<s> import warnings <EOL> from time import time <EOL> import numpy as np <EOL> from sklearn . utils import check_random_state <EOL> from pystruct . learners . ssvm import BaseSSVM <EOL> from pystruct . utils import find_constraint <EOL> class FrankWolfeSSVM ( BaseSSVM ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , model , max_iter = <NUM_LIT:1000> , C = <NUM_LIT:1.0> , verbose = <NUM_LIT:0> , n_jobs = <NUM_LIT:1> , <EOL> show_loss_every = <NUM_LIT:0> , logger = None , batch_mode = False , <EOL> line_search = True , check_dual_every = <NUM_LIT:10> , tol = <NUM_LIT> , <EOL> do_averaging = True , sample_method = '<STR_LIT>' , random_state = None ) : <EOL> if n_jobs != <NUM_LIT:1> : <EOL> warnings . warn ( "<STR_LIT>" <EOL> "<STR_LIT>" ) <EOL> if sample_method not in [ '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ] : <EOL> raise ValueError ( "<STR_LIT>" ) <EOL> BaseSSVM . __init__ ( self , model , max_iter , C , verbose = verbose , <EOL> n_jobs = n_jobs , show_loss_every = show_loss_every , <EOL> logger = logger ) <EOL> self . tol = tol <EOL> self . batch_mode = batch_mode <EOL> self . line_search = line_search <EOL> self . check_dual_every = check_dual_every <EOL> self . do_averaging = do_averaging <EOL> self . sample_method = sample_method <EOL> self . random_state = random_state <EOL> def _calc_dual_gap ( self , X , Y ) : <EOL> n_samples = len ( X ) <EOL> joint_feature_gt = self . model . batch_joint_feature ( X , Y , Y ) <EOL> Y_hat = self . model . batch_loss_augmented_inference ( X , Y , self . w , <EOL> relaxed = True ) <EOL> djoint_feature = joint_feature_gt - self . model . batch_joint_feature ( X , Y_hat ) <EOL> ls = np . sum ( self . model . batch_loss ( Y , Y_hat ) ) <EOL> ws = djoint_feature * self . C <EOL> l_rescaled = self . l * n_samples * self . C <EOL> dual_val = - <NUM_LIT:0.5> * np . sum ( self . w ** <NUM_LIT:2> ) + l_rescaled <EOL> w_diff = self . w - ws <EOL> dual_gap = w_diff . T . dot ( self . w ) - l_rescaled + ls * self . C <EOL> primal_val = dual_val + dual_gap <EOL> return dual_val , dual_gap , primal_val <EOL> def _frank_wolfe_batch ( self , X , Y ) : <EOL> """<STR_LIT>""" <EOL> l = <NUM_LIT:0.0> <EOL> n_samples = float ( len ( X ) ) <EOL> joint_feature_gt = self . model . batch_joint_feature ( X , Y , Y ) <EOL> for iteration in range ( self . max_iter ) : <EOL> Y_hat = self . model . batch_loss_augmented_inference ( X , Y , self . w , <EOL> relaxed = True ) <EOL> djoint_feature = joint_feature_gt - self . model . batch_joint_feature ( X , Y_hat ) <EOL> ls = np . mean ( self . model . batch_loss ( Y , Y_hat ) ) <EOL> ws = djoint_feature * self . C <EOL> w_diff = self . w - ws <EOL> dual_gap = <NUM_LIT:1.0> / ( self . C * n_samples ) * w_diff . T . dot ( self . w ) - l + ls <EOL> if self . line_search : <EOL> eps = <NUM_LIT> <EOL> gamma = dual_gap / ( np . sum ( w_diff ** <NUM_LIT:2> ) / ( self . C * n_samples ) + eps ) <EOL> gamma = max ( <NUM_LIT:0.0> , min ( <NUM_LIT:1.0> , gamma ) ) <EOL> else : <EOL> gamma = <NUM_LIT> / ( iteration + <NUM_LIT> ) <EOL> dual_val = - <NUM_LIT:0.5> * np . sum ( self . w ** <NUM_LIT:2> ) + l * ( n_samples * self . C ) <EOL> dual_gap_display = dual_gap * n_samples * self . C <EOL> primal_val = dual_val + dual_gap_display <EOL> self . primal_objective_curve_ . append ( primal_val ) <EOL> self . objective_curve_ . append ( dual_val ) <EOL> self . timestamps_ . append ( time ( ) - self . timestamps_ [ <NUM_LIT:0> ] ) <EOL> if self . verbose > <NUM_LIT:0> : <EOL> print ( "<STR_LIT>" <EOL> % ( iteration , dual_val , dual_gap_display , primal_val , gamma ) ) <EOL> self . w = ( <NUM_LIT:1.0> - gamma ) * self . w + gamma * ws <EOL> l = ( <NUM_LIT:1.0> - gamma ) * l + gamma * ls <EOL> if self . logger is not None : <EOL> self . logger ( self , iteration ) <EOL> if dual_gap < self . tol : <EOL> return <EOL> def _frank_wolfe_bc ( self , X , Y ) : <EOL> """<STR_LIT>""" <EOL> n_samples = len ( X ) <EOL> w = self . w . copy ( ) <EOL> w_mat = np . zeros ( ( n_samples , self . model . size_joint_feature ) ) <EOL> l_mat = np . zeros ( n_samples ) <EOL> l = <NUM_LIT:0.0> <EOL> k = <NUM_LIT:0> <EOL> rng = check_random_state ( self . random_state ) <EOL> for iteration in range ( self . max_iter ) : <EOL> if self . verbose > <NUM_LIT:0> : <EOL> print ( "<STR_LIT>" % iteration ) <EOL> perm = np . arange ( n_samples ) <EOL> if self . sample_method == '<STR_LIT>' : <EOL> rng . shuffle ( perm ) <EOL> elif self . sample_method == '<STR_LIT>' : <EOL> perm = rng . randint ( low = <NUM_LIT:0> , high = n_samples , size = n_samples ) <EOL> for j in range ( n_samples ) : <EOL> i = perm [ j ] <EOL> x , y = X [ i ] , Y [ i ] <EOL> y_hat , delta_joint_feature , slack , loss = find_constraint ( self . model , x , y , w ) <EOL> ws = delta_joint_feature * self . C <EOL> ls = loss / n_samples <EOL> if self . line_search : <EOL> eps = <NUM_LIT> <EOL> w_diff = w_mat [ i ] - ws <EOL> gamma = ( w_diff . T . dot ( w ) <EOL> - ( self . C * n_samples ) * ( l_mat [ i ] - ls ) ) / ( np . sum ( w_diff ** <NUM_LIT:2> ) + eps ) <EOL> gamma = max ( <NUM_LIT:0.0> , min ( <NUM_LIT:1.0> , gamma ) ) <EOL> else : <EOL> gamma = <NUM_LIT> * n_samples / ( k + <NUM_LIT> * n_samples ) <EOL> w -= w_mat [ i ] <EOL> w_mat [ i ] = ( <NUM_LIT:1.0> - gamma ) * w_mat [ i ] + gamma * ws <EOL> w += w_mat [ i ] <EOL> l -= l_mat [ i ] <EOL> l_mat [ i ] = ( <NUM_LIT:1.0> - gamma ) * l_mat [ i ] + gamma * ls <EOL> l += l_mat [ i ] <EOL> if self . do_averaging : <EOL> rho = <NUM_LIT> / ( k + <NUM_LIT> ) <EOL> self . w = ( <NUM_LIT:1.> - rho ) * self . w + rho * w <EOL> self . l = ( <NUM_LIT:1.> - rho ) * self . l + rho * l <EOL> else : <EOL> self . w = w <EOL> self . l = l <EOL> k += <NUM_LIT:1> <EOL> if ( self . check_dual_every != <NUM_LIT:0> ) and ( iteration % self . check_dual_every == <NUM_LIT:0> ) : <EOL> dual_val , dual_gap , primal_val = self . _calc_dual_gap ( X , Y ) <EOL> self . primal_objective_curve_ . append ( primal_val ) <EOL> self . objective_curve_ . append ( dual_val ) <EOL> self . timestamps_ . append ( time ( ) - self . timestamps_ [ <NUM_LIT:0> ] ) <EOL> if self . verbose > <NUM_LIT:0> : <EOL> print ( "<STR_LIT>" <EOL> % ( dual_val , dual_gap , primal_val ) ) <EOL> if self . logger is not None : <EOL> self . logger ( self , iteration ) <EOL> if dual_gap < self . tol : <EOL> return <EOL> def fit ( self , X , Y , constraints = None , initialize = True ) : <EOL> """<STR_LIT>""" <EOL> if initialize : <EOL> self . model . initialize ( X , Y ) <EOL> self . objective_curve_ , self . primal_objective_curve_ = [ ] , [ ] <EOL> self . timestamps_ = [ time ( ) ] <EOL> self . w = getattr ( self , "<STR_LIT:w>" , np . zeros ( self . model . size_joint_feature ) ) <EOL> self . l = getattr ( self , "<STR_LIT:l>" , <NUM_LIT:0> ) <EOL> try : <EOL> if self . batch_mode : <EOL> self . _frank_wolfe_batch ( X , Y ) <EOL> else : <EOL> self . _frank_wolfe_bc ( X , Y ) <EOL> except KeyboardInterrupt : <EOL> pass <EOL> if self . verbose : <EOL> print ( "<STR_LIT>" ) <EOL> self . timestamps_ . append ( time ( ) - self . timestamps_ [ <NUM_LIT:0> ] ) <EOL> self . primal_objective_curve_ . append ( self . _objective ( X , Y ) ) <EOL> self . objective_curve_ . append ( self . objective_curve_ [ - <NUM_LIT:1> ] ) <EOL> if self . logger is not None : <EOL> self . logger ( self , '<STR_LIT>' ) <EOL> return self </s>
