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skryptBibliotekiDL.py

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+import os
+import pandas as pd
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+file_path = os.path.join("C:", os.sep, "Users", "reyva", "OneDrive", "Pulpit", "studia", "InzynieriaUczeniaMaszynowego", 'Train1.csv')
+
+train_data = pd.read_csv(file_path)
+
+train_data = train_data.dropna(subset=['price_range'])
+
+valid_values = {0.0, 1.0, 2.0, 3.0}
+assert set(train_data['price_range'].unique()) <= valid_values, "Unexpected values in price_range"
+
+class SimpleNN(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(SimpleNN, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, output_size)
+        self.softmax = nn.Softmax(dim=1)
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = self.fc2(x)
+        return self.softmax(x)
+
+input_size = len(train_data.columns) - 2  
+hidden_size = 50
+output_size = len(valid_values)
+
+model = SimpleNN(input_size, hidden_size, output_size)
+
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+epochs = 10
+for epoch in range(epochs):
+    inputs = torch.tensor(train_data.drop(['price_range', 'ID'], axis=1).values, dtype=torch.float32)
+    labels = torch.tensor(train_data['price_range'].values, dtype=torch.long)
+
+    optimizer.zero_grad()
+
+    outputs = model(inputs)
+    loss = criterion(outputs, labels)
+    loss.backward()
+    optimizer.step()
+
+    print(f"Epoch {epoch+1}/{epochs}, Loss: {loss.item()}")
+
+save_path = os.path.join("C:", os.sep, "Users", "reyva", "OneDrive", "Pulpit", "studia", "InzynieriaUczeniaMaszynowego", "model.pth")
+torch.save(model.state_dict(), save_path)
+

skryptBibliotekiDL2.py

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+import os
+import pandas as pd
+import torch
+import torch.nn as nn
+
+class SimpleNN(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(SimpleNN, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, output_size)
+        self.softmax = nn.Softmax(dim=1)
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = self.fc2(x)
+        return self.softmax(x)
+
+input_size = 20 
+hidden_size = 50
+output_size = 4 
+
+model = SimpleNN(input_size, hidden_size, output_size)
+model.load_state_dict(torch.load("model.pth"))
+model.eval() 
+
+file_path = os.path.join("C:", os.sep, "Users", "reyva", "OneDrive", "Pulpit", "studia", "InzynieriaUczeniaMaszynowego", 'Test1.csv')
+test_data = pd.read_csv(file_path)
+
+inputs = torch.tensor(test_data.drop(['price_range', 'ID'], axis=1).values, dtype=torch.float32)
+with torch.no_grad():
+    predictions = model(inputs)
+predicted_classes = torch.argmax(predictions, dim=1)
+
+predicted_classes_df = pd.DataFrame(predicted_classes.numpy(), columns=['Predicted_Price_Range'])
+
+predicted_classes_df['Actual_Price_Range'] = test_data['price_range'].values
+
+output_path = os.path.join("C:", os.sep, "Users", "reyva", "OneDrive", "Pulpit", "studia", "InzynieriaUczeniaMaszynowego", 'predictions.csv')
+predicted_classes_df.to_csv(output_path, index=False)