def test_simulated_historical_forecasts_default_value_check(self):
m = Prophet()
m.fit(self.__df)
# Default value of period should be equal to 0.5 * horizon
df_shf1 = diagnostics.simulated_historical_forecasts(m,
horizon='10 days',
k=1)
df_shf2 = diagnostics.simulated_historical_forecasts(m,
horizon='10 days',
k=1,
period='5 days')
self.assertAlmostEqual(
((df_shf1 - df_shf2)**2)[['y', 'yhat']].sum().sum(), 0.0)
def test_simulated_historical_forecasts(self):
m = Prophet()
m.fit(self.__df)
k = 2
for p in [1, 10]:
for h in [1, 3]:
period = '{} days'.format(p)
horizon = '{} days'.format(h)
df_shf = diagnostics.simulated_historical_forecasts(
m, horizon=horizon, k=k, period=period)
# All cutoff dates should be less than ds dates
self.assertTrue((df_shf['cutoff'] < df_shf['ds']).all())
# The unique size of output cutoff should be equal to 'k'
self.assertEqual(len(np.unique(df_shf['cutoff'])), k)
self.assertEqual(
max(df_shf['ds'] - df_shf['cutoff']),
pd.Timedelta(horizon),
)
dc = df_shf['cutoff'].diff()
dc = dc[dc > pd.Timedelta(0)].min()
self.assertTrue(dc >= pd.Timedelta(period))
# Each y in df_shf and self.__df with same ds should be equal
df_merged = pd.merge(df_shf, self.__df, 'left', on='ds')
self.assertAlmostEqual(
np.sum((df_merged['y_x'] - df_merged['y_y'])**2), 0.0)
def test_simulated_historical_forecasts_logistic(self):
m = Prophet(growth='logistic')
df = self.__df.copy()
df['cap'] = 40
m.fit(df)
df_shf = diagnostics.simulated_historical_forecasts(m,
horizon='3 days',
k=2,
period='3 days')
# All cutoff dates should be less than ds dates
self.assertTrue((df_shf['cutoff'] < df_shf['ds']).all())
# The unique size of output cutoff should be equal to 'k'
self.assertEqual(len(np.unique(df_shf['cutoff'])), 2)
# Each y in df_shf and self.__df with same ds should be equal
df_merged = pd.merge(df_shf, df, 'left', on='ds')
self.assertAlmostEqual(
np.sum((df_merged['y_x'] - df_merged['y_y'])**2), 0.0)
def test_simulated_historical_forecasts_extra_regressors(self):
m = Prophet()
m.add_seasonality(name='monthly', period=30.5, fourier_order=5)
m.add_regressor('extra')
df = self.__df.copy()
df['cap'] = 40
df['extra'] = range(df.shape[0])
m.fit(df)
df_shf = diagnostics.simulated_historical_forecasts(m,
horizon='3 days',
k=2,
period='3 days')
# All cutoff dates should be less than ds dates
self.assertTrue((df_shf['cutoff'] < df_shf['ds']).all())
# The unique size of output cutoff should be equal to 'k'
self.assertEqual(len(np.unique(df_shf['cutoff'])), 2)
# Each y in df_shf and self.__df with same ds should be equal
df_merged = pd.merge(df_shf, df, 'left', on='ds')
self.assertAlmostEqual(
np.sum((df_merged['y_x'] - df_merged['y_y'])**2), 0.0)
