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Code/python/notebooks/02_test_rnn.py

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+# %%
+import torch
+from aiRNN import dataloader, models, losses
+import pathlib
+
+# %%
+file_list = pathlib.Path("/home/lars/Documents/Studium/UiO/data_analysis/project3/Code/cpp/known_routes_and_aircraft.csv")
+base_path = file_list.parent
+file_list = file_list.read_text().splitlines()
+file_list = [(base_path / f).resolve() for f in file_list]
+
+# %%
+dataset = dataloader.EvenlySpacedDataset(
+    filepaths=file_list[:500],
+    n_input=30*10, # 10 minutes input
+    n_output=30*1,  # 1 minutes output
+    n_windows_per_file=5,
+    step=1,
+    feature_columns=("lat", "lon", "alt", "ias"),
+    context_columns=("last_lat", "last_lon", "last_alt", "last_ias"),
+    time_columns=("timestamp", "dt"),
+    target_columns=("lat", "lon", "alt"),
+)
+
+# %%
+len(dataset)
+
+# %%
+test_model = models.ThreeInputRNN(
+    time_in=2,
+    feat_in=4,
+    context_in=4,
+    hidden_size=128,
+    rnn_size=256,
+    out_size=3,
+)
+
+# %%
+# Example training
+optimizer = torch.optim.Adam(test_model.parameters(), lr=1e-2)
+scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=5, factor=0.5)
+criterion = torch.nn.HuberLoss()
+for epoch in range(50):
+    losses = []
+    for X_f, X_t, y, X_c in torch.utils.data.DataLoader(dataset, batch_size=256, shuffle=True):
+        optimizer.zero_grad()
+        y_pred, _ = test_model(X_t, X_f, X_c, 300, 30)
+        loss = criterion(y_pred, y)
+        loss.backward()
+        optimizer.step()
+        losses.append(loss.item())
+    loss = sum(losses) / len(losses)
+    scheduler.step(loss)
+    print(f"Epoch {epoch}: loss={loss}, lr={optimizer.param_groups[0]['lr']}")
+
+# %%
+
+
+