<s> import re , sys , os , math , tempfile , collections , subprocess <EOL> import sbd_util , word_tokenize <EOL> """<STR_LIT>""" <EOL> SVM_LEARN = os . path . abspath ( "<STR_LIT>" ) + "<STR_LIT>" <EOL> SVM_CLASSIFY = os . path . abspath ( "<STR_LIT>" ) + "<STR_LIT>" <EOL> def get_open_fds ( ) : <EOL> '''<STR_LIT>''' <EOL> pid = os . getpid ( ) <EOL> procs = subprocess . check_output ( <EOL> [ "<STR_LIT>" , '<STR_LIT>' , '<STR_LIT>' , "<STR_LIT>" , str ( pid ) ] ) <EOL> nprocs = len ( <EOL> filter ( <EOL> lambda s : s and s [ <NUM_LIT:0> ] == '<STR_LIT:f>' and s [ <NUM_LIT:1> : ] . isdigit ( ) , <EOL> procs . split ( '<STR_LIT:\n>' ) ) <EOL> ) <EOL> return nprocs <EOL> def unannotate ( t ) : <EOL> """<STR_LIT>""" <EOL> t = re . sub ( '<STR_LIT>' , '<STR_LIT>' , t ) <EOL> return t <EOL> def clean ( t ) : <EOL> """<STR_LIT>""" <EOL> t = re . sub ( '<STR_LIT>' , '<STR_LIT>' , t ) <EOL> t = re . sub ( '<STR_LIT>' , '<STR_LIT>' , t ) <EOL> t = t . replace ( '<STR_LIT>' , '<STR_LIT:U+0020>' ) <EOL> return t <EOL> def get_features ( frag , model ) : <EOL> """<STR_LIT>""" <EOL> words1 = clean ( frag . tokenized ) . split ( ) <EOL> if not words1 : w1 = '<STR_LIT>' <EOL> else : w1 = words1 [ - <NUM_LIT:1> ] <EOL> if frag . next : <EOL> words2 = clean ( frag . next . tokenized ) . split ( ) <EOL> if not words2 : w2 = '<STR_LIT>' <EOL> else : w2 = words2 [ <NUM_LIT:0> ] <EOL> else : <EOL> words2 = [ ] <EOL> w2 = '<STR_LIT>' <EOL> c1 = re . sub ( '<STR_LIT>' , '<STR_LIT>' , w1 ) <EOL> c2 = re . sub ( '<STR_LIT>' , '<STR_LIT>' , w2 ) <EOL> feats = { } <EOL> feats [ '<STR_LIT>' ] = c1 <EOL> feats [ '<STR_LIT>' ] = c2 <EOL> feats [ '<STR_LIT>' ] = c1 + '<STR_LIT:_>' + c2 <EOL> len1 = min ( <NUM_LIT:10> , len ( re . sub ( '<STR_LIT>' , '<STR_LIT>' , c1 ) ) ) <EOL> if c1 . replace ( '<STR_LIT:.>' , '<STR_LIT>' ) . isalpha ( ) : <EOL> feats [ '<STR_LIT>' ] = str ( len1 ) <EOL> try : feats [ '<STR_LIT>' ] = str ( int ( math . log ( <NUM_LIT:1> + model . non_abbrs [ c1 [ : - <NUM_LIT:1> ] ] ) ) ) <EOL> except : feats [ '<STR_LIT>' ] = str ( int ( math . log ( <NUM_LIT:1> ) ) ) <EOL> if c2 . replace ( '<STR_LIT:.>' , '<STR_LIT>' ) . isalpha ( ) : <EOL> feats [ '<STR_LIT>' ] = str ( c2 [ <NUM_LIT:0> ] . isupper ( ) ) <EOL> try : feats [ '<STR_LIT>' ] = str ( int ( math . log ( <NUM_LIT:1> + model . lower_words [ c2 . lower ( ) ] ) ) ) <EOL> except : feats [ '<STR_LIT>' ] = str ( int ( math . log ( <NUM_LIT:1> ) ) ) <EOL> feats [ '<STR_LIT>' ] = c1 + '<STR_LIT:_>' + str ( c2 [ <NUM_LIT:0> ] . isupper ( ) ) <EOL> return feats <EOL> def is_sbd_hyp ( word ) : <EOL> """<STR_LIT>""" <EOL> if not '<STR_LIT:.>' in word : return False <EOL> c = unannotate ( word ) <EOL> if c . endswith ( '<STR_LIT:.>' ) : return True <EOL> if re . match ( '<STR_LIT>' , c ) : return True <EOL> return False <EOL> def get_data ( files , expect_labels = True , tokenize = False , verbose = False ) : <EOL> """<STR_LIT>""" <EOL> if type ( files ) == type ( '<STR_LIT>' ) : files = [ files ] <EOL> frag_list = None <EOL> word_index = <NUM_LIT:0> <EOL> frag_index = <NUM_LIT:0> <EOL> curr_words = [ ] <EOL> lower_words , non_abbrs = sbd_util . Counter ( ) , sbd_util . Counter ( ) <EOL> for file in files : <EOL> sys . stderr . write ( '<STR_LIT>' % file ) <EOL> fh = open ( file ) <EOL> for line in fh : <EOL> if ( not line . strip ( ) ) and frag_list : <EOL> if not curr_words : frag . ends_seg = True <EOL> else : <EOL> frag = Frag ( '<STR_LIT:U+0020>' . join ( curr_words ) ) <EOL> frag . ends_seg = True <EOL> if expect_labels : frag . label = True <EOL> prev . next = frag <EOL> if tokenize : <EOL> tokens = word_tokenize . tokenize ( frag . orig ) <EOL> frag . tokenized = tokens <EOL> frag_index += <NUM_LIT:1> <EOL> prev = frag <EOL> curr_words = [ ] <EOL> for word in line . split ( ) : <EOL> curr_words . append ( word ) <EOL> if is_sbd_hyp ( word ) : <EOL> frag = Frag ( '<STR_LIT:U+0020>' . join ( curr_words ) ) <EOL> if not frag_list : frag_list = frag <EOL> else : prev . next = frag <EOL> if expect_labels : frag . label = int ( '<STR_LIT>' in word ) <EOL> if tokenize : <EOL> tokens = word_tokenize . tokenize ( frag . orig ) <EOL> else : tokens = frag . orig <EOL> tokens = re . sub ( '<STR_LIT>' , '<STR_LIT>' , tokens ) <EOL> frag . tokenized = tokens <EOL> frag_index += <NUM_LIT:1> <EOL> prev = frag <EOL> curr_words = [ ] <EOL> word_index += <NUM_LIT:1> <EOL> fh . close ( ) <EOL> frag = Frag ( '<STR_LIT:U+0020>' . join ( curr_words ) ) <EOL> if not frag_list : frag_list = frag <EOL> else : prev . next = frag <EOL> if expect_labels : frag . label = int ( '<STR_LIT>' in word ) <EOL> if tokenize : <EOL> tokens = word_tokenize . tokenize ( frag . orig ) <EOL> else : tokens = frag . orig <EOL> tokens = re . sub ( '<STR_LIT>' , '<STR_LIT>' , tokens ) <EOL> frag . tokenized = tokens <EOL> frag . ends_seg = True <EOL> frag_index += <NUM_LIT:1> <EOL> if verbose : sys . stderr . write ( '<STR_LIT>' % ( word_index , frag_index ) ) <EOL> doc = Doc ( frag_list ) <EOL> return doc <EOL> def get_text_data ( text , expect_labels = True , tokenize = False , verbose = False ) : <EOL> """<STR_LIT>""" <EOL> frag_list = None <EOL> word_index = <NUM_LIT:0> <EOL> frag_index = <NUM_LIT:0> <EOL> curr_words = [ ] <EOL> lower_words , non_abbrs = sbd_util . Counter ( ) , sbd_util . Counter ( ) <EOL> for line in text . splitlines ( ) : <EOL> if ( not line . strip ( ) ) and frag_list : <EOL> if not curr_words : frag . ends_seg = True <EOL> else : <EOL> frag = Frag ( '<STR_LIT:U+0020>' . join ( curr_words ) ) <EOL> frag . ends_seg = True <EOL> if expect_labels : frag . label = True <EOL> prev . next = frag <EOL> if tokenize : <EOL> tokens = word_tokenize . tokenize ( frag . orig ) <EOL> frag . tokenized = tokens <EOL> frag_index += <NUM_LIT:1> <EOL> prev = frag <EOL> curr_words = [ ] <EOL> for word in line . split ( ) : <EOL> curr_words . append ( word ) <EOL> if is_sbd_hyp ( word ) : <EOL> frag = Frag ( '<STR_LIT:U+0020>' . join ( curr_words ) ) <EOL> if not frag_list : frag_list = frag <EOL> else : prev . next = frag <EOL> if expect_labels : frag . label = int ( '<STR_LIT>' in word ) <EOL> if tokenize : <EOL> tokens = word_tokenize . tokenize ( frag . orig ) <EOL> else : tokens = frag . orig <EOL> tokens = re . sub ( '<STR_LIT>' , '<STR_LIT>' , tokens ) <EOL> frag . tokenized = tokens <EOL> frag_index += <NUM_LIT:1> <EOL> prev = frag <EOL> curr_words = [ ] <EOL> word_index += <NUM_LIT:1> <EOL> frag = Frag ( '<STR_LIT:U+0020>' . join ( curr_words ) ) <EOL> if not frag_list : frag_list = frag <EOL> else : prev . next = frag <EOL> if expect_labels : frag . label = int ( '<STR_LIT>' in word ) <EOL> if tokenize : <EOL> tokens = word_tokenize . tokenize ( frag . orig ) <EOL> else : tokens = frag . orig <EOL> tokens = re . sub ( '<STR_LIT>' , '<STR_LIT>' , tokens ) <EOL> frag . tokenized = tokens <EOL> frag . ends_seg = True <EOL> frag_index += <NUM_LIT:1> <EOL> if verbose : sys . stderr . write ( '<STR_LIT>' % ( word_index , frag_index ) ) <EOL> doc = Doc ( frag_list ) <EOL> return doc <EOL> class Model : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , path ) : <EOL> self . feats , self . lower_words , self . non_abbrs = { } , { } , { } <EOL> self . path = path <EOL> def prep ( self , doc ) : <EOL> self . lower_words , self . non_abbrs = doc . get_stats ( verbose = False ) <EOL> self . lower_words = dict ( self . lower_words ) <EOL> self . non_abbrs = dict ( self . non_abbrs ) <EOL> def train ( self , doc ) : <EOL> abstract <EOL> def classify ( self , doc , verbose = False ) : <EOL> abstract <EOL> def save ( self ) : <EOL> """<STR_LIT>""" <EOL> sbd_util . save_pickle ( self . feats , self . path + '<STR_LIT>' ) <EOL> sbd_util . save_pickle ( self . lower_words , self . path + '<STR_LIT>' ) <EOL> sbd_util . save_pickle ( self . non_abbrs , self . path + '<STR_LIT>' ) <EOL> def load ( self ) : <EOL> """<STR_LIT>""" <EOL> self . feats = sbd_util . load_pickle ( self . path + '<STR_LIT>' ) <EOL> self . lower_words = sbd_util . load_pickle ( self . path + '<STR_LIT>' ) <EOL> self . non_abbrs = sbd_util . load_pickle ( self . path + '<STR_LIT>' ) <EOL> class NB_Model ( Model ) : <EOL> """<STR_LIT>""" <EOL> def train ( self , doc ) : <EOL> sys . stderr . write ( '<STR_LIT>' ) <EOL> feats = collections . defaultdict ( sbd_util . Counter ) <EOL> totals = sbd_util . Counter ( ) <EOL> frag = doc . frag <EOL> while frag : <EOL> for feat , val in frag . features . items ( ) : <EOL> feats [ frag . label ] [ feat + '<STR_LIT:_>' + val ] += <NUM_LIT:1> <EOL> totals [ frag . label ] += len ( frag . features ) <EOL> frag = frag . next <EOL> sys . stderr . write ( '<STR_LIT>' ) <EOL> smooth_inc = <NUM_LIT:0.1> <EOL> all_feat_names = set ( feats [ True ] . keys ( ) ) . union ( set ( feats [ False ] . keys ( ) ) ) <EOL> for label in [ <NUM_LIT:0> , <NUM_LIT:1> ] : <EOL> totals [ label ] += ( len ( all_feat_names ) * smooth_inc ) <EOL> for feat in all_feat_names : <EOL> feats [ label ] [ feat ] += smooth_inc <EOL> feats [ label ] [ feat ] /= totals [ label ] <EOL> self . feats [ ( label , feat ) ] = feats [ label ] [ feat ] <EOL> feats [ label ] [ '<STR_LIT>' ] = totals [ label ] / totals . totalCount ( ) <EOL> self . feats [ ( label , '<STR_LIT>' ) ] = feats [ label ] [ '<STR_LIT>' ] <EOL> sys . stderr . write ( '<STR_LIT>' ) <EOL> def classify_nb_one ( self , frag ) : <EOL> probs = sbd_util . Counter ( [ ( label , self . feats [ label , '<STR_LIT>' ] ** <NUM_LIT:4> ) for label in [ <NUM_LIT:0> , <NUM_LIT:1> ] ] ) <EOL> for label in probs : <EOL> for feat , val in frag . features . items ( ) : <EOL> key = ( label , feat + '<STR_LIT:_>' + val ) <EOL> if not key in self . feats : continue <EOL> probs [ label ] *= self . feats [ key ] <EOL> probs = sbd_util . normalize ( probs ) <EOL> return probs [ <NUM_LIT:1> ] <EOL> def classify ( self , doc , verbose = False ) : <EOL> if verbose : sys . stderr . write ( '<STR_LIT>' ) <EOL> frag = doc . frag <EOL> while frag : <EOL> pred = self . classify_nb_one ( frag ) <EOL> frag . pred = pred <EOL> frag = frag . next <EOL> if verbose : sys . stderr . write ( '<STR_LIT>' ) <EOL> class SVM_Model ( Model ) : <EOL> """<STR_LIT>""" <EOL> def train ( self , doc ) : <EOL> """<STR_LIT>""" <EOL> model_file = '<STR_LIT>' % self . path <EOL> sys . stderr . write ( '<STR_LIT>' ) <EOL> feat_list = set ( ) <EOL> frag = doc . frag <EOL> while frag : <EOL> feats = [ f + '<STR_LIT:_>' + v for f , v in frag . features . items ( ) ] <EOL> for feat in feats : feat_list . add ( feat ) <EOL> frag = frag . next <EOL> self . feats = dict ( zip ( feat_list , range ( <NUM_LIT:1> , len ( feat_list ) + <NUM_LIT:1> ) ) ) <EOL> sys . stderr . write ( '<STR_LIT>' ) <EOL> lines = [ ] <EOL> frag = doc . frag <EOL> while frag : <EOL> if frag . label == None : sbd_util . die ( '<STR_LIT>' % frag ) <EOL> elif frag . label > <NUM_LIT:0.5> : svm_label = '<STR_LIT>' <EOL> elif frag . label < <NUM_LIT:0.5> : svm_label = '<STR_LIT>' <EOL> else : continue <EOL> line = '<STR_LIT>' % svm_label <EOL> feats = [ f + '<STR_LIT:_>' + v for f , v in frag . features . items ( ) ] <EOL> svm_feats = [ self . feats [ f ] for f in feats ] <EOL> svm_feats . sort ( lambda x , y : x - y ) <EOL> line += '<STR_LIT:U+0020>' . join ( [ '<STR_LIT>' % x for x in svm_feats ] ) <EOL> lines . append ( line ) <EOL> frag = frag . next <EOL> unused , train_file = tempfile . mkstemp ( ) <EOL> fh = open ( train_file , '<STR_LIT:w>' ) <EOL> fh . write ( '<STR_LIT:\n>' . join ( lines ) + '<STR_LIT:\n>' ) <EOL> fh . close ( ) <EOL> sys . stderr . write ( '<STR_LIT>' ) <EOL> options = '<STR_LIT>' <EOL> cmd = '<STR_LIT>' % ( SVM_LEARN , options , train_file , model_file ) <EOL> os . system ( cmd ) <EOL> sys . stderr . write ( '<STR_LIT>' ) <EOL> os . remove ( train_file ) <EOL> def classify ( self , doc , verbose = False ) : <EOL> model_file = '<STR_LIT>' % self . path <EOL> if not self . feats : sbd_util . die ( '<STR_LIT>' ) <EOL> if not os . path . isfile ( model_file ) : sbd_util . die ( '<STR_LIT>' % model_file ) <EOL> if verbose : sys . stderr . write ( '<STR_LIT>' ) <EOL> lines = [ ] <EOL> frag = doc . frag <EOL> while frag : <EOL> if frag . label == None : svm_label = '<STR_LIT:0>' <EOL> elif frag . label : svm_label = '<STR_LIT>' <EOL> else : svm_label = '<STR_LIT>' <EOL> line = '<STR_LIT>' % svm_label <EOL> feats = [ f + '<STR_LIT:_>' + v for f , v in frag . features . items ( ) ] <EOL> svm_feats = [ self . feats [ f ] for f in feats if f in self . feats ] <EOL> svm_feats . sort ( lambda x , y : x - y ) <EOL> line += '<STR_LIT:U+0020>' . join ( [ '<STR_LIT>' % x for x in svm_feats ] ) <EOL> lines . append ( line ) <EOL> frag = frag . next <EOL> test_file = "<STR_LIT>" <EOL> fh = open ( test_file , '<STR_LIT:w>' ) <EOL> fh . write ( '<STR_LIT:\n>' . join ( lines ) + '<STR_LIT:\n>' ) <EOL> fh . close ( ) <EOL> pred_file = "<STR_LIT>" <EOL> options = '<STR_LIT>' <EOL> cmd = '<STR_LIT>' % ( SVM_CLASSIFY , options , test_file , model_file , pred_file ) <EOL> os . system ( cmd ) <EOL> total = <NUM_LIT:0> <EOL> pf = open ( pred_file , '<STR_LIT:r>' ) <EOL> preds = map ( float , pf . read ( ) . splitlines ( ) ) <EOL> frag = doc . frag <EOL> while frag : <EOL> frag . pred = sbd_util . logit ( preds [ total ] ) <EOL> frag = frag . next <EOL> total += <NUM_LIT:1> <EOL> pf . close ( ) <EOL> os . remove ( test_file ) <EOL> os . remove ( pred_file ) <EOL> if verbose : sys . stderr . write ( '<STR_LIT>' ) <EOL> class Doc : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , frag ) : <EOL> self . frag = frag <EOL> def __str__ ( self ) : <EOL> s = [ ] <EOL> curr = self . frag <EOL> while curr : s . append ( curr ) <EOL> return '<STR_LIT:\n>' . join ( s ) <EOL> def get_stats ( self , verbose ) : <EOL> if verbose : sys . stderr . write ( '<STR_LIT>' ) <EOL> lower_words = sbd_util . Counter ( ) <EOL> non_abbrs = sbd_util . Counter ( ) <EOL> frag = self . frag <EOL> while frag : <EOL> for word in frag . tokenized . split ( ) : <EOL> if word . replace ( '<STR_LIT:.>' , '<STR_LIT>' ) . isalpha ( ) : <EOL> if word . islower ( ) : lower_words [ word . replace ( '<STR_LIT:.>' , '<STR_LIT>' ) ] += <NUM_LIT:1> <EOL> if not word . endswith ( '<STR_LIT:.>' ) : non_abbrs [ word ] += <NUM_LIT:1> <EOL> frag = frag . next <EOL> if verbose : sys . stderr . write ( '<STR_LIT>' <EOL> % ( len ( lower_words ) , len ( non_abbrs ) ) ) <EOL> return lower_words , non_abbrs <EOL> def featurize ( self , model , verbose = False ) : <EOL> if verbose : sys . stderr . write ( '<STR_LIT>' ) <EOL> frag = self . frag <EOL> while frag : <EOL> frag . features = get_features ( frag , model ) <EOL> frag = frag . next <EOL> if verbose : sys . stderr . write ( '<STR_LIT>' ) <EOL> def segment ( self , use_preds = False , tokenize = False , output = None , list_only = False ) : <EOL> """<STR_LIT>""" <EOL> sents = [ ] <EOL> thresh = <NUM_LIT:0.5> <EOL> sent = [ ] <EOL> frag = self . frag <EOL> while frag : <EOL> if tokenize : text = frag . tokenized <EOL> else : text = frag . orig <EOL> sent . append ( text ) <EOL> if frag . ends_seg or ( use_preds and frag . pred > thresh ) or ( not use_preds and frag . label > thresh ) : <EOL> if not frag . orig : break <EOL> sent_text = '<STR_LIT:U+0020>' . join ( sent ) <EOL> if frag . ends_seg : spacer = '<STR_LIT>' <EOL> else : spacer = '<STR_LIT:\n>' <EOL> if output : output . write ( sent_text + spacer ) <EOL> elif not list_only : sys . stdout . write ( sent_text + spacer ) <EOL> sents . append ( sent_text ) <EOL> sent = [ ] <EOL> frag = frag . next <EOL> return sents <EOL> def show_results ( self , verbose = False ) : <EOL> thresh = <NUM_LIT:0.5> <EOL> total , correct = <NUM_LIT:0> , <NUM_LIT:0> <EOL> frag = self . frag <EOL> while frag : <EOL> total += <NUM_LIT:1> <EOL> if frag . label == ( frag . pred > thresh ) : <EOL> correct += <NUM_LIT:1> <EOL> else : <EOL> w1 = '<STR_LIT:U+0020>' . join ( frag . tokenized . split ( ) [ - <NUM_LIT:2> : ] ) <EOL> if frag . next : w2 = '<STR_LIT:U+0020>' . join ( frag . next . tokenized . split ( ) [ : <NUM_LIT:2> ] ) <EOL> else : w2 = '<STR_LIT>' <EOL> if verbose : <EOL> print '<STR_LIT>' % ( frag . label , frag . pred , w1 , w2 ) <EOL> frag = frag . next <EOL> error = <NUM_LIT:1> - ( <NUM_LIT:1.0> * correct / total ) <EOL> print '<STR_LIT>' % ( correct , total , error ) <EOL> class Frag : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , orig ) : <EOL> self . orig = orig <EOL> self . next = None <EOL> self . ends_seg = False <EOL> self . tokenized = False <EOL> self . pred = None <EOL> self . label = None <EOL> self . features = None <EOL> def __str__ ( self ) : <EOL> s = self . orig <EOL> if self . ends_seg : s += '<STR_LIT>' <EOL> return s <EOL> def build_model ( files , options ) : <EOL> train_corpus = get_data ( files , tokenize = options . tokenize ) <EOL> if options . svm : model = SVM_Model ( options . model_path ) <EOL> else : model = NB_Model ( options . model_path ) <EOL> model . prep ( train_corpus ) <EOL> train_corpus . featurize ( model , verbose = True ) <EOL> model . train ( train_corpus ) <EOL> model . save ( ) <EOL> return model <EOL> def load_sbd_model ( model_path = '<STR_LIT>' , use_svm = False ) : <EOL> sys . stderr . write ( '<STR_LIT>' % model_path ) <EOL> if use_svm : model = SVM_Model ( model_path ) <EOL> else : model = NB_Model ( model_path ) <EOL> model . load ( ) <EOL> sys . stderr . write ( '<STR_LIT>' ) <EOL> return model <EOL> def sbd_text ( model , text , do_tok = True ) : <EOL> """<STR_LIT>""" <EOL> data = get_text_data ( text , expect_labels = False , tokenize = True ) <EOL> data . featurize ( model , verbose = False ) <EOL> model . classify ( data , verbose = False ) <EOL> sents = data . segment ( use_preds = True , tokenize = do_tok , list_only = True ) <EOL> return sents <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> modelpath = '<STR_LIT>' <EOL> model = load_sbd_model ( modelpath , True ) <EOL> sam = '<STR_LIT>' <EOL> sents = [ l for l in open ( sam ) ] <EOL> toseg = "<STR_LIT>" <EOL> for s in sents : <EOL> if len ( s . strip ( ) ) > <NUM_LIT:0> : <EOL> print "<STR_LIT:>>" , s <EOL> toseg += "<STR_LIT:U+0020>" + s . strip ( ) <EOL> print toseg <EOL> ss = sbd_text ( model , toseg ) <EOL> for s in ss : <EOL> print "<STR_LIT:!>" , s <EOL> '''<STR_LIT>''' <EOL> def poo ( ) : <EOL> data_root = '<STR_LIT>' <EOL> brown_data = data_root + '<STR_LIT>' <EOL> wsj_data = data_root + '<STR_LIT>' <EOL> poe_data = data_root + '<STR_LIT>' <EOL> new_wsj_data = data_root + '<STR_LIT>' <EOL> install_root = '<STR_LIT>' <EOL> from optparse import OptionParser <EOL> usage = '<STR_LIT>' <EOL> parser = OptionParser ( usage = usage ) <EOL> parser . add_option ( '<STR_LIT>' , '<STR_LIT>' , dest = '<STR_LIT>' , default = False , <EOL> action = '<STR_LIT:store_true>' , help = '<STR_LIT>' ) <EOL> parser . add_option ( '<STR_LIT>' , '<STR_LIT>' , dest = '<STR_LIT>' , default = False , <EOL> action = '<STR_LIT:store_true>' , help = '<STR_LIT>' ) <EOL> parser . add_option ( '<STR_LIT>' , '<STR_LIT>' , dest = '<STR_LIT>' , type = '<STR_LIT:str>' , default = '<STR_LIT>' , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_option ( '<STR_LIT>' , '<STR_LIT>' , dest = '<STR_LIT>' , type = '<STR_LIT:str>' , default = None , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_option ( '<STR_LIT>' , '<STR_LIT>' , dest = '<STR_LIT:train>' , type = '<STR_LIT:str>' , default = None , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_option ( '<STR_LIT:-c>' , '<STR_LIT>' , dest = '<STR_LIT>' , default = False , <EOL> action = '<STR_LIT:store_true>' , help = '<STR_LIT>' ) <EOL> ( options , args ) = parser . parse_args ( ) <EOL> if len ( args ) > <NUM_LIT:0> : <EOL> options . test = args [ <NUM_LIT:0> ] <EOL> if not os . path . isfile ( options . test ) : sbd_util . die ( '<STR_LIT>' % options . test ) <EOL> else : <EOL> options . test = None <EOL> if not options . train : sbd_util . die ( '<STR_LIT>' ) <EOL> if not options . model_path . endswith ( '<STR_LIT:/>' ) : options . model_path += '<STR_LIT:/>' <EOL> if options . train : <EOL> if not os . path . isfile ( options . train ) : sbd_util . die ( '<STR_LIT>' % options . train ) <EOL> if os . path . isdir ( options . model_path ) : sbd_util . die ( '<STR_LIT>' % options . model_path ) <EOL> else : os . mkdir ( options . model_path ) <EOL> else : <EOL> if not os . path . isdir ( options . model_path ) : <EOL> options . model_path = install_root + options . model_path <EOL> if not os . path . isdir ( options . model_path ) : <EOL> sbd_util . die ( '<STR_LIT>' % options . model_path ) <EOL> if options . train : <EOL> model = build_model ( options . train , options ) <EOL> if not options . test : sys . exit ( ) <EOL> print options . svm <EOL> print options . test <EOL> if not options . train : <EOL> if '<STR_LIT>' in options . model_path : options . svm = True <EOL> model = load_sbd_model ( options . model_path , options . svm ) <EOL> if options . output : options . output = open ( options . output , '<STR_LIT:w>' ) <EOL> test = get_data ( options . test , tokenize = True ) <EOL> test . featurize ( model , verbose = True ) <EOL> model . classify ( test , verbose = True ) <EOL> print options . tokenize <EOL> print options . output <EOL> test . segment ( use_preds = True , tokenize = options . tokenize , output = options . output ) </s>
