def test_zscore_predict():
time = pd.date_range(start='2018-01-01', end='2020-01-01')
data_X = np.linspace(0, 1, len(time))
X = xr.DataArray(data_X,
name='foo',
dims=['index'],
coords={
'index': time
}).to_dataframe()
shift = xr.DataArray(np.zeros(364),
name='foo',
dims=['day'],
coords={
'day': np.arange(1, 365)
}).to_series()
scale = xr.DataArray(np.ones(364),
name='foo',
dims=['day'],
coords={
'day': np.arange(1, 365)
}).to_series()
zscore = ZScoreRegressor()
zscore.shift_ = shift
zscore.scale_ = scale
i = int(zscore.window_width / 2)
expected = xr.DataArray(data_X,
name='foo',
dims=['index'],
coords={
'index': time
}).to_dataframe()
expected[0:i] = 'NaN'
expected[-i:] = 'NaN'
out = zscore.predict(X)
np.testing.assert_allclose(out.astype(float), expected.astype(float))
def test_zscore_predict():
time = pd.date_range(start="2018-01-01", end="2020-01-01")
data_X = np.linspace(0, 1, len(time))
X = xr.DataArray(data_X,
name="foo",
dims=["index"],
coords={
"index": time
}).to_dataframe()
shift = xr.DataArray(np.zeros(364),
name="foo",
dims=["day"],
coords={
"day": np.arange(1, 365)
}).to_series()
scale = xr.DataArray(np.ones(364),
name="foo",
dims=["day"],
coords={
"day": np.arange(1, 365)
}).to_series()
zscore = ZScoreRegressor()
zscore.shift_ = shift
zscore.scale_ = scale
i = int(zscore.window_width / 2)
expected = xr.DataArray(data_X,
name="foo",
dims=["index"],
coords={
"index": time
}).to_dataframe()
expected[0:i] = "NaN"
expected[-i:] = "NaN"
np.testing.assert_allclose(
zscore.predict(X).astype(float), expected.astype(float))