<s> from time import time <EOL> import numpy as np <EOL> from sklearn . externals . joblib import Parallel , delayed , cpu_count <EOL> from sklearn . utils import gen_even_slices , shuffle <EOL> from . ssvm import BaseSSVM <EOL> from . . utils import find_constraint <EOL> class SubgradientSSVM ( BaseSSVM ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , model , max_iter = <NUM_LIT:100> , C = <NUM_LIT:1.0> , verbose = <NUM_LIT:0> , momentum = <NUM_LIT:0.0> , <EOL> learning_rate = '<STR_LIT>' , n_jobs = <NUM_LIT:1> , <EOL> show_loss_every = <NUM_LIT:0> , decay_exponent = <NUM_LIT:1> , <EOL> break_on_no_constraints = True , logger = None , batch_size = None , <EOL> decay_t0 = <NUM_LIT:10> , averaging = None , shuffle = False ) : <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 . averaging = averaging <EOL> self . break_on_no_constraints = break_on_no_constraints <EOL> self . momentum = momentum <EOL> self . learning_rate = learning_rate <EOL> self . t = <NUM_LIT:0> <EOL> self . decay_exponent = decay_exponent <EOL> self . decay_t0 = decay_t0 <EOL> self . batch_size = batch_size <EOL> self . shuffle = shuffle <EOL> def _solve_subgradient ( self , djoint_feature , n_samples , w ) : <EOL> """<STR_LIT>""" <EOL> grad = ( djoint_feature - w / ( self . C * n_samples ) ) <EOL> self . grad_old = ( ( <NUM_LIT:1> - self . momentum ) * grad <EOL> + self . momentum * self . grad_old ) <EOL> if self . decay_exponent == <NUM_LIT:0> : <EOL> effective_lr = self . learning_rate_ <EOL> else : <EOL> effective_lr = ( self . learning_rate_ <EOL> / ( self . t + self . decay_t0 ) <EOL> ** self . decay_exponent ) <EOL> w += effective_lr * self . grad_old <EOL> if self . averaging == '<STR_LIT>' : <EOL> rho = <NUM_LIT> / ( self . t + <NUM_LIT> ) <EOL> self . w = ( <NUM_LIT:1.> - rho ) * self . w + rho * w <EOL> elif self . averaging == '<STR_LIT>' : <EOL> rho = <NUM_LIT> * ( self . t + <NUM_LIT:1> ) / ( ( self . t + <NUM_LIT:2> ) * ( <NUM_LIT:2> * self . t + <NUM_LIT:3> ) ) <EOL> self . w = ( <NUM_LIT:1.> - rho ) * self . w + rho * w <EOL> else : <EOL> self . w = w <EOL> self . t += <NUM_LIT:1.> <EOL> return w <EOL> def fit ( self , X , Y , constraints = None , warm_start = False , initialize = True ) : <EOL> """<STR_LIT>""" <EOL> if initialize : <EOL> self . model . initialize ( X , Y ) <EOL> if self . verbose : <EOL> print ( "<STR_LIT>" ) <EOL> self . grad_old = np . zeros ( self . model . size_joint_feature ) <EOL> self . w = getattr ( self , "<STR_LIT:w>" , np . zeros ( self . model . size_joint_feature ) ) <EOL> w = self . w . copy ( ) <EOL> if not warm_start : <EOL> self . objective_curve_ = [ ] <EOL> self . timestamps_ = [ time ( ) ] <EOL> if self . learning_rate == "<STR_LIT>" : <EOL> self . learning_rate_ = self . C * len ( X ) <EOL> else : <EOL> self . learning_rate_ = self . learning_rate <EOL> else : <EOL> self . timestamps_ = ( np . array ( self . timestamps_ ) - time ( ) ) . tolist ( ) <EOL> try : <EOL> for iteration in range ( self . max_iter ) : <EOL> if self . shuffle : <EOL> X , Y = shuffle ( X , Y ) <EOL> if self . n_jobs == <NUM_LIT:1> : <EOL> objective , positive_slacks , w = self . _sequential_learning ( X , Y , w ) <EOL> else : <EOL> objective , positive_slacks , w = self . _parallel_learning ( X , Y , w ) <EOL> objective = objective * self . C + np . sum ( w ** <NUM_LIT:2> ) / <NUM_LIT> <EOL> if positive_slacks == <NUM_LIT:0> : <EOL> if self . verbose : <EOL> print ( "<STR_LIT>" ) <EOL> if self . break_on_no_constraints : <EOL> break <EOL> if self . verbose > <NUM_LIT:0> : <EOL> print ( self ) <EOL> print ( "<STR_LIT>" % iteration ) <EOL> print ( "<STR_LIT>" <EOL> "<STR_LIT>" % <EOL> ( positive_slacks , objective ) ) <EOL> self . timestamps_ . append ( time ( ) - self . timestamps_ [ <NUM_LIT:0> ] ) <EOL> self . objective_curve_ . append ( self . _objective ( X , Y ) ) <EOL> if self . verbose > <NUM_LIT:2> : <EOL> print ( self . w ) <EOL> self . _compute_training_loss ( X , Y , iteration ) <EOL> if self . logger is not None : <EOL> self . logger ( self , iteration ) <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 . objective_curve_ . append ( self . _objective ( X , Y ) ) <EOL> if self . logger is not None : <EOL> self . logger ( self , '<STR_LIT>' ) <EOL> if self . verbose : <EOL> if self . objective_curve_ : <EOL> print ( "<STR_LIT>" % self . objective_curve_ [ - <NUM_LIT:1> ] ) <EOL> if self . verbose and self . n_jobs == <NUM_LIT:1> : <EOL> print ( "<STR_LIT>" % self . model . inference_calls ) <EOL> return self <EOL> def _parallel_learning ( self , X , Y , w ) : <EOL> n_samples = len ( X ) <EOL> objective , positive_slacks = <NUM_LIT:0> , <NUM_LIT:0> <EOL> verbose = max ( <NUM_LIT:0> , self . verbose - <NUM_LIT:3> ) <EOL> if self . batch_size is not None : <EOL> raise ValueError ( "<STR_LIT>" <EOL> "<STR_LIT>" ) <EOL> if self . n_jobs == - <NUM_LIT:1> : <EOL> n_jobs = cpu_count ( ) <EOL> else : <EOL> n_jobs = self . n_jobs <EOL> n_batches = int ( np . ceil ( float ( len ( X ) ) / n_jobs ) ) <EOL> slices = gen_even_slices ( n_samples , n_batches ) <EOL> for batch in slices : <EOL> X_b = X [ batch ] <EOL> Y_b = Y [ batch ] <EOL> candidate_constraints = Parallel ( <EOL> n_jobs = self . n_jobs , <EOL> verbose = verbose ) ( delayed ( find_constraint ) ( <EOL> self . model , x , y , w ) <EOL> for x , y in zip ( X_b , Y_b ) ) <EOL> djoint_feature = np . zeros ( self . model . size_joint_feature ) <EOL> for x , y , constraint in zip ( X_b , Y_b , <EOL> candidate_constraints ) : <EOL> y_hat , delta_joint_feature , slack , loss = constraint <EOL> if slack > <NUM_LIT:0> : <EOL> objective += slack <EOL> djoint_feature += delta_joint_feature <EOL> positive_slacks += <NUM_LIT:1> <EOL> w = self . _solve_subgradient ( djoint_feature , n_samples , w ) <EOL> return objective , positive_slacks , w <EOL> def _sequential_learning ( self , X , Y , w ) : <EOL> n_samples = len ( X ) <EOL> objective , positive_slacks = <NUM_LIT:0> , <NUM_LIT:0> <EOL> if self . batch_size in [ None , <NUM_LIT:1> ] : <EOL> for x , y in zip ( X , Y ) : <EOL> y_hat , delta_joint_feature , slack , loss = find_constraint ( self . model , x , y , w ) <EOL> objective += slack <EOL> if slack > <NUM_LIT:0> : <EOL> positive_slacks += <NUM_LIT:1> <EOL> self . _solve_subgradient ( delta_joint_feature , n_samples , w ) <EOL> else : <EOL> if self . batch_size == - <NUM_LIT:1> : <EOL> slices = [ slice ( <NUM_LIT:0> , len ( X ) ) ] <EOL> else : <EOL> n_batches = int ( np . ceil ( float ( len ( X ) ) / self . batch_size ) ) <EOL> slices = gen_even_slices ( n_samples , n_batches ) <EOL> for batch in slices : <EOL> X_b = X [ batch ] <EOL> Y_b = Y [ batch ] <EOL> Y_hat = self . model . batch_loss_augmented_inference ( <EOL> X_b , Y_b , w , relaxed = True ) <EOL> delta_joint_feature = ( self . model . batch_joint_feature ( X_b , Y_b ) <EOL> - self . model . batch_joint_feature ( X_b , Y_hat ) ) <EOL> loss = np . sum ( self . model . batch_loss ( Y_b , Y_hat ) ) <EOL> violation = np . maximum ( <NUM_LIT:0> , loss - np . dot ( w , delta_joint_feature ) ) <EOL> objective += violation <EOL> positive_slacks += self . batch_size <EOL> self . _solve_subgradient ( delta_joint_feature / len ( X_b ) , n_samples , w ) <EOL> return objective , positive_slacks , w </s>
