Spaces:
Running on Zero
Running on Zero
File size: 9,183 Bytes
c054088 8763d43 c054088 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 | import os
import torch
import numpy as np
from typing import List, Dict, Set
from src.until.page_similarity_graph import construct_page_similarity_graph
class DocumentHypergraph:
def __init__(self):
self.page_similarity_graph = {}
self.query_specific_hypergraph = {}
self.graph = {}
def construct_page_similarity_graph(
self,
page_embeds,
threshold: float = 0.7,
k_value: int = 5,
similarity_measure: str = "cosine"
) -> Dict[int, List[int]]:
self.page_similarity_graph = construct_page_similarity_graph(
page_embeds=page_embeds,
threshold=threshold,
k_value=k_value,
similarity_measure=similarity_measure
)
def _get_specific_page(
self,
basis_pages: List[int],
target_pages: List[int]
) -> Set[int]:
set_basis, set_target = set(basis_pages), set(target_pages)
new_pages = set_target - set_basis
improved_pages = {
p for p in set_basis & set_target
if target_pages.index(p) < basis_pages.index(p)
}
return new_pages | improved_pages
def construct_query_specific_hypergraph(
self,
bandit,
vlm,
dataset,
top_page_indices: List[List[int]],
top_page_scores: List[List[float]],
queries: List[str],
col_score_dict: Dict[int, float],
sample: Dict,
best_first_hit_score: int = 20
):
document_hypergraph = {}
extra_pages = set()
default_idx = len(top_page_indices) - 1
bf_query_idx, bf_vlm_score, bf_score, bf_page = default_idx, -999, -999, 0
def _load_node(
page_num: int,
query_idx: int,
use_vlm: bool = True,
first_hit: int = 1,
multi_hit: int = 1
):
nonlocal bf_query_idx, bf_vlm_score, bf_score, bf_page
if page_num in document_hypergraph:
node = document_hypergraph[page_num]
node["hits"] += multi_hit
cb_score = bandit.sample(node.get("query_idx", set()))
node["score"] = bandit._compute_score(
col_score=node["col_score"],
vlm_score=node["vlm_score"],
cb_score=0.5,
hits=node["hits"],
)
if query_idx != default_idx and query_idx not in node["query_idx"]:
node["query_idx"].add(query_idx)
node["query"] += f",{queries[query_idx]}"
else:
vlm_score = -1
if use_vlm:
vlm_score = bandit.query_vlm_relevance(
vlm=vlm,
dataset=dataset,
sample=sample,
page=page_num,
priori=queries[query_idx],
)
bandit.update({query_idx}, vlm_score)
specific_pages = self._get_specific_page(
top_page_indices[default_idx],
top_page_indices[query_idx]
) if query_idx != default_idx else set(top_page_indices[default_idx])
neighbors = specific_pages | set(self.page_similarity_graph.get(page_num, []))
extra_pages.update(neighbors - specific_pages)
cb_score = bandit.sample({query_idx})
document_hypergraph[page_num] = {
"query_idx": {query_idx},
"query": queries[query_idx],
"col_score": col_score_dict[page_num],
"vlm_score": vlm_score,
"hits": first_hit,
"cb_score": cb_score,
"score": bandit._compute_score(col_score_dict[page_num], vlm_score, 0.5, first_hit),
"neighbor": neighbors,
}
if use_vlm:
_vlm = document_hypergraph[page_num]['vlm_score']
_col = document_hypergraph[page_num]['col_score']
print(f"{queries[query_idx]} [page:{page_num}]: \n - vlm: {_vlm}\n - col: {_col}")
if (vlm_score > bf_vlm_score or (vlm_score == bf_vlm_score and document_hypergraph[page_num]["score"] > bf_score + 0.0001)):
bf_query_idx, bf_vlm_score, bf_score, bf_page = query_idx, vlm_score, document_hypergraph[page_num]["score"], page_num
for query_idx in reversed(range(len(top_page_indices))):
_load_node(
page_num=top_page_indices[query_idx][0],
query_idx=query_idx,
use_vlm=True,
first_hit=best_first_hit_score,
)
if bf_page in document_hypergraph:
print(f" [DEBUG] best_first_queries: {queries[bf_query_idx]}")
sample["best_first_queries"] = queries[bf_query_idx]
document_hypergraph[bf_page]["hits"] += best_first_hit_score
cb_score = bandit.sample(document_hypergraph[bf_page]["query_idx"])
document_hypergraph[bf_page]["score"] = bandit._compute_score(
col_score=document_hypergraph[bf_page]["col_score"],
vlm_score=document_hypergraph[bf_page]["vlm_score"],
cb_score=cb_score,
hits=document_hypergraph[bf_page]["hits"],
)
for page_num in top_page_indices[bf_query_idx][:10]:
_load_node(
page_num=page_num,
query_idx=bf_query_idx,
use_vlm=False,
first_hit=best_first_hit_score
)
for query_idx in range(len(top_page_indices)):
for page_num in top_page_indices[query_idx]:
_load_node(
page_num=page_num,
query_idx=query_idx,
use_vlm=False,
first_hit=1
)
for page_num in extra_pages:
if page_num not in document_hypergraph:
document_hypergraph[page_num] = {
"query_idx": set(),
"query": queries[default_idx],
"col_score": col_score_dict[page_num],
"hits": 1,
"cb_score": bandit.sample({default_idx}),
"neighbor": set(),
}
self.query_specific_hypergraph = document_hypergraph
def _evaluate_rag_one_sample(self, gt, pred, top_k=[1, 3, 5]):
metrics = 0
len_gt = len(gt)
for k in top_k:
cur_pred = pred[:k]
intersect = len(set(cur_pred) & set(gt))
metrics += intersect / len_gt * 100.0
metrics += intersect / len(cur_pred) * 100.0
return int(metrics)
def _debug(self, dataset, sample, queries, scores):
if "evidence_pages" not in sample:
print("[Warning]: No ground_truth.")
return
ground_truth = sample["evidence_pages"]
if isinstance(ground_truth, str):
try:
ground_truth = [int(page.strip()) for page in
ground_truth.strip('[]').split(',')] if ground_truth != "[]" else []
except (ValueError, AttributeError):
print("Error parsing evidence_pages string")
ground_truth = []
else:
ground_truth = list(ground_truth)
if ground_truth != [] and len(ground_truth) > 0:
if dataset.dataset_name == "MMLongBench":
ground_truth = [g - 1 if g > 0 else g for g in ground_truth]
else:
ground_truth = [g if g > 0 else g for g in ground_truth]
query_top = torch.topk(torch.tensor(np.array(scores)), min(10, len(scores[0])), dim=-1)
query_top_indices = query_top.indices.tolist()
query_top_scores = query_top.values.tolist()
metrics_list = []
for i, top_indices in enumerate(query_top_indices):
metrics = self._evaluate_rag_one_sample(ground_truth, top_indices)
metrics_list.append((metrics, queries[i], query_top_indices[i], query_top_scores[i]))
metrics_list.sort(key=lambda x: x[0], reverse=True)
sample["sorted_queries"] = {query: metric for metric, query, _, _ in metrics_list}
print("\n", "#" * 30)
print("[DEBUG] Ground Truth: ", ground_truth)
result = "".join([f" [{query}, {metric}]\n" for metric, query, _, _ in metrics_list])
print("[DEBUG] True top Indices and Scores: \n" + result)
print("#" * 30, "\n")
def clean_up_page_similarity_graph(self):
self.page_similarity_graph = {}
def clean_up_query_specific_hypergraph(self):
self.query_specific_hypergraph = {}
|