[BUG] TimeGapSplit mutates the data

TimeGapSplit mutates the pandas.DataFrame it operates on if the date column is not a datetime type. For example:

from pandas.api.types import (
    is_object_dtype,
    is_datetime64_any_dtype
)

date_range = pd.date_range(start='1/1/2018', end='1/30/2018')
dates = [date.strftime('%m-%d-%Y') for date in date_range]

df = (
    pd.DataFrame(
        data=np.random.randint(0, 30, size=(30, 4)),
        columns=list('ABCy')
    )
    .assign(
        date=dates
    )
)

assert is_object_dtype(df['date'])

cv = TimeGapSplit(
    df=df,
    date_col='date',
    train_duration=timedelta(days=3),
    valid_duration=timedelta(days=1),
)

assert is_datetime64_any_dtype(df['date'])

Is this desirable behavior?

Possible remedies are:

• Only accept the datetime type
• Accept str type, but make a copy and leave the pandas.DataFrame as is.

Happy to hear your thoughts @kayhoogland @stephanecollot @koaning

[koaning]

copy feels like a safer option for now. it allows for flexibility in the future.

[stephanecollot]

@koaning can you close?