[MLLIB] Add fit intercept api to ml logisticregression

I have the fit intercept enabled by default for logistic regression, I
wonder what others think here. I understand that it enables allocation
by default which is undesirable, but one needs to have a very strong
reason for not having an intercept term enabled so it is the safer
default from a statistical sense.

Explicitly modeling the intercept by adding a column of all 1s does not
work. I believe the reason is that since the API for
LogisticRegressionWithLBFGS forces column normalization, and a column of all
1s has 0 variance so dividing by 0 kills it.

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

@@ -31,7 +31,7 @@ import org.apache.spark.storage.StorageLevel
  * Params for logistic regression.
  */
 private[classification] trait LogisticRegressionParams extends ProbabilisticClassifierParams
-  with HasRegParam with HasMaxIter with HasThreshold
+  with HasRegParam with HasMaxIter with HasFitIntercept with HasThreshold
 
 
 /**
@@ -46,6 +46,7 @@ class LogisticRegression
   with LogisticRegressionParams {
 
   setRegParam(0.1)
+  setFitIntercept(true)
   setMaxIter(100)
   setThreshold(0.5)
 
@@ -55,6 +56,9 @@ class LogisticRegression
   /** @group setParam */
   def setMaxIter(value: Int): this.type = set(maxIter, value)
 
+  /** @group setParam */
+  def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
+
   /** @group setParam */
   def setThreshold(value: Double): this.type = set(threshold, value)
 
@@ -71,6 +75,7 @@ class LogisticRegression
     lr.optimizer
       .setRegParam(paramMap(regParam))
       .setNumIterations(paramMap(maxIter))
+      .addIntercept(paramMap(fitIntercept))
     val oldModel = lr.run(oldDataset)
     val lrm = new LogisticRegressionModel(this, paramMap, oldModel.weights, oldModel.intercept)
 

mllib/src/main/scala/org/apache/spark/ml/param/sharedParams.scala

@@ -106,6 +106,17 @@ private[ml] trait HasProbabilityCol extends Params {
   def getProbabilityCol: String = get(probabilityCol)
 }
 
+private[ml] trait HasFitIntercept extends Params {
+  /**
+   * param for fitting the intercept term
+   * @group param
+   */
+  val fitIntercept: BooleanParam = new BooleanParam(this, "fitIntercept", "fits the intercept term or not")
+
+  /** @group getParam */
+  def getFitIntercept: Boolean = get(fitIntercept)
+}
+
 private[ml] trait HasThreshold extends Params {
   /**
    * param for threshold in (binary) prediction