<s> from __future__ import division , print_function , unicode_literals <EOL> from collections import OrderedDict <EOL> import numpy as np <EOL> from brainstorm . describable import Describable <EOL> class Scorer ( Describable ) : <EOL> def __init__ ( self , out_name = '<STR_LIT>' , targets_name = '<STR_LIT>' , mask_name = '<STR_LIT>' , <EOL> name = None ) : <EOL> self . out_name = out_name <EOL> self . targets_name = targets_name <EOL> self . mask_name = mask_name <EOL> self . __name__ = name if name is not None else self . __class__ . __name__ <EOL> def __call__ ( self , true_labels , predicted , mask = None ) : <EOL> pass <EOL> @ staticmethod <EOL> def aggregate ( errors ) : <EOL> errors = np . array ( errors ) <EOL> assert errors . ndim == <NUM_LIT:2> and errors . shape [ <NUM_LIT:1> ] == <NUM_LIT:2> <EOL> return np . sum ( errors [ : , <NUM_LIT:1> ] ) / np . sum ( errors [ : , <NUM_LIT:0> ] ) <EOL> def gather_losses_and_scores ( net , scorers , scores , out_name = '<STR_LIT>' , <EOL> targets_name = '<STR_LIT>' , mask_name = '<STR_LIT>' ) : <EOL> ls = net . get_loss_values ( ) <EOL> for name , loss in ls . items ( ) : <EOL> scores [ name ] . append ( ( net . _buffer_manager . batch_size , loss ) ) <EOL> for sc in scorers : <EOL> name = sc . __name__ <EOL> predicted = net . get ( sc . out_name or out_name or net . output_name ) <EOL> true_labels = net . get_input ( sc . targets_name ) if sc . targets_name else net . get_input ( targets_name ) <EOL> mask = net . get_input ( sc . mask_name ) if sc . mask_name else ( net . get_input ( mask_name ) if mask_name else None ) <EOL> predicted = _flatten_all_but_last ( predicted ) <EOL> true_labels = _flatten_all_but_last ( true_labels ) <EOL> mask = _flatten_all_but_last ( mask ) <EOL> weight = mask . sum ( ) if mask is not None else predicted . shape [ <NUM_LIT:0> ] <EOL> scores [ name ] . append ( ( weight , sc ( true_labels , predicted , mask ) ) ) <EOL> def aggregate_losses_and_scores ( scores , net , scorers ) : <EOL> results = OrderedDict ( ) <EOL> for name in net . get_loss_values ( ) : <EOL> results [ name ] = _weighted_average ( scores [ name ] ) <EOL> for sc in scorers : <EOL> results [ sc . __name__ ] = sc . aggregate ( scores [ sc . __name__ ] ) <EOL> return results <EOL> class Accuracy ( Scorer ) : <EOL> def __call__ ( self , true_labels , predicted , mask = None ) : <EOL> if predicted . shape [ <NUM_LIT:1> ] > <NUM_LIT:1> : <EOL> predicted = predicted . argmax ( <NUM_LIT:1> ) . reshape ( - <NUM_LIT:1> , <NUM_LIT:1> ) <EOL> correct = ( predicted == true_labels ) . astype ( np . float ) <EOL> if mask is not None : <EOL> correct *= mask <EOL> return np . sum ( correct ) <EOL> class Hamming ( Scorer ) : <EOL> def __init__ ( self , threshold = <NUM_LIT:0.5> , out_name = '<STR_LIT>' , targets_name = '<STR_LIT>' , <EOL> mask_name = '<STR_LIT>' , name = None ) : <EOL> super ( Hamming , self ) . __init__ ( out_name , targets_name , mask_name , name ) <EOL> self . threshold = threshold <EOL> def __call__ ( self , true_labels , predicted , mask = None ) : <EOL> correct = np . logical_xor ( predicted < self . threshold , <EOL> true_labels ) . astype ( np . float ) <EOL> if mask is not None : <EOL> correct *= mask <EOL> return np . sum ( correct ) / true_labels . shape [ <NUM_LIT:1> ] <EOL> class MeanSquaredError ( Scorer ) : <EOL> def __call__ ( self , true_labels , predicted , mask = None ) : <EOL> errors = ( true_labels - predicted ) ** <NUM_LIT:2> <EOL> if mask is not None : <EOL> errors *= mask <EOL> return <NUM_LIT:0.5> * np . sum ( errors ) <EOL> def _flatten_all_but_last ( a ) : <EOL> if a is None : <EOL> return None <EOL> return a . reshape ( - <NUM_LIT:1> , a . shape [ - <NUM_LIT:1> ] ) <EOL> def _weighted_average ( errors ) : <EOL> errors = np . array ( errors ) <EOL> assert errors . ndim == <NUM_LIT:2> and errors . shape [ <NUM_LIT:1> ] == <NUM_LIT:2> <EOL> return np . sum ( errors [ : , <NUM_LIT:1> ] * errors [ : , <NUM_LIT:0> ] / np . sum ( errors [ : , <NUM_LIT:0> ] ) ) </s>
