d3dd01d3
 tangwang
自动寻参：
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target_path: coarse_rank.fusion
baseline:
es_bias: 10.0
es_exponent: 0.05
text_bias: 0.1
text_exponent: 0.35
text_translation_weight: 1.0
knn_text_weight: 1.0
knn_image_weight: 2.0
knn_tie_breaker: 0.3
knn_bias: 0.2
knn_exponent: 5.6
knn_text_bias: 0.2
knn_text_exponent: 0.0
knn_image_bias: 0.2
knn_image_exponent: 0.0
parameters:
es_bias: {min: 2.0, max: 20.0, scale: log, round: 4}
es_exponent: {min: 0.03, max: 0.28, scale: linear, round: 4}
text_bias: {min: 0.01, max: 4.0, scale: log, round: 4}
text_exponent: {min: 0.2, max: 1.6, scale: linear, round: 4}
text_translation_weight: {min: 0.7, max: 1.8, scale: linear, round: 4}
knn_text_weight: {min: 0.05, max: 1.8, scale: linear, round: 4}
knn_image_weight: {min: 1.2, max: 6.0, scale: linear, round: 4}
knn_tie_breaker: {min: 0.0, max: 0.4, scale: linear, round: 4}
knn_bias: {min: 0.001, max: 2.5, scale: log, round: 4}
knn_exponent: {min: 0.05, max: 12.0, scale: log, round: 4}
knn_text_bias: {min: 0.001, max: 4.0, scale: log, round: 4}
knn_text_exponent: {min: 0.0, max: 2.0, scale: linear, round: 4}
knn_image_bias: {min: 0.01, max: 1.5, scale: log, round: 4}
knn_image_exponent: {min: 0.0, max: 6.0, scale: linear, round: 4}
seed_experiments:
- name: seed_low_knn_global
description: 先验证 021002 中出现的低 knn 全局指数，去掉 reranker 后是否仍有收益。
params:
knn_bias: 0.6
knn_exponent: 0.4
- name: seed_bigset_knn_soft
description: 从低 knn 全局指数出发，继续平滑 knn 非线性。
params:
text_exponent: 0.42
text_translation_weight: 1.05
knn_text_weight: 0.85
knn_image_weight: 2.4
knn_tie_breaker: 0.18
knn_bias: 0.9
knn_exponent: 0.18
knn_image_exponent: 0.2
- name: seed_bigset_knn_mid
description: 保留平滑 knn，但让 image 通路再强一点，验证大集是否需要适度非线性。
params:
es_bias: 8.0
es_exponent: 0.08
text_bias: 0.15
text_exponent: 0.5
text_translation_weight: 1.15
knn_text_weight: 0.65
knn_image_weight: 3.1
knn_tie_breaker: 0.12
knn_bias: 0.45
knn_exponent: 0.85
knn_text_bias: 0.35
knn_text_exponent: 0.2
knn_image_bias: 0.22
knn_image_exponent: 0.8
- name: seed_bigset_text_stable
description: 提高 lexical 区分度，观察大集是否更偏好稳健文本排序。
params:
es_bias: 7.0
es_exponent: 0.12
text_bias: 0.25
text_exponent: 0.72
text_translation_weight: 1.0
knn_text_weight: 0.55
knn_image_weight: 2.2
knn_tie_breaker: 0.08
knn_bias: 0.7
knn_exponent: 0.35
knn_text_bias: 0.5
knn_text_exponent: 0.4
knn_image_bias: 0.18
knn_image_exponent: 0.35
- name: seed_hybrid_transfer
description: 以大集 baseline 为主，温和吸收小集历史赢家中的 image/text 强化模式。
params:
es_bias: 7.2
es_exponent: 0.15
text_bias: 0.6
text_exponent: 0.82
text_translation_weight: 1.28
knn_text_weight: 0.45
knn_image_weight: 4.0
knn_tie_breaker: 0.08
knn_bias: 0.2
knn_exponent: 1.2
knn_text_bias: 0.8
knn_text_exponent: 0.45
knn_image_bias: 0.3
knn_image_exponent: 1.4
- name: seed_legacy_bo234
description: 直接验证 53 条集历史最优在 771 条集上的迁移表现。
params:
es_bias: 7.214
es_exponent: 0.2025
text_bias: 4.0
text_exponent: 1.584
text_translation_weight: 1.4441
knn_text_weight: 0.1
knn_image_weight: 5.6232
knn_tie_breaker: 0.021
knn_bias: 0.0019
knn_exponent: 11.8477
knn_text_bias: 2.3125
knn_text_exponent: 1.1547
knn_image_bias: 0.9641
knn_image_exponent: 5.8671
- name: seed_legacy_bo340
description: 验证小集冠军参数在大集上是否仍有价值。
params:
es_bias: 5.887
es_exponent: 0.2145
text_bias: 4.0
text_exponent: 1.6
text_translation_weight: 1.4788
knn_text_weight: 0.3693
knn_image_weight: 5.7028
knn_tie_breaker: 0.0174
knn_bias: 0.0016
knn_exponent: 12.0
knn_text_bias: 2.6071
knn_text_exponent: 1.0458
knn_image_bias: 0.8282
knn_image_exponent: 6.0
- name: seed_image_guard
description: 控制 image 权重但允许 image 子项指数，检查 recall 与 precision 的平衡点。
params:
es_bias: 9.0
es_exponent: 0.09
text_bias: 0.12
text_exponent: 0.45
text_translation_weight: 1.1
knn_text_weight: 0.7
knn_image_weight: 2.8
knn_tie_breaker: 0.1
knn_bias: 0.55
knn_exponent: 0.55
knn_text_bias: 0.25
knn_text_exponent: 0.15
knn_image_bias: 0.28
knn_image_exponent: 1.0
optimizer:
init_random: 2
candidate_pool_size: 160
explore_probability: 0.12
local_jitter_probability: 0.62
elite_fraction: 0.25
min_normalized_distance: 0.08
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