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)
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)
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)
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])
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', }, )
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) k, m = model.flat_growth_init(history) self.assertEqual(k, 0) self.assertAlmostEqual(m, 0.49335657, places=4)
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') m.fit(df) self.assertEqual(m.extra_regressors['constant_feature']['std'], 1)
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.country_holidays = 'US' 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) if m1.changepoints is None: self.assertEqual(m1.changepoints, m2.changepoints) else: self.assertTrue(m1.changepoints.equals(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.country_holidays, m2.country_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)