fix LRSuite

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

@@ -216,7 +216,7 @@ class LogisticRegression(override val uid: String)
       (weightsWithIntercept, 0.0)
     }
 
-    new LogisticRegressionModel(this, weights.compressed, intercept)
+    new LogisticRegressionModel(uid, weights.compressed, intercept)
   }
 }
 

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

@@ -19,13 +19,12 @@ package org.apache.spark.ml.classification
 
 import org.scalatest.FunSuite
 
-import org.apache.spark.mllib.classification.LogisticRegressionSuite
+import org.apache.spark.mllib.classification.LogisticRegressionSuite._
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.{DataFrame, Row, SQLContext}
 
-
 class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
 
   @transient var sqlContext: SQLContext = _
@@ -37,8 +36,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
     super.beforeAll()
     sqlContext = new SQLContext(sc)
 
-    dataset = sqlContext.createDataFrame(sc.parallelize(LogisticRegressionSuite
-      .generateLogisticInput(1.0, 1.0, nPoints = 100, seed = 42), 4))
+    dataset = sqlContext.createDataFrame(generateLogisticInput(1.0, 1.0, nPoints = 100, seed = 42))
 
     /**
      * Here is the instruction describing how to export the test data into CSV format
@@ -60,31 +58,30 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
       val xMean = Array(5.843, 3.057, 3.758, 1.199)
       val xVariance = Array(0.6856, 0.1899, 3.116, 0.581)
 
-      val testData = LogisticRegressionSuite.generateMultinomialLogisticInput(
-        weights, xMean, xVariance, true, nPoints, 42)
+      val testData = generateMultinomialLogisticInput(weights, xMean, xVariance, true, nPoints, 42)
 
-      sqlContext.createDataFrame(sc.parallelize(LogisticRegressionSuite
-        .generateMultinomialLogisticInput(weights, xMean, xVariance, true, nPoints, 42), 4))
+      sqlContext.createDataFrame(
+        generateMultinomialLogisticInput(weights, xMean, xVariance, true, nPoints, 42))
     }
   }
 
   test("logistic regression: default params") {
     val lr = new LogisticRegression
-    assert(lr.getLabelCol == "label")
-    assert(lr.getFeaturesCol == "features")
-    assert(lr.getPredictionCol == "prediction")
-    assert(lr.getRawPredictionCol == "rawPrediction")
-    assert(lr.getProbabilityCol == "probability")
-    assert(lr.getFitIntercept == true)
+    assert(lr.getLabelCol === "label")
+    assert(lr.getFeaturesCol === "features")
+    assert(lr.getPredictionCol === "prediction")
+    assert(lr.getRawPredictionCol === "rawPrediction")
+    assert(lr.getProbabilityCol === "probability")
+    assert(lr.getFitIntercept)
     val model = lr.fit(dataset)
     model.transform(dataset)
       .select("label", "probability", "prediction", "rawPrediction")
       .collect()
     assert(model.getThreshold === 0.5)
-    assert(model.getFeaturesCol == "features")
-    assert(model.getPredictionCol == "prediction")
-    assert(model.getRawPredictionCol == "rawPrediction")
-    assert(model.getProbabilityCol == "probability")
+    assert(model.getFeaturesCol === "features")
+    assert(model.getPredictionCol === "prediction")
+    assert(model.getRawPredictionCol === "rawPrediction")
+    assert(model.getProbabilityCol === "probability")
     assert(model.intercept !== 0.0)
   }
 
@@ -134,7 +131,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
     assert(parent2.getRegParam === 0.1)
     assert(parent2.getThreshold === 0.4)
     assert(model2.getThreshold === 0.4)
-    assert(model2.getProbabilityCol == "theProb")
+    assert(model2.getProbabilityCol === "theProb")
   }
 
   test("logistic regression: Predictor, Classifier methods") {