From 592f64985d0d58b4f6a0366bf975e04ca496bdbe Mon Sep 17 00:00:00 2001 From: zero323 Date: Thu, 7 Jan 2016 10:32:56 -0800 Subject: [SPARK-12006][ML][PYTHON] Fix GMM failure if initialModel is not None If initial model passed to GMM is not empty it causes net.razorvine.pickle.PickleException. It can be fixed by converting initialModel.weights to list. Author: zero323 Closes #10644 from zero323/SPARK-12006. --- python/pyspark/mllib/clustering.py | 2 +- python/pyspark/mllib/tests.py | 12 ++++++++++++ 2 files changed, 13 insertions(+), 1 deletion(-) (limited to 'python') diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py index c9e6f1dec6..48daa87e82 100644 --- a/python/pyspark/mllib/clustering.py +++ b/python/pyspark/mllib/clustering.py @@ -346,7 +346,7 @@ class GaussianMixture(object): if initialModel.k != k: raise Exception("Mismatched cluster count, initialModel.k = %s, however k = %s" % (initialModel.k, k)) - initialModelWeights = initialModel.weights + initialModelWeights = list(initialModel.weights) initialModelMu = [initialModel.gaussians[i].mu for i in range(initialModel.k)] initialModelSigma = [initialModel.gaussians[i].sigma for i in range(initialModel.k)] java_model = callMLlibFunc("trainGaussianMixtureModel", rdd.map(_convert_to_vector), diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 6ed03e3582..3436a28b29 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -475,6 +475,18 @@ class ListTests(MLlibTestCase): for c1, c2 in zip(clusters1.weights, clusters2.weights): self.assertEqual(round(c1, 7), round(c2, 7)) + def test_gmm_with_initial_model(self): + from pyspark.mllib.clustering import GaussianMixture + data = self.sc.parallelize([ + (-10, -5), (-9, -4), (10, 5), (9, 4) + ]) + + gmm1 = GaussianMixture.train(data, 2, convergenceTol=0.001, + maxIterations=10, seed=63) + gmm2 = GaussianMixture.train(data, 2, convergenceTol=0.001, + maxIterations=10, seed=63, initialModel=gmm1) + self.assertAlmostEqual((gmm1.weights - gmm2.weights).sum(), 0.0) + def test_classification(self): from pyspark.mllib.classification import LogisticRegressionWithSGD, SVMWithSGD, NaiveBayes from pyspark.mllib.tree import DecisionTree, DecisionTreeModel, RandomForest,\ -- cgit v1.2.3