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/*
* 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._
import org.apache.spark.ml.param._
import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
import org.apache.spark.ml.util._
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.functions.{col, udf}
import org.apache.spark.sql.types.{DoubleType, NumericType, StructType}
/**
* :: Experimental ::
* A one-hot encoder that maps a column of category indices to a column of binary vectors, with
* at most a single one-value per row that indicates the input category index.
* For example with 5 categories, an input value of 2.0 would map to an output vector of
* `[0.0, 0.0, 1.0, 0.0]`.
* The last category is not included by default (configurable via [[OneHotEncoder!.dropLast]]
* because it makes the vector entries sum up to one, and hence linearly dependent.
* So an input value of 4.0 maps to `[0.0, 0.0, 0.0, 0.0]`.
* Note that this is different from scikit-learn's OneHotEncoder, which keeps all categories.
* The output vectors are sparse.
*
* @see [[StringIndexer]] for converting categorical values into category indices
*/
@Experimental
class OneHotEncoder(override val uid: String) extends Transformer
with HasInputCol with HasOutputCol with DefaultParamsWritable {
def this() = this(Identifiable.randomUID("oneHot"))
/**
* Whether to drop the last category in the encoded vector (default: true)
* @group param
*/
final val dropLast: BooleanParam =
new BooleanParam(this, "dropLast", "whether to drop the last category")
setDefault(dropLast -> true)
/** @group setParam */
def setDropLast(value: Boolean): this.type = set(dropLast, value)
/** @group setParam */
def setInputCol(value: String): this.type = set(inputCol, value)
/** @group setParam */
def setOutputCol(value: String): this.type = set(outputCol, value)
override def transformSchema(schema: StructType): StructType = {
val inputColName = $(inputCol)
val outputColName = $(outputCol)
require(schema(inputColName).dataType.isInstanceOf[NumericType],
s"Input column must be of type NumericType but got ${schema(inputColName).dataType}")
val inputFields = schema.fields
require(!inputFields.exists(_.name == outputColName),
s"Output column $outputColName already exists.")
val inputAttr = Attribute.fromStructField(schema(inputColName))
val outputAttrNames: Option[Array[String]] = inputAttr match {
case nominal: NominalAttribute =>
if (nominal.values.isDefined) {
nominal.values
} else if (nominal.numValues.isDefined) {
nominal.numValues.map(n => Array.tabulate(n)(_.toString))
} else {
None
}
case binary: BinaryAttribute =>
if (binary.values.isDefined) {
binary.values
} else {
Some(Array.tabulate(2)(_.toString))
}
case _: NumericAttribute =>
throw new RuntimeException(
s"The input column $inputColName cannot be numeric.")
case _ =>
None // optimistic about unknown attributes
}
val filteredOutputAttrNames = outputAttrNames.map { names =>
if ($(dropLast)) {
require(names.length > 1,
s"The input column $inputColName should have at least two distinct values.")
names.dropRight(1)
} else {
names
}
}
val outputAttrGroup = if (filteredOutputAttrNames.isDefined) {
val attrs: Array[Attribute] = filteredOutputAttrNames.get.map { name =>
BinaryAttribute.defaultAttr.withName(name)
}
new AttributeGroup($(outputCol), attrs)
} else {
new AttributeGroup($(outputCol))
}
val outputFields = inputFields :+ outputAttrGroup.toStructField()
StructType(outputFields)
}
override def transform(dataset: DataFrame): DataFrame = {
// schema transformation
val inputColName = $(inputCol)
val outputColName = $(outputCol)
val shouldDropLast = $(dropLast)
var outputAttrGroup = AttributeGroup.fromStructField(
transformSchema(dataset.schema)(outputColName))
if (outputAttrGroup.size < 0) {
// If the number of attributes is unknown, we check the values from the input column.
val numAttrs = dataset.select(col(inputColName).cast(DoubleType)).rdd.map(_.getDouble(0))
.aggregate(0.0)(
(m, x) => {
assert(x <= Int.MaxValue,
s"OneHotEncoder only supports up to ${Int.MaxValue} indices, but got $x")
assert(x >= 0.0 && x == x.toInt,
s"Values from column $inputColName must be indices, but got $x.")
math.max(m, x)
},
(m0, m1) => {
math.max(m0, m1)
}
).toInt + 1
val outputAttrNames = Array.tabulate(numAttrs)(_.toString)
val filtered = if (shouldDropLast) outputAttrNames.dropRight(1) else outputAttrNames
val outputAttrs: Array[Attribute] =
filtered.map(name => BinaryAttribute.defaultAttr.withName(name))
outputAttrGroup = new AttributeGroup(outputColName, outputAttrs)
}
val metadata = outputAttrGroup.toMetadata()
// data transformation
val size = outputAttrGroup.size
val oneValue = Array(1.0)
val emptyValues = Array[Double]()
val emptyIndices = Array[Int]()
val encode = udf { label: Double =>
if (label < size) {
Vectors.sparse(size, Array(label.toInt), oneValue)
} else {
Vectors.sparse(size, emptyIndices, emptyValues)
}
}
dataset.select(col("*"), encode(col(inputColName).cast(DoubleType)).as(outputColName, metadata))
}
override def copy(extra: ParamMap): OneHotEncoder = defaultCopy(extra)
}
@Since("1.6.0")
object OneHotEncoder extends DefaultParamsReadable[OneHotEncoder] {
@Since("1.6.0")
override def load(path: String): OneHotEncoder = super.load(path)
}
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