Create node_optimizer.py

blockchain_integration/pi_network/pibrain/ai/models/node_optimizer.py

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+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import Dataset, DataLoader
+import numpy as np
+import pandas as pd
+
+class NodeOptimizer(nn.Module):
+    def __init__(self, input_dim, hidden_dim, output_dim):
+        super(NodeOptimizer, self).__init__()
+        self.fc1 = nn.Linear(input_dim, hidden_dim)
+        self.fc2 = nn.Linear(hidden_dim, hidden_dim)
+        self.fc3 = nn.Linear(hidden_dim, output_dim)
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+class NodeOptimizerDataset(Dataset):
+    def __init__(self, data, labels):
+        self.data = data
+        self.labels = labels
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        x = self.data[idx]
+        y = self.labels[idx]
+        return {
+            'x': torch.tensor(x, dtype=torch.float),
+            'y': torch.tensor(y, dtype=torch.float)
+        }
+
+def train_node_optimizer(model, dataset, batch_size, epochs, learning_rate):
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    model.to(device)
+
+    criterion = nn.MSELoss()
+    optimizer = optim.Adam(model.parameters(), lr=learning_rate)
+
+    data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
+
+    for epoch in range(epochs):
+        for batch in data_loader:
+            x = batch['x'].to(device)
+            y = batch['y'].to(device)
+
+            optimizer.zero_grad()
+            outputs = model(x)
+            loss = criterion(outputs, y)
+            loss.backward()
+            optimizer.step()
+
+        print(f'Epoch {epoch+1}, Loss: {loss.item()}')
+
+    return model
+
+def load_node_optimizer_data(file_path):
+    data = pd.read_csv(file_path)
+    X = data.drop(['node_performance'], axis=1).values
+    y = data['node_performance'].values
+    return X, y
+
+# Example usage:
+X, y = load_node_optimizer_data('node_performance_data.csv')
+dataset = NodeOptimizerDataset(X, y)
+model = NodeOptimizer(input_dim=X.shape[1], hidden_dim=128, output_dim=1)
+model = train_node_optimizer(model, dataset, batch_size=32, epochs=100, learning_rate=0.001)