def _split(self, y):
# cutoffs
cutoffs = check_cutoffs(self.cutoffs)
if np.max(cutoffs) >= y.shape[0]:
raise ValueError("`cutoffs` are incompatible with given `y`.")
fh = _check_fh(self.fh)
if np.max(cutoffs) + np.max(fh) > y.shape[0]:
raise ValueError("`fh` is incompatible with given `cutoffs` and `y`.")
window_length = check_window_length(self.window_length)
for cutoff in cutoffs:
training_window = np.arange(cutoff - window_length, cutoff) + 1
test_window = cutoff + fh
yield training_window, test_window
def _split_windows(self, y):
# cutoffs
cutoffs = check_cutoffs(self.cutoffs)
if not np.max(cutoffs) < len(y):
raise ValueError("`cutoffs` are out-of-bounds for given `y`.")
fh = self._check_fh()
if np.max(cutoffs) + np.max(fh) > len(y):
raise ValueError("`fh` is out-of-bounds for given `cutoffs` and `y`.")
window_length = check_window_length(self.window_length)
for cutoff in cutoffs:
training_window = np.arange(cutoff - window_length, cutoff) + 1
test_window = cutoff + fh
yield training_window, test_window
def get_cutoffs(self, y: Optional[ACCEPTED_Y_TYPES] = None) -> np.ndarray:
"""Return the cutoff points.
This method trivially returns the cutoffs given during instance initialization.
The only change is that the set of cutoffs is sorted from smallest to largest.
Parameters
----------
y : pd.Series or pd.Index, optional (default=None)
Time series to split
Returns
-------
cutoffs : np.array
The array of cutoff points.
"""
return check_cutoffs(self.cutoffs)
def _split(self, y: ACCEPTED_Y_TYPES) -> SPLIT_GENERATOR_TYPE:
cutoffs = check_cutoffs(self.cutoffs)
if np.max(cutoffs) >= y.shape[0]:
raise ValueError("`cutoffs` are incompatible with given `y`.")
fh = _check_fh(self.fh)
n_timepoints = y.shape[0]
if np.max(cutoffs) + np.max(fh) > y.shape[0]:
raise ValueError("`fh` is incompatible with given `cutoffs` and `y`.")
window_length = check_window_length(self.window_length, n_timepoints)
for cutoff in cutoffs:
if is_timedelta_or_date_offset(x=window_length):
train_start = y.get_loc(max(y[0], y[cutoff] - window_length))
else:
train_start = cutoff - window_length
training_window = np.arange(train_start, cutoff) + 1
test_window = cutoff + fh
yield training_window, test_window
def _split(self, y: ACCEPTED_Y_TYPES) -> SPLIT_GENERATOR_TYPE:
n_timepoints = y.shape[0]
cutoffs = check_cutoffs(cutoffs=self.cutoffs)
fh = _check_fh(fh=self.fh)
window_length = check_window_length(window_length=self.window_length,
n_timepoints=n_timepoints)
_check_cutoffs_fh_window_length(cutoffs=cutoffs,
fh=fh,
window_length=window_length)
_check_cutoffs_and_y(cutoffs=cutoffs, y=y)
_check_cutoffs_fh_y(cutoffs=cutoffs, fh=fh, y=y)
max_fh = fh.max()
max_cutoff = np.max(cutoffs)
for cutoff in cutoffs:
if is_int(x=window_length) and is_int(x=cutoff):
train_start = cutoff - window_length
elif is_timedelta_or_date_offset(x=window_length) and is_datetime(
x=cutoff):
train_start = y.get_loc(max(y[0], cutoff - window_length))
else:
raise TypeError(f"Unsupported combination of types: "
f"`window_length`: {type(window_length)}, "
f"`cutoff`: {type(cutoff)}")
if is_int(x=cutoff):
training_window = np.arange(train_start, cutoff) + 1
else:
training_window = np.arange(train_start, y.get_loc(cutoff)) + 1
test_window = cutoff + fh.to_numpy()
if is_datetime(x=max_cutoff) and is_timedelta(x=max_fh):
test_window = test_window[test_window >= y.min()]
test_window = np.array(
[y.get_loc(timestamp) for timestamp in test_window])
yield training_window, test_window
def get_cutoffs(self, y=None):
"""Return the cutoff points"""
return check_cutoffs(self.cutoffs)
