[GitHub] [spark] srowen commented on a change in pull request #26124: [SPARK-29224][ML]Implement Factorization Machines as a ml-pipeline component

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srowen commented on a change in pull request #26124: [SPARK-29224][ML]Implement Factorization Machines as a ml-pipeline component 
URL: https://github.com/apache/spark/pull/26124#discussion_r356147752
 
 

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 File path: mllib/src/main/scala/org/apache/spark/ml/classification/FMClassifier.scala
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+/*
+ * 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.classification
+
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark.annotation.Since
+import org.apache.spark.internal.Logging
+import org.apache.spark.ml.linalg._
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.regression.{FactorizationMachines, FactorizationMachinesParams}
+import org.apache.spark.ml.regression.FactorizationMachines._
+import org.apache.spark.ml.util._
+import org.apache.spark.ml.util.Instrumentation.instrumented
+import org.apache.spark.mllib.linalg.{Vector => OldVector}
+import org.apache.spark.mllib.linalg.VectorImplicits._
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{Dataset, Row}
+import org.apache.spark.sql.functions.col
+import org.apache.spark.storage.StorageLevel
+
+/**
+ * Params for FMClassifier.
+ */
+private[classification] trait FMClassifierParams extends ProbabilisticClassifierParams
+  with FactorizationMachinesParams {
+}
+
+/**
+ * Factorization Machines learning algorithm for classification.
+ * It supports normal gradient descent and AdamW solver.
+ *
+ * The implementation is based upon:
+ * <a href="https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf">
+ * S. Rendle. "Factorization machines" 2010</a>.
+ *
+ * FM is able to estimate interactions even in problems with huge sparsity
+ * (like advertising and recommendation system).
+ * FM formula is:
+ * {{{
+ *   y = \sigma\left( w_0 + \sum\limits^n_{i-1} w_i x_i +
+ *     \sum\limits^n_{i=1} \sum\limits^n_{j=i+1} \langle v_i, v_j \rangle x_i x_j \right)
+ * }}}
+ * First two terms denote global bias and linear term (as same as linear regression),
+ * and last term denotes pairwise interactions term. {{{v_i}}} describes the i-th variable
+ * with k factors.
+ *
+ * FM classification model uses logistic loss which can be solved by gradient descent method, and
+ * regularization terms like L2 are usually added to the loss function to prevent overfitting.
+ *
+ * @note Multiclass labels are not currently supported.
+ */
+@Since("3.0.0")
+class FMClassifier @Since("3.0.0") (
 
 Review comment:
   It's not a big deal, but if the class is "since 3.0.0" then implicitly everything else is too. I don't think we need the annotations everywhere, but, I guess a bunch of classes do this, so w/e.

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