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210 lines
8.3 KiB
Go
210 lines
8.3 KiB
Go
// Copyright (c) 2025 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 fusion
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import (
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"testing"
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"github.com/blevesearch/bleve/v2/search"
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)
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func TestRelativeScoreFusion(t *testing.T) {
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tests := []struct {
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name string
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hits search.DocumentMatchCollection
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weights []float64
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windowSize int
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numKNNQueries int
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want FusionResult
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}{
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{
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name: "empty hits",
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hits: search.DocumentMatchCollection{},
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weights: []float64{0.5, 0.5},
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windowSize: 10,
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numKNNQueries: 1,
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want: FusionResult{
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Hits: search.DocumentMatchCollection{},
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Total: 0,
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MaxScore: 0.0,
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},
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},
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{
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name: "single knn query",
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hits: search.DocumentMatchCollection{
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{ID: "a", Score: 0.9, ScoreBreakdown: map[int]float64{0: 0.8}},
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{ID: "b", Score: 0.8, ScoreBreakdown: map[int]float64{0: 0.9}},
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{ID: "c", Score: 0.7, ScoreBreakdown: map[int]float64{0: 0.7}},
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},
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weights: []float64{0.4, 0.6},
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windowSize: 3,
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numKNNQueries: 1,
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want: FusionResult{
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Hits: search.DocumentMatchCollection{
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{ID: "b", Score: 0.8}, // FTS: (0.8-0.7)/(0.9-0.7) * 0.4 + KNN: (0.9-0.7)/(0.9-0.7) * 0.6 = 0.2 + 0.6 = 0.8
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{ID: "a", Score: 0.7}, // FTS: (0.9-0.7)/(0.9-0.7) * 0.4 + KNN: (0.8-0.7)/(0.9-0.7) * 0.6 = 0.4 + 0.3 = 0.7
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{ID: "c", Score: 0.0}, // FTS: (0.7-0.7)/(0.9-0.7) * 0.4 + KNN: (0.7-0.7)/(0.9-0.7) * 0.6 = 0.0 + 0.0 = 0.0
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},
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Total: 3,
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MaxScore: 0.8,
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},
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},
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{
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name: "multiple knn queries",
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hits: search.DocumentMatchCollection{
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{ID: "a", Score: 0.9, ScoreBreakdown: map[int]float64{0: 0.8, 1: 0.6}},
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{ID: "b", Score: 0.8, ScoreBreakdown: map[int]float64{0: 0.9, 1: 0.5}},
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{ID: "c", Score: 0.7, ScoreBreakdown: map[int]float64{0: 0.7, 1: 0.7}},
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},
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weights: []float64{0.3, 0.4, 0.3},
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windowSize: 3,
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numKNNQueries: 2,
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want: FusionResult{
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Hits: search.DocumentMatchCollection{
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{ID: "a", Score: 0.65}, // FTS: (0.9-0.7)/(0.9-0.7)*0.3 + KNN0: (0.8-0.7)/(0.9-0.7)*0.4 + KNN1: (0.6-0.5)/(0.7-0.5)*0.3 = 1.0*0.3 + 0.5*0.4 + 0.5*0.3 = 0.65
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{ID: "b", Score: 0.55}, // FTS: (0.8-0.7)/(0.9-0.7)*0.3 + KNN0: (0.9-0.7)/(0.9-0.7)*0.4 + KNN1: (0.5-0.5)/(0.7-0.5)*0.3 = 0.5*0.3 + 1.0*0.4 + 0.0*0.3 = 0.55
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{ID: "c", Score: 0.3}, // FTS: (0.7-0.7)/(0.9-0.7)*0.3 + KNN0: (0.7-0.7)/(0.9-0.7)*0.4 + KNN1: (0.7-0.5)/(0.7-0.5)*0.3 = 0.0*0.3 + 0.0*0.4 + 1.0*0.3 = 0.3
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},
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Total: 3,
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MaxScore: 0.65,
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},
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},
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{
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name: "all scores identical should normalize to 1.0",
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hits: search.DocumentMatchCollection{
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{ID: "a", Score: 0.8, ScoreBreakdown: map[int]float64{0: 0.9}},
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{ID: "b", Score: 0.8, ScoreBreakdown: map[int]float64{0: 0.9}},
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{ID: "c", Score: 0.8, ScoreBreakdown: map[int]float64{0: 0.9}},
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},
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weights: []float64{0.4, 0.6},
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windowSize: 3,
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numKNNQueries: 1,
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want: FusionResult{
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Hits: search.DocumentMatchCollection{
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{ID: "a", Score: 1.0}, // All scores identical: 1.0 * 0.4 + 1.0 * 0.6 = 1.0
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{ID: "b", Score: 1.0},
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{ID: "c", Score: 1.0},
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},
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Total: 3,
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MaxScore: 1.0,
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},
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},
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{
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name: "window size smaller than hits",
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hits: search.DocumentMatchCollection{
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{ID: "a", Score: 0.9, ScoreBreakdown: map[int]float64{0: 0.7}},
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{ID: "b", Score: 0.8, ScoreBreakdown: map[int]float64{0: 0.9}},
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{ID: "c", Score: 0.7, ScoreBreakdown: map[int]float64{0: 0.8}},
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},
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weights: []float64{0.4, 0.6},
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windowSize: 2,
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numKNNQueries: 1,
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want: FusionResult{
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Hits: search.DocumentMatchCollection{
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{ID: "b", Score: 0.6}, // Using top 2 for min/max: FTS min/max from [0.9,0.8] = [0.8,0.9], KNN min/max from [0.9,0.7] = [0.7,0.9]
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{ID: "a", Score: 0.4}, // FTS: (0.9-0.8)/(0.9-0.8) * 0.4 + KNN: (0.7-0.7)/(0.9-0.7) * 0.6 = 0.4 + 0 = 0.4
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},
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Total: 2,
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MaxScore: 0.6,
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},
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},
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{
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name: "documents with partial scores missing KNN scores",
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hits: search.DocumentMatchCollection{
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{ID: "a", Score: 0.9, ScoreBreakdown: map[int]float64{0: 0.8}}, // has FTS and KNN query 0, missing KNN query 1
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{ID: "b", Score: 0.8, ScoreBreakdown: map[int]float64{1: 0.7}}, // has FTS and KNN query 1, missing KNN query 0
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{ID: "c", Score: 0.7, ScoreBreakdown: map[int]float64{0: 0.6, 1: 0.9}}, // has all scores
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{ID: "d", Score: 0.6, ScoreBreakdown: map[int]float64{}}, // has only FTS, missing all KNN scores
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},
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weights: []float64{0.3, 0.4, 0.3}, // FTS, KNN query 0, KNN query 1
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windowSize: 4,
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numKNNQueries: 2,
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want: FusionResult{
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Hits: search.DocumentMatchCollection{
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{ID: "a", Score: 0.7}, // FTS: (0.9-0.6)/(0.9-0.6)*0.3 + KNN0: (0.8-0.6)/(0.8-0.6)*0.4 + KNN1: 0 = 1.0*0.3 + 1.0*0.4 + 0 = 0.7
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{ID: "c", Score: 0.4}, // FTS: (0.7-0.6)/(0.9-0.6)*0.3 + KNN0: (0.6-0.6)/(0.8-0.6)*0.4 + KNN1: (0.9-0.7)/(0.9-0.7)*0.3 = 0.33*0.3 + 0.0*0.4 + 1.0*0.3 = 0.1 + 0 + 0.3 = 0.4
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{ID: "b", Score: 0.2}, // FTS: (0.8-0.6)/(0.9-0.6)*0.3 + KNN0: 0 + KNN1: (0.7-0.7)/(0.9-0.7)*0.3 = 0.67*0.3 + 0 + 0.0*0.3 = 0.2 + 0 + 0 = 0.2
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{ID: "d", Score: 0.0}, // FTS: (0.6-0.6)/(0.9-0.6)*0.3 + KNN0: 0 + KNN1: 0 = 0.0*0.3 + 0 + 0 = 0
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},
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Total: 4,
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MaxScore: 0.7,
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},
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},
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{
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name: "documents with only KNN scores",
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hits: search.DocumentMatchCollection{
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{ID: "a", Score: 0.0, ScoreBreakdown: map[int]float64{0: 0.9}}, // no FTS rank (Score 0.0), only KNN query 0
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{ID: "b", Score: 0.0, ScoreBreakdown: map[int]float64{1: 0.8}}, // no FTS rank (Score 0.0), only KNN query 1
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{ID: "c", Score: 0.0, ScoreBreakdown: map[int]float64{0: 0.7, 1: 0.6}}, // no FTS rank (Score 0.0), both KNN queries
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},
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weights: []float64{0.5, 0.3, 0.2}, // FTS, KNN query 0, KNN query 1
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windowSize: 3,
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numKNNQueries: 2,
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want: FusionResult{
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Hits: search.DocumentMatchCollection{
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{ID: "a", Score: 0.3}, // FTS: 0 + KNN0: 1.0 * 0.3 + KNN1: 0 = 0.3
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{ID: "b", Score: 0.2}, // FTS: 0 + KNN0: 0 + KNN1: 1.0 * 0.2 = 0.2
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{ID: "c", Score: 0.0}, // FTS: 0 + KNN0: 0 * 0.3 + KNN1: 0 * 0.2 = 0
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},
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Total: 3,
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MaxScore: 0.3,
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},
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},
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{
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name: "mixed scenario with different score ranges",
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hits: search.DocumentMatchCollection{
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{ID: "a", Score: 1.0, ScoreBreakdown: map[int]float64{0: 0.1}}, // high FTS, low KNN
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{ID: "b", Score: 0.1, ScoreBreakdown: map[int]float64{0: 1.0}}, // low FTS, high KNN
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{ID: "c", Score: 0.5, ScoreBreakdown: map[int]float64{0: 0.5}}, // mid FTS, mid KNN
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},
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weights: []float64{0.5, 0.5}, // Equal weights
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windowSize: 3,
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numKNNQueries: 1,
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want: FusionResult{
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Hits: search.DocumentMatchCollection{
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{ID: "a", Score: 0.5}, // FTS: (1.0-0.1)/(1.0-0.1)*0.5 + KNN: (0.1-0.1)/(1.0-0.1)*0.5 = 1.0*0.5 + 0.0*0.5 = 0.5
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{ID: "b", Score: 0.5}, // FTS: (0.1-0.1)/(1.0-0.1)*0.5 + KNN: (1.0-0.1)/(1.0-0.1)*0.5 = 0.0*0.5 + 1.0*0.5 = 0.5
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{ID: "c", Score: 0.444}, // FTS: (0.5-0.1)/(1.0-0.1)*0.5 + KNN: (0.5-0.1)/(1.0-0.1)*0.5 = 0.444*0.5 + 0.444*0.5 = 0.444
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},
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Total: 3,
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MaxScore: 0.5,
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},
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},
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}
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for _, tt := range tests {
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t.Run(tt.name, func(t *testing.T) {
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for i, hit := range tt.hits {
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hit.HitNumber = uint64(i)
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}
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if got := RelativeScoreFusion(tt.hits, tt.weights, tt.windowSize, tt.numKNNQueries, false); !compareFusionResults(*got, tt.want) {
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t.Errorf("RelativeScoreFusion() = %v, want %v", got, tt.want)
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// Print detailed comparison for debugging
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t.Logf("Got hits:")
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for i, hit := range got.Hits {
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t.Logf(" [%d] ID: %s, Score: %.6f", i, hit.ID, hit.Score)
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}
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t.Logf("Want hits:")
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for i, hit := range tt.want.Hits {
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t.Logf(" [%d] ID: %s, Score: %.6f", i, hit.ID, hit.Score)
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}
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t.Logf("Got Total: %d, MaxScore: %.6f", got.Total, got.MaxScore)
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t.Logf("Want Total: %d, MaxScore: %.6f", tt.want.Total, tt.want.MaxScore)
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}
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})
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}
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}
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