Spark の MLLIB を使用して、Python で指定された初期モデルを使用してガウス混合モデル (GMM) をトレーニングしようとしています。pyspark の Doc 1.5.1 には、GaussianMixtureModel オブジェクトを GaussianMixture.train メソッドへの「initialModel」パラメータの入力として使用する必要があると記載されています。独自の初期モデルを作成する前に (たとえば、Kmean の結果を使用する予定です)、単純にこのシナリオをテストしたかっただけです。そこで、1 番目のトレーニングの出力から GaussianMixtureModel を使用して 2 番目のトレーニングを初期化しようとします。しかし、この簡単なシナリオではエラーがスローされます。ここで何が起こっているのかを判断するのを手伝ってくれませんか? どうもありがとうギヨーム
PS: Hadoop 2.6 で (py) spark 1.5.1 を実行します
以下は、簡単なシナリオ コードとエラーです。
from pyspark.mllib.clustering import GaussianMixture
from numpy import array
import sys
import os
import pyspark
### Local default options
K=2 # "k" (int) Set the number of Gaussians in the mixture model. Default: 2
convergenceTol=1e-3 # "convergenceTol" (double) Set the largest change in log-likelihood at which convergence is considered to have occurred.
maxIterations=100 # "maxIterations" (int) Set the maximum number of iterations to run. Default: 100
seed=None # "seed" (long) Set the random seed
initialModel=None
### Load and parse the sample data
data = sc.textFile("gmm_data.txt") # Data from the dummy set here: data/mllib/gmm_data.txt
parsedData = data.map(lambda line: array([float(x) for x in line.strip().split(' ')]))
print type(parsedData)
print type(parsedData.first())
### 1st training: Build the GMM
gmm = GaussianMixture.train(parsedData, K, convergenceTol,
maxIterations, seed, initialModel)
# output parameters of model
for i in range(2):
print ("weight = ", gmm.weights[i], "mu = ", gmm.gaussians[i].mu,
"sigma = ", gmm.gaussians[i].sigma.toArray())
### 2nd training: Re-build a GMM using an initial model
initialModel = gmm
print type(initialModel)
gmm = GaussianMixture.train(parsedData, K, convergenceTol, maxIterations, seed, initialModel)
そして、これはエラーで出力されます：
<class 'pyspark.rdd.PipelinedRDD'>
<type 'numpy.ndarray'>
('weight = ', 0.51945003367044018, 'mu = ', DenseVector([-0.1045,
0.0429]), 'sigma = ', array([[ 4.90706817, -2.00676881],
[-2.00676881, 1.01143891]]))
('weight = ', 0.48054996632955982, 'mu = ', DenseVector([0.0722,
0.0167]), 'sigma = ', array([[ 4.77975653, 1.87624558],
[ 1.87624558, 0.91467242]]))
<class 'pyspark.mllib.clustering.GaussianMixtureModel'>
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-30-0008fe75eb61> in <module>()
33 initialModel = gmm
34 print type(initialModel)
---> 35 gmm = GaussianMixture.train(parsedData, K, convergenceTol,
maxIterations, seed, initialModel) #
/opt/spark/spark-1.5.1-bin-hadoop2.6/python/pyspark/mllib/clustering.pyc
in train(cls, rdd, k, convergenceTol, maxIterations, seed,
initialModel)
306 java_model =
callMLlibFunc("trainGaussianMixtureModel",
rdd.map(_convert_to_vector),
307 k, convergenceTol,
maxIterations, seed,
--> 308 initialModelWeights,
initialModelMu, initialModelSigma)
309 return GaussianMixtureModel(java_model)
310
/opt/spark/spark-1.5.1-bin-hadoop2.6/python/pyspark/mllib/common.pyc
in callMLlibFunc(name, *args)
128 sc = SparkContext._active_spark_context
129 api = getattr(sc._jvm.PythonMLLibAPI(), name)
--> 130 return callJavaFunc(sc, api, *args)
131
132
/opt/spark/spark-1.5.1-bin-hadoop2.6/python/pyspark/mllib/common.pyc
in callJavaFunc(sc, func, *args)
120 def callJavaFunc(sc, func, *args):
121 """ Call Java Function """
--> 122 args = [_py2java(sc, a) for a in args]
123 return _java2py(sc, func(*args))
124
/opt/spark/spark-1.5.1-bin-hadoop2.6/python/pyspark/mllib/common.pyc
in _py2java(sc, obj)
86 else:
87 data = bytearray(PickleSerializer().dumps(obj))
---> 88 obj = sc._jvm.SerDe.loads(data)
89 return obj
90
/opt/spark/spark-1.5.1-bin-hadoop2.6/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py
in __call__(self, *args)
536 answer = self.gateway_client.send_command(command)
537 return_value = get_return_value(answer, self.gateway_client,
--> 538 self.target_id, self.name)
539
540 for temp_arg in temp_args:
/opt/spark/spark-1.5.1-bin-hadoop2.6/python/pyspark/sql/utils.pyc in
deco(*a, **kw)
34 def deco(*a, **kw):
35 try:
---> 36 return f(*a, **kw)
37 except py4j.protocol.Py4JJavaError as e:
38 s = e.java_exception.toString()
/opt/spark/spark-1.5.1-bin-hadoop2.6/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py
in get_return_value(answer, gateway_client, target_id, name)
298 raise Py4JJavaError(
299 'An error occurred while calling {0}{1}{2}.\n'.
--> 300 format(target_id, '.', name), value)
301 else:
302 raise Py4JError(
Py4JJavaError: An error occurred while calling
z:org.apache.spark.mllib.api.python.SerDe.loads.
: net.razorvine.pickle.PickleException: expected zero arguments for
construction of ClassDict (for numpy.core.multiarray._reconstruct)
at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:701)
at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:171)
at net.razorvine.pickle.Unpickler.load(Unpickler.java:85)
at net.razorvine.pickle.Unpickler.loads(Unpickler.java:98)
at org.apache.spark.mllib.api.python.SerDe$.loads(PythonMLLibAPI.scala:1462)
at org.apache.spark.mllib.api.python.SerDe.loads(PythonMLLibAPI.scala)
at sun.reflect.GeneratedMethodAccessor31.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
at py4j.Gateway.invoke(Gateway.java:259)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:207)
at java.lang.Thread.run(Thread.java:745)