diff options
| -rw-r--r-- | R/pkg/R/mllib.R | 13 | ||||
| -rw-r--r-- | R/pkg/inst/tests/testthat/test_mllib.R | 19 | ||||
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/r/MultilayerPerceptronClassifierWrapper.scala | 8 |
3 files changed, 33 insertions, 7 deletions
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R index 98db367a85..971c16658f 100644 --- a/R/pkg/R/mllib.R +++ b/R/pkg/R/mllib.R @@ -694,12 +694,19 @@ setMethod("predict", signature(object = "KMeansModel"), #' } #' @note spark.mlp since 2.1.0 setMethod("spark.mlp", signature(data = "SparkDataFrame"), - function(data, blockSize = 128, layers = c(3, 5, 2), solver = "l-bfgs", maxIter = 100, - tol = 0.5, stepSize = 1, seed = 1) { + function(data, layers, blockSize = 128, solver = "l-bfgs", maxIter = 100, + tol = 1E-6, stepSize = 0.03, seed = NULL) { + layers <- as.integer(na.omit(layers)) + if (length(layers) <= 1) { + stop ("layers must be a integer vector with length > 1.") + } + if (!is.null(seed)) { + seed <- as.character(as.integer(seed)) + } jobj <- callJStatic("org.apache.spark.ml.r.MultilayerPerceptronClassifierWrapper", "fit", data@sdf, as.integer(blockSize), as.array(layers), as.character(solver), as.integer(maxIter), as.numeric(tol), - as.numeric(stepSize), as.integer(seed)) + as.numeric(stepSize), seed) new("MultilayerPerceptronClassificationModel", jobj = jobj) }) diff --git a/R/pkg/inst/tests/testthat/test_mllib.R b/R/pkg/inst/tests/testthat/test_mllib.R index 24c40a8823..a1eaaf2091 100644 --- a/R/pkg/inst/tests/testthat/test_mllib.R +++ b/R/pkg/inst/tests/testthat/test_mllib.R @@ -391,6 +391,25 @@ test_that("spark.mlp", { unlink(modelPath) + # Test default parameter + model <- spark.mlp(df, layers = c(4, 5, 4, 3)) + mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction")) + expect_equal(head(mlpPredictions$prediction, 10), c(1, 1, 1, 1, 0, 1, 2, 2, 1, 0)) + + # Test illegal parameter + expect_error(spark.mlp(df, layers = NULL), "layers must be a integer vector with length > 1.") + expect_error(spark.mlp(df, layers = c()), "layers must be a integer vector with length > 1.") + expect_error(spark.mlp(df, layers = c(3)), "layers must be a integer vector with length > 1.") + + # Test random seed + # default seed + model <- spark.mlp(df, layers = c(4, 5, 4, 3), maxIter = 10) + mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction")) + expect_equal(head(mlpPredictions$prediction, 12), c(1, 1, 1, 1, 0, 1, 2, 2, 1, 2, 0, 1)) + # seed equals 10 + model <- spark.mlp(df, layers = c(4, 5, 4, 3), maxIter = 10, seed = 10) + mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction")) + expect_equal(head(mlpPredictions$prediction, 12), c(1, 1, 1, 1, 2, 1, 2, 2, 1, 0, 0, 1)) }) test_that("spark.naiveBayes", { diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/MultilayerPerceptronClassifierWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/MultilayerPerceptronClassifierWrapper.scala index be51e74187..1067300353 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/r/MultilayerPerceptronClassifierWrapper.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/r/MultilayerPerceptronClassifierWrapper.scala @@ -53,26 +53,26 @@ private[r] object MultilayerPerceptronClassifierWrapper def fit( data: DataFrame, blockSize: Int, - layers: Array[Double], + layers: Array[Int], solver: String, maxIter: Int, tol: Double, stepSize: Double, - seed: Int + seed: String ): MultilayerPerceptronClassifierWrapper = { // get labels and feature names from output schema val schema = data.schema // assemble and fit the pipeline val mlp = new MultilayerPerceptronClassifier() - .setLayers(layers.map(_.toInt)) + .setLayers(layers) .setBlockSize(blockSize) .setSolver(solver) .setMaxIter(maxIter) .setTol(tol) .setStepSize(stepSize) - .setSeed(seed) .setPredictionCol(PREDICTED_LABEL_COL) + if (seed != null && seed.length > 0) mlp.setSeed(seed.toInt) val pipeline = new Pipeline() .setStages(Array(mlp)) .fit(data) |