def test_copy(self):
df = DATA_all.copy()
df['cap'] = 200.
df['binary_feature'] = [0] * 255 + [1] * 255
# These values are created except for its default values
holiday = pd.DataFrame({
'ds': pd.to_datetime(['2016-12-25']),
'holiday': ['x']
})
append_holidays = 'US'
products = itertools.product(
['linear', 'logistic'], # growth
[None, pd.to_datetime(['2016-12-25'])], # changepoints
[3], # n_changepoints
[0.9], # changepoint_range
[True, False], # yearly_seasonality
[True, False], # weekly_seasonality
[True, False], # daily_seasonality
[None, holiday], # holidays
[None, append_holidays], # append_holidays
['additive', 'multiplicative'], # seasonality_mode
[1.1], # seasonality_prior_scale
[1.1], # holidays_prior_scale
[0.1], # changepoint_prior_scale
[100], # mcmc_samples
[0.9], # interval_width
[200] # uncertainty_samples
)
# Values should be copied correctly
for product in products:
m1 = Prophet(*product)
m1.history = m1.setup_dataframe(df.copy(), initialize_scales=True)
m1.set_auto_seasonalities()
m2 = diagnostics.prophet_copy(m1)
self.assertEqual(m1.growth, m2.growth)
self.assertEqual(m1.n_changepoints, m2.n_changepoints)
self.assertEqual(m1.changepoint_range, m2.changepoint_range)
self.assertEqual(m1.changepoints, m2.changepoints)
self.assertEqual(False, m2.yearly_seasonality)
self.assertEqual(False, m2.weekly_seasonality)
self.assertEqual(False, m2.daily_seasonality)
self.assertEqual(m1.yearly_seasonality, 'yearly'
in m2.seasonalities)
self.assertEqual(m1.weekly_seasonality, 'weekly'
in m2.seasonalities)
self.assertEqual(m1.daily_seasonality, 'daily' in m2.seasonalities)
if m1.holidays is None:
self.assertEqual(m1.holidays, m2.holidays)
else:
self.assertTrue((m1.holidays == m2.holidays).values.all())
self.assertEqual(m1.append_holidays, m2.append_holidays)
self.assertEqual(m1.seasonality_mode, m2.seasonality_mode)
self.assertEqual(m1.seasonality_prior_scale,
m2.seasonality_prior_scale)
self.assertEqual(m1.changepoint_prior_scale,
m2.changepoint_prior_scale)
self.assertEqual(m1.holidays_prior_scale, m2.holidays_prior_scale)
self.assertEqual(m1.mcmc_samples, m2.mcmc_samples)
self.assertEqual(m1.interval_width, m2.interval_width)
self.assertEqual(m1.uncertainty_samples, m2.uncertainty_samples)
# Check for cutoff and custom seasonality and extra regressors
changepoints = pd.date_range('2012-06-15', '2012-09-15')
cutoff = pd.Timestamp('2012-07-25')
m1 = Prophet(changepoints=changepoints)
m1.add_seasonality('custom', 10, 5)
m1.add_regressor('binary_feature')
m1.fit(df)
m2 = diagnostics.prophet_copy(m1, cutoff=cutoff)
changepoints = changepoints[changepoints <= cutoff]
self.assertTrue((changepoints == m2.changepoints).all())
self.assertTrue('custom' in m2.seasonalities)
self.assertTrue('binary_feature' in m2.extra_regressors)
def test_copy(self):
df = DATA_all.copy()
df['cap'] = 200.
df['binary_feature'] = [0] * 255 + [1] * 255
# These values are created except for its default values
holiday = pd.DataFrame(
{'ds': pd.to_datetime(['2016-12-25']), 'holiday': ['x']})
products = itertools.product(
['linear', 'logistic'], # growth
[None, pd.to_datetime(['2016-12-25'])], # changepoints
[3], # n_changepoints
[0.9], # changepoint_range
[True, False], # yearly_seasonality
[True, False], # weekly_seasonality
[True, False], # daily_seasonality
[None, holiday], # holidays
['additive', 'multiplicative'], # seasonality_mode
[1.1], # seasonality_prior_scale
[1.1], # holidays_prior_scale
[0.1], # changepoint_prior_scale
[100], # mcmc_samples
[0.9], # interval_width
[200] # uncertainty_samples
)
# Values should be copied correctly
for product in products:
m1 = Prophet(*product)
m1.history = m1.setup_dataframe(
df.copy(), initialize_scales=True)
m1.set_auto_seasonalities()
m2 = diagnostics.prophet_copy(m1)
self.assertEqual(m1.growth, m2.growth)
self.assertEqual(m1.n_changepoints, m2.n_changepoints)
self.assertEqual(m1.changepoint_range, m2.changepoint_range)
self.assertEqual(m1.changepoints, m2.changepoints)
self.assertEqual(False, m2.yearly_seasonality)
self.assertEqual(False, m2.weekly_seasonality)
self.assertEqual(False, m2.daily_seasonality)
self.assertEqual(
m1.yearly_seasonality, 'yearly' in m2.seasonalities)
self.assertEqual(
m1.weekly_seasonality, 'weekly' in m2.seasonalities)
self.assertEqual(
m1.daily_seasonality, 'daily' in m2.seasonalities)
if m1.holidays is None:
self.assertEqual(m1.holidays, m2.holidays)
else:
self.assertTrue((m1.holidays == m2.holidays).values.all())
self.assertEqual(m1.seasonality_mode, m2.seasonality_mode)
self.assertEqual(m1.seasonality_prior_scale, m2.seasonality_prior_scale)
self.assertEqual(m1.changepoint_prior_scale, m2.changepoint_prior_scale)
self.assertEqual(m1.holidays_prior_scale, m2.holidays_prior_scale)
self.assertEqual(m1.mcmc_samples, m2.mcmc_samples)
self.assertEqual(m1.interval_width, m2.interval_width)
self.assertEqual(m1.uncertainty_samples, m2.uncertainty_samples)
# Check for cutoff and custom seasonality and extra regressors
changepoints = pd.date_range('2012-06-15', '2012-09-15')
cutoff = pd.Timestamp('2012-07-25')
m1 = Prophet(changepoints=changepoints)
m1.add_seasonality('custom', 10, 5)
m1.add_regressor('binary_feature')
m1.fit(df)
m2 = diagnostics.prophet_copy(m1, cutoff=cutoff)
changepoints = changepoints[changepoints <= cutoff]
self.assertTrue((changepoints == m2.changepoints).all())
self.assertTrue('custom' in m2.seasonalities)
self.assertTrue('binary_feature' in m2.extra_regressors)