Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1727#discussion_r15725375
--- Diff: python/pyspark/mllib/tree.py ---
@@ -0,0 +1,219 @@
+#
+# 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.
+#
+
+from py4j.java_collections import MapConverter
+
+from pyspark import SparkContext, RDD
+from pyspark.mllib._common import \
+ _get_unmangled_rdd, _get_unmangled_double_vector_rdd,
_serialize_double_vector, \
+ _deserialize_labeled_point, _get_unmangled_labeled_point_rdd, \
+ _deserialize_double
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.serializers import NoOpSerializer
+
+class DecisionTreeModel(object):
+ """
+ A decision tree model for classification or regression.
+
+ WARNING: This is an experimental API. It will probably be modified
for Spark v1.2.
+ """
+
+ def __init__(self, sc, java_model):
+ """
+ :param sc: Spark context
+ :param java_model: Handle to Java model object
+ """
+ self._sc = sc
+ self._java_model = java_model
+
+ def __del__(self):
+ self._sc._gateway.detach(self._java_model)
+
+ def predict(self, x):
+ """
+ Predict the label of one or more examples.
+ NOTE: This currently does NOT support batch prediction.
+
+ :param x: Data point: feature vector, or a LabeledPoint (whose
label is ignored).
+ """
+ pythonAPI = self._sc._jvm.PythonMLLibAPI()
+ if isinstance(x, RDD):
+ # Bulk prediction
+ if x.count() == 0:
+ raise RuntimeError("DecisionTreeModel.predict(x) given
empty RDD x.")
+ elementType = type(x.take(1)[0])
+ if elementType == LabeledPoint:
+ x = x.map(lambda x: x.features)
+ dataBytes = _get_unmangled_double_vector_rdd(x)
+ jSerializedPreds =
pythonAPI.predictDecisionTreeModel(self._java_model, dataBytes._jrdd)
+ dataBytes.unpersist()
+ serializedPreds = RDD(jSerializedPreds, self._sc,
NoOpSerializer())
+ return serializedPreds.map(lambda bytes:
_deserialize_double(bytearray(bytes)))
+ else:
+ if type(x) == LabeledPoint:
--- End diff --
ditto: Maybe we should remove the support of predicting `LabeledPoint`.
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