[SPARK-31925][ML] Summary.totalIterations greater than maxIters

mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala

@@ -594,7 +594,7 @@ class LogisticRegression @Since("1.2.0") (
         Vectors.dense(if (numClasses == 2) Double.PositiveInfinity else Double.NegativeInfinity)
       }
       if (instances.getStorageLevel != StorageLevel.NONE) instances.unpersist()
-      return createModel(dataset, numClasses, coefMatrix, interceptVec, Array.empty)
+      return createModel(dataset, numClasses, coefMatrix, interceptVec, Array(0.0))
     }
 
     if (!$(fitIntercept) && isConstantLabel) {
@@ -1545,13 +1545,19 @@ sealed trait LogisticRegressionSummary extends Serializable {
  */
 sealed trait LogisticRegressionTrainingSummary extends LogisticRegressionSummary {
 
-  /** objective function (scaled loss + regularization) at each iteration. */
+  /**
+   *  objective function (scaled loss + regularization) at each iteration.
+   *  It contains one more element, the initial state, than number of iterations.
+   */
   @Since("1.5.0")
   def objectiveHistory: Array[Double]
 
   /** Number of training iterations. */
   @Since("1.5.0")
-  def totalIterations: Int = objectiveHistory.length
+  def totalIterations: Int = {
+    assert(objectiveHistory.length > 0, s"objectiveHistory length should be greater than 1.")
+    objectiveHistory.length - 1
+  }
 
 }
 

mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala

@@ -433,7 +433,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
         Vectors.dense(Array.fill(dim)(1.0))
     }
 
-    val  (parameters, objectiveHistory) = if ($(blockSize) == 1) {
+    val (parameters, objectiveHistory) = if ($(blockSize) == 1) {
       trainOnRows(instances, yMean, yStd, featuresMean, featuresStd,
         initialValues, regularization, optimizer)
     } else {
@@ -939,8 +939,10 @@ class LinearRegressionTrainingSummary private[regression] (
    * @see `LinearRegression.solver`
    */
   @Since("1.5.0")
-  val totalIterations = objectiveHistory.length
-
+  val totalIterations = {
+    assert(objectiveHistory.length > 0, s"objectiveHistory length should be greater than 1.")
+    objectiveHistory.length - 1
+  }
 }
 
 /**

mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java

@@ -143,6 +143,6 @@ public void logisticRegressionTrainingSummary() {
     LogisticRegressionModel model = lr.fit(dataset);
 
     LogisticRegressionTrainingSummary summary = model.summary();
-    Assert.assertEquals(summary.totalIterations(), summary.objectiveHistory().length);
+    Assert.assertEquals(summary.totalIterations(), summary.objectiveHistory().length - 1);
   }
 }

mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala

@@ -266,6 +266,8 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
     assert(blorModel.summary.isInstanceOf[BinaryLogisticRegressionTrainingSummary])
     assert(blorModel.summary.asBinary.isInstanceOf[BinaryLogisticRegressionSummary])
     assert(blorModel.binarySummary.isInstanceOf[BinaryLogisticRegressionTrainingSummary])
+    assert(blorModel.summary.totalIterations == 1)
+    assert(blorModel.binarySummary.totalIterations == 1)
 
     val mlorModel = lr.setFamily("multinomial").fit(smallMultinomialDataset)
     assert(mlorModel.summary.isInstanceOf[LogisticRegressionTrainingSummary])
@@ -279,6 +281,7 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
         mlorModel.summary.asBinary
       }
     }
+    assert(mlorModel.summary.totalIterations == 1)
 
     val mlorBinaryModel = lr.setFamily("multinomial").fit(smallBinaryDataset)
     assert(mlorBinaryModel.summary.isInstanceOf[BinaryLogisticRegressionTrainingSummary])
@@ -2570,7 +2573,7 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
         rows.map(_.getDouble(0)).toArray === binaryExpected
       }
     }
-    assert(model2.summary.totalIterations === 1)
+    assert(model2.summary.totalIterations === 0)
 
     val lr3 = new LogisticRegression().setFamily("multinomial")
     val model3 = lr3.fit(smallMultinomialDataset)
@@ -2585,7 +2588,7 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
         rows.map(_.getDouble(0)).toArray === multinomialExpected
       }
     }
-    assert(model4.summary.totalIterations === 1)
+    assert(model4.summary.totalIterations === 0)
   }
 
   test("binary logistic regression with all labels the same") {
@@ -2605,13 +2608,15 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
     assert(allZeroInterceptModel.coefficients ~== Vectors.dense(0.0) absTol 1E-3)
     assert(allZeroInterceptModel.intercept === Double.NegativeInfinity)
     assert(allZeroInterceptModel.summary.totalIterations === 0)
+    assert(allZeroInterceptModel.summary.objectiveHistory(0) ~== 0.0 absTol 1e-4)
 
     val allOneInterceptModel = lrIntercept
       .setLabelCol("oneLabel")
       .fit(sameLabels)
     assert(allOneInterceptModel.coefficients ~== Vectors.dense(0.0) absTol 1E-3)
     assert(allOneInterceptModel.intercept === Double.PositiveInfinity)
     assert(allOneInterceptModel.summary.totalIterations === 0)
+    assert(allOneInterceptModel.summary.objectiveHistory(0) ~== 0.0 absTol 1e-4)
 
     // fitIntercept=false
     val lrNoIntercept = new LogisticRegression()
@@ -2647,6 +2652,7 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
         assert(pred === 4.0)
     }
     assert(model.summary.totalIterations === 0)
+    assert(model.summary.objectiveHistory(0) ~== 0.0 absTol 1e-4)
 
     // force the model to be trained with only one class
     val constantZeroData = Seq(
@@ -2660,7 +2666,7 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
         assert(prob === Vectors.dense(Array(1.0)))
         assert(pred === 0.0)
     }
-    assert(modelZeroLabel.summary.totalIterations > 0)
+    assert(modelZeroLabel.summary.totalIterations === 0)
 
     // ensure that the correct value is predicted when numClasses passed through metadata
     val labelMeta = NominalAttribute.defaultAttr.withName("label").withNumValues(6).toMetadata()
@@ -2675,6 +2681,7 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
         assert(pred === 4.0)
     }
     require(modelWithMetadata.summary.totalIterations === 0)
+    assert(model.summary.objectiveHistory(0) ~== 0.0 absTol 1e-4)
   }
 
   test("compressed storage for constant label") {

mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala

@@ -761,13 +761,15 @@ class LinearRegressionSuite extends MLTest with DefaultReadWriteTest with PMMLRe
           .fit(datasetWithWeightConstantLabel)
         if (fitIntercept) {
           assert(model1.summary.objectiveHistory(0) ~== 0.0 absTol 1e-4)
+          assert(model1.summary.totalIterations === 0)
         }
         val model2 = new LinearRegression()
           .setFitIntercept(fitIntercept)
           .setWeightCol("weight")
           .setSolver("l-bfgs")
           .fit(datasetWithWeightZeroLabel)
         assert(model2.summary.objectiveHistory(0) ~== 0.0 absTol 1e-4)
+        assert(model2.summary.totalIterations === 0)
       }
     }
   }
@@ -940,6 +942,19 @@ class LinearRegressionSuite extends MLTest with DefaultReadWriteTest with PMMLRe
     }
   }
 
+  test("linear regression training summary totalIterations") {
+    Seq(1, 5, 10, 20).foreach { maxIter =>
+      val trainer = new LinearRegression().setSolver("l-bfgs").setMaxIter(maxIter)
+      val model = trainer.fit(datasetWithDenseFeature)
+      assert(model.summary.totalIterations <= maxIter)
+    }
+    Seq("auto", "normal").foreach { solver =>
+      val trainer = new LinearRegression().setSolver(solver)
+      val model = trainer.fit(datasetWithDenseFeature)
+      assert(model.summary.totalIterations === 0)
+    }
+  }
+
   test("linear regression with weighted samples") {
     val sqlContext = spark.sqlContext
     import sqlContext.implicits._

python/pyspark/ml/classification.py

@@ -1119,7 +1119,8 @@ class LogisticRegressionTrainingSummary(LogisticRegressionSummary):
     def objectiveHistory(self):
         """
         Objective function (scaled loss + regularization) at each
-        iteration.
+        iteration. It contains one more element, the initial state,
+        than number of iterations.
         """
         return self._call_java("objectiveHistory")
 

python/pyspark/ml/tests/test_training_summary.py

@@ -42,7 +42,7 @@ def test_linear_regression_summary(self):
         self.assertTrue(model.hasSummary)
         s = model.summary
         # test that api is callable and returns expected types
-        self.assertGreater(s.totalIterations, 0)
+        self.assertEqual(s.totalIterations, 0)
         self.assertTrue(isinstance(s.predictions, DataFrame))
         self.assertEqual(s.predictionCol, "prediction")
         self.assertEqual(s.labelCol, "label")