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mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
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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. | ||
*/ | ||
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package org.apache.spark.ml.feature | ||
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import org.apache.spark.annotation.AlphaComponent | ||
import org.apache.spark.ml.attribute.NominalAttribute | ||
import org.apache.spark.ml.param._ | ||
import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol} | ||
import org.apache.spark.ml.util.SchemaUtils | ||
import org.apache.spark.ml.{Estimator, Model} | ||
import org.apache.spark.sql._ | ||
import org.apache.spark.sql.functions._ | ||
import org.apache.spark.sql.types.{DoubleType, StructField, StructType} | ||
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/** | ||
* :: AlphaComponent :: | ||
* `Bucketizer` maps a column of continuous features to a column of feature buckets. | ||
*/ | ||
@AlphaComponent | ||
final class Bucketizer private[ml] (override val parent: Estimator[Bucketizer]) | ||
extends Model[Bucketizer] with HasInputCol with HasOutputCol { | ||
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def this() = this(null) | ||
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/** | ||
* Parameter for mapping continuous features into buckets. With n splits, there are n+1 buckets. | ||
* A bucket defined by splits x,y holds values in the range [x,y). Splits should be strictly | ||
* increasing. Values at -inf, inf must be explicitly provided to cover all Double values; | ||
* otherwise, values outside the splits specified will be treated as errors. | ||
* @group param | ||
*/ | ||
val splits: Param[Array[Double]] = new Param[Array[Double]](this, "splits", | ||
"Split points for mapping continuous features into buckets. With n splits, there are n+1 " + | ||
"buckets. A bucket defined by splits x,y holds values in the range [x,y). The splits " + | ||
"should be strictly increasing. Values at -inf, inf must be explicitly provided to cover" + | ||
" all Double values; otherwise, values outside the splits specified will be treated as" + | ||
" errors.", | ||
Bucketizer.checkSplits) | ||
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/** @group getParam */ | ||
def getSplits: Array[Double] = $(splits) | ||
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/** @group setParam */ | ||
def setSplits(value: Array[Double]): this.type = set(splits, value) | ||
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/** @group setParam */ | ||
def setInputCol(value: String): this.type = set(inputCol, value) | ||
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/** @group setParam */ | ||
def setOutputCol(value: String): this.type = set(outputCol, value) | ||
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override def transform(dataset: DataFrame): DataFrame = { | ||
transformSchema(dataset.schema) | ||
val bucketizer = udf { feature: Double => | ||
Bucketizer.binarySearchForBuckets($(splits), feature) | ||
} | ||
val newCol = bucketizer(dataset($(inputCol))) | ||
val newField = prepOutputField(dataset.schema) | ||
dataset.withColumn($(outputCol), newCol.as($(outputCol), newField.metadata)) | ||
} | ||
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private def prepOutputField(schema: StructType): StructField = { | ||
val buckets = $(splits).sliding(2).map(bucket => bucket.mkString(", ")).toArray | ||
val attr = new NominalAttribute(name = Some($(outputCol)), isOrdinal = Some(true), | ||
values = Some(buckets)) | ||
attr.toStructField() | ||
} | ||
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override def transformSchema(schema: StructType): StructType = { | ||
SchemaUtils.checkColumnType(schema, $(inputCol), DoubleType) | ||
SchemaUtils.appendColumn(schema, prepOutputField(schema)) | ||
} | ||
} | ||
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private[feature] object Bucketizer { | ||
/** We require splits to be of length >= 3 and to be in strictly increasing order. */ | ||
def checkSplits(splits: Array[Double]): Boolean = { | ||
if (splits.length < 3) { | ||
false | ||
} else { | ||
var i = 0 | ||
while (i < splits.length - 1) { | ||
if (splits(i) >= splits(i + 1)) return false | ||
i += 1 | ||
} | ||
true | ||
} | ||
} | ||
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/** | ||
* Binary searching in several buckets to place each data point. | ||
* @throws RuntimeException if a feature is < splits.head or >= splits.last | ||
*/ | ||
def binarySearchForBuckets( | ||
splits: Array[Double], | ||
feature: Double): Double = { | ||
// Check bounds. We make an exception for +inf so that it can exist in some bin. | ||
if ((feature < splits.head) || (feature >= splits.last && feature != Double.PositiveInfinity)) { | ||
throw new RuntimeException(s"Feature value $feature out of Bucketizer bounds" + | ||
s" [${splits.head}, ${splits.last}). Check your features, or loosen " + | ||
s"the lower/upper bound constraints.") | ||
} | ||
var left = 0 | ||
var right = splits.length - 2 | ||
while (left < right) { | ||
val mid = (left + right) / 2 | ||
val split = splits(mid + 1) | ||
if (feature < split) { | ||
right = mid | ||
} else { | ||
left = mid + 1 | ||
} | ||
} | ||
left | ||
} | ||
} |
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