61 lines
1.8 KiB
Python
61 lines
1.8 KiB
Python
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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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import itertools
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.metrics import f1_score as f1
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from sklearn.metrics import confusion_matrix
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from sklearn.metrics import *
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from torch.utils.data import TensorDataset
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from torch.utils.data import DataLoader
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test_file = pd.read_csv('test.data').drop('Unnamed: 0', axis=1)
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df_test = test_file.dropna()
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X_test = df_test.drop('class', axis=1)
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Y_test = df_test['class']
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scaler = MinMaxScaler()
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X_test = scaler.fit_transform(X_test)
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x_test_tensor = torch.tensor(X_test).float()
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y_test_tensor = torch.tensor(Y_test.values).float()
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test_ds = TensorDataset(x_test_tensor, y_test_tensor.unsqueeze(1))
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test_dl = DataLoader(test_ds, batch_size=64)
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model = torch.jit.load('model_scripted.pt')
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model.eval()
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y_pred_list = []
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model.eval()
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with torch.no_grad():
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for xb_test,yb_test in test_dl:
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y_test_pred = model(xb_test)
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y_pred_tag = torch.round(y_test_pred)
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y_pred_list.append(y_pred_tag.detach().numpy())
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y_pred_list = [a.squeeze().tolist() for a in y_pred_list]
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ytest_pred = list(itertools.chain.from_iterable(y_pred_list))
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y_true_test = Y_test.values.ravel()
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conf_matrix = confusion_matrix(y_true_test ,ytest_pred)
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print("Confusion Matrix of the Test Set")
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print("-----------")
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print(conf_matrix)
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print("Precision of the MLP :\t"+str(precision_score(y_true_test,ytest_pred)))
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print("Accuracy of the MLP :\t"+str(accuracy_score(y_true_test,ytest_pred)))
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print("Recall of the MLP :\t"+str(recall_score(y_true_test,ytest_pred)))
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print("F1 Score of the Model :\t"+str(f1_score(y_true_test,ytest_pred)))
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print(*map(int, ytest_pred))
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print(*y_true_test)
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with open('predicted_values.txt', 'w') as f:
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f.write(" ".join(map(str, map(int, ytest_pred))))
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