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| author | zero323 <matthew.szymkiewicz@gmail.com> | 2016-01-06 11:58:33 -0800 |
|---|---|---|
| committer | Joseph K. Bradley <joseph@databricks.com> | 2016-01-06 11:58:33 -0800 |
| commit | fcd013cf70e7890aa25a8fe3cb6c8b36bf0e1f04 (patch) | |
| tree | 48c21e17e12296e7724deadf1b129555c26a4348 /python | |
| parent | ea489f14f11b2fdfb44c86634d2e2c2167b6ea18 (diff) | |
| download | spark-fcd013cf70e7890aa25a8fe3cb6c8b36bf0e1f04.tar.gz spark-fcd013cf70e7890aa25a8fe3cb6c8b36bf0e1f04.tar.bz2 spark-fcd013cf70e7890aa25a8fe3cb6c8b36bf0e1f04.zip | |
[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 <matthew.szymkiewicz@gmail.com>
Closes #9986 from zero323/SPARK-12006.
Diffstat (limited to 'python')
| -rw-r--r-- | python/pyspark/mllib/clustering.py | 2 | ||||
| -rw-r--r-- | python/pyspark/mllib/tests.py | 12 |
2 files changed, 13 insertions, 1 deletions
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..97fed7662e 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,\ |