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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/{cat}/terms.pkl')
if __name__ == "__main__":
cat = 'mf'
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/predict_concat_test{}.csv'.format(cat), sep='|')
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(';')])
train = pd.read_csv('/cluster/home/wenkai/LAVIS/data/sim_split/train_{}.csv'.format(cat), sep='|')
train = train.drop_duplicates()
train['function'] = train['function'].apply(lambda x: x.lower().strip())
train_dict = dict(zip(train['function'], train['GO_label']))
test = pd.read_csv('/cluster/home/wenkai/LAVIS/data/sim_split/test_{}.csv'.format(cat), sep='|')
test = test.drop_duplicates()
test['function'] = test['function'].apply(lambda x: x.lower().strip())
test_dict = dict(zip(test['function'], test['GO_label']))
GO_dict.update(train_dict)
GO_dict.update(test_dict)
choices = []
for x in data['label_list'].tolist() + train['function'].tolist():
choices.extend(x)
choices = list(set(choices))
### 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 = 40
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))
# sims = []
# for text, label in zip(data['pred_list'].tolist(), data['label_list'].tolist()):
# a = get_sim(text, label)
# sims.append(a)
#
# data['sim'] = sims
# data['avg_sim'] = data['sim'].apply(lambda x: round(np.mean(x), 3))
# print("simlarity: {}".format(data['avg_sim'].mean()))
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])
labels = []
pred_labels = []
for l in data['label_list_go']:
if type(l) == str:
l = eval(l)
labels.extend(l)
label_count = {}
for x in labels:
if x not in label_count:
label_count[x] = 1
else:
label_count[x] += 1
labels = list(set(labels))
total = len(labels)
recalls = []
precisions = []
tp_dict, fp_dict, fn_dict = dict(zip(labels, [0]*len(labels))), dict(zip(labels, [0]*len(labels))), dict(zip(labels, [0]*len(labels)))
for preds, label in zip(data['pred_list_go'], data['label_list_go']):
if type(label) == str:
label = eval(label)
if type(preds) == str:
txts = eval(preds)
ll = len(label)
for t in label:
supgo = go.get_anchestors(t)
if supgo.intersection(set(preds)):
tp_dict[t] += 1
else:
fn_dict[t] += 1
for p in preds:
supgo = go.get_anchestors(p)
if not supgo.intersection(set(label)):
if p in fp_dict:
fp_dict[p] += 1
else:
fp_dict[p] = 1
pred_labels.extend(preds)
p_total = len(set(pred_labels))
recall, pr = 0., 0.
for x in labels:
recall += tp_dict[x] / (1.0 * (tp_dict[x] + fn_dict[x] + 1e-8))
pr += tp_dict[x] / (1.0 * (tp_dict[x] + fp_dict[x] + 1e-8))
r = recall / total
p = pr / p_total
f1 = 2 * p * r / (p + r)
print("preds not in labels: {}".format(len(list(fp_dict.keys())) - total))
print("f1 score: {}".format(f1))
'''
cat_f1 = {}
for x in labels:
if tp_dict[x] + fn_dict[x] > 0:
re = tp_dict[x] / (1.0 * (tp_dict[x] + fn_dict[x] + 1e-8))
pr = tp_dict[x] / (1.0 * (tp_dict[x] + fp_dict[x] + 1e-8))
cat_f1[x] = 2 * pr * re / (pr + re + 1e-10)
plt.xlabel('f score')
plt.ylabel('count')
print(np.mean(list(cat_f1.values())))
plt.hist(list(cat_f1.values()), color='red', bins=30)
plt.show()
xs, ys = [], []
for x in labels:
xs.append(label_count[x])
ys.append(cat_f1[x])
df_count = pd.DataFrame({'xs': xs, 'ys': ys})
df_count['xs'].loc[df_count['xs'] > 10] = 11
df_count['xs'] = df_count['xs'].astype(str)
df_count1 = df_count.groupby('xs').mean().reset_index()
df_count2 = df_count.groupby('xs').count().reset_index()
plt.xlabel('label count')
plt.ylabel('f score mean')
df_count1['xs'] = df_count1['xs'].astype(int)
plt.scatter(df_count1['xs'], df_count1['ys'], color='red')
plt.show()
plt.xlabel('label count')
plt.ylabel('protein num')
df_count2['xs'] = df_count2['xs'].astype(int)
plt.bar(df_count2['xs'], df_count2['ys'], color='red')
plt.show()
'''
# 准备数据:blip2预测的Go标签作为feature,label加入祖先后作为预测的Y
print("准备加入祖先后的数据......")
train = pd.read_csv('/cluster/home/wenkai/LAVIS/data/sim_split/train_{}.csv'.format(cat), sep='|')
test = pd.read_csv('/cluster/home/wenkai/LAVIS/data/sim_split/test_{}.csv'.format(cat), sep='|')
train = train.groupby('name').agg({'GO_label': list}).reset_index()
test = test.groupby('name').agg({'GO_label': list}).reset_index()
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
train = prop(train)
test = prop(test)
train_test = pd.concat([train, test])
get_term(train_test)
del train_test
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 = 40
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
train_pred = pd.read_csv('/cluster/home/wenkai/LAVIS/output/predict_concat_train{}.csv'.format(cat), sep='|')
test_pred = pd.read_csv('/cluster/home/wenkai/LAVIS/output/predict_concat_test{}.csv'.format(cat), sep='|')
train_pred = pred_text_to_go(train_pred)
test_pred = pred_text_to_go(test_pred)
train_data = pd.merge(train[['name', 'prop_annotations']],
train_pred[['name', 'pred_list_go']],
on='name', how='inner')
train_data = train_data.drop_duplicates('name')
train_data.to_pickle('/cluster/home/wenkai/deepgozero/data/blip2/{}/train_data.pkl'.format(cat))
test_data = pd.merge(test[['name', 'prop_annotations']],
test_pred[['name', 'pred_list_go']],
on='name', how='inner')
test_data = test_data.drop_duplicates('name')
test_data.to_pickle('/cluster/home/wenkai/deepgozero/data/blip2/{}/test_data.pkl'.format(cat))
test_data.to_pickle('/cluster/home/wenkai/deepgozero/data/blip2/{}/valid_data.pkl'.format(cat))
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