def test_get_duration(n_timepoints, index_type):
"""Test getting of duration."""
if index_type != "timedelta":
index = _make_index(n_timepoints, index_type)
duration = _get_duration(index)
# check output type is duration type
assert isinstance(
duration,
(pd.Timedelta, pd.tseries.offsets.BaseOffset, int, np.integer))
# check integer output
duration = _get_duration(index, coerce_to_int=True)
assert isinstance(duration, (int, np.integer))
assert duration == n_timepoints - 1
else:
match = "index_class: timedelta is not supported"
with pytest.raises(ValueError, match=match):
_make_index(n_timepoints, index_type)
def test_get_duration(n_timepoints, index_type):
index = _make_index(n_timepoints, index_type)
duration = _get_duration(index)
# check output type is duration type
assert isinstance(
duration,
(pd.Timedelta, pd.tseries.offsets.BaseOffset, int, np.integer))
# check integer output
duration = _get_duration(index, coerce_to_int=True)
assert isinstance(duration, (int, np.integer))
assert duration == n_timepoints - 1
def _make_hierarchical(
hierarchy_levels: Tuple = (2, 4),
max_timepoints: int = 12,
min_timepoints: int = 12,
same_cutoff: bool = True,
n_columns: int = 1,
all_positive: bool = True,
index_type: str = None,
random_state: Union[int, np.random.RandomState] = None,
add_nan: bool = False,
) -> pd.DataFrame:
"""Generate hierarchical multiindex mtype for testing.
Parameters
----------
hierarchy_levels : Tuple, optional
the number of groups at each hierarchy level, by default (2, 4)
max_timepoints : int, optional
maximum time points a series can have, by default 12
min_timepoints : int, optional
minimum time points a seires can have, by default 12
same_cutoff : bool, optional
If it's True all series will end at the same date, by default True
n_columns : int, optional
number of columns in the output dataframe, by default 1
all_positive : bool, optional
If True the time series will be , by default True
index_type : str, optional
type of index, by default None
Supported types are "period", "datetime", "range" or "int".
If it's not provided, "datetime" is selected.
random_state : int, np.random.RandomState or None
Controls the randomness of the estimator, by default None
add_nan : bool, optional
If it's true the series will contain NaNs, by default False
Returns
-------
pd.DataFrame
hierarchical mtype dataframe
"""
levels = [[f"h{i}_{j}" for j in range(hierarchy_levels[i])]
for i in range(len(hierarchy_levels))]
level_names = [f"h{i}" for i in range(len(hierarchy_levels))]
rng = check_random_state(random_state)
if min_timepoints == max_timepoints:
time_index = _make_index(max_timepoints, index_type)
index = pd.MultiIndex.from_product(levels + [time_index],
names=level_names + ["time"])
else:
df_list = []
for levels_tuple in product(*levels):
n_timepoints = rng.randint(low=min_timepoints, high=max_timepoints)
if same_cutoff:
time_index = _make_index(max_timepoints,
index_type)[-n_timepoints:]
else:
time_index = _make_index(n_timepoints, index_type)
d = dict(zip(level_names, levels_tuple))
d["time"] = time_index
df_list.append(pd.DataFrame(d))
index = pd.MultiIndex.from_frame(pd.concat(df_list),
names=level_names + ["time"])
total_time_points = len(index)
data = rng.normal(size=(total_time_points, n_columns))
if add_nan:
# add some nan values
data[int(len(data) / 2)] = np.nan
data[0] = np.nan
data[-1] = np.nan
if all_positive:
data -= np.min(data, axis=0) - 1
df = pd.DataFrame(data=data,
index=index,
columns=[f"c{i}" for i in range(n_columns)])
return df