Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/7388#discussion_r37099213
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala ---
@@ -0,0 +1,196 @@
+/*
+ * 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.feature
+
+import scala.collection.mutable
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
+import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.mllib.linalg.{VectorUDT, Vectors}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.DataFrame
+
+/**
+ * Params for [[CountVectorizer]] and [[CountVectorizerModel]].
+ */
+private[feature] trait CountVectorizerParams extends Params with
HasInputCol with HasOutputCol {
+
+ /**
+ * Max size of the vocabulary.
+ * CountVectorizer will build a vocabulary that only considers the top
+ * vocabSize terms ordered by term frequency across the corpus.
+ *
+ * Default: 10000
+ * @group param
+ */
+ val vocabSize: IntParam =
+ new IntParam(this, "vocabSize", "size of the vocabulary",
ParamValidators.gt(0))
+
+ /** @group getParam */
+ def getVocabSize: Int = $(vocabSize)
+
+ /**
+ * The minimum number of times a token must appear in the corpus to be
included in the vocabulary.
+ * Note that this is not the same as document frequency:
[[minTokenCount]] counts tokens including
+ * duplicates of terms, whereas document frequency counts unique terms.
Support for document
+ * frequency will be added in the future.
+ *
+ * Default: 1
+ * @group param
+ */
+ val minTokenCount: IntParam = new IntParam(this, "minTokenCount",
+ "minimum number of times a token must appear in the corpus to be
included in the vocabulary."
+ , ParamValidators.gtEq(1))
+
+ /** @group getParam */
+ def getMinTokenCount: Int = $(minTokenCount)
+
+ /** Validates and transforms the input schema. */
+ protected def validateAndTransformSchema(schema: StructType): StructType
= {
+ SchemaUtils.checkColumnType(schema, $(inputCol), new
ArrayType(StringType, true))
+ SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT)
+ }
+
+ /**
+ * Filter to ignore scarce words in a document. For each document, terms
with
+ * frequency (count) less than the given threshold are ignored.
+ * Default: 1
+ * @group param
+ */
+ val minTermFreq: IntParam = new IntParam(this, "minTermFreq",
+ "minimum frequency (count) filter used to neglect scarce words (>= 1).
For each document, " +
+ "terms with frequency less than the given threshold are ignored.",
ParamValidators.gtEq(1))
+ setDefault(minTermFreq -> 1)
+
+ /** @group getParam */
+ def getMinTermFreq: Int = $(minTermFreq)
+}
+
+/**
+ * :: Experimental ::
+ * Extracts a vocabulary from document collections and generates a
[[CountVectorizerModel]].
+ */
+@Experimental
+class CountVectorizer(override val uid: String)
+ extends Estimator[CountVectorizerModel] with CountVectorizerParams {
+
+ def this() = this(Identifiable.randomUID("cntVec"))
+
+ /** @group setParam */
+ def setInputCol(value: String): this.type = set(inputCol, value)
+
+ /** @group setParam */
+ def setOutputCol(value: String): this.type = set(outputCol, value)
+
+ /** @group setParam */
+ def setVocabSize(value: Int): this.type = set(vocabSize, value)
+
+ /** @group setParam */
+ def setMinTokenCount(value: Int): this.type = set(minTokenCount, value)
+
+ /** @group setParam */
+ def setMinTermFreq(value: Int): this.type = set(minTermFreq, value)
+
+ setDefault(vocabSize -> 10000, minTokenCount -> 1)
+
+ override def fit(dataset: DataFrame): CountVectorizerModel = {
+ transformSchema(dataset.schema, logging = true)
+ val minCnt = $(minTokenCount)
+ val vocSize = $(vocabSize)
+ val input = dataset.select($(inputCol)).map(_.getAs[Seq[String]](0))
+ val wordCounts: RDD[(String, Long)] = input
+ .flatMap { case (tokens) => tokens.map(_ -> 1L) }
+ .reduceByKey(_ + _)
+ .filter(_._2 >= minCnt)
+ wordCounts.cache()
+ val fullVocabSize = wordCounts.count()
+ val vocab: Array[String] = {
+ val tmpSortedWC: Array[(String, Long)] = if (fullVocabSize <=
vocSize) {
+ // Use all terms
+ wordCounts.collect().sortBy(-_._2)
+ } else {
+ // Sort terms to select vocab
+ wordCounts.sortBy(_._2, ascending = false).take(vocSize)
+ }
+ tmpSortedWC.map(_._1)
+ }
+
+ require(vocab.length > 0,
+ "The vocabulary size should be > 0. Lower minTokenCount as
necessary.")
+ copyValues(new CountVectorizerModel(uid, vocab).setParent(this))
+ }
+
+ override def transformSchema(schema: StructType): StructType = {
+ validateAndTransformSchema(schema)
+ }
+
+ override def copy(extra: ParamMap): CountVectorizer = defaultCopy(extra)
+}
+
+/**
+ * :: Experimental ::
+ * Converts a text document to a sparse vector of token counts.
+ * @param vocabulary An Array over terms. Only the terms in the vocabulary
will be counted.
+ */
+@Experimental
+class CountVectorizerModel(override val uid: String, val vocabulary:
Array[String])
+ extends Model[CountVectorizerModel] with CountVectorizerParams {
+
+ def this(vocabulary: Array[String]) = {
+ this(Identifiable.randomUID("cntVecModel"), vocabulary)
+ set(vocabSize, vocabulary.length)
+ }
+
+ /** @group setParam */
+ def setInputCol(value: String): this.type = set(inputCol, value)
+
+ /** @group setParam */
+ def setOutputCol(value: String): this.type = set(outputCol, value)
+
+ /** @group setParam */
+ def setMinTermFreq(value: Int): this.type = set(minTermFreq, value)
+
+ override def transform(dataset: DataFrame): DataFrame = {
+ val dict = vocabulary.zipWithIndex.toMap
--- End diff --
Hm, this is an issue multiple places in MLlib. I'll make a JIRA for it...
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