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From zero323 <...@git.apache.org>
Subject [GitHub] spark pull request #17170: [SPARK-19825][R][ML] spark.ml R API for FPGrowth
Date Tue, 07 Mar 2017 22:53:54 GMT
Github user zero323 commented on a diff in the pull request:

    https://github.com/apache/spark/pull/17170#discussion_r104802147
  
    --- 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 --
    
    To be honest I am not sure. If you think that setting `predictionCol` should  be disabled
I am fine with that but I don't see how formulas could be useful here. `FPGrowth` doesn't
really conform to the conventions used in other ML algorithms. It doesn't use vectors and
fixed size buckets are unlikely to happen.


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