Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/9513#discussion_r44202484
--- 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
--- End diff --
IMO this validation logic is quite confusing and was there for backwards
compatibility. Since we have this opportunity to implement a new API, I suggest:
* Ditching the singleton vector option, requiring the user to specify a
length `k` vector
* Keeping the automatic init as the default, making the API easy for
novice users
The only feature that is lost is replication of `docConcentration` > 0 to a
symmetric prior
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