Python Prophet.setup_dataframe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 setup_dataframe [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 setup_dataframe [as 别名]
    def test_override_n_changepoints(self):
        m = Prophet()
        history = DATA.head(20).copy()

        history = m.setup_dataframe(history, initialize_scales=True)
        m.history = history

        m.set_changepoints()
        self.assertEqual(m.n_changepoints, 15)
        cp = m.changepoints_t
        self.assertEqual(cp.shape[0], 15)

# 需要导入模块: from fbprophet import Prophet [as 别名]
# 或者: from fbprophet.Prophet import setup_dataframe [as 别名]
    def test_get_zero_changepoints(self):
        m = Prophet(n_changepoints=0)
        N = DATA.shape[0]
        history = DATA.head(N // 2).copy()

        history = m.setup_dataframe(history, initialize_scales=True)
        m.history = history

        m.set_changepoints()
        cp = m.changepoints_t
        self.assertEqual(cp.shape[0], 1)
        self.assertEqual(cp[0], 0)

# 需要导入模块: from fbprophet import Prophet [as 别名]
# 或者: from fbprophet.Prophet import setup_dataframe [as 别名]
    def test_setup_dataframe(self):
        m = Prophet()
        N = DATA.shape[0]
        history = DATA.head(N // 2).copy()

        history = m.setup_dataframe(history, initialize_scales=True)

        self.assertTrue('t' in history)
        self.assertEqual(history['t'].min(), 0.0)
        self.assertEqual(history['t'].max(), 1.0)

        self.assertTrue('y_scaled' in history)
        self.assertEqual(history['y_scaled'].max(), 1.0)

# 需要导入模块: from fbprophet import Prophet [as 别名]
# 或者: from fbprophet.Prophet import setup_dataframe [as 别名]
    def test_growth_init(self):
        model = Prophet(growth='logistic')
        history = DATA.iloc[:468].copy()
        history['cap'] = history['y'].max()

        history = model.setup_dataframe(history, initialize_scales=True)

        k, m = model.linear_growth_init(history)
        self.assertAlmostEqual(k, 0.3055671)
        self.assertAlmostEqual(m, 0.5307511)

        k, m = model.logistic_growth_init(history)

        self.assertAlmostEqual(k, 1.507925, places=4)
        self.assertAlmostEqual(m, -0.08167497, places=4)

# 需要导入模块: from fbprophet import Prophet [as 别名]
# 或者: from fbprophet.Prophet import setup_dataframe [as 别名]
    def test_get_changepoints(self):
        m = Prophet()
        N = DATA.shape[0]
        history = DATA.head(N // 2).copy()

        history = m.setup_dataframe(history, initialize_scales=True)
        m.history = history

        m.set_changepoints()

        cp = m.changepoints_t
        self.assertEqual(cp.shape[0], m.n_changepoints)
        self.assertEqual(len(cp.shape), 1)
        self.assertTrue(cp.min() > 0)
        cp_indx = int(np.ceil(0.8 * history.shape[0]))
        self.assertTrue(cp.max() <= history['t'].values[cp_indx])

# 需要导入模块: from fbprophet import Prophet [as 别名]
# 或者: from fbprophet.Prophet import setup_dataframe [as 别名]
    def test_get_changepoints(self):
        m = Prophet()
        N = DATA.shape[0]
        history = DATA.head(N // 2).copy()

        history = m.setup_dataframe(history, initialize_scales=True)
        m.history = history

        m.set_changepoints()

        cp = m.changepoints_t
        self.assertEqual(cp.shape[0], m.n_changepoints)
        self.assertEqual(len(cp.shape), 1)
        self.assertTrue(cp.min() > 0)
        self.assertTrue(cp.max() < N)

        mat = m.get_changepoint_matrix()
        self.assertEqual(mat.shape[0], N // 2)
        self.assertEqual(mat.shape[1], m.n_changepoints)

# 需要导入模块: from fbprophet import Prophet [as 别名]
# 或者: from fbprophet.Prophet import setup_dataframe [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.........

# 需要导入模块: from fbprophet import Prophet [as 别名]
# 或者: from fbprophet.Prophet import setup_dataframe [as 别名]
    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)