Exemple #1
0
    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)
Exemple #2
0
    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)