Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/5266#discussion_r27924848
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala ---
@@ -0,0 +1,114 @@
+/*
+ * 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 org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.ml._
+import org.apache.spark.ml.param._
+import org.apache.spark.mllib.feature
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.sql._
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{StructField, StructType}
+
+/**
+ * Params for [[IDF]] and [[IDFModel]].
+ */
+private[feature] trait IDFParams extends Params with HasInputCol with
HasOutputCol {
+ val minDocFreq = new IntParam(
+ this, "minDocFreq", "minimum of documents in which a term should
appear for filtering", Some(0))
+
+ def getMinDocFreq: Int = {
+ get(minDocFreq)
+ }
+
+ def setMinDocFreq(value: Int): this.type = {
+ set(minDocFreq, value)
+ }
+
+ /**
+ * Validate and transform the input schema.
+ */
+ protected def validateAndTransformSchema(schema: StructType, paramMap:
ParamMap): StructType = {
+ val map = this.paramMap ++ paramMap
+ val inputType = schema(map(inputCol)).dataType
+ require(inputType.isInstanceOf[VectorUDT],
+ s"Input column ${map(inputCol)} must be a vector column")
+ require(!schema.fieldNames.contains(map(outputCol)),
+ s"Output column ${map(outputCol)} already exists.")
+ val outputFields = schema.fields :+ StructField(map(outputCol), new
VectorUDT, false)
+ StructType(outputFields)
+ }
+}
+
+/**
+ * :: AlphaComponent ::
+ * Compute the Inverse Document Frequency (IDF) given a collection of
documents.
+ */
+@AlphaComponent
+class IDF extends Estimator[IDFModel] with IDFParams {
+
+ /** @group setParam */
+ def setInputCol(value: String): this.type = set(inputCol, value)
+
+ /** @group setParam */
+ def setOutputCol(value: String): this.type = set(outputCol, value)
+
+ override def fit(dataset: DataFrame, paramMap: ParamMap): IDFModel = {
+ transformSchema(dataset.schema, paramMap, logging = true)
+ val map = this.paramMap ++ paramMap
+ val input = dataset.select(map(inputCol)).map { case Row(v: Vector) =>
v }
+ val idf = new feature.IDF(getMinDocFreq).fit(input)
+ val model = new IDFModel(this, map, idf)
+ Params.inheritValues(map, this, model)
+ model
+ }
+
+ override def transformSchema(schema: StructType, paramMap: ParamMap):
StructType = {
+ validateAndTransformSchema(schema, paramMap)
+ }
+}
+
+/**
+ * :: AlphaComponent ::
+ * Model fitted by [[IDF]].
+ */
+@AlphaComponent
+class IDFModel private[ml] (
+ override val parent: IDF,
+ override val fittingParamMap: ParamMap,
+ idfModel: feature.IDFModel)
+ extends Model[IDFModel] with IDFParams {
+
+ /** @group setParam */
+ def setInputCol(value: String): this.type = set(inputCol, value)
+
+ /** @group setParam */
+ def setOutputCol(value: String): this.type = set(outputCol, value)
+
+ override def transform(dataset: DataFrame, paramMap: ParamMap):
DataFrame = {
+ transformSchema(dataset.schema, paramMap, logging = true)
+ val map = this.paramMap ++ paramMap
+ val idf = udf((v: Vector) => { idfModel.transform(v) })
--- End diff --
Let's be consistent on the `udf` code style: `udf { (v: Vector) =>
idfModel.transform(v) }`. See examples at
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala#L139
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