Github user MLnick commented on a diff in the pull request: https://github.com/apache/spark/pull/1671#discussion_r15775472 --- Diff: mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala --- @@ -0,0 +1,79 @@ +/* + * 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.mllib.feature + +import java.lang.{Iterable => JavaIterable} + +import scala.collection.JavaConverters._ +import scala.collection.mutable + +import org.apache.spark.annotation.Experimental +import org.apache.spark.api.java.JavaRDD +import org.apache.spark.mllib.linalg.{Vector, Vectors} +import org.apache.spark.rdd.RDD +import org.apache.spark.util.Utils + +/** + * :: Experimental :: + * Maps a sequence of terms to their term frequencies using the hashing trick. + * + * @param numFeatures number of features (default: 1000000) + */ +@Experimental +class HashingTF(val numFeatures: Int) extends Serializable { + + def this() = this(1000000) + --- End diff -- @mengxr @mateiz this looks really awesome! I know this is merged already, but one comment: when using the hashing trick, should the vector size not usually be a power of 2? This is mentioned [here](http://scikit-learn.org/stable/modules/feature_extraction.html#implementation-details) for example. Pretty much every library uses powers of 2 - Vowpal Wabbit, sophia-ml, scikit-learn and shogun for example. So it may be worth mentioning, and making the default 2^20 (or 2^18 which is also a common default).
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