Spaces:
Sleeping
Sleeping
File size: 876 Bytes
9df4cc0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 |
import numpy as np
def f1_score(confusion_matrix):
num_classes = confusion_matrix.shape[0]
f1_scores = np.zeros(num_classes)
for i in range(num_classes):
tp = confusion_matrix[i, i]
fp = sum(confusion_matrix[j, i] for j in range(num_classes) if j != i)
fn = sum(confusion_matrix[i, j] for j in range(num_classes) if j != i)
precision = tp / (tp + fp) if (tp + fp) != 0 else 0
recall = tp / (tp + fn) if (tp + fn) != 0 else 0
f1_scores[i] = 2 * precision * recall / (precision + recall) if (precision + recall) != 0 else 0
macro_f1 = np.mean(f1_scores)
return f1_scores, macro_f1
# Example 3x3 confusion matrix
confusion = np.array([[ 80, 90, 2],[ 1 , 146 ,55 ],[ 11, 22 ,634 ]])
f1_scores, macro_f1 = f1_score(confusion)
print("F1-scores for each class:", f1_scores)
print("Macro F1-score:", macro_f1) |