Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/18513#discussion_r129839492
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/FeatureHasher.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 org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.ml.Transformer
+import org.apache.spark.ml.attribute.AttributeGroup
+import org.apache.spark.ml.linalg.Vectors
+import org.apache.spark.ml.param.{IntParam, ParamMap, ParamValidators}
+import org.apache.spark.ml.param.shared.{HasInputCols, HasOutputCol}
+import org.apache.spark.ml.util.{DefaultParamsReadable,
DefaultParamsWritable, Identifiable, SchemaUtils}
+import org.apache.spark.mllib.feature.{HashingTF => OldHashingTF}
+import org.apache.spark.sql.{DataFrame, Dataset, Row}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types._
+import org.apache.spark.util.Utils
+import org.apache.spark.util.collection.OpenHashMap
+
+/**
+ * Feature hashing projects a set of categorical or numerical features
into a feature vector of
+ * specified dimension (typically substantially smaller than that of the
original feature
+ * space). This is done using the hashing trick
(https://en.wikipedia.org/wiki/Feature_hashing)
+ * to map features to indices in the feature vector.
+ *
+ * The [[FeatureHasher]] transformer operates on multiple columns. Each
column may contain either
+ * numeric or categorical features. Behavior and handling of column data
types is as follows:
+ * -Numeric columns: For numeric features, the hash value of the column
name is used to map the
+ * feature value to its index in the feature vector.
Numeric features are never
+ * treated as categorical, even when they are integers.
You must explicitly
+ * convert numeric columns containing categorical
features to strings first.
+ * -String columns: For categorical features, the hash value of the
string "column_name=value"
+ * is used to map to the vector index, with an indicator
value of `1.0`.
+ * Thus, categorical features are "one-hot" encoded
+ * (similarly to using [[OneHotEncoder]] with
`dropLast=false`).
+ * -Boolean columns: Boolean values are treated in the same way as string
columns. That is,
+ * boolean features are represented as
"column_name=true" or "column_name=false",
+ * with an indicator value of `1.0`.
+ *
+ * Null (missing) values are ignored (implicitly zero in the resulting
feature vector).
+ *
+ * Since a simple modulo is used to transform the hash function to a
vector index,
+ * it is advisable to use a power of two as the numFeatures parameter;
+ * otherwise the features will not be mapped evenly to the vector indices.
+ *
+ * {{{
+ * val df = Seq(
+ * (2.0, true, "1", "foo"),
+ * (3.0, false, "2", "bar")
+ * ).toDF("real", "bool", "stringNum", "string")
+ *
+ * val hasher = new FeatureHasher()
+ * .setInputCols("real", "bool", "stringNum", "num")
+ * .setOutputCol("features")
+ *
+ * hasher.transform(df).show()
+ *
+ * +----+-----+---------+------+--------------------+
+ * |real| bool|stringNum|string| features|
+ * +----+-----+---------+------+--------------------+
+ * | 2.0| true| 1| foo|(262144,[51871,63...|
+ * | 3.0|false| 2| bar|(262144,[6031,806...|
+ * +----+-----+---------+------+--------------------+
+ * }}}
+ */
+@Experimental
+@Since("2.3.0")
+class FeatureHasher(@Since("2.3.0") override val uid: String) extends
Transformer
+ with HasInputCols with HasOutputCol with DefaultParamsWritable {
+
+ @Since("2.3.0")
+ def this() = this(Identifiable.randomUID("featureHasher"))
+
+ /**
+ * Number of features. Should be greater than 0.
+ * (default = 2^18^)
+ * @group param
+ */
+ @Since("2.3.0")
+ val numFeatures = new IntParam(this, "numFeatures", "number of features
(> 0)",
+ ParamValidators.gt(0))
+
+ setDefault(numFeatures -> (1 << 18))
+
+ /** @group getParam */
+ @Since("2.3.0")
+ def getNumFeatures: Int = $(numFeatures)
+
+ /** @group setParam */
+ @Since("2.3.0")
+ def setNumFeatures(value: Int): this.type = set(numFeatures, value)
+
+ /** @group setParam */
+ @Since("2.3.0")
+ def setInputCols(values: String*): this.type =
setInputCols(values.toArray)
+
+ /** @group setParam */
+ @Since("2.3.0")
+ def setInputCols(value: Array[String]): this.type = set(inputCols, value)
+
+ /** @group setParam */
+ @Since("2.3.0")
+ def setOutputCol(value: String): this.type = set(outputCol, value)
+
+ @Since("2.3.0")
+ override def transform(dataset: Dataset[_]): DataFrame = {
+ val hashFunc: Any => Int = OldHashingTF.murmur3Hash
+ val n = $(numFeatures)
+ val localInputCols = $(inputCols)
+
+ val outputSchema = transformSchema(dataset.schema)
+ val realFields = outputSchema.fields.filter { f =>
+ f.dataType.isInstanceOf[NumericType]
+ }.map(_.name).toSet
+
+ def getDouble(x: Any): Double = {
+ x match {
+ case n: java.lang.Number =>
+ n.doubleValue()
+ case other =>
+ // will throw ClassCastException if it cannot be cast, as would
row.getDouble
+ other.asInstanceOf[Double]
+ }
+ }
+
+ val hashFeatures = udf { row: Row =>
+ val map = new OpenHashMap[Int, Double]()
+ localInputCols.foreach { colName =>
+ val fieldIndex = row.fieldIndex(colName)
+ if (!row.isNullAt(fieldIndex)) {
+ val (rawIdx, value) = if (realFields(colName)) {
+ // numeric values are kept as is, with vector index based on
hash of "column_name"
+ val value = getDouble(row.get(fieldIndex))
+ val hash = hashFunc(colName)
+ (hash, value)
+ } else {
+ // string and boolean values are treated as categorical, with
an indicator value of 1.0
+ // and vector index based on hash of "column_name=value"
+ val value = row.get(fieldIndex).toString
+ val fieldName = s"$colName=$value"
+ val hash = hashFunc(fieldName)
+ (hash, 1.0)
+ }
+ val idx = Utils.nonNegativeMod(rawIdx, n)
+ map.changeValue(idx, value, v => v + value)
+ }
+ }
+ Vectors.sparse(n, map.toSeq)
+ }
+
+ val metadata = outputSchema($(outputCol)).metadata
+ dataset.select(
+ col("*"),
+ hashFeatures(struct($(inputCols).map(col): _*)).as($(outputCol),
metadata))
+ }
+
+ @Since("2.3.0")
+ override def copy(extra: ParamMap): FeatureHasher = defaultCopy(extra)
+
+ @Since("2.3.0")
+ override def transformSchema(schema: StructType): StructType = {
+ val fields = schema($(inputCols).toSet)
+ fields.foreach { fieldSchema =>
+ val dataType = fieldSchema.dataType
+ val fieldName = fieldSchema.name
+ require(dataType.isInstanceOf[NumericType] ||
+ dataType.isInstanceOf[StringType] ||
+ dataType.isInstanceOf[BooleanType],
+ s"FeatureHasher requires columns to be of NumericType, BooleanType
or StringType. " +
+ s"Column $fieldName was $dataType")
+ }
+ val attrGroup = new AttributeGroup($(outputCol), $(numFeatures))
--- End diff --
@MLnick Thanks for clarifying.
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