tutorial from ai is uhjhhhhh

.gitignore

@@ -184,4 +184,4 @@ ipython_config.py
 
 
 
-.conda/
+.conda*/

.vscode/extensions.json

@@ -0,0 +1,3 @@
+{
+  "recommendations": ["ms-python.python", "ms-toolsai.jupyter"]
+}

tutorial/pt_tuto.py

@@ -0,0 +1,231 @@
+"""
+This script demonstrates how to port a TensorFlow LSTM model to PyTorch.
+It includes detailed explanations for every step.
+"""
+
+# Step 1: Import necessary libraries
+# ----------------------------------
+# PyTorch is the core library for building and training neural networks.
+# Torch.nn contains modules for defining layers and loss functions.
+# Torch.optim provides optimizers like Adam for training.
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+from torch import nn, optim
+from torch.utils.data import DataLoader, TensorDataset
+
+from load_data import load_this_data
+
+# Step 2: Data Preparation
+# ------------------------
+# In PyTorch, data must be converted into tensors. We'll simulate the data
+# preprocessing steps from your TensorFlow code.
+
+
+# Simulate some data (replace this with your actual data loading logic)
+np.random.seed(42)  # For reproducibility
+# data = np.sin(np.linspace(0, 100, 1000))  # Example: Sine wave data
+# data = data.reshape(-1, 1)  # Reshape to (samples, features)
+
+
+# Load the dataset (replace "dataset.csv" with your actual file path)
+data = load_this_data("dataset.csv")
+
+
+# Extract the "Humidity" column as the target variable
+data = data["Humidity"].values.reshape(-1, 1)
+
+# Normalize the data using MinMaxScaler (same as in TensorFlow)
+scaler = MinMaxScaler()
+data_scaled = scaler.fit_transform(data)
+
+# Define lookback window (sequence length for LSTM)
+n_lookback = 100  # Number of past timesteps to consider
+n_features = 1  # Number of features (e.g., 1 for univariate time series)
+
+
+# Helper function to create sequences
+def create_sequences(data, n_lookback):
+    """
+    Create sequences of length n_lookback for LSTM input.
+
+    Args:
+        data: Scaled data as a NumPy array or PyTorch tensor.
+        n_lookback: Number of past timesteps to consider.
+
+    Returns:
+        x: Input sequences of shape (num_samples, n_lookback, n_features).
+        y: Target values of shape (num_samples, 1).
+    """
+    x, y = [], []
+    for i in range(len(data) - n_lookback):
+        x.append(data[i : i + n_lookback])
+        y.append(data[i + n_lookback])
+    return np.array(x), np.array(y)
+
+
+# Split into training and testing sets (80% train, 20% test)
+train_data, test_data = train_test_split(data_scaled, test_size=0.2, shuffle=False)
+
+# Create sequences for training and testing
+train_x, train_y = create_sequences(train_data, n_lookback)
+test_x, test_y = create_sequences(test_data, n_lookback)
+
+# Convert sequences to PyTorch tensors
+train_x = torch.tensor(train_x, dtype=torch.float32)
+train_y = torch.tensor(train_y, dtype=torch.float32)
+test_x = torch.tensor(test_x, dtype=torch.float32)
+test_y = torch.tensor(test_y, dtype=torch.float32)
+
+# Create datasets and dataloaders
+train_dataset = TensorDataset(train_x, train_y)
+test_dataset = TensorDataset(test_x, test_y)
+
+train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
+test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False)
+
+# Step 3: Define the LSTM Model
+# -----------------------------
+# PyTorch models are defined by subclassing `nn.Module`.
+# The forward method specifies how data flows through the model.
+
+
+class LSTMModel(nn.Module):
+    def __init__(self, n_features, n_lookback, n_neurons):
+        """
+        Initialize the LSTM model.
+
+        Args:
+            n_features: Number of input features (e.g., 1 for univariate time series).
+            n_lookback: Number of past timesteps to consider.
+            n_neurons: Number of neurons in each LSTM layer.
+        """
+        super(LSTMModel, self).__init__()
+        self.lstm = nn.LSTM(
+            input_size=n_features,  # Number of input features
+            hidden_size=n_neurons,  # Number of neurons in the LSTM
+            num_layers=2,  # Number of LSTM layers
+            batch_first=True,  # Input shape is (batch, seq, feature)
+            dropout=0.2,  # Dropout for regularization
+        )
+        self.fc = nn.Linear(n_neurons, 1)  # Fully connected layer for output
+
+    def forward(self, x):
+        """
+        Forward pass through the model.
+
+        Args:
+            x: Input tensor of shape (batch, seq_len, n_features).
+
+        Returns:
+            Output tensor of shape (batch, 1).
+        """
+        lstm_out, _ = self.lstm(x)  # LSTM outputs (ignore hidden states)
+        out = self.fc(lstm_out[:, -1, :])  # Use the last timestep's output
+        return out
+
+
+# Instantiate the model
+n_neurons = 64  # Number of neurons in the LSTM
+model = LSTMModel(n_features, n_lookback, n_neurons)
+
+# Step 4: Define Loss Function and Optimizer
+# ------------------------------------------
+# Loss function: Mean Squared Error (MSE) for regression tasks.
+# Optimizer: Adam optimizer for efficient training.
+criterion = nn.MSELoss()
+optimizer = optim.Adam(model.parameters(), lr=0.0001)
+
+# Step 5: Train the Model
+# -----------------------
+# PyTorch requires an explicit training loop. This gives you full control
+# over the training process.
+
+epochs = 100  # Number of training epochs
+
+# Check for CUDA and set the device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
+
+# Move the model to the device
+model = LSTMModel(n_features, n_lookback, n_neurons).to(device)
+
+# Move data to the device during training and evaluation
+for epoch in range(epochs):
+    model.train()  # Set the model to training mode
+    train_loss = 0.0
+
+    for x_batch, y_batch in train_loader:
+        # Move data to the device
+        x_batch = x_batch.to(device)
+        y_batch = y_batch.to(device)
+        # Reshape input to (batch, seq_len, n_features)
+        x_batch = x_batch.view(-1, n_lookback, n_features)
+        y_batch = y_batch.view(-1, 1)
+
+        # Zero the gradients from the previous step
+        optimizer.zero_grad()
+
+        # Forward pass: Compute predictions
+        predictions = model(x_batch)
+
+        # Compute the loss
+        loss = criterion(predictions, y_batch)
+
+        # Backward pass: Compute gradients
+        loss.backward()
+
+        # Update model parameters
+        optimizer.step()
+
+        # Accumulate the loss for reporting
+        train_loss += loss.item()
+
+    # Print epoch loss
+    print(f"Epoch {epoch+1}/{epochs}, Loss: {train_loss/len(train_loader):.4f}")
+
+    if epoch % 10 == 0:  # Print every 10 epochs
+        print(f"Sample prediction: {predictions[0].item()}, Actual: {y_batch[0].item()}")
+
+# Step 6: Evaluate the Model
+# --------------------------
+# Switch to evaluation mode and disable gradient computation.
+model.eval()
+with torch.no_grad():
+    predictions = []
+    actuals = []
+
+    for x_batch, y_batch in test_loader:
+        # Move data to the device
+        x_batch = x_batch.to(device)
+        y_batch = y_batch.to(device)
+
+        # Reshape input to (batch, seq_len, n_features)
+        x_batch = x_batch.view(-1, n_lookback, n_features)
+
+        # Make predictions
+        preds = model(x_batch)
+
+        # Move predictions and actuals back to CPU for further processing
+        predictions.append(preds.cpu().numpy())
+        actuals.append(y_batch.cpu().numpy())
+
+# Convert predictions and actuals to NumPy arrays for comparison
+predictions = np.concatenate(predictions)
+actuals = np.concatenate(actuals)
+
+# Step 7: Post-Processing and Visualization
+# -----------------------------------------
+# Inverse transform the predictions and actuals to get original scale
+predictions = scaler.inverse_transform(predictions)
+actuals = scaler.inverse_transform(actuals)
+
+# Plot the results
+
+plt.figure(figsize=(20, 5))
+plt.plot(actuals, label="Actual")
+plt.plot(predictions, label="Predicted")
+plt.legend()
+plt.show()

tutorial/pytorch_test.py

@@ -0,0 +1,7 @@
+""" this uses new tech
+make by conda new -p path python=3.12; conda activate -p path; conda install all those shi
+
+conda install pytorch torchvision torchaudio pytorch-cuda -c pytorch -c nvidia
+"""
+
+