872fc669b4
Create several helper functions to call MLlib Java API, convert the arguments to Java type and convert return value to Python object automatically, this simplify serialization in MLlib Python API very much. After this, the MLlib Python API does not need to deal with serialization details anymore, it's easier to add new API. cc mengxr Author: Davies Liu <davies@databricks.com> Closes #2995 from davies/cleanup and squashes the following commits: 8fa6ec6 [Davies Liu] address comments 16b85a0 [Davies Liu] Merge branch 'master' of github.com:apache/spark into cleanup 43743e5 [Davies Liu] bugfix 731331f [Davies Liu] simplify serialization in MLlib Python API
136 lines
4 KiB
Python
136 lines
4 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import py4j.protocol
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from py4j.protocol import Py4JJavaError
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from py4j.java_gateway import JavaObject
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from py4j.java_collections import MapConverter, ListConverter, JavaArray, JavaList
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from pyspark import RDD, SparkContext
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from pyspark.serializers import PickleSerializer, AutoBatchedSerializer
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# Hack for support float('inf') in Py4j
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_old_smart_decode = py4j.protocol.smart_decode
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_float_str_mapping = {
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'nan': 'NaN',
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'inf': 'Infinity',
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'-inf': '-Infinity',
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}
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def _new_smart_decode(obj):
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if isinstance(obj, float):
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s = unicode(obj)
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return _float_str_mapping.get(s, s)
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return _old_smart_decode(obj)
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py4j.protocol.smart_decode = _new_smart_decode
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_picklable_classes = [
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'LinkedList',
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'SparseVector',
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'DenseVector',
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'DenseMatrix',
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'Rating',
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'LabeledPoint',
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]
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# this will call the MLlib version of pythonToJava()
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def _to_java_object_rdd(rdd, cache=False):
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""" Return an JavaRDD of Object by unpickling
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It will convert each Python object into Java object by Pyrolite, whenever the
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RDD is serialized in batch or not.
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"""
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rdd = rdd._reserialize(AutoBatchedSerializer(PickleSerializer()))
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if cache:
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rdd.cache()
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return rdd.ctx._jvm.SerDe.pythonToJava(rdd._jrdd, True)
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def _py2java(sc, obj):
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""" Convert Python object into Java """
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if isinstance(obj, RDD):
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obj = _to_java_object_rdd(obj)
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elif isinstance(obj, SparkContext):
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obj = obj._jsc
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elif isinstance(obj, dict):
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obj = MapConverter().convert(obj, sc._gateway._gateway_client)
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elif isinstance(obj, (list, tuple)):
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obj = ListConverter().convert(obj, sc._gateway._gateway_client)
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elif isinstance(obj, JavaObject):
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pass
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elif isinstance(obj, (int, long, float, bool, basestring)):
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pass
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else:
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bytes = bytearray(PickleSerializer().dumps(obj))
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obj = sc._jvm.SerDe.loads(bytes)
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return obj
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def _java2py(sc, r):
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if isinstance(r, JavaObject):
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clsName = r.getClass().getSimpleName()
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# convert RDD into JavaRDD
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if clsName != 'JavaRDD' and clsName.endswith("RDD"):
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r = r.toJavaRDD()
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clsName = 'JavaRDD'
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if clsName == 'JavaRDD':
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jrdd = sc._jvm.SerDe.javaToPython(r)
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return RDD(jrdd, sc, AutoBatchedSerializer(PickleSerializer()))
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elif isinstance(r, (JavaArray, JavaList)) or clsName in _picklable_classes:
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r = sc._jvm.SerDe.dumps(r)
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if isinstance(r, bytearray):
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r = PickleSerializer().loads(str(r))
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return r
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def callJavaFunc(sc, func, *args):
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""" Call Java Function """
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args = [_py2java(sc, a) for a in args]
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return _java2py(sc, func(*args))
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def callMLlibFunc(name, *args):
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""" Call API in PythonMLLibAPI """
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sc = SparkContext._active_spark_context
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api = getattr(sc._jvm.PythonMLLibAPI(), name)
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return callJavaFunc(sc, api, *args)
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class JavaModelWrapper(object):
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"""
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Wrapper for the model in JVM
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"""
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def __init__(self, java_model):
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self._sc = SparkContext._active_spark_context
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self._java_model = java_model
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def __del__(self):
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self._sc._gateway.detach(self._java_model)
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def call(self, name, *a):
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"""Call method of java_model"""
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return callJavaFunc(self._sc, getattr(self._java_model, name), *a)
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