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ENTYFI/attentionNER/create_dicts.py
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# -*- coding: utf-8 -*- | |
from sklearn.externals import joblib | |
import pickle | |
import numpy as np | |
import sys | |
def load_X2id(file_path): | |
X2id = {} | |
id2X = {} | |
with open(file_path) as f: | |
for line in f: | |
temp = line.strip().split() | |
id,X = temp[0],temp[1] | |
X2id[X] = int(id) | |
id2X[int(id)] = X | |
return X2id, id2X | |
def load_word2vec(file_path): | |
word2vec = {} | |
with open(file_path) as lines: | |
for line in lines: | |
try: | |
split = line.split(" ") | |
word = split[0] | |
vector_strings = split[1:] | |
vector = [float(num) for num in vector_strings] | |
word2vec[word] = np.array(vector) | |
except Exception: | |
pass | |
return word2vec | |
def create_id2vec(word2id,word2vec): | |
unk_vec = word2vec["unk"] | |
dim_of_vector = len(unk_vec) | |
num_of_tokens = len(word2id) | |
id2vec = np.zeros((num_of_tokens,dim_of_vector)) | |
for word,t_id in word2id.items(): | |
id2vec[t_id,:] = word2vec[word] if word in word2vec else unk_vec | |
return id2vec | |
def main(): | |
print ("word2id...") | |
word2id, id2word = load_X2id(sys.argv[1]) | |
print ("feature2id...") | |
feature2id, id2feature = load_X2id(sys.argv[2]) | |
print ("label2id...") | |
label2id, id2label = load_X2id(sys.argv[3]) | |
print ("word2vec...") | |
word2vec = load_word2vec(sys.argv[4]) | |
print ("id2vec...") | |
id2vec = create_id2vec(word2id,word2vec) | |
print ("done!") | |
dicts = {"id2vec":id2vec,"word2id":word2id,"id2word":id2word,"label2id":label2id,"id2label":id2label,"feature2id":feature2id,"id2feature":id2feature} | |
print ("dicts save...") | |
joblib.dump(dicts,sys.argv[5]) | |
if(__name__=='__main__'): | |
main() |