<s> from __future__ import ( absolute_import , unicode_literals , print_function ) <EOL> import sys <EOL> import pickle <EOL> import numpy as np <EOL> PRETRAIN = False <EOL> def load ( name ) : <EOL> compat = { '<STR_LIT>' : '<STR_LIT>' } if sys . version_info [ <NUM_LIT:0> ] == <NUM_LIT:3> else { } <EOL> print ( "<STR_LIT:\t>" + name ) <EOL> try : <EOL> with open ( name , '<STR_LIT:rb>' ) as f : <EOL> return pickle . load ( f , ** compat ) <EOL> except IOError : <EOL> import gzip <EOL> with gzip . open ( name + '<STR_LIT>' , '<STR_LIT:rb>' ) as f : <EOL> return pickle . load ( f , ** compat ) <EOL> print ( "<STR_LIT>" ) <EOL> dataset1 = load ( '<STR_LIT>' ) <EOL> dataset2 = load ( '<STR_LIT>' ) <EOL> dataset3 = load ( '<STR_LIT>' ) <EOL> dataset4 = load ( '<STR_LIT>' ) <EOL> dataset5 = load ( '<STR_LIT>' ) <EOL> dataset0 = load ( '<STR_LIT>' ) <EOL> print ( "<STR_LIT>" ) <EOL> data_train = np . vstack ( [ dataset1 [ '<STR_LIT:data>' ] ] ) <EOL> labels_train = np . hstack ( [ dataset1 [ '<STR_LIT>' ] ] ) <EOL> data_train = data_train . astype ( '<STR_LIT:float>' ) / <NUM_LIT> <EOL> labels_train = labels_train <EOL> data_test = dataset0 [ '<STR_LIT:data>' ] . astype ( '<STR_LIT:float>' ) / <NUM_LIT> <EOL> labels_test = np . array ( dataset0 [ '<STR_LIT>' ] ) <EOL> n_feat = data_train . shape [ <NUM_LIT:1> ] <EOL> n_targets = labels_train . max ( ) + <NUM_LIT:1> <EOL> from sknn import mlp <EOL> nn = mlp . Classifier ( <EOL> layers = [ <EOL> mlp . Layer ( "<STR_LIT>" , units = n_feat / <NUM_LIT:8> ) , <EOL> mlp . Layer ( "<STR_LIT>" , units = n_feat / <NUM_LIT:16> ) , <EOL> mlp . Layer ( "<STR_LIT>" , units = n_targets ) ] , <EOL> n_iter = <NUM_LIT:50> , <EOL> n_stable = <NUM_LIT:10> , <EOL> batch_size = <NUM_LIT> , <EOL> learning_rate = <NUM_LIT> , <EOL> learning_rule = "<STR_LIT>" , <EOL> valid_size = <NUM_LIT:0.1> , <EOL> verbose = <NUM_LIT:1> ) <EOL> if PRETRAIN : <EOL> from sknn import ae <EOL> ae = ae . AutoEncoder ( <EOL> layers = [ <EOL> ae . Layer ( "<STR_LIT>" , units = n_feat / <NUM_LIT:8> ) , <EOL> ae . Layer ( "<STR_LIT>" , units = n_feat / <NUM_LIT:16> ) ] , <EOL> learning_rate = <NUM_LIT> , <EOL> n_iter = <NUM_LIT:10> , <EOL> verbose = <NUM_LIT:1> ) <EOL> ae . fit ( data_train ) <EOL> ae . transfer ( nn ) <EOL> nn . fit ( data_train , labels_train ) <EOL> from sklearn . metrics import classification_report <EOL> from sklearn . metrics import confusion_matrix <EOL> expected = labels_test <EOL> predicted = nn . predict ( data_test ) <EOL> print ( "<STR_LIT>" % ( <EOL> nn , classification_report ( expected , predicted ) ) ) <EOL> print ( "<STR_LIT>" % confusion_matrix ( expected , predicted ) ) </s>
