Github user wangmiao1981 commented on a diff in the pull request:
https://github.com/apache/spark/pull/17170#discussion_r106587292
--- Diff: R/pkg/R/mllib_fpm.R ---
@@ -0,0 +1,152 @@
+#
+# 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
+#'
+#' A parallel FP-growth algorithm to mine frequent itemsets. The algorithm
is described in
+#' Li et al., PFP: Parallel FP-Growth for Query
+#' Recommendation <\url{http://dx.doi.org/10.1145/1454008.1454027}>. PFP
distributes computation in such a way that each worker executes an
+#' independent group of mining tasks. The FP-Growth algorithm is described
in
+#' Han et al., Mining frequent patterns without
+#' candidate generation <\url{http://dx.doi.org/10.1145/335191.335372}>.
+#'
+#' @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 numPartitions Number of partitions used for fitting.
+#' @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{
+#' raw_data <- read.df(
+#' "data/mllib/sample_fpgrowth.txt",
+#' source = "csv",
+#' schema = structType(structField("raw_features", "string")))
+#'
+#' data <- selectExpr(raw_data, "split(raw_features, ' ') as features")
+#' model <- spark.fpGrowth(data)
+#'
+#' # Show frequent itemsets
+#' frequent_itemsets <- spark.freqItemsets(model)
+#' showDF(frequent_itemsets)
+#'
+#' # Show association rules
+#' association_rules <- spark.associationRules(model)
+#' showDF(association_rules)
+#'
+#' # Predict on new data
+#' new_itemsets <- data.frame(features = c("t", "t,s"))
+#' new_data <- selectExpr(createDataFrame(new_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",
+#' numPartitions = 10)
+#' }
+#' @references \url{http://en.wikipedia.org/wiki/Association_rule_learning}
+#' @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",
+ numPartitions = -1) {
+ if (!is.numeric(minSupport) || minSupport < 0 || minSupport >
1) {
+ stop("minSupport should be a number [0, 1].")
+ }
+ if (!is.numeric(minConfidence) || minConfidence < 0 ||
minConfidence > 1) {
+ stop("minConfidence should be a number [0, 1].")
+ }
+
+ jobj <- callJStatic("org.apache.spark.ml.r.FPGrowthWrapper",
"fit",
+ data@sdf, as.numeric(minSupport),
as.numeric(minConfidence),
+ featuresCol, predictionCol,
as.integer(numPartitions))
+ new("FPGrowthModel", jobj = jobj)
+ })
+
+# Get frequent itemsets.
+#' @param object a fitted FPGrowth model.
+#' @return A DataFrame with frequent itemsets.
+#'
--- End diff --
no blank line here.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
