61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
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import torch
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import matplotlib.pyplot as plt
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import os
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import json
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from sklearn.metrics import accuracy_score, precision_score, recall_score
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import pandas as pd
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# Wczytywanie
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# Nie potrzebuje wczytywać modelu i danych testowych bo w jobie trenującym już stworzyłem csv z wynikami
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# model = torch.load('model.pkl')
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# test_set = pd.read_csv('d_test.csv', encoding='latin-1')
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test_results = pd.read_csv('neural_network_prediction_results.csv')
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# Ewaluacja
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y_true = test_results['Testing Y']
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y_predicted = test_results['Predicted Y']
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accuracy = accuracy_score(y_true, y_predicted)
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precision = precision_score(y_true, y_predicted, average='macro')
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recall = recall_score(y_true, y_predicted, average='macro')
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eval_results = {
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'Accuracy': accuracy,
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'Macro-Avg Precision': precision,
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'Macro-Avg Recall': recall
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}
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filename = 'eval_results.json'
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if not os.path.exists(filename):
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with open(filename, 'w') as file:
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json.dump({'eval_results': []}, file, indent=2)
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with open(filename, 'r+') as file:
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file_data = json.load(file)
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file_data['eval_results'].append(eval_results)
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file.seek(0)
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json.dump(file_data, file, indent=2)
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with open(filename, 'r') as file:
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results = json.load(file)['eval_results']
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f_acc = []
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f_prc = []
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f_rec = []
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for res in results:
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f_acc.append(res['Accuracy'])
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f_prc.append(res['Macro-Avg Precision'])
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f_rec.append(res['Macro-Avg Recall'])
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build_axis = [i+1 for i in range(len(f_acc))]
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plt.xlabel('Build')
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plt.ylabel('Score')
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plt.plot(build_axis, f_acc, label='Accuracy')
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plt.plot(build_axis, f_prc, label='Macro-Avg Precision')
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plt.plot(build_axis, f_rec, label='Macro-Avg Recall')
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plt.legend()
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plt.show()
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plt.savefig('metrics.png')
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