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author | WeichenXu <WeichenXu123@outlook.com> | 2016-10-25 21:42:59 -0700 |
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committer | Felix Cheung <felixcheung@apache.org> | 2016-10-25 21:42:59 -0700 |
commit | 12b3e8d2e02788c3bebfecdd69755e94d80011c9 (patch) | |
tree | a8577ebadef6f612401fb7bd92d22d23f4a30ced /R/pkg | |
parent | c329a568b58d65c492a43926bf0f588f2ae6a66e (diff) | |
download | spark-12b3e8d2e02788c3bebfecdd69755e94d80011c9.tar.gz spark-12b3e8d2e02788c3bebfecdd69755e94d80011c9.tar.bz2 spark-12b3e8d2e02788c3bebfecdd69755e94d80011c9.zip |
[SPARK-18007][SPARKR][ML] update SparkR MLP - add initalWeights parameter
## What changes were proposed in this pull request?
update SparkR MLP, add initalWeights parameter.
## How was this patch tested?
test added.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes #15552 from WeichenXu123/mlp_r_add_initialWeight_param.
Diffstat (limited to 'R/pkg')
-rw-r--r-- | R/pkg/R/mllib.R | 14 | ||||
-rw-r--r-- | R/pkg/inst/tests/testthat/test_mllib.R | 15 |
2 files changed, 25 insertions, 4 deletions
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R index b901307f8f..bf182be8e2 100644 --- a/R/pkg/R/mllib.R +++ b/R/pkg/R/mllib.R @@ -665,6 +665,8 @@ setMethod("predict", signature(object = "KMeansModel"), #' @param tol convergence tolerance of iterations. #' @param stepSize stepSize parameter. #' @param seed seed parameter for weights initialization. +#' @param initialWeights initialWeights parameter for weights initialization, it should be a +#' numeric vector. #' @param ... additional arguments passed to the method. #' @return \code{spark.mlp} returns a fitted Multilayer Perceptron Classification Model. #' @rdname spark.mlp @@ -677,8 +679,9 @@ setMethod("predict", signature(object = "KMeansModel"), #' df <- read.df("data/mllib/sample_multiclass_classification_data.txt", source = "libsvm") #' #' # fit a Multilayer Perceptron Classification Model -#' model <- spark.mlp(df, blockSize = 128, layers = c(4, 5, 4, 3), solver = "l-bfgs", -#' maxIter = 100, tol = 0.5, stepSize = 1, seed = 1) +#' model <- spark.mlp(df, blockSize = 128, layers = c(4, 3), solver = "l-bfgs", +#' maxIter = 100, tol = 0.5, stepSize = 1, seed = 1, +#' initialWeights = c(0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 9, 9, 9, 9, 9)) #' #' # get the summary of the model #' summary(model) @@ -695,7 +698,7 @@ setMethod("predict", signature(object = "KMeansModel"), #' @note spark.mlp since 2.1.0 setMethod("spark.mlp", signature(data = "SparkDataFrame"), function(data, layers, blockSize = 128, solver = "l-bfgs", maxIter = 100, - tol = 1E-6, stepSize = 0.03, seed = NULL) { + tol = 1E-6, stepSize = 0.03, seed = NULL, initialWeights = NULL) { if (is.null(layers)) { stop ("layers must be a integer vector with length > 1.") } @@ -706,10 +709,13 @@ setMethod("spark.mlp", signature(data = "SparkDataFrame"), if (!is.null(seed)) { seed <- as.character(as.integer(seed)) } + if (!is.null(initialWeights)) { + initialWeights <- as.array(as.numeric(na.omit(initialWeights))) + } 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), seed) + as.numeric(stepSize), seed, initialWeights) 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 c99315726a..33cc069f14 100644 --- a/R/pkg/inst/tests/testthat/test_mllib.R +++ b/R/pkg/inst/tests/testthat/test_mllib.R @@ -410,6 +410,21 @@ test_that("spark.mlp", { 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$prediction, 12), c(1, 1, 1, 1, 2, 1, 2, 2, 1, 0, 0, 1)) + + # test initialWeights + model <- spark.mlp(df, layers = c(4, 3), maxIter = 2, initialWeights = + c(0, 0, 0, 0, 0, 5, 5, 5, 5, 5, 9, 9, 9, 9, 9)) + mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction")) + expect_equal(head(mlpPredictions$prediction, 12), c(1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1)) + + model <- spark.mlp(df, 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$prediction, 12), c(1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1)) + + model <- spark.mlp(df, layers = c(4, 3), maxIter = 2) + mlpPredictions <- collect(select(predict(model, mlpTestDF), "prediction")) + expect_equal(head(mlpPredictions$prediction, 12), c(1, 1, 1, 1, 0, 1, 0, 2, 1, 0, 0, 1)) }) test_that("spark.naiveBayes", { |