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Enable seasonalities automatically depending on history length / frequency

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This commit is contained in:
Ben Letham 2017-04-13 01:25:03 -07:00
parent c164367c08
commit d937f47612
7 changed files with 255 additions and 16 deletions
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41
R/R/prophet.R
View file

@ -27,8 +27,8 @@ globalVariables(c(
#' if input `changepoints` is supplied. If `changepoints` is not supplied,
#' then n.changepoints potential changepoints are selected uniformly from the
#' first 80 percent of df$ds.
#' @param yearly.seasonality Boolean, fit yearly seasonality.
#' @param weekly.seasonality Boolean, fit weekly seasonality.
#' @param yearly.seasonality Fit yearly seasonality; 'auto', TRUE, or FALSE.
#' @param weekly.seasonality Fit weekly seasonality; 'auto', TRUE, or FALSE.
#' @param holidays data frame with columns holiday (character) and ds (date
#' type)and optionally columns lower_window and upper_window which specify a
#' range of days around the date to be included as holidays. lower_window=-2
@ -70,8 +70,8 @@ prophet <- function(df = df,
growth = 'linear',
changepoints = NULL,
n.changepoints = 25,
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
yearly.seasonality = 'auto',
weekly.seasonality = 'auto',
holidays = NULL,
seasonality.prior.scale = 10,
holidays.prior.scale = 10,
@ -401,6 +401,38 @@ make_all_seasonality_features <- function(m, df) {
return(seasonal.features)
}
#' Set seasonalities that were left on auto.
#'
#' Turns on yearly seasonality if there is >=2 years of history.
#' Turns on weekly seasonality if there is >=2 weeks of history, and the
#' spacing between dates in the history is <7 days.
#'
#' @param m Prophet object.
#'
#' @return The prophet model with seasonalities set.
#'
set_auto_seasonalities <- function(m) {
first <- min(m$history$ds)
last <- max(m$history$ds)
if (m$yearly.seasonality == 'auto') {
if (last - first < 730) {
m$yearly.seasonality <- FALSE
} else {
m$yearly.seasonality <- TRUE
}
}
if (m$weekly.seasonality == 'auto') {
dt <- diff(m$history$ds)
min.dt <- min(dt[dt > 0])
if ((last - first < 14) || (min.dt >= 7)) {
m$weekly.seasonality <- FALSE
} else {
m$weekly.seasonality <- TRUE
}
}
return(m)
}
#' Initialize linear growth.
#'
#' Provides a strong initialization for linear growth by calculating the
@ -484,6 +516,7 @@ fit.prophet <- function(m, df, ...) {
history <- out$df
m <- out$m
m$history <- history
m <- set_auto_seasonalities(m)
seasonal.features <- make_all_seasonality_features(m, history)
m <- set_changepoints(m)

8
R/man/prophet.Rd
View file

@ -5,8 +5,8 @@
\title{Prophet forecaster.}
\usage{
prophet(df = df, growth = "linear", changepoints = NULL,
n.changepoints = 25, yearly.seasonality = TRUE,
weekly.seasonality = TRUE, holidays = NULL,
n.changepoints = 25, yearly.seasonality = "auto",
weekly.seasonality = "auto", holidays = NULL,
seasonality.prior.scale = 10, holidays.prior.scale = 10,
changepoint.prior.scale = 0.05, mcmc.samples = 0, interval.width = 0.8,
uncertainty.samples = 1000, fit = TRUE, ...)
@ -28,9 +28,9 @@ if input `changepoints` is supplied. If `changepoints` is not supplied,
then n.changepoints potential changepoints are selected uniformly from the
first 80 percent of df$ds.}
\item{yearly.seasonality}{Boolean, fit yearly seasonality.}
\item{yearly.seasonality}{Fit yearly seasonality; 'auto', TRUE, or FALSE.}
\item{weekly.seasonality}{Boolean, fit weekly seasonality.}
\item{weekly.seasonality}{Fit weekly seasonality; 'auto', TRUE, or FALSE.}
\item{holidays}{data frame with columns holiday (character) and ds (date
type)and optionally columns lower_window and upper_window which specify a

20
R/man/set_auto_seasonalities.Rd Normal file
View file

@ -0,0 +1,20 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/prophet.R
\name{set_auto_seasonalities}
\alias{set_auto_seasonalities}
\title{Set seasonalities that were left on auto.}
\usage{
set_auto_seasonalities(m)
}
\arguments{
\item{m}{Prophet object.}
}
\value{
The prophet model with seasonalities set.
}
\description{
Turns on yearly seasonality if there is >=2 years of history.
Turns on weekly seasonality if there is >=2 weeks of history, and the
spacing between dates in the history is <7 days.
}

42
R/tests/testthat/data.csv
View file

@ -467,3 +467,45 @@ ds,y
2014-03-27,60.97
2014-03-28,60.01
2014-03-31,60.24
2014-04-01,62.62
2014-04-02,62.72
2014-04-03,59.49
2014-04-04,56.75
2014-04-07,56.95
2014-04-08,58.19
2014-04-09,62.41
2014-04-10,59.16
2014-04-11,58.53
2014-04-14,58.89
2014-04-15,59.09
2014-04-16,59.72
2014-04-17,58.94
2014-04-21,61.24
2014-04-22,63.03
2014-04-23,61.36
2014-04-24,60.87
2014-04-25,57.71
2014-04-28,56.14
2014-04-29,58.15
2014-04-30,59.78
2014-05-01,61.15
2014-05-02,60.46
2014-05-05,61.22
2014-05-06,58.53
2014-05-07,57.39
2014-05-08,56.76
2014-05-09,57.24
2014-05-12,59.83
2014-05-13,59.83
2014-05-14,59.23
2014-05-15,57.92
2014-05-16,58.02
2014-05-19,59.21
2014-05-20,58.56
2014-05-21,60.49
2014-05-22,60.52
2014-05-23,61.35
2014-05-27,63.48
2014-05-28,63.51
2014-05-29,63.83
2014-05-30,63.30

1 ds y
467 2014-03-27 60.97
468 2014-03-28 60.01
469 2014-03-31 60.24
470 2014-04-01 62.62
471 2014-04-02 62.72
472 2014-04-03 59.49
473 2014-04-04 56.75
474 2014-04-07 56.95
475 2014-04-08 58.19
476 2014-04-09 62.41
477 2014-04-10 59.16
478 2014-04-11 58.53
479 2014-04-14 58.89
480 2014-04-15 59.09
481 2014-04-16 59.72
482 2014-04-17 58.94
483 2014-04-21 61.24
484 2014-04-22 63.03
485 2014-04-23 61.36
486 2014-04-24 60.87
487 2014-04-25 57.71
488 2014-04-28 56.14
489 2014-04-29 58.15
490 2014-04-30 59.78
491 2014-05-01 61.15
492 2014-05-02 60.46
493 2014-05-05 61.22
494 2014-05-06 58.53
495 2014-05-07 57.39
496 2014-05-08 56.76
497 2014-05-09 57.24
498 2014-05-12 59.83
499 2014-05-13 59.83
500 2014-05-14 59.23
501 2014-05-15 57.92
502 2014-05-16 58.02
503 2014-05-19 59.21
504 2014-05-20 58.56
505 2014-05-21 60.49
506 2014-05-22 60.52
507 2014-05-23 61.35
508 2014-05-27 63.48
509 2014-05-28 63.51
510 2014-05-29 63.83
511 2014-05-30 63.30

43
R/tests/testthat/test_prophet.R
View file

@ -119,7 +119,7 @@ test_that("fourier_series_yearly", {
})
test_that("growth_init", {
history <- DATA
history <- DATA[1:468, ]
history$cap <- max(history$y)
m <- prophet(history, growth = 'logistic', fit = FALSE)
@ -209,7 +209,8 @@ test_that("fit_with_holidays", {
test_that("make_future_dataframe", {
skip_if_not(Sys.getenv('R_ARCH') != '/i386')
m <- prophet(train)
train.t <- DATA[1:234, ]
m <- prophet(train.t)
future <- make_future_dataframe(m, periods = 3, freq = 'd',
include_history = FALSE)
correct <- as.Date(c('2013-04-26', '2013-04-27', '2013-04-28'))
@ -220,3 +221,41 @@ test_that("make_future_dataframe", {
correct <- as.Date(c('2013-05-25', '2013-06-25', '2013-07-25'))
expect_equal(future$ds, correct)
})
test_that("auto_weekly_seasonality", {
skip_if_not(Sys.getenv('R_ARCH') != '/i386')
# Should be True
N.w <- 15
train.w <- DATA[1:N.w, ]
m <- prophet(train.w, fit = FALSE)
expect_equal(m$weekly.seasonality, 'auto')
m <- prophet:::fit.prophet(m, train.w)
expect_equal(m$weekly.seasonality, TRUE)
# Should be False due to too short history
N.w <- 9
train.w <- DATA[1:N.w, ]
m <- prophet(train.w)
expect_equal(m$weekly.seasonality, FALSE)
m <- prophet(train.w, weekly.seasonality = TRUE)
expect_equal(m$weekly.seasonality, TRUE)
# Should be False due to weekly spacing
train.w <- DATA[seq(1, nrow(DATA), 7), ]
m <- prophet(train.w)
expect_equal(m$weekly.seasonality, FALSE)
})
test_that("auto_yearly_seasonality", {
skip_if_not(Sys.getenv('R_ARCH') != '/i386')
# Should be True
m <- prophet(DATA, fit = FALSE)
expect_equal(m$yearly.seasonality, 'auto')
m <- prophet:::fit.prophet(m, DATA)
expect_equal(m$yearly.seasonality, TRUE)
# Should be False due to too short history
N.w <- 240
train.y <- DATA[1:N.w, ]
m <- prophet(train.y)
expect_equal(m$yearly.seasonality, FALSE)
m <- prophet(train.y, yearly.seasonality = TRUE)
expect_equal(m$yearly.seasonality, TRUE)
})

32
python/fbprophet/forecaster.py
View file

@ -46,8 +46,8 @@ class Prophet(object):
if input `changepoints` is supplied. If `changepoints` is not supplied,
then n.changepoints potential changepoints are selected uniformly from
the first 80 percent of the history.
yearly_seasonality: Boolean, fit yearly seasonality.
weekly_seasonality: Boolean, fit weekly seasonality.
yearly_seasonality: Fit yearly seasonality. Can be 'auto', True, or False.
weekly_seasonality: Fit weekly seasonality. Can be 'auto', True, or False.
holidays: pd.DataFrame with columns holiday (string) and ds (date type)
and optionally columns lower_window and upper_window which specify a
range of days around the date to be included as holidays.
@ -77,8 +77,8 @@ class Prophet(object):
growth='linear',
changepoints=None,
n_changepoints=25,
yearly_seasonality=True,
weekly_seasonality=True,
yearly_seasonality='auto',
weekly_seasonality='auto',
holidays=None,
seasonality_prior_scale=10.0,
holidays_prior_scale=10.0,
@ -392,6 +392,29 @@ class Prophet(object):
seasonal_features.append(self.make_holiday_features(df['ds']))
return pd.concat(seasonal_features, axis=1)
def set_auto_seasonalities(self):
"""Set seasonalities that were left on auto.
Turns on yearly seasonality if there is >=2 years of history.
Turns on weekly seasonality if there is >=2 weeks of history, and the
spacing between dates in the history is <7 days.
"""
first = self.history['ds'].min()
last = self.history['ds'].max()
if self.yearly_seasonality == 'auto':
if last - first < pd.Timedelta(days=730):
self.yearly_seasonality = False
else:
self.yearly_seasonality = True
if self.weekly_seasonality == 'auto':
dt = self.history['ds'].diff()
min_dt = dt.iloc[dt.nonzero()[0]].min()
if ((last - first < pd.Timedelta(weeks=2)) or
(min_dt >= pd.Timedelta(weeks=1))):
self.weekly_seasonality = False
else:
self.weekly_seasonality = True
@staticmethod
def linear_growth_init(df):
"""Initialize linear growth.
@ -487,6 +510,7 @@ class Prophet(object):
history = self.setup_dataframe(history, initialize_scales=True)
self.history = history
self.set_auto_seasonalities()
seasonal_features = self.make_all_seasonality_features(history)
self.set_changepoints()

85
python/fbprophet/tests/test_prophet.py
View file

@ -145,7 +145,7 @@ class TestProphet(TestCase):
def test_growth_init(self):
model = Prophet(growth='logistic')
history = DATA.copy()
history = DATA.iloc[:468].copy()
history['cap'] = history['y'].max()
history = model.setup_dataframe(history, initialize_scales=True)
@ -237,7 +237,7 @@ class TestProphet(TestCase):
model.fit(DATA).predict()
def test_make_future_dataframe(self):
N = DATA.shape[0]
N = 468
train = DATA.head(N // 2)
forecaster = Prophet()
forecaster.fit(train)
@ -255,6 +255,45 @@ class TestProphet(TestCase):
for i in range(3):
self.assertEqual(future.iloc[i]['ds'], correct[i])
def test_auto_weekly_seasonality(self):
# Should be True
N = 15
train = DATA.head(N)
m = Prophet()
self.assertEqual(m.weekly_seasonality, 'auto')
m.fit(train)
self.assertEqual(m.weekly_seasonality, True)
# Should be False due to too short history
N = 9
train = DATA.head(N)
m = Prophet()
m.fit(train)
self.assertEqual(m.weekly_seasonality, False)
m = Prophet(weekly_seasonality=True)
m.fit(train)
self.assertEqual(m.weekly_seasonality, True)
# Should be False due to weekly spacing
train = DATA.iloc[::7, :]
m = Prophet()
m.fit(train)
self.assertEqual(m.weekly_seasonality, False)
def test_auto_yearly_seasonality(self):
# Should be True
m = Prophet()
self.assertEqual(m.yearly_seasonality, 'auto')
m.fit(DATA)
self.assertEqual(m.yearly_seasonality, True)
# Should be False due to too short history
N = 240
train = DATA.head(N)
m = Prophet()
m.fit(train)
self.assertEqual(m.yearly_seasonality, False)
m = Prophet(yearly_seasonality=True)
m.fit(train)
self.assertEqual(m.yearly_seasonality, True)
DATA = pd.read_csv(StringIO("""
ds,y
@ -726,4 +765,46 @@ ds,y
2014-03-27,60.97
2014-03-28,60.01
2014-03-31,60.24
2014-04-01,62.62
2014-04-02,62.72
2014-04-03,59.49
2014-04-04,56.75
2014-04-07,56.95
2014-04-08,58.19
2014-04-09,62.41
2014-04-10,59.16
2014-04-11,58.53
2014-04-14,58.89
2014-04-15,59.09
2014-04-16,59.72
2014-04-17,58.94
2014-04-21,61.24
2014-04-22,63.03
2014-04-23,61.36
2014-04-24,60.87
2014-04-25,57.71
2014-04-28,56.14
2014-04-29,58.15
2014-04-30,59.78
2014-05-01,61.15
2014-05-02,60.46
2014-05-05,61.22
2014-05-06,58.53
2014-05-07,57.39
2014-05-08,56.76
2014-05-09,57.24
2014-05-12,59.83
2014-05-13,59.83
2014-05-14,59.23
2014-05-15,57.92
2014-05-16,58.02
2014-05-19,59.21
2014-05-20,58.56
2014-05-21,60.49
2014-05-22,60.52
2014-05-23,61.35
2014-05-27,63.48
2014-05-28,63.51
2014-05-29,63.83
2014-05-30,63.30
"""), parse_dates=['ds'])