def test_compute_occupancy_feature(even_occupancy):
index = pd.date_range("2017-01-01", periods=1000, freq="H", tz="UTC")
time_features = compute_time_features(index, hour_of_week=True)
hour_of_week = time_features.hour_of_week
occupancy = compute_occupancy_feature(hour_of_week, even_occupancy)
assert occupancy.name == "occupancy"
assert occupancy.shape == (1000, )
assert occupancy.sum().sum() == 500
def test_compute_occupancy_feature_hour_of_week_has_nan(even_occupancy):
index = pd.date_range("2017-01-01", periods=72, freq="H", tz="UTC")
time_features = compute_time_features(index, hour_of_week=True)
hour_of_week = time_features.hour_of_week
hour_of_week.iloc[-1] = np.nan
occupancy = compute_occupancy_feature(hour_of_week, even_occupancy)
assert occupancy.name == "occupancy"
assert occupancy.shape == (72, )
assert occupancy.sum() == 36
def test_compute_occupancy_feature_with_nans(even_occupancy):
"""If there are less than 168 periods, the NaN at the end causes problems"""
index = pd.date_range("2017-01-01", periods=100, freq="H", tz="UTC")
time_features = compute_time_features(index, hour_of_week=True)
hour_of_week = time_features.hour_of_week
hour_of_week[-1] = np.nan
# comment out line below to see the error from not dropping na when
# calculationg _add_weights when there are less than 168 periods.
# TODO (ssuffian): Refactor so get_missing_hours_warnings propogates.
# right now, it will error if the dropna below isn't used.
hour_of_week.dropna(inplace=True)
occupancy = compute_occupancy_feature(hour_of_week, even_occupancy)
