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get_example.py
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import jsonlines
import numpy as np
import copy
import csv
import argparse
from tabulate import tabulate
from transformers import GPT2Tokenizer
def parse_name(f, cleaner):
f = f.replace("_1.jsonl",".jsonl")
f = f.replace("_111.jsonl",".jsonl")
name = f.replace(cleaner,"").replace(".jsonl","").split("_")
classifer = " ".join(name[:name.index('class')])
label = " ".join(name[name.index('class')+1:name.index('iter')])
itr = " ".join(name[name.index('iter')+1:name.index('step')])
stp = " ".join(name[name.index('step')+1:name.index('sample')])
# bce = False
# if("bce" in name):
sample = " ".join(name[name.index('sample')+1:name.index('wd')])
wd = eval(" ".join(name[name.index('wd')+1:name.index('bce')]))
bce = eval(" ".join(name[name.index('bce')+1:]))
return classifer,label,itr,stp,sample,wd,bce
def print_starter(conversation):
for c in conversation:
print(f"Human >>> {c}")
def print_example(table,label,starter,human, number=10):
template = {"HUMAN":8,
"DGPT":2,
"DGPT+R":3,
"DGPT+R+WD":4,
"PPLM":5,
"PPLM+R":6,
"ADAPTER+R":7}
index_mapper = []
table = [item for item in table if label == item["label"]]
for i_s, stat in enumerate(starter):
temp_table = []
for s in stat:
temp_table.append({"Model":"HUMAN","Response":s})
temp_table += ["null"]*7
for i_t, item in enumerate(table):
temp_table[template[item["model"]]] = {"Model":item["model"],"Response":item["resp"][i_s]}
temp_table[template["HUMAN"]] = {"Model":"HUMAN","Response":human[i_s]["response"]}
print(tabulate(temp_table,tablefmt='simple').replace(r"\begin{tabular}{ll}",r"\begin{tabularx}{\textwidth}{lX}").replace(r"\end{tabular}",r"\end{tabularx}"))
print()
print()
if(i_s==number):break
def save_example_csv(table, label, starter, human, number=10):
csv_columns = ['HUMAN1', 'HUMAN2', 'HUMAN3', 'DGPT', 'DGPT+R', 'DGPT+R+WD', 'PPLM', 'PPLM+R', 'ADAPTER+R']
table = [item for item in table if label == item["label"]]
gen_table = []
for i_s, stat in enumerate(starter):
temp_table = ['null'] * len(csv_columns)
for i_t, s in enumerate(stat):
temp_table[i_t] = s
temp_table[csv_columns.index('HUMAN3')] = human[i_s]["response"]
for i_t, item in enumerate(table):
temp_table[csv_columns.index(item['model'])] = item["resp"][i_s]
gen_table.append(temp_table)
if (i_s == number): break
save_file = 'results/evaluate/sample_' + label + '.csv'
with open(save_file, 'w') as f:
writer = csv.writer(f)
writer.writerow(csv_columns)
writer.writerows(gen_table)
def get_human_responses():
tokenizer = GPT2Tokenizer.from_pretrained('models/dialoGPT/medium/')
with open("data/human.txt") as f:
data = []
conversation = []
for d in f:
if len(d)>5:
if("Human Conversation" in d):
if (len(conversation)>0):
data.append(conversation)
conversation = []
i = 0
else:
if("Human 1: " in d):
_, text_turn = d.split("Human 1: ")
conversation.append({"turn":i,"speaker":"Human 1","text":text_turn.strip('\n').strip()})
else:
_, text_turn = d.split("Human 2: ")
conversation.append({"turn":i,"speaker":"Human 2","text":text_turn.strip('\n').strip()})
i += 1
conversation = data
list_starters = []
for i_c,conv in enumerate(conversation):
for index in range(len(conv)-2):
history = [conv[index]["text"],conv[index+1]["text"]]
context_tokens = len(sum([tokenizer.encode(h) + [1111] for h in history],[]))
if(context_tokens <= 70):
if(index+2 <=len(conv)):
list_starters.append({"conversation":[conv[index]["text"],conv[index+1]["text"]],"response":conv[index+2]["text"]})
else:
list_starters.append({"conversation":[conv[index]["text"],conv[index+1]["text"]],"response":"DUMMY"})
return list_starters
def score(save=False):
# if save=True, CSV file is generated and saved instead of printing results (for figure eight)
# # evaluate
row_DGPT = []
row_PPLM = []
row = []
files = {
("results/evaluate/sentiment_class_very_negative/","results/evaluate/sentiment_class_very_negative/sentiment_class_very_negative_iter_75_step_0.02_sample_10_wd_False_bce_False.jsonl"),
("results/evaluate/sentiment_class_very_negative/","results/evaluate/sentiment_class_very_negative/sentiment_class_very_negative_iter_10_step_0.02_sample_10_wd_True_bce_False.jsonl"),
("results/evaluate/sentiment_class_very_negative/","results/evaluate/sentiment_class_very_negative/sentiment_class_very_negative_iter_0_step_0.02_sample_10_wd_False_bce_False_111.jsonl"),
# ("results/evaluate/sentiment_class_very_negative/","results/evaluate/sentiment_class_very_negative/sentiment_class_very_negative_iter_0_step_0.02_sample_10_wd_False_bce_False.jsonl"),
# ("results/evaluate/sentiment_class_very_negative/","results/evaluate/sentiment_class_very_negative/sentiment_class_very_negative_iter_3_step_0.02_sample_10_wd_False_bce_False.jsonl"),
("results/evaluate/sentiment_class_very_positive/","results/evaluate/sentiment_class_very_positive/sentiment_class_very_positive_iter_25_step_0.02_sample_10_wd_False_bce_False.jsonl"),
("results/evaluate/sentiment_class_very_positive/","results/evaluate/sentiment_class_very_positive/sentiment_class_very_positive_iter_10_step_0.02_sample_10_wd_True_bce_False.jsonl"),
("results/evaluate/sentiment_class_very_positive/","results/evaluate/sentiment_class_very_positive/sentiment_class_very_positive_iter_0_step_0.02_sample_10_wd_False_bce_False_111.jsonl"),
# ("results/evaluate/sentiment_class_very_positive/","results/evaluate/sentiment_class_very_positive/sentiment_class_very_positive_iter_0_step_0.02_sample_10_wd_False_bce_False.jsonl"),
("results/evaluate/daily_dialogue_act_class_question/","results/evaluate/daily_dialogue_act_class_question/daily_dialogue_act_class_question_iter_75_step_0.02_sample_10_wd_False_bce_False_1.jsonl"),
("results/evaluate/daily_dialogue_act_class_question/","results/evaluate/daily_dialogue_act_class_question/daily_dialogue_act_class_question_iter_10_step_0.02_sample_10_wd_True_bce_False.jsonl"),
("results/evaluate/daily_dialogue_act_class_question/","results/evaluate/daily_dialogue_act_class_question/daily_dialogue_act_class_question_iter_0_step_0.02_sample_10_wd_False_bce_False_111.jsonl"),
# ("results/evaluate/daily_dialogue_act_class_question/","results/evaluate/daily_dialogue_act_class_question/daily_dialogue_act_class_question_iter_0_step_0.02_sample_10_wd_False_bce_False.jsonl"),
("results/evaluate/toxicity_class_toxic/","results/evaluate/toxicity_class_toxic/toxicity_class_toxic_iter_75_step_0.02_sample_10_wd_False_bce_False.jsonl"),
("results/evaluate/toxicity_class_toxic/","results/evaluate/toxicity_class_toxic/toxicity_class_toxic_iter_10_step_0.02_sample_10_wd_True_bce_False.jsonl"),
("results/evaluate/toxicity_class_toxic/","results/evaluate/toxicity_class_toxic/toxicity_class_toxic_iter_0_step_0.02_sample_10_wd_False_bce_False_111.jsonl"),
# ("results/evaluate/toxicity_class_toxic/","results/evaluate/toxicity_class_toxic/toxicity_class_toxic_iter_0_step_0.02_sample_10_wd_False_bce_False.jsonl"),
("results/evaluate/AG_NEWS_class_Business/","results/evaluate/AG_NEWS_class_Business/AG_NEWS_class_Business_iter_75_step_0.02_sample_10_wd_False_bce_False_1.jsonl"),
("results/evaluate/AG_NEWS_class_Business/","results/evaluate/AG_NEWS_class_Business/AG_NEWS_class_Business_iter_10_step_0.02_sample_10_wd_True_bce_False_1.jsonl"),
("results/evaluate/AG_NEWS_class_Business/","results/evaluate/AG_NEWS_class_Business/AG_NEWS_class_Business_iter_0_step_0.02_sample_10_wd_False_bce_False_111.jsonl"),
("results/evaluate/AG_NEWS_class_SciTech/","results/evaluate/AG_NEWS_class_SciTech/AG_NEWS_class_SciTech_iter_75_step_0.02_sample_10_wd_False_bce_False_1.jsonl"),
("results/evaluate/AG_NEWS_class_SciTech/","results/evaluate/AG_NEWS_class_SciTech/AG_NEWS_class_SciTech_iter_10_step_0.02_sample_10_wd_True_bce_False_1.jsonl"),
("results/evaluate/AG_NEWS_class_SciTech/","results/evaluate/AG_NEWS_class_SciTech/AG_NEWS_class_SciTech_iter_0_step_0.02_sample_10_wd_False_bce_False_111.jsonl"),
("results/evaluate/AG_NEWS_class_World/","results/evaluate/AG_NEWS_class_World/AG_NEWS_class_World_iter_75_step_0.02_sample_10_wd_False_bce_False_1.jsonl"),
("results/evaluate/AG_NEWS_class_World/","results/evaluate/AG_NEWS_class_World/AG_NEWS_class_World_iter_10_step_0.02_sample_10_wd_True_bce_False_1.jsonl"),
("results/evaluate/AG_NEWS_class_World/","results/evaluate/AG_NEWS_class_World/AG_NEWS_class_World_iter_0_step_0.02_sample_10_wd_False_bce_False_111.jsonl"),
("results/evaluate/AG_NEWS_class_Sports/","results/evaluate/AG_NEWS_class_Sports/AG_NEWS_class_Sports_iter_75_step_0.02_sample_10_wd_False_bce_False_1.jsonl"),
("results/evaluate/AG_NEWS_class_Sports/","results/evaluate/AG_NEWS_class_Sports/AG_NEWS_class_Sports_iter_10_step_0.02_sample_10_wd_True_bce_False_1.jsonl"),
("results/evaluate/AG_NEWS_class_Sports/","results/evaluate/AG_NEWS_class_Sports/AG_NEWS_class_Sports_iter_0_step_0.02_sample_10_wd_False_bce_False_111.jsonl"),
}
human = get_human_responses()
done = set()
for (cleaner,f) in files:
classifer,label,itr,stp,sample, wd,bce = parse_name(f,cleaner)
print(itr,label, stp,sample, wd,bce)
if wd:
acc_DGPT_RE_WD = []
resp_DGPT_RE_WD = []
starter = []
with jsonlines.open(f) as reader:
for i_, obj in enumerate(reader):
acc_DGPT_RE_WD.append(obj["acc"]["PPLM"])
resp_DGPT_RE_WD.append(obj["hyp"]["PPLM"][0][-1])
starter.append(obj["conversation"]["conversation"])
if(i_ == 10):break
row.append({"model":"DGPT+R+WD","label":label,"sample":sample,"iter":itr
,"Step":None,"Acc":np.mean(acc_DGPT_RE_WD),
"ppl":0,"dist":0,"resp":resp_DGPT_RE_WD,
"score":0,"vater":0,"emoji":0})
elif(not wd and int(itr) in [0]):
acc_ADAPTER = []
resp_ADAPTER = []
starter = []
with jsonlines.open(f) as reader:
for i_, obj in enumerate(reader):
acc_ADAPTER.append(obj["acc"]["DGPT"])
resp_ADAPTER.append(obj["hyp"]["DGPT"][0][-1])
starter.append(obj["conversation"]["conversation"])
if(i_ == 10):break
row.append({"model":"ADAPTER+R","label":label,"sample":sample,"iter":itr
,"Step":None,"Acc":np.mean(acc_ADAPTER),
"ppl":0,"dist":0,"resp":resp_ADAPTER,
"score":0,"vater":0,"emoji":0})
# if(not wd and int(itr)==75 and int(sample)==10 and float(stp)==0.02 and not bce):
elif(not wd):
# print(itr,label, stp,sample, wd,bce)
acc_PPLM, acc_DGPT, acc_PPLM_WD, acc_DGPT_WD, resp_PPLM, resp_DGPT, resp_PPLM_WD, resp_DGPT_WD = [],[],[],[],[],[],[],[]
starter = []
with jsonlines.open(f) as reader:
for i_, obj in enumerate(reader):
## DGPT
acc_DGPT.append(str(sorted(obj["hyp"]["DGPT"])[0][-2])==label)
resp_DGPT.append(sorted(obj["hyp"]["DGPT"])[0][-1])
## DGPT +WD
acc_DGPT_WD.append(obj["acc"]["DGPT"])
resp_DGPT_WD.append(obj["hyp"]["DGPT"][0][-1])
## PPLM
acc_PPLM.append(str(sorted(obj["hyp"]["PPLM"])[0][-2])==label)
resp_PPLM.append(sorted(obj["hyp"]["PPLM"])[0][-1])
## PPLM + WD
acc_PPLM_WD.append(obj["acc"]["PPLM"])
resp_PPLM_WD.append(obj["hyp"]["PPLM"][0][-1])
starter.append(obj["conversation"]["conversation"])
if(i_ == 10):break
if(len(resp_DGPT) and len(resp_PPLM) and len(acc_DGPT_WD) and len(acc_PPLM_WD)):
if(label not in done):
row.append({"model":"DGPT","label":label,"sample":1,"iter":None
,"Step":None,"Acc":np.mean(acc_DGPT),
"ppl":0,"dist":0,"resp":resp_DGPT,
"score":0,"vater":0,"emoji":0})
row.append({"model":"DGPT+R","label":label,"sample":sample,"iter":None,
"Step":None,"Acc":np.mean(acc_DGPT_WD),
"ppl":0,"dist":0,"resp":resp_DGPT_WD,
"score":0,"vater":0,"emoji":0})
row.append({"model":"PPLM","label":label,"sample":1,"iter":itr,
"Step":stp,"Acc":np.mean(acc_PPLM),
"ppl":0,"dist":0,"resp":resp_PPLM,
"score":0,"vater":0,"emoji":0})
row.append({"model":"PPLM+R","label":label,"sample":sample,"iter":itr,
"Step":stp,"Acc":np.mean(acc_PPLM_WD),
"ppl":0,"dist":0,"resp":resp_PPLM_WD,
"score":0,"vater":0,"emoji":0})
done.add(label)
print("Sentiment")
if save:
save_example_csv(copy.deepcopy(row), "very negative",starter, human)
save_example_csv(copy.deepcopy(row), "very positive",starter, human)
else:
print_example(copy.deepcopy(row), "very positive",starter, human)
print("Toxic")
print()
save_example_csv(copy.deepcopy(row), "toxic", starter, human)
# merge_table(copy.deepcopy(row),["toxic"],["resp","lable"],starter)
# print("Emotion")
# print()
# make_table(copy.deepcopy(row),"angry",["resp","vater","lable"])
# make_table(copy.deepcopy(row),"disgusted",["resp","vater","lable"])
# make_table(copy.deepcopy(row),"terrified",["resp","vater","lable"])
# make_table(copy.deepcopy(row),"joyful",["resp","vater","lable"])
# make_table(copy.deepcopy(row),"sad",["resp","vater","lable"])
# make_table(copy.deepcopy(row),"surprised",["resp","vater","lable"])
# merge_table(copy.deepcopy(row),["angry","disgusted","terrified","joyful","sad","surprised"],["resp","vater","lable"],starter)
print("Question")
print()
save_example_csv(copy.deepcopy(row), "question", starter, human)
# print("AG_NEWS")
# print()
# make_table(copy.deepcopy(row),"Business", ["resp","emoji","vater","lable"],text_class=False)
# make_table(copy.deepcopy(row),"Sports", ["resp","emoji","vater","lable"],text_class=False)
# make_table(copy.deepcopy(row),"SciTech", ["resp","emoji","vater","lable"],text_class=False)
# make_table(copy.deepcopy(row),"World", ["resp","emoji","vater","lable"],text_class=False)
# merge_table(copy.deepcopy(row),["Business","Sports","SciTech","World"],["resp","emoji","vater","lable"],starter)
# print("DBpedia")
# print()
# make_table(copy.deepcopy(row),"Company", ["resp","emoji","vater","lable"],text_class=False)
# make_table(copy.deepcopy(row),"EducationalInstitution", ["resp","emoji","vater","lable"],text_class=False)
# make_table(copy.deepcopy(row),"Artist", ["resp","emoji","vater","lable"],text_class=False)
# make_table(copy.deepcopy(row),"Athlete", ["resp","emoji","vater","lable"],text_class=False)
# make_table(copy.deepcopy(row),"OfficeHolder", ["resp","emoji","vater","lable"],text_class=False)
# make_table(copy.deepcopy(row),"MeanOfTransportation", ["resp","emoji","vater","lable"],text_class=False)
# make_table(copy.deepcopy(row),"Building", ["resp","emoji","vater","lable"],text_class=False)
# make_table(copy.deepcopy(row),"NaturalPlace", ["resp","emoji","vater","lable"],text_class=False)
# make_table(copy.deepcopy(row),"Village", ["resp","emoji","vater","lable"],text_class=False)
# make_table(copy.deepcopy(row),"Animal", ["resp","emoji","vater","lable"],text_class=False)
# make_table(copy.deepcopy(row),"Plant", ["resp","emoji","vater","lable"],text_class=False)
# make_table(copy.deepcopy(row),"Album", ["resp","emoji","vater","lable"],text_class=False)
# make_table(copy.deepcopy(row),"Film", ["resp","emoji","vater","lable"],text_class=False)
# make_table(copy.deepcopy(row),"WrittenWork", ["resp","emoji","vater","lable"],text_class=False)
# merge_table(copy.deepcopy(row),["Company","EducationalInstitution","Artist","Athlete",
# "OfficeHolder","MeanOfTransportation","Building",
# "NaturalPlace","Village","Animal","Plant","Album",
# "Film","WrittenWork"],["resp","emoji","vater","lable"],starter)
# row = [s for s in sorted(row,key=lambda x: (x['model'],x['score']))]
# print(tabulate(row,headers="keys",tablefmt='simple',floatfmt=".2f"))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--save', help="saving the results in csv", action='store_true')
args = parser.parse_args()
score(save=args.save)