46 lines
1.3 KiB
Python
46 lines
1.3 KiB
Python
import pandas as pd
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import re
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from sklearn import metrics
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import numpy as np
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import csv
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import matplotlib.pyplot as plt
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f = open("result.txt", "r")
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list_result, list_predicted=[],[]
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for x in f:
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data = x.split(' ')
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result = re.findall(r'\d+', data[1])
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predicted = re.findall(r'\d+', data[5])
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result=int(result[0])
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predicted=float('.'.join(predicted))
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list_result.append(result)
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list_predicted.append(predicted)
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metrics = metrics.mean_absolute_error(list_result, list_predicted), metrics.mean_squared_error(list_result, list_predicted),np.sqrt(metrics.mean_absolute_error(list_result, list_predicted))
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print("MAE: ", metrics[0])
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print("MSE: ",metrics[1])
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print("RMSE: ",metrics[2])
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with open('eval.csv', 'a+', newline='') as f:
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writer = csv.writer(f)
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writer.writerow((metrics[0],metrics[1], metrics[2]))
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MAE,MSE,RMSE=[],[],[]
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with open('eval.csv', 'r') as r:
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for row in r:
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# row variable is a list that represents a row in csv
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row=row.split(',')
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MAE.append(float(row[0]))
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MSE.append(float(row[1]))
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RMSE.append(float(row[2]))
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plt.xlabel('build')
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plt.plot(np.arange(0, len(MAE)), MAE, label="MAE")
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plt.plot(np.arange(0, len(MSE)), MSE, label="MSE")
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plt.plot(np.arange(0, len(RMSE)), RMSE, label="RMSE")
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plt.legend()
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plt.savefig('metrics.png') |