viirya commented on a change in pull request #27570: [SPARK-30820][SPARKR][ML] 
Add FMClassifier to SparkR
URL: https://github.com/apache/spark/pull/27570#discussion_r398361136
 
 

 ##########
 File path: mllib/src/main/scala/org/apache/spark/ml/r/FMClassifierWrapper.scala
 ##########
 @@ -0,0 +1,177 @@
+/*
+ * 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.classification.{FMClassificationModel, FMClassifier}
+import org.apache.spark.ml.feature.{IndexToString, RFormula}
+import org.apache.spark.ml.r.RWrapperUtils._
+import org.apache.spark.ml.util._
+import org.apache.spark.sql.{DataFrame, Dataset}
+
+private[r] class FMClassifierWrapper private (
+    val pipeline: PipelineModel,
+    val features: Array[String],
+    val labels: Array[String]) extends MLWritable {
+  import FMClassifierWrapper._
+
+  private val fmClassificationModel: FMClassificationModel =
+    pipeline.stages(1).asInstanceOf[FMClassificationModel]
+
+  lazy val rFeatures: Array[String] = if 
(fmClassificationModel.getFitIntercept) {
+    Array("(Intercept)") ++ features
+  } else {
+    features
+  }
+
+  lazy val rCoefficients: Array[Double] = if 
(fmClassificationModel.getFitIntercept) {
+    Array(fmClassificationModel.intercept) ++ 
fmClassificationModel.linear.toArray
+  } else {
+    fmClassificationModel.linear.toArray
+  }
+
+  lazy val rFactors = fmClassificationModel.factors.toArray
+
+  lazy val numClasses: Int = fmClassificationModel.numClasses
+
+  lazy val numFeatures: Int = fmClassificationModel.numFeatures
+
+  lazy val factorSize: Int = fmClassificationModel.getFactorSize
+
+  def transform(dataset: Dataset[_]): DataFrame = {
+    pipeline.transform(dataset)
+      .drop(PREDICTED_LABEL_INDEX_COL)
+      .drop(fmClassificationModel.getFeaturesCol)
+      .drop(fmClassificationModel.getLabelCol)
+  }
+
+  override def write: MLWriter = new 
FMClassifierWrapper.FMClassifierWrapperWriter(this)
+}
+
+private[r] object FMClassifierWrapper
+  extends MLReadable[FMClassifierWrapper] {
+
+  val PREDICTED_LABEL_INDEX_COL = "pred_label_idx"
+  val PREDICTED_LABEL_COL = "prediction"
+
+  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,
+      thresholds: Array[Double],
+      handleInvalid: String): FMClassifierWrapper = {
+
+    val rFormula = new RFormula()
+      .setFormula(formula)
+      .setForceIndexLabel(true)
+      .setHandleInvalid(handleInvalid)
+    checkDataColumns(rFormula, data)
+    val rFormulaModel = rFormula.fit(data)
+
+    val fitIntercept = rFormula.hasIntercept
+
+    // get labels and feature names from output schema
+    val (features, labels) = getFeaturesAndLabels(rFormulaModel, data)
+
+    // assemble and fit the pipeline
+    val fmc = new FMClassifier()
+      .setFactorSize(factorSize)
+      .setFitIntercept(fitIntercept)
+      .setFitLinear(fitLinear)
+      .setRegParam(regParam)
+      .setMiniBatchFraction(miniBatchFraction)
+      .setInitStd(initStd)
+      .setMaxIter(maxIter)
+      .setStepSize(stepSize)
+      .setTol(tol)
+      .setSolver(solver)
+      .setFeaturesCol(rFormula.getFeaturesCol)
+      .setLabelCol(rFormula.getLabelCol)
+      .setPredictionCol(PREDICTED_LABEL_INDEX_COL)
+
+    if (seed != null && seed.length > 0) {
+      fmc.setSeed(seed.toLong)
+    }
+
+    if (thresholds != null) {
+      fmc.setThresholds(thresholds)
+    }
+
+    val idxToStr = new IndexToString()
+      .setInputCol(PREDICTED_LABEL_INDEX_COL)
+      .setOutputCol(PREDICTED_LABEL_COL)
+      .setLabels(labels)
+
+    val pipeline = new Pipeline()
+      .setStages(Array(rFormulaModel, fmc, idxToStr))
+      .fit(data)
+
+    new FMClassifierWrapper(pipeline, features, labels)
+  }
+
+  override def read: MLReader[FMClassifierWrapper] = new 
FMClassifierWrapperReader
+
+  override def load(path: String): FMClassifierWrapper = super.load(path)
 
 Review comment:
   Is this necessary? Seems it is redundant.

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