CR feedback: make the setInitialWeights function private, don't mess …

…with the weights when they are user supploed, validate that the user supplied weights are reasonable.

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

@@ -102,18 +102,27 @@ class LogisticRegression(override val uid: String)
 
   private var optInitialWeights: Option[Vector] = None
   /** @group setParam */
-  def setInitialWeights(value: Vector): this.type = {
+  private[spark] def setInitialWeights(value: Vector): this.type = {
     this.optInitialWeights = Some(value)
     this
   }
 
+  /** Validate the initial weights, return an Option, if not the expected size return None and log */
+  private def validateWeights(vectorOpt: Option[Vector], numFeatures: Int): Option[Vector] = {
+    vectorOpt.flatMap(vec =>
+      if (vec.size == numFeatures) {
+        Some(vec)
+      } else {
+        logWarning(s"""Initial weights provided (${vec})did not match the expected size ${numFeatures}""")
+        None
+      })
+  }
+
   override protected[spark] def train(dataset: DataFrame): LogisticRegressionModel = {
-    val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
-    // Extract columns from data.  If dataset is persisted, do not persist instances.
     val instances = extractLabeledPoints(dataset).map {
       case LabeledPoint(label: Double, features: Vector) => (label, features)
     }
-
+    val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
     if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
 
     val (summarizer, labelSummarizer) = instances.treeAggregate(
@@ -168,10 +177,12 @@ class LogisticRegression(override val uid: String)
       new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, regParamL1Fun, $(tol))
     }
 
-    val initialWeightsWithIntercept = optInitialWeights.getOrElse(
-      Vectors.zeros(if ($(fitIntercept)) numFeatures + 1 else numFeatures))
+    val numFeaturesWithIntercept = if ($(fitIntercept)) numFeatures + 1 else numFeatures
+    val userSuppliedWeights = validateWeights(optInitialWeights, numFeaturesWithIntercept)
+    val initialWeightsWithIntercept = userSuppliedWeights.getOrElse(
+      Vectors.zeros(numFeaturesWithIntercept))
 
-    if ($(fitIntercept)) {
+    if ($(fitIntercept) && !userSuppliedWeights.isDefined) {
       /**
        * For binary logistic regression, when we initialize the weights as zeros,
        * it will converge faster if we initialize the intercept such that