File size: 7,554 Bytes
dbc6c3e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 |
import json
import sys
import re
import pandas as pd
import numpy as np
def parse_jsonl(file_name):
with open(file_name, 'r', encoding='utf-8') as f:
lines = f.readlines()
return [json.loads(line) for line in lines]
def unwrap_answer(data):
if isinstance(data, dict) and "answer" in data:
return data["answer"]
return data
def extract_json_content(text):
if not text:
return None
text = text.strip()
start = text.find('{')
end = text.rfind('}') + 1
if start == -1 or end <= start:
return None
try:
return json.loads(text[start:end])
except json.JSONDecodeError:
return None
def normalize_val(val):
if val is None:
return ""
s = re.sub(r"[<>]", "", str(val)).strip()
if not s:
return ""
num_candidate = s.replace(",", "")
try:
num = float(num_candidate)
except ValueError:
return s
return str(int(num)) if num.is_integer() else str(num)
def flatten_json(data, prefix=""):
items = set()
if isinstance(data, dict):
for k, v in data.items():
items.update(flatten_json(v, f"{prefix}.{k}" if prefix else k))
elif isinstance(data, list):
for v in data:
if isinstance(v, list):
row_tuple = tuple(normalize_val(sub_item) for sub_item in v)
items.add((prefix, row_tuple))
else:
items.update(flatten_json(v, prefix))
else:
s = normalize_val(data)
parts = re.split(r'[，,]', s)
for part in parts:
part = part.strip()
if part:
items.add((prefix, part))
return items
def calculate_metrics(pred_json, gt_json, doc_id=None):
def filter_present_categories(obj):
return {"present_categories": obj.get("present_categories", [])} if isinstance(obj, dict) else {}
if doc_id and doc_id.startswith("DTR_003"):
pred_json = filter_present_categories(pred_json)
gt_json = filter_present_categories(gt_json)
if doc_id and (doc_id.startswith("NC_001") or doc_id.startswith("NC_002")):
if pred_json is None:
return None
try:
return 1.0 if abs(float(pred_json) - float(gt_json)) <= 2 else 0.0
except Exception:
return 0.0
pred_set = flatten_json(pred_json) if pred_json is not None else set()
gt_set = flatten_json(gt_json)
if not gt_set:
return 1.0 if not pred_set else 0.0
tp = len(pred_set & gt_set)
fp = len(pred_set) - tp
fn = len(gt_set) - tp
precision = tp / (tp + fp) if (tp + fp) else 0.0
recall = tp / (tp + fn) if (tp + fn) else 0.0
f1 = (2 * precision * recall) / (precision + recall) if (precision + recall) else 0.0
return f1
def evaluate_samples(pred_file, gt_file):
predictions = parse_jsonl(pred_file)
gts = parse_jsonl(gt_file)
gt_map = {gt['id']: gt for gt in gts}
raw_scores = []
for pred_data in predictions:
doc_id = pred_data['id']
# task NC_005 is currently under construction
if doc_id.startswith('NC_005'):
continue
pred_json = extract_json_content(pred_data['response'])
gt_json = json.loads(gt_map[doc_id]['response'])
pred_json = unwrap_answer(pred_json)
gt_json = unwrap_answer(gt_json)
f1 = calculate_metrics(pred_json, gt_json, doc_id)
raw_score = {
"id": doc_id,
"task": gt_map[doc_id]['task'],
"subtask": gt_map[doc_id]['subtask'],
"robustness": gt_map[doc_id]['robustness'],
"f1": f1
}
raw_scores.append(raw_score)
return raw_scores
def evaluate_tasks(raw_scores):
df = pd.DataFrame(raw_scores)
subtask_mean = (
df.groupby(["task", "subtask"], as_index=False)
.agg(subtask_f1_mean=("f1", "mean"))
)
task_mean = (
subtask_mean.groupby("task", as_index=False)
.agg(task_f1=("subtask_f1_mean", "mean"))
)
order = ["DTR", "KIE", "IQE", "CC", "VC", "NC", "RR"]
task_mean_sorted = (
task_mean.assign(task=pd.Categorical(task_mean["task"], categories=order, ordered=True))
.sort_values("task")
.reset_index(drop=True)
)
print("Performance by Task:\n", task_mean_sorted)
def evaluate_robustness(raw_scores):
df = pd.DataFrame(raw_scores)
subtask_mean = (
df.loc[df["task"].ne("IQE")]
.groupby(["robustness", "task", "subtask"], as_index=False)
.agg(subtask_f1_mean=("f1", "mean"))
)
normal_sub_mean = (
subtask_mean[subtask_mean["robustness"].eq("Normal Captures")]
)
normal_task_mean = (
normal_sub_mean.groupby("task", as_index=False)
.agg(task_f1=("subtask_f1_mean", "mean"))
)
normal_overall_mean = normal_task_mean["task_f1"].mean()
sec_keys = (
subtask_mean.loc[subtask_mean["robustness"].eq("Secondary Captures"), ["task", "subtask"]]
.drop_duplicates()
)
sec_normal_sub_mean = (
subtask_mean[subtask_mean["robustness"].eq("Normal Captures")]
.merge(sec_keys, on=["task", "subtask"], how="inner")
)
sec_normal_task_mean = (
sec_normal_sub_mean.groupby("task", as_index=False)
.agg(task_f1=("subtask_f1_mean", "mean"))
)
sec_normal_overall_mean = sec_normal_task_mean["task_f1"].mean()
multi_subtask_mean = (
df.loc[df["subtask"].isin([
"DTR_001_001",
"DTR_003_001",
"KIE_002_001",
])]
.groupby(["robustness", "task", "subtask"], as_index=False)
.agg(subtask_f1_mean=("f1", "mean"))
)
multi_normal_sub_mean = (
multi_subtask_mean[multi_subtask_mean["robustness"].eq("Normal Captures")]
)
multi_normal_task_mean = (
multi_normal_sub_mean.groupby("task", as_index=False)
.agg(task_f1=("subtask_f1_mean", "mean"))
)
multi_normal_overall_mean = multi_normal_task_mean["task_f1"].mean()
task_mean = (
subtask_mean.groupby(["robustness", "task"], as_index=False)
.agg(task_f1=("subtask_f1_mean", "mean"))
)
robustness_scores = (
task_mean.groupby("robustness", as_index=False)
.agg(robustness_macro_f1=("task_f1", "mean"))
.sort_values("robustness_macro_f1", ascending=False)
.reset_index(drop=True)
)
use_special_denoms = {
"Secondary Captures": sec_normal_overall_mean,
"Cluttered Background": sec_normal_overall_mean,
"Multi-doc Images": multi_normal_overall_mean}
robustness_scores = robustness_scores.copy()
robustness_scores["normal_denom"] = robustness_scores["robustness"].apply(
lambda r: use_special_denoms.get(r, normal_overall_mean)
)
robustness_scores["relative_to_normal"] = (
robustness_scores["robustness_macro_f1"] / robustness_scores["normal_denom"].replace(0, np.nan)
)
robustness_scores = robustness_scores.drop(columns=["normal_denom", "robustness_macro_f1"])
robustness_scores = robustness_scores.sort_values('relative_to_normal', ascending=False)
print("Performance by Robustness:\n", robustness_scores)
if __name__ == "__main__":
p_file = sys.argv[1]
g_file = sys.argv[2]
raw_scores = evaluate_samples(p_file, g_file)
evaluate_tasks(raw_scores)
evaluate_robustness(raw_scores)
|