Calculating precision, recall and F1 score per class in a multilabel classification problem

I'm trying to calculate the precision, the recall and the F1-Score per class in my multilabel classification problem. However, I think I'm doing something wrong, because I am getting really high values, and the F1 Score for the whole problem is 0.66. However, I'm getting +0.8 f1-score in the individual classes.

This is how I am doing it right now:

confusion_matrix = multilabel_confusion_matrix(gold_labels, predictions)

assert(len(confusion_matrix) == 6)

for label in range(len(labels_reduced)):

    tp = confusion_matrix[label][0][0]
    fp = confusion_matrix[label][0][1]
    fn = confusion_matrix[label][1][0]
    tn = confusion_matrix[label][1][1]

    precision = tp+fp
    precision = tp/precision

    recall = tp+fn
    recall = tp/recall

    f1_score_up = precision * recall
    f1_score_down = precision + recall
    f1_score = f1_score_up/f1_score_down
    f1_score = 2 * f1_score

    print(f"Metrics for {labels_reduced[label]}.")
    print(f"Precision: {precision}")
    print(f"Recall: {recall}")
    print(f"F1-Score: {f1_score}")

Are these results okay? Do they make sense? Am I doing something wrong? How would you calculate those metrics? I'm using huggingface transformers for loading the models and getting the predictions, and sklearn for calculating the metrics.

You could use the classification_report function from sklearn:

from sklearn.metrics import classification_report

labels = [[0, 1, 1], [1, 0, 0], [1, 0, 1]]
predictions = [[[0, 0, 1], [1, 0, 0], [1, 1, 1]]

report = classification_report(labels, predictions)
print(report)

Which outputs:

              precision    recall  f1-score   support

           0       1.00      1.00      1.00         2
           1       0.00      0.00      0.00         1
           2       1.00      1.00      1.00         2

   micro avg       0.80      0.80      0.80         5
   macro avg       0.67      0.67      0.67         5
weighted avg       0.80      0.80      0.80         5
 samples avg       0.89      0.83      0.82         5