saymrwulf/prophet
1
0
Fork 0
mirror of https://github.com/saymrwulf/prophet.git synced 2026-06-30 03:37:53 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Implement cross-validation of time series(a rolling forecast origin) (#261)

Browse source 
* Resolve conflict

* Change comments and add error column to output DataFrame

* Change file structure

* Update

* Modified diagnostics

* Update diagnostics.py following the advice on Github

* Add tests and documentation

* Change copy method into Prophet class and reflect comments
This commit is contained in:
Nagi Teramo 2017-08-11 03:14:23 +09:00 committed by Ben Letham
parent fd125c4760
commit 79d0793ce4
5 changed files with 304 additions and 2 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

131
python/fbprophet/diagnostics.py Normal file
View file

@ -0,0 +1,131 @@
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree. An additional grant
# of patent rights can be found in the PATENTS file in the same directory.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import logging
logger = logging.getLogger(__name__)
import numpy as np
import pandas as pd
from functools import reduce
def _cutoffs(df, horizon, k, period):
"""Generate cutoff dates
Parameters
----------
df: pd.DataFrame with historical data
horizon: pd.Timedelta.
Forecast horizon
k: Int number.
The number of forecasts point.
period: pd.Timedelta.
Simulated Forecast will be done at every this period.
Returns
-------
list of pd.Timestamp
"""
# Last cutoff is 'latest date in data - horizon' date
cutoff = df['ds'].max() - horizon
result = [cutoff]
for i in range(1, k):
cutoff -= period
# If data does not exist in data range (cutoff, cutoff + horizon]
if not (((df['ds'] > cutoff) & (df['ds'] <= cutoff + horizon)).any()):
# Next cutoff point is 'closest date before cutoff in data - horizon'
closest_date = df[df['ds'] <= cutoff].max()['ds']
cutoff = closest_date - horizon
if cutoff < df['ds'].min():
logger.warning('Not enough data for requested number of cutoffs! Using {}.'.format(k))
break
result.append(cutoff)
# Sort lines in ascending order
return reversed(result)
def simulated_historical_forecasts(model, horizon, k, period=None):
"""Simulated Historical Forecasts.
If you would like to know it in detail, read the original paper
https://facebookincubator.github.io/prophet/static/prophet_paper_20170113.pdf
Parameters
----------
model: Prophet class object.
Fitted Prophet model
horizon: string which has pd.Timedelta compatible style.
Forecast horizon ('5 days', '3 hours', '10 seconds' etc)
k: Int number.
The number of forecasts point.
period: string which has pd.Timedelta compatible style or None, default None.
Simulated Forecast will be done at every this period.
0.5 * horizon is used when it is None.
Returns
-------
A pd.DataFrame with the forecast, actual value and cutoff.
"""
df = model.history.copy().reset_index(drop=True)
horizon = pd.Timedelta(horizon)
period = 0.5 * horizon if period is None else pd.Timedelta(period)
cutoffs = _cutoffs(df, horizon, k, period)
predicts = []
for cutoff in cutoffs:
# Generate new object with copying fitting options
m = model.copy(cutoff)
# Train model
m.fit(df[df['ds'] <= cutoff])
# Calculate yhat
index_predicted = (df['ds'] > cutoff) & (df['ds'] <= cutoff + horizon)
columns = ['ds'] + (['cap'] if m.growth == 'logistic' else [])
yhat = m.predict(df[index_predicted][columns])
# Merge yhat(predicts), y(df, original data) and cutoff
predicts.append(pd.concat([
yhat[['ds', 'yhat', 'yhat_lower', 'yhat_upper']],
df[index_predicted][['y']].reset_index(drop=True),
pd.DataFrame({'cutoff': [cutoff] * len(yhat)})
], axis=1))
# Combine all predicted pd.DataFrame into one pd.DataFrame
return reduce(lambda x, y: x.append(y), predicts).reset_index(drop=True)
def cross_validation(model, horizon, period, initial=None):
"""Cross-Validation for time-series.
This function is the same with Time series cross-validation described in https://robjhyndman.com/hyndsight/tscv/
when the value of period is equal to the time interval of data.
Parameters
----------
model: Prophet class object. Fitted Prophet model
horizon: string which has pd.Timedelta compatible style.
Forecast horizon ('5 days', '3 hours', '10 seconds' etc)
period: string which has pd.Timedelta compatible style.
Simulated Forecast will be done at every this period.
initial: string which has pd.Timedelta compatible style or None, default None.
First training period.
3 * horizon is used when it is None.
Returns
-------
A pd.DataFrame with the forecast, actual value and cutoff.
"""
te = model.history['ds'].max()
ts = model.history['ds'].min()
horizon = pd.Timedelta(horizon)
period = pd.Timedelta(period)
initial = 3 * horizon if initial is None else pd.Timedelta(initial)
k = int(np.floor(((te - horizon) - (ts + initial)) / period))
return simulated_historical_forecasts(model, horizon, k, period)

37
python/fbprophet/forecaster.py
View file

@ -36,7 +36,6 @@ except ImportError:
# fb-block 2
class Prophet(object):
"""Prophet forecaster.
@ -1395,3 +1394,39 @@ class Prophet(object):
ax.set_xlabel('ds')
ax.set_ylabel(name)
return artists
def copy(self, cutoff=None):
"""Copy Prophet object
Parameters
----------
cutoff: pd.Timestamp or None, default None.
cuttoff Timestamp for changepoints member variable.
changepoints are only remained if 'changepoints <= cutoff'
Returns
-------
Prophet class object with the same parameter with model variable
"""
if self.changepoints is not None and cutoff is not None:
# Filter change points '<= cutoff'
self.changepoints = self.changepoints[self.changepoints <= cutoff]
self.n_changepoints = len(self.changepoints)
return Prophet(
growth=self.growth,
n_changepoints=self.n_changepoints,
changepoints=self.changepoints,
yearly_seasonality=self.yearly_seasonality,
weekly_seasonality=self.weekly_seasonality,
daily_seasonality=self.daily_seasonality,
holidays=self.holidays,
seasonality_prior_scale=self.seasonality_prior_scale,
changepoint_prior_scale=self.changepoint_prior_scale,
holidays_prior_scale=self.holidays_prior_scale,
mcmc_samples=self.mcmc_samples,
interval_width=self.interval_width,
uncertainty_samples=self.uncertainty_samples
)

88
python/fbprophet/tests/test_diagnostics.py Normal file
View file

@ -0,0 +1,88 @@
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree. An additional grant
# of patent rights can be found in the PATENTS file in the same directory.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import numpy as np
import pandas as pd
from unittest import TestCase
from fbprophet import Prophet
from fbprophet import diagnostics
class TestDiagnostics(TestCase):
def __init__(self, *args, **kwargs):
super(TestDiagnostics, self).__init__(*args, **kwargs)
# Use first 100 record in data.csv
self.__df = pd.read_csv(os.path.join(os.path.dirname(__file__), 'data.csv'), parse_dates=['ds']).head(100)
def test_simulated_historical_forecasts(self):
m = Prophet()
m.fit(self.__df)
k = 3
for p in [1, 10]:
for h in [1, 3]:
period = '{} days'.format(p)
horizon = '{} days'.format(h)
df_shf = diagnostics.simulated_historical_forecasts(m, horizon=horizon, k=k, period=period)
# All cutoff dates should be less than ds dates
self.assertTrue((df_shf['cutoff'] < df_shf['ds']).all())
# The unique size of output cutoff should be equal to 'k'
self.assertEqual(len(np.unique(df_shf['cutoff'])), k)
# Each y in df_shf and self.__df with same ds should be equal
df_merged = pd.merge(df_shf, self.__df, 'left', on='ds')
self.assertAlmostEqual(np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0)
def test_simulated_historical_forecasts_logistic(self):
m = Prophet(growth='logistic')
df = self.__df.copy()
df['cap'] = 40
m.fit(df)
df_shf = diagnostics.simulated_historical_forecasts(m, horizon='3 days', k=2, period='3 days')
# All cutoff dates should be less than ds dates
self.assertTrue((df_shf['cutoff'] < df_shf['ds']).all())
# The unique size of output cutoff should be equal to 'k'
self.assertEqual(len(np.unique(df_shf['cutoff'])), 2)
# Each y in df_shf and self.__df with same ds should be equal
df_merged = pd.merge(df_shf, df, 'left', on='ds')
self.assertAlmostEqual(np.sum((df_merged['y_x'] - df_merged['y_y']) ** 2), 0.0)
def test_simulated_historical_forecasts_default_value_check(self):
m = Prophet()
m.fit(self.__df)
# Default value of period should be equal to 0.5 * horizon
df_shf1 = diagnostics.simulated_historical_forecasts(m, horizon='10 days', k=1)
df_shf2 = diagnostics.simulated_historical_forecasts(m, horizon='10 days', k=1, period='5 days')
self.assertAlmostEqual(((df_shf1 - df_shf2)**2)[['y', 'yhat']].sum().sum(), 0.0)
def test_cross_validation(self):
m = Prophet()
m.fit(self.__df)
# Calculate the number of cutoff points(k)
te = self.__df['ds'].max()
ts = self.__df['ds'].min()
horizon = pd.Timedelta('4 days')
period = pd.Timedelta('1 days')
initial = pd.Timedelta('90 days')
k = int(np.floor(((te - horizon) - (ts + initial)) / period))
df_cv = diagnostics.cross_validation(m, horizon=horizon, period=period, initial=initial)
# The unique size of output cutoff should be equal to 'k'
self.assertEqual(len(np.unique(df_cv['cutoff'])), k)
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='1 days')
df_cv2 = diagnostics.cross_validation(m, horizon='32 days', period='1 days', initial='96 days')
self.assertAlmostEqual(((df_cv1 - df_cv2)**2)[['y', 'yhat']].sum().sum(), 0.0)

47
python/fbprophet/tests/test_prophet.py
View file

@ -15,6 +15,7 @@ import pandas as pd
# fb-block 1 start
import os
import itertools
from unittest import TestCase
from fbprophet import Prophet
@ -421,3 +422,49 @@ class TestProphet(TestCase):
fcst['yhat'][0],
fcst['trend'][0] + fcst['seasonal'][0],
)
def test_copy(self):
# These values are created except for its default values
products = itertools.product(
['linear', 'logistic'], # growth
[None, pd.to_datetime(['2016-12-25'])], # changepoints
[3], # n_changepoints
[True, False], # yearly_seasonality
[True, False], # weekly_seasonality
[True, False], # daily_seasonality
[None, pd.DataFrame({'ds': pd.to_datetime(['2016-12-25']), 'holiday': ['x']})], # holidays
[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)
m2 = m1.copy()
self.assertEqual(m1.growth, m2.growth)
self.assertEqual(m1.n_changepoints, m2.n_changepoints)
self.assertEqual(m1.changepoints, m2.changepoints)
self.assertEqual(m1.yearly_seasonality, m2.yearly_seasonality)
self.assertEqual(m1.weekly_seasonality, m2.weekly_seasonality)
self.assertEqual(m1.daily_seasonality, m2.daily_seasonality)
if m1.holidays is None:
self.assertEqual(m1.holidays, m2.holidays)
else:
self.assertTrue((m1.holidays == m2.holidays).values.all())
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
changepoints = pd.date_range('2016-12-15', '2017-01-15')
cutoff = pd.Timestamp('2016-12-25')
m1 = Prophet(changepoints=changepoints)
m2 = m1.copy(cutoff=cutoff)
changepoints = changepoints[changepoints <= cutoff]
self.assertTrue((changepoints == m2.changepoints).all())

3
python/setup.py
View file

@ -94,6 +94,7 @@ class TestCommand(test_command):
sys.modules.update(old_modules)
working_set.__init__()
setup(
name='fbprophet',
version='0.1.1',
@ -117,7 +118,7 @@ setup(
'develop': DevelopCommand,
'test': TestCommand,
},
test_suite='fbprophet.tests.test_prophet',
test_suite='fbprophet.tests',
long_description="""
Implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly and weekly seasonality, plus holidays. It works best with daily periodicity data with at least one year of historical data. Prophet is robust to missing data, shifts in the trend, and large outliers.
"""