[SPARK-14712][ML]spark.ml.LogisticRegressionModel.toString should sum…

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

@@ -625,6 +625,13 @@ class LogisticRegressionModel private[spark] (
    */
   @Since("1.6.0")
   override def write: MLWriter = new LogisticRegressionModel.LogisticRegressionModelWriter(this)
+
+  @Since("2.0.0")
+  override def toString: String = {
+    val td = getDefault(threshold)
+    s"${super.toString}, numClasses = $numClasses, " +
+      s"numFeatures = $numFeatures threshold = ${td.getOrElse("None")}"
+  }
 }
 
 

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

@@ -943,6 +943,13 @@ class LogisticRegressionSuite
         assert(expected.coefficients.toArray === actual.coefficients.toArray)
       }
   }
+
+  test("toString") {
+    val lrModel = new LogisticRegressionModel(uid = "lrModeltest",
+      coefficients = Vectors.dense(0.1, 0.2, 0.3), intercept = 0.5)
+    val expected: String = "lrModeltest, numClasses = 2, numFeatures = 3 threshold = 0.5"
+    assert(lrModel.toString === expected)
+  }
 }
 
 object LogisticRegressionSuite {

python/pyspark/ml/classification.py

@@ -273,6 +273,10 @@ def evaluate(self, dataset):
         java_blr_summary = self._call_java("evaluate", dataset)
         return BinaryLogisticRegressionSummary(java_blr_summary)
 
+    @since("2.0.0")
+    def __repr__(self):
+        return self._call_java("toString")
+
 
 class LogisticRegressionSummary(JavaWrapper):
     """

python/pyspark/mllib/classification.py

@@ -262,6 +262,10 @@ def load(cls, sc, path):
         model.setThreshold(threshold)
         return model
 
+    @since("2.0.0")
+    def __repr__(self):
+        return self._call_java("toString")
+
 
 class LogisticRegressionWithSGD(object):
     """