R unit tests for added regressors

R/R/prophet.R

@@ -625,8 +625,8 @@ make_all_seasonality_features <- function(m, df) {
 
   # Additional regressors
   for (name in names(m$extra_regressors)) {
-    seasonal.features <- cbind(seasonal.features, df[[name]])
-    prior.scales <- cbind(prior.scales, m$extra_regressors[[name]]$prior.scale)
+    seasonal.features[[name]] <- df[[name]]
+    prior.scales <- c(prior.scales, m$extra_regressors[[name]]$prior.scale)
   }
 
   if (ncol(seasonal.features) == 0) {

R/tests/testthat/test_prophet.R

@@ -329,6 +329,73 @@ test_that("custom_seasonality", {
   m <- prophet(holidays=holidays)
   m <- add_seasonality(m, name='monthly', period=30, fourier.order=5)
   expect_equal(m$seasonalities[['monthly']], c(30, 5))
+  expect_error(
+    add_seasonality(m, name='special_day', period=30, fourier_order=5)
+  )
+  expect_error(
+    add_seasonality(m, name='trend', period=30, fourier_order=5)
+  )
+  m <- add_seasonality(m, name='weekly', period=30, fourier.order=5)
+})
+
+test_that("added_regressors", {
+  skip_if_not(Sys.getenv('R_ARCH') != '/i386')
+  m <- prophet()
+  m <- add_regressor(m, 'binary_feature', prior.scale=0.2)
+  m <- add_regressor(m, 'numeric_feature', prior.scale=0.5)
+  m <- add_regressor(m, 'binary_feature2', standardize=TRUE)
+  df <- DATA
+  df$binary_feature <- c(rep(0, 255), rep(1, 255))
+  df$numeric_feature <- 0:509
+  # Require all regressors in df
+  expect_error(
+    fit.prophet(m, df)
+  )
+  df$binary_feature2 <- c(rep(1, 100), rep(0, 410))
+  m <- fit.prophet(m, df)
+  # Check that standardizations are correctly set
+  true <- list(prior.scale = 0.2, mu = 0, std = 1, standardize = 'auto')
+  for (name in names(true)) {
+    expect_equal(true[[name]], m$extra_regressors$binary_feature[[name]])
+  }
+  true <- list(prior.scale = 0.5, mu = 254.5, std = 147.368585)
+  for (name in names(true)) {
+    expect_equal(true[[name]], m$extra_regressors$numeric_feature[[name]],
+                 tolerance = 1e-5)
+  }
+  true <- list(prior.scale = 10., mu = 0.1960784, std = 0.3974183)
+  for (name in names(true)) {
+    expect_equal(true[[name]], m$extra_regressors$binary_feature2[[name]],
+                 tolerance = 1e-5)
+  }
+  # Check that standardization is done correctly
+  df2 <- prophet:::setup_dataframe(m, df)$df
+  expect_equal(df2$binary_feature[1], 0)
+  expect_equal(df2$numeric_feature[1], -1.726962, tolerance = 1e-4)
+  expect_equal(df2$binary_feature2[1], 2.022859, tolerance = 1e-4)
+  # Check that feature matrix and prior scales are correctly constructed
+  out <- prophet:::make_all_seasonality_features(m, df2)
+  seasonal.features <- out$seasonal.features
+  prior.scales <- out$prior.scales
+  expect_true('binary_feature' %in% colnames(seasonal.features))
+  expect_true('numeric_feature' %in% colnames(seasonal.features))
+  expect_true('binary_feature2' %in% colnames(seasonal.features))
+  expect_equal(ncol(seasonal.features), 29)
+  expect_true(all(sort(prior.scales[27:29]) == c(0.2, 0.5, 10.)))
+  # Check that forecast components are reasonable
+  future <- data.frame(
+    ds = c('2014-06-01'), binary_feature = c(0), numeric_feature = c(10))
+  expect_error(predict(m, future))
+  future$binary_feature2 <- 0.
+  fcst <- predict(m, future)
+  expect_equal(ncol(fcst), 31)
+  expect_equal(fcst$binary_feature[1], 0)
+  expect_equal(fcst$extra_regressors[1],
+               fcst$numeric_feature[1] + fcst$binary_feature2[1])
+  expect_equal(fcst$seasonalities[1], fcst$yearly[1] + fcst$weekly[1])
+  expect_equal(fcst$seasonal[1],
+               fcst$seasonalities[1] + fcst$extra_regressors[1])
+  expect_equal(fcst$yhat[1], fcst$trend[1] + fcst$seasonal[1])
 })
 
 test_that("copy", {