Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/10242#discussion_r60470048
--- Diff: python/pyspark/ml/clustering.py ---
@@ -310,6 +311,374 @@ def _create_model(self, java_model):
return BisectingKMeansModel(java_model)
+class LDAModel(JavaModel):
+ """
+ A clustering model derived from the LDA method.
+
+ .. versionadded:: 2.0.0
+ """
+
+ @since("2.0.0")
+ def isDistributed(self):
+ """Indicates whether this instance is of type
DistributedLDAModel"""
+ return self._call_java("isDistributed")
+
+ @since("2.0.0")
+ def vocabSize(self):
+ """Vocabulary size (number of terms or terms in the vocabulary)"""
+ return self._call_java("vocabSize")
+
+ @since("2.0.0")
+ def topicsMatrix(self):
+ """ Inferred topics, where each topic is represented by a
distribution over terms.
+ This is a matrix of size vocabSize x k, where each column is a
topic.
+ No guarantees are given about the ordering of the topics.
+
+ WARNING: If this model is actually a [[DistributedLDAModel]]
instance produced by
+ the Expectation-Maximization ("em") [[optimizer]], then this
method could involve
+ collecting a large amount of data to the driver (on the order of
vocabSize x k).
+ """
+ return self._call_java("topicsMatrix")
+
+ @since("2.0.0")
+ def logLikelihood(self, dataset):
+ """Calculates a lower bound on the log likelihood of the entire
corpus.
+ See Equation (16) in the Online LDA paper (Hoffman et al., 2010).
+
+ WARNING: If this model is an instance of [[DistributedLDAModel]]
(produced when
+ [[optimizer]] is set to "em"), this involves collecting a large
[[topicsMatrix]] to the
+ driver. This implementation may be changed in the future.
+ """
+ return self._call_java("logLikelihood", dataset)
+
+ @since("2.0.0")
+ def logPerplexity(self, dataset):
+ """Calculate an upper bound bound on perplexity. (Lower is
better.)
+ See Equation (16) in the Online LDA paper (Hoffman et al., 2010).
+
+ WARNING: If this model is an instance of [[DistributedLDAModel]]
(produced when
+ [[optimizer]] is set to "em"), this involves collecting a large
[[topicsMatrix]] to the
+ driver. This implementation may be changed in the future.
+ """
+ return self._call_java("logPerplexity", dataset)
+
+ @since("2.0.0")
+ def describeTopics(self, maxTermsPerTopic=10):
+ """Return the topics described by their top-weighted terms.
+
+ WARNING: If vocabSize and k are large, this can return a large
object!
+ """
+ return self._call_java("describeTopics", maxTermsPerTopic)
+
+ @since("2.0.0")
+ def estimatedDocConcentration(self):
+ """Value for [[docConcentration]] estimated from data.
+ If Online LDA was used and [[optimizeDocConcentration]] was set to
false,
+ then this returns the fixed (given) value for the
[[docConcentration]] parameter.
+ """
+ return self._call_java("estimatedDocConcentration")
+
+
+class DistributedLDAModel(LDAModel):
+ """
+ Model fitted by LDA.
+
+ .. versionadded:: 2.0.0
+ """
+ def toLocal(self):
+ return self._call_java("toLocal")
+
+
+class LocalLDAModel(LDAModel):
+ """
+ Model fitted by LDA.
--- End diff --
Copy doc from Scala for this class
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