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@@ -10,6 +10,8 @@ from __future__ import division
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from __future__ import print_function
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from __future__ import print_function
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from __future__ import unicode_literals
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from __future__ import unicode_literals
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+import itertools
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+
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import numpy as np
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import numpy as np
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import pandas as pd
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import pandas as pd
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@@ -19,9 +21,10 @@ from unittest import TestCase
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from fbprophet import Prophet
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from fbprophet import Prophet
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from fbprophet import diagnostics
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from fbprophet import diagnostics
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-DATA = pd.read_csv(
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+DATA_all = pd.read_csv(
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os.path.join(os.path.dirname(__file__), 'data.csv'), parse_dates=['ds']
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os.path.join(os.path.dirname(__file__), 'data.csv'), parse_dates=['ds']
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-).head(100)
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+)
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+DATA = DATA_all.head(100)
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# fb-block 1 end
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# fb-block 1 end
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# fb-block 2
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# fb-block 2
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@@ -165,3 +168,68 @@ class TestDiagnostics(TestCase):
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set(df_horizon.columns),
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set(df_horizon.columns),
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{'coverage', 'mse', 'horizon'},
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{'coverage', 'mse', 'horizon'},
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)
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)
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+
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+ def test_copy(self):
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+ df = DATA_all.copy()
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+ df['cap'] = 200.
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+ df['binary_feature'] = [0] * 255 + [1] * 255
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+ # These values are created except for its default values
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+ holiday = pd.DataFrame(
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+ {'ds': pd.to_datetime(['2016-12-25']), 'holiday': ['x']})
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+ products = itertools.product(
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+ ['linear', 'logistic'], # growth
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+ [None, pd.to_datetime(['2016-12-25'])], # changepoints
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+ [3], # n_changepoints
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+ [True, False], # yearly_seasonality
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+ [True, False], # weekly_seasonality
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+ [True, False], # daily_seasonality
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+ [None, holiday], # holidays
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+ [1.1], # seasonality_prior_scale
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+ [1.1], # holidays_prior_scale
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+ [0.1], # changepoint_prior_scale
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+ [100], # mcmc_samples
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+ [0.9], # interval_width
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+ [200] # uncertainty_samples
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+ )
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+ # Values should be copied correctly
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+ for product in products:
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+ m1 = Prophet(*product)
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+ m1.history = m1.setup_dataframe(
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+ df.copy(), initialize_scales=True)
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+ m1.set_auto_seasonalities()
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+ m2 = diagnostics.prophet_copy(m1)
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+ self.assertEqual(m1.growth, m2.growth)
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+ self.assertEqual(m1.n_changepoints, m2.n_changepoints)
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+ self.assertEqual(m1.changepoints, m2.changepoints)
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+ self.assertEqual(False, m2.yearly_seasonality)
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+ self.assertEqual(False, m2.weekly_seasonality)
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+ self.assertEqual(False, m2.daily_seasonality)
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+ self.assertEqual(
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+ m1.yearly_seasonality, 'yearly' in m2.seasonalities)
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+ self.assertEqual(
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+ m1.weekly_seasonality, 'weekly' in m2.seasonalities)
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+ self.assertEqual(
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+ m1.daily_seasonality, 'daily' in m2.seasonalities)
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+ if m1.holidays is None:
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+ self.assertEqual(m1.holidays, m2.holidays)
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+ else:
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+ self.assertTrue((m1.holidays == m2.holidays).values.all())
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+ self.assertEqual(m1.seasonality_prior_scale, m2.seasonality_prior_scale)
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+ self.assertEqual(m1.changepoint_prior_scale, m2.changepoint_prior_scale)
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+ self.assertEqual(m1.holidays_prior_scale, m2.holidays_prior_scale)
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+ self.assertEqual(m1.mcmc_samples, m2.mcmc_samples)
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+ self.assertEqual(m1.interval_width, m2.interval_width)
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+ self.assertEqual(m1.uncertainty_samples, m2.uncertainty_samples)
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+
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+ # Check for cutoff and custom seasonality and extra regressors
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+ changepoints = pd.date_range('2012-06-15', '2012-09-15')
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+ cutoff = pd.Timestamp('2012-07-25')
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+ m1 = Prophet(changepoints=changepoints)
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+ m1.add_seasonality('custom', 10, 5)
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+ m1.add_regressor('binary_feature')
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+ m1.fit(df)
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+ m2 = diagnostics.prophet_copy(m1, cutoff=cutoff)
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+ changepoints = changepoints[changepoints <= cutoff]
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+ self.assertTrue((changepoints == m2.changepoints).all())
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+ self.assertTrue('custom' in m2.seasonalities)
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+ self.assertTrue('binary_feature' in m2.extra_regressors)
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