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Validation and tests for setting range for automatic changepoints

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This commit is contained in:
Ben Letham 2018-05-25 16:45:24 -07:00
parent ba1bac834e
commit cb0b47994b
8 changed files with 67 additions and 29 deletions
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1
R/R/diagnostics.R
View file

@ -175,6 +175,7 @@ prophet_copy <- function(m, cutoff = NULL) {
growth = m$growth,
changepoints = changepoints,
n.changepoints = m$n.changepoints,
changepoint.range = m$changepoint.range,
yearly.seasonality = FALSE,
weekly.seasonality = FALSE,
daily.seasonality = FALSE,

25
R/R/prophet.R
View file

@ -28,9 +28,10 @@ utils::globalVariables(c(
#' @param n.changepoints Number of potential changepoints to include. Not used
#' if input `changepoints` is supplied. If `changepoints` is not supplied,
#' then n.changepoints potential changepoints are selected uniformly from the
#' first `changepoint.threshold` percent of df$ds.
#' @param changepoint.threshold Parameter controling where to select the changepoints.
#' Not used if input `changepoints` is supplied.
#' first `changepoint.range` proportion of df$ds.
#' @param changepoint.range Proportion of history in which trend changepoints
#' will be estimated. Defaults to 0.8 for the first 80%. Not used if
#' `changepoints` is specified.
#' @param yearly.seasonality Fit yearly seasonality. Can be 'auto', TRUE,
#' FALSE, or a number of Fourier terms to generate.
#' @param weekly.seasonality Fit weekly seasonality. Can be 'auto', TRUE,
@ -81,7 +82,7 @@ prophet <- function(df = NULL,
growth = 'linear',
changepoints = NULL,
n.changepoints = 25,
changepoint.threshold = 0.8,
changepoint.range = 0.8,
yearly.seasonality = 'auto',
weekly.seasonality = 'auto',
daily.seasonality = 'auto',
@ -106,7 +107,7 @@ prophet <- function(df = NULL,
growth = growth,
changepoints = changepoints,
n.changepoints = n.changepoints,
changepoint.threshold = changepoint.threshold,
changepoint.range = changepoint.range,
yearly.seasonality = yearly.seasonality,
weekly.seasonality = weekly.seasonality,
daily.seasonality = daily.seasonality,
@ -152,6 +153,9 @@ validate_inputs <- function(m) {
if (!(m$growth %in% c('linear', 'logistic'))) {
stop("Parameter 'growth' should be 'linear' or 'logistic'.")
}
if ((m$changepoint.range < 0) | (m$changepoint.range > 1)) {
stop("Parameter 'changepoint.range' must be in [0, 1]")
}
if (!is.null(m$holidays)) {
if (!(exists('holiday', where = m$holidays))) {
stop('Holidays dataframe must have holiday field.')
@ -455,14 +459,9 @@ set_changepoints <- function(m) {
}
}
} else {
# Place potential changepoints evenly through the first changepoint.threshold pcnt of
# the history.
if (m$changepoint.threshold > 1 || m$changepoint.threshold <= 0){
m$changepoint.threshold <- .8
message('changepoint.threshold greater than 1 or less than equal to 0. Using ',
m$changepoint.threshold)
}
hist.size <- floor(nrow(m$history) * m$changepoint.threshold)
# Place potential changepoints evenly through the first changepoint.range
# proportion of the history.
hist.size <- floor(nrow(m$history) * m$changepoint.range)
if (m$n.changepoints + 1 > hist.size) {
m$n.changepoints <- hist.size - 1
message('n.changepoints greater than number of observations. Using ',

3
R/tests/testthat/test_diagnostics.R
View file

@ -158,6 +158,7 @@ test_that("copy", {
growth = as.character(products$growth[i]),
changepoints = NULL,
n.changepoints = 3,
changepoint.range = 0.9,
yearly.seasonality = products$yearly.seasonality[i],
weekly.seasonality = products$weekly.seasonality[i],
daily.seasonality = products$daily.seasonality[i],
@ -179,7 +180,7 @@ test_that("copy", {
args <- c('growth', 'changepoints', 'n.changepoints', 'holidays',
'seasonality.prior.scale', 'holidays.prior.scale',
'changepoints.prior.scale', 'mcmc.samples', 'interval.width',
'uncertainty.samples', 'seasonality.mode')
'uncertainty.samples', 'seasonality.mode', 'changepoint.range')
for (arg in args) {
expect_equal(m1[[arg]], m2[[arg]])
}

19
R/tests/testthat/test_prophet.R
View file

@ -121,7 +121,24 @@ test_that("get_changepoints", {
cp <- m$changepoints.t
expect_equal(length(cp), m$n.changepoints)
expect_true(min(cp) > 0)
expect_true(max(cp) < 1)
expect_true(max(cp) <= history$t[ceiling(0.8 * length(history$t))])
})
test_that("set_changepoint_range", {
history <- train
m <- prophet(history, fit = FALSE, changepoint.range = 0.4)
out <- prophet:::setup_dataframe(m, history, initialize_scales = TRUE)
history <- out$df
m <- out$m
m$history <- history
m <- prophet:::set_changepoints(m)
cp <- m$changepoints.t
expect_equal(length(cp), m$n.changepoints)
expect_true(min(cp) > 0)
expect_true(max(cp) <= history$t[ceiling(0.4 * length(history$t))])
})
test_that("get_zero_changepoints", {

1
python/fbprophet/diagnostics.py
View file

@ -179,6 +179,7 @@ def prophet_copy(m, cutoff=None):
m2 = m.__class__(
growth=m.growth,
n_changepoints=m.n_changepoints,
changepoint_range=m.changepoint_range,
changepoints=changepoints,
yearly_seasonality=False,
weekly_seasonality=False,

25
python/fbprophet/forecaster.py
View file

@ -57,8 +57,10 @@ class Prophet(object):
n_changepoints: Number of potential changepoints to include. Not used
if input `changepoints` is supplied. If `changepoints` is not supplied,
then n_changepoints potential changepoints are selected uniformly from
the first `changepoint_threshold` percent of the history.
changepoint_threshold: Parameter controling where to select the changepoints.
the first `changepoint_range` proportion of the history.
changepoint_range: Proportion of history in which trend changepoints will
be estimated. Defaults to 0.8 for the first 80%. Not used if
`changepoints` is specified.
Not used if input `changepoints` is supplied.
yearly_seasonality: Fit yearly seasonality.
Can be 'auto', True, False, or a number of Fourier terms to generate.
@ -99,7 +101,7 @@ class Prophet(object):
growth='linear',
changepoints=None,
n_changepoints=25,
changepoint_threshold=0.8,
changepoint_range=0.8,
yearly_seasonality='auto',
weekly_seasonality='auto',
daily_seasonality='auto',
@ -122,7 +124,7 @@ class Prophet(object):
self.n_changepoints = n_changepoints
self.specified_changepoints = False
self.changepoint_threshold = changepoint_threshold
self.changepoint_range = changepoint_range
self.yearly_seasonality = yearly_seasonality
self.weekly_seasonality = weekly_seasonality
self.daily_seasonality = daily_seasonality
@ -168,6 +170,8 @@ class Prophet(object):
if self.growth not in ('linear', 'logistic'):
raise ValueError(
"Parameter 'growth' should be 'linear' or 'logistic'.")
if ((self.changepoint_range < 0) or (self.changepoint_range > 1)):
raise ValueError("Parameter 'changepoint_range' must be in [0, 1]")
if self.holidays is not None:
has_lower = 'lower_window' in self.holidays
has_upper = 'upper_window' in self.holidays
@ -336,15 +340,10 @@ class Prophet(object):
raise ValueError(
'Changepoints must fall within training data.')
else:
# Place potential changepoints evenly through first changepoint_threshold
# of history
if (self.changepoint_threshold > 1 or self.changepoint_threshold <= 0):
self.changepoint_threshold = 0.8
logger.info(
'changepoint_threshold greater than 1 or less than equal to 0.'
'Using {}.'.format(self.changepoint_threshold)
)
hist_size = np.floor(self.history.shape[0] * self.changepoint_threshold)
# Place potential changepoints evenly through first
# changepoint_range proportion of the history
hist_size = np.floor(
self.history.shape[0] * self.changepoint_range)
if self.n_changepoints + 1 > hist_size:
self.n_changepoints = hist_size - 1
logger.info(

2
python/fbprophet/tests/test_diagnostics.py
View file

@ -180,6 +180,7 @@ class TestDiagnostics(TestCase):
['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
@ -201,6 +202,7 @@ class TestDiagnostics(TestCase):
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)

20
python/fbprophet/tests/test_prophet.py
View file

@ -150,7 +150,25 @@ class TestProphet(TestCase):
self.assertEqual(cp.shape[0], m.n_changepoints)
self.assertEqual(len(cp.shape), 1)
self.assertTrue(cp.min() > 0)
self.assertTrue(cp.max() < 1)
cp_indx = int(np.ceil(0.8 * history.shape[0]))
self.assertTrue(cp.max() <= history['t'].values[cp_indx])
def test_set_changepoint_range(self):
m = Prophet(changepoint_range=0.4)
N = DATA.shape[0]
history = DATA.head(N // 2).copy()
history = m.setup_dataframe(history, initialize_scales=True)
m.history = history
m.set_changepoints()
cp = m.changepoints_t
self.assertEqual(cp.shape[0], m.n_changepoints)
self.assertEqual(len(cp.shape), 1)
self.assertTrue(cp.min() > 0)
cp_indx = int(np.ceil(0.4 * history.shape[0]))
self.assertTrue(cp.max() <= history['t'].values[cp_indx])
def test_get_zero_changepoints(self):
m = Prophet(n_changepoints=0)