Merge pull request apache#247 from palantir/resync-apache

[NOSQUASH] Resync from Apache

Author: ash211

.gitignore

@@ -47,6 +47,8 @@ dev/pr-deps/
 dist/
 docs/_site
 docs/api
+sql/docs
+sql/site
 lib_managed/
 lint-r-report.log
 log/

R/pkg/NAMESPACE

@@ -286,6 +286,8 @@ exportMethods("%<=>%",
               "lower",
               "lpad",
               "ltrim",
+              "map_keys",
+              "map_values",
               "max",
               "md5",
               "mean",

R/pkg/R/DataFrame.R

@@ -593,7 +593,7 @@ setMethod("cache",
 #'
 #' Persist this SparkDataFrame with the specified storage level. For details of the
 #' supported storage levels, refer to
-#' \url{http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence}.
+#' \url{http://spark.apache.org/docs/latest/rdd-programming-guide.html#rdd-persistence}.
 #'
 #' @param x the SparkDataFrame to persist.
 #' @param newLevel storage level chosen for the persistance. See available options in

R/pkg/R/RDD.R

@@ -227,7 +227,7 @@ setMethod("cacheRDD",
 #'
 #' Persist this RDD with the specified storage level. For details of the
 #' supported storage levels, refer to
-#'\url{http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence}.
+#'\url{http://spark.apache.org/docs/latest/rdd-programming-guide.html#rdd-persistence}.
 #'
 #' @param x The RDD to persist
 #' @param newLevel The new storage level to be assigned

R/pkg/R/functions.R

@@ -195,7 +195,10 @@ NULL
 #' head(tmp2)
 #' head(select(tmp, posexplode(tmp$v1)))
 #' head(select(tmp, sort_array(tmp$v1)))
-#' head(select(tmp, sort_array(tmp$v1, asc = FALSE)))}
+#' head(select(tmp, sort_array(tmp$v1, asc = FALSE)))
+#' tmp3 <- mutate(df, v3 = create_map(df$model, df$cyl))
+#' head(select(tmp3, map_keys(tmp3$v3)))
+#' head(select(tmp3, map_values(tmp3$v3)))}
 NULL
 
 #' Window functions for Column operations
@@ -3055,6 +3058,34 @@ setMethod("array_contains",
             column(jc)
           })
 
+#' @details
+#' \code{map_keys}: Returns an unordered array containing the keys of the map.
+#'
+#' @rdname column_collection_functions
+#' @aliases map_keys map_keys,Column-method
+#' @export
+#' @note map_keys since 2.3.0
+setMethod("map_keys",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "map_keys", x@jc)
+            column(jc)
+         })
+
+#' @details
+#' \code{map_values}: Returns an unordered array containing the values of the map.
+#'
+#' @rdname column_collection_functions
+#' @aliases map_values map_values,Column-method
+#' @export
+#' @note map_values since 2.3.0
+setMethod("map_values",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "map_values", x@jc)
+            column(jc)
+          })
+
 #' @details
 #' \code{explode}: Creates a new row for each element in the given array or map column.
 #'

R/pkg/R/generics.R

@@ -1213,6 +1213,16 @@ setGeneric("lpad", function(x, len, pad) { standardGeneric("lpad") })
 #' @name NULL
 setGeneric("ltrim", function(x) { standardGeneric("ltrim") })
 
+#' @rdname column_collection_functions
+#' @export
+#' @name NULL
+setGeneric("map_keys", function(x) { standardGeneric("map_keys") })
+
+#' @rdname column_collection_functions
+#' @export
+#' @name NULL
+setGeneric("map_values", function(x) { standardGeneric("map_values") })
+
 #' @rdname column_misc_functions
 #' @export
 #' @name NULL

R/pkg/R/mllib_classification.R

@@ -69,6 +69,11 @@ setClass("NaiveBayesModel", representation(jobj = "jobj"))
 #' @param aggregationDepth The depth for treeAggregate (greater than or equal to 2). If the dimensions of features
 #'                         or the number of partitions are large, this param could be adjusted to a larger size.
 #'                         This is an expert parameter. Default value should be good for most cases.
+#' @param handleInvalid How to handle invalid data (unseen labels or NULL values) in features and label
+#'                      column of string type.
+#'                      Supported options: "skip" (filter out rows with invalid data),
+#'                                         "error" (throw an error), "keep" (put invalid data in a special additional
+#'                                         bucket, at index numLabels). Default is "error".
 #' @param ... additional arguments passed to the method.
 #' @return \code{spark.svmLinear} returns a fitted linear SVM model.
 #' @rdname spark.svmLinear
@@ -98,7 +103,8 @@ setClass("NaiveBayesModel", representation(jobj = "jobj"))
 #' @note spark.svmLinear since 2.2.0
 setMethod("spark.svmLinear", signature(data = "SparkDataFrame", formula = "formula"),
           function(data, formula, regParam = 0.0, maxIter = 100, tol = 1E-6, standardization = TRUE,
-                   threshold = 0.0, weightCol = NULL, aggregationDepth = 2) {
+                   threshold = 0.0, weightCol = NULL, aggregationDepth = 2,
+                   handleInvalid = c("error", "keep", "skip")) {
             formula <- paste(deparse(formula), collapse = "")
 
             if (!is.null(weightCol) && weightCol == "") {
@@ -107,10 +113,12 @@ setMethod("spark.svmLinear", signature(data = "SparkDataFrame", formula = "formu
               weightCol <- as.character(weightCol)
             }
 
+            handleInvalid <- match.arg(handleInvalid)
+
             jobj <- callJStatic("org.apache.spark.ml.r.LinearSVCWrapper", "fit",
                                 data@sdf, formula, as.numeric(regParam), as.integer(maxIter),
                                 as.numeric(tol), as.logical(standardization), as.numeric(threshold),
-                                weightCol, as.integer(aggregationDepth))
+                                weightCol, as.integer(aggregationDepth), handleInvalid)
             new("LinearSVCModel", jobj = jobj)
           })
 
@@ -218,6 +226,11 @@ function(object, path, overwrite = FALSE) {
 #' @param upperBoundsOnIntercepts The upper bounds on intercepts if fitting under bound constrained optimization.
 #'                                The bound vector size must be equal to 1 for binomial regression, or the number
 #'                                of classes for multinomial regression.
+#' @param handleInvalid How to handle invalid data (unseen labels or NULL values) in features and label
+#'                      column of string type.
+#'                      Supported options: "skip" (filter out rows with invalid data),
+#'                                         "error" (throw an error), "keep" (put invalid data in a special additional
+#'                                         bucket, at index numLabels). Default is "error".
 #' @param ... additional arguments passed to the method.
 #' @return \code{spark.logit} returns a fitted logistic regression model.
 #' @rdname spark.logit
@@ -257,7 +270,8 @@ setMethod("spark.logit", signature(data = "SparkDataFrame", formula = "formula")
                    tol = 1E-6, family = "auto", standardization = TRUE,
                    thresholds = 0.5, weightCol = NULL, aggregationDepth = 2,
                    lowerBoundsOnCoefficients = NULL, upperBoundsOnCoefficients = NULL,
-                   lowerBoundsOnIntercepts = NULL, upperBoundsOnIntercepts = NULL) {
+                   lowerBoundsOnIntercepts = NULL, upperBoundsOnIntercepts = NULL,
+                   handleInvalid = c("error", "keep", "skip")) {
             formula <- paste(deparse(formula), collapse = "")
             row <- 0
             col <- 0
@@ -304,6 +318,8 @@ setMethod("spark.logit", signature(data = "SparkDataFrame", formula = "formula")
               upperBoundsOnCoefficients <- as.array(as.vector(upperBoundsOnCoefficients))
             }
 
+            handleInvalid <- match.arg(handleInvalid)
+
             jobj <- callJStatic("org.apache.spark.ml.r.LogisticRegressionWrapper", "fit",
                                 data@sdf, formula, as.numeric(regParam),
                                 as.numeric(elasticNetParam), as.integer(maxIter),
@@ -312,7 +328,8 @@ setMethod("spark.logit", signature(data = "SparkDataFrame", formula = "formula")
                                 weightCol, as.integer(aggregationDepth),
                                 as.integer(row), as.integer(col),
                                 lowerBoundsOnCoefficients, upperBoundsOnCoefficients,
-                                lowerBoundsOnIntercepts, upperBoundsOnIntercepts)
+                                lowerBoundsOnIntercepts, upperBoundsOnIntercepts,
+                                handleInvalid)
             new("LogisticRegressionModel", jobj = jobj)
           })
 
@@ -394,7 +411,12 @@ setMethod("write.ml", signature(object = "LogisticRegressionModel", path = "char
 #' @param stepSize stepSize parameter.
 #' @param seed seed parameter for weights initialization.
 #' @param initialWeights initialWeights parameter for weights initialization, it should be a
-#' numeric vector.
+#'        numeric vector.
+#' @param handleInvalid How to handle invalid data (unseen labels or NULL values) in features and label
+#'                      column of string type.
+#'                      Supported options: "skip" (filter out rows with invalid data),
+#'                                         "error" (throw an error), "keep" (put invalid data in a special additional
+#'                                         bucket, at index numLabels). Default is "error".
 #' @param ... additional arguments passed to the method.
 #' @return \code{spark.mlp} returns a fitted Multilayer Perceptron Classification Model.
 #' @rdname spark.mlp
@@ -426,7 +448,8 @@ setMethod("write.ml", signature(object = "LogisticRegressionModel", path = "char
 #' @note spark.mlp since 2.1.0
 setMethod("spark.mlp", signature(data = "SparkDataFrame", formula = "formula"),
           function(data, formula, layers, blockSize = 128, solver = "l-bfgs", maxIter = 100,
-                   tol = 1E-6, stepSize = 0.03, seed = NULL, initialWeights = NULL) {
+                   tol = 1E-6, stepSize = 0.03, seed = NULL, initialWeights = NULL,
+                   handleInvalid = c("error", "keep", "skip")) {
             formula <- paste(deparse(formula), collapse = "")
             if (is.null(layers)) {
               stop ("layers must be a integer vector with length > 1.")
@@ -441,10 +464,11 @@ setMethod("spark.mlp", signature(data = "SparkDataFrame", formula = "formula"),
             if (!is.null(initialWeights)) {
               initialWeights <- as.array(as.numeric(na.omit(initialWeights)))
             }
+            handleInvalid <- match.arg(handleInvalid)
             jobj <- callJStatic("org.apache.spark.ml.r.MultilayerPerceptronClassifierWrapper",
                                 "fit", data@sdf, formula, as.integer(blockSize), as.array(layers),
                                 as.character(solver), as.integer(maxIter), as.numeric(tol),
-                                as.numeric(stepSize), seed, initialWeights)
+                                as.numeric(stepSize), seed, initialWeights, handleInvalid)
             new("MultilayerPerceptronClassificationModel", jobj = jobj)
           })
 
@@ -514,6 +538,11 @@ setMethod("write.ml", signature(object = "MultilayerPerceptronClassificationMode
 #' @param formula a symbolic description of the model to be fitted. Currently only a few formula
 #'               operators are supported, including '~', '.', ':', '+', and '-'.
 #' @param smoothing smoothing parameter.
+#' @param handleInvalid How to handle invalid data (unseen labels or NULL values) in features and label
+#'                      column of string type.
+#'                      Supported options: "skip" (filter out rows with invalid data),
+#'                                         "error" (throw an error), "keep" (put invalid data in a special additional
+#'                                         bucket, at index numLabels). Default is "error".
 #' @param ... additional argument(s) passed to the method. Currently only \code{smoothing}.
 #' @return \code{spark.naiveBayes} returns a fitted naive Bayes model.
 #' @rdname spark.naiveBayes
@@ -543,10 +572,12 @@ setMethod("write.ml", signature(object = "MultilayerPerceptronClassificationMode
 #' }
 #' @note spark.naiveBayes since 2.0.0
 setMethod("spark.naiveBayes", signature(data = "SparkDataFrame", formula = "formula"),
-          function(data, formula, smoothing = 1.0) {
+          function(data, formula, smoothing = 1.0,
+                   handleInvalid = c("error", "keep", "skip")) {
             formula <- paste(deparse(formula), collapse = "")
+            handleInvalid <- match.arg(handleInvalid)
             jobj <- callJStatic("org.apache.spark.ml.r.NaiveBayesWrapper", "fit",
-            formula, data@sdf, smoothing)
+                                formula, data@sdf, smoothing, handleInvalid)
             new("NaiveBayesModel", jobj = jobj)
           })
 

R/pkg/R/mllib_regression.R

@@ -76,6 +76,8 @@ setClass("IsotonicRegressionModel", representation(jobj = "jobj"))
 #'                               "frequencyDesc", "frequencyAsc", "alphabetDesc", and "alphabetAsc".
 #'                               The default value is "frequencyDesc". When the ordering is set to
 #'                               "alphabetDesc", this drops the same category as R when encoding strings.
+#' @param offsetCol the offset column name. If this is not set or empty, we treat all instance offsets
+#'                  as 0.0. The feature specified as offset has a constant coefficient of 1.0.
 #' @param ... additional arguments passed to the method.
 #' @aliases spark.glm,SparkDataFrame,formula-method
 #' @return \code{spark.glm} returns a fitted generalized linear model.
@@ -127,7 +129,8 @@ setMethod("spark.glm", signature(data = "SparkDataFrame", formula = "formula"),
           function(data, formula, family = gaussian, tol = 1e-6, maxIter = 25, weightCol = NULL,
                    regParam = 0.0, var.power = 0.0, link.power = 1.0 - var.power,
                    stringIndexerOrderType = c("frequencyDesc", "frequencyAsc",
-                                              "alphabetDesc", "alphabetAsc")) {
+                                              "alphabetDesc", "alphabetAsc"),
+                   offsetCol = NULL) {
 
             stringIndexerOrderType <- match.arg(stringIndexerOrderType)
             if (is.character(family)) {
@@ -159,12 +162,19 @@ setMethod("spark.glm", signature(data = "SparkDataFrame", formula = "formula"),
               weightCol <- as.character(weightCol)
             }
 
+            if (!is.null(offsetCol)) {
+              offsetCol <- as.character(offsetCol)
+              if (nchar(offsetCol) == 0) {
+                offsetCol <- NULL
+              }
+            }
+
             # For known families, Gamma is upper-cased
             jobj <- callJStatic("org.apache.spark.ml.r.GeneralizedLinearRegressionWrapper",
                                 "fit", formula, data@sdf, tolower(family$family), family$link,
                                 tol, as.integer(maxIter), weightCol, regParam,
                                 as.double(var.power), as.double(link.power),
-                                stringIndexerOrderType)
+                                stringIndexerOrderType, offsetCol)
             new("GeneralizedLinearRegressionModel", jobj = jobj)
           })
 
@@ -192,6 +202,8 @@ setMethod("spark.glm", signature(data = "SparkDataFrame", formula = "formula"),
 #'                               "frequencyDesc", "frequencyAsc", "alphabetDesc", and "alphabetAsc".
 #'                               The default value is "frequencyDesc". When the ordering is set to
 #'                               "alphabetDesc", this drops the same category as R when encoding strings.
+#' @param offsetCol the offset column name. If this is not set or empty, we treat all instance offsets
+#'                  as 0.0. The feature specified as offset has a constant coefficient of 1.0.
 #' @return \code{glm} returns a fitted generalized linear model.
 #' @rdname glm
 #' @export
@@ -209,10 +221,12 @@ setMethod("glm", signature(formula = "formula", family = "ANY", data = "SparkDat
           function(formula, family = gaussian, data, epsilon = 1e-6, maxit = 25, weightCol = NULL,
                    var.power = 0.0, link.power = 1.0 - var.power,
                    stringIndexerOrderType = c("frequencyDesc", "frequencyAsc",
-                                              "alphabetDesc", "alphabetAsc")) {
+                                              "alphabetDesc", "alphabetAsc"),
+                   offsetCol = NULL) {
             spark.glm(data, formula, family, tol = epsilon, maxIter = maxit, weightCol = weightCol,
                       var.power = var.power, link.power = link.power,
-                      stringIndexerOrderType = stringIndexerOrderType)
+                      stringIndexerOrderType = stringIndexerOrderType,
+                      offsetCol = offsetCol)
           })
 
 #  Returns the summary of a model produced by glm() or spark.glm(), similarly to R's summary().