-
Notifications
You must be signed in to change notification settings - Fork 115
/
run_demo.py
executable file
·171 lines (150 loc) · 6.45 KB
/
run_demo.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
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
import random
from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
import numpy as np
import json
import matplotlib.pyplot as plt
import argparse
from utils import fix_seed
def parse_arguments():
parser = argparse.ArgumentParser(description="Zero-shot-CoT")
parser.add_argument(
"--task", type=str, default="multiarith",
choices=["aqua", "gsm8k", "commonsensqa", "addsub", "multiarith", "strategyqa", "svamp", "singleeq", "coin_flip", "last_letters"], help="dataset used for experiment"
)
parser.add_argument(
"--max_ra_len", type=int, default=5, help="maximum number of reasoning chains"
)
parser.add_argument(
"--pred_file", type=str, default="log/multiarith_zero_shot_cot.log",
help="use the reasoning chains generated by zero-shot-cot."
)
parser.add_argument(
"--demo_save_dir", type=str, default="demos/multiarith", help="where to save the contructed demonstrations"
)
parser.add_argument("--random_seed", type=int, default=192, help="random seed")
parser.add_argument(
"--encoder", type=str, default="all-MiniLM-L6-v2", help="which sentence-transformer encoder for clustering"
)
parser.add_argument(
"--sampling", type=str, default="center", help="whether to sample the cluster center first"
)
parser.add_argument(
"--debug", type=bool, default=True, help="debug mode"
)
args = parser.parse_args()
return args
def main():
args = parse_arguments()
fix_seed(args.random_seed)
encoder = SentenceTransformer(args.encoder)
task = args.task
pred_file = args.pred_file
save_file = args.demo_save_dir
max_ra_len = args.max_ra_len
if task == "last_letters":
max_ra_len = 7
if task == "aqua" or task == "last_letters":
num_clusters = 4
elif task == "commonsensqa":
num_clusters = 7
elif task == "strategyqa":
num_clusters = 6
else:
num_clusters = 8
corpus = []
question = []
rationale = []
gold_ans = []
pred_ans = []
with open(pred_file, "r", encoding="utf-8") as fp:
answer_seg = ""
for line in fp:
if "Q: " in line:
c_question = line.strip()
if "A: " in line:
answer_seg = line
elif "Therefore" in line and "the answer" in line:
c_rationale = answer_seg
elif answer_seg != "":
answer_seg += line
if "pred_mode" in line:
c_pred_ans = line.split(":")[1].strip()
if "GT :" in line:
c_gold_ans = line.split(":")[1].strip()
c_rationale = c_rationale.replace("A: Let's think step by step.", "Let's think step by step.")
c_question = c_question + "\nA:"
corpus.append(c_question)
question.append(c_question)
rationale.append(c_rationale)
pred_ans.append(c_pred_ans)
if args.debug:
gold_ans.append(c_gold_ans)
answer_seg = ""
corpus_embeddings = encoder.encode(corpus)
# Perform kmean clustering
clustering_model = KMeans(n_clusters=num_clusters, random_state=args.random_seed)
clustering_model.fit(corpus_embeddings)
cluster_assignment = clustering_model.labels_
clustered_sentences = [[] for i in range(num_clusters)]
dist = clustering_model.transform(corpus_embeddings)
clustered_dists = [[] for i in range(num_clusters)]
clustered_idx = [[] for i in range(num_clusters)]
for sentence_id, cluster_id in enumerate(cluster_assignment):
clustered_sentences[cluster_id].append(corpus[sentence_id])
clustered_dists[cluster_id].append(dist[sentence_id][cluster_id])
clustered_idx[cluster_id].append(sentence_id)
demos = []
for i in range(len(clustered_dists)):
print("Cluster ", i+1)
tmp = list(map(list, zip(range(len(clustered_dists[i])), clustered_dists[i])))
top_min_dist = sorted(tmp, key=lambda x: x[1], reverse=False)
if not args.sampling == "center":
random.shuffle(top_min_dist)
for element in top_min_dist:
min_idx = element[0]
c_rationale = rationale[clustered_idx[i][min_idx]].strip()
c_pred_ans = pred_ans[clustered_idx[i][min_idx]].strip()
if len(question[clustered_idx[i][min_idx]].strip().split()) <= 60 \
and len(c_rationale.replace("\n\n", "\n").split("\n")) <= max_ra_len and c_rationale[-1] == "." and c_pred_ans != "":
if args.task in ["gsm8k", "multiarith", "singleeq", "addsub", "svamp"]:
if not (c_pred_ans.strip() in c_rationale.split(".")[-2] or c_pred_ans.strip() in c_rationale.split()[-10:]):
continue
c_question = question[clustered_idx[i][min_idx]]
c_rationale = c_rationale.replace("\n\n", "\n").replace("\n", " ").strip()
c_rationale = " ".join(c_rationale.split())
if args.debug:
c_gold_ans = gold_ans[clustered_idx[i][min_idx]]
else:
c_gold_ans = None
demo_element = {
"question": c_question,
"rationale": c_rationale,
"pred_ans": c_pred_ans,
"gold_ans": c_gold_ans,
}
demos.append(demo_element)
print(c_question)
print(c_rationale)
print(c_pred_ans)
print(c_gold_ans)
print("")
break
demos = {"demo": demos}
with open(args.demo_save_dir, 'w', encoding="utf-8") as write_f:
json.dump(demos, write_f, indent=4, ensure_ascii=False)
y_km = clustering_model.fit_predict(corpus_embeddings)
pca_model = PCA(n_components=2, random_state=args.random_seed)
transformed = pca_model.fit_transform(corpus_embeddings)
centers = pca_model.transform(clustering_model.cluster_centers_)
plt.scatter(x=transformed[:, 0], y=transformed[:, 1], c=y_km, s=50, cmap=plt.cm.Paired, alpha=0.4)
plt.scatter(centers[:, 0],centers[:, 1],
s=250, marker='*', label='centroids',
edgecolor='black',
c=np.arange(0,num_clusters),cmap=plt.cm.Paired,)
plt.xticks([])
plt.yticks([])
plt.savefig(save_file+".png", dpi=600)
if __name__ == "__main__":
main()