CountEncoder incorrectly counts Timestamp columns about category_encoders HOT 3 CLOSED

@vkhodygo It was but I just fixed it myself.
@bmreiniger thanks for the suggestion. I've implemented this and wrote a test based on @willsthompson 's data.

from category_encoders.

It looks like this is due to the underlying ordinal encoder converting the timestamps (and so is likely to affect other encoders as well):

>>> cat_encoder.ordinal_encoder.mapping
[{'col': 'TIMESTAMPS', 'mapping': 8.732448e+17    1
9.679392e+17    2
9.364032e+17    3
9.994752e+17    4
NaN            -2
dtype: int64, 'data_type': dtype('<M8[ns]')}, {'col': 'FLOATS', 'mapping': 0.285652    1
0.900217    2
0.928511    3
0.566326    4
NaN        -2
dtype: int64, 'data_type': dtype('float64')}]

and then

>>> cat_encoder.mapping['TIMESTAMPS']   
NaN    1.0
Name: TIMESTAMPS, dtype: float64

In this diff: d19f69e
it seems that categories.tolist() converts numpy timestamps where list(categories) doesn't (?).

from category_encoders.

Do you still think this is actual?

from category_encoders.