<s> import random <EOL> import os <EOL> import re <EOL> import numpy as np <EOL> from sklearn . feature_extraction . text import CountVectorizer <EOL> from sklearn import linear_model <EOL> from sklearn import tree <EOL> from sklearn import svm <EOL> POLARITY_PATH = '<STR_LIT>' <EOL> def LoadDataset ( dataset_name ) : <EOL> if dataset_name . endswith ( '<STR_LIT>' ) : <EOL> if dataset_name == '<STR_LIT>' : <EOL> cats = [ '<STR_LIT>' , '<STR_LIT>' ] <EOL> class_names = [ '<STR_LIT>' , '<STR_LIT>' ] <EOL> if dataset_name == '<STR_LIT>' : <EOL> cats = [ '<STR_LIT>' , '<STR_LIT>' ] <EOL> class_names = [ '<STR_LIT>' , '<STR_LIT>' ] <EOL> if dataset_name == '<STR_LIT>' : <EOL> cats = [ '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ] <EOL> class_names = [ '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ] <EOL> newsgroups_train = fetch_20newsgroups ( subset = '<STR_LIT:train>' , categories = cats ) <EOL> newsgroups_test = fetch_20newsgroups ( subset = '<STR_LIT:test>' , categories = cats ) <EOL> train_data = newsgroups_train . data <EOL> train_labels = newsgroups_train . target <EOL> test_data = newsgroups_test . data <EOL> test_labels = newsgroups_test . target <EOL> return train_data , train_labels , test_data , test_labels , class_names <EOL> if dataset_name . startswith ( '<STR_LIT>' ) : <EOL> name = dataset_name . split ( '<STR_LIT:_>' ) [ <NUM_LIT:2> ] <EOL> return LoadMultiDomainDataset ( POLARITY_PATH + name ) <EOL> def LoadMultiDomainDataset ( path_data , remove_bigrams = True ) : <EOL> random . seed ( <NUM_LIT:1> ) <EOL> pos = [ ] <EOL> neg = [ ] <EOL> def get_words ( line , remove_bigrams = True ) : <EOL> z = [ tuple ( x . split ( '<STR_LIT::>' ) ) for x in re . findall ( '<STR_LIT>' , line ) ] <EOL> if remove_bigrams : <EOL> z = '<STR_LIT:U+0020>' . join ( [ '<STR_LIT:U+0020>' . join ( [ x [ <NUM_LIT:0> ] ] * int ( x [ <NUM_LIT:1> ] ) ) for x in z if '<STR_LIT:_>' not in x [ <NUM_LIT:0> ] ] ) <EOL> else : <EOL> z = '<STR_LIT:U+0020>' . join ( [ '<STR_LIT:U+0020>' . join ( [ x [ <NUM_LIT:0> ] ] * int ( x [ <NUM_LIT:1> ] ) ) for x in z ] ) <EOL> return z <EOL> for line in open ( os . path . join ( path_data , '<STR_LIT>' ) ) : <EOL> neg . append ( get_words ( line , remove_bigrams ) ) <EOL> for line in open ( os . path . join ( path_data , '<STR_LIT>' ) ) : <EOL> pos . append ( get_words ( line , remove_bigrams ) ) <EOL> random . shuffle ( pos ) <EOL> random . shuffle ( neg ) <EOL> split_pos = int ( len ( pos ) * <NUM_LIT> ) <EOL> split_neg = int ( len ( neg ) * <NUM_LIT> ) <EOL> train_data = pos [ : split_pos ] + neg [ : split_neg ] <EOL> test_data = pos [ split_pos : ] + neg [ split_neg : ] <EOL> train_labels = [ <NUM_LIT:1> ] * len ( pos [ : split_pos ] ) + [ <NUM_LIT:0> ] * len ( neg [ : split_neg ] ) <EOL> test_labels = [ <NUM_LIT:1> ] * len ( pos [ split_pos : ] ) + [ <NUM_LIT:0> ] * len ( neg [ split_neg : ] ) <EOL> return train_data , np . array ( train_labels ) , test_data , np . array ( test_labels ) , [ '<STR_LIT>' , '<STR_LIT>' ] </s>
