enable non-seasonal forecast

DESCRIPTION

@@ -14,7 +14,7 @@ Encoding: UTF-8
 LazyData: true
 SystemRequirements: Python (>= 3.6)
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.1.2
+RoxygenNote: 7.2.0
 Imports: 
     dplyr (>= 1.0.7),
     forecast (>= 8.15),

R/ngbfor.R

@@ -183,9 +183,15 @@ NGBforecast <- R6::R6Class(
                 train_loss_monitor = train_loss_monitor,
                 val_loss_monitor = val_loss_monitor,
                 early_stopping_rounds = early_stopping_rounds)
-      method = paste0("NGBforecast", " with ",
-                      gsub("[()]", "", paste0(private$Base)),
-                      "(",max_lag,", ", K, ")")
+      if(seasonal){
+        method = paste0("NGBforecast", " with ",
+                        gsub("[()]", "", paste0(private$Base)),
+                        "(",max_lag,", ", K, ")")
+      } else {
+        method = paste0("NGBforecast", " with ",
+                        gsub("[()]", "", paste0(private$Base)),
+                        "(",max_lag,", ", 0, ")")
+      }
 
       fitted_int <- ts(c(rep(NA, max_lag), c(model$predict(X))), 
                        start = start(y), frequency = frequency(y))

R/ngbforcv.R

@@ -94,37 +94,37 @@ NGBforecastCV <- R6::R6Class(
                           tol = 0.0001,
                           random_state = NULL){
 
-      if(class(Dist)!="list"){
+      if(!inherits(Dist, "list")){
         stop(
           "Please profide a list of Dist object with at least one spesification"
           )
       }
-      if(class(Score)!="list"){
+      if(!inherits(Score, "list")){
         stop(
           "Please profide a list of Score object with at least one spesification"
         )
       }
-      if(class(Base)!="list"){
+      if(!inherits(Base, "list")){
         stop(
           "Please profide a list of Base base learnesr with at least one spesification"
         )
       }
-      if(class(n_estimators)!="list"){
+      if(!inherits(n_estimators, "list")){
         stop(
           "Please profide a list of n_estimators object with at least one spesification"
         )
       }
-      if(class(learning_rate)!="list"){
+      if(!inherits(learning_rate, "list")){
         stop(
           "Please profide a list of learning_rate object with at least one spesification"
         )
       }
-      if(class(minibatch_frac)!="list"){
+      if(!inherits(minibatch_frac, "list")){
         stop(
           "Please profide a list of minibatch_frac object with at least one spesification"
         )
       }
-      if(class(col_sample)!="list"){
+      if(!inherits(col_sample, "list")){
         stop(
           "Please profide a list of col_sample object with at least one spesification"
         )

R/utils.R

@@ -267,7 +267,9 @@ prepare_data <- function(y,
   x <- matrix(0, nrow = c(length_y - max_lag), ncol = ncolx)
 
   x[, seq_len(max_lag)] <- lag_maker(y, max_lag)
-
+
+  fourier_s <- NULL
+
   if (seasonal == TRUE & freq > 1)
   {
     fourier_s <- forecast::fourier(modified_y, K = K)

tests/testthat/test-ngbforecastcv.R

@@ -0,0 +1,38 @@
+if(require(testthat)){
+  test_that("tests for some arguments in NGBforecastCV", {
+
+    if_not_ngboost_exist_skip()
+
+    dists <- list(Dist("Normal"))
+    base_learners <- list(sklearner(module = "tree", class = "DecisionTreeRegressor",
+                                    max_depth = 6),
+                          sklearner(module = "tree", class = "DecisionTreeRegressor",
+                                    max_depth = 7))
+    scores <-  list(Scores("LogScore"))
+
+    model <- NGBforecastCV$new(Dist = dists,
+                               Base = base_learners,
+                               Score = scores,
+                               natural_gradient = TRUE,
+                               n_estimators = list(10, 12),
+                               learning_rate = list(0.1),
+                               minibatch_frac = list(0.1),
+                               col_sample = list(0.3),
+                               verbose = FALSE,
+                               verbose_eval = 100,
+                               tol = 1e-5)
+
+    params <- model$tune(y = AirPassengers,
+                         seasonal = TRUE,
+                         max_lag = 12,
+                         xreg = NULL,
+                         early_stopping_rounds = NULL,
+                         n_splits = 4L)
+
+    out <- class(params)
+
+    expect_equal(out, "list")
+
+  })
+
+}

tests/testthat/test-ngboostfor.R

@@ -25,4 +25,89 @@ if(require(testthat)){
     expect_equal(fc, c(454.3927, 425.1821), tolerance = 4)
 
   })
+
+  test_that("tests for non-seasonal NGBforecast", {
+
+    if_not_ngboost_exist_skip()
+
+    model <- NGBforecast$new(Dist = Dist("LogNormal"),
+                             Base = sklearner(),
+                             Score = Scores("LogScore"),
+                             natural_gradient = TRUE,
+                             n_estimators = 200,
+                             learning_rate =  0.1,
+                             minibatch_frac = 1,
+                             col_sample = 1,
+                             verbose = TRUE,
+                             verbose_eval = 100,
+                             tol = 1e-5)
+
+    model$fit(y = AirPassengers, seasonal = FALSE, max_lag = 12, xreg = NULL, 
+              early_stopping_rounds = 10L)
+
+
+    fc <- c(model$forecast(h = 2, level = c(90, 80), xreg = NULL)$mean)
+
+    expect_equal(fc, c(468, 461), tolerance = 5)
+
+  })
+
+  test_that("tests for non-seasonal xreg NGBforecast", {
+
+    if_not_ngboost_exist_skip()
+
+    model <- NGBforecast$new(Dist = Dist("LogNormal"),
+                             Base = sklearner(),
+                             Score = Scores("LogScore"),
+                             natural_gradient = TRUE,
+                             n_estimators = 200,
+                             learning_rate =  0.1,
+                             minibatch_frac = 1,
+                             col_sample = 1,
+                             verbose = TRUE,
+                             verbose_eval = 100,
+                             tol = 1e-5)
+
+    xreg <- matrix(rnorm(length(AirPassengers)*2, mean = 3), ncol = 2)
+    newxreg <- matrix(rnorm(4, mean = 3), ncol = 2)
+
+    model$fit(y = AirPassengers, seasonal = FALSE, max_lag = 12, xreg = xreg, 
+              early_stopping_rounds = 10L)
+
+
+    fc <- c(model$forecast(h = 2, level = c(90, 80), xreg = newxreg)$mean)
+
+    expect_equal(fc, c(463, 461), tolerance = 5)
+
+  })
+
+  test_that("tests for seasonal xreg NGBforecast", {
+
+    if_not_ngboost_exist_skip()
+
+    model <- NGBforecast$new(Dist = Dist("LogNormal"),
+                             Base = sklearner(),
+                             Score = Scores("LogScore"),
+                             natural_gradient = TRUE,
+                             n_estimators = 200,
+                             learning_rate =  0.1,
+                             minibatch_frac = 1,
+                             col_sample = 1,
+                             verbose = TRUE,
+                             verbose_eval = 100,
+                             tol = 1e-5)
+
+    xreg <- matrix(rnorm(length(AirPassengers)*2, mean = 3), ncol = 2)
+    newxreg <- matrix(rnorm(4, mean = 3), ncol = 2)
+
+    model$fit(y = AirPassengers, seasonal = TRUE, max_lag = 12, xreg = xreg, 
+              early_stopping_rounds = 10L)
+
+
+    fc <- c(model$forecast(h = 2, level = c(90, 80), xreg = newxreg)$mean)
+
+    expect_equal(fc, c(470, 463), tolerance = 5)
+
+  })
+
 }