ScaleShift, fit_scale_shift | |
| def evaluate(prediction: np.ndarray, ground_truth: np.ndarray, valid: np.ndarray) -> tuple[ScaleShift, np.ndarray, dict]: | |
| if prediction.shape != ground_truth.shape or valid.shape != ground_truth.shape: | |
| raise ValueError("Prediction, ground truth, and validity mask must have matching shapes") | |
| mask = valid & np.isfinite(prediction) & np.isfinite(ground_truth) | |
| calibration = fit_scale_shift(prediction, ground_truth, mask) | |
| aligned = calibration.apply(prediction) | |
| errors = aligned[mask].astype(np.float64) - ground_truth[mask].astype(np.float64) | |
| corr = float(np.corrcoef(aligned[mask], ground_truth[mask])[0, 1]) | |
| ss_res = float(np.sum(errors ** 2)) | |
| ss_tot = float(np.sum((ground_truth[mask] - ground_truth[mask].mean()) ** 2)) | |
| metrics = { | |
| "method": "scale/shift aligned baseline", | |
| "valid_pixels": int(mask.sum()), | |
| "scale_coefficient": calibration.scale, | |
| "shift_coefficient_m": calibration.shift, | |
| "mae_m": float(np.mean(np.abs(errors))), | |
| "rmse_m": float(np.sqrt(np.mean(errors ** 2))), | |
| "pearson_r": corr, | |
| "r_squared": float(1 - ss_res / ss_tot) if ss_tot > 0 else None, | |
| "prediction_raw_min": float(np.min(prediction[mask])), | |
| "prediction_raw_max": float(np.max(prediction[mask])), | |
| "prediction_raw_mean": float(np.mean(prediction[mask])), | |
| "ground_truth_min_m": float(np.min(ground_truth[mask])), | |
| "ground_truth_max_m": float(np.max(ground_truth[mask])), | |
| "ground_truth_mean_m": float(np.mean(ground_truth[mask])), | |
| "ground_truth_median_m": float(np.median(ground_truth[mask])), | |
| } | |
| return calibration, aligned, metrics | |