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)