epic.sequences.CRFModel.scala Maven / Gradle / Ivy
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package epic.sequences
import epic.framework._
import breeze.util._
import breeze.linalg._
import epic.sequences.CRF.{AnchoredFeaturizer, TransitionVisitor}
import breeze.features.FeatureVector
import epic.features._
import epic.lexicon.SimpleLexicon
import java.util
import epic.util.{ProgressLog, SafeLogging, NotProvided, Optional}
import epic.constraints.TagConstraints
/**
*
* @author dlwh
*/
@SerialVersionUID(1L)
class CRFModel[L, W](val featureIndex: Index[Feature],
val lexicon: TagConstraints.Factory[L, W],
val featurizer: CRF.IndexedFeaturizer[L, W],
initialWeights: Feature=>Double = {(_: Feature) => 0.0}) extends Model[TaggedSequence[L, W]] with StandardExpectedCounts.Model[TaggedSequence[L, W]] with Serializable {
def labelIndex: Index[L] = featurizer.labelIndex
def extractCRF(weights: DenseVector[Double]) = {
inferenceFromWeights(weights)
}
type Inference = CRFInference[L, W]
type Marginal = CRF.Marginal[L, W]
type Scorer = CRF.Anchoring[L, W]
def initialValueForFeature(f: Feature): Double = initialWeights(f)
def inferenceFromWeights(weights: DenseVector[Double]): Inference =
new CRFInference(weights, featureIndex, lexicon, featurizer)
def accumulateCounts(inf: Inference, s: Scorer, d: TaggedSequence[L, W], marg: Marginal, counts: ExpectedCounts, scale: Double): Unit = {
counts.loss += marg.logPartition * scale
val localization = s.asInstanceOf[inf.Anchoring].localization
val visitor = new TransitionVisitor[L, W] {
def apply(pos: Int, prev: Int, cur: Int, count: Double) {
val feats = localization.featuresForTransition(pos, prev, cur)
if(count != 0) assert(feats ne null, (pos, prev, cur, marg.length, marg.anchoring.validSymbols(pos), marg.anchoring.validSymbols(pos-1)))
axpy(scale * count, feats, counts)
}
}
marg.visit(visitor)
}
}
@SerialVersionUID(1)
class CRFInference[L, W](val weights: DenseVector[Double],
val featureIndex: Index[Feature],
val lexicon: TagConstraints.Factory[L, W],
featurizer: CRF.IndexedFeaturizer[L, W]) extends AugmentableInference[TaggedSequence[L, W], CRF.Anchoring[L, W]] with CRF[L, W] with AnnotatingInference[TaggedSequence[L, W]] with Serializable {
def scorer(v: TaggedSequence[L, W]): Scorer = new Anchoring(v.words)
def viterbi(sentence: IndexedSeq[W], anchoring: CRF.Anchoring[L, W]): TaggedSequence[L, W] = {
CRF.viterbi(new Anchoring(sentence) * anchoring)
}
def annotate(datum: TaggedSequence[L, W], m: Marginal): TaggedSequence[L, W] = {
CRF.posteriorDecode(m)
}
type Marginal = CRF.Marginal[L, W]
type ExpectedCounts = StandardExpectedCounts[Feature]
type Scorer = CRF.Anchoring[L, W]
def emptyCounts = StandardExpectedCounts.zero(this.featureIndex)
def anchor(w: IndexedSeq[W]) = new Anchoring(w)
def labelIndex = featurizer.labelIndex
def startSymbol = featurizer.startSymbol
def marginal(scorer: Scorer, v: TaggedSequence[L, W], aug: CRF.Anchoring[L, W]): CRFInference[L, W]#Marginal = {
CRF.Marginal(scorer * aug)
}
def goldMarginal(scorer: Scorer, v: TaggedSequence[L, W], aug: CRF.Anchoring[L, W]): Marginal = {
CRF.Marginal.goldMarginal[L, W](new Anchoring(v.words) * aug, v.label)
}
private val allLabels = (0 until labelIndex.size).toSet
def baseAugment(v: TaggedSequence[L, W]): CRF.Anchoring[L, W] = {
new CRF.IdentityAnchoring(v.words, IndexedSeq.fill(v.words.length)(allLabels),labelIndex, featurizer.startSymbol)
}
class Anchoring(val words: IndexedSeq[W]) extends CRF.Anchoring[L, W] {
val localization = featurizer.anchor(words)
val transCache = Array.ofDim[Double](labelIndex.size, labelIndex.size, length)
for(a <- transCache; b <- a) util.Arrays.fill(b, Double.NegativeInfinity)
for(i <- 0 until length; c <- validSymbols(i); p <- validSymbols(i-1)) {
val feats = localization.featuresForTransition(i, p, c)
if(feats ne null)
transCache(p)(c)(i) = weights dot feats
else transCache(p)(c)(i) = Double.NegativeInfinity
}
def validSymbols(pos: Int): Set[Int] = localization.validSymbols(pos)
def scoreTransition(pos: Int, prev: Int, cur: Int): Double = {
transCache(prev)(cur)(pos)
}
def labelIndex: Index[L] = featurizer.labelIndex
def startSymbol = featurizer.startSymbol
}
def posteriorDecode(m: Marginal):TaggedSequence[L, W] = {
CRF.posteriorDecode(m)
}
}
class TaggedSequenceModelFactory[L](val startSymbol: L,
gazetteer: Optional[Gazetteer[Any, String]] = NotProvided,
wordFeaturizer: Optional[WordFeaturizer[String]] = NotProvided,
transitionFeaturizer: Optional[WordFeaturizer[String]] = NotProvided,
weights: Feature=>Double = { (f:Feature) => 0.0},
hashFeatureScale: Double = 0.0) extends SafeLogging {
import TaggedSequenceModelFactory._
def makeModel(train: IndexedSeq[TaggedSequence[L, String]]): CRFModel[L, String] = {
val labelIndex: Index[L] = Index[L](Iterator(startSymbol) ++ train.iterator.flatMap(_.label))
val counts: Counter2[L, String, Double] = Counter2.count(train.flatMap(p => p.label zip p.words)).mapValues(_.toDouble)
val lexicon:TagConstraints.Factory[L, String] = new SimpleLexicon[L, String](labelIndex, counts)
var featurizer: WordFeaturizer[String] = wordFeaturizer.getOrElse(WordFeaturizer.goodPOSTagFeaturizer(counts))
featurizer = gazetteer.foldLeft(featurizer)(_ + _)
val l2featurizer: WordFeaturizer[String] = transitionFeaturizer.getOrElse(WordFeaturizer.goodPOSTagTransitionFeaturizer(counts))
val indexedFeaturizer = IndexedWordFeaturizer.fromData(featurizer, train.map{_.words})
val indexedL2featurizer = IndexedWordFeaturizer.fromData(l2featurizer, train.map{_.words})
val lfBuilder = new CrossProductIndex.Builder(labelIndex, indexedFeaturizer.featureIndex, includeLabelOnlyFeatures = false, hashFeatures = HashFeature.Relative(hashFeatureScale))
val label2Index = Index[(L, L)]()
val label2Features = Array.tabulate(labelIndex.size, labelIndex.size) { (l1, l2) =>
label2Index.index(labelIndex.get(l1) -> labelIndex.get(l2))
}
val l2Builder = new CrossProductIndex.Builder(label2Index, indexedL2featurizer.featureIndex, includeLabelOnlyFeatures = true, hashFeatures = HashFeature.Relative(hashFeatureScale))
val progress = new ProgressLog(logger,train.length, frequency=1000, name= "NumFeatures")
for(s <- train) {
val loc = indexedFeaturizer.anchor(s.words)
val l2loc = indexedL2featurizer.anchor(s.words)
val lexLoc = lexicon.anchor(s.words)
for {
b <- 0 until s.length
l <- lexLoc.allowedTags(b)
} {
lfBuilder.add(l, loc.featuresForWord(b))
if(lexLoc.allowedTags(b).size > 1) {
for(prevTag <- if(b == 0) Set(labelIndex(startSymbol)) else lexLoc.allowedTags(b-1)) {
l2Builder.add(label2Features(prevTag)(l), l2loc.featuresForWord(b))
}
}
}
progress.info(s"${lfBuilder.size + l2Builder.size}")
}
val indexed = new IndexedStandardFeaturizer[L, String](indexedFeaturizer,
indexedL2featurizer, lexicon, startSymbol, labelIndex, label2Features, lfBuilder.result(), l2Builder.result())
logger.info(s"There are ${indexed.label2FeatureIndex.size} and ${indexed.labelFeatureIndex.size} features total.")
val model = new CRFModel(indexed.featureIndex, lexicon, indexed, weights)
model
}
}
object TaggedSequenceModelFactory {
@SerialVersionUID(1L)
class IndexedStandardFeaturizer[L, String](wordFeaturizer: IndexedWordFeaturizer[String],
l2WordFeaturizer: IndexedWordFeaturizer[String],
val lexicon: TagConstraints.Factory[L, String],
val startSymbol: L,
val labelIndex: Index[L],
label2Features: Array[Array[Int]],
val labelFeatureIndex: CrossProductIndex[L, Feature],
val label2FeatureIndex: CrossProductIndex[(L, L), Feature]
) extends CRF.IndexedFeaturizer[L,String] with Serializable { outer =>
val featureIndex = SegmentedIndex(labelFeatureIndex, label2FeatureIndex)
private val loff = featureIndex.componentOffset(0)
private val l2off = featureIndex.componentOffset(1)
private val startSymbolSet = Set(labelIndex(startSymbol))
def anchor(w: IndexedSeq[String]): AnchoredFeaturizer[L, String] = new AnchoredFeaturizer[L, String] {
val loc = wordFeaturizer.anchor(w)
val l2loc = l2WordFeaturizer.anchor(w)
val lexLoc = lexicon.anchor(w)
def featureIndex: Index[Feature] = outer.featureIndex
def validSymbols(pos: Int): Set[Int] = if(pos < 0 || pos >= w.length) startSymbolSet else lexLoc.allowedTags(pos)
def length = w.length
val featureArray = Array.ofDim[FeatureVector](length, labelIndex.size, labelIndex.size)
private val posNeedsAmbiguity = Array.tabulate(length)(i => validSymbols(i).size > 1)
for {
pos <- 0 until length
curTag <- validSymbols(pos)
features = loc.featuresForWord(pos)
justLabel = labelFeatureIndex.crossProduct(Array(curTag), features, offset = loff, usePlainLabelFeatures = false)
prevTag <- validSymbols(pos-1)
} {
val l2feats = l2loc.featuresForWord(pos)
val feats = if(posNeedsAmbiguity(pos)) {
justLabel++ label2FeatureIndex.crossProduct(Array(label2Features(prevTag)(curTag)), l2feats, offset = l2off, usePlainLabelFeatures = true)
} else {
justLabel
}
featureArray(pos)(prevTag)(curTag) = new FeatureVector(feats)
}
def featuresForTransition(pos: Int, prev: Int, cur: Int): FeatureVector = {
featureArray(pos)(prev)(cur)
}
}
}
}
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