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[GitHub] [spark] zero323 opened a new pull request #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

zero323 opened a new pull request #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571
 
 
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   This pull request adds SparkR wrapper for `FMRegressor`:
   
   - Supporting ` org.apache.spark.ml.r.FMRegressorWrapper`.
   - `FMRegressionModel` S4 class.
   - Corresponding `spark.fmRegressor`, `predict`, `summary` and `write.ml` generics.
   - Corresponding docs and tests.
   
   ### Why are the changes needed?
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   Feature parity.
   
   
   ### Does this PR introduce any user-facing change?
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   New unit tests.

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[GitHub] [spark] SparkQA commented on issue #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
SparkQA commented on issue #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#issuecomment-596161286
 
 
   **[Test build #119524 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/119524/testReport)** for PR 27571 at commit [`0adb2cf`](https://github.com/apache/spark/commit/0adb2cfc36623537e91b213297987854838cd642).
    * This patch passes all tests.
    * This patch merges cleanly.
    * This patch adds no public classes.

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[GitHub] [spark] SparkQA commented on issue #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
SparkQA commented on issue #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#issuecomment-594045864
 
 
   **[Test build #119240 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/119240/testReport)** for PR 27571 at commit [`4c5b2a5`](https://github.com/apache/spark/commit/4c5b2a59574f59927b962f9657f82837f88db74b).

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Posted by GitBox <gi...@apache.org>.
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URL: https://github.com/apache/spark/pull/27571#issuecomment-596157194
 
 
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Posted by GitBox <gi...@apache.org>.
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[GitHub] [spark] huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#discussion_r379882358
 
 

 ##########
 File path: R/pkg/R/mllib_regression.R
 ##########
 @@ -540,3 +546,150 @@ setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "c
           function(object, path, overwrite = FALSE) {
             write_internal(object, path, overwrite)
           })
+
+
+#' Factorization Machines Regression Model Model
+#'
+#' \code{spark.fmRegressor} fits a factorization regression model against a SparkDataFrame.
+#' Users can call \code{predict} to make
+#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
+#'
+#' @param data a \code{SparkDataFrame} of observations and labels for model fitting.
+#' @param formula a symbolic description of the model to be fitted. Currently only a few formula
+#'                operators are supported, including '~', '.', ':', '+', and '-'.
+#' @param factorSize dimensionality of the factors.
+#' @param fitLinear whether to fit linear term.  # TODO Can we express this with formula?
+#' @param regParam the regularization parameter.
+#' @param miniBatchFraction the mini-batch fraction parameter.
+#' @param initStd the standard deviation of initial coefficients.
+#' @param maxIter maximum iteration number.
+#' @param stepSize stepSize parameter.
+#' @param tol convergence tolerance of iterations.
+#' @param solver solver parameter, supported options: "gd" (minibatch gradient descent) or "adamW".
+#' @param seed seed parameter for weights initialization.
+#' @param stringIndexerOrderType how to order categories of a string feature column. This is used to
+#'                               decide the base level of a string feature as the last category
+#'                               after ordering is dropped when encoding strings. Supported options
+#'                               are "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 ... additional arguments passed to the method.
+#' @return \code{spark.fmRegressor} returns a fitted Factorization Machines Regression Model.
+#'
+#' @rdname spark.fmRegressor
+#' @aliases spark.fmRegressor,SparkDataFrame,formula-method
+#' @name spark.fmRegressor
+#' @seealso \link{read.ml}
+#' @examples
+#' \dontrun{
+#' df <- read.df("data/mllib/sample_linear_regression_data.txt", source = "libsvm")
+#'
+#' # fit Factorization Machines Regression Model
+#' model <- spark.fmRegressor(
+#'            df, label ~ features,
+#'            regParam = 0.01, maxIter = 10, fitLinear = TRUE
+#'          )
+#'
+#' # get the summary of the model
+#' summary(model)
+#'
+#' # make predictions
+#' predictions <- predict(model, df)
+#'
+#' # save and load the model
+#' path <- "path/to/model"
+#' write.ml(model, path)
+#' savedModel <- read.ml(path)
+#' summary(savedModel)
+#' }
+#' @note spark.fmRegressor since 3.1.0
+setMethod("spark.fmRegressor", signature(data = "SparkDataFrame", formula = "formula"),
+          function(data, formula, factorSize = 8, fitLinear = TRUE, regParam = 0.0,
+                   miniBatchFraction = 1.0, initStd = 0.01, maxIter = 100, stepSize=1.0,
+                   tol = 1e-6, solver = c("adamW", "gd"), seed = NULL,
+                   stringIndexerOrderType = c("frequencyDesc", "frequencyAsc",
+                                              "alphabetDesc", "alphabetAsc")) {
+
+            formula <- paste(deparse(formula), collapse = "")
+
+            if (!is.null(seed)) {
+              seed <- as.character(as.integer(seed))
+            }
+
+            solver <- match.arg(solver)
+            stringIndexerOrderType <- match.arg(stringIndexerOrderType)
+
+            jobj <- callJStatic("org.apache.spark.ml.r.FMRegressorWrapper",
+                                "fit",
+                                data@sdf,
+                                formula,
+                                as.integer(factorSize),
+                                as.logical(fitLinear),
+                                as.numeric(regParam),
+                                as.numeric(miniBatchFraction),
+                                as.numeric(initStd),
+                                as.integer(maxIter),
+                                as.numeric(stepSize),
+                                as.numeric(tol),
+                                solver,
+                                seed,
+                                stringIndexerOrderType)
+            new("FMRegressionModel", jobj = jobj)
+          })
+
+
+#  Returns the summary of a FM Regression model produced by \code{spark.fmRegressor}
+
+#' @param object a FM Regression Model model fitted by \code{spark.fmRegressor}.
+#' @return \code{summary} returns summary information of the fitted model, which is a list.
+#'
+#' @rdname spark.fmRegressor
+#' @note summary(FMRegressionModel) since 3.1.0
+setMethod("summary", signature(object = "FMRegressionModel"),
+          function(object) {
+            jobj <- object@jobj
+            features <- callJMethod(jobj, "rFeatures")
+            coefficients <- callJMethod(jobj, "rCoefficients")
+            coefficients <- as.matrix(unlist(coefficients))
+            colnames(coefficients) <- c("Estimate")
+            rownames(coefficients) <- unlist(features)
+            numFeatures <- callJMethod(jobj, "numFeatures")
+            raw_factors <- unlist(callJMethod(jobj, "rFactors"))
+            factor_size <- callJMethod(jobj, "factorSize")
+
+            list(
+              coefficients = coefficients,
+              factors = matrix(raw_factors, ncol = factor_size),
+              numFeatures = numFeatures,
+              factorSize = factor_size
+            )
+          })
+
+
 
 Review comment:
   diddo

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[GitHub] [spark] SparkQA commented on issue #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
SparkQA commented on issue #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#issuecomment-596258797
 
 
   **[Test build #119544 has started](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/119544/testReport)** for PR 27571 at commit [`82b99c1`](https://github.com/apache/spark/commit/82b99c1466e0428f09e1eb61f8ecd48d0a074e57).

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[GitHub] [spark] huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#discussion_r379882600
 
 

 ##########
 File path: examples/src/main/r/ml/fmRegressor.R
 ##########
 @@ -0,0 +1,40 @@
+#
+# 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.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/decisionTree.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-fmRegressor-example")
+
+# $example on
+# Load training data
+df <- read.df("data/mllib/sample_linear_regression_data.txt", source = "libsvm")
+training_test <- randomSplit(df, c(0.7, 0.3))
+training <- training_test[[1]]
+test <- training_test[[2]]
+
+
 
 Review comment:
   nit: delete extra line

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[GitHub] [spark] huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#discussion_r379882502
 
 

 ##########
 File path: R/pkg/tests/fulltests/test_mllib_regression.R
 ##########
 @@ -551,4 +551,33 @@ test_that("spark.survreg", {
   }
 })
 
+
+test_that("spark.fmRegressor", {
+  df <- suppressWarnings(createDataFrame(iris))
+
+  model <- spark.fmRegressor(
+    df,  Sepal_Width ~ .,
+    regParam = 0.01, maxIter = 10, fitLinear = TRUE
+  )
+
+  prediction1 <- predict(model, df)
+  expect_is(prediction1, "SparkDataFrame")
+
+  # Test model save/load
+  if (windows_with_hadoop()) {
+    modelPath <- tempfile(pattern = "spark-fmregressor", fileext = ".tmp")
+    write.ml(model, modelPath)
+    model2 <- read.ml(modelPath)
+
+    expect_is(model2, "FMRegressionModel")
+
+    prediction2 <- predict(model2, df)
+    expect_equal(
+      collect(prediction1),
+      collect(prediction2)
+    )
+  }
+})
+
+
 
 Review comment:
   nit: delete extra line

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huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#discussion_r379882027
 
 

 ##########
 File path: R/pkg/R/mllib_regression.R
 ##########
 @@ -540,3 +546,150 @@ setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "c
           function(object, path, overwrite = FALSE) {
             write_internal(object, path, overwrite)
           })
+
+
+#' Factorization Machines Regression Model Model
 
 Review comment:
   nit: ```Model Model```

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[GitHub] [spark] huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#discussion_r405098967
 
 

 ##########
 File path: R/pkg/R/mllib_regression.R
 ##########
 @@ -540,3 +546,147 @@ setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "c
           function(object, path, overwrite = FALSE) {
             write_internal(object, path, overwrite)
           })
+
+#' Factorization Machines Regression Model
+#'
+#' \code{spark.fmRegressor} fits a factorization regression model against a SparkDataFrame.
+#' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to make
+#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
+#'
+#' @param data a \code{SparkDataFrame} of observations and labels for model fitting.
+#' @param formula a symbolic description of the model to be fitted. Currently only a few formula
+#'                operators are supported, including '~', '.', ':', '+', and '-'.
+#' @param factorSize dimensionality of the factors.
+#' @param fitLinear whether to fit linear term.  # TODO Can we express this with formula?
+#' @param regParam the regularization parameter.
+#' @param miniBatchFraction the mini-batch fraction parameter.
+#' @param initStd the standard deviation of initial coefficients.
+#' @param maxIter maximum iteration number.
+#' @param stepSize stepSize parameter.
+#' @param tol convergence tolerance of iterations.
+#' @param solver solver parameter, supported options: "gd" (minibatch gradient descent) or "adamW".
+#' @param seed seed parameter for weights initialization.
+#' @param stringIndexerOrderType how to order categories of a string feature column. This is used to
+#'                               decide the base level of a string feature as the last category
+#'                               after ordering is dropped when encoding strings. Supported options
+#'                               are "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 ... additional arguments passed to the method.
+#' @return \code{spark.fmRegressor} returns a fitted Factorization Machines Regression Model.
+#'
+#' @rdname spark.fmRegressor
+#' @aliases spark.fmRegressor,SparkDataFrame,formula-method
+#' @name spark.fmRegressor
+#' @seealso \link{read.ml}
+#' @examples
+#' \dontrun{
+#' df <- read.df("data/mllib/sample_linear_regression_data.txt", source = "libsvm")
+#'
+#' # fit Factorization Machines Regression Model
+#' model <- spark.fmRegressor(
+#'            df, label ~ features,
 
 Review comment:
   nit: The indents seem not right? 

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[GitHub] [spark] SparkQA commented on issue #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

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SparkQA commented on issue #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
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    * This patch passes all tests.
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     * `  class FMRegressorWrapperReader extends MLReader[FMRegressorWrapper] `

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[GitHub] [spark] huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#discussion_r405099515
 
 

 ##########
 File path: mllib/src/main/scala/org/apache/spark/ml/r/FMRegressorWrapper.scala
 ##########
 @@ -0,0 +1,158 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.r
+
+import org.apache.hadoop.fs.Path
+import org.json4s._
+import org.json4s.JsonDSL._
+import org.json4s.jackson.JsonMethods._
+
+import org.apache.spark.ml.{Pipeline, PipelineModel}
+import org.apache.spark.ml.attribute.AttributeGroup
+import org.apache.spark.ml.feature.RFormula
+import org.apache.spark.ml.r.RWrapperUtils._
+import org.apache.spark.ml.regression.{FMRegressionModel, FMRegressor}
+import org.apache.spark.ml.util._
+import org.apache.spark.sql.{DataFrame, Dataset}
+
+private[r] class FMRegressorWrapper private (
+    val pipeline: PipelineModel,
+    val features: Array[String]) extends MLWritable {
+  import FMRegressorWrapper._
+
+  private val fmRegressionModel: FMRegressionModel =
+    pipeline.stages(1).asInstanceOf[FMRegressionModel]
+
+  lazy val rFeatures: Array[String] = if (fmRegressionModel.getFitIntercept) {
+    Array("(Intercept)") ++ features
+  } else {
+    features
+  }
+
+  lazy val rCoefficients: Array[Double] = if (fmRegressionModel.getFitIntercept) {
+    Array(fmRegressionModel.intercept) ++ fmRegressionModel.linear.toArray
+  } else {
+    fmRegressionModel.linear.toArray
+  }
+
+  lazy val rFactors = fmRegressionModel.factors.toArray
+
+  lazy val numFeatures: Int = fmRegressionModel.numFeatures
+
+  lazy val factorSize: Int = fmRegressionModel.getFactorSize
+
+  def transform(dataset: Dataset[_]): DataFrame = {
+    pipeline.transform(dataset)
+      .drop(fmRegressionModel.getFeaturesCol)
+  }
+
+  override def write: MLWriter = new FMRegressorWrapper.FMRegressorWrapperWriter(this)
+}
+
+private[r] object FMRegressorWrapper
+  extends MLReadable[FMRegressorWrapper] {
+
+  def fit(  // scalastyle:ignore
+      data: DataFrame,
+      formula: String,
+      factorSize: Int,
+      fitLinear: Boolean,
+      regParam: Double,
+      miniBatchFraction: Double,
+      initStd: Double,
+      maxIter: Int,
+      stepSize: Double,
+      tol: Double,
+      solver: String,
+      seed: String,
+      stringIndexerOrderType: String): FMRegressorWrapper = {
+
+    val rFormula = new RFormula()
+      .setFormula(formula)
+      .setStringIndexerOrderType(stringIndexerOrderType)
+    checkDataColumns(rFormula, data)
+    val rFormulaModel = rFormula.fit(data)
+
+    val fitIntercept = rFormula.hasIntercept
+
+    // get feature names from output schema
+    val schema = rFormulaModel.transform(data).schema
+    val featureAttrs = AttributeGroup.fromStructField(schema(rFormulaModel.getFeaturesCol))
+      .attributes.get
+    val features = featureAttrs.map(_.name.get)
+
+    // assemble and fit the pipeline
+    val fmr = new FMRegressor()
+      .setFactorSize(factorSize)
+      .setFitIntercept(fitIntercept)
+      .setFitLinear(fitLinear)
+      .setRegParam(regParam)
+      .setMiniBatchFraction(miniBatchFraction)
+      .setInitStd(initStd)
+      .setMaxIter(maxIter)
+      .setStepSize(stepSize)
+      .setTol(tol)
+      .setSolver(solver)
+      .setFeaturesCol(rFormula.getFeaturesCol)
+
+    if (seed != null && seed.length > 0) {
+      fmr.setSeed(seed.toLong)
+    }
+
+    val pipeline = new Pipeline()
+      .setStages(Array(rFormulaModel, fmr))
+      .fit(data)
+
+    new FMRegressorWrapper(pipeline, features)
+  }
+
+  override def read: MLReader[FMRegressorWrapper] = new FMRegressorWrapperReader
+
+  override def load(path: String): FMRegressorWrapper = super.load(path)
 
 Review comment:
   nit: remove this?

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[GitHub] [spark] huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#discussion_r379882343
 
 

 ##########
 File path: R/pkg/R/mllib_regression.R
 ##########
 @@ -540,3 +546,150 @@ setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "c
           function(object, path, overwrite = FALSE) {
             write_internal(object, path, overwrite)
           })
+
+
+#' Factorization Machines Regression Model Model
+#'
+#' \code{spark.fmRegressor} fits a factorization regression model against a SparkDataFrame.
+#' Users can call \code{predict} to make
+#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
+#'
+#' @param data a \code{SparkDataFrame} of observations and labels for model fitting.
+#' @param formula a symbolic description of the model to be fitted. Currently only a few formula
+#'                operators are supported, including '~', '.', ':', '+', and '-'.
+#' @param factorSize dimensionality of the factors.
+#' @param fitLinear whether to fit linear term.  # TODO Can we express this with formula?
+#' @param regParam the regularization parameter.
+#' @param miniBatchFraction the mini-batch fraction parameter.
+#' @param initStd the standard deviation of initial coefficients.
+#' @param maxIter maximum iteration number.
+#' @param stepSize stepSize parameter.
+#' @param tol convergence tolerance of iterations.
+#' @param solver solver parameter, supported options: "gd" (minibatch gradient descent) or "adamW".
+#' @param seed seed parameter for weights initialization.
+#' @param stringIndexerOrderType how to order categories of a string feature column. This is used to
+#'                               decide the base level of a string feature as the last category
+#'                               after ordering is dropped when encoding strings. Supported options
+#'                               are "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 ... additional arguments passed to the method.
+#' @return \code{spark.fmRegressor} returns a fitted Factorization Machines Regression Model.
+#'
+#' @rdname spark.fmRegressor
+#' @aliases spark.fmRegressor,SparkDataFrame,formula-method
+#' @name spark.fmRegressor
+#' @seealso \link{read.ml}
+#' @examples
+#' \dontrun{
+#' df <- read.df("data/mllib/sample_linear_regression_data.txt", source = "libsvm")
+#'
+#' # fit Factorization Machines Regression Model
+#' model <- spark.fmRegressor(
+#'            df, label ~ features,
+#'            regParam = 0.01, maxIter = 10, fitLinear = TRUE
+#'          )
+#'
+#' # get the summary of the model
+#' summary(model)
+#'
+#' # make predictions
+#' predictions <- predict(model, df)
+#'
+#' # save and load the model
+#' path <- "path/to/model"
+#' write.ml(model, path)
+#' savedModel <- read.ml(path)
+#' summary(savedModel)
+#' }
+#' @note spark.fmRegressor since 3.1.0
+setMethod("spark.fmRegressor", signature(data = "SparkDataFrame", formula = "formula"),
+          function(data, formula, factorSize = 8, fitLinear = TRUE, regParam = 0.0,
+                   miniBatchFraction = 1.0, initStd = 0.01, maxIter = 100, stepSize=1.0,
+                   tol = 1e-6, solver = c("adamW", "gd"), seed = NULL,
+                   stringIndexerOrderType = c("frequencyDesc", "frequencyAsc",
+                                              "alphabetDesc", "alphabetAsc")) {
+
+            formula <- paste(deparse(formula), collapse = "")
+
+            if (!is.null(seed)) {
+              seed <- as.character(as.integer(seed))
+            }
+
+            solver <- match.arg(solver)
+            stringIndexerOrderType <- match.arg(stringIndexerOrderType)
+
+            jobj <- callJStatic("org.apache.spark.ml.r.FMRegressorWrapper",
+                                "fit",
+                                data@sdf,
+                                formula,
+                                as.integer(factorSize),
+                                as.logical(fitLinear),
+                                as.numeric(regParam),
+                                as.numeric(miniBatchFraction),
+                                as.numeric(initStd),
+                                as.integer(maxIter),
+                                as.numeric(stepSize),
+                                as.numeric(tol),
+                                solver,
+                                seed,
+                                stringIndexerOrderType)
+            new("FMRegressionModel", jobj = jobj)
+          })
+
+
 
 Review comment:
   nit: delete extra line?

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[GitHub] [spark] huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#discussion_r379882860
 
 

 ##########
 File path: examples/src/main/r/ml/fmRegressor.R
 ##########
 @@ -0,0 +1,40 @@
+#
+# 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.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/decisionTree.R
+
+# Load SparkR library into your R session
+library(SparkR)
+
+# Initialize SparkSession
+sparkR.session(appName = "SparkR-ML-fmRegressor-example")
+
+# $example on
+# Load training data
+df <- read.df("data/mllib/sample_linear_regression_data.txt", source = "libsvm")
+training_test <- randomSplit(df, c(0.7, 0.3))
+training <- training_test[[1]]
+test <- training_test[[2]]
+
+
+# Fit a FM regression model
+model <- spark.fmRegressor(training, label ~ features)
+
+# Prediction
+predictions <- predict(model, test)
 
 Review comment:
   same as the classifier example, I guess add ```summary(model)```, ```head(predictions)``` and also add ```sparkR.session.stop()``` in the end?
   

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[GitHub] [spark] zero323 commented on issue #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

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[GitHub] [spark] srowen commented on issue #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

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srowen commented on issue #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
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   @zero323 I imagine this one is good to go too but now has a merge conflict, b/c FMClassifier was merged

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SparkQA commented on issue #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#issuecomment-586023829
 
 
   **[Test build #118381 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/118381/testReport)** for PR 27571 at commit [`55f6761`](https://github.com/apache/spark/commit/55f67613cb83c40a6b7dc4f31ccae888215a2840).
    * This patch **fails Scala style tests**.
    * This patch merges cleanly.
    * This patch adds the following public classes _(experimental)_:
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Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#discussion_r379882615
 
 

 ##########
 File path: examples/src/main/r/ml/fmRegressor.R
 ##########
 @@ -0,0 +1,40 @@
+#
+# 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.
+#
+
+# To run this example use
+# ./bin/spark-submit examples/src/main/r/ml/decisionTree.R
 
 Review comment:
   change this ```decisionTree.R```?

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[GitHub] [spark] huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#discussion_r379883014
 
 

 ##########
 File path: mllib/src/main/scala/org/apache/spark/ml/r/FMRegressorWrapper.scala
 ##########
 @@ -0,0 +1,157 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.r
+
+import org.apache.hadoop.fs.Path
+import org.json4s._
+import org.json4s.JsonDSL._
+import org.json4s.jackson.JsonMethods._
+
+import org.apache.spark.ml.{Pipeline, PipelineModel}
+import org.apache.spark.ml.attribute.AttributeGroup
+import org.apache.spark.ml.feature.RFormula
+import org.apache.spark.ml.r.RWrapperUtils._
+import org.apache.spark.ml.regression.{FMRegressionModel, FMRegressor}
+import org.apache.spark.ml.util._
+import org.apache.spark.sql.{DataFrame, Dataset}
+
+private[r] class FMRegressorWrapper private (
+    val pipeline: PipelineModel,
+    val features: Array[String]) extends MLWritable {
+  import FMRegressorWrapper._
+
+  private val fmRegressionModel: FMRegressionModel =
+    pipeline.stages(1).asInstanceOf[FMRegressionModel]
+
+  lazy val rFeatures: Array[String] = if (fmRegressionModel.getFitIntercept) {
+    Array("(Intercept)") ++ features
+  } else {
+    features
+  }
+
+  lazy val rCoefficients: Array[Double] = if (fmRegressionModel.getFitIntercept) {
+    Array(fmRegressionModel.intercept) ++ fmRegressionModel.linear.toArray
+  } else {
+    fmRegressionModel.linear.toArray
+  }
+
+  lazy val rFactors = fmRegressionModel.factors.toArray
+
+  lazy val numFeatures: Int = fmRegressionModel.numFeatures
+
+  lazy val factorSize: Int = fmRegressionModel.getFactorSize
+
+  def transform(dataset: Dataset[_]): DataFrame = {
+    pipeline.transform(dataset)
+      .drop(fmRegressionModel.getFeaturesCol)
+  }
+
+  override def write: MLWriter = new FMRegressorWrapper.FMRegressorWrapperWriter(this)
+}
+
+private[r] object FMRegressorWrapper
+  extends MLReadable[FMRegressorWrapper] {
+
+  def fit(  // scalastyle:ignore
+      data: DataFrame,
+      formula: String,
+      factorSize: Int,
+      fitLinear: Boolean,
+      regParam: Double,
+      miniBatchFraction: Double,
+      initStd: Double,
+      maxIter: Int,
+      stepSize: Double,
+      tol: Double,
+      solver: String,
+      seed: String,
+      stringIndexerOrderType: String): FMRegressorWrapper = {
+
+    val rFormula = new RFormula()
+      .setFormula(formula)
+      .setStringIndexerOrderType(stringIndexerOrderType)
+    checkDataColumns(rFormula, data)
+    val rFormulaModel = rFormula.fit(data)
+
+    val fitIntercept = rFormula.hasIntercept
+
+    // get feature names from output schema
+    val schema = rFormulaModel.transform(data).schema
+    val featureAttrs = AttributeGroup.fromStructField(schema(rFormulaModel.getFeaturesCol))
+      .attributes.get
+    val features = featureAttrs.map(_.name.get)
+
+    // assemble and fit the pipeline
+    val fmr = new FMRegressor()
+      .setFactorSize(factorSize)
+      .setFitLinear(fitLinear)
+      .setRegParam(regParam)
+      .setMiniBatchFraction(miniBatchFraction)
+      .setInitStd(initStd)
+      .setMaxIter(maxIter)
+      .setTol(tol)
+      .setSolver(solver)
+      .setFitIntercept(fitIntercept)
+      .setFeaturesCol(rFormula.getFeaturesCol)
+
+    if (seed != null) {
 
 Review comment:
   also check ```seed.length > 0```?

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[GitHub] [spark] huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#discussion_r379881943
 
 

 ##########
 File path: R/pkg/R/mllib_regression.R
 ##########
 @@ -540,3 +546,150 @@ setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "c
           function(object, path, overwrite = FALSE) {
             write_internal(object, path, overwrite)
           })
+
+
 
 Review comment:
   nit: delete extra line?

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[GitHub] [spark] zero323 opened a new pull request #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

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zero323 opened a new pull request #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571
 
 
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   This pull request adds SparkR wrapper for `FMRegressor`:
   
   - Supporting ` org.apache.spark.ml.r.FMRegressorWrapper`.
   - `FMRegressionModel` S4 class.
   - Corresponding `spark.fmRegressor`, `predict`, `summary` and `write.ml` generics.
   - Corresponding docs and tests.
   
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[GitHub] [spark] zero323 commented on issue #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
zero323 commented on issue #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#issuecomment-594175279
 
 
   Retest this please.

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[GitHub] [spark] huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#discussion_r379882476
 
 

 ##########
 File path: R/pkg/tests/fulltests/test_mllib_regression.R
 ##########
 @@ -551,4 +551,33 @@ test_that("spark.survreg", {
   }
 })
 
+
+test_that("spark.fmRegressor", {
+  df <- suppressWarnings(createDataFrame(iris))
+
+  model <- spark.fmRegressor(
+    df,  Sepal_Width ~ .,
+    regParam = 0.01, maxIter = 10, fitLinear = TRUE
+  )
+
+  prediction1 <- predict(model, df)
+  expect_is(prediction1, "SparkDataFrame")
 
 Review comment:
   I guess we may want to check the predict result too?

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[GitHub] [spark] SparkQA commented on issue #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
SparkQA commented on issue #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#issuecomment-594092951
 
 
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    * This patch passes all tests.
    * This patch merges cleanly.
    * This patch adds no public classes.

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[GitHub] [spark] huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#discussion_r379882920
 
 

 ##########
 File path: mllib/src/main/scala/org/apache/spark/ml/r/FMRegressorWrapper.scala
 ##########
 @@ -0,0 +1,157 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.r
+
+import org.apache.hadoop.fs.Path
+import org.json4s._
+import org.json4s.JsonDSL._
+import org.json4s.jackson.JsonMethods._
+
+import org.apache.spark.ml.{Pipeline, PipelineModel}
+import org.apache.spark.ml.attribute.AttributeGroup
+import org.apache.spark.ml.feature.RFormula
+import org.apache.spark.ml.r.RWrapperUtils._
+import org.apache.spark.ml.regression.{FMRegressionModel, FMRegressor}
+import org.apache.spark.ml.util._
+import org.apache.spark.sql.{DataFrame, Dataset}
+
+private[r] class FMRegressorWrapper private (
+    val pipeline: PipelineModel,
+    val features: Array[String]) extends MLWritable {
+  import FMRegressorWrapper._
+
+  private val fmRegressionModel: FMRegressionModel =
+    pipeline.stages(1).asInstanceOf[FMRegressionModel]
+
+  lazy val rFeatures: Array[String] = if (fmRegressionModel.getFitIntercept) {
+    Array("(Intercept)") ++ features
+  } else {
+    features
+  }
+
+  lazy val rCoefficients: Array[Double] = if (fmRegressionModel.getFitIntercept) {
+    Array(fmRegressionModel.intercept) ++ fmRegressionModel.linear.toArray
+  } else {
+    fmRegressionModel.linear.toArray
+  }
+
+  lazy val rFactors = fmRegressionModel.factors.toArray
+
+  lazy val numFeatures: Int = fmRegressionModel.numFeatures
+
+  lazy val factorSize: Int = fmRegressionModel.getFactorSize
+
+  def transform(dataset: Dataset[_]): DataFrame = {
+    pipeline.transform(dataset)
+      .drop(fmRegressionModel.getFeaturesCol)
+  }
+
+  override def write: MLWriter = new FMRegressorWrapper.FMRegressorWrapperWriter(this)
+}
+
+private[r] object FMRegressorWrapper
+  extends MLReadable[FMRegressorWrapper] {
+
+  def fit(  // scalastyle:ignore
+      data: DataFrame,
+      formula: String,
+      factorSize: Int,
+      fitLinear: Boolean,
+      regParam: Double,
+      miniBatchFraction: Double,
+      initStd: Double,
+      maxIter: Int,
+      stepSize: Double,
+      tol: Double,
+      solver: String,
+      seed: String,
+      stringIndexerOrderType: String): FMRegressorWrapper = {
+
+    val rFormula = new RFormula()
+      .setFormula(formula)
+      .setStringIndexerOrderType(stringIndexerOrderType)
+    checkDataColumns(rFormula, data)
+    val rFormulaModel = rFormula.fit(data)
+
+    val fitIntercept = rFormula.hasIntercept
+
+    // get feature names from output schema
+    val schema = rFormulaModel.transform(data).schema
+    val featureAttrs = AttributeGroup.fromStructField(schema(rFormulaModel.getFeaturesCol))
+      .attributes.get
+    val features = featureAttrs.map(_.name.get)
+
+    // assemble and fit the pipeline
+    val fmr = new FMRegressor()
+      .setFactorSize(factorSize)
+      .setFitLinear(fitLinear)
+      .setRegParam(regParam)
+      .setMiniBatchFraction(miniBatchFraction)
+      .setInitStd(initStd)
+      .setMaxIter(maxIter)
+      .setTol(tol)
+      .setSolver(solver)
+      .setFitIntercept(fitIntercept)
+      .setFeaturesCol(rFormula.getFeaturesCol)
 
 Review comment:
   add ```setStepSize```?

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[GitHub] [spark] huaxingao commented on issue #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
huaxingao commented on issue #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#issuecomment-610611767
 
 
   LGTM except a few minor comments.

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[GitHub] [spark] SparkQA commented on issue #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
SparkQA commented on issue #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#issuecomment-611360631
 
 
   **[Test build #121002 has finished](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/121002/testReport)** for PR 27571 at commit [`2d1f8b1`](https://github.com/apache/spark/commit/2d1f8b1e1546301dee709e7d4ae38dec5deafac0).
    * This patch passes all tests.
    * This patch merges cleanly.
    * This patch adds the following public classes _(experimental)_:
     * `  class FMRegressorWrapperWriter(instance: FMRegressorWrapper) extends MLWriter `
     * `  class FMRegressorWrapperReader extends MLReader[FMRegressorWrapper] `

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[GitHub] [spark] huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#discussion_r379882145
 
 

 ##########
 File path: R/pkg/R/mllib_regression.R
 ##########
 @@ -540,3 +546,150 @@ setMethod("write.ml", signature(object = "AFTSurvivalRegressionModel", path = "c
           function(object, path, overwrite = FALSE) {
             write_internal(object, path, overwrite)
           })
+
+
+#' Factorization Machines Regression Model Model
+#'
+#' \code{spark.fmRegressor} fits a factorization regression model against a SparkDataFrame.
+#' Users can call \code{predict} to make
+#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
 
 Review comment:
   I guess also mention ```summary``` here?

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[GitHub] [spark] huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML] Add FMRegressor wrapper to SparkR

Posted by GitBox <gi...@apache.org>.
huaxingao commented on a change in pull request #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#discussion_r405099205
 
 

 ##########
 File path: mllib/src/main/scala/org/apache/spark/ml/r/FMRegressorWrapper.scala
 ##########
 @@ -0,0 +1,158 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.ml.r
+
+import org.apache.hadoop.fs.Path
+import org.json4s._
+import org.json4s.JsonDSL._
+import org.json4s.jackson.JsonMethods._
+
+import org.apache.spark.ml.{Pipeline, PipelineModel}
+import org.apache.spark.ml.attribute.AttributeGroup
+import org.apache.spark.ml.feature.RFormula
+import org.apache.spark.ml.r.RWrapperUtils._
+import org.apache.spark.ml.regression.{FMRegressionModel, FMRegressor}
+import org.apache.spark.ml.util._
+import org.apache.spark.sql.{DataFrame, Dataset}
+
+private[r] class FMRegressorWrapper private (
+    val pipeline: PipelineModel,
+    val features: Array[String]) extends MLWritable {
+  import FMRegressorWrapper._
 
 Review comment:
   nit: remove this?

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AmplabJenkins commented on issue #27571: [SPARK-30819][SPARKR][ML]  Add FMRegressor wrapper to SparkR
URL: https://github.com/apache/spark/pull/27571#issuecomment-596161361
 
 
   Merged build finished. Test PASSed.

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