2022-05-02 16:45:31 +02:00
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from model import AvocadoDataset, evaluate_model
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from torch.utils.data import DataLoader
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from torch.jit import load as load_model
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import pandas as pd
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import matplotlib.pyplot as plt
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import matplotlib
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matplotlib.style.use('ggplot')
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# * Load the test data
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test_data = DataLoader(AvocadoDataset(
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'./data/avocado.data.test'), batch_size=1, shuffle=False)
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# * Load the model
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model = load_model('./data/model_scripted.pt')
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model.eval()
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# * Append new inference data
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with open('./data/evaluation_results.csv', 'a+') as f:
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f.write("{0},{1},{2}\n".format(*evaluate_model(test_data, model)))
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# * Load all inference data gathered (till the current one)
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2022-05-02 18:40:14 +02:00
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results = pd.read_csv('./data/evaluation_results.csv',
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names=['MSE', 'RMSE', 'MAE'])
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2022-05-02 16:45:31 +02:00
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# * Plot the results
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plt.plot(range(1, len(results)+1), results['MSE'], color='green')
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plt.scatter(range(1, len(results)+1),
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results['MSE'], label='MSE', color='green', marker='.')
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plt.plot(range(1, len(results)+1), results['RMSE'], color='darkred')
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plt.scatter(range(1, len(results)+1),
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results['RMSE'], label='RMSE', color='darkorange', marker='.')
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plt.plot(range(1, len(results)+1), results['MAE'], color='blue')
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plt.scatter(range(1, len(results)+1),
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results['MAE'], label='MAE', color='blue', marker='.')
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plt.xticks(range(1, len(results)+1))
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plt.ylabel('Metric value')
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plt.xlabel('Build number')
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plt.legend()
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# * Save figure
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plt.savefig('data/plots.png')
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