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