8096d9fb90
this originated from a misunderstanding of mine going back several years. the values need not be float64 just because we plan to serialize them as json. there are still larger questions about what the right type should be, and where should any conversions go. but, this commit simply attempts to address the most egregious problems
183 lines
5.2 KiB
Go
183 lines
5.2 KiB
Go
// Copyright (c) 2014 Couchbase, Inc.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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package scorer
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import (
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"fmt"
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"math"
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"github.com/blevesearch/bleve/index"
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"github.com/blevesearch/bleve/search"
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)
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type TermQueryScorer struct {
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queryTerm string
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queryField string
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queryBoost float64
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docTerm uint64
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docTotal uint64
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idf float64
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options search.SearcherOptions
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idfExplanation *search.Explanation
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queryNorm float64
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queryWeight float64
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queryWeightExplanation *search.Explanation
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}
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func NewTermQueryScorer(queryTerm string, queryField string, queryBoost float64, docTotal, docTerm uint64, options search.SearcherOptions) *TermQueryScorer {
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rv := TermQueryScorer{
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queryTerm: queryTerm,
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queryField: queryField,
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queryBoost: queryBoost,
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docTerm: docTerm,
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docTotal: docTotal,
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idf: 1.0 + math.Log(float64(docTotal)/float64(docTerm+1.0)),
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options: options,
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queryWeight: 1.0,
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}
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if options.Explain {
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rv.idfExplanation = &search.Explanation{
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Value: rv.idf,
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Message: fmt.Sprintf("idf(docFreq=%d, maxDocs=%d)", docTerm, docTotal),
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}
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}
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return &rv
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}
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func (s *TermQueryScorer) Weight() float64 {
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sum := s.queryBoost * s.idf
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return sum * sum
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}
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func (s *TermQueryScorer) SetQueryNorm(qnorm float64) {
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s.queryNorm = qnorm
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// update the query weight
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s.queryWeight = s.queryBoost * s.idf * s.queryNorm
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if s.options.Explain {
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childrenExplanations := make([]*search.Explanation, 3)
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childrenExplanations[0] = &search.Explanation{
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Value: s.queryBoost,
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Message: "boost",
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}
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childrenExplanations[1] = s.idfExplanation
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childrenExplanations[2] = &search.Explanation{
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Value: s.queryNorm,
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Message: "queryNorm",
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}
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s.queryWeightExplanation = &search.Explanation{
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Value: s.queryWeight,
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Message: fmt.Sprintf("queryWeight(%s:%s^%f), product of:", s.queryField, string(s.queryTerm), s.queryBoost),
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Children: childrenExplanations,
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}
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}
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}
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func (s *TermQueryScorer) Score(ctx *search.SearchContext, termMatch *index.TermFieldDoc) *search.DocumentMatch {
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var scoreExplanation *search.Explanation
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// need to compute score
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var tf float64
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if termMatch.Freq < MaxSqrtCache {
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tf = SqrtCache[int(termMatch.Freq)]
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} else {
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tf = math.Sqrt(float64(termMatch.Freq))
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}
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score := tf * termMatch.Norm * s.idf
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if s.options.Explain {
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childrenExplanations := make([]*search.Explanation, 3)
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childrenExplanations[0] = &search.Explanation{
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Value: tf,
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Message: fmt.Sprintf("tf(termFreq(%s:%s)=%d", s.queryField, string(s.queryTerm), termMatch.Freq),
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}
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childrenExplanations[1] = &search.Explanation{
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Value: termMatch.Norm,
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Message: fmt.Sprintf("fieldNorm(field=%s, doc=%s)", s.queryField, termMatch.ID),
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}
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childrenExplanations[2] = s.idfExplanation
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scoreExplanation = &search.Explanation{
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Value: score,
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Message: fmt.Sprintf("fieldWeight(%s:%s in %s), product of:", s.queryField, string(s.queryTerm), termMatch.ID),
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Children: childrenExplanations,
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}
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}
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// if the query weight isn't 1, multiply
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if s.queryWeight != 1.0 {
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score = score * s.queryWeight
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if s.options.Explain {
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childExplanations := make([]*search.Explanation, 2)
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childExplanations[0] = s.queryWeightExplanation
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childExplanations[1] = scoreExplanation
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scoreExplanation = &search.Explanation{
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Value: score,
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Message: fmt.Sprintf("weight(%s:%s^%f in %s), product of:", s.queryField, string(s.queryTerm), s.queryBoost, termMatch.ID),
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Children: childExplanations,
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}
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}
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}
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rv := ctx.DocumentMatchPool.Get()
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rv.IndexInternalID = append(rv.IndexInternalID, termMatch.ID...)
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rv.Score = score
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if s.options.Explain {
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rv.Expl = scoreExplanation
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}
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if termMatch.Vectors != nil && len(termMatch.Vectors) > 0 {
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locs := make([]search.Location, len(termMatch.Vectors))
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locsUsed := 0
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totalPositions := 0
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for _, v := range termMatch.Vectors {
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totalPositions += len(v.ArrayPositions)
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}
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positions := make(search.ArrayPositions, totalPositions)
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positionsUsed := 0
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rv.Locations = make(search.FieldTermLocationMap)
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for _, v := range termMatch.Vectors {
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tlm := rv.Locations[v.Field]
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if tlm == nil {
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tlm = make(search.TermLocationMap)
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rv.Locations[v.Field] = tlm
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}
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loc := &locs[locsUsed]
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locsUsed++
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loc.Pos = v.Pos
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loc.Start = v.Start
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loc.End = v.End
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if len(v.ArrayPositions) > 0 {
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loc.ArrayPositions = positions[positionsUsed : positionsUsed+len(v.ArrayPositions)]
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for i, ap := range v.ArrayPositions {
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loc.ArrayPositions[i] = ap
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}
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positionsUsed += len(v.ArrayPositions)
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}
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tlm[s.queryTerm] = append(tlm[s.queryTerm], loc)
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}
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}
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return rv
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}
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