def mapper():
seg_path = './dict_seg_wordrank/worddict/'
tag_path = './dict_seg_wordrank/tagdict/'
ner_path = './dict_seg_wordrank/wordner.conf'
stop_words_path = "./dict_seg_wordrank/stop_words.txt"
seg_handle = wordrank_ecom.Seg_Rank();
seg_handle.seg_dict_init(seg_path, tag_path, ner_path, stop_words_path);
for line in sys.stdin:
if not line:
continue
line = line.strip()
data = line.split("\t")
length = len(data)
if length == 3:
tag = data[1]
if tag == "0": #word vector dict
term = process_str.handle_str(data[0]).strip()
if len(term) == 0:
continue
vec = data[2].strip().split(' ');
sum = 0
for ele in vec:
sum = sum + float(ele) * float(ele);
sum = math.sqrt(sum);
vec = [str(float(x)/sum) for x in vec];
print "%s\t0\t%s" %(term, " ".join(vec))
elif tag == "1": #url vector dict
print "%s\t0\t%s" %(data[0], data[2])
else:
continue
else:
if data[1] != "2":
continue
winfoid = data[0];
bidword = data[2];
for idx in xrange(3, len(data)):
target_url = data[idx]
print "%s\t1\turl_info\t%s" %(target_url, winfoid)
if len(bidword) == 0:
continue
try:
(ret, token, netoken) = seg_handle.get_seg(bidword);
if ret < 0:
continue
for term, postag in token:
term = term.strip()
if len(term) == 0:
continue
print "%s\t1\tterm_info\t%s" %(term, winfoid)
except:
continue
def mapper(fea_common_conf_filename):
'''@desc mapper阶段 对不同资源打上不同tag方便拼接 '''
user_trade = read_user_trade()
parse_shitu_log_obj = parse_shitu_log(fea_common_conf_filename)
parse_shitu_log_obj.initialize()
ps_dict = {}
bd_trade = {}
for line in sys.stdin:
if not line:
continue
line = line.strip()
data = line.split("\t")
if len(line) == 0 or len(data) < 1:
continue
part = data[0].split(",")
if len(part) == 3 and (data[1] == "pc" or data[1] == "wise"): # log
key = urllib.unquote(data[parse_shitu_log.join_key_index].strip()).replace("\t", " ")
key = key.replace("\n", " ")
key = process_str.handle_str(key)
if len(key) == 0:
continue
userid = data[parse_shitu_log.userid_index]
trade = user_trade.get(userid, 0)
print "%s\t3\t%s\t%s" %(key, line, trade) #打上tag 3
elif len(data) == 3: #bidword trade
key = process_str.handle_str(data[1])
if len(key) == 0:
continue
if bd_trade.has_key(key):
continue
bd_trade[key] = 1
print "%s\t0\t%s" %(key, data[2]) #打上tag 0
elif data[1] == "300": #plsa_pzd 300
try:
key = process_str.handle_str(data[0])
except:
print >>sys.stderr, data[0]
continue
if len(key) == 0:
continue
value = "\N"
if data[4] == "0": #data[4] = 0 表示plsa向量为空
print "%s\t1\t%s" %(key, value)
else:
idx = 5
to_print = []
length = len(data)
while idx < length and (idx + 1) < length:
value = "%s:%s" %(data[idx], data[idx + 1])
to_print.append(value)
idx += 2
print "%s\t1\t%s" %(key, ",".join(to_print)) #打上tag 1
else: #ps term
key = process_str.handle_str(data[0])
if len(key) == 0:
continue
if ps_dict.has_key(key):
continue
value = "\N"
if int(data[2]) == 0:
print "%s\t2\t%s" %(key, value)
else:
idx = 3
to_print = []
length = len(data)
while idx < length and (idx + 1) < length:
value = "%s:%s" %(data[idx], data[idx + 1])
to_print.append(value)
idx += 2
print "%s\t2\t%s" %(key, ",".join(to_print)) #打上tag 2
ps_dict[key] = 1
def pretreatment(model_type, fea_common_conf_filename):
'''@desc 对shitu log 进行预处理(抽取字段，过滤等） '''
white_dict = read_white_list()
parse_shitu_log_obj = parse_shitu_log(fea_common_conf_filename)
parse_shitu_log_obj.initialize()
log_pc_show = 0
log_pc_click = 0
log_pc_price = 0
log_wise_show = 0
log_wise_click = 0
log_wise_price = 0
for line in sys.stdin:
if not line:
continue
line = line.strip()
data = line.split("\t")
if line == "" or len(data) == 0:
continue
wise_cmatch = ["222", "223", "228", "229"]
pc_cmatch = ["201", "204", "225"]
if model_type == "predict": # tag:predict 表示对shitu log 进行预估
bidword = urllib.unquote(data[23]).replace("\t", " ") #解码
bidword = process_str.handle_str(bidword).strip()
if len(bidword) == 0:
continue
rec_field = []
cmatch = data[9]
wmatch = data[10]
winfoid = data[11]
key = data[6] + "," + data[9] + "," + data[12] #searchid + cmatch + rank
rec_field.append(winfoid)
rec_field.append("record_field")
rec_field.append(cmatch)
rec_field.append(wmatch)
rec_field.append(bidword)
rec_field.append(data[0]) #show
rec_field.append(data[1]) #click
rec_field.append(data[2]) #price
if cmatch in wise_cmatch: #wise log
showurl = urllib.unquote(data[50]).strip()
log_wise_show += int(data[0])
log_wise_click += int(data[1])
log_wise_price += int(data[2])
target_url = urllib.unquote(data[92]).strip();
rec_field.append(target_url)
print "\t".join(rec_field)
elif cmatch in pc_cmatch: #pc log
showurl = urllib.unquote(data[51]).strip()
log_pc_show += int(data[0])
log_pc_click += int(data[1])
log_pc_price += int(data[2])
target_url = urllib.unquote(data[73]).strip();
rec_field.append(target_url)
print "\t".join(rec_field)
else:
rec_field.append("NULL")
print "\t".join(rec_field)
continue
else: # 表示训练和测试过程, 训练和测试数据做了一些处理
bidword = urllib.unquote(data[24]).replace("\t", " ")
bidword = process_str.handle_str(bidword).strip()
if len(bidword) == 0:
continue
cmatch = data[10]
wmatch = data[11]
winfoid = data[12]
key = data[7] + "," + data[10] + "," + data[13] #searchid + cmatch + rank
rec_field = []
rec_field.append(winfoid)
rec_field.append("record_field")
rec_field.append(cmatch)
rec_field.append(wmatch)
rec_field.append(bidword)
rec_field.append(data[1]) #show
rec_field.append(data[2]) #click
rec_field.append(data[3]) #price
if cmatch in wise_cmatch: #wise log
showurl = urllib.unquote(data[51]).strip()
target_url = urllib.unquote(data[93]).strip();
rec_field.append(target_url)
print "%s#C" %("\t".join(rec_field))
elif cmatch in pc_cmatch: #pc log
showurl = urllib.unquote(data[52]).strip()
target_url = urllib.unquote(data[74]).strip();
rec_field.append(target_url)
print "%s#C" %("\t".join(rec_field))
else:
rec_field.append("NULL")
print "%s#C" %("\t".join(rec_field))
continue
# 一些白名单过滤，词表格式(bidword \t showurl)
if model_type == "predict" and white_dict.has_key(bidword):
part = white_dict[bidword].split(",")
#proto, rest = urllib.splittype(showurl)
#host, rest = urllib.splithost(rest)
if showurl in part:
continue
'''
# 只对精确匹配的广告进行预估
if model_type == "predict" and wmatch != "63":
continue
'''
if cmatch in wise_cmatch:
source = "wise"
elif cmatch in pc_cmatch:
source = "pc"
else:
continue
p_value = 0
label = 0
# winfo + desc1 为key对log 进行去重
if model_type == "predict":
winfo = data[11]
desc1 = data[25]
nline = "%s\t%s\t%s\t%s\t%s" %(key, source, p_value, label, line)
fea_value = parse_shitu_log_obj.parse_field(nline, source)
if fea_value == "NULL" or fea_value == "":
continue
merge_key = winfo + "," + wmatch + "," + source + "\t" + desc1
print "%s\t%s" %(merge_key, fea_value)
else:
winfo = data[12]
nline = "%s\t%s\t%s\t%s" %(key, source, p_value, line)
fea_value = parse_shitu_log_obj.parse_field(nline, source)
if fea_value == "NULL" or fea_value == "":
continue
print "%s#A" %(fea_value)
if model_type == "predict":
print "tongji\tpc_log\t%s\t%s\t%s" %(log_pc_show, log_pc_click, log_pc_price)
print "tongji\twise_log\t%s\t%s\t%s" %(log_wise_show, log_wise_click, log_wise_price)
seg_handle.seg_dict_init(seg_path, tag_path, ner_path, stop_words_path);
# input:
# 1. term \t tag(0) \t vector
# 2. target_url \t query \t click \t show \t ctr \t price
# output:
# 1. term \t tag(1) \t vector
# 2. term \t tag(2) \t target_url \t click
for line in sys.stdin:
flds = line.strip().split("\t");
flds_size = len(flds);
if flds_size != 6 and flds_size != 3:
continue;
if flds_size == 3: # word vector dict
word = process_str.handle_str(flds[0]).strip()
if len(word) == 0:
continue
length = 0;
vec = flds[2].strip().split(' ');
for ele in vec:
length = length + float(ele) * float(ele);
length = math.sqrt(length);
vec = [str(float(x)/length) for x in vec];
print "%s\t1\t%s" % (word, " ".join(vec));
elif flds_size == 6:
target_url = flds[0];
if len(target_url.split("\t")) != 1 or target_url.isdigit():
continue;
