mirror of
https://github.com/saymrwulf/prophet.git
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729 lines
16 KiB
Python
729 lines
16 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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try:
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from StringIO import StringIO
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except ImportError:
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from io import StringIO
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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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from unittest import TestCase
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from fbprophet import Prophet
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# fb-block 1 end
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# fb-block 2
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class TestProphet(TestCase):
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def test_load_models(self):
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forecaster = Prophet()
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forecaster.get_linear_model()
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forecaster.get_logistic_model()
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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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train[(train['ds'] > '2014-01-01')] += 20
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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_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_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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self.assertTrue(cp.max() < N)
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mat = m.get_changepoint_matrix()
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self.assertEqual(mat.shape[0], N // 2)
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self.assertEqual(mat.shape[1], m.n_changepoints)
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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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mat = m.get_changepoint_matrix()
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self.assertEqual(mat.shape[0], N // 2)
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self.assertEqual(mat.shape[1], 1)
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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.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 = model.make_holiday_features(df['ds'])
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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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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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feats = Prophet(holidays=holidays).make_holiday_features(df['ds'])
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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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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_make_future_dataframe(self):
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N = DATA.shape[0]
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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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DATA = pd.read_csv(StringIO("""
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ds,y
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2012-05-18,38.23
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2012-05-21,34.03
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2012-05-22,31.0
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2012-05-23,32.0
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2012-05-24,33.03
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2012-05-25,31.91
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2012-05-29,28.84
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2012-05-30,28.19
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2012-05-31,29.6
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2012-06-01,27.72
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2012-06-04,26.9
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2012-06-05,25.87
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2012-06-06,26.81
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2012-06-07,26.31
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2012-06-08,27.1
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2012-06-11,27.01
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2012-06-12,27.4
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2012-06-13,27.27
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2012-06-14,28.29
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2012-06-15,30.01
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2012-06-18,31.41
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2012-06-19,31.91
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2012-06-20,31.6
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2012-06-21,31.84
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2012-06-22,33.05
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2012-06-25,32.06
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2012-06-26,33.1
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2012-06-27,32.23
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2012-06-28,31.36
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2012-06-29,31.1
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2012-07-02,30.77
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2012-07-03,31.2
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2012-07-05,31.47
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2012-07-06,31.73
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2012-07-09,32.17
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2012-07-10,31.47
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2012-07-11,30.97
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2012-07-12,30.81
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2012-07-13,30.72
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2012-07-16,28.25
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2012-07-17,28.09
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2012-07-18,29.11
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2012-07-19,29.0
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2012-07-20,28.76
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2012-07-23,28.75
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2012-07-24,28.45
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2012-07-25,29.34
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2012-07-26,26.85
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2012-07-27,23.71
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2012-07-30,23.15
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2012-07-31,21.71
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2012-08-01,20.88
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2012-08-02,20.04
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2012-08-03,21.09
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2012-08-06,21.92
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2012-08-07,20.72
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2012-08-08,20.72
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2012-08-09,21.01
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2012-08-10,21.81
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2012-08-13,21.6
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2012-08-14,20.38
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2012-08-15,21.2
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2012-08-16,19.87
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2012-08-17,19.05
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2012-08-20,20.01
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2012-08-21,19.16
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2012-08-22,19.44
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2012-08-23,19.44
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2012-08-24,19.41
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2012-08-27,19.15
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2012-08-28,19.34
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2012-08-29,19.1
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2012-08-30,19.09
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2012-08-31,18.06
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2012-09-04,17.73
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2012-09-05,18.58
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2012-09-06,18.96
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2012-09-07,18.98
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2012-09-10,18.81
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2012-09-11,19.43
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2012-09-12,20.93
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2012-09-13,20.71
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2012-09-14,22.0
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2012-09-17,21.52
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2012-09-18,21.87
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2012-09-19,23.29
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2012-09-20,22.59
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2012-09-21,22.86
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2012-09-24,20.79
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2012-09-25,20.28
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2012-09-26,20.62
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2012-09-27,20.32
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2012-09-28,21.66
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2012-10-01,21.99
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2012-10-02,22.27
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2012-10-03,21.83
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2012-10-04,21.95
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2012-10-05,20.91
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2012-10-08,20.4
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2012-10-09,20.23
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2012-10-10,19.64
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2012-10-11,19.75
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2012-10-12,19.52
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2012-10-15,19.52
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2012-10-16,19.48
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2012-10-17,19.88
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|
2012-10-18,18.98
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2012-10-19,19.0
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2012-10-22,19.32
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2012-10-23,19.5
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2012-10-24,23.23
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2012-10-25,22.56
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2012-10-26,21.94
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2012-10-31,21.11
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2012-11-01,21.21
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2012-11-02,21.18
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2012-11-05,21.25
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2012-11-06,21.17
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2012-11-07,20.47
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2012-11-08,19.99
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|
2012-11-09,19.21
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|
2012-11-12,20.07
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|
2012-11-13,19.86
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|
2012-11-14,22.36
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|
2012-11-15,22.17
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|
2012-11-16,23.56
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|
2012-11-19,22.92
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|
2012-11-20,23.1
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|
2012-11-21,24.32
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|
2012-11-23,24.0
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|
2012-11-26,25.94
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|
2012-11-27,26.15
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|
2012-11-28,26.36
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|
2012-11-29,27.32
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|
2012-11-30,28.0
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|
2012-12-03,27.04
|
|
2012-12-04,27.46
|
|
2012-12-05,27.71
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|
2012-12-06,26.97
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|
2012-12-07,27.49
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|
2012-12-10,27.84
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|
2012-12-11,27.98
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|
2012-12-12,27.58
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|
2012-12-13,28.24
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|
2012-12-14,26.81
|
|
2012-12-17,26.75
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|
2012-12-18,27.71
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|
2012-12-19,27.41
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|
2012-12-20,27.36
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|
2012-12-21,26.26
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|
2012-12-24,26.93
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|
2012-12-26,26.51
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|
2012-12-27,26.05
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|
2012-12-28,25.91
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|
2012-12-31,26.62
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|
2013-01-02,28.0
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|
2013-01-03,27.77
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|
2013-01-04,28.76
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|
2013-01-07,29.42
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|
2013-01-08,29.06
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|
2013-01-09,30.59
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|
2013-01-10,31.3
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|
2013-01-11,31.72
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|
2013-01-14,30.95
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|
2013-01-15,30.1
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|
2013-01-16,29.85
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|
2013-01-17,30.14
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|
2013-01-18,29.66
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|
2013-01-22,30.73
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|
2013-01-23,30.82
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|
2013-01-24,31.08
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|
2013-01-25,31.54
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|
2013-01-28,32.47
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|
2013-01-29,30.79
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|
2013-01-30,31.24
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|
2013-01-31,30.98
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|
2013-02-01,29.73
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|
2013-02-04,28.11
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|
2013-02-05,28.64
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|
2013-02-06,29.05
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|
2013-02-07,28.65
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|
2013-02-08,28.55
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|
2013-02-11,28.26
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2013-02-12,27.37
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2013-02-13,27.91
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|
2013-02-14,28.5
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|
2013-02-15,28.32
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|
2013-02-19,28.93
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|
2013-02-20,28.46
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|
2013-02-21,27.28
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|
2013-02-22,27.13
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|
2013-02-25,27.27
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|
2013-02-26,27.39
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|
2013-02-27,26.87
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|
2013-02-28,27.25
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|
2013-03-01,27.78
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|
2013-03-04,27.72
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|
2013-03-05,27.52
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|
2013-03-06,27.45
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|
2013-03-07,28.58
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|
2013-03-08,27.96
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|
2013-03-11,28.14
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|
2013-03-12,27.83
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|
2013-03-13,27.08
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|
2013-03-14,27.04
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2013-03-15,26.65
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|
2013-03-18,26.49
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|
2013-03-19,26.55
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|
2013-03-20,25.86
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|
2013-03-21,25.74
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|
2013-03-22,25.73
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|
2013-03-25,25.13
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|
2013-03-26,25.21
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2013-03-27,26.09
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2013-03-28,25.58
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|
2013-04-01,25.53
|
|
2013-04-02,25.42
|
|
2013-04-03,26.25
|
|
2013-04-04,27.07
|
|
2013-04-05,27.39
|
|
2013-04-08,26.85
|
|
2013-04-09,26.59
|
|
2013-04-10,27.57
|
|
2013-04-11,28.02
|
|
2013-04-12,27.4
|
|
2013-04-15,26.52
|
|
2013-04-16,26.92
|
|
2013-04-17,26.63
|
|
2013-04-18,25.69
|
|
2013-04-19,25.73
|
|
2013-04-22,25.97
|
|
2013-04-23,25.98
|
|
2013-04-24,26.11
|
|
2013-04-25,26.14
|
|
2013-04-26,26.85
|
|
2013-04-29,26.98
|
|
2013-04-30,27.77
|
|
2013-05-01,27.43
|
|
2013-05-02,28.97
|
|
2013-05-03,28.31
|
|
2013-05-06,27.57
|
|
2013-05-07,26.89
|
|
2013-05-08,27.12
|
|
2013-05-09,27.04
|
|
2013-05-10,26.68
|
|
2013-05-13,26.82
|
|
2013-05-14,27.07
|
|
2013-05-15,26.6
|
|
2013-05-16,26.13
|
|
2013-05-17,26.25
|
|
2013-05-20,25.76
|
|
2013-05-21,25.66
|
|
2013-05-22,25.16
|
|
2013-05-23,25.06
|
|
2013-05-24,24.31
|
|
2013-05-28,24.1
|
|
2013-05-29,23.32
|
|
2013-05-30,24.55
|
|
2013-05-31,24.35
|
|
2013-06-03,23.85
|
|
2013-06-04,23.52
|
|
2013-06-05,22.9
|
|
2013-06-06,22.97
|
|
2013-06-07,23.29
|
|
2013-06-10,24.33
|
|
2013-06-11,24.03
|
|
2013-06-12,23.77
|
|
2013-06-13,23.73
|
|
2013-06-14,23.63
|
|
2013-06-17,24.02
|
|
2013-06-18,24.21
|
|
2013-06-19,24.31
|
|
2013-06-20,23.9
|
|
2013-06-21,24.53
|
|
2013-06-24,23.94
|
|
2013-06-25,24.25
|
|
2013-06-26,24.16
|
|
2013-06-27,24.66
|
|
2013-06-28,24.88
|
|
2013-07-01,24.81
|
|
2013-07-02,24.41
|
|
2013-07-03,24.52
|
|
2013-07-05,24.37
|
|
2013-07-08,24.71
|
|
2013-07-09,25.48
|
|
2013-07-10,25.8
|
|
2013-07-11,25.81
|
|
2013-07-12,25.91
|
|
2013-07-15,26.28
|
|
2013-07-16,26.32
|
|
2013-07-17,26.65
|
|
2013-07-18,26.18
|
|
2013-07-19,25.88
|
|
2013-07-22,26.05
|
|
2013-07-23,26.13
|
|
2013-07-24,26.51
|
|
2013-07-25,34.36
|
|
2013-07-26,34.01
|
|
2013-07-29,35.43
|
|
2013-07-30,37.63
|
|
2013-07-31,36.8
|
|
2013-08-01,37.49
|
|
2013-08-02,38.05
|
|
2013-08-05,39.19
|
|
2013-08-06,38.55
|
|
2013-08-07,38.87
|
|
2013-08-08,38.54
|
|
2013-08-09,38.5
|
|
2013-08-12,38.22
|
|
2013-08-13,37.02
|
|
2013-08-14,36.65
|
|
2013-08-15,36.56
|
|
2013-08-16,37.08
|
|
2013-08-19,37.81
|
|
2013-08-20,38.41
|
|
2013-08-21,38.32
|
|
2013-08-22,38.55
|
|
2013-08-23,40.55
|
|
2013-08-26,41.34
|
|
2013-08-27,39.64
|
|
2013-08-28,40.55
|
|
2013-08-29,41.28
|
|
2013-08-30,41.29
|
|
2013-09-03,41.87
|
|
2013-09-04,41.78
|
|
2013-09-05,42.66
|
|
2013-09-06,43.95
|
|
2013-09-09,44.04
|
|
2013-09-10,43.6
|
|
2013-09-11,45.04
|
|
2013-09-12,44.75
|
|
2013-09-13,44.31
|
|
2013-09-16,42.51
|
|
2013-09-17,45.07
|
|
2013-09-18,45.23
|
|
2013-09-19,45.98
|
|
2013-09-20,47.49
|
|
2013-09-23,47.19
|
|
2013-09-24,48.45
|
|
2013-09-25,49.46
|
|
2013-09-26,50.39
|
|
2013-09-27,51.24
|
|
2013-09-30,50.23
|
|
2013-10-01,50.42
|
|
2013-10-02,50.28
|
|
2013-10-03,49.18
|
|
2013-10-04,51.04
|
|
2013-10-07,50.52
|
|
2013-10-08,47.14
|
|
2013-10-09,46.77
|
|
2013-10-10,49.05
|
|
2013-10-11,49.11
|
|
2013-10-14,49.51
|
|
2013-10-15,49.5
|
|
2013-10-16,51.14
|
|
2013-10-17,52.21
|
|
2013-10-18,54.22
|
|
2013-10-21,53.85
|
|
2013-10-22,52.68
|
|
2013-10-23,51.9
|
|
2013-10-24,52.45
|
|
2013-10-25,51.95
|
|
2013-10-28,50.23
|
|
2013-10-29,49.4
|
|
2013-10-30,49.01
|
|
2013-10-31,50.21
|
|
2013-11-01,49.75
|
|
2013-11-04,48.22
|
|
2013-11-05,50.11
|
|
2013-11-06,49.12
|
|
2013-11-07,47.56
|
|
2013-11-08,47.53
|
|
2013-11-11,46.2
|
|
2013-11-12,46.61
|
|
2013-11-13,48.71
|
|
2013-11-14,48.99
|
|
2013-11-15,49.01
|
|
2013-11-18,45.83
|
|
2013-11-19,46.36
|
|
2013-11-20,46.43
|
|
2013-11-21,46.7
|
|
2013-11-22,46.23
|
|
2013-11-25,44.82
|
|
2013-11-26,45.89
|
|
2013-11-27,46.49
|
|
2013-11-29,47.01
|
|
2013-12-02,47.06
|
|
2013-12-03,46.73
|
|
2013-12-04,48.62
|
|
2013-12-05,48.34
|
|
2013-12-06,47.94
|
|
2013-12-09,48.84
|
|
2013-12-10,50.25
|
|
2013-12-11,49.38
|
|
2013-12-12,51.83
|
|
2013-12-13,53.32
|
|
2013-12-16,53.81
|
|
2013-12-17,54.86
|
|
2013-12-18,55.57
|
|
2013-12-19,55.05
|
|
2013-12-20,55.12
|
|
2013-12-23,57.77
|
|
2013-12-24,57.96
|
|
2013-12-26,57.73
|
|
2013-12-27,55.44
|
|
2013-12-30,53.71
|
|
2013-12-31,54.65
|
|
2014-01-02,54.71
|
|
2014-01-03,54.56
|
|
2014-01-06,57.2
|
|
2014-01-07,57.92
|
|
2014-01-08,58.23
|
|
2014-01-09,57.22
|
|
2014-01-10,57.94
|
|
2014-01-13,55.91
|
|
2014-01-14,57.74
|
|
2014-01-15,57.6
|
|
2014-01-16,57.19
|
|
2014-01-17,56.3
|
|
2014-01-21,58.51
|
|
2014-01-22,57.51
|
|
2014-01-23,56.63
|
|
2014-01-24,54.45
|
|
2014-01-27,53.55
|
|
2014-01-28,55.14
|
|
2014-01-29,53.53
|
|
2014-01-30,61.08
|
|
2014-01-31,62.57
|
|
2014-02-03,61.48
|
|
2014-02-04,62.75
|
|
2014-02-05,62.19
|
|
2014-02-06,62.16
|
|
2014-02-07,64.32
|
|
2014-02-10,63.55
|
|
2014-02-11,64.85
|
|
2014-02-12,64.45
|
|
2014-02-13,67.33
|
|
2014-02-14,67.09
|
|
2014-02-18,67.3
|
|
2014-02-19,68.06
|
|
2014-02-20,69.63
|
|
2014-02-21,68.59
|
|
2014-02-24,70.78
|
|
2014-02-25,69.85
|
|
2014-02-26,69.26
|
|
2014-02-27,68.94
|
|
2014-02-28,68.46
|
|
2014-03-03,67.41
|
|
2014-03-04,68.8
|
|
2014-03-05,71.57
|
|
2014-03-06,70.84
|
|
2014-03-07,69.8
|
|
2014-03-10,72.03
|
|
2014-03-11,70.1
|
|
2014-03-12,70.88
|
|
2014-03-13,68.83
|
|
2014-03-14,67.72
|
|
2014-03-17,68.74
|
|
2014-03-18,69.19
|
|
2014-03-19,68.24
|
|
2014-03-20,66.97
|
|
2014-03-21,67.24
|
|
2014-03-24,64.1
|
|
2014-03-25,64.89
|
|
2014-03-26,60.39
|
|
2014-03-27,60.97
|
|
2014-03-28,60.01
|
|
2014-03-31,60.24
|
|
"""), parse_dates=['ds'])
|