Version bump; test fixes; R build

R/DESCRIPTION

@@ -1,6 +1,6 @@
 Package: prophet
 Title: Automatic Forecasting Procedure
-Version: 0.3.0.1
+Version: 0.4
 Date: 2018-06-13
 Authors@R: c(
   person("Sean", "Taylor", email = "sjt@fb.com", role = c("cre", "aut")),

R/NAMESPACE

@@ -17,4 +17,5 @@ export(predictive_samples)
 export(prophet)
 export(prophet_plot_components)
 import(Rcpp)
+import(rlang)
 importFrom(dplyr,"%>%")

R/R/diagnostics.R

@@ -154,7 +154,6 @@ prophet_copy <- function(m, cutoff = NULL) {
     weekly.seasonality = FALSE,
     daily.seasonality = FALSE,
     holidays = m$holidays,
-    append.holidays = m$append.holidays,
     seasonality.mode = m$seasonality.mode,
     seasonality.prior.scale = m$seasonality.prior.scale,
     changepoint.prior.scale = m$changepoint.prior.scale,
@@ -166,6 +165,7 @@ prophet_copy <- function(m, cutoff = NULL) {
   )
   m2$extra_regressors <- m$extra_regressors
   m2$seasonalities <- m$seasonalities
+  m2$country_holidays <- m$country_holidays
   return(m2)
 }
 

R/R/make_holidays.R

@@ -11,7 +11,7 @@
 #' @param country.name Country name (character).
 #'
 #' @return A vector of all possible holiday names (unique) of given country.
-#' @internal
+#' @keywords internal
 get_holiday_names <- function(country.name){
     holidays <- generated_holidays %>% 
       dplyr::filter(country == country.name) %>%
@@ -28,7 +28,7 @@ get_holiday_names <- function(country.name){
 #'
 #' @return Dataframe with 'ds' and 'holiday', which can directly feed
 #'  to 'holidays' params in Prophet
-#' @internal
+#' @keywords internal
 make_holidays_df <- function(years, country.name){
   country.holidays = generated_holidays %>%
     dplyr::filter(country == country.name)

R/R/prophet.R

@@ -80,6 +80,7 @@ globalVariables(c(
 #' @export
 #' @importFrom dplyr "%>%"
 #' @import Rcpp
+#' @import rlang
 prophet <- function(df = NULL,
                     growth = 'linear',
                     changepoints = NULL,
@@ -1219,7 +1220,7 @@ fit.prophet <- function(m, df, ...) {
       init = stan_init,
       iter = m$mcmc.samples
     )
-    args <- modifyList(args, list(...))
+    args <- utils::modifyList(args, list(...))
     stan.fit <- do.call(rstan::sampling, args)
     m$params <- rstan::extract(stan.fit)
     n.iteration <- length(m$params$k)
@@ -1231,7 +1232,7 @@ fit.prophet <- function(m, df, ...) {
       iter = 1e4,
       as_vector = FALSE
     )
-    args <- modifyList(args, list(...))
+    args <- utils::modifyList(args, list(...))
     stan.fit <- do.call(rstan::optimizing, args)
     m$params <- stan.fit$par
     n.iteration <- 1

R/man/get_holiday_names.Rd

@@ -15,3 +15,4 @@ A vector of all possible holiday names (unique) of given country.
 \description{
 Return all possible holiday names of given country
 }
+\keyword{internal}

R/man/make_holidays_df.Rd

@@ -18,3 +18,4 @@ Dataframe with 'ds' and 'holiday', which can directly feed
 \description{
 Make dataframe of holidays for given years and countries
 }
+\keyword{internal}

R/tests/testthat/test_diagnostics.R

@@ -121,7 +121,6 @@ test_that("copy", {
     weekly.seasonality = c(TRUE, FALSE),
     daily.seasonality = c(TRUE, FALSE),
     holidays = c('null', 'insert_dataframe'),
-    append.holidays = c(NULL, 'US'),
     seasonality.mode = c('additive', 'multiplicative')
   )
   products <- expand.grid(inputs)
@@ -140,7 +139,6 @@ test_that("copy", {
       weekly.seasonality = products$weekly.seasonality[i],
       daily.seasonality = products$daily.seasonality[i],
       holidays = holidays,
-      append.holidays = products$append.holidays[i],
       seasonality.prior.scale = 1.1,
       holidays.prior.scale = 1.1,
       changepoints.prior.scale = 0.1,
@@ -149,13 +147,14 @@ test_that("copy", {
       uncertainty.samples = 200,
       fit = FALSE
     )
+    m1$country_holidays <- 'US'
     out <- prophet:::setup_dataframe(m1, df, initialize_scales = TRUE)
     m1 <- out$m
     m1$history <- out$df
     m1 <- prophet:::set_auto_seasonalities(m1)
     m2 <- prophet:::prophet_copy(m1)
     # Values should be copied correctly
-    args <- c('growth', 'changepoints', 'n.changepoints', 'holidays', 'append.holidays',
+    args <- c('growth', 'changepoints', 'n.changepoints', 'holidays', 'country_holidays',
               'seasonality.prior.scale', 'holidays.prior.scale',
               'changepoints.prior.scale', 'mcmc.samples', 'interval.width',
               'uncertainty.samples', 'seasonality.mode', 'changepoint.range')

python/fbprophet/__init__.py

@@ -7,4 +7,4 @@
 
 from fbprophet.forecaster import Prophet
 
-__version__ = '0.4.dev'
+__version__ = '0.4'

python/fbprophet/diagnostics.py

@@ -159,7 +159,6 @@ def prophet_copy(m, cutoff=None):
         weekly_seasonality=False,
         daily_seasonality=False,
         holidays=m.holidays,
-        append_holidays=m.append_holidays,
         seasonality_mode=m.seasonality_mode,
         seasonality_prior_scale=m.seasonality_prior_scale,
         changepoint_prior_scale=m.changepoint_prior_scale,
@@ -170,6 +169,7 @@ def prophet_copy(m, cutoff=None):
     )
     m2.extra_regressors = deepcopy(m.extra_regressors)
     m2.seasonalities = deepcopy(m.seasonalities)
+    m2.country_holidays = deepcopy(m.country_holidays)
     return m2
 
 

python/fbprophet/tests/test_diagnostics.py

@@ -141,7 +141,6 @@ class TestDiagnostics(TestCase):
         # These values are created except for its default values
         holiday = pd.DataFrame(
             {'ds': pd.to_datetime(['2016-12-25']), 'holiday': ['x']})
-        append_holidays = 'US'
         products = itertools.product(
             ['linear', 'logistic'],  # growth
             [None, pd.to_datetime(['2016-12-25'])],  # changepoints
@@ -151,7 +150,6 @@ class TestDiagnostics(TestCase):
             [True, False],  # weekly_seasonality
             [True, False],  # daily_seasonality
             [None, holiday],  # holidays
-            [None, append_holidays],  # append_holidays
             ['additive', 'multiplicative'],  # seasonality_mode
             [1.1],  # seasonality_prior_scale
             [1.1],  # holidays_prior_scale
@@ -163,6 +161,7 @@ class TestDiagnostics(TestCase):
         # 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()
@@ -184,7 +183,7 @@ class TestDiagnostics(TestCase):
                 self.assertEqual(m1.holidays, m2.holidays)
             else:
                 self.assertTrue((m1.holidays == m2.holidays).values.all())
-            self.assertEqual(m1.append_holidays, m2.append_holidays)
+            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)

python/setup.py

@@ -98,7 +98,7 @@ with open('requirements.txt', 'r') as f:
 
 setup(
     name='fbprophet',
-    version='0.3.post2',
+    version='0.4',
     description='Automatic Forecasting Procedure',
     url='https://facebook.github.io/prophet/',
     author='Sean J. Taylor <sjt@fb.com>, Ben Letham <bletham@fb.com>',