Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6791#discussion_r33828784
--- Diff: python/pyspark/mllib/clustering.py ---
@@ -562,5 +564,67 @@ def _test():
exit(-1)
+class LDAModel(JavaModelWrapper):
+
+ """ A clustering model derived from the LDA method.
+
+ Latent Dirichlet Allocation (LDA), a topic model designed for text
documents.
+ Terminology
+ - "word" = "term": an element of the vocabulary
+ - "token": instance of a term appearing in a document
+ - "topic": multinomial distribution over words representing some
concept
+ References:
+ - Original LDA paper (journal version):
+ Blei, Ng, and Jordan. "Latent Dirichlet Allocation." JMLR, 2003.
+
+ >>> from pyspark.mllib.linalg import Vectors
+ >>> from numpy.testing import assert_almost_equal
+ >>> data = [
+ ... [1, Vectors.dense([0.0, 1.0])],
+ ... [2, SparseVector(2, {0: 1.0})],
+ ... ]
+ >>> rdd = sc.parallelize(data)
+ >>> model = LDA.train(rdd, k=2)
+ >>> model.vocabSize()
+ 2
+ >>> topics = model.topicsMatrix()
+ >>> topics_expect = array([[0.5, 0.5], [0.5, 0.5]])
+ >>> assert_almost_equal(topics, topics_expect, 1)
+ """
+
+ def topicsMatrix(self):
+ """Inferred topics, where each topic is represented by a
distribution over terms."""
+ return self.call("topicsMatrix").toArray()
+
+ def vocabSize(self):
+ """Vocabulary size (number of terms or terms in the vocabulary)"""
+ return self.call("vocabSize")
+
+
+class LDA():
+
+ @classmethod
+ def train(cls, rdd, k=10, maxIterations=20, docConcentration=-1.0,
+ topicConcentration=-1.0, seed=None, checkpointInterval=10,
optimizer="em"):
+ """Train a LDA model.
+
+ :param rdd: RDD of data points
+ :param k: Number of clusters you want
+ :param maxIterations: Number of iterations. Default to 20
+ :param docConcentration: Concentration parameter (commonly
named "alpha")
+ for the prior placed on documents' distributions over topics
("theta").
+ :param topicConcentration: Concentration parameter (commonly
named "beta" or "eta")
+ for the prior placed on topics' distributions over terms.
+ :param seed: Random Seed
+ :param checkpointInterval: Period (in iterations) between
checkpoints.
+ :param optimizer: LDAOptimizer used to perform the
actual calculation
+ (default = EMLDAOptimizer)
--- End diff --
Sorry, I should have noticed this earlier: This should say "em" since that
is the actual value specified. Can it also say the 2 supported values (em and
online)?
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