Hi all,
We're trying to train a Gaussian Mixture Model (GMM) with a specified
initial model.
Doc 1.5.1 says we should use a GaussianMixtureModel object as input
for the "initialModel" parameter to the GaussianMixture.train method.
Before creating our own initial model (the plan is to use a Kmean
result for instance), we simply wanted to test case this scenario.
So we try to initialize a 2nd training using the GaussianMixtureModel
from the output a 1st training.
But this trivial scenario throws an error.
Could you please help us determine what's going on here ?
Thanks a lot
guillaume
PS: we run (py)spark 1.5.1 with hadoop 2.6
Below is the trivial scenario code and the error:
============================================ SOURCE CODE
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)
============================================ OUTPUT WITH ERROR:
<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)
---------------------------------------------------------------------------
Guillaume Maze, Research Scientist at Ifremer
LPO, UMR 6523: wwz.ifremer.fr/lpo
SO Argo-France: wwz.ifremer.fr/lpo/SO-Argo
+33 (0)2 98 22 43 39
http://www.guillaumemaze.org
---------------------------------------------------------------------------
