mirror of
https://github.com/fxsjy/jieba.git
synced 2025-07-10 00:01:33 +08:00
don't compile re every time; autopep8
This commit is contained in:
Dingyuan Wang
parent
22bcf8be7a
commit
32a0e92a09
@ -25,17 +25,20 @@ user_word_tag_tab = {}
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initialized = False
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pool = None
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_curpath = os.path.normpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
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_curpath = os.path.normpath(
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os.path.join(os.getcwd(), os.path.dirname(__file__)))
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log_console = logging.StreamHandler(sys.stderr)
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.DEBUG)
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logger.addHandler(log_console)
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def setLogLevel(log_level):
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global logger
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logger.setLevel(log_level)
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def gen_pfdict(f_name):
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lfreq = {}
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pfdict = set()
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@ -56,6 +59,7 @@ def gen_pfdict(f_name):
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raise e
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return pfdict, lfreq, ltotal
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def initialize(dictionary=None):
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global pfdict, FREQ, total, initialized, DICTIONARY, DICT_LOCK
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if not dictionary:
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@ -67,10 +71,12 @@ def initialize(dictionary=None):
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abs_path = os.path.join(_curpath, dictionary)
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logger.debug("Building prefix dict from %s ..." % abs_path)
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t1 = time.time()
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if abs_path == os.path.join(_curpath, "dict.txt"): #default dictionary
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# default dictionary
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if abs_path == os.path.join(_curpath, "dict.txt"):
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cache_file = os.path.join(tempfile.gettempdir(), "jieba.cache")
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else: # custom dictionary
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cache_file = os.path.join(tempfile.gettempdir(), "jieba.u%s.cache" % md5(abs_path.encode('utf-8', 'replace')).hexdigest())
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cache_file = os.path.join(tempfile.gettempdir(), "jieba.u%s.cache" % md5(
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abs_path.encode('utf-8', 'replace')).hexdigest())
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load_from_cache_fail = True
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if os.path.isfile(cache_file) and os.path.getmtime(cache_file) > os.path.getmtime(abs_path):
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@ -136,7 +142,9 @@ def calc(sentence, DAG, route):
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N = len(sentence)
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route[N] = (0.0, '')
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for idx in xrange(N - 1, -1, -1):
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route[idx] = max((log(FREQ.get(sentence[idx:x+1], 1)) - log(total) + route[x+1][0], x) for x in DAG[idx])
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route[idx] = max((log(FREQ.get(sentence[idx:x + 1], 1)) -
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log(total) + route[x + 1][0], x) for x in DAG[idx])
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@require_initialized
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def get_DAG(sentence):
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@ -157,8 +165,10 @@ def get_DAG(sentence):
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DAG[k] = tmplist
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return DAG
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def __cut_DAG_NO_HMM(sentence):
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re_eng = re.compile(r'[a-zA-Z0-9]', re.U)
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def __cut_DAG_NO_HMM(sentence):
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DAG = get_DAG(sentence)
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route = {}
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calc(sentence, DAG, route)
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@ -181,6 +191,7 @@ def __cut_DAG_NO_HMM(sentence):
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yield buf
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buf = ''
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def __cut_DAG(sentence):
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DAG = get_DAG(sentence)
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route = {}
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@ -221,6 +232,12 @@ def __cut_DAG(sentence):
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for elem in buf:
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yield elem
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re_han_default = re.compile("([\u4E00-\u9FA5a-zA-Z0-9+#&\._]+)", re.U)
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re_skip_default = re.compile("(\r\n|\s)", re.U)
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re_han_cut_all = re.compile("([\u4E00-\u9FA5]+)", re.U)
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re_skip_cut_all = re.compile("[^a-zA-Z0-9+#\n]", re.U)
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def cut(sentence, cut_all=False, HMM=True):
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'''The main function that segments an entire sentence that contains
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Chinese characters into seperated words.
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@ -235,9 +252,11 @@ def cut(sentence, cut_all=False, HMM=True):
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# \r\n|\s : whitespace characters. Will not be handled.
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if cut_all:
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re_han, re_skip = re.compile("([\u4E00-\u9FA5]+)", re.U), re.compile("[^a-zA-Z0-9+#\n]", re.U)
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re_han = re_han_cut_all
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re_skip = re_skip_cut_all
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else:
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re_han, re_skip = re.compile("([\u4E00-\u9FA5a-zA-Z0-9+#&\._]+)", re.U), re.compile("(\r\n|\s)", re.U)
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re_han = re_han_default
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re_skip = re_skip_default
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blocks = re_han.split(sentence)
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if cut_all:
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cut_block = __cut_all
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@ -262,6 +281,7 @@ def cut(sentence, cut_all=False, HMM=True):
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else:
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yield x
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def cut_for_search(sentence, HMM=True):
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words = cut(sentence, HMM=HMM)
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for w in words:
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@ -277,6 +297,7 @@ def cut_for_search(sentence, HMM=True):
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yield gram3
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yield w
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@require_initialized
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def load_userdict(f):
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''' Load personalized dict to improve detect rate.
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@ -300,6 +321,7 @@ def load_userdict(f):
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if tup[1].isdigit():
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add_word(*tup)
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@require_initialized
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def add_word(word, freq, tag=None):
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global FREQ, pfdict, total, user_word_tag_tab
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@ -314,12 +336,19 @@ def add_word(word, freq, tag=None):
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__ref_cut = cut
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__ref_cut_for_search = cut_for_search
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def __lcut(sentence):
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return list(__ref_cut(sentence, False))
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def __lcut_no_hmm(sentence):
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return list(__ref_cut(sentence, False, False))
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def __lcut_all(sentence):
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return list(__ref_cut(sentence, True))
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def __lcut_for_search(sentence):
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return list(__ref_cut_for_search(sentence))
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@ -356,6 +385,7 @@ def enable_parallel(processnum=None):
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cut = pcut
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cut_for_search = pcut_for_search
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def disable_parallel():
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global pool, cut, cut_for_search
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if pool:
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@ -364,6 +394,7 @@ def disable_parallel():
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cut = __ref_cut
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cut_for_search = __ref_cut_for_search
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def set_dictionary(dictionary_path):
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global initialized, DICTIONARY
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with DICT_LOCK:
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@ -373,9 +404,11 @@ def set_dictionary(dictionary_path):
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DICTIONARY = abs_path
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initialized = False
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def get_abs_path_dict():
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return os.path.join(_curpath, DICTIONARY)
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def tokenize(unicode_sentence, mode="default", HMM=True):
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"""Tokenize a sentence and yields tuples of (word, start, end)
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Parameter:
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@ -13,14 +13,16 @@ PROB_EMIT_P = "prob_emit.p"
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PrevStatus = {
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'B':('E','S'),
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'M':('M','B'),
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'S':('S','E'),
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'E':('B','M')
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'B': 'ES',
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'M': 'MB',
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'S': 'SE',
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'E': 'BM'
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}
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def load_model():
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_curpath = os.path.normpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
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_curpath = os.path.normpath(
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os.path.join(os.getcwd(), os.path.dirname(__file__)))
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start_p = {}
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abs_path = os.path.join(_curpath, PROB_START_P)
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@ -46,6 +48,7 @@ else:
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from .prob_trans import P as trans_P
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from .prob_emit import P as emit_P
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def viterbi(obs, states, start_p, trans_p, emit_p):
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V = [{}] # tabular
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path = {}
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@ -57,19 +60,20 @@ def viterbi(obs, states, start_p, trans_p, emit_p):
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newpath = {}
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for y in states:
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em_p = emit_p[y].get(obs[t], MIN_FLOAT)
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(prob,state ) = max([(V[t-1][y0] + trans_p[y0].get(y, MIN_FLOAT) + em_p, y0) for y0 in PrevStatus[y]])
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(prob, state) = max(
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[(V[t - 1][y0] + trans_p[y0].get(y, MIN_FLOAT) + em_p, y0) for y0 in PrevStatus[y]])
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V[t][y] = prob
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newpath[y] = path[state] + [y]
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path = newpath
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(prob, state) = max([(V[len(obs)-1][y], y) for y in ('E','S')])
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(prob, state) = max((V[len(obs) - 1][y], y) for y in 'ES')
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return (prob, path[state])
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def __cut(sentence):
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global emit_P
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prob, pos_list = viterbi(sentence, ('B','M','E','S'), start_P, trans_P, emit_P)
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prob, pos_list = viterbi(sentence, 'BMES', start_P, trans_P, emit_P)
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begin, nexti = 0, 0
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# print pos_list, sentence
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for i, char in enumerate(sentence):
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@ -85,9 +89,12 @@ def __cut(sentence):
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if nexti < len(sentence):
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yield sentence[nexti:]
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re_han = re.compile("([\u4E00-\u9FA5]+)")
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re_skip = re.compile("(\d+\.\d+|[a-zA-Z0-9]+)")
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def cut(sentence):
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sentence = strdecode(sentence)
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re_han, re_skip = re.compile("([\u4E00-\u9FA5]+)"), re.compile("(\d+\.\d+|[a-zA-Z0-9]+)")
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blocks = re_han.split(sentence)
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for blk in blocks:
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if re_han.match(blk):
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@ -13,8 +13,20 @@ PROB_TRANS_P = "prob_trans.p"
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PROB_EMIT_P = "prob_emit.p"
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CHAR_STATE_TAB_P = "char_state_tab.p"
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re_han_detail = re.compile("([\u4E00-\u9FA5]+)")
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re_skip_detail = re.compile("([\.0-9]+|[a-zA-Z0-9]+)")
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re_han_internal = re.compile("([\u4E00-\u9FA5a-zA-Z0-9+#&\._]+)")
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re_skip_internal = re.compile("(\r\n|\s)")
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re_eng = re.compile("[a-zA-Z0-9]+")
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re_num = re.compile("[\.0-9]+")
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re_eng1 = re.compile('^[a-zA-Z0-9]$', re.U)
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def load_model(f_name, isJython=True):
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_curpath = os.path.normpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
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_curpath = os.path.normpath(
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os.path.join(os.getcwd(), os.path.dirname(__file__)))
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result = {}
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with open(f_name, "rb") as f:
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@ -53,7 +65,8 @@ def load_model(f_name, isJython=True):
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return state, start_p, trans_p, emit_p, result
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if sys.platform.startswith("java"):
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char_state_tab_P, start_P, trans_P, emit_P, word_tag_tab = load_model(jieba.get_abs_path_dict())
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char_state_tab_P, start_P, trans_P, emit_P, word_tag_tab = load_model(
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jieba.get_abs_path_dict())
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else:
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from .char_state_tab import P as char_state_tab_P
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from .prob_start import P as start_P
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@ -62,6 +75,7 @@ else:
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word_tag_tab = load_model(jieba.get_abs_path_dict(), isJython=False)
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def makesure_userdict_loaded(fn):
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@wraps(fn)
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@ -73,7 +87,9 @@ def makesure_userdict_loaded(fn):
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return wrapped
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class pair(object):
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def __init__(self, word, flag):
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self.word = word
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self.flag = flag
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@ -93,8 +109,10 @@ class pair(object):
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def encode(self, arg):
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return self.__unicode__().encode(arg)
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def __cut(sentence):
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prob, pos_list = viterbi(sentence, char_state_tab_P, start_P, trans_P, emit_P)
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prob, pos_list = viterbi(
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sentence, char_state_tab_P, start_P, trans_P, emit_P)
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begin, nexti = 0, 0
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for i, char in enumerate(sentence):
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@ -110,16 +128,15 @@ def __cut(sentence):
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if nexti < len(sentence):
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yield pair(sentence[nexti:], pos_list[nexti][1])
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def __cut_detail(sentence):
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re_han, re_skip = re.compile("([\u4E00-\u9FA5]+)"), re.compile("([\.0-9]+|[a-zA-Z0-9]+)")
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re_eng, re_num = re.compile("[a-zA-Z0-9]+"), re.compile("[\.0-9]+")
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blocks = re_han.split(sentence)
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blocks = re_han_detail.split(sentence)
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for blk in blocks:
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if re_han.match(blk):
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if re_han_detail.match(blk):
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for word in __cut(blk):
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yield word
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else:
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tmp = re_skip.split(blk)
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tmp = re_skip_detail.split(blk)
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for x in tmp:
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if x:
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if re_num.match(x):
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@ -129,6 +146,7 @@ def __cut_detail(sentence):
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else:
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yield pair(x, 'x')
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def __cut_DAG_NO_HMM(sentence):
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DAG = jieba.get_DAG(sentence)
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route = {}
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@ -136,11 +154,10 @@ def __cut_DAG_NO_HMM(sentence):
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x = 0
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N = len(sentence)
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buf = ''
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re_eng = re.compile('[a-zA-Z0-9]',re.U)
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while x < N:
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y = route[x][1] + 1
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l_word = sentence[x:y]
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if re_eng.match(l_word) and len(l_word) == 1:
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if re_eng1.match(l_word):
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buf += l_word
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x = y
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else:
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@ -153,6 +170,7 @@ def __cut_DAG_NO_HMM(sentence):
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yield pair(buf, 'eng')
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buf = ''
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def __cut_DAG(sentence):
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DAG = jieba.get_DAG(sentence)
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route = {}
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@ -193,24 +211,23 @@ def __cut_DAG(sentence):
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for elem in buf:
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yield pair(elem, word_tag_tab.get(elem, 'x'))
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def __cut_internal(sentence, HMM=True):
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sentence = strdecode(sentence)
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re_han, re_skip = re.compile("([\u4E00-\u9FA5a-zA-Z0-9+#&\._]+)"), re.compile("(\r\n|\s)")
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re_eng, re_num = re.compile("[a-zA-Z0-9]+"), re.compile("[\.0-9]+")
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blocks = re_han.split(sentence)
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blocks = re_han_internal.split(sentence)
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if HMM:
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__cut_blk = __cut_DAG
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else:
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__cut_blk = __cut_DAG_NO_HMM
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for blk in blocks:
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if re_han.match(blk):
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if re_han_internal.match(blk):
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for word in __cut_blk(blk):
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yield word
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else:
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tmp = re_skip.split(blk)
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tmp = re_skip_internal.split(blk)
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for x in tmp:
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if re_skip.match(x):
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if re_skip_internal.match(x):
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yield pair(x, 'x')
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else:
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for xx in x:
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@ -221,8 +238,11 @@ def __cut_internal(sentence, HMM=True):
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else:
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yield pair(xx, 'x')
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def __lcut_internal(sentence):
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return list(__cut_internal(sentence))
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def __lcut_internal_no_hmm(sentence):
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return list(__cut_internal(sentence, False))
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@ -241,4 +261,3 @@ def cut(sentence, HMM=True):
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for r in result:
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for w in r:
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yield w
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@ -6,9 +6,11 @@ MIN_INF = float("-inf")
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if sys.version_info[0] > 2:
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xrange = range
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def get_top_states(t_state_v, K=4):
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return sorted(t_state_v, key=t_state_v.__getitem__, reverse=True)[:K]
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def viterbi(obs, states, start_p, trans_p, emit_p):
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V = [{}] # tabular
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mem_path = [{}]
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@ -20,16 +22,20 @@ def viterbi(obs, states, start_p, trans_p, emit_p):
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V.append({})
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mem_path.append({})
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#prev_states = get_top_states(V[t-1])
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prev_states = [x for x in mem_path[t-1].keys() if len(trans_p[x]) > 0]
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prev_states = [
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x for x in mem_path[t - 1].keys() if len(trans_p[x]) > 0]
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prev_states_expect_next = set((y for x in prev_states for y in trans_p[x].keys()))
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obs_states = set(states.get(obs[t], all_states)) & prev_states_expect_next
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prev_states_expect_next = set(
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(y for x in prev_states for y in trans_p[x].keys()))
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obs_states = set(
|
||||
states.get(obs[t], all_states)) & prev_states_expect_next
|
||||
|
||||
if not obs_states:
|
||||
obs_states = prev_states_expect_next if prev_states_expect_next else all_states
|
||||
|
||||
for y in obs_states:
|
||||
prob, state = max((V[t-1][y0] + trans_p[y0].get(y,MIN_INF) + emit_p[y].get(obs[t],MIN_FLOAT), y0) for y0 in prev_states)
|
||||
prob, state = max((V[t - 1][y0] + trans_p[y0].get(y, MIN_INF) +
|
||||
emit_p[y].get(obs[t], MIN_FLOAT), y0) for y0 in prev_states)
|
||||
V[t][y] = prob
|
||||
mem_path[t][y] = state
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user