# Different definitions of Macro F1 score, which one is used in Scikit-learn?

In this article Macro F1 and Macro F1 two different definitions of the F1 used in the literature are demonstrated. The first F1 score is computed such as:

F1 scores are computed for each class and then averaged via arithmetic mean

The second such as:

The harmonic mean is computed over the arithmetic means of precision and recall

I was wondering which definition is actually implemented in Scikit-learn. From the docs I cannot derive which definition is used:

Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.

The first variant is implemented: $$F1_{macro}= \ \sum_{classes} \frac{F1\text{ }of \text{ }class}{number\text{ }of\text{ }classes}$$