epic.sequences.CRF.scala Maven / Gradle / Ivy
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package epic.sequences
import breeze.util.Index
import breeze.numerics
import breeze.linalg.{argmax, softmax, DenseVector}
import epic.framework._
import java.util
import collection.mutable.ArrayBuffer
import breeze.features.FeatureVector
import epic.constraints.{LabeledSpanConstraints, TagConstraints}
import epic.util.{NotProvided, Optional, CacheBroker}
import breeze.optimize.FirstOrderMinimizer.OptParams
import breeze.optimize.CachedBatchDiffFunction
import epic.features.WordFeaturizer
/**
* A Linear Chain Conditional Random Field. Useful for POS tagging, etc.
*
* As usual in Epic, all the heavy lifting is done in the companion object and Marginals.
*
* CRFs can produce [[epic.sequences.TaggedSequence]] from an input sequence of words.
* They can also produce marginals, etc.
* @author dlwh
*/
@SerialVersionUID(1L)
trait CRF[L, W] extends Serializable {
def labelIndex : Index[L]
def startSymbol: L
def anchor(w: IndexedSeq[W]):CRF.Anchoring[L, W]
def marginal(w: IndexedSeq[W]) = {
CRF.Marginal(anchor(w))
}
def goldMarginal(tags: IndexedSeq[L], w: IndexedSeq[W]):CRF.Marginal[L, W] = {
CRF.Marginal.goldMarginal(anchor(w), tags)
}
def bestSequence(w: IndexedSeq[W], id: String = ""): TaggedSequence[L, W] = {
CRF.viterbi(anchor(w), id)
}
}
object CRF {
/**
* Builds a CRF from a corpus of TaggedSequences using reasonable defaults.
* @param data
* @param startSymbol
* @param gazetteer
* @param opt
* @tparam L
* @return
*/
def buildSimple[L](data: IndexedSeq[TaggedSequence[L, String]],
startSymbol: L,
gazetteer: Gazetteer[Any, String] = Gazetteer.empty[Any, String],
wordFeaturizer: Optional[WordFeaturizer[String]] = NotProvided,
transitionFeaturizer: Optional[WordFeaturizer[String]] = NotProvided,
opt: OptParams = OptParams(),
hashFeatures: Double = 1.0)(implicit cache: CacheBroker = CacheBroker()):CRF[L, String] = {
val model: CRFModel[L, String] = new TaggedSequenceModelFactory[L](startSymbol, gazetteer = gazetteer, wordFeaturizer = wordFeaturizer, transitionFeaturizer = transitionFeaturizer, hashFeatureScale = hashFeatures).makeModel(data)
val obj = new ModelObjective(model, data)
val cached = new CachedBatchDiffFunction(obj)
val weights = opt.minimize(cached, obj.initialWeightVector(randomize = false))
model.extractCRF(weights)
}
def buildIOModel[L](data: IndexedSeq[TaggedSequence[L, String]],
outsideSymbol: L,
gazetteer: Gazetteer[Any, String] = Gazetteer.empty[Any, String],
opt: OptParams = OptParams()): CRF[Boolean, String] = {
val fixedData: IndexedSeq[TaggedSequence[Boolean, String]] = data.map{s =>
s.copy(tags=s.tags.map{l => (l != outsideSymbol)})
}
buildSimple(fixedData, false, gazetteer, opt = opt)
}
trait Anchoring[L, W] extends TagConstraints[L] {
def words : IndexedSeq[W]
def length: Int = words.length
def scoreTransition(pos: Int, prev: Int, cur: Int):Double
def labelIndex: Index[L]
def startSymbol: L
def validSymbols(pos: Int): Set[Int]
override def allowedTags(pos: Int): Set[Int] = validSymbols(pos)
def *(other: Anchoring[L, W]):Anchoring[L, W] = {
(this, other) match {
case (x: IdentityAnchoring[L, W], _) => other
case (_, x: IdentityAnchoring[L, W]) => this
case (x, y) => new ProductAnchoring(this, other)
}
}
}
trait Marginal[L, W] extends VisitableMarginal[TransitionVisitor[L, W]] {
def anchoring: Anchoring[L, W]
def words: IndexedSeq[W] = anchoring.words
def length: Int = anchoring.length
/** Visits spans with non-zero score, useful for expected counts */
def visit( f: TransitionVisitor[L, W])
/** normalized probability of seeing segment with transition */
def transitionMarginal(pos: Int, prev:Int, cur: Int):Double
def logPartition: Double
def positionMarginal(pos: Int, label: L):Double = positionMarginal(pos, anchoring.labelIndex(label))
def positionMarginal(pos: Int):DenseVector[Double] = DenseVector.tabulate(anchoring.labelIndex.size)(positionMarginal(pos, _))
def positionMarginal(pos: Int, label: Int):Double = {
var prev = 0
val numLabels: Int = anchoring.labelIndex.size
var sum = 0.0
while(prev < numLabels) {
sum += transitionMarginal(pos, prev, label)
prev += 1
}
sum
}
}
object Marginal {
def apply[L, W](scorer: Anchoring[L, W]):Marginal[L, W] = {
val forwardScores: Array[Array[Double]] = this.forwardScores(scorer)
val backwardScore: Array[Array[Double]] = this.backwardScores(scorer)
val partition = softmax(forwardScores.last)
val _s = scorer
new Marginal[L, W] {
def anchoring: Anchoring[L, W] = _s
def visit(f: TransitionVisitor[L, W]) {
val numLabels = scorer.labelIndex.size
var pos = 0
while (pos < length) {
var label = 0
while (label < numLabels) {
if(!backwardScore(pos+1)(label).isInfinite) {
var prevLabel = 0
while (prevLabel < numLabels) {
val score = transitionMarginal(pos, prevLabel, label)
if(score != 0.0)
f(pos, prevLabel, label, score)
prevLabel += 1
}
}
label += 1
}
pos += 1
}
}
/** Log-normalized probability of seing segment with transition */
def transitionMarginal(pos: Int, prev: Int, cur: Int): Double = {
val withoutTrans = forwardScores(pos)(prev) + backwardScore(pos+1)(cur)
if(withoutTrans.isInfinite) 0.0
else math.exp(withoutTrans + anchoring.scoreTransition(pos, prev, cur) - logPartition)
}
def logPartition: Double = partition
// println(words + " " + partition)
}
}
def goldMarginal[L, W](scorer: Anchoring[L, W], tags: IndexedSeq[L]):Marginal[L, W] = {
var lastSymbol = scorer.labelIndex(scorer.startSymbol)
var score = 0.0
for( (l, pos) <- tags.zipWithIndex) {
val symbol = scorer.labelIndex(l)
assert(symbol != -1, s"$l not in index: ${scorer.labelIndex}")
score += scorer.scoreTransition(pos, lastSymbol, symbol)
lastSymbol = symbol
}
val s = scorer
new Marginal[L, W] {
def anchoring: Anchoring[L, W] = s
def visit(f: TransitionVisitor[L, W]) {
var lastSymbol = scorer.labelIndex(scorer.startSymbol)
for( (l,pos) <- tags.zipWithIndex) {
val symbol = scorer.labelIndex(l)
f.apply(pos, lastSymbol, symbol, 1.0)
lastSymbol = symbol
}
}
val indexedSymbols = scorer.labelIndex(scorer.startSymbol) +: tags.map(scorer.labelIndex(_))
def transitionMarginal(pos: Int, prev: Int, cur: Int): Double = {
numerics.I(prev == indexedSymbols(pos) && cur == indexedSymbols(pos + 1))
}
def logPartition: Double = score
}
}
/**
*
* @param scorer
* @return forwardScore(end position)(label) = forward score of ending a segment labeled label in position end position
*/
private def forwardScores[L, W](scorer: CRF.Anchoring[L, W]): Array[Array[Double]] = {
val length = scorer.length
val numLabels = scorer.labelIndex.size
// total weight (logSum) for reaching the fence post before position i with label l. i.e. forward(0) is the start state
val forwardScores = Array.fill(length+1, numLabels)(Double.NegativeInfinity)
forwardScores(0)(scorer.labelIndex(scorer.startSymbol)) = 0.0
val cache = new Array[Double](numLabels * length)
// forward
for(i <- 0 until length) {
val cur = forwardScores(i+1)
for ( next <- scorer.validSymbols(i)) {
var offset = 0
for ( previous <- if(i == 0) IndexedSeq(scorer.labelIndex(scorer.startSymbol)) else scorer.validSymbols(i-1)) {
val score = scorer.scoreTransition(i, previous, next) + forwardScores(i)(previous)
if(score != Double.NegativeInfinity) {
cache(offset) = score
offset += 1
}
}
cur(next) = softmax.array(cache, offset)
}
}
forwardScores
}
/**
* computes the sum of all completions of derivations, starting with label l at pos.
* at the end of the sequence
* @param scorer
* @tparam L
* @tparam W
* @return backwardScore(pos)(label)
*/
private def backwardScores[L, W](scorer: CRF.Anchoring[L, W]): Array[Array[Double]] = {
val length = scorer.length
val numLabels = scorer.labelIndex.size
// total completion weight (logSum) for being in state l at fencepost i + 1,
val backwardScores = Array.fill(length+1, numLabels)(Double.NegativeInfinity)
util.Arrays.fill(backwardScores(length), 0.0)
val accumArray = new Array[Double](numLabels)
for(i <- (length-1) until 0 by -1) {
val cur = backwardScores(i)
for ( curLabel <- scorer.validSymbols(i-1)) {
var offset = 0
for( next <- scorer.validSymbols(i)) {
val nextScore = backwardScores(i+1)(next)
val score = scorer.scoreTransition(i, curLabel, next) + nextScore
if(score != Double.NegativeInfinity) {
accumArray(offset) = score
offset += 1
}
}
cur(curLabel) = softmax(new DenseVector(accumArray, 0, 1, offset))
}
}
backwardScores
}
}
trait TransitionVisitor[L, W] {
def apply(pos: Int, prev: Int, cur: Int, count: Double)
}
trait IndexedFeaturizer[L, W] {
def anchor(w: IndexedSeq[W]):AnchoredFeaturizer[L, W]
def startSymbol: L
def labelIndex: Index[L]
def featureIndex: Index[Feature]
}
trait AnchoredFeaturizer[L, W] {
def featureIndex: Index[Feature]
def featuresForTransition(pos: Int, prev: Int, cur: Int):FeatureVector
def validSymbols(pos: Int):Set[Int]
}
def viterbi[L, W](scorer: Anchoring[L ,W], id: String=""):TaggedSequence[L, W] = {
val length = scorer.length
val numLabels = scorer.labelIndex.size
// total weight (logSum) for reaching the fence post before position i with label l. i.e. forward(0) is the start state
val forwardScores = Array.fill(length+1, numLabels)(Double.NegativeInfinity)
forwardScores(0)(scorer.labelIndex(scorer.startSymbol)) = 0.0
val backPointer = Array.fill(length, numLabels)(-1)
// forward
for(i <- 0 until length) {
val cur = forwardScores(i+1)
for ( next <- scorer.validSymbols(i)) {
var currentMax = Double.NegativeInfinity
var currentArgMax = -1
for ( previous <- scorer.validSymbols(i-1)) {
val score = scorer.scoreTransition(i, previous, next) + forwardScores(i)(previous)
if(score > currentMax) {
currentMax = score
currentArgMax = previous
}
}
assert(!currentMax.isNaN)
assert(!currentMax.isInfinite)
cur(next) = currentMax
backPointer(i)(next) = currentArgMax
}
}
val tags = ArrayBuffer[L]()
def rec(end: Int, label: Int) {
tags += scorer.labelIndex.get(label)
if(end > 0) {
val bestCurrentLabel = backPointer(end)(label)
rec(end-1, bestCurrentLabel)
}
}
rec(length-1, (0 until numLabels).maxBy(forwardScores(length)(_)))
TaggedSequence(tags.reverse, scorer.words, id)
}
def posteriorDecode[L, W](m: Marginal[L, W], id: String = "") = {
val length = m.length
val labels = (0 until length).map(pos => (0 until m.anchoring.labelIndex.size).maxBy(m.positionMarginal(pos, _)))
TaggedSequence(labels.map(m.anchoring.labelIndex.get), m.words, id)
}
case class ProductAnchoring[L, W](a: Anchoring[L ,W], b: Anchoring[L, W]) extends Anchoring[L, W] {
if((a.labelIndex ne b.labelIndex) && (a.labelIndex != b.labelIndex)) throw new IllegalArgumentException("Elements of product anchoring must have the same labelIndex!")
if(a.startSymbol != b.startSymbol) throw new IllegalArgumentException("Elements of product anchoring must have the same startSymbol!")
def words: IndexedSeq[W] = a.words
def scoreTransition(i: Int, prev: Int, cur: Int): Double = {
var score = a.scoreTransition(i, prev, cur)
if (score != Double.NegativeInfinity) {
score += b.scoreTransition(i, prev, cur)
}
score
}
def validSymbols(pos: Int): Set[Int] = a.validSymbols(pos)
def labelIndex: Index[L] = a.labelIndex
def startSymbol: L = a.startSymbol
}
class IdentityAnchoring[L, W](val words: IndexedSeq[W], val validSyms: IndexedSeq[Set[Int]], val labelIndex: Index[L], val startSymbol: L) extends Anchoring[L, W] {
def scoreTransition(pos: Int, prev: Int, cur: Int): Double = 0.0
def validSymbols(pos: Int): Set[Int] = validSyms(pos)
def canStartLongSegment(pos: Int): Boolean = true
}
}
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