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package org.apache.spark.ml.odkl
import java.util.concurrent.ThreadLocalRandom
import odkl.analysis.spark.util.RDDOperations._
import odkl.analysis.spark.util.collection.CompactBuffer
import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.ml.{Estimator, Model, Transformer}
import org.apache.spark.ml.param.shared.{HasInputCol, HasLabelCol}
import org.apache.spark.ml.param.{DoubleParam, IntParam, ParamMap, Params}
import org.apache.spark.ml.util.{DefaultParamsWritable, Identifiable}
import org.apache.spark.ml.linalg.{BLAS, Vector, Vectors}
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.{DataFrame, Dataset, Row, functions}
/**
* Follows ideas from Combined Regression and Ranking paper (http://www.decom.ufop.br/menotti/rp122/sem/sem2-alex-art.pdf)
*
* Can model pair-wise ranking task (sample pairs, substract features and label 1/0), can model point-wise regression,
* or can combine both by choosing whenever to sample single item or a pair.
*/
trait CRRSamplerParams extends HasInputCol with HasGroupByColumns with HasLabelCol {
val groupSampleRate = new DoubleParam(this, "groupSampleRate",
"The percentage of lists (users) to keep in the taken sample.", (x: Double) => x > 0 && x <= 1)
val itemSampleRate = new DoubleParam(this, "itemSampleRate",
"The percentage of items to keep in the sample (comparing to the amount of items, not to amount of possible pairs).", (x: Double) => x > 0 && x <= 1)
val rankingPower = new DoubleParam(this, "rankingPower",
"The percentage of paired sample (for each selected item we decide whenever to return it as is or to select a pair.", (x: Double) => x >= 0 && x <= 1)
val shuffleToPartitions = new IntParam(this, "shuffleToPartitions",
"Since after sampling the size might decrease, it is worth to shuffle dataset to fewer partitions. Shuffle also can improve" +
"convergence by providing better order randomization.", (x: Int) => x > 0)
def setGroupSampleRate(value: Double): this.type = set(groupSampleRate, value)
def setItemSampleRate(value: Double): this.type = set(itemSampleRate, value)
def setRankingPower(value: Double): this.type = set(rankingPower, value)
def setShufflerToPartitions(value: Int) : this.type = set(shuffleToPartitions, value)
setDefault(
rankingPower -> 0.0,
inputCol -> "features",
labelCol -> "label"
)
}
/**
* Model applied as a transformer, but the resulting data set is not determenistic (each pass produces different
* results). Results must not be cached.
*/
class CRRSamplerModel(override val uid: String) extends
Model[CRRSamplerModel] with DefaultParamsWritable with CRRSamplerParams with HasNetlibBlas{
def this() = this(Identifiable.randomUID("crrSampler"))
setDefault(
groupSampleRate -> 1.0,
itemSampleRate -> 1.0
)
override def transform(dataset: Dataset[_]): DataFrame = {
val (data, keyIndex, toDrop) = if (isDefined(groupByColumns) && $(groupByColumns).length > 0) {
if ($(groupByColumns).length == 1) {
(dataset, dataset.schema.fieldIndex($(groupByColumns).head), None)
} else {
val keyStruct = functions.struct($(groupByColumns).map(x => dataset(x)): _*)
val keyName = s"${uid}_tmpKey"
val data = dataset.withColumn(keyName, keyStruct)
(data, data.schema.fieldIndex(keyName), Some(keyName))
}
} else {
val keyName = s"${uid}_tmpKey"
val data = dataset.withColumn(keyName, functions.lit(keyName))
(data, data.schema.fieldIndex(keyName), Some(keyName))
}
val featuresIndex = data.schema.fieldIndex($(inputCol))
val labelIndex = data.schema.fieldIndex($(labelCol))
val result = dataset.sqlContext.createDataFrame(
data.toDF
.rdd
.groupWithinPartitionsBy(x => x.get(keyIndex))
.flatMap(x => sampleRows(x._2, labelIndex, featuresIndex)),
data.schema
)
val noExtraColumn = toDrop.map(x => result.drop(x)).getOrElse(result)
get(shuffleToPartitions).map(x => noExtraColumn.repartition(x)).getOrElse(noExtraColumn)
}
def sampleRows(rows: Iterator[Row], labelIndex: Int, featureIndex: Int): Iterator[Row] = {
if ($(groupSampleRate) < 1 && ThreadLocalRandom.current().nextDouble() > $(groupSampleRate)) {
// Skip entire group
Iterator.empty
} else if ($(rankingPower) > 0) {
val positives = new CompactBuffer[Row]()
val negatives = new CompactBuffer[Row]()
rows.foreach(x => if (x.getDouble(labelIndex) > 0) {
positives += x
} else {
negatives += x
})
if (positives.isEmpty || negatives.isEmpty) {
// Could not use single class groups when ranking part is enabled.
Iterator.empty
} else {
counterSample(positives.iterator, negatives, featureIndex) ++ counterSample(negatives.iterator, positives, featureIndex)
}
} else {
counterSample(rows, Seq.empty, featureIndex)
}
}
def counterSample(source: Iterator[Row], counterPart: Seq[Row], featureIndex: Int): Iterator[Row] = {
val sampled = if ($(itemSampleRate) < 1) {
source.filter(_ => ThreadLocalRandom.current().nextDouble() < $(itemSampleRate))
} else {
source
}
if ($(rankingPower) <= 0) {
// Pure regression
sampled
} else if ($(rankingPower) < 1) {
// Pure ranking
sampled.map(x => pairSample(x, counterPart, featureIndex))
} else {
// Mixture
sampled.map(x => {
if (ThreadLocalRandom.current().nextDouble() < $(rankingPower)) {
pairSample(x, counterPart, featureIndex)
} else {
x
}
})
}
}
def pairSample(sample: Row, counterPart: Seq[Row], featureIndex: Int): Row = {
val counterSample: Vector = counterPart(ThreadLocalRandom.current().nextInt(counterPart.size)).getAs[Vector](featureIndex)
val result = Array.tabulate(sample.length) { i =>
if (i == featureIndex) {
val features = sample.getAs[Vector](featureIndex)
val result = Vectors.zeros(features.size).toDense
axpy(1.0, features, result.values)
axpy(-1.0, counterSample, result.values)
result
} else {
sample.get(i)
}
}
Row.fromSeq(result)
}
override def copy(extra: ParamMap): CRRSamplerModel = defaultCopy(extra)
@DeveloperApi
override def transformSchema(schema: StructType): StructType = schema
}
/**
* Estimator is used to select the proper item sample rate to achive desired size of the resulting
* sample. Takes into consideration the source dataset size and the amount of valid for ranking lists
* (list with samples of different rank).
*/
class CRRSamplerEstimator(override val uid: String) extends Estimator[CRRSamplerModel]
with DefaultParamsWritable with CRRSamplerParams {
val expectedNumSamples = new IntParam(this, "expectedNumSamples",
"The expected number of samples in the result. Required.", (x: Int) => x > 0)
def setExpectedNumberOfSamples(value: Int) : this.type = set(expectedNumSamples, value)
setDefault(
groupSampleRate -> 1.0
)
def this() = this(Identifiable.randomUID("crrSamplerEstimator"))
override def fit(dataset: Dataset[_]): CRRSamplerModel = {
val totalSamples: Double = if ($(rankingPower) > 0 && isDefined(groupByColumns) && $(groupByColumns).length > 0) {
val (withKey, keyIndex) = if ($(groupByColumns).length == 1) {
(dataset, dataset.schema.fieldIndex($(groupByColumns).head))
} else {
val key = functions.struct($(groupByColumns).map(x => dataset(x)): _*)
val keyName = s"${uid}_tmpKey"
val data = dataset.withColumn(keyName, key)
(data, data.schema.fieldIndex(keyName))
}
val labelIndex = withKey.schema.fieldIndex($(labelCol))
withKey.toDF
.rdd
.groupWithinPartitionsBy(x => x.get(keyIndex))
.map(x => {
var numPositives: Long = 0
var numNegatives: Long = 0
x._2.foreach(row => if (row.getDouble(labelIndex) > 0) numPositives += 1 else numNegatives += 1)
if(numNegatives > 0 && numPositives > 0) numNegatives + numPositives else 0
}).sum()
} else {
dataset.count()
}
val discountedByGroupRate = totalSamples * $(groupSampleRate)
val requiredItemSampleRate = Math.min(1.0, $(expectedNumSamples) / discountedByGroupRate)
logInfo(s"Estimated total number of samples $totalSamples, after groups sampling $discountedByGroupRate. Chosen item sample rate is $requiredItemSampleRate")
val model: CRRSamplerModel = copyValues(new CRRSamplerModel(), ParamMap(itemSampleRate -> requiredItemSampleRate))
model.setParent(this)
}
override def copy(extra: ParamMap): CRRSamplerEstimator = defaultCopy(extra)
@DeveloperApi
override def transformSchema(schema: StructType): StructType = schema
}