Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/9513#discussion_r44354690
--- Diff: mllib/src/main/scala/org/apache/spark/ml/clustering/LDA.scala ---
@@ -0,0 +1,668 @@
+/*
+ * 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) to infer. Must be > 1.
Default: 10.
+ * @group param
+ */
+ @Since("1.6.0")
+ final val k = new IntParam(this, "k", "number of topics (clusters) to
infer",
+ 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)
+
+ /**
+ * 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)
+
+ /** Supported values for Param [[optimizer]]. */
+ final val supportedOptimizers: Array[String] = Array("online", "em")
+
+ /**
+ * Optimizer or inference algorithm used to estimate the LDA model.
+ * Currently supported (case-insensitive):
+ * - "online": Online Variational Bayes (default)
+ * - "em": Expectation-Maximization
+ *
+ * For details, see the following papers:
+ * - Online LDA:
+ * Hoffman, Blei and Bach. "Online Learning for Latent Dirichlet
Allocation."
+ * Neural Information Processing Systems, 2010.
+ *
[[http://www.cs.columbia.edu/~blei/papers/HoffmanBleiBach2010b.pdf]]
+ * - EM:
+ * Asuncion et al. "On Smoothing and Inference for Topic Models."
+ * Uncertainty in Artificial Intelligence, 2009.
+ * [[http://arxiv.org/pdf/1205.2662.pdf]]
+ *
+ * @group param
+ */
+ @Since("1.6.0")
+ final val optimizer = new Param[String](this, "optimizer", "Optimizer or
inference" +
+ " algorithm used to estimate the LDA model. Supported: " +
supportedOptimizers.mkString(", "),
+ (o: String) =>
ParamValidators.inArray(supportedOptimizers).apply(o.toLowerCase))
+
+ /** @group getParam */
+ @Since("1.6.0")
+ def getOptimizer: String = $(optimizer)
+
+ /**
+ * 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)
+
+ /**
+ * A (positive) learning parameter that downweights early iterations.
Larger values make early
+ * iterations count less.
+ * Default: 1024, following the Online LDA paper (Hoffman et al., 2010).
+ * @group expertParam
+ */
+ @Since("1.6.0")
+ final val tau0 = new DoubleParam(this, "tau0", "A (positive) learning
parameter that" +
+ " downweights early iterations. Larger values make early iterations
count less.",
+ ParamValidators.gt(0))
+
+ /** @group expertGetParam */
+ @Since("1.6.0")
+ def getTau0: Double = $(tau0)
+
+ /**
+ * Learning rate, set as an exponential decay rate.
+ * This should be between (0.5, 1.0] to guarantee asymptotic convergence.
+ * Default: 0.51, based on the Online LDA paper (Hoffman et al., 2010).
+ * @group expertParam
+ */
+ @Since("1.6.0")
+ final val kappa = new DoubleParam(this, "kappa", "Learning rate, set as
an exponential decay" +
+ " rate. This should be between (0.5, 1.0] to guarantee asymptotic
convergence.",
+ ParamValidators.gt(0))
+
+ /** @group expertGetParam */
+ @Since("1.6.0")
+ def getKappa: Double = $(kappa)
+
+ /**
+ * Fraction of the corpus to be sampled and used in each iteration of
mini-batch gradient descent,
+ * in range (0, 1].
+ *
+ * Note that this should be adjusted in synch with [[LDA.maxIter]]
+ * so the entire corpus is used. Specifically, set both so that
+ * maxIterations * miniBatchFraction >= 1.
+ *
+ * Note: This is the same as the `miniBatchFraction` parameter in
+ * [[org.apache.spark.mllib.clustering.OnlineLDAOptimizer]].
+ *
+ * Default: 0.05, i.e., 5% of total documents.
+ * @group param
+ */
+ @Since("1.6.0")
+ final val subsamplingRate = new DoubleParam(this, "subsamplingRate",
"Fraction of the corpus" +
+ " to be sampled and used in each iteration of mini-batch gradient
descent, in range (0, 1].",
+ ParamValidators.inRange(0.0, 1.0, lowerInclusive = false,
upperInclusive = true))
+
+ /** @group getParam */
+ @Since("1.6.0")
+ def getSubsamplingRate: Double = $(subsamplingRate)
+
+ /**
+ * Indicates whether the docConcentration (Dirichlet parameter for
+ * document-topic distribution) will be optimized during training.
+ * Setting this to true will make the model more expressive and fit the
training data better.
+ * Default: false
+ * @group expertParam
+ */
+ @Since("1.6.0")
+ final val optimizeDocConcentration = new BooleanParam(this,
"optimizeDocConcentration",
+ "Indicates whether the docConcentration (Dirichlet parameter for
document-topic" +
+ " distribution) will be optimized during training.")
+
+ /** @group expertGetParam */
+ @Since("1.6.0")
+ def getOptimizeDocConcentration: Boolean = $(optimizeDocConcentration)
+
+ /**
+ * 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)
+ }
+
+ override def validateParams(): Unit = {
+ if (getDocConcentration.length != 1) {
+ require(getDocConcentration.length == getK, s"LDA docConcentration
was of length" +
+ s" ${getDocConcentration.length}, but k = $getK. docConcentration
must be either" +
+ s" length 1 (scalar) or an array of length k.")
+ }
+ }
+
+ private[clustering] def getOldOptimizer: OldLDAOptimizer = getOptimizer
match {
+ case "online" =>
+ new OldOnlineLDAOptimizer()
+ .setTau0($(tau0))
+ .setKappa($(kappa))
+ .setMiniBatchFraction($(subsamplingRate))
+ .setOptimizeDocConcentration($(optimizeDocConcentration))
+ case "em" =>
+ new OldEMLDAOptimizer()
+ }
+}
+
+
+/**
+ * :: Experimental ::
+ * Model fitted by [[LDA]].
+ *
+ * @param vocabSize Vocabulary size (number of terms or terms in the
vocabulary)
+ * @param oldLocalModel Underlying spark.mllib model.
+ * If this model was produced by Online LDA, then
this is the
+ * only model representation.
+ * If this model was produced by EM, then this local
+ * representation may be built lazily.
+ * @param sqlContext Used to construct local DataFrames for returning
query results
+ */
+@Since("1.6.0")
+@Experimental
+class LDAModel private[ml] (
+ @Since("1.6.0") override val uid: String,
+ @Since("1.6.0") val vocabSize: Int,
+ @Since("1.6.0") protected var oldLocalModel: Option[OldLocalLDAModel],
+ @Since("1.6.0") @transient protected val sqlContext: SQLContext)
+ extends Model[LDAModel] with LDAParams with Logging {
+
+ /** Returns underlying spark.mllib model */
+ @Since("1.6.0")
+ protected def getModel: OldLDAModel = oldLocalModel match {
+ case Some(m) => m
+ case None =>
+ // Should never happen.
+ throw new RuntimeException("LDAModel required local model format," +
+ " but the underlying model is missing.")
+ }
+
+ /**
+ * The features for LDA should be a [[Vector]] representing the word
counts in a document.
+ * The vector should be of length vocabSize, with counts for each term
(word).
+ * @group setParam
+ */
+ @Since("1.6.0")
+ def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+ /** @group setParam */
+ @Since("1.6.0")
+ def setSeed(value: Long): this.type = set(seed, value)
+
+ @Since("1.6.0")
+ override def copy(extra: ParamMap): LDAModel = {
+ val copied = new LDAModel(uid, vocabSize, oldLocalModel, sqlContext)
+ copyValues(copied, extra).setParent(parent)
+ }
+
+ @Since("1.6.0")
+ override def transform(dataset: DataFrame): DataFrame = {
+ if ($(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" +
--- End diff --
nit: "as a noop" -> "without an output column"
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