mirror of
https://github.com/saymrwulf/prophet.git
synced 2026-05-14 20:48:08 +00:00
246 lines
11 KiB
Python
246 lines
11 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
|
|
|
|
# This source code is licensed under the MIT license found in the
|
|
# LICENSE file in the root directory of this source tree.
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
from __future__ import unicode_literals
|
|
|
|
import itertools
|
|
import os
|
|
from unittest import TestCase
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
from fbprophet import Prophet
|
|
from fbprophet import diagnostics
|
|
|
|
DATA_all = pd.read_csv(
|
|
os.path.join(os.path.dirname(__file__), 'data.csv'), parse_dates=['ds']
|
|
)
|
|
DATA = DATA_all.head(100)
|
|
|
|
|
|
class TestDiagnostics(TestCase):
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super(TestDiagnostics, self).__init__(*args, **kwargs)
|
|
# Use first 100 record in data.csv
|
|
self.__df = DATA
|
|
|
|
def test_cross_validation(self):
|
|
m = Prophet()
|
|
m.fit(self.__df)
|
|
# Calculate the number of cutoff points(k)
|
|
horizon = pd.Timedelta('4 days')
|
|
period = pd.Timedelta('10 days')
|
|
initial = pd.Timedelta('115 days')
|
|
df_cv = diagnostics.cross_validation(
|
|
m, horizon='4 days', period='10 days', initial='115 days')
|
|
self.assertEqual(len(np.unique(df_cv['cutoff'])), 3)
|
|
self.assertEqual(max(df_cv['ds'] - df_cv['cutoff']), horizon)
|
|
self.assertTrue(min(df_cv['cutoff']) >= min(self.__df['ds']) + initial)
|
|
dc = df_cv['cutoff'].diff()
|
|
dc = dc[dc > pd.Timedelta(0)].min()
|
|
self.assertTrue(dc >= period)
|
|
self.assertTrue((df_cv['cutoff'] < df_cv['ds']).all())
|
|
# Each y in df_cv and self.__df with same ds should be equal
|
|
df_merged = pd.merge(df_cv, self.__df, 'left', on='ds')
|
|
self.assertAlmostEqual(
|
|
np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0)
|
|
df_cv = diagnostics.cross_validation(
|
|
m, horizon='4 days', period='10 days', initial='135 days')
|
|
self.assertEqual(len(np.unique(df_cv['cutoff'])), 1)
|
|
with self.assertRaises(ValueError):
|
|
diagnostics.cross_validation(
|
|
m, horizon='10 days', period='10 days', initial='140 days')
|
|
|
|
def test_cross_validation_logistic(self):
|
|
df = self.__df.copy()
|
|
df['cap'] = 40
|
|
m = Prophet(growth='logistic').fit(df)
|
|
df_cv = diagnostics.cross_validation(
|
|
m, horizon='1 days', period='1 days', initial='140 days')
|
|
self.assertEqual(len(np.unique(df_cv['cutoff'])), 2)
|
|
self.assertTrue((df_cv['cutoff'] < df_cv['ds']).all())
|
|
df_merged = pd.merge(df_cv, self.__df, 'left', on='ds')
|
|
self.assertAlmostEqual(
|
|
np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0)
|
|
|
|
def test_cross_validation_extra_regressors(self):
|
|
df = self.__df.copy()
|
|
df['extra'] = range(df.shape[0])
|
|
df['is_conditional_week'] = np.arange(df.shape[0]) // 7 % 2
|
|
m = Prophet()
|
|
m.add_seasonality(name='monthly', period=30.5, fourier_order=5)
|
|
m.add_seasonality(name='conditional_weekly', period=7, fourier_order=3,
|
|
prior_scale=2., condition_name='is_conditional_week')
|
|
m.add_regressor('extra')
|
|
m.fit(df)
|
|
df_cv = diagnostics.cross_validation(
|
|
m, horizon='4 days', period='4 days', initial='135 days')
|
|
self.assertEqual(len(np.unique(df_cv['cutoff'])), 2)
|
|
period = pd.Timedelta('4 days')
|
|
dc = df_cv['cutoff'].diff()
|
|
dc = dc[dc > pd.Timedelta(0)].min()
|
|
self.assertTrue(dc >= period)
|
|
self.assertTrue((df_cv['cutoff'] < df_cv['ds']).all())
|
|
df_merged = pd.merge(df_cv, self.__df, 'left', on='ds')
|
|
self.assertAlmostEqual(
|
|
np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0)
|
|
|
|
def test_cross_validation_default_value_check(self):
|
|
m = Prophet()
|
|
m.fit(self.__df)
|
|
# Default value of initial should be equal to 3 * horizon
|
|
df_cv1 = diagnostics.cross_validation(
|
|
m, horizon='32 days', period='10 days')
|
|
df_cv2 = diagnostics.cross_validation(
|
|
m, horizon='32 days', period='10 days', initial='96 days')
|
|
self.assertAlmostEqual(
|
|
((df_cv1['y'] - df_cv2['y']) ** 2).sum(), 0.0)
|
|
self.assertAlmostEqual(
|
|
((df_cv1['yhat'] - df_cv2['yhat']) ** 2).sum(), 0.0)
|
|
|
|
def test_performance_metrics(self):
|
|
m = Prophet()
|
|
m.fit(self.__df)
|
|
df_cv = diagnostics.cross_validation(
|
|
m, horizon='4 days', period='10 days', initial='90 days')
|
|
# Aggregation level none
|
|
df_none = diagnostics.performance_metrics(df_cv, rolling_window=-1)
|
|
self.assertEqual(
|
|
set(df_none.columns),
|
|
{'horizon', 'coverage', 'mae', 'mape', 'mse', 'rmse'},
|
|
)
|
|
self.assertEqual(df_none.shape[0], 16)
|
|
# Aggregation level 0
|
|
df_0 = diagnostics.performance_metrics(df_cv, rolling_window=0)
|
|
self.assertEqual(len(df_0), 4)
|
|
self.assertEqual(len(df_0['horizon'].unique()), 4)
|
|
# Aggregation level 0.2
|
|
df_horizon = diagnostics.performance_metrics(df_cv, rolling_window=0.2)
|
|
self.assertEqual(len(df_horizon), 4)
|
|
self.assertEqual(len(df_horizon['horizon'].unique()), 4)
|
|
# Aggregation level all
|
|
df_all = diagnostics.performance_metrics(df_cv, rolling_window=1)
|
|
self.assertEqual(df_all.shape[0], 1)
|
|
for metric in ['mse', 'mape', 'mae', 'coverage']:
|
|
self.assertAlmostEqual(df_all[metric].values[0], df_none[metric].mean())
|
|
# Custom list of metrics
|
|
df_horizon = diagnostics.performance_metrics(
|
|
df_cv, metrics=['coverage', 'mse'],
|
|
)
|
|
self.assertEqual(
|
|
set(df_horizon.columns),
|
|
{'coverage', 'mse', 'horizon'},
|
|
)
|
|
# Skip MAPE
|
|
df_cv.loc[0, 'y'] = 0.
|
|
df_horizon = diagnostics.performance_metrics(
|
|
df_cv, metrics=['coverage', 'mape'],
|
|
)
|
|
self.assertEqual(
|
|
set(df_horizon.columns),
|
|
{'coverage', 'horizon'},
|
|
)
|
|
df_horizon = diagnostics.performance_metrics(
|
|
df_cv, metrics=['mape'],
|
|
)
|
|
self.assertIsNone(df_horizon)
|
|
|
|
def test_rolling_mean(self):
|
|
x = np.arange(10)
|
|
h = np.arange(10)
|
|
df = diagnostics.rolling_mean_by_h(x=x, h=h, w=1, name='x')
|
|
self.assertTrue(np.array_equal(x, df['x'].values))
|
|
self.assertTrue(np.array_equal(h, df['horizon'].values))
|
|
|
|
df = diagnostics.rolling_mean_by_h(x, h, w=4, name='x')
|
|
self.assertTrue(np.allclose(x[3:] - 1.5, df['x'].values))
|
|
self.assertTrue(np.array_equal(np.arange(3, 10), df['horizon'].values))
|
|
|
|
h = np.array([1., 2., 3., 4., 4., 4., 4., 4., 7., 7.])
|
|
x_true = np.array([1.0, 5.0 , 22. / 3])
|
|
h_true = np.array([3., 4., 7.])
|
|
df = diagnostics.rolling_mean_by_h(x, h, w=3, name='x')
|
|
self.assertTrue(np.allclose(x_true, df['x'].values))
|
|
self.assertTrue(np.array_equal(h_true, df['horizon'].values))
|
|
|
|
df = diagnostics.rolling_mean_by_h(x, h, w=10, name='x')
|
|
self.assertTrue(np.allclose(np.array([7.]), df['horizon'].values))
|
|
self.assertTrue(np.allclose(np.array([4.5]), df['x'].values))
|
|
|
|
def test_copy(self):
|
|
df = DATA_all.copy()
|
|
df['cap'] = 200.
|
|
df['binary_feature'] = [0] * 255 + [1] * 255
|
|
# These values are created except for its default values
|
|
holiday = pd.DataFrame(
|
|
{'ds': pd.to_datetime(['2016-12-25']), 'holiday': ['x']})
|
|
products = itertools.product(
|
|
['linear', 'logistic'], # growth
|
|
[None, pd.to_datetime(['2016-12-25'])], # changepoints
|
|
[3], # n_changepoints
|
|
[0.9], # changepoint_range
|
|
[True, False], # yearly_seasonality
|
|
[True, False], # weekly_seasonality
|
|
[True, False], # daily_seasonality
|
|
[None, holiday], # holidays
|
|
['additive', 'multiplicative'], # seasonality_mode
|
|
[1.1], # seasonality_prior_scale
|
|
[1.1], # holidays_prior_scale
|
|
[0.1], # changepoint_prior_scale
|
|
[100], # mcmc_samples
|
|
[0.9], # interval_width
|
|
[200] # uncertainty_samples
|
|
)
|
|
# Values should be copied correctly
|
|
for product in products:
|
|
m1 = Prophet(*product)
|
|
m1.country_holidays = 'US'
|
|
m1.history = m1.setup_dataframe(
|
|
df.copy(), initialize_scales=True)
|
|
m1.set_auto_seasonalities()
|
|
m2 = diagnostics.prophet_copy(m1)
|
|
self.assertEqual(m1.growth, m2.growth)
|
|
self.assertEqual(m1.n_changepoints, m2.n_changepoints)
|
|
self.assertEqual(m1.changepoint_range, m2.changepoint_range)
|
|
self.assertEqual(m1.changepoints, m2.changepoints)
|
|
self.assertEqual(False, m2.yearly_seasonality)
|
|
self.assertEqual(False, m2.weekly_seasonality)
|
|
self.assertEqual(False, m2.daily_seasonality)
|
|
self.assertEqual(
|
|
m1.yearly_seasonality, 'yearly' in m2.seasonalities)
|
|
self.assertEqual(
|
|
m1.weekly_seasonality, 'weekly' in m2.seasonalities)
|
|
self.assertEqual(
|
|
m1.daily_seasonality, 'daily' in m2.seasonalities)
|
|
if m1.holidays is None:
|
|
self.assertEqual(m1.holidays, m2.holidays)
|
|
else:
|
|
self.assertTrue((m1.holidays == m2.holidays).values.all())
|
|
self.assertEqual(m1.country_holidays, m2.country_holidays)
|
|
self.assertEqual(m1.seasonality_mode, m2.seasonality_mode)
|
|
self.assertEqual(m1.seasonality_prior_scale, m2.seasonality_prior_scale)
|
|
self.assertEqual(m1.changepoint_prior_scale, m2.changepoint_prior_scale)
|
|
self.assertEqual(m1.holidays_prior_scale, m2.holidays_prior_scale)
|
|
self.assertEqual(m1.mcmc_samples, m2.mcmc_samples)
|
|
self.assertEqual(m1.interval_width, m2.interval_width)
|
|
self.assertEqual(m1.uncertainty_samples, m2.uncertainty_samples)
|
|
|
|
# Check for cutoff and custom seasonality and extra regressors
|
|
changepoints = pd.date_range('2012-06-15', '2012-09-15')
|
|
cutoff = pd.Timestamp('2012-07-25')
|
|
m1 = Prophet(changepoints=changepoints)
|
|
m1.add_seasonality('custom', 10, 5)
|
|
m1.add_regressor('binary_feature')
|
|
m1.fit(df)
|
|
m2 = diagnostics.prophet_copy(m1, cutoff=cutoff)
|
|
changepoints = changepoints[changepoints <= cutoff]
|
|
self.assertTrue((changepoints == m2.changepoints).all())
|
|
self.assertTrue('custom' in m2.seasonalities)
|
|
self.assertTrue('binary_feature' in m2.extra_regressors)
|