1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
|
#
# 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.
#
import sys
if sys.version >= '3':
long = int
unicode = str
import py4j.protocol
from py4j.protocol import Py4JJavaError
from py4j.java_gateway import JavaObject
from py4j.java_collections import ListConverter, JavaArray, JavaList
from pyspark import RDD, SparkContext
from pyspark.serializers import PickleSerializer, AutoBatchedSerializer
from pyspark.sql import DataFrame, SQLContext
# Hack for support float('inf') in Py4j
_old_smart_decode = py4j.protocol.smart_decode
_float_str_mapping = {
'nan': 'NaN',
'inf': 'Infinity',
'-inf': '-Infinity',
}
def _new_smart_decode(obj):
if isinstance(obj, float):
s = str(obj)
return _float_str_mapping.get(s, s)
return _old_smart_decode(obj)
py4j.protocol.smart_decode = _new_smart_decode
_picklable_classes = [
'LinkedList',
'SparseVector',
'DenseVector',
'DenseMatrix',
'Rating',
'LabeledPoint',
]
# this will call the MLlib version of pythonToJava()
def _to_java_object_rdd(rdd):
""" Return a JavaRDD of Object by unpickling
It will convert each Python object into Java object by Pyrolite, whenever the
RDD is serialized in batch or not.
"""
rdd = rdd._reserialize(AutoBatchedSerializer(PickleSerializer()))
return rdd.ctx._jvm.org.apache.spark.mllib.api.python.SerDe.pythonToJava(rdd._jrdd, True)
def _py2java(sc, obj):
""" Convert Python object into Java """
if isinstance(obj, RDD):
obj = _to_java_object_rdd(obj)
elif isinstance(obj, DataFrame):
obj = obj._jdf
elif isinstance(obj, SparkContext):
obj = obj._jsc
elif isinstance(obj, list):
obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client)
elif isinstance(obj, JavaObject):
pass
elif isinstance(obj, (int, long, float, bool, bytes, unicode)):
pass
else:
data = bytearray(PickleSerializer().dumps(obj))
obj = sc._jvm.org.apache.spark.mllib.api.python.SerDe.loads(data)
return obj
def _java2py(sc, r, encoding="bytes"):
if isinstance(r, JavaObject):
clsName = r.getClass().getSimpleName()
# convert RDD into JavaRDD
if clsName != 'JavaRDD' and clsName.endswith("RDD"):
r = r.toJavaRDD()
clsName = 'JavaRDD'
if clsName == 'JavaRDD':
jrdd = sc._jvm.org.apache.spark.mllib.api.python.SerDe.javaToPython(r)
return RDD(jrdd, sc)
if clsName == 'Dataset':
return DataFrame(r, SQLContext.getOrCreate(sc))
if clsName in _picklable_classes:
r = sc._jvm.org.apache.spark.mllib.api.python.SerDe.dumps(r)
elif isinstance(r, (JavaArray, JavaList)):
try:
r = sc._jvm.org.apache.spark.mllib.api.python.SerDe.dumps(r)
except Py4JJavaError:
pass # not pickable
if isinstance(r, (bytearray, bytes)):
r = PickleSerializer().loads(bytes(r), encoding=encoding)
return r
def callJavaFunc(sc, func, *args):
""" Call Java Function """
args = [_py2java(sc, a) for a in args]
return _java2py(sc, func(*args))
def callMLlibFunc(name, *args):
""" Call API in PythonMLLibAPI """
sc = SparkContext.getOrCreate()
api = getattr(sc._jvm.PythonMLLibAPI(), name)
return callJavaFunc(sc, api, *args)
class JavaModelWrapper(object):
"""
Wrapper for the model in JVM
"""
def __init__(self, java_model):
self._sc = SparkContext.getOrCreate()
self._java_model = java_model
def __del__(self):
self._sc._gateway.detach(self._java_model)
def call(self, name, *a):
"""Call method of java_model"""
return callJavaFunc(self._sc, getattr(self._java_model, name), *a)
def inherit_doc(cls):
"""
A decorator that makes a class inherit documentation from its parents.
"""
for name, func in vars(cls).items():
# only inherit docstring for public functions
if name.startswith("_"):
continue
if not func.__doc__:
for parent in cls.__bases__:
parent_func = getattr(parent, name, None)
if parent_func and getattr(parent_func, "__doc__", None):
func.__doc__ = parent_func.__doc__
break
return cls
|
