added the model

roadcast/diagnostics.py

@@ -0,0 +1,67 @@
+import traceback
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+
+from data import CSVDataset
+from models import create_model
+
+
+def print_arr_stats(name, arr):
+    arr = np.asarray(arr)
+    print(f"{name}: shape={arr.shape} dtype={arr.dtype}")
+    print(f"  mean={np.nanmean(arr):.6f} std={np.nanstd(arr):.6f} min={np.nanmin(arr):.6f} max={np.nanmax(arr):.6f}")
+    print(f"  any_nan={np.isnan(arr).any()} any_inf={np.isinf(arr).any()}")
+
+
+def main():
+    try:
+        print('Loading dataset...')
+        ds = CSVDataset('data.csv', generate_labels=True, n_buckets=100)
+        print(f'dataset length: {len(ds)}')
+
+        X = ds.features.numpy()
+        y = ds.labels.numpy()
+
+        print_arr_stats('X (all)', X)
+        # per-column NaN/inf counts
+        nan_counts = np.isnan(X).sum(axis=0)
+        inf_counts = np.isinf(X).sum(axis=0)
+        print('per-column nan counts (first 20):', nan_counts[:20].tolist())
+        print('per-column inf counts (first 20):', inf_counts[:20].tolist())
+
+        print('Labels stats: unique count=', len(np.unique(y)))
+        print('Labels min/max:', int(y.min()), int(y.max()))
+        vals, counts = np.unique(y, return_counts=True)
+        print('Label distribution (first 20):', list(zip(vals[:20].tolist(), counts[:20].tolist())))
+
+        # get a small batch
+        dl = DataLoader(ds, batch_size=64, shuffle=False)
+        xb, yb = next(iter(dl))
+        print_arr_stats('xb batch', xb.numpy())
+        print('yb batch unique:', np.unique(yb.numpy()))
+
+        # build model
+        print('Building model...')
+        model = create_model(device='cpu', model_type='mlp', input_dim=X.shape[1], num_classes=100)
+        model.eval()
+        with torch.no_grad():
+            out = model(xb)
+        out_np = out.numpy()
+        print_arr_stats('model outputs', out_np)
+
+        # check for rows with NaN/Inf in features
+        bad_rows = np.where(np.isnan(X).any(axis=1) | np.isinf(X).any(axis=1))[0]
+        print('bad rows count:', len(bad_rows))
+        if len(bad_rows) > 0:
+            print('first bad row index:', bad_rows[0])
+            print('row values:', X[bad_rows[0]].tolist())
+            print('label:', int(y[bad_rows[0]]))
+
+    except Exception as e:
+        print('Exception during diagnostics:')
+        traceback.print_exc()
+
+
+if __name__ == '__main__':
+    main()