Github user jkbradley commented on a diff in the pull request: https://github.com/apache/spark/pull/9513#discussion_r44239866 --- 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. --- End diff -- That seems hard to do with DistributedLDAModel, for which we don't want to collect the topicsMatrix unless we have to. It might work if LDAModel were abstract, but I don't see a good way when LDAModel is concrete.
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