[SPARK-6705][MLLIB] Add fit intercept api to ml logisticregression

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 +106,18 @@ 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", "indicates whether to fit an intercept term")
+
+  /** @group getParam */
+  def getFitIntercept: Boolean = get(fitIntercept)
+}
+
 private[ml] trait HasThreshold extends Params {
   /**
    * param for threshold in (binary) prediction

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

@@ -46,6 +46,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
     assert(lr.getPredictionCol == "prediction")
     assert(lr.getRawPredictionCol == "rawPrediction")
     assert(lr.getProbabilityCol == "probability")
+    assert(lr.getFitIntercept == true)
     val model = lr.fit(dataset)
     model.transform(dataset)
       .select("label", "probability", "prediction", "rawPrediction")
@@ -55,6 +56,14 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
     assert(model.getPredictionCol == "prediction")
     assert(model.getRawPredictionCol == "rawPrediction")
     assert(model.getProbabilityCol == "probability")
+    assert(model.intercept != 0.0)
+  }
+
+  test("logistic regression doesn't fit intercept when fitIntercept is off") {
+    val lr = new LogisticRegression
+    lr.setFitIntercept(false)
+    val model = lr.fit(dataset)
+    assert(model.intercept == 0.0)
   }
 
   test("logistic regression with setters") {