32 lines
1002 B
Python
32 lines
1002 B
Python
# Mając skumulowane wartości metryk z wszystkich dotychczasowych buildów,
|
|
# stwórz wykres: na osi X numer builda, na osi Y wartość metryk(i)
|
|
|
|
import pandas as pd
|
|
import matplotlib.pyplot as plt
|
|
|
|
# wczytanie pliku csv z metrykami
|
|
metrics_df = pd.read_csv('metrics.csv')
|
|
|
|
# Podział wartości
|
|
build_numbers = metrics_df['Build Number']
|
|
accuracy_values = metrics_df['Accuracy']
|
|
precision_values = metrics_df['Micro-avg Precision']
|
|
recall_values = metrics_df['Micro-avg Recall']
|
|
f1_score_values = metrics_df['F1 Score']
|
|
rmse_values = metrics_df['RMSE']
|
|
|
|
# Plotowanie wykresu
|
|
plt.plot(build_numbers, accuracy_values, label='Accuracy')
|
|
plt.plot(build_numbers, precision_values, label='Micro-avg Precision')
|
|
plt.plot(build_numbers, recall_values, label='Micro-avg Recall')
|
|
plt.plot(build_numbers, f1_score_values, label='F1 Score')
|
|
plt.plot(build_numbers, rmse_values, label='RMSE')
|
|
|
|
plt.legend()
|
|
plt.xlabel('Build number')
|
|
plt.ylabel('Value metric')
|
|
|
|
plt.savefig('metrics_chart_plot.png')
|
|
|
|
plt.show()
|