func (s *ConjunctionQueryScorer) Score(constituents []*search.DocumentMatch) *search.DocumentMatch {
rv := search.DocumentMatch{
ID: constituents[0].ID,
}
var sum float64
var childrenExplanations []*search.Explanation
if s.explain {
childrenExplanations = make([]*search.Explanation, len(constituents))
}
locations := []search.FieldTermLocationMap{}
for i, docMatch := range constituents {
sum += docMatch.Score
if s.explain {
childrenExplanations[i] = docMatch.Expl
}
if docMatch.Locations != nil {
locations = append(locations, docMatch.Locations)
}
}
rv.Score = sum
if s.explain {
rv.Expl = &search.Explanation{Value: sum, Message: "sum of:", Children: childrenExplanations}
}
if len(locations) == 1 {
rv.Locations = locations[0]
} else if len(locations) > 1 {
rv.Locations = search.MergeLocations(locations)
}
return &rv
}
func (s *DisjunctionQueryScorer) Score(constituents []*search.DocumentMatch, countMatch, countTotal int) *search.DocumentMatch {
rv := search.DocumentMatch{
ID: constituents[0].ID,
}
var sum float64
var childrenExplanations []*search.Explanation
if s.explain {
childrenExplanations = make([]*search.Explanation, len(constituents))
}
locations := []search.FieldTermLocationMap{}
for i, docMatch := range constituents {
sum += docMatch.Score
if s.explain {
childrenExplanations[i] = docMatch.Expl
}
if docMatch.Locations != nil {
locations = append(locations, docMatch.Locations)
}
}
var rawExpl *search.Explanation
if s.explain {
rawExpl = &search.Explanation{Value: sum, Message: "sum of:", Children: childrenExplanations}
}
coord := float64(countMatch) / float64(countTotal)
rv.Score = sum * coord
if s.explain {
ce := make([]*search.Explanation, 2)
ce[0] = rawExpl
ce[1] = &search.Explanation{Value: coord, Message: fmt.Sprintf("coord(%d/%d)", countMatch, countTotal)}
rv.Expl = &search.Explanation{Value: rv.Score, Message: "product of:", Children: ce}
}
if len(locations) == 1 {
rv.Locations = locations[0]
} else if len(locations) > 1 {
rv.Locations = search.MergeLocations(locations)
}
return &rv
}
func (s *ConstantScorer) Score(id string) *search.DocumentMatch {
var scoreExplanation *search.Explanation
score := s.constant
if s.explain {
scoreExplanation = &search.Explanation{
Value: score,
Message: fmt.Sprintf("ConstantScore()"),
}
}
// if the query weight isn't 1, multiply
if s.queryWeight != 1.0 {
score = score * s.queryWeight
if s.explain {
childExplanations := make([]*search.Explanation, 2)
childExplanations[0] = s.queryWeightExplanation
childExplanations[1] = scoreExplanation
scoreExplanation = &search.Explanation{
Value: score,
Message: fmt.Sprintf("weight(^%f), product of:", s.boost),
Children: childExplanations,
}
}
}
rv := search.DocumentMatch{
ID: id,
Score: score,
}
if s.explain {
rv.Expl = scoreExplanation
}
return &rv
}
func (s *PhraseSearcher) Next() (*search.DocumentMatch, error) {
if !s.initialized {
err := s.initSearchers()
if err != nil {
return nil, err
}
}
var rv *search.DocumentMatch
for s.currMust != nil {
rvftlm := make(search.FieldTermLocationMap, 0)
freq := 0
firstTerm := s.terms[0]
for field, termLocMap := range s.currMust.Locations {
rvtlm := make(search.TermLocationMap, 0)
locations, ok := termLocMap[firstTerm]
if ok {
OUTER:
for _, location := range locations {
crvtlm := make(search.TermLocationMap, 0)
INNER:
for i := 0; i < len(s.terms); i++ {
nextTerm := s.terms[i]
if nextTerm != "" {
// look through all these term locations
// to try and find the correct offsets
nextLocations, ok := termLocMap[nextTerm]
if ok {
for _, nextLocation := range nextLocations {
if nextLocation.Pos == location.Pos+float64(i) && nextLocation.SameArrayElement(location) {
// found a location match for this term
crvtlm.AddLocation(nextTerm, nextLocation)
continue INNER
}
}
// if we got here we didn't find a location match for this term
continue OUTER
} else {
continue OUTER
}
}
}
// if we got here all the terms matched
freq++
search.MergeTermLocationMaps(rvtlm, crvtlm)
rvftlm[field] = rvtlm
}
}
}
if freq > 0 {
// return match
rv = s.currMust
rv.Locations = rvftlm
err := s.advanceNextMust()
if err != nil {
return nil, err
}
return rv, nil
}
err := s.advanceNextMust()
if err != nil {
return nil, err
}
}
return nil, nil
}
func (s *Highlighter) BestFragmentsInField(dm *search.DocumentMatch, doc *document.Document, field string, num int) []string {
tlm := dm.Locations[field]
orderedTermLocations := highlight.OrderTermLocations(tlm)
scorer := NewFragmentScorer(tlm)
// score the fragments and put them into a priority queue ordered by score
fq := make(FragmentQueue, 0)
heap.Init(&fq)
for _, f := range doc.Fields {
if f.Name() == field {
_, ok := f.(*document.TextField)
if ok {
termLocationsSameArrayPosition := make(highlight.TermLocations, 0)
for _, otl := range orderedTermLocations {
if highlight.SameArrayPositions(f.ArrayPositions(), otl.ArrayPositions) {
termLocationsSameArrayPosition = append(termLocationsSameArrayPosition, otl)
}
}
fieldData := f.Value()
fragments := s.fragmenter.Fragment(fieldData, termLocationsSameArrayPosition)
for _, fragment := range fragments {
fragment.ArrayPositions = f.ArrayPositions()
scorer.Score(fragment)
heap.Push(&fq, fragment)
}
}
}
}
// now find the N best non-overlapping fragments
var bestFragments []*highlight.Fragment
if len(fq) > 0 {
candidate := heap.Pop(&fq)
OUTER:
for candidate != nil && len(bestFragments) < num {
// see if this overlaps with any of the best already identified
if len(bestFragments) > 0 {
for _, frag := range bestFragments {
if candidate.(*highlight.Fragment).Overlaps(frag) {
if len(fq) < 1 {
break OUTER
}
candidate = heap.Pop(&fq)
continue OUTER
}
}
bestFragments = append(bestFragments, candidate.(*highlight.Fragment))
} else {
bestFragments = append(bestFragments, candidate.(*highlight.Fragment))
}
if len(fq) < 1 {
break
}
candidate = heap.Pop(&fq)
}
}
// now that we have the best fragments, we can format them
orderedTermLocations.MergeOverlapping()
formattedFragments := make([]string, len(bestFragments))
for i, fragment := range bestFragments {
formattedFragments[i] = ""
if fragment.Start != 0 {
formattedFragments[i] += s.sep
}
formattedFragments[i] += s.formatter.Format(fragment, orderedTermLocations)
if fragment.End != len(fragment.Orig) {
formattedFragments[i] += s.sep
}
}
if dm.Fragments == nil {
dm.Fragments = make(search.FieldFragmentMap, 0)
}
if len(formattedFragments) > 0 {
dm.Fragments[field] = formattedFragments
}
return formattedFragments
}
func (s *TermQueryScorer) Score(termMatch *index.TermFieldDoc) *search.DocumentMatch {
var scoreExplanation *search.Explanation
// need to compute score
var tf float64
if termMatch.Freq < MaxSqrtCache {
tf = SqrtCache[int(termMatch.Freq)]
} else {
tf = math.Sqrt(float64(termMatch.Freq))
}
score := tf * termMatch.Norm * s.idf
if s.explain {
childrenExplanations := make([]*search.Explanation, 3)
childrenExplanations[0] = &search.Explanation{
Value: tf,
Message: fmt.Sprintf("tf(termFreq(%s:%s)=%d", s.queryField, string(s.queryTerm), termMatch.Freq),
}
childrenExplanations[1] = &search.Explanation{
Value: termMatch.Norm,
Message: fmt.Sprintf("fieldNorm(field=%s, doc=%s)", s.queryField, termMatch.ID),
}
childrenExplanations[2] = s.idfExplanation
scoreExplanation = &search.Explanation{
Value: score,
Message: fmt.Sprintf("fieldWeight(%s:%s in %s), product of:", s.queryField, string(s.queryTerm), termMatch.ID),
Children: childrenExplanations,
}
}
// if the query weight isn't 1, multiply
if s.queryWeight != 1.0 {
score = score * s.queryWeight
if s.explain {
childExplanations := make([]*search.Explanation, 2)
childExplanations[0] = s.queryWeightExplanation
childExplanations[1] = scoreExplanation
scoreExplanation = &search.Explanation{
Value: score,
Message: fmt.Sprintf("weight(%s:%s^%f in %s), product of:", s.queryField, string(s.queryTerm), s.queryBoost, termMatch.ID),
Children: childExplanations,
}
}
}
rv := search.DocumentMatch{
ID: termMatch.ID,
Score: score,
}
if s.explain {
rv.Expl = scoreExplanation
}
if termMatch.Vectors != nil && len(termMatch.Vectors) > 0 {
rv.Locations = make(search.FieldTermLocationMap)
for _, v := range termMatch.Vectors {
tlm := rv.Locations[v.Field]
if tlm == nil {
tlm = make(search.TermLocationMap)
}
loc := search.Location{
Pos: float64(v.Pos),
Start: float64(v.Start),
End: float64(v.End),
}
if len(v.ArrayPositions) > 0 {
loc.ArrayPositions = make([]float64, len(v.ArrayPositions))
for i, ap := range v.ArrayPositions {
loc.ArrayPositions[i] = float64(ap)
}
}
locations := tlm[s.queryTerm]
if locations == nil {
locations = make(search.Locations, 1)
locations[0] = &loc
} else {
locations = append(locations, &loc)
}
tlm[s.queryTerm] = locations
rv.Locations[v.Field] = tlm
}
}
return &rv
}
func (hc *TopNCollector) collectSingle(ctx *search.SearchContext, reader index.IndexReader, d *search.DocumentMatch) error {
// increment total hits
hc.total++
d.HitNumber = hc.total
// update max score
if d.Score > hc.maxScore {
hc.maxScore = d.Score
}
var err error
// see if we need to load ID (at this early stage, for example to sort on it)
if hc.needDocIds {
d.ID, err = reader.ExternalID(d.IndexInternalID)
if err != nil {
return err
}
}
// see if we need to load the stored fields
if len(hc.neededFields) > 0 {
// find out which fields haven't been loaded yet
fieldsToLoad := d.CachedFieldTerms.FieldsNotYetCached(hc.neededFields)
// look them up
fieldTerms, err := reader.DocumentFieldTerms(d.IndexInternalID, fieldsToLoad)
if err != nil {
return err
}
// cache these as well
if d.CachedFieldTerms == nil {
d.CachedFieldTerms = make(map[string][]string)
}
d.CachedFieldTerms.Merge(fieldTerms)
}
// compute this hits sort value
if len(hc.sort) == 1 && hc.cachedScoring[0] {
d.Sort = sortByScoreOpt
} else {
hc.sort.Value(d)
}
// optimization, we track lowest sorting hit already removed from heap
// with this one comparison, we can avoid all heap operations if
// this hit would have been added and then immediately removed
if hc.lowestMatchOutsideResults != nil {
cmp := hc.sort.Compare(hc.cachedScoring, hc.cachedDesc, d, hc.lowestMatchOutsideResults)
if cmp >= 0 {
// this hit can't possibly be in the result set, so avoid heap ops
ctx.DocumentMatchPool.Put(d)
return nil
}
}
hc.store.Add(d)
if hc.store.Len() > hc.size+hc.skip {
removed := hc.store.RemoveLast()
if hc.lowestMatchOutsideResults == nil {
hc.lowestMatchOutsideResults = removed
} else {
cmp := hc.sort.Compare(hc.cachedScoring, hc.cachedDesc, removed, hc.lowestMatchOutsideResults)
if cmp < 0 {
tmp := hc.lowestMatchOutsideResults
hc.lowestMatchOutsideResults = removed
ctx.DocumentMatchPool.Put(tmp)
}
}
}
return nil
}
