fix predict

Author: Davies Liu

mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala

@@ -17,6 +17,7 @@
 
 package org.apache.spark.mllib.tree.model
 
+import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.rdd.RDD
@@ -52,6 +53,17 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
     features.map(x => predict(x))
   }
 
+
+  /**
+   * Predict values for the given data set using the model trained.
+   *
+   * @param features JavaRDD representing data points to be predicted
+   * @return JavaRDD of predictions for each of the given data points
+   */
+  def predict(features: JavaRDD[Vector]): JavaRDD[Double] = {
+    predict(features.rdd)
+  }
+
   /**
    * Get number of nodes in tree, including leaf nodes.
    */

python/pyspark/mllib/tree.py

@@ -124,10 +124,13 @@ def trainClassifier(data, numClasses, categoricalFeaturesInfo,
            Predict: 0.0
           Else (feature 0 > 0.0)
            Predict: 1.0
-        >>> model.predict(array([1.0])) > 0
-        True
-        >>> model.predict(array([0.0])) == 0
-        True
+        >>> model.predict(array([1.0]))
+        1.0
+        >>> model.predict(array([0.0]))
+        0.0
+        >>> rdd = sc.parallelize([[1.0], [0.0]])
+        >>> model.predict(rdd).collect()
+        [1.0, 0.0]
         """
         return DecisionTree._train(data, "classification", numClasses, categoricalFeaturesInfo,
                                    impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
@@ -170,14 +173,13 @@ def trainRegressor(data, categoricalFeaturesInfo,
         ... ]
         >>>
         >>> model = DecisionTree.trainRegressor(sc.parallelize(sparse_data), {})
-        >>> model.predict(array([0.0, 1.0])) == 1
-        True
-        >>> model.predict(array([0.0, 0.0])) == 0
-        True
-        >>> model.predict(SparseVector(2, {1: 1.0})) == 1
-        True
-        >>> model.predict(SparseVector(2, {1: 0.0})) == 0
-        True
+        >>> model.predict(SparseVector(2, {1: 1.0}))
+        1.0
+        >>> model.predict(SparseVector(2, {1: 0.0}))
+        0.0
+        >>> rdd = sc.parallelize([[0.0, 1.0], [0.0, 0.0]])
+        >>> model.predict(rdd).collect()
+        [1.0, 0.0]
         """
         return DecisionTree._train(data, "regression", 0, categoricalFeaturesInfo,
                                    impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)