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Adding KNN scorer explanation (#1899)
This PR adds a score Explanation to the KNN scorer, along with a unit test. Also, contains a minor refactor of scoring related code. --------- Co-authored-by: Abhinav Dangeti <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,178 @@ | ||
| // Copyright (c) 2023 Couchbase, Inc. | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
|
|
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| //go:build vectors | ||
| // +build vectors | ||
|
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| package scorer | ||
|
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| import ( | ||
| "reflect" | ||
| "testing" | ||
|
|
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| "github.com/blevesearch/bleve/v2/search" | ||
| index "github.com/blevesearch/bleve_index_api" | ||
| ) | ||
|
|
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| func TestKNNScorerExplanation(t *testing.T) { | ||
| var queryVector []float32 | ||
| // arbitrary vector of dims: 64 | ||
| for i := 0; i < 64; i++ { | ||
| queryVector = append(queryVector, float32(i)) | ||
| } | ||
|
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| var resVector []float32 | ||
| // arbitrary res vector. | ||
| for i := 0; i < 64; i++ { | ||
| resVector = append(resVector, float32(i)) | ||
| } | ||
|
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| tests := []struct { | ||
| vectorMatch *index.VectorDoc | ||
| scorer *KNNQueryScorer | ||
| norm float64 | ||
| result *search.DocumentMatch | ||
| }{ | ||
| { | ||
| vectorMatch: &index.VectorDoc{ | ||
| ID: index.IndexInternalID("one"), | ||
| Score: 0.5, | ||
| Vector: resVector, | ||
| }, | ||
| norm: 1.0, | ||
| scorer: NewKNNQueryScorer(queryVector, "desc", 1.0, | ||
| search.SearcherOptions{Explain: true}, index.EuclideanDistance), | ||
| // Specifically testing EuclideanDistance since that involves score inversion. | ||
| result: &search.DocumentMatch{ | ||
| IndexInternalID: index.IndexInternalID("one"), | ||
| Score: 0.5, | ||
| Expl: &search.Explanation{ | ||
| Value: 1 / 0.5, | ||
| Message: "fieldWeight(desc in doc one), score of:", | ||
| Children: []*search.Explanation{ | ||
| {Value: 1 / 0.5, | ||
| Message: "vector(field(desc:one) with similarity_metric(l2_norm)=2.000000e+00", | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| { | ||
| vectorMatch: &index.VectorDoc{ | ||
| ID: index.IndexInternalID("one"), | ||
| Score: 0.0, | ||
| // Result vector is an exact match of an existing vector. | ||
| Vector: queryVector, | ||
| }, | ||
| norm: 1.0, | ||
| scorer: NewKNNQueryScorer(queryVector, "desc", 1.0, | ||
| search.SearcherOptions{Explain: true}, index.EuclideanDistance), | ||
| // Specifically testing EuclideanDistance with 0 score. | ||
| result: &search.DocumentMatch{ | ||
| IndexInternalID: index.IndexInternalID("one"), | ||
| Score: 0.0, | ||
| Expl: &search.Explanation{ | ||
| Value: maxKNNScore, | ||
| Message: "fieldWeight(desc in doc one), score of:", | ||
| Children: []*search.Explanation{ | ||
| {Value: maxKNNScore, | ||
| Message: "vector(field(desc:one) with similarity_metric(l2_norm)=1.797693e+308", | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| { | ||
| vectorMatch: &index.VectorDoc{ | ||
| ID: index.IndexInternalID("one"), | ||
| Score: 0.5, | ||
| Vector: resVector, | ||
| }, | ||
| norm: 1.0, | ||
| scorer: NewKNNQueryScorer(queryVector, "desc", 1.0, | ||
| search.SearcherOptions{Explain: true}, index.CosineSimilarity), | ||
| result: &search.DocumentMatch{ | ||
| IndexInternalID: index.IndexInternalID("one"), | ||
| Score: 0.5, | ||
| Expl: &search.Explanation{ | ||
| Value: 0.5, | ||
| Message: "fieldWeight(desc in doc one), score of:", | ||
| Children: []*search.Explanation{ | ||
| {Value: 0.5, | ||
| Message: "vector(field(desc:one) with similarity_metric(dot_product)=5.000000e-01", | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| { | ||
| vectorMatch: &index.VectorDoc{ | ||
| ID: index.IndexInternalID("one"), | ||
| Score: 0.25, | ||
| Vector: resVector, | ||
| }, | ||
| norm: 0.5, | ||
| scorer: NewKNNQueryScorer(queryVector, "desc", 1.0, | ||
| search.SearcherOptions{Explain: true}, index.CosineSimilarity), | ||
| result: &search.DocumentMatch{ | ||
| IndexInternalID: index.IndexInternalID("one"), | ||
| Score: 0.25, | ||
| Expl: &search.Explanation{ | ||
| Value: 0.125, | ||
| Message: "weight(desc:query Vector^1.000000 in one), product of:", | ||
| Children: []*search.Explanation{ | ||
| { | ||
| Value: 0.5, | ||
| Message: "queryWeight(desc:query Vector^1.000000), product of:", | ||
| Children: []*search.Explanation{ | ||
| { | ||
| Value: 1, | ||
| Message: "boost", | ||
| }, | ||
| { | ||
| Value: 0.5, | ||
| Message: "queryNorm", | ||
| }, | ||
| }, | ||
| }, | ||
| { | ||
| Value: 0.25, | ||
| Message: "fieldWeight(desc in doc one), score of:", | ||
| Children: []*search.Explanation{ | ||
| { | ||
| Value: 0.25, | ||
| Message: "vector(field(desc:one) with similarity_metric(dot_product)=2.500000e-01", | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| }, | ||
| } | ||
|
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| for _, test := range tests { | ||
| ctx := &search.SearchContext{ | ||
| DocumentMatchPool: search.NewDocumentMatchPool(1, 0), | ||
| } | ||
| test.scorer.SetQueryNorm(test.norm) | ||
| actual := test.scorer.Score(ctx, test.vectorMatch) | ||
| actual.Complete(nil) | ||
|
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| if !reflect.DeepEqual(actual.Expl, test.result.Expl) { | ||
| t.Errorf("expected %#v got %#v for %#v", test.result.Expl, | ||
| actual.Expl, test.vectorMatch) | ||
| } | ||
| } | ||
| } |
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