-
Notifications
You must be signed in to change notification settings - Fork 3
/
compare_axes.py
152 lines (126 loc) · 5.01 KB
/
compare_axes.py
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
import os
import json
import argparse
import pandas as pd
from langchain_core.output_parsers import JsonOutputParser
from langchain.prompts import PromptTemplate
from openai import OpenAI
from prompts.compare_axes import *
from parsers import CompareAxesScore as Score
#defining prompt templates here
metrics = {
'halu': hallucination,
'task_qual': task_quality,
'cont_qual': content_quality,
'reas_acc': reasoning_accuracy
}
prompt = PromptTemplate(
template=general_schema,
input_variables=["question", "answer_a", "answer_b", "format_instruction", "metric"],
)
parser = JsonOutputParser(pydantic_object=Score)
model = "gpt-4-turbo"
def write_jsonl(data, filename):
with open(filename, 'w') as f:
for item in data:
f.write(json.dumps(item) + '\n')
def create_dict(id, question, answer_a, answer_b, metric):
#processing the original answer first
orig_prompt = prompt.invoke(
{
"question": question,
"answer_a": answer_a,
"answer_b": answer_b,
"format_instruction": parser.get_format_instructions(),
"metric": metrics[metric]
}
)
orig_dict = {
"custom_id": id,
"method": "POST",
"url": "/v1/chat/completions",
"body": {
"model": model,
"messages": [
{
"role": "system",
"content": system_prompt},
{
"role": "user",
"content": orig_prompt.text}
],
"max_tokens": 1000,
"temperature": 0}
}
return orig_dict
def process_instance(data_row, p_mode, metric):
if p_mode:
id = data_row['cdx']
prompt = data_row['question']
original_answer = data_row['perturbed_gpt4']
perturbed_answer = data_row['og']
orig_id = f"{id}~compare_axes_cot~{metric}~perturb_orig"
else:
id = data_row['cdx']
prompt = data_row['question']
original_answer = data_row['og']
perturbed_answer = data_row['perturbed_gpt4']
orig_id = f"{id}~compare_axer_cot~{metric}~orig_perturb"
#processing the original answer
orig_dict = create_dict(orig_id, prompt, original_answer, perturbed_answer, metric)
return [orig_dict]
def parse_args():
parser = argparse.ArgumentParser(description='Axes')
parser.add_argument("--file_name", type=str,
help="File name of the data")
parser.add_argument("--out_file_name", type=str,
help="Output jsonl File name")
parser.add_argument("--batch_mode", action="store_true", default=True,
help="Run evaluation in batch mode")
parser.add_argument("--model", type=str, choices=['gpt-4o', "gpt-4-turbo", "gpt-3.5-turbo-0125", "llama3-70b", "claude3-opus", 'gemini-1.5-flash', 'gemini-1.5-pro'],
help="Model name")
parser.add_argument("--p_mode", action="store_true", default=False, help="Run in perturbed first mode")
group = parser.add_mutually_exclusive_group()
group.add_argument("--all", action="store_true",
help="Run all metrics")
group.add_argument("--axes", type=str, nargs='+', choices=metrics.keys(),
help="Run specific metrics")
args = parser.parse_args()
return args
def main(args):
global model
df = pd.read_csv(args.file_name, sep='\t')
df_dict = df.to_dict('records')
model = args.model
if model in ('gpt-4o', "gpt-4-turbo", "gpt-3.5-turbo-0125", "llama3-70b", "claude3-opus", 'gemini-1.5-flash', 'gemini-1.5-pro'):
if args.batch_mode:
final_jsonl = []
#running all axes
if args.all:
for metric in metrics.keys():
for row in df_dict:
row_dicts = process_instance(row, args.p_mode, metric)
final_jsonl.extend(row_dicts)
if args.p_mode:
out_file_name = args.out_file_name.split(".json")[0] + "_perturb.jsonl"
write_jsonl(final_jsonl, out_file_name)
else:
write_jsonl(final_jsonl, args.out_file_name)
#running only the selected axes
else:
for metric in args.axes:
for row in df_dict:
row_dicts = process_instance(row, args.p_mode, metric)
final_jsonl.extend(row_dicts)
if args.p_mode:
out_file_name = args.out_file_name.split(".json")[0] + "_perturb.jsonl"
write_jsonl(final_jsonl, out_file_name)
else:
write_jsonl(final_jsonl, args.out_file_name)
else:
print("2Still pending")
else:
print("3Still pending")
if __name__ == '__main__':
args = parse_args()
main(args)