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729 lines
26 KiB
Python
729 lines
26 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 os
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from unittest import TestCase
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import numpy as np
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import pandas as pd
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from fbprophet import Prophet
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DATA = pd.read_csv(
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os.path.join(os.path.dirname(__file__), 'data.csv'),
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parse_dates=['ds'],
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)
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DATA2 = pd.read_csv(
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os.path.join(os.path.dirname(__file__), 'data2.csv'),
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parse_dates=['ds'],
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)
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class TestProphet(TestCase):
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def test_fit_predict(self):
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N = DATA.shape[0]
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train = DATA.head(N // 2)
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future = DATA.tail(N // 2)
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forecaster = Prophet()
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forecaster.fit(train)
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forecaster.predict(future)
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def test_fit_predict_no_seasons(self):
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N = DATA.shape[0]
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train = DATA.head(N // 2)
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future = DATA.tail(N // 2)
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forecaster = Prophet(weekly_seasonality=False, yearly_seasonality=False)
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forecaster.fit(train)
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forecaster.predict(future)
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def test_fit_predict_no_changepoints(self):
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N = DATA.shape[0]
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train = DATA.head(N // 2)
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future = DATA.tail(N // 2)
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forecaster = Prophet(n_changepoints=0)
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forecaster.fit(train)
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forecaster.predict(future)
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def test_fit_changepoint_not_in_history(self):
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train = DATA[(DATA['ds'] < '2013-01-01') | (DATA['ds'] > '2014-01-01')]
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future = pd.DataFrame({'ds': DATA['ds']})
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forecaster = Prophet(changepoints=['2013-06-06'])
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forecaster.fit(train)
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forecaster.predict(future)
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def test_fit_predict_duplicates(self):
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N = DATA.shape[0]
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train1 = DATA.head(N // 2).copy()
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train2 = DATA.head(N // 2).copy()
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train2['y'] += 10
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train = train1.append(train2)
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future = pd.DataFrame({'ds': DATA['ds'].tail(N // 2)})
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forecaster = Prophet()
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forecaster.fit(train)
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forecaster.predict(future)
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def test_fit_predict_constant_history(self):
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N = DATA.shape[0]
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train = DATA.head(N // 2).copy()
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train['y'] = 20
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future = pd.DataFrame({'ds': DATA['ds'].tail(N // 2)})
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m = Prophet()
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m.fit(train)
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fcst = m.predict(future)
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self.assertEqual(fcst['yhat'].values[-1], 20)
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train['y'] = 0
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future = pd.DataFrame({'ds': DATA['ds'].tail(N // 2)})
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m = Prophet()
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m.fit(train)
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fcst = m.predict(future)
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self.assertEqual(fcst['yhat'].values[-1], 0)
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def test_setup_dataframe(self):
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m = Prophet()
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N = DATA.shape[0]
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history = DATA.head(N // 2).copy()
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history = m.setup_dataframe(history, initialize_scales=True)
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self.assertTrue('t' in history)
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self.assertEqual(history['t'].min(), 0.0)
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self.assertEqual(history['t'].max(), 1.0)
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self.assertTrue('y_scaled' in history)
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self.assertEqual(history['y_scaled'].max(), 1.0)
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def test_logistic_floor(self):
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m = Prophet(growth='logistic')
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N = DATA.shape[0]
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history = DATA.head(N // 2).copy()
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history['floor'] = 10.
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history['cap'] = 80.
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future = DATA.tail(N // 2).copy()
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future['cap'] = 80.
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future['floor'] = 10.
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m.fit(history, algorithm='Newton')
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self.assertTrue(m.logistic_floor)
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self.assertTrue('floor' in m.history)
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self.assertAlmostEqual(m.history['y_scaled'][0], 1.)
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fcst1 = m.predict(future)
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m2 = Prophet(growth='logistic')
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history2 = history.copy()
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history2['y'] += 10.
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history2['floor'] += 10.
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history2['cap'] += 10.
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future['cap'] += 10.
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future['floor'] += 10.
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m2.fit(history2, algorithm='Newton')
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self.assertAlmostEqual(m2.history['y_scaled'][0], 1.)
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fcst2 = m2.predict(future)
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fcst2['yhat'] -= 10.
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# Check for approximate shift invariance
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self.assertTrue((np.abs(fcst1['yhat'] - fcst2['yhat']) < 1).all())
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def test_get_changepoints(self):
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m = Prophet()
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N = DATA.shape[0]
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history = DATA.head(N // 2).copy()
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history = m.setup_dataframe(history, initialize_scales=True)
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m.history = history
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m.set_changepoints()
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cp = m.changepoints_t
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self.assertEqual(cp.shape[0], m.n_changepoints)
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self.assertEqual(len(cp.shape), 1)
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self.assertTrue(cp.min() > 0)
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cp_indx = int(np.ceil(0.8 * history.shape[0]))
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self.assertTrue(cp.max() <= history['t'].values[cp_indx])
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def test_set_changepoint_range(self):
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m = Prophet(changepoint_range=0.4)
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N = DATA.shape[0]
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history = DATA.head(N // 2).copy()
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history = m.setup_dataframe(history, initialize_scales=True)
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m.history = history
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m.set_changepoints()
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cp = m.changepoints_t
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self.assertEqual(cp.shape[0], m.n_changepoints)
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self.assertEqual(len(cp.shape), 1)
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self.assertTrue(cp.min() > 0)
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cp_indx = int(np.ceil(0.4 * history.shape[0]))
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self.assertTrue(cp.max() <= history['t'].values[cp_indx])
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with self.assertRaises(ValueError):
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m = Prophet(changepoint_range=-0.1)
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with self.assertRaises(ValueError):
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m = Prophet(changepoint_range=2)
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def test_get_zero_changepoints(self):
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m = Prophet(n_changepoints=0)
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N = DATA.shape[0]
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history = DATA.head(N // 2).copy()
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history = m.setup_dataframe(history, initialize_scales=True)
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m.history = history
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m.set_changepoints()
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cp = m.changepoints_t
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self.assertEqual(cp.shape[0], 1)
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self.assertEqual(cp[0], 0)
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def test_override_n_changepoints(self):
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m = Prophet()
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history = DATA.head(20).copy()
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history = m.setup_dataframe(history, initialize_scales=True)
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m.history = history
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m.set_changepoints()
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self.assertEqual(m.n_changepoints, 15)
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cp = m.changepoints_t
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self.assertEqual(cp.shape[0], 15)
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def test_fourier_series_weekly(self):
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mat = Prophet.fourier_series(DATA['ds'], 7, 3)
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# These are from the R forecast package directly.
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true_values = np.array([
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0.7818315, 0.6234898, 0.9749279, -0.2225209, 0.4338837, -0.9009689,
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])
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self.assertAlmostEqual(np.sum((mat[0] - true_values)**2), 0.0)
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def test_fourier_series_yearly(self):
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mat = Prophet.fourier_series(DATA['ds'], 365.25, 3)
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# These are from the R forecast package directly.
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true_values = np.array([
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0.7006152, -0.7135393, -0.9998330, 0.01827656, 0.7262249, 0.6874572,
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])
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self.assertAlmostEqual(np.sum((mat[0] - true_values)**2), 0.0)
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def test_growth_init(self):
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model = Prophet(growth='logistic')
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history = DATA.iloc[:468].copy()
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history['cap'] = history['y'].max()
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history = model.setup_dataframe(history, initialize_scales=True)
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k, m = model.linear_growth_init(history)
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self.assertAlmostEqual(k, 0.3055671)
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self.assertAlmostEqual(m, 0.5307511)
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k, m = model.logistic_growth_init(history)
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self.assertAlmostEqual(k, 1.507925, places=4)
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self.assertAlmostEqual(m, -0.08167497, places=4)
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def test_piecewise_linear(self):
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model = Prophet()
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t = np.arange(11.)
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m = 0
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k = 1.0
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deltas = np.array([0.5])
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changepoint_ts = np.array([5])
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y = model.piecewise_linear(t, deltas, k, m, changepoint_ts)
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y_true = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0,
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6.5, 8.0, 9.5, 11.0, 12.5])
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self.assertEqual((y - y_true).sum(), 0.0)
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t = t[8:]
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y_true = y_true[8:]
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y = model.piecewise_linear(t, deltas, k, m, changepoint_ts)
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self.assertEqual((y - y_true).sum(), 0.0)
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def test_piecewise_logistic(self):
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model = Prophet()
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t = np.arange(11.)
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cap = np.ones(11) * 10
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m = 0
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k = 1.0
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deltas = np.array([0.5])
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changepoint_ts = np.array([5])
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y = model.piecewise_logistic(t, cap, deltas, k, m, changepoint_ts)
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y_true = np.array([5.000000, 7.310586, 8.807971, 9.525741, 9.820138,
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9.933071, 9.984988, 9.996646, 9.999252, 9.999833,
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9.999963])
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self.assertAlmostEqual((y - y_true).sum(), 0.0, places=5)
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t = t[8:]
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y_true = y_true[8:]
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cap = cap[8:]
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y = model.piecewise_logistic(t, cap, deltas, k, m, changepoint_ts)
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self.assertAlmostEqual((y - y_true).sum(), 0.0, places=5)
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def test_holidays(self):
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holidays = pd.DataFrame({
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'ds': pd.to_datetime(['2016-12-25']),
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'holiday': ['xmas'],
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'lower_window': [-1],
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'upper_window': [0],
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})
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model = Prophet(holidays=holidays)
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df = pd.DataFrame({
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'ds': pd.date_range('2016-12-20', '2016-12-31')
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})
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feats, priors, names = model.make_holiday_features(df['ds'], model.holidays)
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# 11 columns generated even though only 8 overlap
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self.assertEqual(feats.shape, (df.shape[0], 2))
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self.assertEqual((feats.sum(0) - np.array([1.0, 1.0])).sum(), 0)
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self.assertEqual(priors, [10., 10.]) # Default prior
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self.assertEqual(names, ['xmas'])
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holidays = pd.DataFrame({
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'ds': pd.to_datetime(['2016-12-25']),
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'holiday': ['xmas'],
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'lower_window': [-1],
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'upper_window': [10],
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})
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m = Prophet(holidays=holidays)
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feats, priors, names = m.make_holiday_features(df['ds'], m.holidays)
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# 12 columns generated even though only 8 overlap
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self.assertEqual(feats.shape, (df.shape[0], 12))
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self.assertEqual(priors, list(10. * np.ones(12)))
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self.assertEqual(names, ['xmas'])
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# Check prior specifications
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holidays = pd.DataFrame({
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'ds': pd.to_datetime(['2016-12-25', '2017-12-25']),
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'holiday': ['xmas', 'xmas'],
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'lower_window': [-1, -1],
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'upper_window': [0, 0],
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'prior_scale': [5., 5.],
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})
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m = Prophet(holidays=holidays)
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feats, priors, names = m.make_holiday_features(df['ds'], m.holidays)
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self.assertEqual(priors, [5., 5.])
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self.assertEqual(names, ['xmas'])
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# 2 different priors
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holidays2 = pd.DataFrame({
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'ds': pd.to_datetime(['2012-06-06', '2013-06-06']),
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'holiday': ['seans-bday'] * 2,
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'lower_window': [0] * 2,
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'upper_window': [1] * 2,
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'prior_scale': [8] * 2,
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})
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holidays2 = pd.concat((holidays, holidays2))
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m = Prophet(holidays=holidays2)
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feats, priors, names = m.make_holiday_features(df['ds'], m.holidays)
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pn = zip(priors, [s.split('_delim_')[0] for s in feats.columns])
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for t in pn:
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self.assertIn(t, [(8., 'seans-bday'), (5., 'xmas')])
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holidays2 = pd.DataFrame({
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'ds': pd.to_datetime(['2012-06-06', '2013-06-06']),
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'holiday': ['seans-bday'] * 2,
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'lower_window': [0] * 2,
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'upper_window': [1] * 2,
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})
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holidays2 = pd.concat((holidays, holidays2))
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feats, priors, names = Prophet(
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holidays=holidays2, holidays_prior_scale=4
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).make_holiday_features(df['ds'], holidays2)
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self.assertEqual(set(priors), {4., 5.})
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# Check incompatible priors
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holidays = pd.DataFrame({
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'ds': pd.to_datetime(['2016-12-25', '2016-12-27']),
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'holiday': ['xmasish', 'xmasish'],
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'lower_window': [-1, -1],
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'upper_window': [0, 0],
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'prior_scale': [5., 6.],
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})
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with self.assertRaises(ValueError):
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Prophet(holidays=holidays).make_holiday_features(df['ds'], holidays)
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def test_fit_with_holidays(self):
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holidays = pd.DataFrame({
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'ds': pd.to_datetime(['2012-06-06', '2013-06-06']),
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'holiday': ['seans-bday'] * 2,
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'lower_window': [0] * 2,
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'upper_window': [1] * 2,
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})
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model = Prophet(holidays=holidays, uncertainty_samples=0)
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model.fit(DATA).predict()
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def test_fit_predict_with_country_holidays(self):
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holidays = pd.DataFrame({
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'ds': pd.to_datetime(['2012-06-06', '2013-06-06']),
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'holiday': ['seans-bday'] * 2,
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'lower_window': [0] * 2,
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'upper_window': [1] * 2,
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})
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# Test with holidays and country_holidays
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model = Prophet(holidays=holidays, uncertainty_samples=0)
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model.add_country_holidays(country_name='US')
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model.fit(DATA).predict()
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# There are training holidays missing in the test set
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train = DATA.head(154)
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future = DATA.tail(355)
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model = Prophet(uncertainty_samples=0)
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model.add_country_holidays(country_name='US')
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model.fit(train).predict(future)
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# There are test holidays missing in the training set
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train = DATA.tail(355)
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future = DATA2
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model = Prophet(uncertainty_samples=0)
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model.add_country_holidays(country_name='US')
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model.fit(train).predict(future)
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def test_make_future_dataframe(self):
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N = 468
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train = DATA.head(N // 2)
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forecaster = Prophet()
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forecaster.fit(train)
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future = forecaster.make_future_dataframe(periods=3, freq='D',
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include_history=False)
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correct = pd.DatetimeIndex(['2013-04-26', '2013-04-27', '2013-04-28'])
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self.assertEqual(len(future), 3)
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for i in range(3):
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self.assertEqual(future.iloc[i]['ds'], correct[i])
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future = forecaster.make_future_dataframe(periods=3, freq='M',
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include_history=False)
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correct = pd.DatetimeIndex(['2013-04-30', '2013-05-31', '2013-06-30'])
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self.assertEqual(len(future), 3)
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for i in range(3):
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self.assertEqual(future.iloc[i]['ds'], correct[i])
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def test_auto_weekly_seasonality(self):
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# Should be enabled
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N = 15
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train = DATA.head(N)
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m = Prophet()
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self.assertEqual(m.weekly_seasonality, 'auto')
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m.fit(train)
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self.assertIn('weekly', m.seasonalities)
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self.assertEqual(
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m.seasonalities['weekly'],
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{
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'period': 7,
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'fourier_order': 3,
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'prior_scale': 10.,
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'mode': 'additive',
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},
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)
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# Should be disabled due to too short history
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N = 9
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train = DATA.head(N)
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m = Prophet()
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m.fit(train)
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self.assertNotIn('weekly', m.seasonalities)
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m = Prophet(weekly_seasonality=True)
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m.fit(train)
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self.assertIn('weekly', m.seasonalities)
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# Should be False due to weekly spacing
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train = DATA.iloc[::7, :]
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m = Prophet()
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m.fit(train)
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self.assertNotIn('weekly', m.seasonalities)
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m = Prophet(weekly_seasonality=2, seasonality_prior_scale=3.)
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m.fit(DATA)
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self.assertEqual(
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m.seasonalities['weekly'],
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{
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'period': 7,
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'fourier_order': 2,
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'prior_scale': 3.,
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'mode': 'additive',
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},
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)
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def test_auto_yearly_seasonality(self):
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# Should be enabled
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m = Prophet()
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self.assertEqual(m.yearly_seasonality, 'auto')
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m.fit(DATA)
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self.assertIn('yearly', m.seasonalities)
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self.assertEqual(
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m.seasonalities['yearly'],
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{
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'period': 365.25,
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'fourier_order': 10,
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'prior_scale': 10.,
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'mode': 'additive',
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},
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)
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# Should be disabled due to too short history
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N = 240
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train = DATA.head(N)
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m = Prophet()
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m.fit(train)
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self.assertNotIn('yearly', m.seasonalities)
|
|
m = Prophet(yearly_seasonality=True)
|
|
m.fit(train)
|
|
self.assertIn('yearly', m.seasonalities)
|
|
m = Prophet(yearly_seasonality=7, seasonality_prior_scale=3.)
|
|
m.fit(DATA)
|
|
self.assertEqual(
|
|
m.seasonalities['yearly'],
|
|
{
|
|
'period': 365.25,
|
|
'fourier_order': 7,
|
|
'prior_scale': 3.,
|
|
'mode': 'additive',
|
|
},
|
|
)
|
|
|
|
def test_auto_daily_seasonality(self):
|
|
# Should be enabled
|
|
m = Prophet()
|
|
self.assertEqual(m.daily_seasonality, 'auto')
|
|
m.fit(DATA2)
|
|
self.assertIn('daily', m.seasonalities)
|
|
self.assertEqual(
|
|
m.seasonalities['daily'],
|
|
{
|
|
'period': 1,
|
|
'fourier_order': 4,
|
|
'prior_scale': 10.,
|
|
'mode': 'additive',
|
|
},
|
|
)
|
|
# Should be disabled due to too short history
|
|
N = 430
|
|
train = DATA2.head(N)
|
|
m = Prophet()
|
|
m.fit(train)
|
|
self.assertNotIn('daily', m.seasonalities)
|
|
m = Prophet(daily_seasonality=True)
|
|
m.fit(train)
|
|
self.assertIn('daily', m.seasonalities)
|
|
m = Prophet(daily_seasonality=7, seasonality_prior_scale=3.)
|
|
m.fit(DATA2)
|
|
self.assertEqual(
|
|
m.seasonalities['daily'],
|
|
{
|
|
'period': 1,
|
|
'fourier_order': 7,
|
|
'prior_scale': 3.,
|
|
'mode': 'additive',
|
|
},
|
|
)
|
|
m = Prophet()
|
|
m.fit(DATA)
|
|
self.assertNotIn('daily', m.seasonalities)
|
|
|
|
def test_subdaily_holidays(self):
|
|
holidays = pd.DataFrame({
|
|
'ds': pd.to_datetime(['2017-01-02']),
|
|
'holiday': ['special_day'],
|
|
})
|
|
m = Prophet(holidays=holidays)
|
|
m.fit(DATA2)
|
|
fcst = m.predict()
|
|
self.assertEqual(sum(fcst['special_day'] == 0), 575)
|
|
|
|
def test_custom_seasonality(self):
|
|
holidays = pd.DataFrame({
|
|
'ds': pd.to_datetime(['2017-01-02']),
|
|
'holiday': ['special_day'],
|
|
'prior_scale': [4.],
|
|
})
|
|
m = Prophet(holidays=holidays)
|
|
m.add_seasonality(name='monthly', period=30, fourier_order=5,
|
|
prior_scale=2.)
|
|
self.assertEqual(
|
|
m.seasonalities['monthly'],
|
|
{
|
|
'period': 30,
|
|
'fourier_order': 5,
|
|
'prior_scale': 2.,
|
|
'mode': 'additive',
|
|
},
|
|
)
|
|
with self.assertRaises(ValueError):
|
|
m.add_seasonality(name='special_day', period=30, fourier_order=5)
|
|
with self.assertRaises(ValueError):
|
|
m.add_seasonality(name='trend', period=30, fourier_order=5)
|
|
m.add_seasonality(name='weekly', period=30, fourier_order=5)
|
|
# Test priors
|
|
m = Prophet(
|
|
holidays=holidays, yearly_seasonality=False,
|
|
seasonality_mode='multiplicative',
|
|
)
|
|
m.add_seasonality(name='monthly', period=30, fourier_order=5,
|
|
prior_scale=2., mode='additive')
|
|
m.fit(DATA.copy())
|
|
self.assertEqual(m.seasonalities['monthly']['mode'], 'additive')
|
|
self.assertEqual(m.seasonalities['weekly']['mode'], 'multiplicative')
|
|
seasonal_features, prior_scales, component_cols, modes = (
|
|
m.make_all_seasonality_features(m.history)
|
|
)
|
|
self.assertEqual(sum(component_cols['monthly']), 10)
|
|
self.assertEqual(sum(component_cols['special_day']), 1)
|
|
self.assertEqual(sum(component_cols['weekly']), 6)
|
|
self.assertEqual(sum(component_cols['additive_terms']), 10)
|
|
self.assertEqual(sum(component_cols['multiplicative_terms']), 7)
|
|
if seasonal_features.columns[0] == 'monthly_delim_1':
|
|
true = [2.] * 10 + [10.] * 6 + [4.]
|
|
self.assertEqual(sum(component_cols['monthly'][:10]), 10)
|
|
self.assertEqual(sum(component_cols['weekly'][10:16]), 6)
|
|
else:
|
|
true = [10.] * 6 + [2.] * 10 + [4.]
|
|
self.assertEqual(sum(component_cols['weekly'][:6]), 6)
|
|
self.assertEqual(sum(component_cols['monthly'][6:16]), 10)
|
|
self.assertEqual(prior_scales, true)
|
|
|
|
def test_added_regressors(self):
|
|
m = Prophet()
|
|
m.add_regressor('binary_feature', prior_scale=0.2)
|
|
m.add_regressor('numeric_feature', prior_scale=0.5)
|
|
m.add_regressor(
|
|
'numeric_feature2', prior_scale=0.5, mode='multiplicative'
|
|
)
|
|
m.add_regressor('binary_feature2', standardize=True)
|
|
df = DATA.copy()
|
|
df['binary_feature'] = ['0'] * 255 + ['1'] * 255
|
|
df['numeric_feature'] = range(510)
|
|
df['numeric_feature2'] = range(510)
|
|
with self.assertRaises(ValueError):
|
|
# Require all regressors in df
|
|
m.fit(df)
|
|
df['binary_feature2'] = [1] * 100 + [0] * 410
|
|
m.fit(df)
|
|
# Check that standardizations are correctly set
|
|
self.assertEqual(
|
|
m.extra_regressors['binary_feature'],
|
|
{
|
|
'prior_scale': 0.2,
|
|
'mu': 0,
|
|
'std': 1,
|
|
'standardize': 'auto',
|
|
'mode': 'additive',
|
|
},
|
|
)
|
|
self.assertEqual(
|
|
m.extra_regressors['numeric_feature']['prior_scale'], 0.5)
|
|
self.assertEqual(
|
|
m.extra_regressors['numeric_feature']['mu'], 254.5)
|
|
self.assertAlmostEqual(
|
|
m.extra_regressors['numeric_feature']['std'], 147.368585, places=5)
|
|
self.assertEqual(
|
|
m.extra_regressors['numeric_feature2']['mode'], 'multiplicative')
|
|
self.assertEqual(
|
|
m.extra_regressors['binary_feature2']['prior_scale'], 10.)
|
|
self.assertAlmostEqual(
|
|
m.extra_regressors['binary_feature2']['mu'], 0.1960784, places=5)
|
|
self.assertAlmostEqual(
|
|
m.extra_regressors['binary_feature2']['std'], 0.3974183, places=5)
|
|
# Check that standardization is done correctly
|
|
df2 = m.setup_dataframe(df.copy())
|
|
self.assertEqual(df2['binary_feature'][0], 0)
|
|
self.assertAlmostEqual(df2['numeric_feature'][0], -1.726962, places=4)
|
|
self.assertAlmostEqual(df2['binary_feature2'][0], 2.022859, places=4)
|
|
# Check that feature matrix and prior scales are correctly constructed
|
|
seasonal_features, prior_scales, component_cols, modes = (
|
|
m.make_all_seasonality_features(df2)
|
|
)
|
|
self.assertEqual(seasonal_features.shape[1], 30)
|
|
names = ['binary_feature', 'numeric_feature', 'binary_feature2']
|
|
true_priors = [0.2, 0.5, 10.]
|
|
for i, name in enumerate(names):
|
|
self.assertIn(name, seasonal_features)
|
|
self.assertEqual(sum(component_cols[name]), 1)
|
|
self.assertEqual(
|
|
sum(np.array(prior_scales) * component_cols[name]),
|
|
true_priors[i],
|
|
)
|
|
# Check that forecast components are reasonable
|
|
future = pd.DataFrame({
|
|
'ds': ['2014-06-01'],
|
|
'binary_feature': [0],
|
|
'numeric_feature': [10],
|
|
'numeric_feature2': [10],
|
|
})
|
|
with self.assertRaises(ValueError):
|
|
m.predict(future)
|
|
future['binary_feature2'] = 0
|
|
fcst = m.predict(future)
|
|
self.assertEqual(fcst.shape[1], 37)
|
|
self.assertEqual(fcst['binary_feature'][0], 0)
|
|
self.assertAlmostEqual(
|
|
fcst['extra_regressors_additive'][0],
|
|
fcst['numeric_feature'][0] + fcst['binary_feature2'][0],
|
|
)
|
|
self.assertAlmostEqual(
|
|
fcst['extra_regressors_multiplicative'][0],
|
|
fcst['numeric_feature2'][0],
|
|
)
|
|
self.assertAlmostEqual(
|
|
fcst['additive_terms'][0],
|
|
fcst['yearly'][0] + fcst['weekly'][0]
|
|
+ fcst['extra_regressors_additive'][0],
|
|
)
|
|
self.assertAlmostEqual(
|
|
fcst['multiplicative_terms'][0],
|
|
fcst['extra_regressors_multiplicative'][0],
|
|
)
|
|
self.assertAlmostEqual(
|
|
fcst['yhat'][0],
|
|
fcst['trend'][0] * (1 + fcst['multiplicative_terms'][0])
|
|
+ fcst['additive_terms'][0],
|
|
)
|
|
# Check works if constant extra regressor at 0
|
|
df['constant_feature'] = 0
|
|
m = Prophet()
|
|
m.add_regressor('constant_feature')
|
|
m.fit(df)
|
|
self.assertEqual(m.extra_regressors['constant_feature']['std'], 1)
|
|
|
|
def test_set_seasonality_mode(self):
|
|
# Setting attribute
|
|
m = Prophet()
|
|
self.assertEqual(m.seasonality_mode, 'additive')
|
|
m = Prophet(seasonality_mode='multiplicative')
|
|
self.assertEqual(m.seasonality_mode, 'multiplicative')
|
|
with self.assertRaises(ValueError):
|
|
Prophet(seasonality_mode='batman')
|
|
|
|
def test_seasonality_modes(self):
|
|
# Model with holidays, seasonalities, and extra regressors
|
|
holidays = pd.DataFrame({
|
|
'ds': pd.to_datetime(['2016-12-25']),
|
|
'holiday': ['xmas'],
|
|
'lower_window': [-1],
|
|
'upper_window': [0],
|
|
})
|
|
m = Prophet(seasonality_mode='multiplicative', holidays=holidays)
|
|
m.add_seasonality('monthly', period=30, mode='additive', fourier_order=3)
|
|
m.add_regressor('binary_feature', mode='additive')
|
|
m.add_regressor('numeric_feature')
|
|
# Construct seasonal features
|
|
df = DATA.copy()
|
|
df['binary_feature'] = [0] * 255 + [1] * 255
|
|
df['numeric_feature'] = range(510)
|
|
df = m.setup_dataframe(df, initialize_scales=True)
|
|
m.history = df.copy()
|
|
m.set_auto_seasonalities()
|
|
seasonal_features, prior_scales, component_cols, modes = (
|
|
m.make_all_seasonality_features(df))
|
|
self.assertEqual(sum(component_cols['additive_terms']), 7)
|
|
self.assertEqual(sum(component_cols['multiplicative_terms']), 29)
|
|
self.assertEqual(
|
|
set(modes['additive']),
|
|
{'monthly', 'binary_feature', 'additive_terms',
|
|
'extra_regressors_additive'},
|
|
)
|
|
self.assertEqual(
|
|
set(modes['multiplicative']),
|
|
{'weekly', 'yearly', 'xmas', 'numeric_feature',
|
|
'multiplicative_terms', 'extra_regressors_multiplicative',
|
|
'holidays',
|
|
},
|
|
)
|