diff --git a/python/fbprophet/models.py b/python/fbprophet/models.py index 6667465..1a1fdbe 100644 --- a/python/fbprophet/models.py +++ b/python/fbprophet/models.py @@ -2,7 +2,7 @@ # All rights reserved. # # This source code is licensed under the BSD-style license found in the -# LICENSE file in the root directory of this source tree. An additional grant +# LICENSE file in the root directory of this source tree. An additional grant # of patent rights can be found in the PATENTS file in the same directory. from __future__ import absolute_import diff --git a/python/fbprophet/tests/test_diagnostics.py b/python/fbprophet/tests/test_diagnostics.py index f4e3c85..43907e6 100644 --- a/python/fbprophet/tests/test_diagnostics.py +++ b/python/fbprophet/tests/test_diagnostics.py @@ -10,21 +10,28 @@ from __future__ import division from __future__ import print_function from __future__ import unicode_literals -import os import numpy as np import pandas as pd +# fb-block 1 start +import os from unittest import TestCase from fbprophet import Prophet from fbprophet import diagnostics +DATA = pd.read_csv( + os.path.join(os.path.dirname(__file__), 'data.csv'), parse_dates=['ds'] +).head(100) +# fb-block 1 end +# fb-block 2 + class TestDiagnostics(TestCase): def __init__(self, *args, **kwargs): super(TestDiagnostics, self).__init__(*args, **kwargs) # Use first 100 record in data.csv - self.__df = pd.read_csv(os.path.join(os.path.dirname(__file__), 'data.csv'), parse_dates=['ds']).head(100) + self.__df = DATA def test_simulated_historical_forecasts(self): m = Prophet() @@ -34,47 +41,55 @@ class TestDiagnostics(TestCase): for h in [1, 3]: period = '{} days'.format(p) horizon = '{} days'.format(h) - df_shf = diagnostics.simulated_historical_forecasts(m, horizon=horizon, k=k, period=period) + df_shf = diagnostics.simulated_historical_forecasts( + m, horizon=horizon, k=k, period=period) # All cutoff dates should be less than ds dates self.assertTrue((df_shf['cutoff'] < df_shf['ds']).all()) # The unique size of output cutoff should be equal to 'k' self.assertEqual(len(np.unique(df_shf['cutoff'])), k) - self.assertEqual(max(df_shf['ds'] - df_shf['cutoff']), pd.Timedelta(horizon)) + self.assertEqual( + max(df_shf['ds'] - df_shf['cutoff']), + pd.Timedelta(horizon), + ) dc = df_shf['cutoff'].diff() dc = dc[dc > pd.Timedelta(0)].min() self.assertTrue(dc >= pd.Timedelta(period)) # Each y in df_shf and self.__df with same ds should be equal df_merged = pd.merge(df_shf, self.__df, 'left', on='ds') - self.assertAlmostEqual(np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0) + self.assertAlmostEqual( + np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0) def test_simulated_historical_forecasts_logistic(self): m = Prophet(growth='logistic') df = self.__df.copy() df['cap'] = 40 m.fit(df) - df_shf = diagnostics.simulated_historical_forecasts(m, horizon='3 days', k=2, period='3 days') + df_shf = diagnostics.simulated_historical_forecasts( + m, horizon='3 days', k=2, period='3 days') # All cutoff dates should be less than ds dates self.assertTrue((df_shf['cutoff'] < df_shf['ds']).all()) # The unique size of output cutoff should be equal to 'k' self.assertEqual(len(np.unique(df_shf['cutoff'])), 2) # Each y in df_shf and self.__df with same ds should be equal df_merged = pd.merge(df_shf, df, 'left', on='ds') - self.assertAlmostEqual(np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0) + self.assertAlmostEqual( + np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0) def test_simulated_historical_forecasts_default_value_check(self): m = Prophet() m.fit(self.__df) # Default value of period should be equal to 0.5 * horizon - df_shf1 = diagnostics.simulated_historical_forecasts(m, horizon='10 days', k=1) - df_shf2 = diagnostics.simulated_historical_forecasts(m, horizon='10 days', k=1, period='5 days') - self.assertAlmostEqual(((df_shf1 - df_shf2)**2)[['y', 'yhat']].sum().sum(), 0.0) + df_shf1 = diagnostics.simulated_historical_forecasts( + m, horizon='10 days', k=1) + df_shf2 = diagnostics.simulated_historical_forecasts( + m, horizon='10 days', k=1, period='5 days') + self.assertAlmostEqual( + ((df_shf1 - df_shf2)**2)[['y', 'yhat']].sum().sum(), 0.0) def test_cross_validation(self): m = Prophet() m.fit(self.__df) # Calculate the number of cutoff points(k) - te = self.__df['ds'].max() - ts = self.__df['ds'].min() horizon = pd.Timedelta('4 days') period = pd.Timedelta('10 days') k = 5 @@ -91,6 +106,9 @@ class TestDiagnostics(TestCase): m = Prophet() m.fit(self.__df) # Default value of initial should be equal to 3 * horizon - df_cv1 = diagnostics.cross_validation(m, horizon='32 days', period='10 days') - df_cv2 = diagnostics.cross_validation(m, horizon='32 days', period='10 days', initial='96 days') - self.assertAlmostEqual(((df_cv1 - df_cv2)**2)[['y', 'yhat']].sum().sum(), 0.0) + df_cv1 = diagnostics.cross_validation( + m, horizon='32 days', period='10 days') + df_cv2 = diagnostics.cross_validation( + m, horizon='32 days', period='10 days', initial='96 days') + self.assertAlmostEqual( + ((df_cv1 - df_cv2)**2)[['y', 'yhat']].sum().sum(), 0.0) diff --git a/python/fbprophet/tests/test_prophet.py b/python/fbprophet/tests/test_prophet.py index 5b94e3b..ac5b3e9 100644 --- a/python/fbprophet/tests/test_prophet.py +++ b/python/fbprophet/tests/test_prophet.py @@ -10,12 +10,12 @@ from __future__ import division from __future__ import print_function from __future__ import unicode_literals +import itertools import numpy as np import pandas as pd # fb-block 1 start import os -import itertools from unittest import TestCase from fbprophet import Prophet @@ -551,6 +551,8 @@ class TestProphet(TestCase): def test_copy(self): # 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 @@ -558,7 +560,7 @@ class TestProphet(TestCase): [True, False], # yearly_seasonality [True, False], # weekly_seasonality [True, False], # daily_seasonality - [None, pd.DataFrame({'ds': pd.to_datetime(['2016-12-25']), 'holiday': ['x']})], # holidays + [None, holiday], # holidays [1.1], # seasonality_prior_scale [1.1], # holidays_prior_scale [0.1], # changepoint_prior_scale