From 284674327836992acebcf734b999ce0b566e1f1d Mon Sep 17 00:00:00 2001 From: Yin Huai Date: Wed, 6 Jan 2016 22:01:16 -0800 Subject: [PATCH] Revert "[SPARK-12006][ML][PYTHON] Fix GMM failure if initialModel is not None" This reverts commit fcd013cf70e7890aa25a8fe3cb6c8b36bf0e1f04. --- python/pyspark/mllib/clustering.py | 2 +- python/pyspark/mllib/tests.py | 12 ------------ 2 files changed, 1 insertion(+), 13 deletions(-) diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py index 48daa87e82d13..c9e6f1dec6bf8 100644 --- a/python/pyspark/mllib/clustering.py +++ b/python/pyspark/mllib/clustering.py @@ -346,7 +346,7 @@ def train(cls, rdd, k, convergenceTol=1e-3, maxIterations=100, seed=None, initia if initialModel.k != k: raise Exception("Mismatched cluster count, initialModel.k = %s, however k = %s" % (initialModel.k, k)) - initialModelWeights = list(initialModel.weights) + initialModelWeights = 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 97fed7662ea90..6ed03e35828ed 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -475,18 +475,6 @@ def test_gmm_deterministic(self): 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,\