Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/17170#discussion_r107012724
--- Diff: R/pkg/R/mllib_fpm.R ---
@@ -0,0 +1,144 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# mllib_fpm.R: Provides methods for MLlib frequent pattern mining
algorithms integration
+
+#' S4 class that represents a FPGrowthModel
+#'
+#' @param jobj a Java object reference to the backing Scala FPGrowthModel
+#' @export
+#' @note FPGrowthModel since 2.2.0
+setClass("FPGrowthModel", slots = list(jobj = "jobj"))
+
+#' FPGrowth Model
+#'
+#' Provides FP-growth algorithm to mine frequent itemsets.
+#'
+#' @param data A SparkDataFrame for training.
+#' @param minSupport Minimal support level.
+#' @param minConfidence Minimal confidence level.
+#' @param featuresCol Features column name.
+#' @param predictionCol Prediction column name.
+#' @param ... additional argument(s) passed to the method.
+#' @return \code{spark.fpGrowth} returns a fitted FPGrowth model.
+#'
+#' @rdname spark.fpGrowth
+#' @name spark.fpGrowth
+#' @aliases spark.fpGrowth,SparkDataFrame-method
+#' @export
+#' @examples
+#' \dontrun{
+#' itemsets <- data.frame(features = c("a,b", "a,b,c", "c,d"))
+#' data <- selectExpr(createDataFrame(itemsets), "split(features, ',') as
features")
+#' model <- spark.fpGrowth(data)
+#'
+#' # Show frequent itemsets
+#' frequent_itemsets <- freqItemsets(model)
+#' showDF(frequent_itemsets)
+#'
+#' # Show association rules
+#' association_rules <- associationRules(model)
+#' showDF(association_rules)
+#'
+#' # Predict on new data
+#' new_itemsets <- data.frame(features = c("b", "a,c", "d"))
+#' new_data <- selectExpr(createDataFrame(itemsets), "split(features, ',')
as features")
+#' predict(model, new_data)
+#'
+#' # Save and load model
+#' path <- "/path/to/model"
+#' write.ml(model, path)
+#' read.ml(path)
+#'
+#' # Optional arguments
+#' baskets_data <- selectExpr(createDataFrame(itemsets), "split(features,
',') as baskets")
+#' another_model <- spark.fpGrowth(data, minSupport = 0.1, minConfidence =
0.5
+#' featureCol = "baskets", predictionCol =
"predicted")
+#' }
+#' @note spark.fpGrowth since 2.2.0
+setMethod("spark.fpGrowth", signature(data = "SparkDataFrame"),
+ function(data, minSupport = 0.3, minConfidence = 0.8,
+ featuresCol = "features", predictionCol = "prediction")
{
--- End diff --
I believe predictionCol param only allow you to change the name of the
column - prediction is always still going to be there, no?
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