mirror of
https://github.com/saymrwulf/prophet.git
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235 lines
9.9 KiB
Python
235 lines
9.9 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 itertools
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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_all = pd.read_csv(
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os.path.join(os.path.dirname(__file__), 'data.csv'), parse_dates=['ds']
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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 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_extra_regressors(self):
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m = Prophet()
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m.add_seasonality(name='monthly', period=30.5, fourier_order=5)
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m.add_regressor('extra')
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df = self.__df.copy()
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df['cap'] = 40
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df['extra'] = range(df.shape[0])
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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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def test_performance_metrics(self):
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m = Prophet()
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m.fit(self.__df)
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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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# Aggregation level none
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df_none = diagnostics.performance_metrics(df_cv, rolling_window=0)
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self.assertEqual(
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set(df_none.columns),
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{'horizon', 'coverage', 'mae', 'mape', 'mse', 'rmse'},
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)
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self.assertEqual(df_none.shape[0], 14)
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# Aggregation level 0.2
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df_horizon = diagnostics.performance_metrics(df_cv, rolling_window=0.2)
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self.assertEqual(len(df_horizon['horizon'].unique()), 4)
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self.assertEqual(df_horizon.shape[0], 13)
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# Aggregation level all
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df_all = diagnostics.performance_metrics(df_cv, rolling_window=1)
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self.assertEqual(df_all.shape[0], 1)
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for metric in ['mse', 'mape', 'mae', 'coverage']:
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self.assertEqual(df_all[metric].values[0], df_none[metric].mean())
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# Custom list of metrics
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df_horizon = diagnostics.performance_metrics(
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df_cv, metrics=['coverage', 'mse'],
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)
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self.assertEqual(
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set(df_horizon.columns),
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{'coverage', 'mse', 'horizon'},
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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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# 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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