[BUG] Absolute to relative ForecastingHorizon

Describe the bug

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-5-40dcb829593a> in <module>
      4 y_train, y_test = temporal_train_test_split(y, test_size=120)
      5 fh = ForecastingHorizon(y_test.index[0:5], is_relative=False)
----> 6 fh.to_relative(cutoff=y_train.index[-1])

c:\Users\Martin\Desktop\sktime\sktime\sktime\forecasting\base\_fh.py in to_relative(self, cutoff)
    223                 values = _coerce_duration_to_int(values, unit=_get_unit(cutoff))
    224 
--> 225             return self._new(values, is_relative=True)
    226 
    227     @lru_cache(typed=True)

c:\Users\Martin\Desktop\sktime\sktime\sktime\forecasting\base\_fh.py in _new(self, values, is_relative)
    160         if is_relative is None:
    161             is_relative = self.is_relative
--> 162         return type(self)(values, is_relative)
    163 
    164     @property

c:\Users\Martin\Desktop\sktime\sktime\sktime\forecasting\base\_fh.py in __init__(self, values, is_relative)
    137         if not isinstance(is_relative, bool):
    138             raise TypeError("`is_relative` must be a boolean")
--> 139         values = _check_values(values)
    140 
    141         # check types, note that isinstance() does not work here because index

c:\Users\Martin\Desktop\sktime\sktime\sktime\forecasting\base\_fh.py in _check_values(values)
    105     # check values does not contain duplicates
    106     if len(values) != values.nunique():
--> 107         raise ValueError("`values` must not contain duplicates.")
    108 
    109     # return sorted values

ValueError: `values` must not contain duplicates.

To Reproduce

from sktime.forecasting.all import *
y = load_airline()
y.index = y.index.to_timestamp()
y_train, y_test = temporal_train_test_split(y, test_size=120)
fh = ForecastingHorizon(y_test.index[0:5], is_relative=False)
fh.to_relative(cutoff=y_train.index[-1])

Expected behavior No exception

Additional context

Versions Current GitHub master

[mloning]

Yes I asked about it on their Gitter: https://gitter.im/pydata/pandas

The issue is we often work with incomplete indices (e.g. forecasting horizon or windows in temporal CV), not sure how to best handle that yet.

[aiwalter]

@mloning I had a look into it and I think the best way is what you wrote here:

fh.to_pandas().to_period(“M”) - cutoff.to_period(“M”)

So basically what you already said, when doing fh.to_relative() then just converting it to a PerdiodIndex first in case it is a DatetimeIndex. The good thing is that pandas can convert the DatetimIndex also just like this:

from sktime.forecasting.all import *
y = load_airline()
y = y.to_timestamp()
cutoff = y.index[-1]
y.index.to_period()

and it works without giving the freq="M" argument:

PeriodIndex(['1949-01', '1949-02', '1949-03', '1949-04', '1949-05', '1949-06',
             '1949-07', '1949-08', '1949-09', '1949-10',
             ...
             '1960-03', '1960-04', '1960-05', '1960-06', '1960-07', '1960-08',
             '1960-09', '1960-10', '1960-11', '1960-12'],
            dtype='period[M]', name='Period', length=144, freq='M')

The bad thing is that to_period() does not work for a single Timestamp like below. I think I will raise this as an issue to pandas, as this is should then also work imho.

cutoff.to_period()

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
pandas\_libs\tslibs\period.pyx in pandas._libs.tslibs.period.freq_to_dtype_code()

AttributeError: 'pandas._libs.tslibs.offsets.MonthBegin' object has no attribute '_period_dtype_code'

The above exception was the direct cause of the following exception:

ValueError                                Traceback (most recent call last)
<ipython-input-122-1571c54e24e5> in <module>
      1 cutoff = y.index[-1]
----> 2 cutoff.to_period()

pandas\_libs\tslibs\timestamps.pyx in pandas._libs.tslibs.timestamps._Timestamp.to_period()

pandas\_libs\tslibs\period.pyx in pandas._libs.tslibs.period.Period.__new__()

pandas\_libs\tslibs\period.pyx in pandas._libs.tslibs.period.freq_to_dtype_code()

ValueError: Invalid frequency: {0}

So I have now a good workaroud to convert the Timestamp to a Period without including a hardcoded freq value or other if statements:

date = pd.DatetimeIndex([cutoff], freq=cutoff.freq)
cutoff = date.to_period()[0]
cutoff

This results in Period('1949-01', 'M'). 🎉 So now we ca do the delta calculation as usual with the PeriodIndex in order to get the relative fh. What do you think? I can try to implement it.