Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/9513#discussion_r44203542
--- Diff: mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala ---
@@ -0,0 +1,740 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.clustering
+
+import org.apache.spark.Logging
+import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.ml.util.{SchemaUtils, Identifiable}
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.param.shared.{HasCheckpointInterval,
HasFeaturesCol, HasSeed, HasMaxIter}
+import org.apache.spark.ml.param._
+import org.apache.spark.mllib.clustering.{DistributedLDAModel =>
OldDistributedLDAModel,
+ EMLDAOptimizer => OldEMLDAOptimizer, LDA => OldLDA, LDAModel =>
OldLDAModel,
+ LDAOptimizer => OldLDAOptimizer, LocalLDAModel => OldLocalLDAModel,
+ OnlineLDAOptimizer => OldOnlineLDAOptimizer}
+import org.apache.spark.mllib.linalg.{VectorUDT, Vectors, Matrix, Vector}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{SQLContext, DataFrame, Row}
+import org.apache.spark.sql.functions.{col, monotonicallyIncreasingId, udf}
+import org.apache.spark.sql.types.StructType
+
+
+private[clustering] trait LDAParams extends Params with HasFeaturesCol
with HasMaxIter
+ with HasSeed with HasCheckpointInterval {
+
+ /**
+ * Param for the number of topics (clusters). Must be > 1. Default: 10.
+ * @group param
+ */
+ @Since("1.6.0")
+ final val k = new IntParam(this, "k", "number of clusters to create",
ParamValidators.gt(1))
+
+ /** @group getParam */
+ @Since("1.6.0")
+ def getK: Int = $(k)
+
+ /**
+ * Concentration parameter (commonly named "alpha") for the prior placed
on documents'
+ * distributions over topics ("theta").
+ *
+ * This is the parameter to a Dirichlet distribution, where larger
values mean more smoothing
+ * (more regularization).
+ *
+ * If set to a singleton vector [-1], then docConcentration is set
automatically. If set to
+ * singleton vector [alpha] where alpha != -1, then alpha is replicated
to a vector of
+ * length k in fitting. Otherwise, the [[docConcentration]] vector must
be length k.
+ * (default = [-1] = automatic)
+ *
+ * Optimizer-specific parameter settings:
+ * - EM
+ * - Currently only supports symmetric distributions, so all values
in the vector should be
+ * the same.
+ * - Values should be > 1.0
+ * - default = uniformly (50 / k) + 1, where 50/k is common in LDA
libraries and +1 follows
+ * from Asuncion et al. (2009), who recommend a +1 adjustment for
EM.
+ * - Online
+ * - Values should be >= 0
+ * - default = uniformly (1.0 / k), following the implementation from
+ * [[https://github.com/Blei-Lab/onlineldavb]].
+ * @group param
+ */
+ @Since("1.6.0")
+ final val docConcentration = new DoubleArrayParam(this,
"docConcentration",
+ "Concentration parameter (commonly named \"alpha\") for the prior
placed on documents'" +
+ " distributions over topics (\"theta\").", validDocConcentration)
+
+ /** Check that the docConcentration is valid, independently of other
Params */
+ private def validDocConcentration(alpha: Array[Double]): Boolean = {
+ if (alpha.length == 1) {
+ alpha(0) == -1 || alpha(0) >= 1.0
+ } else if (alpha.length > 1) {
+ alpha.forall(_ >= 1.0)
+ } else {
+ false
+ }
+ }
+
+ /** @group getParam */
+ @Since("1.6.0")
+ def getDocConcentration: Array[Double] = $(docConcentration)
+
+ /**
+ * Alias for [[getDocConcentration]]
+ * @group getParam
+ */
+ @Since("1.6.0")
+ def getAlpha: Array[Double] = getDocConcentration
+
+ /**
+ * Concentration parameter (commonly named "beta" or "eta") for the
prior placed on topics'
+ * distributions over terms.
+ *
+ * This is the parameter to a symmetric Dirichlet distribution.
+ *
+ * Note: The topics' distributions over terms are called "beta" in the
original LDA paper
+ * by Blei et al., but are called "phi" in many later papers such as
Asuncion et al., 2009.
+ *
+ * If set to -1, then topicConcentration is set automatically.
+ * (default = -1 = automatic)
+ *
+ * Optimizer-specific parameter settings:
+ * - EM
+ * - Value should be > 1.0
+ * - default = 0.1 + 1, where 0.1 gives a small amount of smoothing
and +1 follows
+ * Asuncion et al. (2009), who recommend a +1 adjustment for EM.
+ * - Online
+ * - Value should be >= 0
+ * - default = (1.0 / k), following the implementation from
+ * [[https://github.com/Blei-Lab/onlineldavb]].
+ * @group param
+ */
+ @Since("1.6.0")
+ final val topicConcentration = new DoubleParam(this,
"topicConcentration",
+ "Concentration parameter (commonly named \"beta\" or \"eta\") for the
prior placed on topic'" +
+ " distributions over terms.", (beta: Double) => beta == -1 || beta
>= 0.0)
+
+ /** @group getParam */
+ @Since("1.6.0")
+ def getTopicConcentration: Double = $(topicConcentration)
+
+ /**
+ * Alias for [[getTopicConcentration]]
+ * @group getParam
+ */
+ @Since("1.6.0")
+ def getBeta: Double = getTopicConcentration
+
+ /**
+ * Optimizer or inference algorithm used to estimate the LDA model,
specified as a
+ * [[LDAOptimizer]] type.
+ * Currently supported:
+ * - Online Variational Bayes: [[OnlineLDAOptimizer]] (default)
+ * - Expectation-Maximization (EM): [[EMLDAOptimizer]]
+ * @group param
+ */
+ @Since("1.6.0")
+ final val optimizer = new Param[LDAOptimizer](this, "optimizer",
"Optimizer or inference" +
+ " algorithm used to estimate the LDA model")
+
+ /** @group getParam */
+ @Since("1.6.0")
+ def getOptimizer: LDAOptimizer = $(optimizer)
+
+ // Developers should override these setOptimizer() methods. These are
defined here to
+ // ensure identical behavior when setting the optimizer using a String.
+ /** @group setParam */
+ @Since("1.6.0")
+ def setOptimizer(value: LDAOptimizer): this.type = set(optimizer, value)
+
+ /**
+ * Set [[optimizer]] by name (case-insensitive):
+ * - "online" = [[OnlineLDAOptimizer]]
+ * - "em" = [[EMLDAOptimizer]]
+ * @group setParam
+ */
+ @Since("1.6.0")
+ def setOptimizer(value: String): this.type = value.toLowerCase match {
+ case "online" => setOptimizer(new OnlineLDAOptimizer)
+ case "em" => setOptimizer(new EMLDAOptimizer)
+ case _ => throw new IllegalArgumentException(
+ s"LDA was given unknown optimizer '$value'. Supported values: em,
online")
+ }
+
+ /**
+ * Output column with estimates of the topic mixture distribution for
each document (often called
+ * "theta" in the literature). Returns a vector of zeros for an empty
document.
+ *
+ * This uses a variational approximation following Hoffman et al.
(2010), where the approximate
+ * distribution is called "gamma." Technically, this method returns
this approximation "gamma"
+ * for each document.
+ * @group param
+ */
+ @Since("1.6.0")
+ final val topicDistributionCol = new Param[String](this,
"topicDistribution", "Output column" +
+ " with estimates of the topic mixture distribution for each document
(often called \"theta\"" +
+ " in the literature). Returns a vector of zeros for an empty
document.")
+
+ setDefault(topicDistributionCol -> "topicDistribution")
+
+ /** @group getParam */
+ @Since("1.6.0")
+ def getTopicDistributionCol: String = $(topicDistributionCol)
+
+ /**
+ * Validates and transforms the input schema.
+ * @param schema input schema
+ * @return output schema
+ */
+ protected def validateAndTransformSchema(schema: StructType): StructType
= {
+ SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT)
+ SchemaUtils.appendColumn(schema, $(topicDistributionCol), new
VectorUDT)
+ }
+}
+
+
+/**
+ * :: Experimental ::
+ * Model fitted by [[LDA]].
+ *
+ * @param vocabSize Vocabulary size (number of terms or terms in the
vocabulary)
+ * @param oldLocalModel Underlying spark.mllib model.
+ * If this model was produced by
[[OnlineLDAOptimizer]], then this is the
+ * only model representation.
+ * If this model was produced by [[EMLDAOptimizer]],
then this local
+ * representation may be built lazily.
+ * @param sqlContext Used to construct local DataFrames for returning
query results
+ */
+@Since("1.6.0")
+@Experimental
+class LDAModel private[ml] (
+ @Since("1.6.0") override val uid: String,
+ @Since("1.6.0") val vocabSize: Int,
+ @Since("1.6.0") protected var oldLocalModel: Option[OldLocalLDAModel],
+ @Since("1.6.0") @transient protected val sqlContext: SQLContext)
+ extends Model[LDAModel] with LDAParams with Logging {
+
+ override def validateParams(): Unit = {
+ if (getDocConcentration.length != 1) {
+ require(getDocConcentration.length == getK, s"LDA docConcentration
was of length" +
+ s" ${getDocConcentration.length}, but k = $getK. docConcentration
must be either" +
+ s" length 1 (scalar) or an array of length k.")
+ }
+ }
+
+ /** Returns underlying spark.mllib model */
+ @Since("1.6.0")
+ protected def getModel: OldLDAModel = oldLocalModel match {
+ case Some(m) => m
+ case None =>
+ // Should never happen.
+ throw new RuntimeException("LDAModel required local model format," +
+ " but the underlying model is missing.")
+ }
+
+ /** @group setParam */
+ @Since("1.6.0")
+ def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+ /** @group setParam */
+ @Since("1.6.0")
+ def setSeed(value: Long): this.type = set(seed, value)
+
+ @Since("1.6.0")
+ override def copy(extra: ParamMap): LDAModel = {
+ val copied = new LDAModel(uid, vocabSize, oldLocalModel, sqlContext)
+ copyValues(copied, extra).setParent(parent)
+ }
+
+ @Since("1.6.0")
+ override def transform(dataset: DataFrame): DataFrame = {
+ if ($(topicDistributionCol).nonEmpty) {
+ val t =
udf(oldLocalModel.get.getTopicDistributionMethod(sqlContext.sparkContext))
+ dataset.withColumn($(topicDistributionCol), t(col($(featuresCol))))
+ } else {
+ logWarning("LDAModel.transform was called as a noop. Set an output
column such as" +
+ " topicDistributionCol to produce results.")
+ dataset
+ }
+ }
+
+ @Since("1.6.0")
+ override def transformSchema(schema: StructType): StructType = {
+ validateAndTransformSchema(schema)
+ }
+
+ /**
+ * Value for [[docConcentration]] estimated from data.
+ * If [[estimatedDocConcentration]] was set to false, then this returns
the fixed (given) value
+ * for the [[docConcentration]] parameter.
+ */
+ @Since("1.6.0")
+ def estimatedDocConcentration: Vector = getModel.docConcentration
+
+ /**
+ * 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
from [[EMLDAOptimizer]],
+ * then this method could involve collecting a large amount of
data to the driver
+ * (on the order of vocabSize x k).
+ */
+ @Since("1.6.0")
+ def topicsMatrix: Matrix = getModel.topicsMatrix
+
+ /** Indicates whether this instance is of type [[DistributedLDAModel]] */
+ @Since("1.6.0")
+ def isDistributed: Boolean = false
+
+ /**
+ * Calculates a lower bound on the log likelihood of the entire corpus.
+ *
+ * See Equation (16) in original Online LDA paper.
+ *
+ * WARNING: If this model was learned via a [[DistributedLDAModel]],
this involves collecting
+ * a large [[topicsMatrix]] to the driver. This implementation
may be changed in the
+ * future.
+ *
+ * @param dataset test corpus to use for calculating log likelihood
+ * @return variational lower bound on the log likelihood of the entire
corpus
+ */
+ @Since("1.6.0")
+ def logLikelihood(dataset: DataFrame): Double = oldLocalModel match {
+ case Some(m) =>
+ val oldDataset = LDA.getOldDataset(dataset, $(featuresCol))
+ m.logLikelihood(oldDataset)
+ case None =>
+ // Should never happen.
+ throw new RuntimeException("LocalLDAModel.logLikelihood was called,"
+
+ " but the underlying model is missing.")
+ }
+
+ /**
+ * Calculate an upper bound bound on perplexity. (Lower is better.)
+ * See Equation (16) in original Online LDA paper.
+ *
+ * @param dataset test corpus to use for calculating perplexity
+ * @return Variational upper bound on log perplexity per token.
+ */
+ @Since("1.6.0")
+ def logPerplexity(dataset: DataFrame): Double = oldLocalModel match {
+ case Some(m) =>
+ val oldDataset = LDA.getOldDataset(dataset, $(featuresCol))
+ m.logPerplexity(oldDataset)
+ case None =>
+ // Should never happen.
+ throw new RuntimeException("LocalLDAModel.logPerplexity was called,"
+
+ " but the underlying model is missing.")
+ }
+
+ /**
+ * Return the topics described by their top-weighted terms.
+ *
+ * @param maxTermsPerTopic Maximum number of terms to collect for each
topic.
+ * Default value of 10.
+ * @return Local DataFrame with one topic per Row, with columns:
+ * - "topic": IntegerType: topic index
+ * - "termIndices": ArrayType(IntegerType): term indices,
sorted in order of decreasing
+ * term importance
+ * - "termWeights": ArrayType(DoubleType): corresponding
sorted term weights
+ */
+ @Since("1.6.0")
+ def describeTopics(maxTermsPerTopic: Int): DataFrame = {
+ val topics =
getModel.describeTopics(maxTermsPerTopic).zipWithIndex.map {
+ case ((termIndices, termWeights), topic) =>
+ (topic, termIndices, termWeights)
+ }
+ sqlContext.createDataFrame(topics).toDF("topic", "termIndices",
"termWeights")
+ }
+
+ @Since("1.6.0")
+ def describeTopics(): DataFrame = describeTopics(10)
+}
+
+
+/**
+ * :: Experimental ::
+ *
+ * Distributed model fitted by [[LDA]] using the [[EMLDAOptimizer]].
+ *
+ * This model stores the inferred topics, the full training dataset, and
the topic distribution
+ * for each training document.
+ */
+@Since("1.6.0")
+@Experimental
+class DistributedLDAModel private[ml] (
+ uid: String,
+ vocabSize: Int,
+ private val oldDistributedModel: OldDistributedLDAModel,
+ sqlContext: SQLContext)
+ extends LDAModel(uid, vocabSize, None, sqlContext) {
+
+ /**
+ * Convert this distributed model to a local representation. This
discards info about the
+ * training dataset.
+ */
+ @Since("1.6.0")
+ def toLocal: LDAModel = {
+ if (oldLocalModel.isEmpty) {
+ oldLocalModel = Some(oldDistributedModel.toLocal)
+ }
+ new LDAModel(uid, vocabSize, oldLocalModel, sqlContext)
+ }
+
+ @Since("1.6.0")
+ override protected def getModel: OldLDAModel = oldDistributedModel
+
+ @Since("1.6.0")
+ override def copy(extra: ParamMap): DistributedLDAModel = {
+ val copied = new DistributedLDAModel(uid, vocabSize,
oldDistributedModel, sqlContext)
+ if (oldLocalModel.nonEmpty) copied.oldLocalModel = oldLocalModel
+ copyValues(copied, extra).setParent(parent)
+ copied
+ }
+
+ @Since("1.6.0")
+ override def topicsMatrix: Matrix = {
+ if (oldLocalModel.isEmpty) {
+ oldLocalModel = Some(oldDistributedModel.toLocal)
+ }
+ super.topicsMatrix
+ }
+
+ @Since("1.6.0")
+ override def isDistributed: Boolean = true
+
+ @Since("1.6.0")
+ override def logLikelihood(dataset: DataFrame): Double = {
+ if (oldLocalModel.isEmpty) {
+ oldLocalModel = Some(oldDistributedModel.toLocal)
+ }
+ super.logLikelihood(dataset)
+ }
+
+ @Since("1.6.0")
+ override def logPerplexity(dataset: DataFrame): Double = {
+ if (oldLocalModel.isEmpty) {
+ oldLocalModel = Some(oldDistributedModel.toLocal)
+ }
+ super.logPerplexity(dataset)
+ }
+
+ /**
+ * Log likelihood of the observed tokens in the training set,
+ * given the current parameter estimates:
+ * log P(docs | topics, topic distributions for docs, alpha, eta)
--- End diff --
`alpha` and especially `eta` are confusing in this context where the
implementation is in a whole different file
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