pytorch port working now. fun.

.gitignore

@@ -184,4 +184,7 @@ ipython_config.py
 
 
 
-.conda*/
+.conda*/
+
+data/
+# for rn

load_data.py

@@ -76,6 +76,12 @@ def validate_view(data: pd.DataFrame, verbose=False):
 # if __name__ == "__main__":
 def load_this_data(filename, verbose=False):
     d = load_data(filename)
+    # if verbose:
+    #     print(d.columns)
     d = date_combine(d)
+    # if verbose:
+    #     print(d.columns)
     d = validate_view(d, verbose)
+    # if verbose:
+    #     print(d.columns)
     return d

pytorch_port.py

@@ -0,0 +1,162 @@
+from sklearn.model_selection import train_test_split
+import torch
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+from sklearn.preprocessing import MinMaxScaler
+
+import numpy as np
+from load_data import load_this_data
+
+a = load_this_data("dataset.csv")
+
+# time series forecasting
+
+
+scaler = MinMaxScaler()
+print(a.columns)
+scaled_data = scaler.fit_transform(a)
+
+
+def create_sequences(data, seq_length):
+    xs = []
+    ys = []
+    for i in range(len(data) - seq_length):
+        x = data[i : (i + seq_length)]
+        y = data[i + seq_length]
+        xs.append(x)
+        ys.append(y)
+    return np.array(xs), np.array(ys)
+
+
+seq_length = 20
+
+X, y = create_sequences(scaled_data, seq_length)
+
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=42
+)
+from torch.utils.data import TensorDataset, DataLoader
+
+X_train_tensor = torch.tensor(X_train, dtype=torch.float32)
+y_train_tensor = torch.tensor(y_train, dtype=torch.float32)
+
+train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
+train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
+
+
+import torch.nn as nn
+
+
+class LSTMModel(nn.Module):
+    def __init__(self, input_size, hidden_size, num_layers, output_size):
+        super(LSTMModel, self).__init__()
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
+        self.fc = nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
+        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
+
+        out, _ = self.lstm(x, (h0, c0))
+        out = self.fc(out[:, -1, :])  # Take the output from the last time step
+        return out
+
+
+model = LSTMModel(
+    input_size=X_train.shape[2],
+    hidden_size=64,
+    num_layers=2,
+    output_size=y_train.shape[1],
+)
+model.to(device)
+criterion = nn.MSELoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+
+train_losses = []
+val_losses = []
+
+
+for epoch in range(100):
+    model.train()
+    running_train_loss = 0.0
+
+    # inputs, targets
+    for batch_X, batch_y in train_loader:
+        batch_X = batch_X.to(device)
+        batch_y = batch_y.to(device)
+
+        outputs = model(batch_X)
+        loss = criterion(outputs, batch_y)
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        running_train_loss += loss.item() * batch_X.size(0)
+    if (epoch + 1) % 10 == 0:
+        print(f"Epoch [{epoch+1}/100], Loss: {loss.item():.4f}")
+
+    epoch_train_loss = running_train_loss / len(train_loader.dataset)
+    train_losses.append(epoch_train_loss)
+
+model.eval()
+
+X_test_tensor = torch.tensor(X_test, dtype=torch.float32).to(device)
+y_test_tensor = torch.tensor(y_test, dtype=torch.float32).to(device)
+val_dataset = TensorDataset(X_test_tensor, y_test_tensor)
+val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)
+
+# with torch.no_grad():
+#     running_val_loss = 0.0
+#     predictions = model(X_test_tensor)
+#     test_loss = criterion(predictions, y_test_tensor)
+#     print(f"Test Loss: {test_loss.item():.4f}")
+
+running_val_loss = 0.0
+predictions = []
+with torch.no_grad():
+    for inputs, targets in val_loader:
+        inputs, targets = inputs.to(device), targets.to(device)
+        outputs = model(inputs)
+        loss = criterion(outputs, targets)
+        running_val_loss += loss.item() * inputs.size(0)
+        predictions.append(outputs.cpu().numpy())  # Collect all batches
+
+epoch_val_loss = running_val_loss / len(val_loader.dataset)  # len(val_dataset)
+val_losses.append(epoch_val_loss)
+
+predictions = np.concatenate(predictions, axis=0)
+import matplotlib.pyplot as plt
+
+y_test_cpu = y_test_tensor.cpu().numpy()
+
+predictions_rescaled = scaler.inverse_transform(predictions)
+y_test_rescaled = scaler.inverse_transform(y_test_cpu)
+
+
+fig, (ax1, ax2) = plt.subplots(
+    1, 2, figsize=(14, 5), gridspec_kw={"width_ratios": [1, 3]}
+)
+
+# Loss curve on the left
+ax1.plot(train_losses, label="Train Loss")
+ax1.plot(val_losses, label="Val Loss")
+ax1.set_title("Loss Curve")
+ax1.set_xlabel("Epoch")
+ax1.set_ylabel("Loss")
+ax1.legend()
+ax1.grid(True)
+
+# Prediction vs Actual on the right
+ax2.plot(y_test_rescaled, label="Actual")
+ax2.plot(predictions_rescaled, label="Predicted")
+ax2.set_title("Actual vs. Predicted")
+ax2.set_xlabel("Sample")
+ax2.set_ylabel("Value")
+ax2.legend()
+
+plt.tight_layout()
+plt.show()