<s> """<STR_LIT>""" <EOL> import numpy as np <EOL> try : <EOL> import cPickle as pickle <EOL> except ImportError : <EOL> import pickle <EOL> from pystruct import learners <EOL> import pystruct . models as crfs <EOL> from pystruct . utils import SaveLogger <EOL> data_train = pickle . load ( open ( "<STR_LIT>" ) ) <EOL> C = <NUM_LIT> <EOL> n_states = <NUM_LIT> <EOL> print ( "<STR_LIT>" % len ( data_train [ '<STR_LIT:X>' ] ) ) <EOL> class_weights = <NUM_LIT:1.> / np . bincount ( np . hstack ( data_train [ '<STR_LIT:Y>' ] ) ) <EOL> class_weights *= <NUM_LIT> / np . sum ( class_weights ) <EOL> print ( class_weights ) <EOL> model = crfs . EdgeFeatureGraphCRF ( inference_method = '<STR_LIT>' , <EOL> class_weight = class_weights , <EOL> symmetric_edge_features = [ <NUM_LIT:0> , <NUM_LIT:1> ] , <EOL> antisymmetric_edge_features = [ <NUM_LIT:2> ] ) <EOL> experiment_name = "<STR_LIT>" % C <EOL> ssvm = learners . NSlackSSVM ( <EOL> model , verbose = <NUM_LIT:2> , C = C , max_iter = <NUM_LIT> , n_jobs = - <NUM_LIT:1> , <EOL> tol = <NUM_LIT> , show_loss_every = <NUM_LIT:5> , <EOL> logger = SaveLogger ( experiment_name + "<STR_LIT>" , save_every = <NUM_LIT:100> ) , <EOL> inactive_threshold = <NUM_LIT> , inactive_window = <NUM_LIT:10> , batch_size = <NUM_LIT:100> ) <EOL> ssvm . fit ( data_train [ '<STR_LIT:X>' ] , data_train [ '<STR_LIT:Y>' ] ) <EOL> data_val = pickle . load ( open ( "<STR_LIT>" ) ) <EOL> y_pred = ssvm . predict ( data_val [ '<STR_LIT:X>' ] ) <EOL> y_pred , y_true = np . hstack ( y_pred ) , np . hstack ( data_val [ '<STR_LIT:Y>' ] ) <EOL> y_pred = y_pred [ y_true != <NUM_LIT:255> ] <EOL> y_true = y_true [ y_true != <NUM_LIT:255> ] <EOL> print ( "<STR_LIT>" % np . mean ( y_true == y_pred ) ) </s>
