def test_string_period(self):
testing.N, testing.K = 20, 1
ts = testing.makeTimeDataFrame(freq="s")
periodic_feature = PeriodicSeasonal(period="1 days")
periodic_feature.transform(ts)
assert type(periodic_feature.period) == pd.Timedelta
def test_too_high_sampling_frequency(self):
start_date = "2018-01-01"
periodic_feature = PeriodicSeasonal(
period="2 days",
start_date=start_date,
index_period=pd.date_range(start=start_date, end="2020-01-01", freq="W"),
)
with pytest.raises(ValueError):
periodic_feature.transform()
def test_correct_sinusoide(self):
testing.N, testing.K = 30, 1
ts = testing.makeTimeDataFrame(freq="MS")
start_date = "2018-01-01"
periodic_feature = PeriodicSeasonal(
period="365 days",
start_date=start_date,
index_period=pd.DatetimeIndex(start=start_date,
end="2020-01-01",
freq="W"),
)
output_sin = periodic_feature.transform(ts)
expected_index = pd.DatetimeIndex(
[
"2000-01-01",
"2000-02-01",
"2000-03-01",
"2000-04-01",
"2000-05-01",
"2000-06-01",
"2000-07-01",
"2000-08-01",
"2000-09-01",
"2000-10-01",
"2000-11-01",
"2000-12-01",
"2001-01-01",
"2001-02-01",
"2001-03-01",
"2001-04-01",
"2001-05-01",
"2001-06-01",
"2001-07-01",
"2001-08-01",
"2001-09-01",
"2001-10-01",
"2001-11-01",
"2001-12-01",
"2002-01-01",
"2002-02-01",
"2002-03-01",
"2002-04-01",
"2002-05-01",
"2002-06-01",
],
dtype="datetime64[ns]",
freq="MS",
)
expected_df = pd.DataFrame.from_dict({
f"0__{periodic_feature.__class__.__name__}": [
0.0,
0.25433547,
0.42938198,
0.49999537,
0.43585316,
0.25062091,
0.0043035,
-0.25062091,
-0.43585316,
-0.49999537,
-0.42938198,
-0.24688778,
0.00860668,
0.2617078,
0.42938198,
0.49999537,
0.43585316,
0.25062091,
0.0043035,
-0.25062091,
-0.43585316,
-0.49999537,
-0.42938198,
-0.24688778,
0.00860668,
0.2617078,
0.42938198,
0.49999537,
0.43585316,
0.25062091,
]
})
expected_df.index = expected_index
pd.testing.assert_frame_equal(output_sin, expected_df)
def test_correct_start_date(self):
testing.N, testing.K = 20, 1
ts = testing.makeTimeDataFrame(freq="s")
start_date = "2018-01-01"
periodic_feature = PeriodicSeasonal(period="1 days",
start_date=start_date)
periodic_feature.transform(ts)
assert periodic_feature.start_date == ts.index.values[0]
periodic_feature = PeriodicSeasonal(period="3 days",
index_period=10,
start_date=start_date)
periodic_feature.transform()
assert periodic_feature.start_date == pd.to_datetime(start_date)
start_date = pd.to_datetime("2018-01-01")
periodic_feature = PeriodicSeasonal(period="3 days",
index_period=10,
start_date=start_date)
periodic_feature.transform()
assert periodic_feature.start_date == start_date
def test_missing_start_date_or_period(self):
periodic_feature = PeriodicSeasonal()
with pytest.raises(ValueError):
periodic_feature.transform()
periodic_feature = PeriodicSeasonal(index_period=1)
with pytest.raises(ValueError):
periodic_feature.transform()
periodic_feature = PeriodicSeasonal(start_date="2010-01-01")
with pytest.raises(ValueError):
periodic_feature.transform()