<s> import numpy as np <EOL> from . ssvm import BaseSSVM <EOL> from . n_slack_ssvm import NSlackSSVM <EOL> from . . utils import find_constraint <EOL> class LatentSSVM ( BaseSSVM ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , base_ssvm , latent_iter = <NUM_LIT:5> , logger = None ) : <EOL> self . base_ssvm = base_ssvm <EOL> self . latent_iter = latent_iter <EOL> self . logger = logger <EOL> def fit ( self , X , Y , H_init = None , initialize = True ) : <EOL> """<STR_LIT>""" <EOL> self . model . initialize ( X , Y ) <EOL> w = np . zeros ( self . model . size_joint_feature ) <EOL> constraints = None <EOL> ws = [ ] <EOL> if H_init is None : <EOL> H_init = self . model . init_latent ( X , Y ) <EOL> self . H_init_ = H_init <EOL> H = H_init <EOL> for iteration in range ( self . latent_iter ) : <EOL> if self . verbose : <EOL> print ( "<STR_LIT>" % iteration ) <EOL> if iteration == <NUM_LIT:0> : <EOL> pass <EOL> else : <EOL> H_new = [ self . model . latent ( x , y , w ) for x , y in zip ( X , Y ) ] <EOL> changes = [ np . any ( h_new != h ) for h_new , h in zip ( H_new , H ) ] <EOL> if not np . any ( changes ) : <EOL> if self . verbose > <NUM_LIT:0> : <EOL> print ( "<STR_LIT>" <EOL> "<STR_LIT>" ) <EOL> break <EOL> if self . verbose : <EOL> print ( "<STR_LIT>" % np . sum ( changes ) ) <EOL> if isinstance ( self . base_ssvm , NSlackSSVM ) : <EOL> constraints = [ [ ] for i in range ( len ( X ) ) ] <EOL> for sample , h , i in zip ( self . base_ssvm . constraints_ , H_new , <EOL> np . arange ( len ( X ) ) ) : <EOL> for constraint in sample : <EOL> const = find_constraint ( self . model , X [ i ] , h , w , <EOL> constraint [ <NUM_LIT:0> ] ) <EOL> y_hat , djoint_feature , _ , loss = const <EOL> constraints [ i ] . append ( [ y_hat , djoint_feature , loss ] ) <EOL> H = H_new <EOL> if iteration > <NUM_LIT:0> : <EOL> self . base_ssvm . fit ( X , H , constraints = constraints , <EOL> warm_start = "<STR_LIT>" , initialize = False ) <EOL> else : <EOL> self . base_ssvm . fit ( X , H , constraints = constraints , <EOL> initialize = False ) <EOL> w = self . base_ssvm . w <EOL> ws . append ( w ) <EOL> if self . logger is not None : <EOL> self . logger ( self , iteration ) <EOL> def predict ( self , X ) : <EOL> prediction = self . base_ssvm . predict ( X ) <EOL> return [ self . model . label_from_latent ( h ) for h in prediction ] <EOL> def predict_latent ( self , X ) : <EOL> return self . base_ssvm . predict ( X ) <EOL> def score ( self , X , Y ) : <EOL> """<STR_LIT>""" <EOL> losses = [ self . model . base_loss ( y , y_pred ) <EOL> for y , y_pred in zip ( Y , self . predict ( X ) ) ] <EOL> max_losses = [ self . model . max_loss ( y ) for y in Y ] <EOL> return <NUM_LIT:1.> - np . sum ( losses ) / float ( np . sum ( max_losses ) ) <EOL> @ property <EOL> def model ( self ) : <EOL> return self . base_ssvm . model <EOL> @ model . setter <EOL> def model ( self , model_ ) : <EOL> self . base_ssvm . model = model_ <EOL> @ property <EOL> def w ( self ) : <EOL> return self . base_ssvm . w <EOL> @ w . setter <EOL> def w ( self , w_ ) : <EOL> self . base_ssvm . w = w_ <EOL> @ property <EOL> def C ( self ) : <EOL> return self . base_ssvm . C <EOL> @ C . setter <EOL> def C ( self , C_ ) : <EOL> self . base_ssvm . w = C_ <EOL> @ property <EOL> def n_jobs ( self ) : <EOL> return self . base_ssvm . n_jobs <EOL> @ n_jobs . setter <EOL> def n_jobs ( self , n_jobs_ ) : <EOL> self . base_ssvm . n_jobs = n_jobs_ <EOL> @ property <EOL> def verbose ( self ) : <EOL> return self . base_ssvm . verbose <EOL> @ verbose . setter <EOL> def verbose ( self , verbose_ ) : <EOL> self . base_ssvm . verbose = verbose_ </s>
