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# -*- coding: utf-8 -*-
import math
import json
import re
import os
import numpy as np
from rag.nlp import huqie
from api.utils.file_utils import get_project_base_directory
class Dealer:
def __init__(self):
self.stop_words = set(["请问",
"您",
"你",
"我",
"他",
"是",
"的",
"就",
"有",
"于",
"及",
"即",
"在",
"为",
"最",
"有",
"从",
"以",
"了",
"将",
"与",
"吗",
"吧",
"中",
"#",
"什么",
"怎么",
"哪个",
"哪些",
"啥",
"相关"])
def load_dict(fnm):
res = {}
f = open(fnm, "r")
while True:
l = f.readline()
if not l:
break
arr = l.replace("\n", "").split("\t")
if len(arr) < 2:
res[arr[0]] = 0
else:
res[arr[0]] = int(arr[1])
c = 0
for _, v in res.items():
c += v
if c == 0:
return set(res.keys())
return res
fnm = os.path.join(get_project_base_directory(), "rag/res")
self.ne, self.df = {}, {}
try:
self.ne = json.load(open(os.path.join(fnm, "ner.json"), "r"))
except Exception as e:
print("[WARNING] Load ner.json FAIL!")
try:
self.df = load_dict(os.path.join(fnm, "term.freq"))
except Exception as e:
print("[WARNING] Load term.freq FAIL!")
def pretoken(self, txt, num=False, stpwd=True):
patt = [
r"[~—\t @#%!<>,\.\?\":;'\{\}\[\]_=\(\)\|，。？》•●○↓《；‘’：“”【¥ 】…￥！、·（）×`&\\/「」\\]"
]
rewt = [
]
for p, r in rewt:
txt = re.sub(p, r, txt)
res = []
for t in huqie.qie(txt).split(" "):
tk = t
if (stpwd and tk in self.stop_words) or (
re.match(r"[0-9]$", tk) and not num):
continue
for p in patt:
if re.match(p, t):
tk = "#"
break
tk = re.sub(r"([\+\\-])", r"\\\1", tk)
if tk != "#" and tk:
res.append(tk)
return res
def tokenMerge(self, tks):
def oneTerm(t): return len(t) == 1 or re.match(r"[0-9a-z]{1,2}$", t)
res, i = [], 0
while i < len(tks):
j = i
if i == 0 and oneTerm(tks[i]) and len(
tks) > 1 and len(tks[i + 1]) > 1: # 多 工位
res.append(" ".join(tks[0:2]))
i = 2
continue
while j < len(
tks) and tks[j] and tks[j] not in self.stop_words and oneTerm(tks[j]):
j += 1
if j - i > 1:
if j - i < 5:
res.append(" ".join(tks[i:j]))
i = j
else:
res.append(" ".join(tks[i:i + 2]))
i = i + 2
else:
if len(tks[i]) > 0:
res.append(tks[i])
i += 1
return [t for t in res if t]
def ner(self, t):
if not self.ne:
return ""
res = self.ne.get(t, "")
if res:
return res
def split(self, txt):
tks = []
for t in re.sub(r"[ \t]+", " ", txt).split(" "):
if tks and re.match(r".*[a-zA-Z]$", tks[-1]) and \
re.match(r".*[a-zA-Z]$", t) and tks and \
self.ne.get(t, "") != "func" and self.ne.get(tks[-1], "") != "func":
tks[-1] = tks[-1] + " " + t
else:
tks.append(t)
return tks
def weights(self, tks):
def skill(t):
if t not in self.sk:
return 1
return 6
def ner(t):
if re.match(r"[0-9,.]{2,}$", t):
return 2
if re.match(r"[a-z]{1,2}$", t):
return 0.01
if not self.ne or t not in self.ne:
return 1
m = {"toxic": 2, "func": 1, "corp": 3, "loca": 3, "sch": 3, "stock": 3,
"firstnm": 1}
return m[self.ne[t]]
def postag(t):
t = huqie.tag(t)
if t in set(["r", "c", "d"]):
return 0.3
if t in set(["ns", "nt"]):
return 3
if t in set(["n"]):
return 2
if re.match(r"[0-9-]+", t):
return 2
return 1
def freq(t):
if re.match(r"[0-9. -]{2,}$", t):
return 3
s = huqie.freq(t)
if not s and re.match(r"[a-z. -]+$", t):
return 300
if not s:
s = 0
if not s and len(t) >= 4:
s = [tt for tt in huqie.qieqie(t).split(" ") if len(tt) > 1]
if len(s) > 1:
s = np.min([freq(tt) for tt in s]) / 6.
else:
s = 0
return max(s, 10)
def df(t):
if re.match(r"[0-9. -]{2,}$", t):
return 5
if t in self.df:
return self.df[t] + 3
elif re.match(r"[a-z. -]+$", t):
return 300
elif len(t) >= 4:
s = [tt for tt in huqie.qieqie(t).split(" ") if len(tt) > 1]
if len(s) > 1:
return max(3, np.min([df(tt) for tt in s]) / 6.)
return 3
def idf(s, N): return math.log10(10 + ((N - s + 0.5) / (s + 0.5)))
tw = []
for tk in tks:
tt = self.tokenMerge(self.pretoken(tk, True))
idf1 = np.array([idf(freq(t), 10000000) for t in tt])
idf2 = np.array([idf(df(t), 1000000000) for t in tt])
wts = (0.3 * idf1 + 0.7 * idf2) * \
np.array([ner(t) * postag(t) for t in tt])
tw.extend(zip(tt, wts))
S = np.sum([s for _, s in tw])
return [(t, s / S) for t, s in tw]
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