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9f29df6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | """Evaluate actual RainNet rollout outputs and create diagnostic figures."""
import json
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
import yaml
ROOT = Path(__file__).resolve().parents[1]
def load_config():
with (ROOT / "conf/config.yaml").open(encoding="utf-8") as handle:
return yaml.safe_load(handle)
def csi(prediction, target, threshold):
pred_event, target_event = prediction >= threshold, target >= threshold
hits = np.logical_and(pred_event, target_event).sum(dtype=np.float64)
false_alarms = np.logical_and(pred_event, ~target_event).sum(dtype=np.float64)
misses = np.logical_and(~pred_event, target_event).sum(dtype=np.float64)
denominator = hits + false_alarms + misses
return float(hits / denominator) if denominator else 0.0
def fss(prediction, target, threshold, window):
pred = torch.from_numpy((prediction >= threshold).astype(np.float32))[None, None]
obs = torch.from_numpy((target >= threshold).astype(np.float32))[None, None]
if window > 1:
padding = window // 2
pred = F.avg_pool2d(pred, window, stride=1, padding=padding)
obs = F.avg_pool2d(obs, window, stride=1, padding=padding)
pred = pred[..., : prediction.shape[0], : prediction.shape[1]]
obs = obs[..., : target.shape[0], : target.shape[1]]
numerator = torch.sum((pred - obs) ** 2)
denominator = torch.sum(pred**2) + torch.sum(obs**2)
return float(1.0 - numerator / denominator) if denominator > 0 else 0.0
def metric_series(predictions, targets, thresholds, windows):
output = []
for index, (prediction, target) in enumerate(zip(predictions, targets)):
pred_rate, target_rate = prediction * 12.0, target * 12.0
output.append(
{
"lead_minutes": (index + 1) * 5,
"mae_mm_h": float(np.mean(np.abs(pred_rate - target_rate), dtype=np.float64)),
"csi": {str(t): csi(pred_rate, target_rate, t) for t in thresholds},
"fss": {
str(t): {str(w): fss(pred_rate, target_rate, t, w) for w in windows}
for t in thresholds
},
}
)
return output
def main():
config = load_config()
output_dir = ROOT / config["inference"]["output_dir"]
required = ["inputs.npy", "predictions.npy", "targets.npy", "persistence.npy"]
missing = [name for name in required if not (output_dir / name).exists()]
if missing:
raise FileNotFoundError(f"Missing inference outputs: {missing}")
inputs = np.load(output_dir / "inputs.npy")
predictions = np.load(output_dir / "predictions.npy")
targets = np.load(output_dir / "targets.npy")
persistence = np.load(output_dir / "persistence.npy")
for name, array in (("prediction", predictions), ("target", targets), ("persistence", persistence)):
if array.shape != predictions.shape or not np.isfinite(array).all():
raise ValueError(f"Invalid {name}: shape={array.shape}, finite={np.isfinite(array).all()}")
thresholds = config["evaluation"]["thresholds_mm_h"]
windows = config["evaluation"]["fss_windows_km"]
rainnet_metrics = metric_series(predictions, targets, thresholds, windows)
persistence_metrics = metric_series(persistence, targets, thresholds, windows)
metrics = {
"units": "mm/h",
"rainnet": rainnet_metrics,
"persistence": persistence_metrics,
}
result_dir = ROOT / config["evaluation"]["result_dir"]
result_dir.mkdir(parents=True, exist_ok=True)
with (result_dir / "metrics.json").open("w", encoding="utf-8") as handle:
json.dump(metrics, handle, indent=2)
history_path = result_dir / "train_history.json"
with history_path.open(encoding="utf-8") as handle:
history = json.load(handle)
fig, ax = plt.subplots(figsize=(6, 4))
ax.plot(history["train_loss"], marker="o", label="Train")
ax.plot(history["validation_loss"], marker="o", label="Validation")
ax.set(xlabel="Epoch", ylabel="Log-Cosh loss", title="RainNet smoke training")
ax.legend()
fig.tight_layout()
fig.savefig(result_dir / "loss.png", dpi=150)
plt.close(fig)
selected = [0, 5, 11]
fig, axes = plt.subplots(3, 5, figsize=(16, 10))
for row, index in enumerate(selected):
panels = [inputs[-1], targets[index], predictions[index], persistence[index], predictions[index] - targets[index]]
titles = ["Last Input", "Truth", "RainNet Prediction", "Persistence", "Prediction Error"]
for axis, panel, title in zip(axes[row], panels, titles):
image = axis.imshow(panel, cmap="RdBu_r" if title == "Prediction Error" else "Blues")
axis.set_title(f"{title}\n{(index + 1) * 5} min")
axis.axis("off")
fig.colorbar(image, ax=axis, fraction=0.046)
fig.tight_layout()
fig.savefig(result_dir / "forecast_comparison.png", dpi=120)
plt.close(fig)
leads = [item["lead_minutes"] for item in rainnet_metrics]
fig, axes = plt.subplots(1, 3, figsize=(15, 4))
axes[0].plot(leads, [item["mae_mm_h"] for item in rainnet_metrics], label="RainNet")
axes[0].plot(leads, [item["mae_mm_h"] for item in persistence_metrics], label="Persistence")
axes[0].set(title="MAE", xlabel="Lead time (min)", ylabel="mm/h")
for threshold in thresholds:
axes[1].plot(leads, [item["csi"][str(threshold)] for item in rainnet_metrics], label=str(threshold))
axes[2].plot(leads, [item["fss"][str(threshold)]["20"] for item in rainnet_metrics], label=str(threshold))
axes[1].set(title="RainNet CSI", xlabel="Lead time (min)", ylabel="CSI")
axes[2].set(title="RainNet FSS (20 km)", xlabel="Lead time (min)", ylabel="FSS")
axes[0].legend()
axes[1].legend(title="mm/h", fontsize=7)
axes[2].legend(title="mm/h", fontsize=7)
fig.tight_layout()
fig.savefig(result_dir / "metrics.png", dpi=150)
plt.close(fig)
print(f"Prediction shape: {predictions.shape}")
print(f"Target shape: {targets.shape}")
print(f"Persistence shape: {persistence.shape}")
for index in selected:
item = rainnet_metrics[index]
print(f"Lead {item['lead_minutes']} min MAE: {item['mae_mm_h']:.8f} mm/h")
print(f"Lead {item['lead_minutes']} min CSI: {item['csi']}")
print(f"Lead {item['lead_minutes']} min FSS: {item['fss']}")
if __name__ == "__main__":
main()
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