forked from s464914/ium_464914
25 lines
994 B
Python
25 lines
994 B
Python
|
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, mean_squared_error
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
true_labels = []
|
||
|
|
predicted_labels = []
|
||
|
|
|
||
|
|
f = open("predictions.txt", "r")
|
||
|
|
for line in f:
|
||
|
|
parts = line.strip().split(' ')
|
||
|
|
true_labels.append(int(parts[3]))
|
||
|
|
predicted_labels.append(int(parts[1]))
|
||
|
|
|
||
|
|
accuracy = accuracy_score(true_labels, predicted_labels)
|
||
|
|
precision_micro = precision_score(true_labels, predicted_labels, average='micro')
|
||
|
|
recall_micro = recall_score(true_labels, predicted_labels, average='micro')
|
||
|
|
f1_micro = f1_score(true_labels, predicted_labels, average='micro')
|
||
|
|
rmse = np.sqrt(mean_squared_error(true_labels, predicted_labels))
|
||
|
|
|
||
|
|
with open(r'metrics.txt', 'a') as fp:
|
||
|
|
fp.write(f"Accuracy: {accuracy}\n")
|
||
|
|
fp.write(f"Micro-average Precision: {precision_micro}\n")
|
||
|
|
fp.write(f"Micro-average Recall: {recall_micro}\n")
|
||
|
|
fp.write(f"Micro-average F1-score: {f1_micro}\n")
|
||
|
|
fp.write(f"RMSE: {rmse}\n")
|
||
|
|
fp.write("--------------------\n")
|