class_weight of type log produces 0.0 weight on most frequent class

[Guillermogsjc]

This makes that training ignores the most frequent class.

Reproduce example:

`import numpy
import pandas
from finetune.imbalance import compute_class_weights

np.random.seed(0)
y = pd.Series(np.random.choice(a=[0, 1, 2], size=1000, p=[0.3, 0.6, 0.1]))
print(y.value_counts(normalize=True))
print(compute_class_weights('log', y))`

[madisonmay]

Good catch, will have to think of a proper way to resolve this. Simply adding one may be sufficient as this is a relatively simple heuristic, but need to give it a bit of thought.

[madisonmay]

I've opted to go for the adding 1 option in PR #183 -- I was just looking for a relatively soft re-weighting option when I added this in, and that should satisfy that requirement. Will be in the development shortly and available as part of the next release (0.5.12). Thanks again for the bug report @Guillermogsjc