Github user jkbradley commented on a diff in the pull request: https://github.com/apache/spark/pull/9513#discussion_r44239881 --- 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) + * + * Notes: + * - This excludes the prior; for that, use [[logPrior]]. + * - Even with [[logPrior]], this is NOT the same as the data log likelihood given the + * hyperparameters. + * - This is computed from the topic distributions computed during training. If you call + * [[logLikelihood()]] on the same training dataset, the topic distributions will be computed + * again, possibly giving different results. + */ + @Since("1.6.0") + lazy val trainingLogLikelihood: Double = oldDistributedModel.logLikelihood + + /** + * Log probability of the current parameter estimate: + * log P(topics, topic distributions for docs | alpha, eta) + */ + @Since("1.6.0") + lazy val logPrior: Double = oldDistributedModel.logPrior +} + + +/** + * :: Experimental :: + * + * Latent Dirichlet Allocation (LDA), a topic model designed for text documents. + * + * Terminology: + * - "term" = "word": an element of the vocabulary + * - "token": instance of a term appearing in a document + * - "topic": multinomial distribution over terms representing some concept + * - "document": one piece of text, corresponding to one row in the input data + * + * References: + * - Original LDA paper (journal version): + * Blei, Ng, and Jordan. "Latent Dirichlet Allocation." JMLR, 2003. + * + * @see [[http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation Latent Dirichlet allocation + * (Wikipedia)]] + */ +@Since("1.6.0") +@Experimental +class LDA @Since("1.6.0") ( + @Since("1.6.0") override val uid: String) extends Estimator[LDAModel] with LDAParams { + + @Since("1.6.0") + def this() = this(Identifiable.randomUID("lda")) + + setDefault(k -> 10, docConcentration -> Array(-1.0), topicConcentration -> -1.0, + optimizer -> new OnlineLDAOptimizer) + + /** @group setParam */ + @Since("1.6.0") + def setFeaturesCol(value: String): this.type = set(featuresCol, value) + + /** @group setParam */ + @Since("1.6.0") + def setMaxIter(value: Int): this.type = set(maxIter, value) + + /** @group setParam */ + @Since("1.6.0") + def setSeed(value: Long): this.type = set(seed, value) + + /** @group setParam */ + @Since("1.6.0") + def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value) + + /** @group setParam */ + @Since("1.6.0") + def setK(value: Int): this.type = set(k, value) + + /** @group setParam */ + @Since("1.6.0") + def setDocConcentration(value: Array[Double]): this.type = set(docConcentration, value) + + /** @group setParam */ + @Since("1.6.0") + def setDocConcentration(value: Double): this.type = set(docConcentration, Array(value)) + + /** @group setParam */ + @Since("1.6.0") + def setAlpha(value: Array[Double]): this.type = set(docConcentration, value) + + /** @group setParam */ + @Since("1.6.0") + def setTopicConcentration(value: Double): this.type = set(topicConcentration, value) + + /** @group setParam */ + @Since("1.6.0") + def setBeta(value: Double): this.type = set(topicConcentration, value) + + override def setOptimizer(value: LDAOptimizer): this.type = set(optimizer, value) + + override def setOptimizer(value: String): this.type = super.setOptimizer(value) + + /** @group setParam */ + @Since("1.6.0") + def setTopicDistributionCol(value: String): this.type = set(topicDistributionCol, value) + + @Since("1.6.0") + override def copy(extra: ParamMap): LDA = defaultCopy(extra) + + @Since("1.6.0") + override def fit(dataset: DataFrame): LDAModel = { + transformSchema(dataset.schema, logging = true) + val oldLDA = new OldLDA() + .setK($(k)) + .setDocConcentration(Vectors.dense($(docConcentration))) + .setTopicConcentration($(topicConcentration)) + .setMaxIterations($(maxIter)) + .setSeed($(seed)) + .setCheckpointInterval($(checkpointInterval)) + .setOptimizer($(optimizer).getOldOptimizer) + // TODO: persist here, or in old LDA? + val oldData = LDA.getOldDataset(dataset, $(featuresCol)) + val oldModel = oldLDA.run(oldData) + val newModel = oldModel match { + case m: OldLocalLDAModel => + new LDAModel(uid, m.vocabSize, Some(m), dataset.sqlContext) --- End diff -- LDAModel = LocalLDAModel in this API
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