com.tencent.angel.sona.ml.feature.MinMaxScaler.scala Maven / Gradle / Ivy
/*
* 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 com.tencent.angel.sona.ml.feature
import org.apache.hadoop.fs.Path
import com.tencent.angel.sona.ml.{Estimator, Model}
import org.apache.spark.linalg.{VectorUDT, Vectors}
import com.tencent.angel.sona.ml.param.{DoubleParam, ParamMap, Params}
import com.tencent.angel.sona.ml.param.shared.{HasInputCol, HasOutputCol}
import com.tencent.angel.sona.ml.stat.Statistics
import com.tencent.angel.sona.ml.util._
import org.apache.spark.rdd.RDD
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.{StructField, StructType}
import org.apache.spark.linalg
import org.apache.spark.sql.util.SONASchemaUtils
/**
* Params for [[MinMaxScaler]] and [[MinMaxScalerModel]].
*/
private[sona] trait MinMaxScalerParams extends Params with HasInputCol with HasOutputCol {
/**
* lower bound after transformation, shared by all features
* Default: 0.0
*
* @group param
*/
val min: DoubleParam = new DoubleParam(this, "min",
"lower bound of the output feature range")
/** @group getParam */
def getMin: Double = $(min)
/**
* upper bound after transformation, shared by all features
* Default: 1.0
*
* @group param
*/
val max: DoubleParam = new DoubleParam(this, "max",
"upper bound of the output feature range")
/** @group getParam */
def getMax: Double = $(max)
/** Validates and transforms the input schema. */
protected def validateAndTransformSchema(schema: StructType): StructType = {
require($(min) < $(max), s"The specified min(${$(min)}) is larger or equal to max(${$(max)})")
SONASchemaUtils.checkColumnType(schema, $(inputCol), new VectorUDT)
require(!schema.fieldNames.contains($(outputCol)),
s"Output column ${$(outputCol)} already exists.")
val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false)
StructType(outputFields)
}
}
/**
* Rescale each feature individually to a common range [min, max] linearly using column summary
* statistics, which is also known as min-max normalization or Rescaling. The rescaled value for
* feature E is calculated as:
*
*
* $$
* Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) + min
* $$
*
*
* For the case \(E_{max} == E_{min}\), \(Rescaled(e_i) = 0.5 * (max + min)\).
*
* @note Since zero values will probably be transformed to non-zero values, output of the
* transformer will be DenseVector even for sparse input.
*/
class MinMaxScaler(override val uid: String)
extends Estimator[MinMaxScalerModel] with MinMaxScalerParams with DefaultParamsWritable {
def this() = this(Identifiable.randomUID("minMaxScal"))
setDefault(min -> 0.0, max -> 1.0)
/** @group setParam */
def setInputCol(value: String): this.type = set(inputCol, value)
/** @group setParam */
def setOutputCol(value: String): this.type = set(outputCol, value)
/** @group setParam */
def setMin(value: Double): this.type = set(min, value)
/** @group setParam */
def setMax(value: Double): this.type = set(max, value)
override def fit(dataset: Dataset[_]): MinMaxScalerModel = {
transformSchema(dataset.schema, logging = true)
val input: RDD[linalg.Vector] = dataset.select($(inputCol)).rdd.map {
case Row(v: linalg.Vector) => v
}
val summary = Statistics.colStats(input)
copyValues(new MinMaxScalerModel(uid, summary.min, summary.max).setParent(this))
}
override def transformSchema(schema: StructType): StructType = {
validateAndTransformSchema(schema)
}
override def copy(extra: ParamMap): MinMaxScaler = defaultCopy(extra)
}
object MinMaxScaler extends DefaultParamsReadable[MinMaxScaler] {
override def load(path: String): MinMaxScaler = super.load(path)
}
/**
* Model fitted by [[MinMaxScaler]].
*
* @param originalMin min value for each original column during fitting
* @param originalMax max value for each original column during fitting
*
* TODO: The transformer does not yet set the metadata in the output column (SPARK-8529).
*/
class MinMaxScalerModel private[angel](
override val uid: String,
val originalMin: linalg.Vector,
val originalMax: linalg.Vector)
extends Model[MinMaxScalerModel] with MinMaxScalerParams with MLWritable {
import MinMaxScalerModel._
/** @group setParam */
def setInputCol(value: String): this.type = set(inputCol, value)
/** @group setParam */
def setOutputCol(value: String): this.type = set(outputCol, value)
/** @group setParam */
def setMin(value: Double): this.type = set(min, value)
/** @group setParam */
def setMax(value: Double): this.type = set(max, value)
override def transform(dataset: Dataset[_]): DataFrame = {
transformSchema(dataset.schema, logging = true)
val originalRange = (originalMax.asBreeze - originalMin.asBreeze).toArray
val minArray = originalMin.toArray
val reScale = udf { (vector: linalg.Vector) =>
val scale = $(max) - $(min)
// 0 in sparse vector will probably be rescaled to non-zero
val values = vector.toArray
val size = values.length
var i = 0
while (i < size) {
if (!values(i).isNaN) {
val raw = if (originalRange(i) != 0) (values(i) - minArray(i)) / originalRange(i) else 0.5
values(i) = raw * scale + $(min)
}
i += 1
}
Vectors.dense(values)
}
dataset.withColumn($(outputCol), reScale(col($(inputCol))))
}
override def transformSchema(schema: StructType): StructType = {
validateAndTransformSchema(schema)
}
override def copy(extra: ParamMap): MinMaxScalerModel = {
val copied = new MinMaxScalerModel(uid, originalMin, originalMax)
copyValues(copied, extra).setParent(parent)
}
override def write: MLWriter = new MinMaxScalerModelWriter(this)
}
object MinMaxScalerModel extends MLReadable[MinMaxScalerModel] {
private[MinMaxScalerModel]
class MinMaxScalerModelWriter(instance: MinMaxScalerModel) extends MLWriter {
private case class Data(originalMin: linalg.Vector, originalMax: linalg.Vector)
override protected def saveImpl(path: String): Unit = {
DefaultParamsWriter.saveMetadata(instance, path, sc)
val data = new Data(instance.originalMin, instance.originalMax)
val dataPath = new Path(path, "data").toString
sparkSession.createDataFrame(Seq(data)).repartition(1).write.parquet(dataPath)
}
}
private class MinMaxScalerModelReader extends MLReader[MinMaxScalerModel] {
private val className = classOf[MinMaxScalerModel].getName
override def load(path: String): MinMaxScalerModel = {
val metadata = DefaultParamsReader.loadMetadata(path, sc, className)
val dataPath = new Path(path, "data").toString
val data = sparkSession.read.parquet(dataPath)
val Row(originalMin: linalg.Vector, originalMax: linalg.Vector) =
MLUtils.convertVectorColumnsToML(data, "originalMin", "originalMax")
.select("originalMin", "originalMax")
.head()
val model = new MinMaxScalerModel(metadata.uid, originalMin, originalMax)
metadata.getAndSetParams(model)
model
}
}
override def read: MLReader[MinMaxScalerModel] = new MinMaxScalerModelReader
override def load(path: String): MinMaxScalerModel = super.load(path)
}
© 2015 - 2025 Weber Informatics LLC | Privacy Policy