Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/17757#discussion_r113361559
--- Diff: R/pkg/inst/tests/testthat/test_mllib_classification.R ---
@@ -284,22 +284,11 @@ test_that("spark.mlp", {
c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "2.0", "2.0",
"1.0", "0.0"))
# test initialWeights
- model <- spark.mlp(df, label ~ features, layers = c(4, 3), maxIter = 2,
initialWeights =
+ model <- spark.mlp(df, label ~ features, layers = c(4, 3),
initialWeights =
c(0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 9, 9, 9, 9, 9))
mlpPredictions <- collect(select(predict(model, mlpTestDF),
"prediction"))
expect_equal(head(mlpPredictions$prediction, 10),
- c("1.0", "1.0", "1.0", "1.0", "2.0", "1.0", "2.0", "2.0",
"1.0", "0.0"))
-
- model <- spark.mlp(df, label ~ features, layers = c(4, 3), maxIter = 2,
initialWeights =
- c(0.0, 0.0, 0.0, 0.0, 0.0, 5.0, 5.0, 5.0, 5.0, 5.0, 9.0, 9.0, 9.0,
9.0, 9.0))
- mlpPredictions <- collect(select(predict(model, mlpTestDF),
"prediction"))
- expect_equal(head(mlpPredictions$prediction, 10),
- c("1.0", "1.0", "1.0", "1.0", "2.0", "1.0", "2.0", "2.0",
"1.0", "0.0"))
-
- model <- spark.mlp(df, label ~ features, layers = c(4, 3), maxIter = 2)
- mlpPredictions <- collect(select(predict(model, mlpTestDF),
"prediction"))
- expect_equal(head(mlpPredictions$prediction, 10),
- c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "0.0", "2.0",
"1.0", "0.0"))
+ c("1.0", "1.0", "1.0", "1.0", "0.0", "1.0", "2.0", "2.0",
"1.0", "0.0"))
--- End diff --
Yeah, here we just removed the unconverged test(with ```maxIter = 2```),
since we can't guarantee any equality during the iteration. I think the best
way to test the api works well is to check number of iterations. If we set
proper initial weights, the number of iterations to converge would be different
from other initial weights or no initial weights. Let's open a separate JIRA to
expose training summary for MLP at MLlib side, and then we can expose them at
SparkR and add check here. Thanks.
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