<s> from __future__ import print_function <EOL> from builtins import zip <EOL> import csv <EOL> import probablepeople <EOL> from parserator import data_prep_utils <EOL> from lxml import etree <EOL> import random <EOL> import pycrfsuite <EOL> def getIncorrect ( name_list , correct_tag ) : <EOL> incorrect_list = [ ] <EOL> for name in name_list : <EOL> labeled_sequence = probablepeople . parse ( name ) <EOL> string , label = labeled_sequence [ <NUM_LIT:0> ] <EOL> if label != correct_tag : <EOL> incorrect_list . append ( [ ( string , correct_tag ) ] ) <EOL> print ( len ( incorrect_list ) , "<STR_LIT:/>" , len ( name_list ) , "<STR_LIT>" , int ( float ( len ( incorrect_list ) ) / float ( len ( name_list ) ) * <NUM_LIT:100> ) , "<STR_LIT:%>" ) <EOL> return incorrect_list <EOL> def makeTaggedData ( filename , correct_tag ) : <EOL> with open ( filename , '<STR_LIT>' ) as f : <EOL> reader = csv . reader ( f ) <EOL> tagged_data = set ( [ ( row [ <NUM_LIT:0> ] , correct_tag ) for row in reader ] ) <EOL> return tagged_data <EOL> def addFailedPreds ( tagged_list , train_file ) : <EOL> print ( "<STR_LIT>" ) <EOL> i = <NUM_LIT:0> <EOL> added = <NUM_LIT:0> <EOL> for index , tagged_item in enumerate ( tagged_list ) : <EOL> if index % <NUM_LIT:20> == <NUM_LIT:0> : <EOL> print ( ) <EOL> if probablepeople . parse ( tagged_item [ <NUM_LIT:0> ] ) == [ tagged_item ] : <EOL> print ( "<STR_LIT:.>" , end = '<STR_LIT:U+0020>' ) <EOL> else : <EOL> data_prep_utils . appendListToXMLfile ( [ [ tagged_item ] ] , train_file ) <EOL> print ( "<STR_LIT:*>" , end = '<STR_LIT:U+0020>' ) <EOL> added += <NUM_LIT:1> <EOL> i += <NUM_LIT:1> <EOL> if added == <NUM_LIT:10> : <EOL> added = <NUM_LIT:0> <EOL> print ( "<STR_LIT:\n>" , "<STR_LIT:->" * <NUM_LIT:50> , "<STR_LIT>" , index ) <EOL> training_data = list ( data_prep_utils . parseTrainingData ( '<STR_LIT>' ) ) <EOL> trainModel ( training_data , '<STR_LIT>' ) <EOL> print ( "<STR_LIT:\n>" , "<STR_LIT:->" * <NUM_LIT:50> , "<STR_LIT>" ) <EOL> training_data = list ( data_prep_utils . parseTrainingData ( '<STR_LIT>' ) ) <EOL> trainModel ( training_data , '<STR_LIT>' ) <EOL> print ( i , "<STR_LIT>" , train_file ) <EOL> def trainModel ( training_data , model_file , <EOL> params_to_set = { '<STR_LIT>' : <NUM_LIT:0.1> , '<STR_LIT>' : <NUM_LIT> , '<STR_LIT>' : <NUM_LIT:0> } ) : <EOL> X = [ ] <EOL> Y = [ ] <EOL> for string_concat , components in training_data : <EOL> tokens , labels = list ( zip ( * components ) ) <EOL> X . append ( probablepeople . tokens2features ( tokens ) ) <EOL> Y . append ( labels ) <EOL> trainer = pycrfsuite . Trainer ( verbose = False , params = params_to_set ) <EOL> for xseq , yseq in zip ( X , Y ) : <EOL> trainer . append ( xseq , yseq ) <EOL> trainer . train ( model_file ) <EOL> reload ( probablepeople ) <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> surname_file = '<STR_LIT>' <EOL> female_file = '<STR_LIT>' <EOL> male_file = '<STR_LIT>' <EOL> surname_list = makeTaggedData ( surname_file , '<STR_LIT>' ) <EOL> female_list = makeTaggedData ( female_file , '<STR_LIT>' ) <EOL> male_list = makeTaggedData ( male_file , '<STR_LIT>' ) <EOL> shuffled_list = list ( surname_list ) + list ( female_list ) + list ( male_list ) <EOL> random . shuffle ( shuffled_list ) <EOL> addFailedPreds ( shuffled_list , '<STR_LIT>' ) </s>
