<s> """<STR_LIT>""" <EOL> import os . path <EOL> import csv <EOL> import copy <EOL> import numpy <EOL> import condor <EOL> import borg <EOL> logger = borg . get_logger ( __name__ , default_level = "<STR_LIT>" ) <EOL> def infer_distributions ( run_data , model_name , instance , exclude ) : <EOL> """<STR_LIT>""" <EOL> filtered_data = copy . deepcopy ( run_data ) <EOL> filtered_runs = filter ( lambda r : r . solver != exclude , filtered_data . run_lists [ instance ] ) <EOL> filtered_data . run_lists [ instance ] = filtered_runs <EOL> model = borg . experiments . common . train_model ( model_name , filtered_data , bins = <NUM_LIT:8> ) <EOL> ( M , S , B ) = model . log_masses . shape <EOL> n = sorted ( filtered_data . run_lists ) . index ( instance ) <EOL> true = run_data . to_bins_array ( run_data . solver_names , B = <NUM_LIT:8> ) [ n ] . astype ( float ) <EOL> true /= numpy . sum ( true , axis = - <NUM_LIT:1> ) [ : , None ] + <NUM_LIT> <EOL> observed = filtered_data . to_bins_array ( run_data . solver_names , B = <NUM_LIT:8> ) [ n ] . astype ( float ) <EOL> observed /= numpy . sum ( observed , axis = - <NUM_LIT:1> ) [ : , None ] + <NUM_LIT> <EOL> mask = model . names == instance <EOL> log_predicted_all = model . log_weights [ mask ] [ : , None , None ] + model . log_masses [ mask , : , : ] <EOL> predicted = numpy . sum ( numpy . exp ( log_predicted_all ) , axis = <NUM_LIT:0> ) <EOL> predicted /= numpy . sum ( predicted , axis = - <NUM_LIT:1> ) [ ... , None ] <EOL> def yield_rows ( ) : <EOL> for s in xrange ( S ) : <EOL> solver_name = run_data . solver_names [ s ] <EOL> for b in xrange ( B ) : <EOL> yield ( model_name , instance , solver_name , b , predicted [ s , b ] ) <EOL> yield ( "<STR_LIT>" , instance , solver_name , b , observed [ s , b ] ) <EOL> yield ( "<STR_LIT:true>" , instance , solver_name , b , true [ s , b ] ) <EOL> return list ( yield_rows ( ) ) <EOL> @ borg . annotations ( <EOL> out_path = ( "<STR_LIT>" ) , <EOL> bundle = ( "<STR_LIT>" , "<STR_LIT>" , None , os . path . abspath ) , <EOL> experiments = ( "<STR_LIT>" , "<STR_LIT>" , None , borg . util . load_json ) , <EOL> workers = ( "<STR_LIT>" , "<STR_LIT>" , "<STR_LIT:w>" , int ) , <EOL> local = ( "<STR_LIT>" , "<STR_LIT>" ) , <EOL> ) <EOL> def main ( out_path , bundle , experiments , workers = <NUM_LIT:0> , local = False ) : <EOL> """<STR_LIT>""" <EOL> def yield_jobs ( ) : <EOL> run_data = borg . storage . RunData . from_bundle ( bundle ) <EOL> for experiment in experiments : <EOL> yield ( <EOL> infer_distributions , <EOL> [ <EOL> run_data , <EOL> experiment [ "<STR_LIT>" ] , <EOL> experiment [ "<STR_LIT>" ] , <EOL> experiment [ "<STR_LIT>" ] , <EOL> ] , <EOL> ) <EOL> with open ( out_path , "<STR_LIT:w>" ) as out_file : <EOL> writer = csv . writer ( out_file ) <EOL> writer . writerow ( [ "<STR_LIT>" , "<STR_LIT>" , "<STR_LIT>" , "<STR_LIT>" , "<STR_LIT>" ] ) <EOL> for ( _ , row ) in condor . do ( yield_jobs ( ) , workers , local ) : <EOL> writer . writerows ( row ) <EOL> if __name__ == "<STR_LIT:__main__>" : <EOL> borg . script ( main ) </s>
