mirror of
https://github.com/saymrwulf/prophet.git
synced 2026-06-10 00:39:01 +00:00
Custom seasonality prior scales R, handle NAs in holiday priors
This commit is contained in:
Ben Letham
parent
ddbb353278
commit
66ea9444eb
7 changed files with 156 additions and 57 deletions
|
|
@ -42,7 +42,8 @@ globalVariables(c(
|
|||
#' a column prior_scale specifying the prior scale for each holiday.
|
||||
#' @param seasonality.prior.scale Parameter modulating the strength of the
|
||||
#' seasonality model. Larger values allow the model to fit larger seasonal
|
||||
#' fluctuations, smaller values dampen the seasonality.
|
||||
#' fluctuations, smaller values dampen the seasonality. Can be specified for
|
||||
#' individual seasonalities using add_seasonality.
|
||||
#' @param holidays.prior.scale Parameter modulating the strength of the holiday
|
||||
#' components model, unless overridden in the holidays input.
|
||||
#' @param changepoint.prior.scale Parameter modulating the flexibility of the
|
||||
|
|
@ -508,37 +509,44 @@ make_holiday_features <- function(m, dates) {
|
|||
} else {
|
||||
offsets <- c(0)
|
||||
}
|
||||
if (exists('prior_scale', where = .) && !is.na(.$prior_scale)) {
|
||||
ps <- .$prior_scale
|
||||
} else {
|
||||
ps <- m$holidays.prior.scale
|
||||
}
|
||||
names <- paste(.$holiday, '_delim_', ifelse(offsets < 0, '-', '+'),
|
||||
abs(offsets), sep = '')
|
||||
dplyr::data_frame(ds = .$ds + offsets * 24 * 3600, holiday = names,
|
||||
prior_scale = ps)
|
||||
dplyr::data_frame(ds = .$ds + offsets * 24 * 3600, holiday = names)
|
||||
}) %>%
|
||||
dplyr::mutate(x = 1.) %>%
|
||||
tidyr::spread(holiday, x, fill = 0)
|
||||
|
||||
holiday.features <- data.frame(ds = set_date(dates)) %>%
|
||||
dplyr::left_join(wide, by = 'ds')
|
||||
dplyr::left_join(wide, by = 'ds') %>%
|
||||
dplyr::select(-ds)
|
||||
|
||||
prior.scales.all <- holiday.features$prior_scale
|
||||
prior.scales <- c()
|
||||
|
||||
holiday.features <- dplyr::select(holiday.features, -ds, -prior_scale)
|
||||
holiday.features[is.na(holiday.features)] <- 0
|
||||
|
||||
for (name in colnames(holiday.features)) {
|
||||
rows <- !is.na(holiday.features[[name]]) & (holiday.features[[name]] == 1)
|
||||
ps <- unique(prior.scales.all[rows])
|
||||
# Prior scales
|
||||
if (!('prior_scale' %in% colnames(m$holidays))) {
|
||||
m$holidays$prior_scale <- m$holidays.prior.scale
|
||||
}
|
||||
prior.scales.list <- list()
|
||||
for (name in unique(m$holidays$holiday)) {
|
||||
df.h <- m$holidays[m$holidays$holiday == name, ]
|
||||
ps <- unique(df.h$prior_scale)
|
||||
if (length(ps) > 1) {
|
||||
sn <- strsplit(name, '_delim_', fixed = TRUE)[[1]][1]
|
||||
stop('Holiday ', sn, ' does not have a consistent prior scale ',
|
||||
stop('Holiday ', name, ' does not have a consistent prior scale ',
|
||||
'specification')
|
||||
}
|
||||
prior.scales <- c(prior.scales, ps)
|
||||
if (is.na(ps)) {
|
||||
ps <- m$holidays.prior.scale
|
||||
}
|
||||
if (ps <= 0) {
|
||||
stop('Prior scale must be > 0.')
|
||||
}
|
||||
prior.scales.list[[name]] <- ps
|
||||
}
|
||||
|
||||
prior.scales <- c()
|
||||
for (name in colnames(holiday.features)) {
|
||||
sn <- strsplit(name, '_delim_', fixed = TRUE)[[1]][1]
|
||||
prior.scales <- c(prior.scales, prior.scales.list[[sn]])
|
||||
}
|
||||
return(list(holiday.features = holiday.features,
|
||||
prior.scales = prior.scales))
|
||||
|
|
@ -584,23 +592,28 @@ add_regressor <- function(m, name, prior.scale = NULL, standardize = 'auto'){
|
|||
return(m)
|
||||
}
|
||||
|
||||
#' Add a seasonal component with specified period and number of Fourier
|
||||
#' components.
|
||||
#' Add a seasonal component with specified period, number of Fourier
|
||||
#' components, and prior scale.
|
||||
#'
|
||||
#' Increasing the number of Fourier components allows the seasonality to change
|
||||
#' more quickly (at risk of overfitting). Default values for yearly and weekly
|
||||
#' seasonalities are 10 and 3 respectively.
|
||||
#'
|
||||
#' Increasing prior scale will allow this seasonality component more
|
||||
#' flexibility, decreasing will dampen it. If not provided, will use the
|
||||
#' seasonality.prior.scale provided on Prophet initialization (defaults to 10).
|
||||
#'
|
||||
#' @param m Prophet object.
|
||||
#' @param name String name of the seasonality component.
|
||||
#' @param period Float number of days in one period.
|
||||
#' @param fourier.order Int number of Fourier components to use.
|
||||
#' @param prior.scale Float prior scale for this component.
|
||||
#'
|
||||
#' @return The prophet model with the seasonality added.
|
||||
#'
|
||||
#' @importFrom dplyr "%>%"
|
||||
#' @export
|
||||
add_seasonality <- function(m, name, period, fourier.order) {
|
||||
add_seasonality <- function(m, name, period, fourier.order, prior.scale = NULL) {
|
||||
if (!is.null(m$history)) {
|
||||
stop("Seasonality must be added prior to model fitting.")
|
||||
}
|
||||
|
|
@ -608,7 +621,19 @@ add_seasonality <- function(m, name, period, fourier.order) {
|
|||
# Allow overriding built-in seasonalities
|
||||
validate_column_name(m, name, check_seasonalities = FALSE)
|
||||
}
|
||||
m$seasonalities[[name]] <- c(period, fourier.order)
|
||||
if (is.null(prior.scale)) {
|
||||
ps <- m$seasonality.prior.scale
|
||||
} else {
|
||||
ps <- prior.scale
|
||||
}
|
||||
if (ps <= 0) {
|
||||
stop('Prior scale must be > 0')
|
||||
}
|
||||
m$seasonalities[[name]] <- list(
|
||||
period = period,
|
||||
fourier.order = fourier.order,
|
||||
prior.scale = ps
|
||||
)
|
||||
return(m)
|
||||
}
|
||||
|
||||
|
|
@ -631,12 +656,12 @@ make_all_seasonality_features <- function(m, df) {
|
|||
|
||||
# Seasonality features
|
||||
for (name in names(m$seasonalities)) {
|
||||
period <- m$seasonalities[[name]][1]
|
||||
series.order <- m$seasonalities[[name]][2]
|
||||
features <- make_seasonality_features(df$ds, period, series.order, name)
|
||||
props <- m$seasonalities[[name]]
|
||||
features <- make_seasonality_features(
|
||||
df$ds, props$period, props$fourier.order, name)
|
||||
seasonal.features <- cbind(seasonal.features, features)
|
||||
prior.scales <- c(prior.scales,
|
||||
m$seasonality.prior.scale * rep(1, ncol(features)))
|
||||
props$prior.scale * rep(1, ncol(features)))
|
||||
}
|
||||
|
||||
# Holiday features
|
||||
|
|
@ -751,21 +776,33 @@ set_auto_seasonalities <- function(m) {
|
|||
fourier.order <- parse_seasonality_args(
|
||||
m, 'yearly', m$yearly.seasonality, yearly.disable, 10)
|
||||
if (fourier.order > 0) {
|
||||
m$seasonalities[['yearly']] <- c(365.25, fourier.order)
|
||||
m$seasonalities[['yearly']] <- list(
|
||||
period = 365.25,
|
||||
fourier.order = fourier.order,
|
||||
prior.scale = m$seasonality.prior.scale
|
||||
)
|
||||
}
|
||||
|
||||
weekly.disable <- ((time_diff(last, first) < 14) || (min.dt >= 7))
|
||||
fourier.order <- parse_seasonality_args(
|
||||
m, 'weekly', m$weekly.seasonality, weekly.disable, 3)
|
||||
if (fourier.order > 0) {
|
||||
m$seasonalities[['weekly']] <- c(7, fourier.order)
|
||||
m$seasonalities[['weekly']] <- list(
|
||||
period = 7,
|
||||
fourier.order = fourier.order,
|
||||
prior.scale = m$seasonality.prior.scale
|
||||
)
|
||||
}
|
||||
|
||||
daily.disable <- ((time_diff(last, first) < 2) || (min.dt >= 1))
|
||||
fourier.order <- parse_seasonality_args(
|
||||
m, 'daily', m$daily.seasonality, daily.disable, 4)
|
||||
if (fourier.order > 0) {
|
||||
m$seasonalities[['daily']] <- c(1, fourier.order)
|
||||
m$seasonalities[['daily']] <- list(
|
||||
period = 1,
|
||||
fourier.order = fourier.order,
|
||||
prior.scale = m$seasonality.prior.scale
|
||||
)
|
||||
}
|
||||
return(m)
|
||||
}
|
||||
|
|
@ -1598,7 +1635,7 @@ plot_yearly <- function(m, uncertainty = TRUE, yearly_start = 0) {
|
|||
plot_seasonality <- function(m, name, uncertainty = TRUE) {
|
||||
# Compute seasonality from Jan 1 through a single period.
|
||||
start <- set_date('2017-01-01')
|
||||
period <- m$seasonalities[[name]][1]
|
||||
period <- m$seasonalities[[name]]$period
|
||||
end <- start + period * 24 * 3600
|
||||
plot.points <- 200
|
||||
days <- seq(from=start, to=end, length.out=plot.points)
|
||||
|
|
|
|||
|
|
@ -2,10 +2,10 @@
|
|||
% Please edit documentation in R/prophet.R
|
||||
\name{add_seasonality}
|
||||
\alias{add_seasonality}
|
||||
\title{Add a seasonal component with specified period and number of Fourier
|
||||
components.}
|
||||
\title{Add a seasonal component with specified period, number of Fourier
|
||||
components, and prior scale.}
|
||||
\usage{
|
||||
add_seasonality(m, name, period, fourier.order)
|
||||
add_seasonality(m, name, period, fourier.order, prior.scale = NULL)
|
||||
}
|
||||
\arguments{
|
||||
\item{m}{Prophet object.}
|
||||
|
|
@ -15,6 +15,8 @@ add_seasonality(m, name, period, fourier.order)
|
|||
\item{period}{Float number of days in one period.}
|
||||
|
||||
\item{fourier.order}{Int number of Fourier components to use.}
|
||||
|
||||
\item{prior.scale}{Float prior scale for this component.}
|
||||
}
|
||||
\value{
|
||||
The prophet model with the seasonality added.
|
||||
|
|
@ -24,3 +26,8 @@ Increasing the number of Fourier components allows the seasonality to change
|
|||
more quickly (at risk of overfitting). Default values for yearly and weekly
|
||||
seasonalities are 10 and 3 respectively.
|
||||
}
|
||||
\details{
|
||||
Increasing prior scale will allow this seasonality component more
|
||||
flexibility, decreasing will dampen it. If not provided, will use the
|
||||
seasonality.prior.scale provided on Prophet initialization (defaults to 10).
|
||||
}
|
||||
|
|
|
|||
|
|
@ -12,7 +12,9 @@ make_holiday_features(m, dates)
|
|||
\item{dates}{Vector with dates used for computing seasonality.}
|
||||
}
|
||||
\value{
|
||||
A dataframe with a column for each holiday.
|
||||
A list with entries
|
||||
holiday.features: dataframe with a column for each holiday.
|
||||
prior.scales: array of prior scales for each holiday column.
|
||||
}
|
||||
\description{
|
||||
Construct a matrix of holiday features.
|
||||
|
|
|
|||
|
|
@ -43,14 +43,16 @@ FALSE, or a number of Fourier terms to generate.}
|
|||
\item{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
|
||||
will include 2 days prior to the date as holidays.}
|
||||
will include 2 days prior to the date as holidays. Also optionally can have
|
||||
a column prior_scale specifying the prior scale for each holiday.}
|
||||
|
||||
\item{seasonality.prior.scale}{Parameter modulating the strength of the
|
||||
seasonality model. Larger values allow the model to fit larger seasonal
|
||||
fluctuations, smaller values dampen the seasonality.}
|
||||
fluctuations, smaller values dampen the seasonality. Can be specified for
|
||||
individual seasonalities using add_seasonality.}
|
||||
|
||||
\item{holidays.prior.scale}{Parameter modulating the strength of the holiday
|
||||
components model.}
|
||||
components model, unless overridden in the holidays input.}
|
||||
|
||||
\item{changepoint.prior.scale}{Parameter modulating the flexibility of the
|
||||
automatic changepoint selection. Large values will allow many changepoints,
|
||||
|
|
|
|||
|
|
@ -247,11 +247,22 @@ test_that("holidays", {
|
|||
upper_window = c(1, 1),
|
||||
prior_scale = c(8, 8)
|
||||
)
|
||||
holiday2 <- rbind(holidays, holidays2)
|
||||
holidays2 <- rbind(holidays, holidays2)
|
||||
m <- prophet(holidays = holidays2, fit = FALSE)
|
||||
out <- prophet:::make_holiday_features(m, df$ds)
|
||||
priors <- out$prior.scales
|
||||
expect_true(all(priors == c(8,8, 5, 5)))
|
||||
expect_true(all(priors == c(8, 8, 5, 5)))
|
||||
holidays2 <- data.frame(
|
||||
ds = prophet:::set_date(c('2012-06-06', '2013-06-06')),
|
||||
holiday = c('seans-bday', 'seans-bday'),
|
||||
lower_window = c(0, 0),
|
||||
upper_window = c(1, 1)
|
||||
)
|
||||
holidays2 <- dplyr::bind_rows(holidays, holidays2)
|
||||
m <- prophet(holidays = holidays2, fit = FALSE, holidays.prior.scale = 4)
|
||||
out <- prophet:::make_holiday_features(m, df$ds)
|
||||
priors <- out$prior.scales
|
||||
expect_true(all(priors == c(4, 4, 5, 5)))
|
||||
# Check incompatible priors
|
||||
holidays <- data.frame(
|
||||
ds = prophet:::set_date(c('2016-12-25', '2016-12-27')),
|
||||
|
|
@ -296,9 +307,12 @@ test_that("auto_weekly_seasonality", {
|
|||
train.w <- DATA[1:N.w, ]
|
||||
m <- prophet(train.w, fit = FALSE)
|
||||
expect_equal(m$weekly.seasonality, 'auto')
|
||||
m <- prophet:::fit.prophet(m, train.w)
|
||||
m <- fit.prophet(m, train.w)
|
||||
expect_true('weekly' %in% names(m$seasonalities))
|
||||
expect_equal(m$seasonalities[['weekly']], c(7, 3))
|
||||
true <- list(period = 7, fourier.order = 3, prior.scale = 10)
|
||||
for (name in names(true)) {
|
||||
expect_equal(m$seasonalities$weekly[[name]], true[[name]])
|
||||
}
|
||||
# Should be disabled due to too short history
|
||||
N.w <- 9
|
||||
train.w <- DATA[1:N.w, ]
|
||||
|
|
@ -310,8 +324,11 @@ test_that("auto_weekly_seasonality", {
|
|||
train.w <- DATA[seq(1, nrow(DATA), 7), ]
|
||||
m <- prophet(train.w)
|
||||
expect_false('weekly' %in% names(m$seasonalities))
|
||||
m <- prophet(DATA, weekly.seasonality=2)
|
||||
expect_equal(m$seasonalities[['weekly']], c(7, 2))
|
||||
m <- prophet(DATA, weekly.seasonality = 2, seasonality.prior.scale = 3)
|
||||
true <- list(period = 7, fourier.order = 2, prior.scale = 3)
|
||||
for (name in names(true)) {
|
||||
expect_equal(m$seasonalities$weekly[[name]], true[[name]])
|
||||
}
|
||||
})
|
||||
|
||||
test_that("auto_yearly_seasonality", {
|
||||
|
|
@ -319,9 +336,12 @@ test_that("auto_yearly_seasonality", {
|
|||
# Should be enabled
|
||||
m <- prophet(DATA, fit = FALSE)
|
||||
expect_equal(m$yearly.seasonality, 'auto')
|
||||
m <- prophet:::fit.prophet(m, DATA)
|
||||
m <- fit.prophet(m, DATA)
|
||||
expect_true('yearly' %in% names(m$seasonalities))
|
||||
expect_equal(m$seasonalities[['yearly']], c(365.25, 10))
|
||||
true <- list(period = 365.25, fourier.order = 10, prior.scale = 10)
|
||||
for (name in names(true)) {
|
||||
expect_equal(m$seasonalities$yearly[[name]], true[[name]])
|
||||
}
|
||||
# Should be disabled due to too short history
|
||||
N.w <- 240
|
||||
train.y <- DATA[1:N.w, ]
|
||||
|
|
@ -329,8 +349,11 @@ test_that("auto_yearly_seasonality", {
|
|||
expect_false('yearly' %in% names(m$seasonalities))
|
||||
m <- prophet(train.y, yearly.seasonality = TRUE)
|
||||
expect_true('yearly' %in% names(m$seasonalities))
|
||||
m <- prophet(DATA, yearly.seasonality=7)
|
||||
expect_equal(m$seasonalities[['yearly']], c(365.25, 7))
|
||||
m <- prophet(DATA, yearly.seasonality = 7, seasonality.prior.scale = 3)
|
||||
true <- list(period = 365.25, fourier.order = 7, prior.scale = 3)
|
||||
for (name in names(true)) {
|
||||
expect_equal(m$seasonalities$yearly[[name]], true[[name]])
|
||||
}
|
||||
})
|
||||
|
||||
test_that("auto_daily_seasonality", {
|
||||
|
|
@ -338,9 +361,12 @@ test_that("auto_daily_seasonality", {
|
|||
# Should be enabled
|
||||
m <- prophet(DATA2, fit = FALSE)
|
||||
expect_equal(m$daily.seasonality, 'auto')
|
||||
m <- prophet:::fit.prophet(m, DATA2)
|
||||
m <- fit.prophet(m, DATA2)
|
||||
expect_true('daily' %in% names(m$seasonalities))
|
||||
expect_equal(m$seasonalities[['daily']], c(1, 4))
|
||||
true <- list(period = 1, fourier.order = 4, prior.scale = 10)
|
||||
for (name in names(true)) {
|
||||
expect_equal(m$seasonalities$daily[[name]], true[[name]])
|
||||
}
|
||||
# Should be disabled due to too short history
|
||||
N.d <- 430
|
||||
train.y <- DATA2[1:N.d, ]
|
||||
|
|
@ -348,8 +374,11 @@ test_that("auto_daily_seasonality", {
|
|||
expect_false('daily' %in% names(m$seasonalities))
|
||||
m <- prophet(train.y, daily.seasonality = TRUE)
|
||||
expect_true('daily' %in% names(m$seasonalities))
|
||||
m <- prophet(DATA2, daily.seasonality=7)
|
||||
expect_equal(m$seasonalities[['daily']], c(1, 7))
|
||||
m <- prophet(DATA2, daily.seasonality = 7, seasonality.prior.scale = 3)
|
||||
true <- list(period = 1, fourier.order = 7, prior.scale = 3)
|
||||
for (name in names(true)) {
|
||||
expect_equal(m$seasonalities$daily[[name]], true[[name]])
|
||||
}
|
||||
m <- prophet(DATA)
|
||||
expect_false('daily' %in% names(m$seasonalities))
|
||||
})
|
||||
|
|
@ -366,10 +395,14 @@ test_that("test_subdaily_holidays", {
|
|||
test_that("custom_seasonality", {
|
||||
skip_if_not(Sys.getenv('R_ARCH') != '/i386')
|
||||
holidays <- data.frame(ds = c('2017-01-02'),
|
||||
holiday = c('special_day'))
|
||||
holiday = c('special_day'),
|
||||
prior_scale = c(4))
|
||||
m <- prophet(holidays=holidays)
|
||||
m <- add_seasonality(m, name='monthly', period=30, fourier.order=5)
|
||||
expect_equal(m$seasonalities[['monthly']], c(30, 5))
|
||||
true <- list(period = 30, fourier.order = 5, prior.scale = 10)
|
||||
for (name in names(true)) {
|
||||
expect_equal(m$seasonalities$monthly[[name]], true[[name]])
|
||||
}
|
||||
expect_error(
|
||||
add_seasonality(m, name='special_day', period=30, fourier_order=5)
|
||||
)
|
||||
|
|
@ -377,6 +410,14 @@ test_that("custom_seasonality", {
|
|||
add_seasonality(m, name='trend', period=30, fourier_order=5)
|
||||
)
|
||||
m <- add_seasonality(m, name='weekly', period=30, fourier.order=5)
|
||||
# Test priors
|
||||
m <- prophet(holidays = holidays, yearly.seasonality = FALSE)
|
||||
m <- add_seasonality(
|
||||
m, name='monthly', period=30, fourier.order=5, prior.scale = 2)
|
||||
m <- fit.prophet(m, DATA)
|
||||
prior.scales <- prophet:::make_all_seasonality_features(
|
||||
m, m$history)$prior.scales
|
||||
expect_true(all(prior.scales == c(rep(2, 10), rep(10, 6), 4)))
|
||||
})
|
||||
|
||||
test_that("added_regressors", {
|
||||
|
|
|
|||
|
|
@ -413,9 +413,8 @@ class Prophet(object):
|
|||
except ValueError:
|
||||
lw = 0
|
||||
uw = 0
|
||||
try:
|
||||
ps = float(row.get('prior_scale', self.holidays_prior_scale))
|
||||
except ValueError:
|
||||
ps = float(row.get('prior_scale', self.holidays_prior_scale))
|
||||
if np.isnan(ps):
|
||||
ps = float(self.holidays_prior_scale)
|
||||
if (
|
||||
row.holiday in prior_scales and prior_scales[row.holiday] != ps
|
||||
|
|
|
|||
|
|
@ -308,6 +308,17 @@ class TestProphet(TestCase):
|
|||
holidays2 = pd.concat((holidays, holidays2))
|
||||
feats, priors = Prophet(holidays=holidays2).make_holiday_features(df['ds'])
|
||||
self.assertEqual(priors, [8., 8., 5., 5.])
|
||||
holidays2 = pd.DataFrame({
|
||||
'ds': pd.to_datetime(['2012-06-06', '2013-06-06']),
|
||||
'holiday': ['seans-bday'] * 2,
|
||||
'lower_window': [0] * 2,
|
||||
'upper_window': [1] * 2,
|
||||
})
|
||||
holidays2 = pd.concat((holidays, holidays2))
|
||||
feats, priors = Prophet(
|
||||
holidays=holidays2, holidays_prior_scale=4
|
||||
).make_holiday_features(df['ds'])
|
||||
self.assertEqual(priors, [4., 4., 5., 5.])
|
||||
# Check incompatible priors
|
||||
holidays = pd.DataFrame({
|
||||
'ds': pd.to_datetime(['2016-12-25', '2016-12-27']),
|
||||
|
|
|
|||
Loading…
Reference in a new issue