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| author | Hrishikesh Subramonian <hrishikesh.subramonian@flytxt.com> | 2015-05-05 07:57:39 -0700 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2015-05-05 07:57:39 -0700 |
| commit | 5995ada96b661546a80657f2c5ed20604593e4aa (patch) | |
| tree | 5ea7879e611aa34fd912d93e253ad69d0799a665 /python/pyspark/mllib/tests.py | |
| parent | 5ab652cdb8bef10214edd079502a7f49017579aa (diff) | |
| download | spark-5995ada96b661546a80657f2c5ed20604593e4aa.tar.gz spark-5995ada96b661546a80657f2c5ed20604593e4aa.tar.bz2 spark-5995ada96b661546a80657f2c5ed20604593e4aa.zip | |
[SPARK-6612] [MLLIB] [PYSPARK] Python KMeans parity
The following items are added to Python kmeans:
kmeans - setEpsilon, setInitializationSteps
KMeansModel - computeCost, k
Author: Hrishikesh Subramonian <hrishikesh.subramonian@flytxt.com>
Closes #5647 from FlytxtRnD/newPyKmeansAPI and squashes the following commits:
b9e451b [Hrishikesh Subramonian] set seed to fixed value in doc test
5fd3ced [Hrishikesh Subramonian] doc test corrections
20b3c68 [Hrishikesh Subramonian] python 3 fixes
4d4e695 [Hrishikesh Subramonian] added arguments in python tests
21eb84c [Hrishikesh Subramonian] Python Kmeans - setEpsilon, setInitializationSteps, k and computeCost added.
Diffstat (limited to 'python/pyspark/mllib/tests.py')
| -rw-r--r-- | python/pyspark/mllib/tests.py | 9 |
1 files changed, 6 insertions, 3 deletions
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 1d9c6ebf3b..d05cfe2af0 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -236,7 +236,8 @@ class ListTests(MLlibTestCase): [1.1, 0], [1.2, 0], ] - clusters = KMeans.train(self.sc.parallelize(data), 2, initializationMode="k-means||") + clusters = KMeans.train(self.sc.parallelize(data), 2, initializationMode="k-means||", + initializationSteps=7, epsilon=1e-4) self.assertEquals(clusters.predict(data[0]), clusters.predict(data[1])) self.assertEquals(clusters.predict(data[2]), clusters.predict(data[3])) @@ -246,9 +247,11 @@ class ListTests(MLlibTestCase): Y = range(0, 100, 10) data = [[x, y] for x, y in zip(X, Y)] clusters1 = KMeans.train(self.sc.parallelize(data), - 3, initializationMode="k-means||", seed=42) + 3, initializationMode="k-means||", + seed=42, initializationSteps=7, epsilon=1e-4) clusters2 = KMeans.train(self.sc.parallelize(data), - 3, initializationMode="k-means||", seed=42) + 3, initializationMode="k-means||", + seed=42, initializationSteps=7, epsilon=1e-4) centers1 = clusters1.centers centers2 = clusters2.centers for c1, c2 in zip(centers1, centers2): |