test_rerank_client.py
6.63 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
from math import isclose
from config.schema import RerankFusionConfig
from search.rerank_client import fuse_scores_and_resort
def test_fuse_scores_and_resort_aggregates_text_components_and_keeps_rerank_primary():
hits = [
{
"_id": "1",
"_score": 3.2,
"matched_queries": {
"base_query": 2.4,
"base_query_trans_zh": 1.8,
"knn_query": 0.8,
},
},
{
"_id": "2",
"_score": 2.8,
"matched_queries": {
"base_query": 9.0,
"knn_query": 0.2,
},
},
]
debug = fuse_scores_and_resort(hits, [0.9, 0.7], debug=True)
expected_text_1 = 2.4 + 0.25 * (0.8 * 1.8)
expected_fused_1 = (0.9 + 0.00001) * ((expected_text_1 + 0.1) ** 0.35) * ((0.8 + 0.6) ** 0.2)
expected_fused_2 = (0.7 + 0.00001) * ((9.0 + 0.1) ** 0.35) * ((0.2 + 0.6) ** 0.2)
by_id = {hit["_id"]: hit for hit in hits}
assert isclose(by_id["1"]["_text_score"], expected_text_1, rel_tol=1e-9)
assert isclose(by_id["1"]["_fused_score"], expected_fused_1, rel_tol=1e-9)
assert isclose(by_id["2"]["_fused_score"], expected_fused_2, rel_tol=1e-9)
assert debug[0]["text_source_score"] == 2.4
assert debug[0]["text_translation_score"] == 1.8
assert isclose(debug[0]["text_weighted_translation_score"], 1.44, rel_tol=1e-9)
assert debug[0]["knn_score"] == 0.8
assert isclose(debug[0]["rerank_factor"], 0.90001, rel_tol=1e-9)
assert [hit["_id"] for hit in hits] == ["2", "1"]
def test_fuse_scores_and_resort_falls_back_when_matched_queries_missing():
hits = [
{"_id": "1", "_score": 0.5},
{"_id": "2", "_score": 2.0},
]
debug = fuse_scores_and_resort(hits, [0.4, 0.3], debug=True)
expected_1 = (0.4 + 0.00001) * ((0.5 + 0.1) ** 0.35) * ((0.0 + 0.6) ** 0.2)
expected_2 = (0.3 + 0.00001) * ((2.0 + 0.1) ** 0.35) * ((0.0 + 0.6) ** 0.2)
by_id = {hit["_id"]: hit for hit in hits}
assert isclose(by_id["1"]["_text_score"], 0.5, rel_tol=1e-9)
assert isclose(by_id["1"]["_fused_score"], expected_1, rel_tol=1e-9)
assert isclose(by_id["2"]["_text_score"], 2.0, rel_tol=1e-9)
assert isclose(by_id["2"]["_fused_score"], expected_2, rel_tol=1e-9)
assert debug[0]["text_score_fallback_to_es"] is True
assert debug[1]["text_score_fallback_to_es"] is True
assert [hit["_id"] for hit in hits] == ["2", "1"]
def test_fuse_scores_and_resort_downweights_text_only_advantage():
hits = [
{
"_id": "lexical-heavy",
"_score": 10.0,
"matched_queries": {
"base_query": 10.0,
"knn_query": 0.0,
},
},
{
"_id": "rerank-better",
"_score": 6.0,
"matched_queries": {
"base_query": 6.0,
"knn_query": 0.0,
},
},
]
fuse_scores_and_resort(hits, [0.72, 0.98])
assert [hit["_id"] for hit in hits] == ["rerank-better", "lexical-heavy"]
def test_fuse_scores_and_resort_uses_configurable_fusion_params():
hits = [
{
"_id": "a",
"_score": 1.0,
"matched_queries": {"base_query": 2.0, "knn_query": 0.5},
},
{
"_id": "b",
"_score": 1.0,
"matched_queries": {"base_query": 3.0, "knn_query": 0.0},
},
]
fusion = RerankFusionConfig(
rerank_bias=0.0,
rerank_exponent=1.0,
text_bias=0.0,
text_exponent=1.0,
knn_bias=0.0,
knn_exponent=1.0,
)
fuse_scores_and_resort(hits, [1.0, 1.0], fusion=fusion)
# b 的 knn 为 0 -> 融合为 0；a 为 1 * 2 * 0.5
assert [h["_id"] for h in hits] == ["a", "b"]
by_id = {h["_id"]: h for h in hits}
assert isclose(by_id["a"]["_fused_score"], 1.0, rel_tol=1e-9)
assert isclose(by_id["b"]["_fused_score"], 0.0, rel_tol=1e-9)
def test_fuse_scores_and_resort_boosts_hits_with_selected_sku():
hits = [
{
"_id": "style-selected",
"_score": 1.0,
"_style_rerank_suffix": "Blue XL",
"matched_queries": {"base_query": 1.0, "knn_query": 0.0},
},
{
"_id": "plain",
"_score": 1.0,
"matched_queries": {"base_query": 1.0, "knn_query": 0.0},
},
]
debug = fuse_scores_and_resort(
hits,
[1.0, 1.0],
style_intent_selected_sku_boost=1.2,
debug=True,
)
by_id = {h["_id"]: h for h in hits}
assert isclose(by_id["style-selected"]["_fused_score"], by_id["plain"]["_fused_score"] * 1.2, rel_tol=1e-9)
assert by_id["style-selected"]["_style_intent_selected_sku_boost"] == 1.2
assert by_id["plain"]["_style_intent_selected_sku_boost"] == 1.0
assert [h["_id"] for h in hits] == ["style-selected", "plain"]
assert debug[0]["style_intent_selected_sku"] is True
assert debug[0]["style_intent_selected_sku_boost"] == 1.2
def test_fuse_scores_and_resort_uses_max_of_text_and_image_knn_scores():
hits = [
{
"_id": "mm-hit",
"_score": 1.0,
"matched_queries": {
"base_query": 1.5,
"knn_query": 0.2,
"image_knn_query": 0.7,
},
}
]
debug = fuse_scores_and_resort(hits, [0.8], debug=True)
assert isclose(hits[0]["_knn_score"], 0.7, rel_tol=1e-9)
assert isclose(debug[0]["knn_score"], 0.7, rel_tol=1e-9)
assert isclose(debug[0]["text_knn_score"], 0.2, rel_tol=1e-9)
assert isclose(debug[0]["image_knn_score"], 0.7, rel_tol=1e-9)
def test_fuse_scores_and_resort_applies_knn_dismax_weights_and_tie_breaker():
hits = [
{
"_id": "mm-hit",
"_score": 1.0,
"matched_queries": {
"base_query": 1.5,
"knn_query": 0.4,
"image_knn_query": 0.5,
},
}
]
fusion = RerankFusionConfig(
rerank_bias=0.00001,
rerank_exponent=1.0,
text_bias=0.1,
text_exponent=0.35,
knn_text_weight=2.0,
knn_image_weight=1.0,
knn_tie_breaker=0.25,
knn_bias=0.0,
knn_exponent=1.0,
)
debug = fuse_scores_and_resort(hits, [0.8], fusion=fusion, debug=True)
expected_knn = 0.8 + 0.25 * 0.5
assert isclose(hits[0]["_knn_score"], expected_knn, rel_tol=1e-9)
assert isclose(debug[0]["weighted_text_knn_score"], 0.8, rel_tol=1e-9)
assert isclose(debug[0]["weighted_image_knn_score"], 0.5, rel_tol=1e-9)
assert isclose(debug[0]["knn_primary_score"], 0.8, rel_tol=1e-9)
assert isclose(debug[0]["knn_support_score"], 0.5, rel_tol=1e-9)