com.tencent.angel.sona.ml.feature.StandardScaler.scala Maven / Gradle / Ivy
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* http://www.apache.org/licenses/LICENSE-2.0
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package com.tencent.angel.sona.ml.feature
import org.apache.hadoop.fs.Path
import com.tencent.angel.sona.ml.{Estimator, Model}
import com.tencent.angel.sona.ml.param.{BooleanParam, ParamMap, Params}
import com.tencent.angel.sona.ml.param.shared.{HasInputCol, HasOutputCol}
import com.tencent.angel.sona.ml.stat.MultivariateOnlineSummarizer
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.linalg._
import org.apache.spark.sql.util.SONASchemaUtils
/**
* Params for [[StandardScaler]] and [[StandardScalerModel]].
*/
private[sona] trait StandardScalerParams extends Params with HasInputCol with HasOutputCol {
/**
* Whether to center the data with mean before scaling.
* It will build a dense output, so take care when applying to sparse input.
* Default: false
*
* @group param
*/
val withMean: BooleanParam = new BooleanParam(this, "withMean",
"Whether to center data with mean")
/** @group getParam */
def getWithMean: Boolean = $(withMean)
/**
* Whether to scale the data to unit standard deviation.
* Default: true
*
* @group param
*/
val withStd: BooleanParam = new BooleanParam(this, "withStd",
"Whether to scale the data to unit standard deviation")
/** @group getParam */
def getWithStd: Boolean = $(withStd)
/** Validates and transforms the input schema. */
protected def validateAndTransformSchema(schema: StructType): StructType = {
SONASchemaUtils.checkColumnType(schema, $(inputCol), new VectorUDT)
require(!schema.fieldNames.contains($(outputCol)),
s"Output column ${$(outputCol)} already exists.")
val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false)
StructType(outputFields)
}
setDefault(withMean -> false, withStd -> true)
}
/**
* Standardizes features by removing the mean and scaling to unit variance using column summary
* statistics on the samples in the training set.
*
* The "unit std" is computed using the
*
* corrected sample standard deviation,
* which is computed as the square root of the unbiased sample variance.
*/
class StandardScaler(override val uid: String)
extends Estimator[StandardScalerModel] with StandardScalerParams with DefaultParamsWritable {
def this() = this(Identifiable.randomUID("stdScal"))
/** @group setParam */
def setInputCol(value: String): this.type = set(inputCol, value)
/** @group setParam */
def setOutputCol(value: String): this.type = set(outputCol, value)
/** @group setParam */
def setWithMean(value: Boolean): this.type = set(withMean, value)
/** @group setParam */
def setWithStd(value: Boolean): this.type = set(withStd, value)
override def fit(dataset: Dataset[_]): StandardScalerModel = {
transformSchema(dataset.schema, logging = true)
val input: RDD[linalg.Vector] = dataset.select($(inputCol)).rdd.map {
case Row(v: linalg.Vector) => v
}
val summary = input.treeAggregate(new MultivariateOnlineSummarizer)(
(aggregator, data) => aggregator.add(data),
(aggregator1, aggregator2) => aggregator1.merge(aggregator2))
val std = summary.variance match {
case DenseVector(values) =>
Vectors.dense(values.map(v => Math.sqrt(v)))
case IntSparseVector(size, index, values) =>
Vectors.sparse(size, index, values.map(v => Math.sqrt(v)))
case _ => throw new Exception("Vector type is not support!")
}
copyValues(new StandardScalerModel(uid, std, summary.mean).setParent(this))
}
override def transformSchema(schema: StructType): StructType = {
validateAndTransformSchema(schema)
}
override def copy(extra: ParamMap): StandardScaler = defaultCopy(extra)
}
object StandardScaler extends DefaultParamsReadable[StandardScaler] {
override def load(path: String): StandardScaler = super.load(path)
}
/**
* Model fitted by [[StandardScaler]].
*
* @param std Standard deviation of the StandardScalerModel
* @param mean Mean of the StandardScalerModel
*/
class StandardScalerModel private[angel](
override val uid: String,
val std: linalg.Vector,
val mean: linalg.Vector)
extends Model[StandardScalerModel] with StandardScalerParams with MLWritable {
import StandardScalerModel._
/** @group setParam */
def setInputCol(value: String): this.type = set(inputCol, value)
/** @group setParam */
def setOutputCol(value: String): this.type = set(outputCol, value)
private lazy val shift: Array[Double] = mean.toArray
override def transform(dataset: Dataset[_]): DataFrame = {
transformSchema(dataset.schema, logging = true)
// TODO: Make the transformer natively in ml framework to avoid extra conversion.
val transformer: linalg.Vector => linalg.Vector = vector => {
require(mean.size == vector.size)
if ($(withMean)) {
// By default, Scala generates Java methods for member variables. So every time when
// the member variables are accessed, `invokespecial` will be called which is expensive.
// This can be avoid by having a local reference of `shift`.
val localShift = shift
// Must have a copy of the values since it will be modified in place
val values = vector match {
// specially handle DenseVector because its toArray does not clone already
case d: DenseVector => d.values.clone()
case v: linalg.Vector => v.toArray
}
val size = values.length
if ($(withStd)) {
var i = 0
while (i < size) {
values(i) = if (std(i) != 0.0) (values(i) - localShift(i)) * (1.0 / std(i)) else 0.0
i += 1
}
} else {
var i = 0
while (i < size) {
values(i) -= localShift(i)
i += 1
}
}
Vectors.dense(values)
} else if ($(withStd)) {
vector match {
case DenseVector(vs) =>
val values = vs.clone()
val size = values.length
var i = 0
while (i < size) {
values(i) *= (if (std(i) != 0.0) 1.0 / std(i) else 0.0)
i += 1
}
Vectors.dense(values)
case IntSparseVector(size, indices, vs) =>
// For sparse vector, the `index` array inside sparse vector object will not be changed,
// so we can re-use it to save memory.
val values = vs.clone()
val nnz = values.length
var i = 0
while (i < nnz) {
values(i) *= (if (std(indices(i)) != 0.0) 1.0 / std(indices(i)) else 0.0)
i += 1
}
Vectors.sparse(size, indices, values)
case v => throw new IllegalArgumentException("Do not support vector type " + v.getClass)
}
} else {
// Note that it's safe since we always assume that the data in RDD should be immutable.
vector
}
}
val scale = udf(transformer)
dataset.withColumn($(outputCol), scale(col($(inputCol))))
}
override def transformSchema(schema: StructType): StructType = {
validateAndTransformSchema(schema)
}
override def copy(extra: ParamMap): StandardScalerModel = {
val copied = new StandardScalerModel(uid, std, mean)
copyValues(copied, extra).setParent(parent)
}
override def write: MLWriter = new StandardScalerModelWriter(this)
}
object StandardScalerModel extends MLReadable[StandardScalerModel] {
private[StandardScalerModel]
class StandardScalerModelWriter(instance: StandardScalerModel) extends MLWriter {
private case class Data(std: linalg.Vector, mean: linalg.Vector)
override protected def saveImpl(path: String): Unit = {
DefaultParamsWriter.saveMetadata(instance, path, sc)
val data = Data(instance.std, instance.mean)
val dataPath = new Path(path, "data").toString
sparkSession.createDataFrame(Seq(data)).repartition(1).write.parquet(dataPath)
}
}
private class StandardScalerModelReader extends MLReader[StandardScalerModel] {
private val className = classOf[StandardScalerModel].getName
override def load(path: String): StandardScalerModel = {
val metadata = DefaultParamsReader.loadMetadata(path, sc, className)
val dataPath = new Path(path, "data").toString
val data = sparkSession.read.parquet(dataPath)
val Row(std: linalg.Vector, mean: linalg.Vector) = MLUtils.convertVectorColumnsToML(data, "std", "mean")
.select("std", "mean")
.head()
val model = new StandardScalerModel(metadata.uid, std, mean)
metadata.getAndSetParams(model)
model
}
}
override def read: MLReader[StandardScalerModel] = new StandardScalerModelReader
override def load(path: String): StandardScalerModel = super.load(path)
}
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