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[SPARK-16445][MLlib][SparkR] Fix @return description for sparkR mlp summary() method #15015

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keypointt wants to merge 3 commits into from
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[SPARK-16445][MLlib][SparkR] Fix @return description for sparkR mlp summary() method #15015

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99d8944
[SPARK-16445] fix return description for mlp.summary
keypointt Sep 8, 2016
91ae8d6
[SPARK-16445] add test for weight list
keypointt Sep 9, 2016
d526890
Merge branch 'master' into SPARK-16445-2
keypointt Sep 9, 2016
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6 changes: 3 additions & 3 deletions R/pkg/R/mllib.R
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Expand Up @@ -720,8 +720,9 @@ setMethod("predict", signature(object = "MultilayerPerceptronClassificationModel
# Returns the summary of a Multilayer Perceptron Classification Model produced by \code{spark.mlp}

#' @param object a Multilayer Perceptron Classification Model fitted by \code{spark.mlp}
#' @return \code{summary} returns a list containing \code{layers}, the label distribution, and
#' \code{tables}, conditional probabilities given the target label.
#' @return \code{summary} returns a list containing \code{labelCount}, \code{layers}, and
#' \code{weights}. For \code{weights}, it is a numeric vector with length equal to
#' the expected given the architecture (i.e., for 8-10-2 network, 100 connection weights).
#' @rdname spark.mlp
#' @export
#' @aliases summary,MultilayerPerceptronClassificationModel-method
Expand All @@ -732,7 +733,6 @@ setMethod("summary", signature(object = "MultilayerPerceptronClassificationModel
labelCount <- callJMethod(jobj, "labelCount")
layers <- unlist(callJMethod(jobj, "layers"))
weights <- callJMethod(jobj, "weights")
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@felixcheung felixcheung Sep 8, 2016

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could we add a test for this output?

weights <- matrix(weights, nrow = length(weights))
list(labelCount = labelCount, layers = layers, weights = weights)
})

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2 changes: 2 additions & 0 deletions R/pkg/inst/tests/testthat/test_mllib.R
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Expand Up @@ -369,6 +369,8 @@ test_that("spark.mlp", {
expect_equal(summary$labelCount, 3)
expect_equal(summary$layers, c(4, 5, 4, 3))
expect_equal(length(summary$weights), 64)
expect_equal(head(summary$weights, 5), list(-0.878743, 0.2154151, -1.16304, -0.6583214, 1.009825),
tolerance = 1e-6)

# Test predict method
mlpTestDF <- df
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