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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.SparkException
import org.apache.spark.annotation.AlphaComponent
import org.apache.spark.ml.{Estimator, Model}
import org.apache.spark.ml.attribute.NominalAttribute
import org.apache.spark.ml.param._
import org.apache.spark.ml.param.shared._
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.{NumericType, StringType, StructType}
import org.apache.spark.util.collection.OpenHashMap
/**
* Base trait for [[StringIndexer]] and [[StringIndexerModel]].
*/
private[feature] trait StringIndexerBase extends Params with HasInputCol with HasOutputCol {
/** Validates and transforms the input schema. */
protected def validateAndTransformSchema(schema: StructType, paramMap: ParamMap): StructType = {
val map = extractParamMap(paramMap)
val inputColName = map(inputCol)
val inputDataType = schema(inputColName).dataType
require(inputDataType == StringType || inputDataType.isInstanceOf[NumericType],
s"The input column $inputColName must be either string type or numeric type, " +
s"but got $inputDataType.")
val inputFields = schema.fields
val outputColName = map(outputCol)
require(inputFields.forall(_.name != outputColName),
s"Output column $outputColName already exists.")
val attr = NominalAttribute.defaultAttr.withName(map(outputCol))
val outputFields = inputFields :+ attr.toStructField()
StructType(outputFields)
}
}
/**
* :: AlphaComponent ::
* A label indexer that maps a string column of labels to an ML column of label indices.
* If the input column is numeric, we cast it to string and index the string values.
* The indices are in [0, numLabels), ordered by label frequencies.
* So the most frequent label gets index 0.
*/
@AlphaComponent
class StringIndexer extends Estimator[StringIndexerModel] with StringIndexerBase {
/** @group setParam */
def setInputCol(value: String): this.type = set(inputCol, value)
/** @group setParam */
def setOutputCol(value: String): this.type = set(outputCol, value)
// TODO: handle unseen labels
override def fit(dataset: DataFrame, paramMap: ParamMap): StringIndexerModel = {
val map = extractParamMap(paramMap)
val counts = dataset.select(col(map(inputCol)).cast(StringType))
.map(_.getString(0))
.countByValue()
val labels = counts.toSeq.sortBy(-_._2).map(_._1).toArray
val model = new StringIndexerModel(this, map, labels)
Params.inheritValues(map, this, model)
model
}
override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
validateAndTransformSchema(schema, paramMap)
}
}
/**
* :: AlphaComponent ::
* Model fitted by [[StringIndexer]].
*/
@AlphaComponent
class StringIndexerModel private[ml] (
override val parent: StringIndexer,
override val fittingParamMap: ParamMap,
labels: Array[String]) extends Model[StringIndexerModel] with StringIndexerBase {
private val labelToIndex: OpenHashMap[String, Double] = {
val n = labels.length
val map = new OpenHashMap[String, Double](n)
var i = 0
while (i < n) {
map.update(labels(i), i)
i += 1
}
map
}
/** @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 = {
val map = extractParamMap(paramMap)
val indexer = udf { label: String =>
if (labelToIndex.contains(label)) {
labelToIndex(label)
} else {
// TODO: handle unseen labels
throw new SparkException(s"Unseen label: $label.")
}
}
val outputColName = map(outputCol)
val metadata = NominalAttribute.defaultAttr
.withName(outputColName).withValues(labels).toMetadata()
dataset.select(col("*"),
indexer(dataset(map(inputCol)).cast(StringType)).as(outputColName, metadata))
}
override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
validateAndTransformSchema(schema, paramMap)
}
}
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