Support for string labels

[michelole]

skift seems to expect integer labels and will fail when using string labels.

For instance, when running

from skift import FirstColFtClassifier
import pandas as pd
df = pd.DataFrame(
    data=[
        ['woof', 'a'],
        ['meow', 'b'],
        ['squick', 'c'],
    ],
    columns=['txt', 'lbl'],
)
sk_clf = FirstColFtClassifier(lr=0.3, epoch=10)
sk_clf.fit(df[['txt']], df['lbl'])
sk_clf.predict([['squick']])

I get

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-32-52a73258e761> in <module>
----> 1 sk_clf.predict([['squick']])

/usr/local/Caskroom/miniconda/base/envs/base/lib/python3.7/site-packages/skift/core.py in predict(self, X)
    165         return np.array([
    166             self._clean_label(res[0][0])
--> 167             for res in self._predict(X)
    168         ], dtype=np.float_)
    169 

/usr/local/Caskroom/miniconda/base/envs/base/lib/python3.7/site-packages/skift/core.py in <listcomp>(.0)
    165         return np.array([
    166             self._clean_label(res[0][0])
--> 167             for res in self._predict(X)
    168         ], dtype=np.float_)
    169 

/usr/local/Caskroom/miniconda/base/envs/base/lib/python3.7/site-packages/skift/core.py in _clean_label(ft_label)
    135     @staticmethod
    136     def _clean_label(ft_label):
--> 137         return int(ft_label[9:])
    138 
    139     def _predict_on_str_arr(self, str_arr, k=1):

ValueError: invalid literal for int() with base 10: 'c'

This is a bit unexpected since neither sklearn nor fasttext require integer labels.

I guess skift could handle that either by:

• passing the string labels directly to fasttext (caveat: might require some cleaning)
• automatically calling LabelEncoder (e.g. as in sklearn's code for LR)

[shaypal5]

Hmmm. Good point!

I would accept a PR solving this either way. Would you consider writing one? :)

[michelole]

Sure, let me just find some spare cycles...

[shaypal5]

:)