huaxingao commented on a change in pull request #27570:
[SPARK-30820][SPARKR][ML] Add FMClassifier to SparkR
URL: https://github.com/apache/spark/pull/27570#discussion_r386517512
##########
File path: R/pkg/tests/fulltests/test_mllib_classification.R
##########
@@ -488,4 +488,36 @@ test_that("spark.naiveBayes", {
expect_equal(class(collect(predictions)$clicked[1]), "character")
})
+test_that("spark.fmClassifier", {
+ df <- withColumn(
+ suppressWarnings(createDataFrame(iris)),
+ "Species", otherwise(when(column("Species") == "Setosa", "Setosa"),
"Not-Setosa")
+ )
+
+ model1 <- spark.fmClassifier(
+ df, Species ~ .,
+ regParam = 0.01, maxIter = 10, fitLinear = TRUE, factorSize = 3
+ )
+
+ prediction1 <- predict(model1, df)
+ expect_is(prediction1, "SparkDataFrame")
+ expect_equal(summary(model1)$factorSize, 3)
+
+ # Test model save/load
+ if (windows_with_hadoop()) {
+ modelPath <- tempfile(pattern = "spark-fmclassifier", fileext = ".tmp")
+ write.ml(model1, modelPath)
+ model2 <- read.ml(modelPath)
+
+ expect_is(model2, "FMClassificationModel")
+
+ prediction2 <- predict(model2, df)
+ expect_equal(
+ collect(drop(prediction1, c("rawPrediction", "probability"))),
+ collect(drop(prediction2, c("rawPrediction", "probability")))
+ )
Review comment:
add ```unlink(modelPath)``` to delete the dir
Also, I prefer to follow what the other tests do for testing model save and
load: checking if the loaded model has the same fields as the saved model. In
scala test, we do this for ```FMClassifier```:
```
assert(model.intercept === model2.intercept)
assert(model.linear.toArray === model2.linear.toArray)
assert(model.factors.toArray === model2.factors.toArray)
assert(model.numFeatures === model2.numFeatures)
```
In LinearSVM R test, it does this:
```
coefs <- summary(model)$coefficients
coefs2 <- summary(model2)$coefficients
expect_equal(coefs, coefs2)
```
Is it OK we do something similar?
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