Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/9513#discussion_r44322164
--- 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"
--- End diff --
OK, that makes sense. Let's leave it
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