SPARK-2272 [MLlib] Feature scaling which standardizes the range of independent variables or features of data
Feature scaling is a method used to standardize the range of independent variables or features of data. In data processing, it is generally performed during the data preprocessing step. In this work, a trait called `VectorTransformer` is defined for generic transformation on a vector. It contains one method to be implemented, `transform` which applies transformation on a vector. There are two implementations of `VectorTransformer` now, and they all can be easily extended with PMML transformation support. 1) `StandardScaler` - Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set. 2) `Normalizer` - Normalizes samples individually to unit L^n norm Author: DB Tsai <dbtsai@alpinenow.com> Closes #1207 from dbtsai/dbtsai-feature-scaling and squashes the following commits: 78c15d3 [DB Tsai] Alpine Data Labs
This commit is contained in:
parent
5507dd8e18
commit
ae58aea2d1
|
@ -0,0 +1,76 @@
|
|||
/*
|
||||
* 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.mllib.feature
|
||||
|
||||
import breeze.linalg.{DenseVector => BDV, SparseVector => BSV}
|
||||
|
||||
import org.apache.spark.annotation.DeveloperApi
|
||||
import org.apache.spark.mllib.linalg.{Vector, Vectors}
|
||||
|
||||
/**
|
||||
* :: DeveloperApi ::
|
||||
* Normalizes samples individually to unit L^p^ norm
|
||||
*
|
||||
* For any 1 <= p < Double.PositiveInfinity, normalizes samples using
|
||||
* sum(abs(vector).^p^)^(1/p)^ as norm.
|
||||
*
|
||||
* For p = Double.PositiveInfinity, max(abs(vector)) will be used as norm for normalization.
|
||||
*
|
||||
* @param p Normalization in L^p^ space, p = 2 by default.
|
||||
*/
|
||||
@DeveloperApi
|
||||
class Normalizer(p: Double) extends VectorTransformer {
|
||||
|
||||
def this() = this(2)
|
||||
|
||||
require(p >= 1.0)
|
||||
|
||||
/**
|
||||
* Applies unit length normalization on a vector.
|
||||
*
|
||||
* @param vector vector to be normalized.
|
||||
* @return normalized vector. If the norm of the input is zero, it will return the input vector.
|
||||
*/
|
||||
override def transform(vector: Vector): Vector = {
|
||||
var norm = vector.toBreeze.norm(p)
|
||||
|
||||
if (norm != 0.0) {
|
||||
// For dense vector, we've to allocate new memory for new output vector.
|
||||
// However, for sparse vector, the `index` array will not be changed,
|
||||
// so we can re-use it to save memory.
|
||||
vector.toBreeze match {
|
||||
case dv: BDV[Double] => Vectors.fromBreeze(dv :/ norm)
|
||||
case sv: BSV[Double] =>
|
||||
val output = new BSV[Double](sv.index, sv.data.clone(), sv.length)
|
||||
var i = 0
|
||||
while (i < output.data.length) {
|
||||
output.data(i) /= norm
|
||||
i += 1
|
||||
}
|
||||
Vectors.fromBreeze(output)
|
||||
case v => throw new IllegalArgumentException("Do not support vector type " + v.getClass)
|
||||
}
|
||||
} else {
|
||||
// Since the norm is zero, return the input vector object itself.
|
||||
// Note that it's safe since we always assume that the data in RDD
|
||||
// should be immutable.
|
||||
vector
|
||||
}
|
||||
}
|
||||
|
||||
}
|
|
@ -0,0 +1,119 @@
|
|||
/*
|
||||
* 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.mllib.feature
|
||||
|
||||
import breeze.linalg.{DenseVector => BDV, SparseVector => BSV, Vector => BV}
|
||||
|
||||
import org.apache.spark.annotation.DeveloperApi
|
||||
import org.apache.spark.mllib.linalg.{Vector, Vectors}
|
||||
import org.apache.spark.mllib.rdd.RDDFunctions._
|
||||
import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
|
||||
import org.apache.spark.rdd.RDD
|
||||
|
||||
/**
|
||||
* :: DeveloperApi ::
|
||||
* Standardizes features by removing the mean and scaling to unit variance using column summary
|
||||
* statistics on the samples in the training set.
|
||||
*
|
||||
* @param withMean False by default. Centers the data with mean before scaling. It will build a
|
||||
* dense output, so this does not work on sparse input and will raise an exception.
|
||||
* @param withStd True by default. Scales the data to unit standard deviation.
|
||||
*/
|
||||
@DeveloperApi
|
||||
class StandardScaler(withMean: Boolean, withStd: Boolean) extends VectorTransformer {
|
||||
|
||||
def this() = this(false, true)
|
||||
|
||||
require(withMean || withStd, s"withMean and withStd both equal to false. Doing nothing.")
|
||||
|
||||
private var mean: BV[Double] = _
|
||||
private var factor: BV[Double] = _
|
||||
|
||||
/**
|
||||
* Computes the mean and variance and stores as a model to be used for later scaling.
|
||||
*
|
||||
* @param data The data used to compute the mean and variance to build the transformation model.
|
||||
* @return This StandardScalar object.
|
||||
*/
|
||||
def fit(data: RDD[Vector]): this.type = {
|
||||
val summary = data.treeAggregate(new MultivariateOnlineSummarizer)(
|
||||
(aggregator, data) => aggregator.add(data),
|
||||
(aggregator1, aggregator2) => aggregator1.merge(aggregator2))
|
||||
|
||||
mean = summary.mean.toBreeze
|
||||
factor = summary.variance.toBreeze
|
||||
require(mean.length == factor.length)
|
||||
|
||||
var i = 0
|
||||
while (i < factor.length) {
|
||||
factor(i) = if (factor(i) != 0.0) 1.0 / math.sqrt(factor(i)) else 0.0
|
||||
i += 1
|
||||
}
|
||||
|
||||
this
|
||||
}
|
||||
|
||||
/**
|
||||
* Applies standardization transformation on a vector.
|
||||
*
|
||||
* @param vector Vector to be standardized.
|
||||
* @return Standardized vector. If the variance of a column is zero, it will return default `0.0`
|
||||
* for the column with zero variance.
|
||||
*/
|
||||
override def transform(vector: Vector): Vector = {
|
||||
if (mean == null || factor == null) {
|
||||
throw new IllegalStateException(
|
||||
"Haven't learned column summary statistics yet. Call fit first.")
|
||||
}
|
||||
|
||||
require(vector.size == mean.length)
|
||||
|
||||
if (withMean) {
|
||||
vector.toBreeze match {
|
||||
case dv: BDV[Double] =>
|
||||
val output = vector.toBreeze.copy
|
||||
var i = 0
|
||||
while (i < output.length) {
|
||||
output(i) = (output(i) - mean(i)) * (if (withStd) factor(i) else 1.0)
|
||||
i += 1
|
||||
}
|
||||
Vectors.fromBreeze(output)
|
||||
case v => throw new IllegalArgumentException("Do not support vector type " + v.getClass)
|
||||
}
|
||||
} else if (withStd) {
|
||||
vector.toBreeze match {
|
||||
case dv: BDV[Double] => Vectors.fromBreeze(dv :* factor)
|
||||
case sv: BSV[Double] =>
|
||||
// For sparse vector, the `index` array inside sparse vector object will not be changed,
|
||||
// so we can re-use it to save memory.
|
||||
val output = new BSV[Double](sv.index, sv.data.clone(), sv.length)
|
||||
var i = 0
|
||||
while (i < output.data.length) {
|
||||
output.data(i) *= factor(output.index(i))
|
||||
i += 1
|
||||
}
|
||||
Vectors.fromBreeze(output)
|
||||
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
|
||||
}
|
||||
}
|
||||
|
||||
}
|
|
@ -0,0 +1,51 @@
|
|||
/*
|
||||
* 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.mllib.feature
|
||||
|
||||
import org.apache.spark.annotation.DeveloperApi
|
||||
import org.apache.spark.mllib.linalg.Vector
|
||||
import org.apache.spark.rdd.RDD
|
||||
|
||||
/**
|
||||
* :: DeveloperApi ::
|
||||
* Trait for transformation of a vector
|
||||
*/
|
||||
@DeveloperApi
|
||||
trait VectorTransformer extends Serializable {
|
||||
|
||||
/**
|
||||
* Applies transformation on a vector.
|
||||
*
|
||||
* @param vector vector to be transformed.
|
||||
* @return transformed vector.
|
||||
*/
|
||||
def transform(vector: Vector): Vector
|
||||
|
||||
/**
|
||||
* Applies transformation on an RDD[Vector].
|
||||
*
|
||||
* @param data RDD[Vector] to be transformed.
|
||||
* @return transformed RDD[Vector].
|
||||
*/
|
||||
def transform(data: RDD[Vector]): RDD[Vector] = {
|
||||
// Later in #1498 , all RDD objects are sent via broadcasting instead of akka.
|
||||
// So it should be no longer necessary to explicitly broadcast `this` object.
|
||||
data.map(x => this.transform(x))
|
||||
}
|
||||
|
||||
}
|
|
@ -19,7 +19,7 @@ package org.apache.spark.mllib.linalg.distributed
|
|||
|
||||
import java.util.Arrays
|
||||
|
||||
import breeze.linalg.{Vector => BV, DenseMatrix => BDM, DenseVector => BDV, SparseVector => BSV}
|
||||
import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, SparseVector => BSV}
|
||||
import breeze.linalg.{svd => brzSvd, axpy => brzAxpy}
|
||||
import breeze.numerics.{sqrt => brzSqrt}
|
||||
import com.github.fommil.netlib.BLAS.{getInstance => blas}
|
||||
|
|
|
@ -0,0 +1,120 @@
|
|||
/*
|
||||
* 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.mllib.feature
|
||||
|
||||
import org.scalatest.FunSuite
|
||||
|
||||
import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vectors}
|
||||
import org.apache.spark.mllib.util.LocalSparkContext
|
||||
import org.apache.spark.mllib.util.TestingUtils._
|
||||
|
||||
class NormalizerSuite extends FunSuite with LocalSparkContext {
|
||||
|
||||
val data = Array(
|
||||
Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))),
|
||||
Vectors.dense(0.0, 0.0, 0.0),
|
||||
Vectors.dense(0.6, -1.1, -3.0),
|
||||
Vectors.sparse(3, Seq((1, 0.91), (2, 3.2))),
|
||||
Vectors.sparse(3, Seq((0, 5.7), (1, 0.72), (2, 2.7))),
|
||||
Vectors.sparse(3, Seq())
|
||||
)
|
||||
|
||||
lazy val dataRDD = sc.parallelize(data, 3)
|
||||
|
||||
test("Normalization using L1 distance") {
|
||||
val l1Normalizer = new Normalizer(1)
|
||||
|
||||
val data1 = data.map(l1Normalizer.transform)
|
||||
val data1RDD = l1Normalizer.transform(dataRDD)
|
||||
|
||||
assert((data, data1, data1RDD.collect()).zipped.forall {
|
||||
case (v1: DenseVector, v2: DenseVector, v3: DenseVector) => true
|
||||
case (v1: SparseVector, v2: SparseVector, v3: SparseVector) => true
|
||||
case _ => false
|
||||
}, "The vector type should be preserved after normalization.")
|
||||
|
||||
assert((data1, data1RDD.collect()).zipped.forall((v1, v2) => v1 ~== v2 absTol 1E-5))
|
||||
|
||||
assert(data1(0).toBreeze.norm(1) ~== 1.0 absTol 1E-5)
|
||||
assert(data1(2).toBreeze.norm(1) ~== 1.0 absTol 1E-5)
|
||||
assert(data1(3).toBreeze.norm(1) ~== 1.0 absTol 1E-5)
|
||||
assert(data1(4).toBreeze.norm(1) ~== 1.0 absTol 1E-5)
|
||||
|
||||
assert(data1(0) ~== Vectors.sparse(3, Seq((0, -0.465116279), (1, 0.53488372))) absTol 1E-5)
|
||||
assert(data1(1) ~== Vectors.dense(0.0, 0.0, 0.0) absTol 1E-5)
|
||||
assert(data1(2) ~== Vectors.dense(0.12765957, -0.23404255, -0.63829787) absTol 1E-5)
|
||||
assert(data1(3) ~== Vectors.sparse(3, Seq((1, 0.22141119), (2, 0.7785888))) absTol 1E-5)
|
||||
assert(data1(4) ~== Vectors.dense(0.625, 0.07894737, 0.29605263) absTol 1E-5)
|
||||
assert(data1(5) ~== Vectors.sparse(3, Seq()) absTol 1E-5)
|
||||
}
|
||||
|
||||
test("Normalization using L2 distance") {
|
||||
val l2Normalizer = new Normalizer()
|
||||
|
||||
val data2 = data.map(l2Normalizer.transform)
|
||||
val data2RDD = l2Normalizer.transform(dataRDD)
|
||||
|
||||
assert((data, data2, data2RDD.collect()).zipped.forall {
|
||||
case (v1: DenseVector, v2: DenseVector, v3: DenseVector) => true
|
||||
case (v1: SparseVector, v2: SparseVector, v3: SparseVector) => true
|
||||
case _ => false
|
||||
}, "The vector type should be preserved after normalization.")
|
||||
|
||||
assert((data2, data2RDD.collect()).zipped.forall((v1, v2) => v1 ~== v2 absTol 1E-5))
|
||||
|
||||
assert(data2(0).toBreeze.norm(2) ~== 1.0 absTol 1E-5)
|
||||
assert(data2(2).toBreeze.norm(2) ~== 1.0 absTol 1E-5)
|
||||
assert(data2(3).toBreeze.norm(2) ~== 1.0 absTol 1E-5)
|
||||
assert(data2(4).toBreeze.norm(2) ~== 1.0 absTol 1E-5)
|
||||
|
||||
assert(data2(0) ~== Vectors.sparse(3, Seq((0, -0.65617871), (1, 0.75460552))) absTol 1E-5)
|
||||
assert(data2(1) ~== Vectors.dense(0.0, 0.0, 0.0) absTol 1E-5)
|
||||
assert(data2(2) ~== Vectors.dense(0.184549876, -0.3383414, -0.922749378) absTol 1E-5)
|
||||
assert(data2(3) ~== Vectors.sparse(3, Seq((1, 0.27352993), (2, 0.96186349))) absTol 1E-5)
|
||||
assert(data2(4) ~== Vectors.dense(0.897906166, 0.113419726, 0.42532397) absTol 1E-5)
|
||||
assert(data2(5) ~== Vectors.sparse(3, Seq()) absTol 1E-5)
|
||||
}
|
||||
|
||||
test("Normalization using L^Inf distance.") {
|
||||
val lInfNormalizer = new Normalizer(Double.PositiveInfinity)
|
||||
|
||||
val dataInf = data.map(lInfNormalizer.transform)
|
||||
val dataInfRDD = lInfNormalizer.transform(dataRDD)
|
||||
|
||||
assert((data, dataInf, dataInfRDD.collect()).zipped.forall {
|
||||
case (v1: DenseVector, v2: DenseVector, v3: DenseVector) => true
|
||||
case (v1: SparseVector, v2: SparseVector, v3: SparseVector) => true
|
||||
case _ => false
|
||||
}, "The vector type should be preserved after normalization.")
|
||||
|
||||
assert((dataInf, dataInfRDD.collect()).zipped.forall((v1, v2) => v1 ~== v2 absTol 1E-5))
|
||||
|
||||
assert(dataInf(0).toArray.map(Math.abs).max ~== 1.0 absTol 1E-5)
|
||||
assert(dataInf(2).toArray.map(Math.abs).max ~== 1.0 absTol 1E-5)
|
||||
assert(dataInf(3).toArray.map(Math.abs).max ~== 1.0 absTol 1E-5)
|
||||
assert(dataInf(4).toArray.map(Math.abs).max ~== 1.0 absTol 1E-5)
|
||||
|
||||
assert(dataInf(0) ~== Vectors.sparse(3, Seq((0, -0.86956522), (1, 1.0))) absTol 1E-5)
|
||||
assert(dataInf(1) ~== Vectors.dense(0.0, 0.0, 0.0) absTol 1E-5)
|
||||
assert(dataInf(2) ~== Vectors.dense(0.2, -0.36666667, -1.0) absTol 1E-5)
|
||||
assert(dataInf(3) ~== Vectors.sparse(3, Seq((1, 0.284375), (2, 1.0))) absTol 1E-5)
|
||||
assert(dataInf(4) ~== Vectors.dense(1.0, 0.12631579, 0.473684211) absTol 1E-5)
|
||||
assert(dataInf(5) ~== Vectors.sparse(3, Seq()) absTol 1E-5)
|
||||
}
|
||||
|
||||
}
|
|
@ -0,0 +1,200 @@
|
|||
/*
|
||||
* 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.mllib.feature
|
||||
|
||||
import org.scalatest.FunSuite
|
||||
|
||||
import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
|
||||
import org.apache.spark.mllib.util.LocalSparkContext
|
||||
import org.apache.spark.mllib.util.TestingUtils._
|
||||
import org.apache.spark.mllib.rdd.RDDFunctions._
|
||||
import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, MultivariateOnlineSummarizer}
|
||||
import org.apache.spark.rdd.RDD
|
||||
|
||||
class StandardScalerSuite extends FunSuite with LocalSparkContext {
|
||||
|
||||
private def computeSummary(data: RDD[Vector]): MultivariateStatisticalSummary = {
|
||||
data.treeAggregate(new MultivariateOnlineSummarizer)(
|
||||
(aggregator, data) => aggregator.add(data),
|
||||
(aggregator1, aggregator2) => aggregator1.merge(aggregator2))
|
||||
}
|
||||
|
||||
test("Standardization with dense input") {
|
||||
val data = Array(
|
||||
Vectors.dense(-2.0, 2.3, 0),
|
||||
Vectors.dense(0.0, -1.0, -3.0),
|
||||
Vectors.dense(0.0, -5.1, 0.0),
|
||||
Vectors.dense(3.8, 0.0, 1.9),
|
||||
Vectors.dense(1.7, -0.6, 0.0),
|
||||
Vectors.dense(0.0, 1.9, 0.0)
|
||||
)
|
||||
|
||||
val dataRDD = sc.parallelize(data, 3)
|
||||
|
||||
val standardizer1 = new StandardScaler(withMean = true, withStd = true)
|
||||
val standardizer2 = new StandardScaler()
|
||||
val standardizer3 = new StandardScaler(withMean = true, withStd = false)
|
||||
|
||||
withClue("Using a standardizer before fitting the model should throw exception.") {
|
||||
intercept[IllegalStateException] {
|
||||
data.map(standardizer1.transform)
|
||||
}
|
||||
}
|
||||
|
||||
standardizer1.fit(dataRDD)
|
||||
standardizer2.fit(dataRDD)
|
||||
standardizer3.fit(dataRDD)
|
||||
|
||||
val data1 = data.map(standardizer1.transform)
|
||||
val data2 = data.map(standardizer2.transform)
|
||||
val data3 = data.map(standardizer3.transform)
|
||||
|
||||
val data1RDD = standardizer1.transform(dataRDD)
|
||||
val data2RDD = standardizer2.transform(dataRDD)
|
||||
val data3RDD = standardizer3.transform(dataRDD)
|
||||
|
||||
val summary = computeSummary(dataRDD)
|
||||
val summary1 = computeSummary(data1RDD)
|
||||
val summary2 = computeSummary(data2RDD)
|
||||
val summary3 = computeSummary(data3RDD)
|
||||
|
||||
assert((data, data1, data1RDD.collect()).zipped.forall {
|
||||
case (v1: DenseVector, v2: DenseVector, v3: DenseVector) => true
|
||||
case (v1: SparseVector, v2: SparseVector, v3: SparseVector) => true
|
||||
case _ => false
|
||||
}, "The vector type should be preserved after standardization.")
|
||||
|
||||
assert((data, data2, data2RDD.collect()).zipped.forall {
|
||||
case (v1: DenseVector, v2: DenseVector, v3: DenseVector) => true
|
||||
case (v1: SparseVector, v2: SparseVector, v3: SparseVector) => true
|
||||
case _ => false
|
||||
}, "The vector type should be preserved after standardization.")
|
||||
|
||||
assert((data, data3, data3RDD.collect()).zipped.forall {
|
||||
case (v1: DenseVector, v2: DenseVector, v3: DenseVector) => true
|
||||
case (v1: SparseVector, v2: SparseVector, v3: SparseVector) => true
|
||||
case _ => false
|
||||
}, "The vector type should be preserved after standardization.")
|
||||
|
||||
assert((data1, data1RDD.collect()).zipped.forall((v1, v2) => v1 ~== v2 absTol 1E-5))
|
||||
assert((data2, data2RDD.collect()).zipped.forall((v1, v2) => v1 ~== v2 absTol 1E-5))
|
||||
assert((data3, data3RDD.collect()).zipped.forall((v1, v2) => v1 ~== v2 absTol 1E-5))
|
||||
|
||||
assert(summary1.mean ~== Vectors.dense(0.0, 0.0, 0.0) absTol 1E-5)
|
||||
assert(summary1.variance ~== Vectors.dense(1.0, 1.0, 1.0) absTol 1E-5)
|
||||
|
||||
assert(summary2.mean !~== Vectors.dense(0.0, 0.0, 0.0) absTol 1E-5)
|
||||
assert(summary2.variance ~== Vectors.dense(1.0, 1.0, 1.0) absTol 1E-5)
|
||||
|
||||
assert(summary3.mean ~== Vectors.dense(0.0, 0.0, 0.0) absTol 1E-5)
|
||||
assert(summary3.variance ~== summary.variance absTol 1E-5)
|
||||
|
||||
assert(data1(0) ~== Vectors.dense(-1.31527964, 1.023470449, 0.11637768424) absTol 1E-5)
|
||||
assert(data1(3) ~== Vectors.dense(1.637735298, 0.156973995, 1.32247368462) absTol 1E-5)
|
||||
assert(data2(4) ~== Vectors.dense(0.865538862, -0.22604255, 0.0) absTol 1E-5)
|
||||
assert(data2(5) ~== Vectors.dense(0.0, 0.71580142, 0.0) absTol 1E-5)
|
||||
assert(data3(1) ~== Vectors.dense(-0.58333333, -0.58333333, -2.8166666666) absTol 1E-5)
|
||||
assert(data3(5) ~== Vectors.dense(-0.58333333, 2.316666666, 0.18333333333) absTol 1E-5)
|
||||
}
|
||||
|
||||
|
||||
test("Standardization with sparse input") {
|
||||
val data = Array(
|
||||
Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))),
|
||||
Vectors.sparse(3, Seq((1, -1.0), (2, -3.0))),
|
||||
Vectors.sparse(3, Seq((1, -5.1))),
|
||||
Vectors.sparse(3, Seq((0, 3.8), (2, 1.9))),
|
||||
Vectors.sparse(3, Seq((0, 1.7), (1, -0.6))),
|
||||
Vectors.sparse(3, Seq((1, 1.9)))
|
||||
)
|
||||
|
||||
val dataRDD = sc.parallelize(data, 3)
|
||||
|
||||
val standardizer1 = new StandardScaler(withMean = true, withStd = true)
|
||||
val standardizer2 = new StandardScaler()
|
||||
val standardizer3 = new StandardScaler(withMean = true, withStd = false)
|
||||
|
||||
standardizer1.fit(dataRDD)
|
||||
standardizer2.fit(dataRDD)
|
||||
standardizer3.fit(dataRDD)
|
||||
|
||||
val data2 = data.map(standardizer2.transform)
|
||||
|
||||
withClue("Standardization with mean can not be applied on sparse input.") {
|
||||
intercept[IllegalArgumentException] {
|
||||
data.map(standardizer1.transform)
|
||||
}
|
||||
}
|
||||
|
||||
withClue("Standardization with mean can not be applied on sparse input.") {
|
||||
intercept[IllegalArgumentException] {
|
||||
data.map(standardizer3.transform)
|
||||
}
|
||||
}
|
||||
|
||||
val data2RDD = standardizer2.transform(dataRDD)
|
||||
|
||||
val summary2 = computeSummary(data2RDD)
|
||||
|
||||
assert((data, data2, data2RDD.collect()).zipped.forall {
|
||||
case (v1: DenseVector, v2: DenseVector, v3: DenseVector) => true
|
||||
case (v1: SparseVector, v2: SparseVector, v3: SparseVector) => true
|
||||
case _ => false
|
||||
}, "The vector type should be preserved after standardization.")
|
||||
|
||||
assert((data2, data2RDD.collect()).zipped.forall((v1, v2) => v1 ~== v2 absTol 1E-5))
|
||||
|
||||
assert(summary2.mean !~== Vectors.dense(0.0, 0.0, 0.0) absTol 1E-5)
|
||||
assert(summary2.variance ~== Vectors.dense(1.0, 1.0, 1.0) absTol 1E-5)
|
||||
|
||||
assert(data2(4) ~== Vectors.sparse(3, Seq((0, 0.865538862), (1, -0.22604255))) absTol 1E-5)
|
||||
assert(data2(5) ~== Vectors.sparse(3, Seq((1, 0.71580142))) absTol 1E-5)
|
||||
}
|
||||
|
||||
test("Standardization with constant input") {
|
||||
// When the input data is all constant, the variance is zero. The standardization against
|
||||
// zero variance is not well-defined, but we decide to just set it into zero here.
|
||||
val data = Array(
|
||||
Vectors.dense(2.0),
|
||||
Vectors.dense(2.0),
|
||||
Vectors.dense(2.0)
|
||||
)
|
||||
|
||||
val dataRDD = sc.parallelize(data, 2)
|
||||
|
||||
val standardizer1 = new StandardScaler(withMean = true, withStd = true)
|
||||
val standardizer2 = new StandardScaler(withMean = true, withStd = false)
|
||||
val standardizer3 = new StandardScaler(withMean = false, withStd = true)
|
||||
|
||||
standardizer1.fit(dataRDD)
|
||||
standardizer2.fit(dataRDD)
|
||||
standardizer3.fit(dataRDD)
|
||||
|
||||
val data1 = data.map(standardizer1.transform)
|
||||
val data2 = data.map(standardizer2.transform)
|
||||
val data3 = data.map(standardizer3.transform)
|
||||
|
||||
assert(data1.forall(_.toArray.forall(_ == 0.0)),
|
||||
"The variance is zero, so the transformed result should be 0.0")
|
||||
assert(data2.forall(_.toArray.forall(_ == 0.0)),
|
||||
"The variance is zero, so the transformed result should be 0.0")
|
||||
assert(data3.forall(_.toArray.forall(_ == 0.0)),
|
||||
"The variance is zero, so the transformed result should be 0.0")
|
||||
}
|
||||
|
||||
}
|
Loading…
Reference in a new issue