* fixed LinearRegression train() to use embedded paramMap

* added Predictor.predict(RDD[Vector]) method
* updated Linear/LogisticRegressionSuites

mllib/src/main/scala/org/apache/spark/ml/impl/estimator/Predictor.scala

@@ -137,6 +137,7 @@ private[ml] abstract class PredictionModel[M <: PredictionModel[M]]
   }
 
   /**
+   * Strongly typed version of [[transform()]].
    * Default implementation using single-instance predict().
    *
    * Developers should override this for efficiency.  E.g., this does not broadcast the model.
@@ -147,6 +148,9 @@ private[ml] abstract class PredictionModel[M <: PredictionModel[M]]
     dataset.map(tmpModel.predict)
   }
 
+  /** Strongly typed version of [[transform()]]. */
+  def predict(dataset: RDD[Vector]): RDD[Double] = predict(dataset, new ParamMap)
+
   /**
    * Predict label for the given features.
    */

mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala

@@ -53,6 +53,7 @@ class LinearRegression extends Regressor[LinearRegression, LinearRegressionModel
    *                  These values override any specified in this Estimator's embedded ParamMap.
    */
   override def train(dataset: RDD[LabeledPoint], paramMap: ParamMap): LinearRegressionModel = {
+    val map = this.paramMap ++ paramMap
     val oldDataset = dataset.map { case LabeledPoint(label: Double, features: Vector, weight) =>
       org.apache.spark.mllib.regression.LabeledPoint(label, features)
     }
@@ -62,10 +63,10 @@ class LinearRegression extends Regressor[LinearRegression, LinearRegressionModel
     }
     val lr = new LinearRegressionWithSGD()
     lr.optimizer
-      .setRegParam(paramMap(regParam))
-      .setNumIterations(paramMap(maxIter))
+      .setRegParam(map(regParam))
+      .setNumIterations(map(maxIter))
     val model = lr.run(oldDataset)
-    val lrm = new LinearRegressionModel(this, paramMap, model.weights, model.intercept)
+    val lrm = new LinearRegressionModel(this, map, model.weights, model.intercept)
     if (handlePersistence) {
       oldDataset.unpersist()
     }

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

@@ -19,15 +19,19 @@ package org.apache.spark.ml.classification
 
 import org.scalatest.FunSuite
 
+import org.apache.spark.ml.LabeledPoint
 import org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInput
+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 = _
   @transient var dataset: DataFrame = _
+  private val eps: Double = 1e-5
 
   override def beforeAll(): Unit = {
     super.beforeAll()
@@ -38,6 +42,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
 
   test("logistic regression: default params") {
     val lr = new LogisticRegression
+    assert(lr.getLabelCol == "label")
     val model = lr.fit(dataset)
     model.transform(dataset)
       .select("label", "prediction")
@@ -96,4 +101,43 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
     assert(model2.getThreshold === 0.4)
     assert(model2.getScoreCol == "theProb")
   }
+
+  test("logistic regression: Predictor, Classifier methods") {
+    val sqlContext = this.sqlContext
+    import sqlContext._
+    val lr = new LogisticRegression
+
+    // fit() vs. train()
+    val model1 = lr.fit(dataset)
+    val rdd = dataset.select('label, 'features).map { case Row(label: Double, features: Vector) =>
+      LabeledPoint(label, features)
+    }
+    val features = rdd.map(_.features)
+    val model2 = lr.train(rdd)
+    assert(model1.intercept == model2.intercept)
+    assert(model1.weights.equals(model2.weights))
+    assert(model1.numClasses == model2.numClasses)
+    assert(model1.numClasses === 2)
+
+    // transform() vs. predict()
+    val trans = model1.transform(dataset).select('prediction)
+    val preds = model1.predict(rdd.map(_.features))
+    trans.zip(preds).collect().foreach { case (Row(pred1: Double), pred2: Double) =>
+      assert(pred1 == pred2)
+    }
+
+    // Check various types of predictions.
+    val allPredictions = features.map { f =>
+      (model1.predictRaw(f), model1.predictProbabilities(f), model1.predict(f))
+    }.collect()
+    val threshold = model1.getThreshold
+    allPredictions.foreach { case (raw: Vector, prob: Vector, pred: Double) =>
+      val computeProbFromRaw: (Double => Double) = (m) => 1.0 / (1.0 + math.exp(-m))
+      raw.toArray.map(computeProbFromRaw).zip(prob.toArray).foreach { case (r, p) =>
+        assert(r ~== p relTol eps)
+      }
+      val predFromProb = prob.toArray.zipWithIndex.maxBy(_._1)._2
+      assert(pred == predFromProb)
+    }
+  }
 }

mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala

@@ -0,0 +1,95 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.regression
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.ml.LabeledPoint
+import org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInput
+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.{Row, SQLContext, SchemaRDD}
+
+class LinearRegressionSuite extends FunSuite with MLlibTestSparkContext {
+
+  @transient var sqlContext: SQLContext = _
+  @transient var dataset: SchemaRDD = _
+
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    sqlContext = new SQLContext(sc)
+    dataset = sqlContext.createSchemaRDD(
+      sc.parallelize(generateLogisticInput(1.0, 1.0, nPoints = 100, seed = 42), 2))
+  }
+
+  test("linear regression: default params") {
+    val sqlContext = this.sqlContext
+    import sqlContext._
+    val lr = new LinearRegression
+    assert(lr.getLabelCol == "label")
+    val model = lr.fit(dataset)
+    model.transform(dataset)
+      .select('label, 'prediction)
+      .collect()
+    // Check defaults
+    assert(model.getFeaturesCol == "features")
+    assert(model.getPredictionCol == "prediction")
+  }
+
+  test("linear regression with setters") {
+    // Set params, train, and check as many as we can.
+    val sqlContext = this.sqlContext
+    import sqlContext._
+    val lr = new LinearRegression()
+      .setMaxIter(10)
+      .setRegParam(1.0)
+    val model = lr.fit(dataset)
+    assert(model.fittingParamMap.get(lr.maxIter) === Some(10))
+    assert(model.fittingParamMap.get(lr.regParam) === Some(1.0))
+
+    // Call fit() with new params, and check as many as we can.
+    val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1, lr.predictionCol -> "thePred")
+    assert(model2.fittingParamMap.get(lr.maxIter) === Some(5))
+    assert(model2.fittingParamMap.get(lr.regParam) === Some(0.1))
+    assert(model2.getPredictionCol == "thePred")
+  }
+
+  test("linear regression: Predictor, Regressor methods") {
+    val sqlContext = this.sqlContext
+    import sqlContext._
+    val lr = new LinearRegression
+
+    // fit() vs. train()
+    val model1 = lr.fit(dataset)
+    val rdd = dataset.select('label, 'features).map { case Row(label: Double, features: Vector) =>
+      LabeledPoint(label, features)
+    }
+    val features = rdd.map(_.features)
+    val model2 = lr.train(rdd)
+    assert(model1.intercept == model2.intercept)
+    assert(model1.weights.equals(model2.weights))
+
+    // transform() vs. predict()
+    val trans = model1.transform(dataset).select('prediction)
+    val preds = model1.predict(rdd.map(_.features))
+    trans.zip(preds).collect().foreach { case (Row(pred1: Double), pred2: Double) =>
+      assert(pred1 == pred2)
+    }
+  }
+}