Create doc2vec model from data

doc2vec.py

from gensim.models.doc2vec import LabeledSentence
from os import listdir
from os.path import isfile, join
import gensim
import DocIterator as DocIt
import MySQLdb


docLabels = []
data = []

conn = MySQLdb.connect(host="XXXXX", user="XXXXX", passwd="XXXXX", db="XXXXX",  charset="utf8")
cur = conn.cursor()

cur.execute('SELECT tweet_id, text from articles where publish_date > "2016-10-01"')

for row in cur:
    docLabels.append(str(row[0]))
    docu = row[1].lower()
    for char in ['.', '"', ',', '(', ')', '!', '?', ';', ':']:
        docu = docu.replace(char, ' ' + char + ' ')
    
    data.append(docu)
    

print("Examples: " + str(len(data)))

it = DocIt.DocIterator(data, docLabels)

    #Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5, hs=0, min_count=2, workers=2),

#model = gensim.models.Doc2Vec(dm=1, dm_concat=1, size=50, window=5, negative=5, hs=0, min_count=2, workers=3, alpha=0.04, min_alpha=0.005) # use fixed learning rate
model = gensim.models.Doc2Vec(dm=1, dm_concat=1, size=100, window=5, negative=5, hs=0, min_count=2, workers=2)

model.build_vocab(it)

for epoch in range(100):
    print("Epoch " + str(epoch))
    model.train(it)
    print(model.docvecs.most_similar(["782943325909291008"], topn=10))
    print(model.docvecs.most_similar(["783641803358670848"], topn=10))



    model.alpha -= 0.002 # decrease the learning rate
    print(model.alpha)
    model.min_alpha = model.alpha # fix the learning rate, no deca
    
    model.train(it)
    print(model.docvecs.most_similar(["782943325909291008"], topn=10))
    print(model.docvecs.most_similar(["783641803358670848"], topn=10))
    
    model.save("doc2vec.model")