def __init__(self, user, consumer_key, consumer_secret):
self.auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
usr = tweepy.API(self.auth).get_user(screen_name=user)
self.user = str(usr.id)
self.url = ''
self.data = config.loadData()
self.dt = { }
Pre-trained models already exist.
No need to run model2.py if predict output.
Please backup the existed method 2 models before running model2.py!
'''
# Create Dir to same model1 (if not exist)
path = './model/model2'
if not os.path.exists(path):
os.makedirs(path)
sys.stdout.write("0%\r")
sys.stdout.flush()
############ Load Training Data set ############
data_sample, data_output = loadData()
data_sample = data_sample.values.tolist()
sys.stdout.write("10%\r")
sys.stdout.flush()
############ Preprocess of Sample Data set ############
def data_flag(data_out):
flag = data_out[:][0].tolist()
return flag
def data_seq(data_out):
data_out = data_out[:][1]
split = data_out.str.split(':', expand=True)
import email import config import string import rfc822 import imaplib import smtplib import datetime import StringIO import email.utils from email.mime.text import MIMEText configt = config.loadData() class MailSpider(): def __init__(self, user, password): self.user = user self.passwd = password try: self.mail = imaplib.IMAP4_SSL(configt['social']['imap_server'], configt['social']['imap_port']) self.mail.login(user, password) except Exception, e: print e def __del__(self): self.mail.logout() def __parse_email(self, raw_addres): return string.lower(email.utils.parseaddr(raw_addres)[1])
import json
import tweepy
import config
import urllib2
import hashlib
import binascii
import threading
import mailspider
import sqlalchemy
import twitterspider
import facebookspider
import socialdistance
from sqlalchemy.orm import create_session
from sqlalchemy.ext.declarative import declarative_base
data = config.loadData()
db = sqlalchemy.create_engine(data["db"]["mysql_connection"])
metadata = sqlalchemy.MetaData(db)
Base = declarative_base()
class SocialPeer(Base):
__table__ = sqlalchemy.Table("socialpeers", metadata, autoload=True)
def get_ip():
return urllib2.urlopen(data["social"]["url_get_ip"]).read()
def get_message(user):
def __init__(self, user): self.data = config.loadData() self.user = hashlib.md5(user.upper()).hexdigest() self.token_fb = '' self.code = '' self.url = ''
8.Type model of 3rd sequence
9.D sequence model of 3rd sequence
Pre-trained models already exist.
No need to run model1.py if predict output.
Please backup the existed method 1 models before running model1.py!
'''
# Create Dir to same model1 (if not exist)
path = './model/model1'
if not os.path.exists(path):
os.makedirs(path)
############ Load Training Data set ############
inputs, output = loadData()
sys.stdout.write("10%\r")
############ Preprocess of Sample Data set ############
# Feature data
output_number = output[:][0]
# Type of first sequence
input_new = pd.concat([inputs, output[:][0]], axis=1)
output_seq = output[:][1]
output_seq_new = output_seq.str.split(':', expand=True)
output_new = output_seq_new[[0]]
# Divide Train set and Output set
output_seq_split = output_seq.str.split(':', expand=True)