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import pandas as pd
import re
import random
import Levenshtein
import numpy as np
import difflib
# from torchmetrics.text import BLEUScore
import time
from multiprocessing import Pool, Queue, Process
import matplotlib.pyplot as plt
from data.evaluate_data.utils import Ontology
# bleu = BLEUScore(n_gram=1)
def fuzzy_match(texts):
text_dict = {}
for context in texts:
if context not in choices:
# txt_dict[txt] = process.extractOne(txt, choices)[0]
text_dict[context] = difflib.get_close_matches(context, choices, n=1, cutoff=0.)[0]
return text_dict
def get_sim(text, label):
all_s = []
for x in label:
s = 0
for y in text:
temp = Levenshtein.ratio(x, y)
if temp > s:
s = temp
all_s.append(s)
all_s = [round(i, 3) for i in all_s]
# bs = [bleu(x, [label]) for x in text]
return all_s
def txt_map(x, txt_dict):
if type(x) == str:
x = eval(x)
x_ = []
for i in x:
if i == '':
continue
if i in txt_dict:
x_.append(txt_dict[i])
else:
x_.append(i)
return x_
def go_map(t):
if t in GO_dict:
return GO_dict[t]
else:
print(t)
def get_term(df):
from collections import Counter
cnt = Counter()
for i, row in enumerate(df.itertuples()):
for term in row.prop_annotations:
cnt[term] += 1
terms = list(cnt.keys())
# remove top
for top_term in ['GO:0005575', 'GO:0003674', 'GO:0008150']:
if top_term in terms:
terms.remove(top_term)
terms_df = pd.DataFrame({'gos': terms})
terms_df.to_pickle(f'/cluster/home/wenkai/deepgozero/data/blip2/terms.pkl')
if __name__ == "__main__":
go = Ontology(f'/cluster/home/wenkai/deepgozero/data/data/go.obo', with_rels=True)
go_des = pd.read_csv('/cluster/home/wenkai/LAVIS/data/go_descriptions_new.txt', sep='|', header=None)
go_des.columns = ['GO', 'function']
go_des = go_des[go_des['function'].notnull()]
go_des['function'] = go_des['function'].apply(lambda x: x.lower().strip())
go_des['GO'] = go_des['GO'].apply(lambda x: re.sub('_', ':', x))
GO_dict = dict(zip(go_des['function'], go_des['GO']))
data = pd.read_csv('/cluster/home/wenkai/LAVIS/output/output_case.txt', sep='|', header=None)
data.columns = ['protein', 'pred', 'label']
data['label'] = data['label'].apply(lambda x: x.lower())
data['pred'] = data['pred'].apply(lambda x: re.sub('</s>', '', x))
data['label_list'] = data['label'].apply(lambda x: [i.strip() for i in x.split(';')])
data['pred_list'] = data['pred'].apply(lambda x: [i.strip() for i in x.split(';')])
test = pd.read_csv('/cluster/home/wenkai/LAVIS/data/pretrain/test.csv', sep='|')
test = test.drop_duplicates()
test['function'] = test['function'].apply(lambda x: x.lower().strip())
test['function'] = test['function'].apply(lambda x: [i.strip() for i in x.split(';')])
test['GO_label'] = test['GO_label'].apply(lambda x: [i.strip() for i in x.split(';')])
test_dict = dict()
for x, y in zip(test['function'], test['GO_label']):
temp = dict(zip(x, y))
test_dict.update(temp)
GO_dict.update(test_dict)
choices = list(test_dict.keys())
### 预测的文本如果不在GO标签词中，则算作最相似的GO标签
'''
print("找到与预测文本最相似的GO标签......")
t0 = time.time()
txt_dict = {}
all_txt = []
for txt in data['pred_list']:
if type(txt) == str:
all_txt.extend(eval(txt))
else:
all_txt.extend(txt)
all_txt = list(set(all_txt))
n = len(all_txt)
thread = 10
size = int(n/thread)
inds = list(range(0, n, size))
inds.append(n)
all_txt_sep = [all_txt[i: min(i+size, n)] for i in inds[:-1]]
with Pool(processes=thread) as pool:
result = pool.map(fuzzy_match, all_txt_sep)
pool.close()
pool.join()
for d in result:
txt_dict.update(d)
# for txt in all_txt[:10]:
# fuzzy_match(txt)
data['pred_list'] = data['pred_list'].apply(lambda x: txt_map(x, txt_dict))
data['pred_list'] = data['pred_list'].apply(lambda x: list(set(x)))
print("fuzzy matching time: {}".format(time.time() - t0))
print("calculating f1 score ......")
data['label_list_go'] = data['label_list'].apply(lambda x: [go_map(i) for i in x])
data['pred_list_go'] = data['pred_list'].apply(lambda x: [go_map(i) for i in x])
'''
# 准备case测试数据：blip2预测的Go标签作为feature，label加入祖先后作为预测的Y
prepare_ancestors = True
if prepare_ancestors:
print("准备加入祖先后的数据......")
def prop(df):
prop_annotations = []
for i, row in df.iterrows():
# Propagate annotations
annot_set = set()
annots = row['GO_label']
for go_id in annots:
annot_set |= go.get_anchestors(go_id)
annots = list(annot_set)
prop_annotations.append(annots)
df['prop_annotations'] = prop_annotations
return df
def pred_text_to_go(df):
df['pred'] = df['pred'].apply(lambda x: re.sub('</s>', '', x))
df['pred_list'] = df['pred'].apply(lambda x: [i.strip() for i in x.split(';')])
### 预测的文本如果不在GO标签词中，则算作最相似的GO标签
t0 = time.time()
txt_dict = {}
all_txt = []
for txt in df['pred_list']:
if type(txt) == str:
all_txt.extend(eval(txt))
else:
all_txt.extend(txt)
all_txt = list(set(all_txt))
if '' in all_txt:
all_txt.remove('')
n = len(all_txt)
thread = 10
size = int(n / thread)
inds = list(range(0, n, size))
inds.append(n)
all_txt_sep = [all_txt[i: min(i + size, n)] for i in inds[:-1]]
with Pool(processes=thread) as pool:
result = pool.map(fuzzy_match, all_txt_sep)
pool.close()
pool.join()
for d in result:
txt_dict.update(d)
# for txt in all_txt[:10]:
# fuzzy_match(txt)
df['pred_list'] = df['pred_list'].apply(lambda x: txt_map(x, txt_dict))
df['pred_list'] = df['pred_list'].apply(lambda x: list(set(x)))
print("fuzzy matching time: {}".format(time.time() - t0))
df['pred_list_go'] = df['pred_list'].apply(lambda x: [go_map(i) for i in x])
return df
test_pred = pd.read_csv('/cluster/home/wenkai/LAVIS/output/output_case.txt', sep='|', header=None)
test_pred.columns = ['protein', 'pred', 'GO_label']
test_pred['GO_label'] = test_pred['GO_label'].apply(lambda x: [i.strip() for i in x.split(';')])
test_pred = prop(test_pred)
test_pred = pred_text_to_go(test_pred)
for cat in ['mf', 'bp', 'cc']:
test_pred.to_pickle('/cluster/home/wenkai/deepgozero/data/blip2/{}/test_case.pkl'.format(cat))
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