mirror of
https://github.com/saymrwulf/prophet.git
synced 2026-05-15 20:50:48 +00:00
3c69ce3312
* modified cross_validation to allow custom cutoffs * moved set period, initials and identify larg. seas * modified the diagnostics and added the test * reverted cv default value tests and added a new custom cutoff test * reorganized to raise the seasonality period warning message even if cutoffs are manually specified * moved the initials vs. seasonality check * changed assertCountEqual to assertItemsEqual in cv * modified to test lengths instread of cutoff values Co-authored-by: Fusi Marco <Marco.Fusi@valuelab.it>
328 lines
14 KiB
Python
328 lines
14 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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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 os
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from unittest import TestCase
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from unittest.mock import patch
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import numpy as np
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import pandas as pd
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import datetime
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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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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_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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initial = pd.Timedelta('115 days')
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# Run for both cases of multiprocess on or off
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for multiprocess in [False, True]:
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df_cv = diagnostics.cross_validation(
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m, horizon='4 days', period='10 days', initial='115 days',
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multiprocess=multiprocess)
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self.assertEqual(len(np.unique(df_cv['cutoff'])), 3)
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self.assertEqual(max(df_cv['ds'] - df_cv['cutoff']), horizon)
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self.assertTrue(min(df_cv['cutoff']) >= min(self.__df['ds']) + initial)
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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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self.assertTrue((df_cv['cutoff'] < df_cv['ds']).all())
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# Each y in df_cv and self.__df with same ds should be equal
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df_merged = pd.merge(df_cv, 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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df_cv = diagnostics.cross_validation(
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m, horizon='4 days', period='10 days', initial='135 days')
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self.assertEqual(len(np.unique(df_cv['cutoff'])), 1)
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with self.assertRaises(ValueError):
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diagnostics.cross_validation(
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m, horizon='10 days', period='10 days', initial='140 days')
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def test_check_single_cutoff_forecast_func_calls(self):
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m = Prophet()
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m.fit(self.__df)
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mock_predict = pd.DataFrame({'ds':pd.date_range(start='2012-09-17', periods=3),
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'yhat':np.arange(16, 19),
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'yhat_lower':np.arange(15, 18),
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'yhat_upper': np.arange(17, 20),
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'y': np.arange(16.5, 19.5),
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'cutoff': [datetime.date(2012, 9, 15)]*3})
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# cross validation with 3 and 7 forecasts
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for args, forecasts in ((['4 days', '10 days', '115 days'], 3),
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(['4 days', '4 days', '115 days'], 7)):
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with patch('fbprophet.diagnostics.single_cutoff_forecast') as mock_func:
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mock_func.return_value = mock_predict
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df_cv = diagnostics.cross_validation(m, *args)
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# check single forecast function called expected number of times
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self.assertEqual(diagnostics.single_cutoff_forecast.call_count,
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forecasts)
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def test_cross_validation_logistic(self):
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df = self.__df.copy()
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df['cap'] = 40
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m = Prophet(growth='logistic').fit(df)
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df_cv = diagnostics.cross_validation(
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m, horizon='1 days', period='1 days', initial='140 days')
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self.assertEqual(len(np.unique(df_cv['cutoff'])), 2)
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self.assertTrue((df_cv['cutoff'] < df_cv['ds']).all())
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df_merged = pd.merge(df_cv, 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_cross_validation_extra_regressors(self):
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df = self.__df.copy()
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df['extra'] = range(df.shape[0])
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df['is_conditional_week'] = np.arange(df.shape[0]) // 7 % 2
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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_seasonality(name='conditional_weekly', period=7, fourier_order=3,
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prior_scale=2., condition_name='is_conditional_week')
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m.add_regressor('extra')
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m.fit(df)
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df_cv = diagnostics.cross_validation(
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m, horizon='4 days', period='4 days', initial='135 days')
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self.assertEqual(len(np.unique(df_cv['cutoff'])), 2)
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period = pd.Timedelta('4 days')
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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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self.assertTrue((df_cv['cutoff'] < df_cv['ds']).all())
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df_merged = pd.merge(df_cv, 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_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_cross_validation_custom_cutoffs(self):
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m = Prophet()
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m.fit(self.__df)
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# When specify a list of cutoffs
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# the cutoff dates in df_cv are those specified
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df_cv1 = diagnostics.cross_validation(
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m,
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horizon='32 days',
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period='10 days',
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cutoffs=[pd.Timestamp('2012-07-31'), pd.Timestamp('2012-08-31')])
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self.assertEqual(len(df_cv1['cutoff'].unique()), 2)
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def test_cross_validation_uncertainty_disabled(self):
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df = self.__df.copy()
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for uncertainty in [0, False]:
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m = Prophet(uncertainty_samples=uncertainty)
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m.fit(df, algorithm='Newton')
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df_cv = diagnostics.cross_validation(
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m, horizon='4 days', period='4 days', initial='115 days')
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expected_cols = ['ds', 'yhat', 'y', 'cutoff']
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self.assertTrue(all(col in expected_cols for col in df_cv.columns.tolist()))
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df_p = diagnostics.performance_metrics(df_cv)
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self.assertTrue('coverage' not in df_p.columns)
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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=-1)
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self.assertEqual(
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set(df_none.columns),
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{'horizon', 'coverage', 'mae', 'mape', 'mdape', 'mse', 'rmse'},
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)
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self.assertEqual(df_none.shape[0], 16)
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# Aggregation level 0
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df_0 = diagnostics.performance_metrics(df_cv, rolling_window=0)
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self.assertEqual(len(df_0), 4)
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self.assertEqual(len(df_0['horizon'].unique()), 4)
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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), 4)
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self.assertEqual(len(df_horizon['horizon'].unique()), 4)
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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.assertAlmostEqual(df_all[metric].values[0], df_none[metric].mean())
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self.assertAlmostEqual(df_all['mdape'].values[0], df_none['mdape'].median())
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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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# Skip MAPE
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df_cv.loc[0, 'y'] = 0.
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df_horizon = diagnostics.performance_metrics(
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df_cv, metrics=['coverage', 'mape'],
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)
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self.assertEqual(
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set(df_horizon.columns),
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{'coverage', 'horizon'},
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)
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df_horizon = diagnostics.performance_metrics(
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df_cv, metrics=['mape'],
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)
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self.assertIsNone(df_horizon)
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# List of metrics containing non-valid metrics
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with self.assertRaises(ValueError):
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diagnostics.performance_metrics(
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df_cv, metrics=['mse', 'error_metric'],
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)
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def test_rolling_mean(self):
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x = np.arange(10)
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h = np.arange(10)
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df = diagnostics.rolling_mean_by_h(x=x, h=h, w=1, name='x')
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self.assertTrue(np.array_equal(x, df['x'].values))
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self.assertTrue(np.array_equal(h, df['horizon'].values))
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df = diagnostics.rolling_mean_by_h(x, h, w=4, name='x')
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self.assertTrue(np.allclose(x[3:] - 1.5, df['x'].values))
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self.assertTrue(np.array_equal(np.arange(3, 10), df['horizon'].values))
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h = np.array([1., 2., 3., 4., 4., 4., 4., 4., 7., 7.])
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x_true = np.array([1.0, 5.0 , 22. / 3])
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h_true = np.array([3., 4., 7.])
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df = diagnostics.rolling_mean_by_h(x, h, w=3, name='x')
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self.assertTrue(np.allclose(x_true, df['x'].values))
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self.assertTrue(np.array_equal(h_true, df['horizon'].values))
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df = diagnostics.rolling_mean_by_h(x, h, w=10, name='x')
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self.assertTrue(np.allclose(np.array([7.]), df['horizon'].values))
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self.assertTrue(np.allclose(np.array([4.5]), df['x'].values))
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def test_rolling_median(self):
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x = np.arange(10)
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h = np.arange(10)
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df = diagnostics.rolling_median_by_h(x=x, h=h, w=1, name='x')
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self.assertTrue(np.array_equal(x, df['x'].values))
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self.assertTrue(np.array_equal(h, df['horizon'].values))
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df = diagnostics.rolling_median_by_h(x, h, w=4, name='x')
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x_true = x[3:] - 1.5
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self.assertTrue(np.allclose(x_true, df['x'].values))
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self.assertTrue(np.array_equal(np.arange(3, 10), df['horizon'].values))
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h = np.array([1., 2., 3., 4., 4., 4., 4., 4., 7., 7.])
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x_true = np.array([1.0, 5.0, 8.0])
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h_true = np.array([3., 4., 7.])
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df = diagnostics.rolling_median_by_h(x, h, w=3, name='x')
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self.assertTrue(np.allclose(x_true, df['x'].values))
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self.assertTrue(np.array_equal(h_true, df['horizon'].values))
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df = diagnostics.rolling_median_by_h(x, h, w=10, name='x')
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self.assertTrue(np.allclose(np.array([7.]), df['horizon'].values))
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self.assertTrue(np.allclose(np.array([4.5]), df['x'].values))
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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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[0.9], # changepoint_range
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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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['additive', 'multiplicative'], # seasonality_mode
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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.country_holidays = 'US'
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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.changepoint_range, m2.changepoint_range)
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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.country_holidays, m2.country_holidays)
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self.assertEqual(m1.seasonality_mode, m2.seasonality_mode)
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self.assertEqual(m1.seasonality_prior_scale,
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m2.seasonality_prior_scale)
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self.assertEqual(m1.changepoint_prior_scale,
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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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