How To Calculate F1-Score For Multilabel Classification?

Question

I try to calculate the f1_score but I get some warnings for some cases when I use the sklearn f1_score method.

I have a multilabel 5 classes problem for a prediction.

import numpy as np
from sklearn.metrics import f1_score

y_true = np.zeros((1,5))
y_true[0,0] = 1 # => label = [[1, 0, 0, 0, 0]]

y_pred = np.zeros((1,5))
y_pred[:] = 1 # => prediction = [[1, 1, 1, 1, 1]]

result_1 = f1_score(y_true=y_true, y_pred=y_pred, labels=None, average="weighted")

print(result_1) # prints 1.0

result_2 = f1_score(y_true=y_ture, y_pred=y_pred, labels=None, average="weighted")

print(result_2) # prints: (1.0, 1.0, 1.0, None) for precision/recall/fbeta_score/support

When I use average="samples" instead of "weighted" I get (0.1, 1.0, 0.1818..., None). Is the "weighted" option not useful for a multilabel problem or how do I use the f1_score method correctly?

I also get a warning when using average="weighted":

"UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples."

Answer 1

It works if you slightly add up data:

y_true = np.array([[1,0,0,0], [1,1,0,0], [1,1,1,1]])
y_pred = np.array([[1,0,0,0], [1,1,1,0], [1,1,1,1]])

recall_score(y_true=y_true, y_pred=y_pred, average='weighted')
>>> 1.0
precision_score(y_true=y_true, y_pred=y_pred, average='weighted')
>>> 0.9285714285714286

f1_score(y_true=y_true, y_pred=y_pred, average='weighted')
>>> 0.95238095238095244

The data suggests we have not missed any true positives and have not predicted any false negatives (recall_score equals 1). However, we have predicted one false positive in the second observation that lead to precision_score equal ~0.93.

As both precision_score and recall_score are not zero with weighted parameter, f1_score, thus, exists. I believe your case is invalid due to lack of information in the example.