63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
import torch
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import os
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import pandas as pd
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import numpy as np
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from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
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import matplotlib.pyplot as plt
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import seaborn as sns
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from NeuralNetwork import NeuralNetwork
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# Load model if it exists
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if os.path.exists('./models/model.pth'):
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# Create model
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model = torch.load('./models/model.pth')
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# Load test data
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test = pd.read_csv('./datasets/test.csv')
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# Split data
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X_test = test.drop(columns=['id', 'diagnosis']).values
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y_test = test['diagnosis'].values
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# Convert data to PyTorch tensors
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X_test = torch.FloatTensor(X_test)
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y_test = torch.FloatTensor(y_test).view(-1, 1)
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# Predict
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with torch.no_grad():
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y_pred = model(X_test)
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y_pred = np.where(y_pred >= 0.5, 1, 0)
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# Save predictions to CSV
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pd.DataFrame(y_pred, columns=['Prediction']).to_csv('predictions.csv', index=False)
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# Calculate metrics
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accuracy = accuracy_score(y_test, y_pred)
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precision = precision_score(y_test, y_pred)
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recall = recall_score(y_test, y_pred)
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f1 = f1_score(y_test, y_pred)
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# Save metrics to CSV (append mode, if file exists, if not, create it)
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if not os.path.exists('metrics.csv'):
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pd.DataFrame([[accuracy, precision, recall, f1]], columns=['Accuracy', 'Precision', 'Recall', 'F1']).to_csv('metrics.csv', index=False)
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else:
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# without header
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metrics = pd.read_csv('metrics.csv')
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metrics = metrics._append({'Accuracy': accuracy, 'Precision': precision, 'Recall': recall, 'F1': f1}, ignore_index=True)
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metrics.to_csv('metrics.csv', index=False, mode='a', header=False)
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# Plot metrics line chart
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sns.set(style='whitegrid')
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plt.figure(figsize=(8, 6))
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sns.lineplot(data=metrics)
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plt.title('Metrics history')
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plt.xlabel('History number')
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plt.ylabel('Value')
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plt.legend()
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plt.savefig('metrics.png')
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else:
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raise FileNotFoundError('Model not found')
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