[GitHub] spark pull request: [SPARK-12010][SQL] Add columnMapping support
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[GitHub] spark pull request: [SPARK-12177] [STREAMING] Update KafkaDStreams...
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[GitHub] spark pull request: [SPARK-12010][SQL] Add columnMapping support
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[GitHub] spark pull request: [SPARK-12269][Streaming][Kinesis] Update aws-j...
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[GitHub] spark pull request: [SPARK-12334][SQL][PYSPARK] Support read from ...
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[GitHub] spark pull request: [SPARK-8519] [ML] [MLlib] Blockify distance co...
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[GitHub] spark pull request: [Streaming][Minor] Fix typo in function name o...
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[GitHub] spark pull request: [SPARK-12270][SQL]remove empty space after get...
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[GitHub] spark pull request: [SPARK-8519] [ML] [MLlib] Blockify distance co...
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[GitHub] spark pull request: [SPARK-12130] Replace shuffleManagerClass with...
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[GitHub] spark pull request: [SPARK-12164] [SQL] Decode the encoded values ...
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[GitHub] spark pull request: [SPARK-9057] [STREAMING] Twitter example joini...
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[GitHub] spark pull request: [SPARK-12235][SPARKR] Enhance mutate() to supp...
Github user 3ourroom commented on a diff in the pull request:
https://github.com/apache/spark/pull/10220#discussion_r47612640
--- Diff: R/pkg/R/DataFrame.R ---
@@ -1357,17 +1357,46 @@ setMethod("mutate",
function(.data, ...) {
x <- .data
cols <- list(...)
-stopifnot(length(cols) > 0)
+if (length(cols) <= 0) {
+ return(x)
+}
+
stopifnot(class(cols[[1]]) == "Column")
+
+# Check if there is any duplicated column name in the DataFrame
+dfCols <- columns(x)
+if (length(unique(dfCols)) != length(dfCols)) {
--- End diff --
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[GitHub] spark pull request: [SPARK-11940][PYSPARK] Python API for ml.clust...
Github user 3ourroom commented on a diff in the pull request:
https://github.com/apache/spark/pull/10242#discussion_r47619640
--- Diff: python/pyspark/ml/clustering.py ---
@@ -167,6 +167,200 @@ def getInitSteps(self):
return self.getOrDefault(self.initSteps)
+class LDAModel(JavaModel):
+""" 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.
+"""
+
+@since("1.7.0")
+def isDistributed(self):
+"""Indicates whether this instance is of type
DistributedLDAModel"""
+return self._call_java("isDistributed")
+
+@since("1.7.0")
+def vocabSize(self):
+"""Vocabulary size (number of terms or terms in the vocabulary)"""
+return self._call_java("vocabSize")
+
+@since("1.7.0")
+def topicsMatrix(self):
+"""Inferred topics, where each topic is represented by a
distribution over terms."""
+return self._call_java("topicsMatrix")
+
+@since("1.7.0")
+def logLikelihood(self, dataset):
+"""Calculates a lower bound on the log likelihood of the entire
corpus."""
+return self._call_java("logLikelihood", dataset)
+
+@since("1.7.0")
+def logPerplexity(self, dataset):
+"""Calculate an upper bound bound on perplexity. (Lower is
better.)"""
+return self._call_java("logPerplexity", dataset)
+
+@since("1.7.0")
+def describeTopics(self, maxTermsPerTopic=10):
+"""Return the topics described by weighted terms.
+
+WARNING: If vocabSize and k are large, this can return a large
object!
+
+:param maxTermsPerTopic: Maximum number of terms to collect for
each topic.
+(default: vocabulary size)
+:return: Array over topics. Each topic is represented as a pair of
matching arrays:
+(term indices, term weights in topic).
+Each topic's terms are sorted in order of decreasing weight.
+"""
+return self._call_java("describeTopics", maxTermsPerTopic)
+
+
+class DistributedLDAModel(LDAModel):
+"""
+Model fitted by LDA.
+
+.. versionadded:: 1.7.0
+"""
+def toLocal(self):
+return self._call_java("toLocal")
+
+
+class LocalLDAModel(LDAModel):
+"""
+Model fitted by LDA.
+
+.. versionadded:: 1.7.0
+"""
+pass
+
+
+class LDA(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed,
HasCheckpointInterval):
+""" 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, SparseVector
+>>> from pyspark.ml.clustering import LDA
+>>> df = sqlContext.createDataFrame([[1, Vectors.dense([0.0, 1.0])], \
+[2, SparseVector(2, {0: 1.0})],], ["id", "features"])
+>>> lda = LDA(k=2, seed=1, optimizer="em")
+>>> model = lda.fit(df)
+>>> model.isDistributed()
+True
+>>> localModel = model.toLocal()
+>>> localModel.isDistributed()
+False
+>>> model.vocabSize()
+2
+>>> model.describeTopics().show()
++-+---++
+|topic|termIndices| termWeights|
++-+-
[GitHub] spark pull request: [SPARK-12337][SPARKR] Implement dropDuplicates...
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[GitHub] spark pull request: [SPARK-11944][PYSPARK][MLLIB] python mllib.clu...
Github user 3ourroom commented on a diff in the pull request:
https://github.com/apache/spark/pull/10150#discussion_r47619760
--- Diff: python/pyspark/mllib/clustering.py ---
@@ -38,13 +38,158 @@
from pyspark.mllib.util import Saveable, Loader, inherit_doc, JavaLoader,
JavaSaveable
from pyspark.streaming import DStream
-__all__ = ['KMeansModel', 'KMeans', 'GaussianMixtureModel',
'GaussianMixture',
- 'PowerIterationClusteringModel', 'PowerIterationClustering',
- 'StreamingKMeans', 'StreamingKMeansModel',
+__all__ = ['BisectingKMeansModel', 'BisectingKMeans', 'KMeansModel',
'KMeans',
+ 'GaussianMixtureModel', 'GaussianMixture',
'PowerIterationClusteringModel',
+ 'PowerIterationClustering', 'StreamingKMeans',
'StreamingKMeansModel',
'LDA', 'LDAModel']
@inherit_doc
+class BisectingKMeansModel(JavaModelWrapper):
+"""
+.. note:: Experimental
+
+A clustering model derived from the bisecting k-means method.
+
+>>> data = array([0.0,0.0, 1.0,1.0, 9.0,8.0, 8.0,9.0]).reshape(4, 2)
+>>> bskm = BisectingKMeans()
+>>> model = bskm.run(sc.parallelize(data))
+>>> model.predict(array([0.0, 0.0])) == model.predict(array([0.0,
0.0]))
+True
+>>> model.k
+4
+>>> model.computeCost(array([0.0, 0.0]))
+0.0
+>>> model.k == len(model.clusterCenters)
+True
+>>> model = bskm.setK(2).run(sc.parallelize(data))
+>>> model.predict(array([0.0, 0.0])) == model.predict(array([1.0,
1.0]))
+True
+>>> model.k
+2
+"""
+
+@property
+def clusterCenters(self):
+"""Get the cluster centers, represented as a list of NumPy
arrays."""
+return [c.toArray() for c in self.call("clusterCenters")]
+
+@property
+def k(self):
+"""Get the number of clusters"""
+return self.call("k")
+
+def predict(self, x):
+"""
+Find the cluster to which x belongs in this model.
+
+:param x: Either the point to determine the cluster for or an RDD
of points to determine
+the clusters for.
+"""
+if isinstance(x, RDD):
+return x.map(self.predict(x))
+
+x = _convert_to_vector(x)
+return self.call("predict", x)
+
+def computeCost(self, point):
+"""
+Return the Bisecting K-means cost (sum of squared distances of
points to
+their nearest center) for this model on the given data.
+
+:param point: the point to compute the cost to
+"""
+return self.call("computeCost", _convert_to_vector(point))
+
+
+class BisectingKMeans:
+"""
+A bisecting k-means algorithm based on the paper "A comparison of
document clustering
+techniques" by Steinbach, Karypis, and Kumar, with modification to fit
Spark.
+The algorithm starts from a single cluster that contains all points.
+Iteratively it finds divisible clusters on the bottom level and
bisects each of them using
+k-means, until there are `k` leaf clusters in total or no leaf
clusters are divisible.
+The bisecting steps of clusters on the same level are grouped together
to increase parallelism.
+If bisecting all divisible clusters on the bottom level would result
more than `k` leaf
+clusters, larger clusters get higher priority.
+
+Based on
[[http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf
+Steinbach, Karypis, and Kumar, A comparison of document clustering
techniques,
+KDD Workshop on Text Mining, 2000.]]
+"""
+def __init__(self):
+self.k = 4
+self.maxIterations = 20
+self.minDivisibleClusterSize = 1.0
+self.seed = -1888008604 # classOf[BisectingKMeans].getName.##
+
+def setK(self, k):
+"""
+Set the number of leaf clusters.
+
+:param k: the desired number of leaf clusters (default: 4). The
actual number could be
+smaller if there are no divisible leaf clusters.
+"""
+self.k = k
+return self
+
+def getK(self):
+"""Return the desired number of leaf clusters."""
+return self.k
+
+def setMaxIterations(self, max
[GitHub] spark pull request: [SPARK-12323][SPARK-12335][SPARK-12336][SQL] M...
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[GitHub] spark pull request: [SPARK-10291] [PySpark] statsByKey method for ...
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[GitHub] spark pull request: [SPARK-12337][SPARKR] Implement dropDuplicates...
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[GitHub] spark pull request: [SPARK-7729][UI]Executor which has been killed...
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[GitHub] spark pull request: [SPARK-12334][SQL][PYSPARK] Support read from ...
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[GitHub] spark pull request: [SPARK-8641][SQL] Native Spark Window function...
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[GitHub] spark pull request: [SPARK-12220][Core]Make Utils.fetchFile suppor...
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[GitHub] spark pull request: [MESOS]Add documentation about submitting Spar...
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[GitHub] spark pull request: [SPARK-7889] [CORE] HistoryServer to refresh c...
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[GitHub] spark pull request: [SPARK-12337][SPARKR] Implement dropDuplicates...
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[GitHub] spark pull request: [SPARK-12337][SPARKR] Implement dropDuplicates...
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[GitHub] spark pull request: [SPARK-12010][SQL] Spark JDBC requires support...
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[GitHub] spark pull request: [SPARK-4117] [YARN] Spark on Yarn handle AM be...
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[GitHub] spark pull request: [SPARK-12311][CORE] Restore previous value of ...
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[GitHub] spark pull request: [SPARK-12332][TRIVIAL][TEST] Fix minor typo in...
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[GitHub] spark pull request: [SPARK-12340][SQL] Fix overstep the bounds of ...
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[GitHub] spark pull request: [SPARK-5210] Support group event log when app ...
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[GitHub] spark pull request: [SPARK-10797] RDD's coalesce should not write ...
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[GitHub] spark pull request: [SPARK-12340][SQL] Fix overstep the bounds of ...
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[GitHub] spark pull request: [SPARK-12321][SQL] JSON format for TreeNode (u...
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[GitHub] spark pull request: [SPARK-12323][SPARK-12335][SPARK-12336][SPARK-...
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[GitHub] spark pull request: [SPARK-12321][SQL] JSON format for TreeNode (u...
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[GitHub] spark pull request: Branch 1.6
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[GitHub] spark pull request: [SPARK-9687] Avoid throwing SparkException for...
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[GitHub] spark pull request: [SPARK-11638] [Mesos + Docker Bridge networkin...
Github user 3ourroom commented on a diff in the pull request:
https://github.com/apache/spark/pull/9608#discussion_r47606048
--- Diff: core/src/main/scala/org/apache/spark/rpc/netty/NettyRpcEnv.scala
---
@@ -122,7 +122,8 @@ private[netty] class NettyRpcEnv(
@Nullable
override lazy val address: RpcAddress = {
-if (server != null) RpcAddress(host, server.getPort()) else null
+if (server != null)
+ RpcAddress(host, conf.getInt("spark.driver.advertisedPort",
server.getPort())) else null
--- End diff --
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[GitHub] spark pull request: [SPARK-12318][SPARKR] Save mode in SparkR shou...
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[GitHub] spark pull request: [Streaming][Minor] Fix typo in function name o...
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[GitHub] spark pull request: [SPARK-7727] [SQL] Avoid inner classes in Rule...
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[GitHub] spark pull request: [SPARK-12168][SPARKR] Add automated tests for ...
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[GitHub] spark pull request: [SPARK-11735] [SQL] Add a check in the constru...
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[GitHub] spark pull request: SPARK-12105 - SPARK-SQL add convenient show fu...
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[GitHub] spark pull request: [SPARK-5095][MESOS] Support capping cores and ...
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