def test_convenience(load_df_and_hier_uv):
hd, hier = load_df_and_hier_uv
hd = hd.head(200)
ht = HTSRegressor(model='holt_winters', revision_method='OLS')
ht = ht.fit(df=hd, nodes=hier)
preds = ht.predict(steps_ahead=10)
rev = revise_forecasts(
'OLS',
nodes=ht.nodes,
forecasts=ht.hts_result.forecasts,
errors=ht.hts_result.errors,
residuals=ht.hts_result.residuals,
)
assert isinstance(rev, pandas.DataFrame)
assert numpy.allclose(preds.values, rev.values)
rev_s = revise_forecasts(
'OLS',
summing_matrix=ht.sum_mat,
forecasts=ht.hts_result.forecasts,
errors=ht.hts_result.errors,
residuals=ht.hts_result.residuals,
)
assert isinstance(rev_s, pandas.DataFrame)
assert numpy.allclose(rev.values, rev_s.values)
rev = revise_forecasts('AHP',
nodes=ht.nodes,
forecasts=ht.hts_result.forecasts)
assert isinstance(rev, pandas.DataFrame)
def test_instantiate_revision(load_df_and_hier_visnights):
hierarchical_visnights_data, visnights_hier = load_df_and_hier_visnights
for method in ['OLS', 'FP', 'WLSS', 'WLSV', 'PHA', 'AHP', 'NONE']:
ht = HTSRegressor(model='holt_winters', revision_method=method)
ht.fit(df=hierarchical_visnights_data, nodes=visnights_hier)
rm = RevisionMethod(method,
sum_mat=ht.sum_mat,
transformer=ht.transform)
_ = ht.predict(steps_ahead=3)
revised = rm.revise(forecasts=ht.hts_result.forecasts,
mse=ht.hts_result.errors,
nodes=ht.nodes)
assert isinstance(revised, numpy.ndarray)
assert revised.shape == (11, len(ht.hts_result.forecasts))
def test_predict_regressor_visnights(load_df_and_hier_visnights):
hierarchical_vis_data, vis_hier = load_df_and_hier_visnights
hvd = hierarchical_vis_data
for model in ["holt_winters", "auto_arima"]:
ht = HTSRegressor(model=model, revision_method="OLS")
ht.fit(df=hvd, nodes=vis_hier)
preds = ht.predict(steps_ahead=4)
assert isinstance(preds, pandas.DataFrame)
# base + steps ahead
assert len(preds) == len(hvd) + 4
assert len(ht.hts_result.forecasts["total"]) == len(hvd) + 4
assert "total" in ht.hts_result.errors
assert "total" in ht.hts_result.forecasts
assert "total" in ht.hts_result.residuals
assert "NSW" in ht.hts_result.errors
assert "NSW" in ht.hts_result.forecasts
assert "NSW" in ht.hts_result.residuals
def test_predict_regressor_uv(load_df_and_hier_uv):
hierarchical_sine_data, sine_hier = load_df_and_hier_uv
hsd = hierarchical_sine_data.head(200)
for model in ["holt_winters", "auto_arima", "sarimax"]:
ht = HTSRegressor(model=model, revision_method="OLS")
ht.fit(df=hsd, nodes=sine_hier)
preds = ht.predict(steps_ahead=10)
assert isinstance(preds, pandas.DataFrame)
# base + steps ahead
assert len(preds) == len(hsd) + 10
assert len(ht.hts_result.forecasts["total"]) == len(hsd) + 10
assert "total" in ht.hts_result.errors
assert "total" in ht.hts_result.forecasts
assert "total" in ht.hts_result.residuals
assert "a" in ht.hts_result.errors
assert "a" in ht.hts_result.forecasts
assert "a" in ht.hts_result.residuals
