diff options
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala | 12 | ||||
| -rw-r--r-- | python/pyspark/mllib/tree.py | 26 |
2 files changed, 26 insertions, 12 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala index ec1d99ab26..ac4d02ee39 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala +++ b/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. */ diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py index 5d1a3c0962..ef0d556fac 100644 --- a/python/pyspark/mllib/tree.py +++ b/python/pyspark/mllib/tree.py @@ -124,10 +124,13 @@ class DecisionTree(object): 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 @@ class DecisionTree(object): ... ] >>> >>> 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) |