def main():
parser = OptionParser()
parser.add_option("-f",
"--filename",
type="string",
dest="file",
default="DataBaseDB",
help="NEED CORRECT PARAMETERS")
(options, args) = parser.parse_args()
if options.file == "error":
print "hello world"
sys.exit(1)
task_file = options.file
t_path = config("../conf/dp.conf")
data_path = t_path["data_path"]
L_list = []
task_list, c_dict = get_task(data_path)
item_list, l_dict = c_to_l(c_dict, min_sup, 1)
i_len = 1
while l_dict != {}:
L_list.append(l_dict)
i_len += 1
com_list = list(itertools.combinations(item_list, i_len))
l_dict = get_ldict(task_list, com_list, min_sup, i_len)
get_rules(L_list)
def main():
parser = OptionParser()
parser.add_option("-f","--filename",type="string",dest="file",default="DataBaseDB",help="NEED CORRECT PARAMETERS")
(options,args) = parser.parse_args()
if options.file == "error":
print "hello world"
sys.exit(1)
task_file = options.file
t_path = config("../conf/dp.conf")
data_path = t_path["data_path"]
L_list = []
task_list,c_dict = get_task(data_path)
item_list,l_dict = c_to_l(c_dict,min_sup,1)
i_len = 1
while l_dict != {}:
L_list.append(l_dict)
i_len += 1
com_list = list(itertools.combinations(item_list,i_len))
l_dict = get_ldict(task_list,com_list,min_sup,i_len)
get_rules(L_list)
def main(options):
dp = config("../conf/dp.conf")
#1 merge
if options.merge == True:
print "合并训练测试集"
merge(dp)
elif options.split==True:
print "将得到的数据分开"
sp(dp,options.tp)
else:
print "error 没有这个选项"
sys.exit(1)
def main():
parser = OptionParser()
parser.add_option("-m", "--model", dest="model", \
help=u"选择模型:可选择的有LR，RF，NB", metavar="your_model",default="LR")
parser.add_option("-t","--tokenize",dest="tokenize",action="store_true",\
help=u"选择是否进行tokenize，tokenize会得到稍微高一点的准确率，但是效率会慢很多,默认是true",\
metavar="your_tokenize",default=False)
parser.add_option("-n","--nontext",dest="nontext",action="store_true",\
help=u"选择是否利用非文本特征，默认是false",default=False)
parser.add_option("-l","--LSA",dest="LSA",action="store_true",\
help=u"选择是否LSA，注意当选用非LR模型的时候，LSA是必须默认开着的，这个在后来我会强制一下逻辑，现在没写",\
default=False)
parser.add_option("-s","--fselect",dest="fs",action="store_true",
help=u"选择是否进行特征选择，默认是否，加上-s后会进行选择",default=False)
parser.add_option("-p","--topic",dest="topic",action="store_true",\
help=u"选择是否读取主题分布，默认是否，加上-tp后会进行读取",default=False)
parser.add_option("-c","--combine",dest="combine",action="store_true",\
help=u"选择是否进行模型融合，默认是否",default=False)
(options, args) = parser.parse_args()
print options
#读入配置文件
dp = config("../conf/dp.conf")
#读入数据
print "读取数据集"
train,test,y,label,train_nontext,test_nontext = data(dp,options.tokenize)
print "train 大小",len(train)
print "test 大小",len(test)
print "读取主题"
total_topic = topic(dp)
train_topic = total_topic[:len(train)]
test_topic = total_topic[len(train):]
print "train 大小",len(train_topic)
print "test 大小",len(test_topic)
if options.combine==False:
result = train_model(train,test,y,options,train_topic,test_topic,train_nontext,test_nontext)
print "产生结果"
gen_submission(dp,result,label)
else:
combine_model(dp,train,test,y,label,train_topic,test_topic,train_nontext,test_nontext)
#coding=utf-8
from word import word
from read_conf import config
from nlp import NLP
import numpy as np
import os
from sklearn import linear_model
from logistic_nd import LogisticRegression
data_conf = config('../conf/dp.conf')
tr_data_path = data_conf['train_path']
te_data_path = data_conf['test_path']
cat_dict = {'acq':0,'corn':1,'crude':2,'earn':3,'grain':4,'interest':5,'money-fx':6,'ship':7,'trade':8,'wheat':9}
nlp = NLP()
def get_doc_num(path):
docs_dict = {'doc_sum':0}
doc_dir = os.listdir(path)
for doc_cat in doc_dir:
file_list = os.listdir(path+doc_cat)
docs_dict[cat_dict[doc_cat]] = len(file_list)
docs_dict['doc_sum'] += docs_dict[cat_dict[doc_cat]]
return docs_dict
def get_voc_set():
word_dict = {}
word_no = 0
doc_dir = os.listdir(tr_data_path)
for doc_cat in doc_dir:
# Implementation of RAKE - Rapid Automtic Keyword Exraction algorithm
# as described in:
# Rose, S., D. Engel, N. Cramer, and W. Cowley (2010).
# Automatic keyword extraction from indi-vidual documents.
# In M. W. Berry and J. Kogan (Eds.), Text Mining: Applications and Theory.unknown: John Wiley and Sons, Ltd.
import re
import operator
import nltk
from read_conf import config
dp = config("../conf/dp.conf")
def is_number(s):
try:
float(s) if '.' in s else int(s)
return True
except ValueError:
return False
def load_stop_words(stop_word_file):
"""
Utility function to load stop words from a file and return as a list of words
@param stop_word_file Path and file name of a file containing stop words.
@return list A list of stop words.
"""
stop_words = []
for line in open(stop_word_file):
if line.strip()[0:1] != "#":
for word in line.split(): # in case more than one per line
import csv
cat_dic = {
'acq': 0,
'corn': 1,
'crude': 2,
'earn': 3,
'grain': 4,
'interest': 5,
'money-fx': 6,
'ship': 7,
'trade': 8,
'wheat': 9
}
t_path = config("../conf/dp.conf")
train_path = t_path["train_path"]
test_path = t_path["test_path"]
wordset_path = t_path["wordset_path"]
stopword = stop_set(t_path["stopword_path"])
pattern = r'''[a-zA-Z]+'''
def get_num():
num_set = {}
doc_num = 0
doc_dir = os.listdir(train_path)
for dd in doc_dir:
f_list = os.listdir(train_path + dd)
num_set[cat_dic[dd]] = len(f_list)
#coding=utf-8
from read_conf import config
import jieba
import re
import os
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
t_path = config('../conf/dp.conf')
def get_stop_list(path):
stop_list = []
with open(path,'rb') as infile:
lines = infile.readlines()
line_num = 1
for line in lines:
if line_num < 278:
stop_list.append(line.rstrip().decode('gbk'))
line_num += 1
else:
break
infile.close()
stop_list.append(' ')
return stop_list
def get_word_pinyin_dict(path):
word_dict = {}
with open(path,'rb') as infile:
lines = infile.readlines()
#coding=utf-8
'''
我也来封装一下呗
'''
from nltk import regexp_tokenize
from nltk.stem import WordNetLemmatizer
#import textblob
#from textblob.tokenizers import SentenceTokenizer as sent_tok
#from textblob.tokenizers import WordTokenizer as word_tok
from read_conf import config
stopwords = open(config('../conf/dp.conf')['stopword_path'])
stopwords = stopwords.readlines()
stopwords = [item.strip() for item in stopwords]
pattern = r'''[a-zA-Z]+'''
class NLP(object):
def __init__(self):
#self.__wordnetlem = WordNetLemmatizer()
#self.__stokenizer = sent_tok()
#self.__wtokenizer = word_tok()
self.__stopwords = set(stopwords)
def word_tokenize(self, document):
tokens = regexp_tokenize(document, pattern)
tokens = [item.lower() for item in tokens]
tokens = [item for item in tokens if item not in stopwords]
return tokens
#coding=utf-8
'''
我也来封装一下呗
'''
from nltk import regexp_tokenize
from nltk.stem import WordNetLemmatizer
#import textblob
#from textblob.tokenizers import SentenceTokenizer as sent_tok
#from textblob.tokenizers import WordTokenizer as word_tok
from read_conf import config
stopwords = open(config('../conf/dp.conf')['stopword_path'])
stopwords = stopwords.readlines()
stopwords = [item.strip() for item in stopwords]
pattern = r'''[a-zA-Z]+'''
class NLP(object):
def __init__(self):
#self.__wordnetlem = WordNetLemmatizer()
#self.__stokenizer = sent_tok()
#self.__wtokenizer = word_tok()
self.__stopwords = set(stopwords)
def word_tokenize(self,document):
tokens = regexp_tokenize(document,pattern)
tokens = [item.lower() for item in tokens]
tokens = [item for item in tokens if item not in stopwords]
return tokens
'''
#coding: utf-8
from read_conf import config
def corpus(file_dir,name):
f = open(file_dir)
result = f.readlines()
if name == "my":
result = [i.split(":")[0] for i in result]
else:
result = [i.split()[0] for i in result]
return set(result)
if __name__ == '__main__':
conf = config("lda.conf")
my_dir = conf["words_dir"]
blei_dir = "/home/lavi/publishrepo/lda/ap/vocab.txt"
my_corpus = corpus(my_dir,"my")
blei_corpus = corpus(blei_dir,"blei")
common = 0
for word in my_corpus:
if word in blei_corpus:
common += 1
print "my:%s"%(1.0*common/len(my_corpus))
print "blei:%s"%(1.0*common/len(blei_corpus))
#coding: utf-8
'''
author:yaoming
这个文件和主体算法无关，只是我用来计算学校ID和老师ID的set的个数来看是否有价值将这两条作为feature加进去
'''
from read_conf import config
import csv, sys
import numpy as np
def count_different(conf, col_num_list):
teacher_id, school_id1, school_id2 = [], [], []
with open(conf["project"], 'r') as pf:
reader = csv.reader(pf)
for line in reader:
teacher_id.append(line[col_num_list[0]])
school_id1.append(line[col_num_list[1]])
school_id2.append(line[col_num_list[2]])
print "teacher id"
print len(set(teacher_id))
print "school id1"
print len(set(school_id1))
print "school id2"
print len(set(school_id2))
if __name__ == "__main__":
dp_conf = config("../conf/dp.conf")
col_list = [1, 2, 3]
count_different(dp_conf, col_list)
'''
数据、配置的载入
'''
from csv import DictReader
from read_conf import config
from item import item
from optparse import OptionParser
import unittest
import pickle
import sys
rawconf_dir = '../conf/raw_data.conf'
dbconf_dir = '../conf/db.conf'
raw_conf = config(rawconf_dir)
db_conf = config(dbconf_dir)
def get_raw_conf():
return raw_conf
def get_db_conf():
return db_conf
def get_one_item(op, data_path):
infile = open(data_path, 'rb')
for idx, row in enumerate(DictReader(infile)):
# coding: utf-8
"""
author:yaoming
这个文件和主体算法无关，只是我用来计算学校ID和老师ID的set的个数来看是否有价值将这两条作为feature加进去
"""
from read_conf import config
import csv, sys
import numpy as np
def count_different(conf, col_num_list):
teacher_id, school_id1, school_id2 = [], [], []
with open(conf["project"], "r") as pf:
reader = csv.reader(pf)
for line in reader:
teacher_id.append(line[col_num_list[0]])
school_id1.append(line[col_num_list[1]])
school_id2.append(line[col_num_list[2]])
print "teacher id"
print len(set(teacher_id))
print "school id1"
print len(set(school_id1))
print "school id2"
print len(set(school_id2))
if __name__ == "__main__":
dp_conf = config("../conf/dp.conf")
col_list = [1, 2, 3]
count_different(dp_conf, col_list)
s = open(conf["trans_dir"])
t = open(conf["reduction_trans_dir"],"w")
reader = csv.reader(s)
a = 0
for line in reader:
if a == 0:
a += 1
continue
if line[3] in category or line[4] in company or line[5] in brand:
write_str = ','.join(line)
t.write(write_str+"\n")
if a % 10000 == 0:
print a
a += 1
if __name__ == '__main__':
print "hello"
data_position_conf = config("../conf/data_position.conf")
offer = extract_offer(data_position_conf)
reduct_transactions(data_position_conf,offer)
print "cross validation",np.mean(cross_validation.cross_val_score(clf,train,y,cv=3,scoring='roc_auc',n_jobs=3))
elif ctype == "predict":
clf.fit(train,y)
predict = clf.predict_proba(test)[:,1]
f = open(conf["result_essay"],"w")
f.write("projectid,is_exciting\n")
for it in range(len(test_id)):
f.write("%s,%s\n"%(test_id[it],predict[it]))
if __name__ == '__main__':
print "hello"
#读取数据文件的conf文件，获取地址
dp = config("../conf/dp.conf")
if len(sys.argv)!=2:
print "usage python essay_bench.py <usage>"
print "usage:split=> train test essay split"
print "usage:get_y=> get well writen y"
print "usage:train=> fit train and predict test"
sys.exit(1)
if sys.argv[1] == "split":
#step 1: 先把train和test分开
#sub step 1: 先获取test文件所有的id
test_id = get_test_id(dp)
#sub step 2: 读取所有的essay文件，在此分割,并且将其写入train和test文件中
import pickle
import os
import nltk
import csv
from read_conf import config
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn import linear_model
from sklearn.neighbors import KNeighborsClassifier
from sklearn import svm
from sklearn.decomposition import PCA
cat_dic = {'acq':0,'corn':1,'crude':2,'earn':3,'grain':4,'interest':5,'money-fx':6,'ship':7,'trade':8,'wheat':9}
t_path = config("../conf/dp.conf")
train_path = t_path["train_path"]
test_path = t_path["test_path"]
def handle_doc(word_set,rs_path):
doc_dir = os.listdir(rs_path)
doc_matrix = []
doc_cat = []
for docs in doc_dir:
files = os.listdir(rs_path+docs)
print "start to handle the --> "+docs
for file_d in files:
d_path = rs_path+docs+'/'+file_d
#get the single file path
with open(d_path,'rb') as text_file:
str_tmp = ''
#coding=utf-8
from word import word
from read_conf import config
from nlp import NLP
import numpy as np
import os
from sklearn import linear_model
from logistic_nd import LogisticRegression
data_conf = config('../conf/dp.conf')
tr_data_path = data_conf['train_path']
te_data_path = data_conf['test_path']
cat_dict = {
'acq': 0,
'corn': 1,
'crude': 2,
'earn': 3,
'grain': 4,
'interest': 5,
'money-fx': 6,
'ship': 7,
'trade': 8,
'wheat': 9
}
nlp = NLP()
def get_doc_num(path):
docs_dict = {'doc_sum': 0}