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import os
import time
import json
import torch
import pandas as pd
import faiss
from datasets import load_dataset
from .model import SharedBiEncoder
from .util import get_tokenizer, query_trans, context_trans
from .preprocess import tokenise, preprocess_question
from pyvi.ViTokenizer import tokenize
class BiRetriever():
def __init__(self, args, encoder=None, biencoder=None, save_type="dpr"):
start = time.time()
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.args = args
if self.args.new_data:
self.train_file = "ttrain_all.csv"
self.test_file = "ttest_all.csv"
self.val_file = "tval_all.csv"
else:
self.train_file = "ttrain.csv"
self.test_file = "ttest.csv"
self.val_file = "tval.csv"
self.save_type = save_type
self.dpr_tokenizer = get_tokenizer(self.args.BE_checkpoint)
if biencoder is not None:
self.biencoder = biencoder
elif encoder is not None:
self.biencoder = SharedBiEncoder(model_checkpoint=self.args.BE_checkpoint,
encoder=encoder,
representation=self.args.BE_representation,
fixed=self.args.bi_fixed)
else:
self.biencoder = SharedBiEncoder(model_checkpoint=self.args.biencoder_path,
representation=self.args.BE_representation,
fixed=self.args.bi_fixed)
#self.biencoder.load_state_dict(torch.load(self.args.biencoder_path))
self.biencoder.to(self.device)
self.encoder = self.biencoder.get_model()
self.corpus = load_dataset("csv", data_files=self.args.corpus_file, split = 'train')
if self.args.index_path:
self.corpus.load_faiss_index('embeddings', self.args.index_path)
else:
self.corpus = self.get_index()
end = time.time()
print(end - start)
def get_index(self):
self.encoder.to("cuda").eval()
with torch.no_grad():
corpus_with_embeddings = self.corpus.map(lambda example: {'embeddings': self.encoder.get_representation(self.dpr_tokenizer.encode_plus(context_trans(example["tokenized_text"], self.dpr_tokenizer),
padding='max_length',
truncation=True,
max_length=self.args.ctx_len,
return_tensors='pt')['input_ids'].to(self.device),
self.dpr_tokenizer.encode_plus(context_trans(example["tokenized_text"], self.dpr_tokenizer),
padding='max_length',
truncation=True,
max_length=self.args.ctx_len,
return_tensors='pt')['attention_mask'].to(self.device))[0].to('cpu').numpy()})
corpus_with_embeddings.add_faiss_index(column='embeddings', metric_type=faiss.METRIC_INNER_PRODUCT)
index_path = self.args.biencoder_path.split("/")[-1]
index_path = "outputs/index/index_"+ self.save_type + ".faiss"
corpus_with_embeddings.save_faiss_index('embeddings', index_path)
return corpus_with_embeddings
def retrieve(self, question, top_k=100, segmented = False):
start = time.time()
self.encoder.to(self.device).eval()
if segmented:
tokenized_question = query_trans(question, self.dpr_tokenizer)
else:
tokenized_question = query_trans(tokenise(preprocess_question(question, remove_end_phrase=False), tokenize), self.dpr_tokenizer)
with torch.no_grad():
Q = self.dpr_tokenizer.encode_plus(tokenized_question, padding='max_length', truncation=True, max_length=self.args.q_len, return_tensors='pt')
question_embedding = self.encoder.get_representation(Q['input_ids'].to(self.device),
Q['attention_mask'].to(self.device))[0].to('cpu').numpy()
scores, retrieved_examples = self.corpus.get_nearest_examples('embeddings', question_embedding, k=top_k)
retrieved_ids = retrieved_examples['id']
end = time.time()
#print(end - start)
return retrieved_ids, scores
def test_on_data(self, top_k =[100], segmented = True, train= True):
result = []
dtest = pd.read_csv(os.path.join(self.args.data_dir, self.test_file))
dval = pd.read_csv(os.path.join(self.args.data_dir, self.val_file))
if train:
dtrain = pd.read_csv(os.path.join(self.args.data_dir, self.train_file))
train_retrieved = self.retrieve_on_data(dtrain, name = 'train', top_k= max(top_k),segmented=segmented)
test_retrieved = self.retrieve_on_data(dtest, name = 'test', top_k= max(top_k), segmented=segmented)
val_retrieved = self.retrieve_on_data(dval, name = 'val', top_k= max(top_k),segmented=segmented)
for k in top_k:
rlt = {}
strk = str(k)
rlt[strk] = {}
test_retrieved_k = [x[:k] for x in test_retrieved]
val_retrieved_k = [x[:k] for x in val_retrieved]
print("Testing hit scores with top_{}:".format(k))
val_hit_acc, val_all_acc = self.calculate_score(dval, val_retrieved_k)
rlt[strk]['val_hit'] = val_hit_acc
rlt[strk]['val_all'] = val_all_acc
print("\tVal hit acc: {:.4f}%".format(val_hit_acc*100))
print("\tVal all acc: {:.4f}%".format(val_all_acc*100))
test_hit_acc, test_all_acc = self.calculate_score(dtest, test_retrieved_k)
rlt[strk]['test_hit'] = test_hit_acc
rlt[strk]['test_all'] = test_all_acc
print("\tTest hit acc: {:.4f}%".format(test_hit_acc*100))
print("\tTest all acc: {:.4f}%".format(test_all_acc*100))
result.append(rlt)
#name = self.args.biencoder_path.split("/")
save_file = "outputs/testdpr_"+ self.save_type + ".json"
with open(save_file, 'w') as f:
json.dump(result, f, ensure_ascii = False, indent =4)
def retrieve_with_result(self, df, name, top_k=[100], segmented=False):
result = []
retrieved = self.retrieve_on_data(df, name, top_k=max(top_k), segmented=segmented)
for k in top_k:
rlt = {}
strk = str(k)
rlt[strk] = {}
retrieved_k = [x[:k] for x in retrieved]
print("Testing hit scores with top_{}:".format(k))
hit_acc, all_acc = self.calculate_score(df, retrieved_k)
rlt[strk]['hit'] = hit_acc
rlt[strk]['all'] = all_acc
print("\tHit acc: {:.4f}%".format(hit_acc*100))
print("\tAll acc: {:.4f}%".format(all_acc*100))
result.append(rlt)
def retrieve_on_data(self, df, name, top_k = 100, segmented = False, saved=True):
count = 0
acc = 0
retrieved_list = []
#retrieved_sub_list = []
if not segmented:
tokenized_questions = []
for i in range(len(df)):
tokenized_question = tokenise(preprocess_question(df['question'][i], remove_end_phrase=False), tokenize)
tokenized_questions.append(tokenized_question)
df['tokenized_question'] = tokenized_questions
for i in range(len(df)):
tokenized_question = df['tokenized_question'][i]
retrieved_ids, _ = self.retrieve(tokenized_question, top_k, segmented=True)
retrieved_list.append(retrieved_ids)
if saved:
save_file = "outputs/" + self.save_type + "_" + name + "_retrieved.json"
with open(save_file, 'w') as f:
json.dump(retrieved_list, f, ensure_ascii = False, indent =4)
return retrieved_list
def find_neg(self, df, name, no_negs=3, segmented=True):
retrieved_list = self.retrieve_on_data(df, name, 100, segmented, saved=False)
ttokenized_ques = df['tokenized_question'].tolist()
tans_id = df['ans_id'].tolist()
tnew_neg = []
tbest_ans_id = df['best_ans_id'].tolist()
nbest_ans_id = []
for i in range(len(df)):
retrieved_ids = retrieved_list[i]
ans_idss = json.loads(tans_id[i])
tbest_ans_idss = json.loads(tbest_ans_id[i])
ans_ids = []
nbest_ans_ids = []
for j in range(len(ans_idss)):
a_ids = ans_idss[j]
tbest_a_id = tbest_ans_idss[j]
ans_ids += a_ids
found = True
ij = 0
while (found and ij < 100):
if retrieved_ids[ij] in a_ids:
nbest_ans_ids.append(retrieved_ids[ij])
found = False
ij += 1
if found:
nbest_ans_ids.append(tbest_a_id)
new_neg_ids = [x for x in retrieved_ids if x not in ans_ids]# and x not in kept_neg_ids]
new_neg_ids = new_neg_ids[:no_negs]
nbest_ans_id.append(nbest_ans_ids)
tnew_neg.append(new_neg_ids)
dn = pd.DataFrame()
dn['tokenized_question'] = ttokenized_ques
dn['ans_id'] = tans_id
dn['best_ans_id'] = nbest_ans_id
dn['neg_ids'] = tnew_neg
dt = pd.DataFrame()
dt['tokenized_question'] = ttokenized_ques
dt['ans_id'] = tans_id
dt['best_ans_id'] = tbest_ans_id
dt['neg_ids'] = tnew_neg
return dt, dn
def increase_neg(self, no_negs=3, segmented=True):
dtrain = pd.read_csv(os.path.join(self.args.data_dir, self.train_file))
dval = pd.read_csv(os.path.join(self.args.data_dir, self.val_file))
dtest = pd.read_csv(os.path.join(self.args.data_dir, self.test_file))
dttrain, dntrain = self.find_neg(dtrain, "train", no_negs, segmented)
dtval, dnval = self.find_neg(dval, "val", no_negs, segmented)
dttest, dntest = self.find_neg(dtest, "test", no_negs, segmented)
dttrain.to_csv("outputs/data/{}/old/{}".format(self.save_type, self.train_file), index=False)
dtval.to_csv("outputs/data/{}/old/{}".format(self.save_type, self.val_file), index=False)
dttest.to_csv("outputs/data/{}/old/{}".format(self.save_type, self.test_file), index=False)
dntrain.to_csv("outputs/data/{}/new/{}".format(self.save_type, self.train_file), index=False)
dnval.to_csv("outputs/data/{}/new/{}".format(self.save_type, self.val_file), index=False)
dntest.to_csv("outputs/data/{}/new/{}".format(self.save_type, self.test_file), index=False)
def calculate_score(self, df, retrieved_list):
top_k = len(retrieved_list[0])
all_count = 0
hit_count = 0
for i in range(len(df)):
all_check = True
hit_check = False
retrieved_ids = retrieved_list[i]
ans_ids = json.loads(df['ans_id'][i])
for a_ids in ans_ids:
com = [a_id for a_id in a_ids if a_id in retrieved_ids]
if len(com) > 0:
hit_check = True
else:
all_check = False
if hit_check:
hit_count += 1
if all_check:
all_count += 1
all_acc = all_count/len(df)
hit_acc = hit_count/len(df)
return hit_acc, all_acc
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