<s> import argparse <EOL> from collections import defaultdict <EOL> import numpy as np <EOL> from glove import Glove , metrics <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> parser = argparse . ArgumentParser ( description = ( '<STR_LIT>' <EOL> '<STR_LIT>' ) ) <EOL> parser . add_argument ( '<STR_LIT>' , '<STR_LIT>' , action = '<STR_LIT:store>' , <EOL> required = True , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , '<STR_LIT>' , action = '<STR_LIT:store>' , <EOL> required = True , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , '<STR_LIT>' , action = '<STR_LIT:store_true>' , <EOL> default = False , <EOL> help = ( '<STR_LIT>' <EOL> '<STR_LIT>' <EOL> '<STR_LIT>' <EOL> '<STR_LIT>' ) ) <EOL> parser . add_argument ( '<STR_LIT>' , '<STR_LIT>' , action = '<STR_LIT:store>' , <EOL> default = <NUM_LIT:1> , <EOL> help = ( '<STR_LIT>' ) ) <EOL> args = parser . parse_args ( ) <EOL> glove = Glove . load ( args . model ) <EOL> if args . encode : <EOL> encode = lambda words : [ x . lower ( ) . encode ( '<STR_LIT:utf-8>' ) for x in words ] <EOL> else : <EOL> encode = lambda words : [ unicode ( x . lower ( ) ) for x in words ] <EOL> sections = defaultdict ( list ) <EOL> evaluation_words = [ sections [ section ] . append ( encode ( words ) ) for section , words in <EOL> metrics . read_analogy_file ( args . test ) ] <EOL> section_ranks = [ ] <EOL> for section , words in sections . items ( ) : <EOL> evaluation_ids = metrics . construct_analogy_test_set ( words , <EOL> glove . dictionary , <EOL> ignore_missing = True ) <EOL> ranks = metrics . analogy_rank_score ( evaluation_ids , glove . word_vectors , <EOL> no_threads = int ( args . parallelism ) ) <EOL> section_ranks . append ( ranks ) <EOL> print ( '<STR_LIT>' % ( section , ranks . mean ( ) , <EOL> ( ranks == <NUM_LIT:0> ) . sum ( ) / float ( len ( ranks ) ) ) ) <EOL> ranks = np . hstack ( section_ranks ) <EOL> print ( '<STR_LIT>' % ( ranks . mean ( ) , <EOL> ( ranks == <NUM_LIT:0> ) . sum ( ) / float ( len ( ranks ) ) ) ) </s>
