<s> from collections import defaultdict <EOL> import time <EOL> import re <EOL> from unidecode import unidecode <EOL> from nltk . tokenize import RegexpTokenizer <EOL> from nltk import bigrams <EOL> from util . build_whoosh import text_iterator <EOL> import clm <EOL> kTOKENIZER = RegexpTokenizer ( '<STR_LIT>' ) . tokenize <EOL> kUNK = "<STR_LIT>" <EOL> kMAX_TEXT_LENGTH = <NUM_LIT> <EOL> kGOODCHAR = re . compile ( r"<STR_LIT>" ) <EOL> class DistCounter : <EOL> def __init__ ( self ) : <EOL> self . _total = <NUM_LIT:0> <EOL> self . _bins = <NUM_LIT:0> <EOL> self . _counts = defaultdict ( int ) <EOL> def __len__ ( self ) : <EOL> return len ( self . _counts ) <EOL> def inc ( self , word , count = <NUM_LIT:1> ) : <EOL> self . _counts [ word ] += count <EOL> self . _total += count <EOL> assert self . _total >= <NUM_LIT:0> , "<STR_LIT>" <EOL> assert self . _counts [ word ] >= <NUM_LIT:0> , "<STR_LIT>" <EOL> if self . _counts [ word ] == <NUM_LIT:0> : <EOL> del self . _counts [ word ] <EOL> def __getitem__ ( self , word ) : <EOL> return self . _counts . get ( word , <NUM_LIT:0> ) <EOL> def __iter__ ( self ) : <EOL> for ii in self . _counts : <EOL> if self . _counts [ ii ] > <NUM_LIT:0> : <EOL> yield ii <EOL> def B ( self ) : <EOL> return len ( self . _counts ) <EOL> def N ( self ) : <EOL> return self . _total <EOL> def freq ( self , word ) : <EOL> if self . _total == <NUM_LIT:0> : <EOL> return <NUM_LIT:0> <EOL> return self . _counts . get ( word , <NUM_LIT:0> ) / float ( self . _total ) <EOL> class LanguageModelBase : <EOL> def vocab_lookup ( self , word ) : <EOL> """<STR_LIT>""" <EOL> assert self . _vocab_final , "<STR_LIT>" <EOL> if word in self . _vocab : <EOL> return self . _vocab [ word ] <EOL> else : <EOL> return self . _vocab [ kUNK ] <EOL> @ staticmethod <EOL> def normalize_title ( corpus , title ) : <EOL> norm_title = corpus + "<STR_LIT>" . join ( x for x in kGOODCHAR . findall ( title ) if x ) <EOL> return norm_title <EOL> @ staticmethod <EOL> def tokenize_without_censor ( sentence ) : <EOL> for ii in kTOKENIZER ( unidecode ( sentence ) ) : <EOL> yield ii . lower ( ) <EOL> def vocab_size ( self ) : <EOL> return len ( self . _vocab ) <EOL> def tokenize_and_censor ( self , sentence ) : <EOL> """<STR_LIT>""" <EOL> if not isinstance ( sentence , basestring ) : <EOL> sentence = '<STR_LIT:U+0020>' . join ( list ( sentence ) ) <EOL> for ii in kTOKENIZER ( unidecode ( sentence ) ) : <EOL> yield self . vocab_lookup ( ii . lower ( ) ) <EOL> class LanguageModelReader ( LanguageModelBase ) : <EOL> def __init__ ( self , lm_file , interp = <NUM_LIT> ) : <EOL> from clm import intArray <EOL> self . _datafile = lm_file <EOL> self . _lm = clm . JelinekMercerFeature ( ) <EOL> self . _lm . set_interpolation ( interp ) <EOL> self . _sentence = intArray ( kMAX_TEXT_LENGTH ) <EOL> self . _sentence_length = <NUM_LIT:0> <EOL> self . _sentence_hash = <NUM_LIT:0> <EOL> self . _vocab_final = True <EOL> def init ( self ) : <EOL> infile = open ( self . _datafile ) <EOL> num_lms = int ( infile . readline ( ) ) <EOL> vocab_size = int ( infile . readline ( ) ) <EOL> self . _vocab = { } <EOL> for ii in xrange ( vocab_size ) : <EOL> self . _vocab [ infile . readline ( ) . strip ( ) ] = ii <EOL> print ( "<STR_LIT>" % vocab_size ) <EOL> self . _corpora = { } <EOL> for ii in xrange ( num_lms ) : <EOL> line = infile . readline ( ) <EOL> corpus , compare = line . split ( ) <EOL> self . _corpora [ corpus ] = ii <EOL> print ( self . _corpora . keys ( ) [ : <NUM_LIT:10> ] ) <EOL> self . _lm . read_counts ( self . _datafile ) <EOL> def feature_line ( self , corpus , guess , sentence ) : <EOL> if self . _sentence_hash != hash ( sentence ) : <EOL> self . _sentence_hash = hash ( sentence ) <EOL> tokenized = list ( self . tokenize_and_censor ( sentence ) ) <EOL> self . _sentence_length = len ( tokenized ) <EOL> assert self . _sentence_length < kMAX_TEXT_LENGTH <EOL> for ii , ww in enumerate ( tokenized ) : <EOL> self . _sentence [ ii ] = ww <EOL> norm_title = self . normalize_title ( corpus , guess ) <EOL> if not norm_title in self . _corpora or self . _sentence_length == <NUM_LIT:0> : <EOL> return "<STR_LIT>" <EOL> else : <EOL> guess_id = self . _corpora [ norm_title ] <EOL> return self . _lm . feature ( corpus , guess_id , self . _sentence , <EOL> self . _sentence_length ) <EOL> class LanguageModelWriter ( LanguageModelBase ) : <EOL> def __init__ ( self , vocab_size , comparison_corpora ) : <EOL> self . _vocab_size = vocab_size <EOL> self . _vocab_final = False <EOL> self . _vocab = { } <EOL> self . _compare = comparison_corpora <EOL> self . _training_counts = DistCounter ( ) <EOL> self . _obs_counts = { } <EOL> self . _unigram = defaultdict ( DistCounter ) <EOL> def train_seen ( self , word , count = <NUM_LIT:1> ) : <EOL> """<STR_LIT>""" <EOL> assert not self . _vocab_final , "<STR_LIT>" <EOL> self . _training_counts . inc ( word , count ) <EOL> def finalize ( self , vocab = None ) : <EOL> """<STR_LIT>""" <EOL> self . _vocab_final = True <EOL> if vocab is None : <EOL> self . _vocab_size = min ( len ( self . _training_counts ) , <EOL> self . _vocab_size ) <EOL> vocab = sorted ( self . _training_counts , <EOL> key = lambda x : self . _training_counts [ x ] , <EOL> reverse = True ) [ : self . _vocab_size ] <EOL> self . _vocab = dict ( ( x , y + <NUM_LIT:1> ) for y , x in enumerate ( vocab ) ) <EOL> assert not kUNK in self . _vocab , "<STR_LIT>" % kUNK <EOL> self . _vocab [ kUNK ] = <NUM_LIT:0> <EOL> else : <EOL> self . _vocab = vocab <EOL> del self . _training_counts <EOL> return self . _vocab <EOL> def add_train ( self , corpus , sentence ) : <EOL> """<STR_LIT>""" <EOL> if not corpus in self . _obs_counts : <EOL> self . _obs_counts [ corpus ] = defaultdict ( DistCounter ) <EOL> for context , word in bigrams ( self . tokenize_and_censor ( sentence ) ) : <EOL> self . _obs_counts [ corpus ] [ context ] . inc ( word ) <EOL> self . _unigram [ corpus ] . inc ( word ) <EOL> def compare ( self , title ) : <EOL> return hash ( title ) % self . _compare <EOL> def write_lm ( self , outfile ) : <EOL> """<STR_LIT>""" <EOL> outfile . write ( "<STR_LIT>" % len ( self . _unigram ) ) <EOL> outfile . write ( "<STR_LIT>" % len ( self . _vocab ) ) <EOL> vocab_size = len ( self . _vocab ) <EOL> for ii , count in sorted ( self . _vocab . iteritems ( ) , <EOL> key = lambda key_value : key_value [ <NUM_LIT:1> ] ) : <EOL> outfile . write ( "<STR_LIT>" % ii ) <EOL> print ( "<STR_LIT>" ) <EOL> corpus_num = <NUM_LIT:0> <EOL> for ii in sorted ( self . _unigram ) : <EOL> corpus_num += <NUM_LIT:1> <EOL> if corpus_num % <NUM_LIT:100> == <NUM_LIT:0> : <EOL> print ( "<STR_LIT>" % <EOL> ( unidecode ( ii ) , corpus_num , len ( self . _unigram ) ) ) <EOL> outfile . write ( "<STR_LIT>" % ( ii , self . compare ( ii ) ) ) <EOL> for ii in sorted ( self . _unigram ) : <EOL> for jj in xrange ( vocab_size ) : <EOL> if jj in self . _obs_counts [ ii ] : <EOL> total = self . _obs_counts [ ii ] [ jj ] . N ( ) <EOL> contexts = self . _obs_counts [ ii ] [ jj ] . B ( ) <EOL> else : <EOL> total = <NUM_LIT:0> <EOL> contexts = <NUM_LIT:0> <EOL> outfile . write ( "<STR_LIT>" % ( jj , total , contexts ) ) <EOL> if jj in self . _obs_counts [ ii ] : <EOL> for kk in self . _obs_counts [ ii ] [ jj ] : <EOL> outfile . write ( "<STR_LIT>" % <EOL> ( kk , self . _obs_counts [ ii ] [ jj ] [ kk ] ) ) <EOL> if __name__ == "<STR_LIT:__main__>" : <EOL> import argparse <EOL> parser = argparse . ArgumentParser ( description = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = str , default = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = str , default = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = str , default = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = int , default = <NUM_LIT:5> , <EOL> help = "<STR_LIT>" ) <EOL> parser . add_argument ( '<STR_LIT>' , type = int , default = <NUM_LIT> ) <EOL> parser . add_argument ( "<STR_LIT>" , type = int , default = - <NUM_LIT:1> , <EOL> help = "<STR_LIT>" ) <EOL> parser . add_argument ( "<STR_LIT>" , type = int , default = - <NUM_LIT:1> , <EOL> help = "<STR_LIT>" ) <EOL> parser . add_argument ( "<STR_LIT>" , type = int , default = <NUM_LIT> , <EOL> help = "<STR_LIT>" ) <EOL> parser . add_argument ( "<STR_LIT>" , type = str , default = '<STR_LIT>' ) <EOL> flags = parser . parse_args ( ) <EOL> min_answers = flags . min_answers <EOL> print ( "<STR_LIT>" % <EOL> min_answers ) <EOL> lm = LanguageModelWriter ( flags . vocab_size , flags . global_lms ) <EOL> num_docs = <NUM_LIT:0> <EOL> background = defaultdict ( int ) <EOL> for title , text in text_iterator ( True , flags . wiki_location , <EOL> True , flags . question_db , <EOL> True , flags . source_location , <EOL> flags . max_pages , <EOL> min_pages = min_answers ) : <EOL> num_docs += <NUM_LIT:1> <EOL> if num_docs % <NUM_LIT> == <NUM_LIT:0> : <EOL> print ( unidecode ( title ) , num_docs ) <EOL> print ( list ( lm . tokenize_without_censor ( text [ <NUM_LIT:100> : <NUM_LIT:200> ] ) ) ) <EOL> for tt in lm . tokenize_without_censor ( text ) : <EOL> background [ tt ] += <NUM_LIT:1> <EOL> for ii in background : <EOL> lm . train_seen ( ii , background [ ii ] ) <EOL> vocab = lm . finalize ( ) <EOL> print ( str ( vocab ) [ : <NUM_LIT> ] ) <EOL> print ( "<STR_LIT>" % <EOL> ( len ( vocab ) , num_docs ) ) <EOL> del background <EOL> doc_num = <NUM_LIT:0> <EOL> for corpus , qb , wiki , source in [ ( "<STR_LIT>" , False , True , False ) , <EOL> ( "<STR_LIT>" , True , False , False ) , <EOL> ( "<STR_LIT:source>" , False , False , True ) <EOL> ] : <EOL> start = time . time ( ) <EOL> for title , text in text_iterator ( wiki , flags . wiki_location , <EOL> qb , flags . question_db , <EOL> source , flags . source_location , <EOL> flags . max_pages , <EOL> min_pages = min_answers ) : <EOL> norm_title = lm . normalize_title ( corpus , title ) <EOL> doc_num += <NUM_LIT:1> <EOL> if doc_num % <NUM_LIT> == <NUM_LIT:0> or time . time ( ) - start > <NUM_LIT:10> : <EOL> print ( "<STR_LIT>" % <EOL> ( doc_num , unidecode ( title ) , corpus ) ) <EOL> start = time . time ( ) <EOL> lm . add_train ( norm_title , text ) <EOL> comp = lm . compare ( norm_title ) <EOL> for ii in xrange ( flags . global_lms ) : <EOL> if comp != ii : <EOL> lm . add_train ( "<STR_LIT>" % ii , text ) <EOL> print ( "<STR_LIT>" ) <EOL> if flags . lm_out : <EOL> o = open ( flags . lm_out , '<STR_LIT:w>' ) <EOL> lm . write_lm ( o ) </s>
