fixed tests

R-package/tests/testthat/test_basic.R

@@ -8,6 +8,7 @@ test <- agaricus.test
 windows_flag <- grepl("Windows", Sys.info()[["sysname"]])
 
 TOLERANCE <- 1e-6
+set.seed(708L)
 
 # [description] Every time this function is called, it adds 0.1
 #               to an accumulator then returns the current value.
@@ -715,7 +716,6 @@ test_that("If first_metric_only is TRUE, lgb.train() decides to stop early based
 test_that("lgb.train() works when a mixture of functions and strings are passed to eval", {
   set.seed(708L)
   nrounds <- 10L
-  early_stopping_rounds <- 3L
   increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv)
   bst <- lgb.train(
     params = list(
@@ -745,51 +745,8 @@ test_that("lgb.train() works when a mixture of functions and strings are passed
 
   # the difference metrics shouldn't have been mixed up with each other
   results <- bst$record_evals[["valid1"]]
-  expect_true(abs(results[["rmse"]][["eval"]][[1L]] - 0.9278173) < TOLERANCE)
-  expect_true(abs(results[["l2"]][["eval"]][[1L]] - 0.8608449) < TOLERANCE)
-  expected_increasing_metric <- increasing_metric_starting_value + 0.1
-  expect_true(
-    abs(
-      results[["increasing_metric"]][["eval"]][[1L]] - expected_increasing_metric
-    ) < TOLERANCE
-  )
-  expect_true(abs(results[["constant_metric"]][["eval"]][[1L]] - CONSTANT_METRIC_VALUE) < TOLERANCE)
-
-})
-
-test_that("lgb.train() works when a character vector is passed to eval", {
-  set.seed(708L)
-  nrounds <- 10L
-  early_stopping_rounds <- 3L
-  increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv)
-  bst <- lgb.train(
-    params = list(
-      objective = "binary"
-      , metric = "None"
-    )
-    , data = DTRAIN_RANDOM_CLASSIFICATION
-    , nrounds = nrounds
-    , valids = list(
-      "valid1" = DVALID_RANDOM_CLASSIFICATION
-    )
-    , eval = c(
-      "binary_error"
-      , "binary_logloss"
-    )
-  )
-
-  # all 4 metrics should have been used
-  expect_named(
-    bst$record_evals[["valid1"]]
-    , expected = c("rmse", "l2", "increasing_metric", "constant_metric")
-    , ignore.order = TRUE
-    , ignore.case = FALSE
-  )
-
-  # the difference metrics shouldn't have been mixed up with each other
-  results <- bst$record_evals[["valid1"]]
-  expect_true(abs(results[["rmse"]][["eval"]][[1L]] - 0.9278173) < TOLERANCE)
-  expect_true(abs(results[["l2"]][["eval"]][[1L]] - 0.8608449) < TOLERANCE)
+  expect_true(abs(results[["rmse"]][["eval"]][[1L]] - 1.105012) < TOLERANCE)
+  expect_true(abs(results[["l2"]][["eval"]][[1L]] - 1.221051) < TOLERANCE)
   expected_increasing_metric <- increasing_metric_starting_value + 0.1
   expect_true(
     abs(
@@ -814,7 +771,6 @@ test_that("lgb.train() works when a list of strings or a character vector is pas
 
     set.seed(708L)
     nrounds <- 10L
-    early_stopping_rounds <- 3L
     increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv)
     bst <- lgb.train(
       params = list(
@@ -840,18 +796,17 @@ test_that("lgb.train() works when a list of strings or a character vector is pas
     # the difference metrics shouldn't have been mixed up with each other
     results <- bst$record_evals[["valid1"]]
     if ("binary_error" %in% unlist(eval_variation)) {
-      expect_true(abs(results[["binary_error"]][["eval"]][[1L]] - 0.5135135) < TOLERANCE)
+      expect_true(abs(results[["binary_error"]][["eval"]][[1L]] - 0.4864865) < TOLERANCE)
     }
     if ("binary_logloss" %in% unlist(eval_variation)) {
-      expect_true(abs(results[["binary_logloss"]][["eval"]][[1L]] - 0.6992222) < TOLERANCE)
+      expect_true(abs(results[["binary_logloss"]][["eval"]][[1L]] - 0.6932548) < TOLERANCE)
     }
   }
 })
 
 test_that("lgb.train() works when you specify both 'metric' and 'eval' with strings", {
   set.seed(708L)
   nrounds <- 10L
-  early_stopping_rounds <- 3L
   increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv)
   bst <- lgb.train(
     params = list(
@@ -876,14 +831,13 @@ test_that("lgb.train() works when you specify both 'metric' and 'eval' with stri
 
   # the difference metrics shouldn't have been mixed up with each other
   results <- bst$record_evals[["valid1"]]
-  expect_true(abs(results[["binary_error"]][["eval"]][[1L]] - 0.5135135) < TOLERANCE)
-  expect_true(abs(results[["binary_logloss"]][["eval"]][[1L]] - 0.6992222) < TOLERANCE)
+  expect_true(abs(results[["binary_error"]][["eval"]][[1L]] - 0.4864865) < TOLERANCE)
+  expect_true(abs(results[["binary_logloss"]][["eval"]][[1L]] - 0.6932548) < TOLERANCE)
 })
 
 test_that("lgb.train() works when you specify both 'metric' and 'eval' with strings", {
   set.seed(708L)
   nrounds <- 10L
-  early_stopping_rounds <- 3L
   increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv)
   bst <- lgb.train(
     params = list(
@@ -908,6 +862,28 @@ test_that("lgb.train() works when you specify both 'metric' and 'eval' with stri
 
   # the difference metrics shouldn't have been mixed up with each other
   results <- bst$record_evals[["valid1"]]
-  expect_true(abs(results[["binary_error"]][["eval"]][[1L]] - 0.5135135) < TOLERANCE)
-  expect_true(abs(results[["binary_logloss"]][["eval"]][[1L]] - 0.6992222) < TOLERANCE)
+  expect_true(abs(results[["binary_error"]][["eval"]][[1L]] - 0.4864865) < TOLERANCE)
+  expect_true(abs(results[["binary_logloss"]][["eval"]][[1L]] - 0.6932548) < TOLERANCE)
+})
+
+test_that("lgb.train() works when you give a function for eval", {
+  set.seed(708L)
+  nrounds <- 10L
+  increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv)
+  bst <- lgb.train(
+    params = list(
+      objective = "binary"
+      , metric = "None"
+    )
+    , data = DTRAIN_RANDOM_CLASSIFICATION
+    , nrounds = nrounds
+    , valids = list(
+      "valid1" = DVALID_RANDOM_CLASSIFICATION
+    )
+    , eval = .constant_metric
+  )
+
+  # the difference metrics shouldn't have been mixed up with each other
+  results <- bst$record_evals[["valid1"]]
+  expect_true(abs(results[["constant_metric"]][["eval"]][[1L]] - CONSTANT_METRIC_VALUE) < TOLERANCE)
 })