mob-ai 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_r356386089
 
 

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 File path: 
mllib/src/test/scala/org/apache/spark/ml/classification/FMClassifierSuite.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
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+ * 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
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+
+package org.apache.spark.ml.classification
+
+import 
org.apache.spark.ml.classification.LogisticRegressionSuite.generateLogisticInput
+import org.apache.spark.ml.linalg.{DenseVector, Vector, Vectors}
+import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.regression.FMRegressorSuite._
+import org.apache.spark.ml.util._
+import org.apache.spark.ml.util.TestingUtils._
+import org.apache.spark.sql.{DataFrame, Row}
+
+class FMClassifierSuite extends MLTest with DefaultReadWriteTest {
 
 Review comment:
   > Is this resolved? I'd also love to have at least one test that compares 
output to some other library, even if the test can only assert some approximate 
match in loss, etc.
   
   Now, test method is FM fit a dataset perfectly (loss closed to zero and 
model's weights closed to real weights), the dataset is generated by a group 
weight (include bias/linear/factor weights). I think it is enough.

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