<s> """<STR_LIT>""" <EOL> import logging <EOL> import numpy as np <EOL> from os . path import splitext <EOL> from neon . data . dataiterator import NervanaDataIterator , ArrayIterator <EOL> from neon . data . datasets import Dataset <EOL> from neon . data . text_preprocessing import pad_sentences , pad_data <EOL> logger = logging . getLogger ( __name__ ) <EOL> class Text ( NervanaDataIterator ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , time_steps , path , vocab = None , tokenizer = None , <EOL> onehot_input = True ) : <EOL> """<STR_LIT>""" <EOL> super ( Text , self ) . __init__ ( name = None ) <EOL> self . seq_length = time_steps <EOL> self . onehot_input = onehot_input <EOL> self . batch_index = <NUM_LIT:0> <EOL> text = open ( path ) . read ( ) <EOL> tokens = self . get_tokens ( text , tokenizer ) <EOL> extra_tokens = len ( tokens ) % ( self . be . bsz * time_steps ) <EOL> if extra_tokens : <EOL> tokens = tokens [ : - extra_tokens ] <EOL> self . nbatches = len ( tokens ) / ( self . be . bsz * time_steps ) <EOL> self . ndata = self . nbatches * self . be . bsz <EOL> self . vocab = sorted ( self . get_vocab ( tokens , vocab ) ) <EOL> self . nclass = len ( self . vocab ) <EOL> self . token_to_index = dict ( ( t , i ) for i , t in enumerate ( self . vocab ) ) <EOL> self . index_to_token = dict ( ( i , t ) for i , t in enumerate ( self . vocab ) ) <EOL> X = np . asarray ( [ self . token_to_index [ t ] for t in tokens ] , dtype = np . uint32 ) <EOL> y = np . concatenate ( ( X [ <NUM_LIT:1> : ] , X [ : <NUM_LIT:1> ] ) ) <EOL> self . X = X . reshape ( self . be . bsz , self . nbatches , time_steps ) <EOL> self . y = y . reshape ( self . be . bsz , self . nbatches , time_steps ) <EOL> self . nout = len ( self . vocab ) <EOL> if self . onehot_input : <EOL> self . shape = ( self . nout , time_steps ) <EOL> self . dev_X = self . be . iobuf ( ( self . nout , time_steps ) ) <EOL> else : <EOL> self . shape = ( time_steps , <NUM_LIT:1> ) <EOL> self . dev_X = self . be . iobuf ( time_steps , dtype = np . int32 ) <EOL> self . dev_y = self . be . iobuf ( ( self . nout , time_steps ) ) <EOL> self . dev_lbl = self . be . iobuf ( time_steps , dtype = np . int32 ) <EOL> self . dev_lblflat = self . dev_lbl . reshape ( ( <NUM_LIT:1> , - <NUM_LIT:1> ) ) <EOL> @ staticmethod <EOL> def create_valid_file ( path , valid_split = <NUM_LIT:0.1> ) : <EOL> """<STR_LIT>""" <EOL> text = open ( path ) . read ( ) <EOL> filename , ext = splitext ( path ) <EOL> train_path = filename + '<STR_LIT>' + ext <EOL> valid_path = filename + '<STR_LIT>' + ext <EOL> train_split = int ( len ( text ) * ( <NUM_LIT:1> - valid_split ) ) <EOL> train_text = text [ : train_split ] <EOL> valid_text = text [ train_split : ] <EOL> with open ( train_path , '<STR_LIT:w>' ) as train_file : <EOL> train_file . write ( train_text ) <EOL> with open ( valid_path , '<STR_LIT:w>' ) as valid_file : <EOL> valid_file . write ( valid_text ) <EOL> return train_path , valid_path <EOL> @ staticmethod <EOL> def get_tokens ( string , tokenizer = None ) : <EOL> """<STR_LIT>""" <EOL> if tokenizer is None : <EOL> return string <EOL> else : <EOL> return tokenizer ( string ) <EOL> @ staticmethod <EOL> def get_vocab ( tokens , vocab = None ) : <EOL> """<STR_LIT>""" <EOL> if vocab is None : <EOL> return set ( tokens ) <EOL> else : <EOL> vocab = set ( vocab ) <EOL> assert vocab >= set ( tokens ) , "<STR_LIT>" <EOL> return vocab <EOL> @ staticmethod <EOL> def pad_sentences ( sentences , sentence_length = None , dtype = np . int32 , pad_val = <NUM_LIT:0.> ) : <EOL> logger . error ( '<STR_LIT>' <EOL> '<STR_LIT>' ) <EOL> return pad_sentences ( sentences , <EOL> sentence_length = sentence_length , <EOL> dtype = dtype , <EOL> pad_val = pad_val ) <EOL> @ staticmethod <EOL> def pad_data ( path , vocab_size = <NUM_LIT> , sentence_length = <NUM_LIT:100> , oov = <NUM_LIT:2> , <EOL> start = <NUM_LIT:1> , index_from = <NUM_LIT:3> , seed = <NUM_LIT> , test_split = <NUM_LIT> ) : <EOL> logger . error ( '<STR_LIT>' <EOL> '<STR_LIT>' ) <EOL> return pad_data ( path , <EOL> vocab_size = vocab_size , <EOL> sentence_length = sentence_length , <EOL> oov = oov , <EOL> start = start , <EOL> index_from = index_from , <EOL> seed = seed , <EOL> test_split = test_split ) <EOL> def reset ( self ) : <EOL> """<STR_LIT>""" <EOL> self . batch_index = <NUM_LIT:0> <EOL> def __iter__ ( self ) : <EOL> """<STR_LIT>""" <EOL> self . batch_index = <NUM_LIT:0> <EOL> while self . batch_index < self . nbatches : <EOL> X_batch = self . X [ : , self . batch_index , : ] . T . astype ( np . float32 , order = '<STR_LIT:C>' ) <EOL> y_batch = self . y [ : , self . batch_index , : ] . T . astype ( np . float32 , order = '<STR_LIT:C>' ) <EOL> if self . onehot_input : <EOL> self . dev_lbl . set ( X_batch ) <EOL> self . dev_X [ : ] = self . be . onehot ( self . dev_lblflat , axis = <NUM_LIT:0> ) <EOL> else : <EOL> self . dev_X . set ( X_batch ) <EOL> self . dev_lbl . set ( y_batch ) <EOL> self . dev_y [ : ] = self . be . onehot ( self . dev_lblflat , axis = <NUM_LIT:0> ) <EOL> self . batch_index += <NUM_LIT:1> <EOL> yield self . dev_X , self . dev_y <EOL> class Shakespeare ( Dataset ) : <EOL> def __init__ ( self , timesteps , path = '<STR_LIT:.>' ) : <EOL> url = '<STR_LIT>' <EOL> super ( Shakespeare , self ) . __init__ ( '<STR_LIT>' , <EOL> url , <EOL> <NUM_LIT> , <EOL> path = path ) <EOL> self . timesteps = timesteps <EOL> def load_data ( self ) : <EOL> self . filepath = self . load_zip ( self . filename , self . size ) <EOL> return self . filepath <EOL> def gen_iterators ( self ) : <EOL> self . load_data ( ) <EOL> train_path , valid_path = Text . create_valid_file ( self . filepath ) <EOL> self . data_dict = { } <EOL> self . data_dict [ '<STR_LIT:train>' ] = Text ( self . timesteps , train_path ) <EOL> vocab = self . data_dict [ '<STR_LIT:train>' ] . vocab <EOL> self . data_dict [ '<STR_LIT>' ] = Text ( self . timesteps , valid_path , vocab = vocab ) <EOL> return self . data_dict <EOL> class PTB ( Dataset ) : <EOL> '''<STR_LIT>''' <EOL> def __init__ ( self , timesteps , path = '<STR_LIT:.>' , <EOL> onehot_input = True , <EOL> tokenizer = None ) : <EOL> url = '<STR_LIT>' <EOL> self . filemap = { '<STR_LIT:train>' : <NUM_LIT> , <EOL> '<STR_LIT:test>' : <NUM_LIT> , <EOL> '<STR_LIT>' : <NUM_LIT> } <EOL> keys = self . filemap . keys ( ) <EOL> filenames = [ self . gen_filename ( phase ) for phase in keys ] <EOL> sizes = [ self . filemap [ phase ] for phase in keys ] <EOL> super ( PTB , self ) . __init__ ( filenames , <EOL> url , <EOL> sizes , <EOL> path = path ) <EOL> self . timesteps = timesteps <EOL> self . onehot_input = onehot_input <EOL> self . tokenizer = tokenizer <EOL> if tokenizer is not None : <EOL> assert hasattr ( self , self . tokenizer ) <EOL> self . tokenizer_func = getattr ( self , self . tokenizer ) <EOL> else : <EOL> self . tokenizer_func = None <EOL> @ staticmethod <EOL> def newline_tokenizer ( s ) : <EOL> return s . replace ( '<STR_LIT:\n>' , '<STR_LIT>' ) . split ( ) <EOL> @ staticmethod <EOL> def gen_filename ( phase ) : <EOL> return '<STR_LIT>' % phase <EOL> def load_data ( self ) : <EOL> self . file_paths = { } <EOL> for phase in self . filemap : <EOL> fn = self . gen_filename ( phase ) <EOL> size = self . filemap [ phase ] <EOL> self . file_paths [ phase ] = self . load_zip ( fn , size ) <EOL> return self . file_paths <EOL> def gen_iterators ( self ) : <EOL> self . load_data ( ) <EOL> self . data_dict = { } <EOL> self . vocab = None <EOL> for phase in [ '<STR_LIT:train>' , '<STR_LIT:test>' , '<STR_LIT>' ] : <EOL> file_path = self . file_paths [ phase ] <EOL> self . data_dict [ phase ] = Text ( self . timesteps , <EOL> file_path , <EOL> tokenizer = self . tokenizer_func , <EOL> onehot_input = self . onehot_input , <EOL> vocab = self . vocab ) <EOL> if self . vocab is None : <EOL> self . vocab = self . data_dict [ '<STR_LIT:train>' ] . vocab <EOL> return self . data_dict <EOL> class HutterPrize ( Dataset ) : <EOL> def __init__ ( self , path = '<STR_LIT:.>' ) : <EOL> super ( HutterPrize , self ) . __init__ ( '<STR_LIT>' , <EOL> '<STR_LIT>' , <EOL> <NUM_LIT> , <EOL> path = path ) <EOL> def load_data ( self ) : <EOL> self . filepath = self . load_zip ( self . filename , self . size ) <EOL> return self . filepath <EOL> class IMDB ( Dataset ) : <EOL> def __init__ ( self , vocab_size , sentence_length , path = '<STR_LIT:.>' ) : <EOL> url = '<STR_LIT>' <EOL> super ( IMDB , self ) . __init__ ( '<STR_LIT>' , <EOL> url , <EOL> <NUM_LIT> , <EOL> path = path ) <EOL> self . vocab_size = vocab_size <EOL> self . sentence_length = sentence_length <EOL> self . filepath = None <EOL> def load_data ( self ) : <EOL> self . filepath = self . load_zip ( self . filename , self . size ) <EOL> return self . filepath <EOL> def gen_iterators ( self ) : <EOL> if self . filepath is None : <EOL> self . load_data ( ) <EOL> data = pad_data ( self . filepath , vocab_size = self . vocab_size , <EOL> sentence_length = self . sentence_length ) <EOL> ( X_train , y_train ) , ( X_test , y_test ) , nclass = data <EOL> self . data_dict = { '<STR_LIT>' : nclass } <EOL> self . data_dict [ '<STR_LIT:train>' ] = ArrayIterator ( X_train , y_train , nclass = <NUM_LIT:2> ) <EOL> self . data_dict [ '<STR_LIT:test>' ] = ArrayIterator ( X_test , y_test , nclass = <NUM_LIT:2> ) <EOL> return self . data_dict </s>
