mirror of
https://github.com/saymrwulf/prophet.git
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e78f583f90
* Update memory requirement description per #326 * Fix R warning with extra regressor; disallow constant extra regressors. * Fix unit test broken in new pandas * Fix diagnostics unit tests for new pandas * Fix copy with extra seasonalities / regressors Py * Fix copy with extra seasonalities / regressors R * Fix weekly_start and yearly_start in R plot_components * Fix plotting in pandas 0.21 by using pydatetime instead of numpy
118 lines
4.7 KiB
Python
118 lines
4.7 KiB
Python
# Copyright (c) 2017-present, Facebook, Inc.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree. An additional grant
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# of patent rights can be found in the PATENTS file in the same directory.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import numpy as np
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import pandas as pd
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# fb-block 1 start
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import os
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from unittest import TestCase
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from fbprophet import Prophet
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from fbprophet import diagnostics
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DATA = pd.read_csv(
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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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# fb-block 1 end
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# fb-block 2
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class TestDiagnostics(TestCase):
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def __init__(self, *args, **kwargs):
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super(TestDiagnostics, self).__init__(*args, **kwargs)
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# Use first 100 record in data.csv
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self.__df = DATA
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def test_simulated_historical_forecasts(self):
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m = Prophet()
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m.fit(self.__df)
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k = 2
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for p in [1, 10]:
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for h in [1, 3]:
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period = '{} days'.format(p)
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horizon = '{} days'.format(h)
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df_shf = diagnostics.simulated_historical_forecasts(
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m, horizon=horizon, k=k, period=period)
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# All cutoff dates should be less than ds dates
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self.assertTrue((df_shf['cutoff'] < df_shf['ds']).all())
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# The unique size of output cutoff should be equal to 'k'
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self.assertEqual(len(np.unique(df_shf['cutoff'])), k)
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self.assertEqual(
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max(df_shf['ds'] - df_shf['cutoff']),
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pd.Timedelta(horizon),
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)
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dc = df_shf['cutoff'].diff()
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dc = dc[dc > pd.Timedelta(0)].min()
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self.assertTrue(dc >= pd.Timedelta(period))
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# Each y in df_shf and self.__df with same ds should be equal
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df_merged = pd.merge(df_shf, self.__df, 'left', on='ds')
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self.assertAlmostEqual(
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np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0)
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def test_simulated_historical_forecasts_logistic(self):
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m = Prophet(growth='logistic')
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df = self.__df.copy()
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df['cap'] = 40
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m.fit(df)
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df_shf = diagnostics.simulated_historical_forecasts(
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m, horizon='3 days', k=2, period='3 days')
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# All cutoff dates should be less than ds dates
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self.assertTrue((df_shf['cutoff'] < df_shf['ds']).all())
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# The unique size of output cutoff should be equal to 'k'
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self.assertEqual(len(np.unique(df_shf['cutoff'])), 2)
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# Each y in df_shf and self.__df with same ds should be equal
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df_merged = pd.merge(df_shf, df, 'left', on='ds')
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self.assertAlmostEqual(
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np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0)
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def test_simulated_historical_forecasts_default_value_check(self):
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m = Prophet()
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m.fit(self.__df)
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# Default value of period should be equal to 0.5 * horizon
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df_shf1 = diagnostics.simulated_historical_forecasts(
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m, horizon='10 days', k=1)
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df_shf2 = diagnostics.simulated_historical_forecasts(
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m, horizon='10 days', k=1, period='5 days')
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self.assertAlmostEqual(
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((df_shf1['y'] - df_shf2['y']) ** 2).sum(), 0.0)
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self.assertAlmostEqual(
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((df_shf1['yhat'] - df_shf2['yhat']) ** 2).sum(), 0.0)
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def test_cross_validation(self):
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m = Prophet()
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m.fit(self.__df)
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# Calculate the number of cutoff points(k)
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horizon = pd.Timedelta('4 days')
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period = pd.Timedelta('10 days')
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k = 5
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df_cv = diagnostics.cross_validation(
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m, horizon='4 days', period='10 days', initial='90 days')
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# The unique size of output cutoff should be equal to 'k'
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self.assertEqual(len(np.unique(df_cv['cutoff'])), k)
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self.assertEqual(max(df_cv['ds'] - df_cv['cutoff']), horizon)
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dc = df_cv['cutoff'].diff()
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dc = dc[dc > pd.Timedelta(0)].min()
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self.assertTrue(dc >= period)
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def test_cross_validation_default_value_check(self):
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m = Prophet()
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m.fit(self.__df)
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# Default value of initial should be equal to 3 * horizon
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df_cv1 = diagnostics.cross_validation(
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m, horizon='32 days', period='10 days')
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df_cv2 = diagnostics.cross_validation(
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m, horizon='32 days', period='10 days', initial='96 days')
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self.assertAlmostEqual(
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((df_cv1['y'] - df_cv2['y']) ** 2).sum(), 0.0)
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self.assertAlmostEqual(
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((df_cv1['yhat'] - df_cv2['yhat']) ** 2).sum(), 0.0)
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