Python Prophet.make_all_seasonality_features方法代码示例

# 需要导入模块: from fbprophet import Prophet [as 别名]
# 或者: from fbprophet.Prophet import make_all_seasonality_features [as 别名]
 def test_seasonality_modes(self):
     # Model with holidays, seasonalities, and extra regressors
     holidays = pd.DataFrame({
         'ds': pd.to_datetime(['2016-12-25']),
         'holiday': ['xmas'],
         'lower_window': [-1],
         'upper_window': [0],
     })
     m = Prophet(seasonality_mode='multiplicative', holidays=holidays)
     m.add_seasonality('monthly', period=30, mode='additive', fourier_order=3)
     m.add_regressor('binary_feature', mode='additive')
     m.add_regressor('numeric_feature')
     # Construct seasonal features
     df = DATA.copy()
     df['binary_feature'] = [0] * 255 + [1] * 255
     df['numeric_feature'] = range(510)
     df = m.setup_dataframe(df, initialize_scales=True)
     m.history = df.copy()
     m.set_auto_seasonalities()
     seasonal_features, prior_scales, component_cols, modes = (
         m.make_all_seasonality_features(df))
     self.assertEqual(sum(component_cols['additive_terms']), 7)
     self.assertEqual(sum(component_cols['multiplicative_terms']), 29)
     self.assertEqual(
         set(modes['additive']),
         {'monthly', 'binary_feature', 'additive_terms',
          'extra_regressors_additive'},
     )
     self.assertEqual(
         set(modes['multiplicative']),
         {'weekly', 'yearly', 'xmas', 'numeric_feature',
          'multiplicative_terms', 'extra_regressors_multiplicative',
          'holidays',
         },
     )

# 需要导入模块: from fbprophet import Prophet [as 别名]
# 或者: from fbprophet.Prophet import make_all_seasonality_features [as 别名]
 def test_custom_seasonality(self):
     holidays = pd.DataFrame({
         'ds': pd.to_datetime(['2017-01-02']),
         'holiday': ['special_day'],
         'prior_scale': [4.],
     })
     m = Prophet(holidays=holidays)
     m.add_seasonality(name='monthly', period=30, fourier_order=5,
                       prior_scale=2.)
     self.assertEqual(
         m.seasonalities['monthly'],
         {
             'period': 30,
             'fourier_order': 5,
             'prior_scale': 2.,
             'mode': 'additive',
         },
     )
     with self.assertRaises(ValueError):
         m.add_seasonality(name='special_day', period=30, fourier_order=5)
     with self.assertRaises(ValueError):
         m.add_seasonality(name='trend', period=30, fourier_order=5)
     m.add_seasonality(name='weekly', period=30, fourier_order=5)
     # Test priors
     m = Prophet(
         holidays=holidays, yearly_seasonality=False,
         seasonality_mode='multiplicative',
     )
     m.add_seasonality(name='monthly', period=30, fourier_order=5,
                       prior_scale=2., mode='additive')
     m.fit(DATA.copy())
     self.assertEqual(m.seasonalities['monthly']['mode'], 'additive')
     self.assertEqual(m.seasonalities['weekly']['mode'], 'multiplicative')
     seasonal_features, prior_scales, component_cols, modes = (
         m.make_all_seasonality_features(m.history)
     )
     self.assertEqual(sum(component_cols['monthly']), 10)
     self.assertEqual(sum(component_cols['special_day']), 1)
     self.assertEqual(sum(component_cols['weekly']), 6)
     self.assertEqual(sum(component_cols['additive_terms']), 10)
     self.assertEqual(sum(component_cols['multiplicative_terms']), 7)
     if seasonal_features.columns[0] == 'monthly_delim_1':
         true = [2.] * 10 + [10.] * 6 + [4.]
         self.assertEqual(sum(component_cols['monthly'][:10]), 10)
         self.assertEqual(sum(component_cols['weekly'][10:16]), 6)
     else:
         true = [10.] * 6 + [2.] * 10 + [4.]
         self.assertEqual(sum(component_cols['weekly'][:6]), 6)
         self.assertEqual(sum(component_cols['monthly'][6:16]), 10)
     self.assertEqual(prior_scales, true)

# 需要导入模块: from fbprophet import Prophet [as 别名]
# 或者: from fbprophet.Prophet import make_all_seasonality_features [as 别名]
 def test_added_regressors(self):
     m = Prophet()
     m.add_regressor('binary_feature', prior_scale=0.2)
     m.add_regressor('numeric_feature', prior_scale=0.5)
     m.add_regressor(
         'numeric_feature2', prior_scale=0.5, mode='multiplicative'
     )
     m.add_regressor('binary_feature2', standardize=True)
     df = DATA.copy()
     df['binary_feature'] = [0] * 255 + [1] * 255
     df['numeric_feature'] = range(510)
     df['numeric_feature2'] = range(510)
     with self.assertRaises(ValueError):
         # Require all regressors in df
         m.fit(df)
     df['binary_feature2'] = [1] * 100 + [0] * 410
     m.fit(df)
     # Check that standardizations are correctly set
     self.assertEqual(
         m.extra_regressors['binary_feature'],
         {
             'prior_scale': 0.2,
             'mu': 0,
             'std': 1,
             'standardize': 'auto',
             'mode': 'additive',
         },
     )
     self.assertEqual(
         m.extra_regressors['numeric_feature']['prior_scale'], 0.5)
     self.assertEqual(
         m.extra_regressors['numeric_feature']['mu'], 254.5)
     self.assertAlmostEqual(
         m.extra_regressors['numeric_feature']['std'], 147.368585, places=5)
     self.assertEqual(
         m.extra_regressors['numeric_feature2']['mode'], 'multiplicative')
     self.assertEqual(
         m.extra_regressors['binary_feature2']['prior_scale'], 10.)
     self.assertAlmostEqual(
         m.extra_regressors['binary_feature2']['mu'], 0.1960784, places=5)
     self.assertAlmostEqual(
         m.extra_regressors['binary_feature2']['std'], 0.3974183, places=5)
     # Check that standardization is done correctly
     df2 = m.setup_dataframe(df.copy())
     self.assertEqual(df2['binary_feature'][0], 0)
     self.assertAlmostEqual(df2['numeric_feature'][0], -1.726962, places=4)
     self.assertAlmostEqual(df2['binary_feature2'][0], 2.022859, places=4)
     # Check that feature matrix and prior scales are correctly constructed
     seasonal_features, prior_scales, component_cols, modes = (
         m.make_all_seasonality_features(df2)
     )
     self.assertEqual(seasonal_features.shape[1], 30)
     names = ['binary_feature', 'numeric_feature', 'binary_feature2']
     true_priors = [0.2, 0.5, 10.]
     for i, name in enumerate(names):
         self.assertIn(name, seasonal_features)
         self.assertEqual(sum(component_cols[name]), 1)
         self.assertEqual(
             sum(np.array(prior_scales) * component_cols[name]),
             true_priors[i],
         )
     # Check that forecast components are reasonable
     future = pd.DataFrame({
         'ds': ['2014-06-01'],
         'binary_feature': [0],
         'numeric_feature': [10],
         'numeric_feature2': [10],
     })
     with self.assertRaises(ValueError):
         m.predict(future)
     future['binary_feature2'] = 0
     fcst = m.predict(future)
     self.assertEqual(fcst.shape[1], 37)
     self.assertEqual(fcst['binary_feature'][0], 0)
     self.assertAlmostEqual(
         fcst['extra_regressors_additive'][0],
         fcst['numeric_feature'][0] + fcst['binary_feature2'][0],
     )
     self.assertAlmostEqual(
         fcst['extra_regressors_multiplicative'][0],
         fcst['numeric_feature2'][0],
     )
     self.assertAlmostEqual(
         fcst['additive_terms'][0],
         fcst['yearly'][0] + fcst['weekly'][0]
             + fcst['extra_regressors_additive'][0],
     )
     self.assertAlmostEqual(
         fcst['multiplicative_terms'][0],
         fcst['extra_regressors_multiplicative'][0],
     )
     self.assertAlmostEqual(
         fcst['yhat'][0],
         fcst['trend'][0] * (1 + fcst['multiplicative_terms'][0])
             + fcst['additive_terms'][0],
     )
     # Check works if constant extra regressor at 0
     df['constant_feature'] = 0
     m = Prophet()
     m.add_regressor('constant_feature')
#.........这里部分代码省略.........