def to_absolute_int(self, start, cutoff=None):
"""Return absolute values as zero-based integer index starting from `start`.
Parameters
----------
start : pd.Period, pd.Timestamp, int
Start value returned as zero.
cutoff : pd.Period, pd.Timestamp, int, optional (default=None)
Cutoff value required to convert a relative forecasting
horizon to an absolute one (and vice versa).
Returns
-------
fh : ForecastingHorizon
Absolute representation of forecasting horizon as zero-based
integer index.
"""
# We here check the start value, the cutoff value is checked when we use it
# to convert the horizon to the absolute representation below
absolute = self.to_absolute(cutoff).to_pandas()
_check_start(start, absolute)
# Note: We should here also coerce to periods for more reliable arithmetic
# operations as in `to_relative` but currently doesn't work with
# `update_predict` and incomplete time indices where the `freq` information
# is lost, see comment on issue #534
integers = absolute - start
if isinstance(absolute, (pd.PeriodIndex, pd.DatetimeIndex)):
integers = _coerce_duration_to_int(integers,
freq=_get_freq(cutoff))
return self._new(integers, is_relative=False)
def _align_seasonal(self, y):
"""Align seasonal components with y's time index"""
shift = (-_get_duration(
y.index[0],
self._y_index[0],
coerce_to_int=True,
unit=_get_freq(self._y_index),
) % self.sp)
return np.resize(np.roll(self.seasonal_, shift=shift), y.shape[0])
def _to_relative(fh: ForecastingHorizon, cutoff=None) -> ForecastingHorizon:
"""Return forecasting horizon values relative to a cutoff.
Parameters
----------
fh : ForecastingHorizon
cutoff : pd.Period, pd.Timestamp, int, optional (default=None)
Cutoff value required to convert a relative forecasting
horizon to an absolute one (and vice versa).
Returns
-------
fh : ForecastingHorizon
Relative representation of forecasting horizon.
"""
if fh.is_relative:
return fh._new()
else:
absolute = fh.to_pandas()
_check_cutoff(cutoff, absolute)
# We cannot use the freq from the ForecastingHorizon itself (or its
# wrapped pd.DatetimeIndex) because it may be none for non-regular
# indices, so instead we use the freq of cutoff.
freq = _get_freq(cutoff)
if isinstance(absolute, pd.DatetimeIndex):
# coerce to pd.Period for reliable arithmetics and computations of
# time deltas
absolute = _coerce_to_period(absolute, freq)
cutoff = _coerce_to_period(cutoff, freq)
# TODO: Replace when we upgrade our lower pandas bound
# to a version where this is fixed
# Compute relative values
# The following line circumvents the bug in pandas
# periods = pd.period_range(start="2021-01-01", periods=3, freq="2H")
# periods - periods[0]
# Out: Index([<0 * Hours>, <4 * Hours>, <8 * Hours>], dtype = 'object')
# [v - periods[0] for v in periods]
# Out: Index([<0 * Hours>, <2 * Hours>, <4 * Hours>], dtype='object')
# TODO: v0.12.0: Check if this comment below can be removed,
# so check if pandas has released the fix to PyPI:
# This bug was reported: https://github.com/pandas-dev/pandas/issues/45999
# and fixed: https://github.com/pandas-dev/pandas/pull/46006
# Most likely it will be released with pandas 1.5
# Once the bug is fixed the line should simply be:
# relative = absolute - cutoff
relative = pd.Index([date - cutoff for date in absolute])
# Coerce durations (time deltas) into integer values for given frequency
if isinstance(absolute, (pd.PeriodIndex, pd.DatetimeIndex)):
relative = _coerce_duration_to_int(relative, freq=freq)
return fh._new(relative, is_relative=True)
def test_get_freq():
"""Test whether get_freq runs without error."""
x = pd.Series(
index=pd.date_range(start="2017-01-01", periods=700, freq="W"),
data=np.random.randn(700),
)
x1 = x.index
x2 = x.resample("W").sum().index
x3 = pd.Series(index=[
datetime.datetime(2017, 1, 1) + datetime.timedelta(days=int(i))
for i in np.arange(1, 100, 7)
]).index
x4 = [
datetime.datetime(2017, 1, 1) + datetime.timedelta(days=int(i))
for i in np.arange(1, 100, 7)
]
assert _get_freq(x1) == "W"
assert _get_freq(x2) == "W"
assert _get_freq(x3) is None
assert _get_freq(x4) is None
def to_relative(self, cutoff=None):
"""Return forecasting horizon values relative to a cutoff.
Parameters
----------
cutoff : pd.Period, pd.Timestamp, int, optional (default=None)
Cutoff value required to convert a relative forecasting
horizon to an absolute one (and vice versa).
Returns
-------
fh : ForecastingHorizon
Relative representation of forecasting horizon.
"""
if self.is_relative:
return self._new()
else:
absolute = self.to_pandas()
_check_cutoff(cutoff, absolute)
if isinstance(absolute, pd.DatetimeIndex):
# We cannot use the freq from the the ForecastingHorizon itself (or its
# wrapped pd.DatetimeIndex) because it may be none for non-regular
# indices, so instead we use the freq of cutoff.
freq = _get_freq(cutoff)
# coerce to pd.Period for reliable arithmetics and computations of
# time deltas
absolute = _coerce_to_period(absolute, freq)
cutoff = _coerce_to_period(cutoff, freq)
# Compute relative values
relative = absolute - cutoff
# Coerce durations (time deltas) into integer values for given frequency
if isinstance(absolute, (pd.PeriodIndex, pd.DatetimeIndex)):
relative = _coerce_duration_to_int(relative,
freq=_get_freq(cutoff))
return self._new(relative, is_relative=True)
def test_coerce_duration_to_int(duration):
ret = _coerce_duration_to_int(duration, freq=_get_freq(duration))
# check output type is always integer
assert type(ret) in (pd.Int64Index, np.integer, int)
# check result
if isinstance(duration, pd.Index):
np.testing.assert_array_equal(ret, range(3))
if isinstance(duration, pd.tseries.offsets.BaseOffset):
assert ret == 3
def test_coerce_duration_to_int(duration):
"""Test coercion of duration to int."""
ret = _coerce_duration_to_int(duration, freq=_get_freq(duration))
# check output type is always integer
assert (type(ret) in (np.integer, int)) or is_integer_index(ret)
# check result
if isinstance(duration, pd.Index):
np.testing.assert_array_equal(ret, range(3))
if isinstance(duration, pd.tseries.offsets.BaseOffset):
assert ret == 3
def _coerce_to_period(x, freq=None):
"""Helper function to coerce pd.Timestamp to pd.Period or pd.DatetimeIndex to
pd.PeriodIndex for more reliable arithmetic operations with time indices"""
if freq is None:
freq = _get_freq(x)
try:
return x.to_period(freq)
except (ValueError, AttributeError) as e:
msg = str(e)
if "Invalid frequency" in msg or "_period_dtype_code" in msg:
raise ValueError(
"Invalid frequency. Please select a frequency that can "
"be converted to a regular `pd.PeriodIndex`. For other "
"frequencies, basic arithmetic operation to compute "
"durations currently do not work reliably.")
else:
raise
def test_coerce_duration_to_int_with_non_allowed_durations(duration):
"""Test coercion of duration to int."""
with pytest.raises(ValueError, match="frequency is missing"):
_coerce_duration_to_int(duration, freq=_get_freq(duration))
