Skip to content

Revert "[SPARK-12006][ML][PYTHON] Fix GMM failure if initialModel is not None" #10632

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 1 commit into from
Closed

Revert "[SPARK-12006][ML][PYTHON] Fix GMM failure if initialModel is not None" #10632

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion python/pyspark/mllib/clustering.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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),
Expand Down
12 changes: 0 additions & 12 deletions python/pyspark/mllib/tests.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -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,\
Expand Down