<s> """<STR_LIT>""" <EOL> import logging <EOL> import numpy as np <EOL> from itertools import izip <EOL> from . import utils <EOL> from . import config <EOL> from . import attributes <EOL> from . metadata import Metadata <EOL> from . pos import POSReader <EOL> from . srl import SRLReader <EOL> from . parse import DependencyReader <EOL> from . network import Network , ConvolutionalNetwork , ConvolutionalDependencyNetwork <EOL> def load_network ( md ) : <EOL> """<STR_LIT>""" <EOL> logger = logging . getLogger ( "<STR_LIT>" ) <EOL> is_srl = md . task . startswith ( '<STR_LIT>' ) and md . task != '<STR_LIT>' <EOL> logger . info ( '<STR_LIT>' ) <EOL> if is_srl : <EOL> net_class = ConvolutionalNetwork <EOL> elif md . task . endswith ( '<STR_LIT>' ) : <EOL> net_class = ConvolutionalDependencyNetwork <EOL> else : <EOL> net_class = Network <EOL> nn = net_class . load_from_file ( md . paths [ md . network ] ) <EOL> logger . info ( '<STR_LIT>' ) <EOL> return nn <EOL> def create_reader ( md , gold_file = None ) : <EOL> """<STR_LIT>""" <EOL> logger = logging . getLogger ( '<STR_LIT>' ) <EOL> logger . info ( '<STR_LIT>' ) <EOL> if md . task == '<STR_LIT>' : <EOL> tr = POSReader ( md , filename = gold_file ) <EOL> elif '<STR_LIT>' in md . task : <EOL> labeled = md . task . startswith ( '<STR_LIT>' ) <EOL> tr = DependencyReader ( md , filename = gold_file , labeled = labeled ) <EOL> elif md . task . startswith ( '<STR_LIT>' ) : <EOL> tr = SRLReader ( md , filename = gold_file , only_boundaries = ( md . task == '<STR_LIT>' ) , <EOL> only_classify = ( md . task == '<STR_LIT>' ) , <EOL> only_predicates = ( md . task == '<STR_LIT>' ) ) <EOL> else : <EOL> raise ValueError ( "<STR_LIT>" % md . task ) <EOL> logger . info ( '<STR_LIT>' ) <EOL> return tr <EOL> def _group_arguments ( tokens , predicate_positions , boundaries , labels ) : <EOL> """<STR_LIT>""" <EOL> arg_structs = [ ] <EOL> for predicate_position , pred_boundaries , pred_labels in izip ( predicate_positions , <EOL> boundaries , <EOL> labels ) : <EOL> structure = { } <EOL> for token , boundary_tag in izip ( tokens , pred_boundaries ) : <EOL> if boundary_tag == '<STR_LIT:O>' : <EOL> continue <EOL> elif boundary_tag == '<STR_LIT:B>' : <EOL> argument_tokens = [ token ] <EOL> elif boundary_tag == '<STR_LIT:I>' : <EOL> argument_tokens . append ( token ) <EOL> elif boundary_tag == '<STR_LIT:E>' : <EOL> argument_tokens . append ( token ) <EOL> tag = pred_labels . pop ( <NUM_LIT:0> ) <EOL> structure [ tag ] = argument_tokens <EOL> else : <EOL> tag = pred_labels . pop ( <NUM_LIT:0> ) <EOL> structure [ tag ] = [ token ] <EOL> predicate = tokens [ predicate_position ] <EOL> arg_structs . append ( ( predicate , structure ) ) <EOL> return arg_structs <EOL> class SRLAnnotatedSentence ( object ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , tokens , arg_structures ) : <EOL> """<STR_LIT>""" <EOL> self . tokens = tokens <EOL> self . arg_structures = arg_structures <EOL> class ParsedSentence ( object ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , tokens , heads , labels , pos = None ) : <EOL> """<STR_LIT>""" <EOL> self . tokens = tokens <EOL> self . heads = heads <EOL> self . labels = labels <EOL> self . pos = pos <EOL> def __len__ ( self ) : <EOL> return len ( self . tokens ) <EOL> def to_conll ( self ) : <EOL> """<STR_LIT>""" <EOL> result = [ ] <EOL> for i in range ( len ( self . tokens ) ) : <EOL> token = self . tokens [ i ] <EOL> head = self . heads [ i ] + <NUM_LIT:1> <EOL> label = self . labels [ i ] <EOL> pos = self . pos [ i ] if self . pos else '<STR_LIT:_>' <EOL> line = u'<STR_LIT>' <EOL> result . append ( line . format ( id = i + <NUM_LIT:1> , pos = pos , head = head , label = label , token = token ) ) <EOL> return '<STR_LIT:\n>' . join ( result ) <EOL> class Tagger ( object ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , data_dir = None , language = '<STR_LIT>' ) : <EOL> """<STR_LIT>""" <EOL> asrt_msg = "<STR_LIT>" <EOL> if data_dir is None : <EOL> assert config . data_dir is not None , asrt_msg <EOL> self . paths = config . FILES <EOL> else : <EOL> self . paths = config . get_config_paths ( data_dir ) <EOL> self . data_dir = data_dir <EOL> self . language = language <EOL> self . _load_data ( ) <EOL> def _load_data ( self ) : <EOL> """<STR_LIT>""" <EOL> pass <EOL> class SRLTagger ( Tagger ) : <EOL> """<STR_LIT>""" <EOL> def _load_data ( self ) : <EOL> """<STR_LIT>""" <EOL> md_boundary = Metadata . load_from_file ( '<STR_LIT>' , self . paths ) <EOL> self . boundary_nn = load_network ( md_boundary ) <EOL> self . boundary_reader = create_reader ( md_boundary ) <EOL> self . boundary_reader . create_converter ( ) <EOL> self . boundary_itd = self . boundary_reader . get_inverse_tag_dictionary ( ) <EOL> md_classify = Metadata . load_from_file ( '<STR_LIT>' , self . paths ) <EOL> self . classify_nn = load_network ( md_classify ) <EOL> self . classify_reader = create_reader ( md_classify ) <EOL> self . classify_reader . create_converter ( ) <EOL> self . classify_itd = self . classify_reader . get_inverse_tag_dictionary ( ) <EOL> md_pred = Metadata . load_from_file ( '<STR_LIT>' , self . paths ) <EOL> self . pred_nn = load_network ( md_pred ) <EOL> self . pred_reader = create_reader ( md_pred ) <EOL> self . pred_reader . create_converter ( ) <EOL> def find_predicates ( self , tokens ) : <EOL> """<STR_LIT>""" <EOL> sent_codified = np . array ( [ self . pred_reader . converter . convert ( token ) <EOL> for token in tokens ] ) <EOL> answer = np . array ( self . pred_nn . tag_sentence ( sent_codified ) ) <EOL> return answer . nonzero ( ) [ <NUM_LIT:0> ] <EOL> def tag ( self , text ) : <EOL> """<STR_LIT>""" <EOL> tokens = utils . tokenize ( text , self . language ) <EOL> result = [ ] <EOL> for sent in tokens : <EOL> tagged = self . tag_tokens ( sent ) <EOL> result . append ( tagged ) <EOL> return result <EOL> def tag_tokens ( self , tokens , no_repeats = False ) : <EOL> """<STR_LIT>""" <EOL> if self . language == '<STR_LIT>' : <EOL> tokens_obj = [ attributes . Token ( utils . clean_text ( t , False ) ) for t in tokens ] <EOL> else : <EOL> tokens_obj = [ attributes . Token ( t ) for t in tokens ] <EOL> converted_bound = np . array ( [ self . boundary_reader . converter . convert ( t ) <EOL> for t in tokens_obj ] ) <EOL> converted_class = np . array ( [ self . classify_reader . converter . convert ( t ) <EOL> for t in tokens_obj ] ) <EOL> pred_positions = self . find_predicates ( tokens_obj ) <EOL> answers = self . boundary_nn . tag_sentence ( converted_bound , pred_positions ) <EOL> boundaries = [ [ self . boundary_itd [ x ] for x in pred_answer ] <EOL> for pred_answer in answers ] <EOL> arg_limits = [ utils . boundaries_to_arg_limits ( pred_boundaries ) <EOL> for pred_boundaries in boundaries ] <EOL> answers = self . classify_nn . tag_sentence ( converted_class , <EOL> pred_positions , arg_limits , <EOL> allow_repeats = not no_repeats ) <EOL> arguments = [ [ self . classify_itd [ x ] for x in pred_answer ] <EOL> for pred_answer in answers ] <EOL> structures = _group_arguments ( tokens , pred_positions , boundaries , arguments ) <EOL> return SRLAnnotatedSentence ( tokens , structures ) <EOL> class DependencyParser ( Tagger ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( self , * args , ** kwargs ) : <EOL> """<STR_LIT>""" <EOL> super ( DependencyParser , self ) . __init__ ( * args , ** kwargs ) <EOL> def _load_data ( self ) : <EOL> """<STR_LIT>""" <EOL> md_udep = Metadata . load_from_file ( '<STR_LIT>' , paths = self . paths ) <EOL> self . unlabeled_nn = load_network ( md_udep ) <EOL> self . unlabeled_reader = create_reader ( md_udep ) <EOL> md_ldep = Metadata . load_from_file ( '<STR_LIT>' , paths = self . paths ) <EOL> self . labeled_nn = load_network ( md_ldep ) <EOL> self . labeled_reader = create_reader ( md_ldep ) <EOL> self . itd = self . labeled_reader . get_inverse_tag_dictionary ( ) <EOL> self . use_pos = md_udep . use_pos or md_ldep . use_pos <EOL> if self . use_pos : <EOL> self . pos_tagger = POSTagger ( self . data_dir , language = self . language ) <EOL> def parse ( self , text ) : <EOL> """<STR_LIT>""" <EOL> sentences = utils . tokenize ( text , self . language ) <EOL> result = [ ] <EOL> for sent in sentences : <EOL> parsed = self . parse_sentence ( sent ) <EOL> result . append ( parsed ) <EOL> return result <EOL> def tag_tokens ( self , tokens ) : <EOL> """<STR_LIT>""" <EOL> return self . parse_sentence ( tokens ) <EOL> def parse_sentence ( self , tokens ) : <EOL> """<STR_LIT>""" <EOL> original_tokens = tokens <EOL> tokens_obj = [ ] <EOL> if self . use_pos : <EOL> tokens = self . pos_tagger . tag_tokens ( tokens , return_tokens = True ) <EOL> for token in tokens : <EOL> if self . use_pos : <EOL> word , pos = token <EOL> else : <EOL> pos = None <EOL> tokens_obj . append ( attributes . Token ( word , pos = pos ) ) <EOL> converted_tokens = self . unlabeled_reader . codify_sentence ( tokens_obj ) <EOL> heads = self . unlabeled_nn . tag_sentence ( converted_tokens ) <EOL> root = heads . argmax ( ) <EOL> heads [ root ] = root <EOL> converted_tokens = self . labeled_reader . codify_sentence ( tokens_obj ) <EOL> label_codes = self . labeled_nn . tag_sentence ( converted_tokens , heads ) <EOL> labels = [ self . itd [ code ] for code in label_codes ] <EOL> heads [ root ] = - <NUM_LIT:1> <EOL> if self . use_pos : <EOL> pos_tags = zip ( * tokens ) [ <NUM_LIT:1> ] <EOL> else : <EOL> pos_tags = None <EOL> parsed = ParsedSentence ( original_tokens , heads , labels , pos_tags ) <EOL> return parsed <EOL> def tag ( self , text ) : <EOL> """<STR_LIT>""" <EOL> return self . parse ( text ) <EOL> class POSTagger ( Tagger ) : <EOL> """<STR_LIT>""" <EOL> def _load_data ( self ) : <EOL> """<STR_LIT>""" <EOL> md = Metadata . load_from_file ( '<STR_LIT>' , self . paths ) <EOL> self . nn = load_network ( md ) <EOL> self . reader = create_reader ( md ) <EOL> self . reader . create_converter ( ) <EOL> self . itd = self . reader . get_inverse_tag_dictionary ( ) <EOL> def tag ( self , text ) : <EOL> """<STR_LIT>""" <EOL> tokens = utils . tokenize ( text , self . language ) <EOL> result = [ ] <EOL> for sent in tokens : <EOL> tagged = self . tag_tokens ( sent , return_tokens = True ) <EOL> result . append ( tagged ) <EOL> return result <EOL> def tag_tokens ( self , tokens , return_tokens = False ) : <EOL> """<STR_LIT>""" <EOL> converter = self . reader . converter <EOL> converted_tokens = np . array ( [ converter . convert ( token ) <EOL> for token in tokens ] ) <EOL> answer = self . nn . tag_sentence ( converted_tokens ) <EOL> tags = [ self . itd [ tag ] for tag in answer ] <EOL> if return_tokens : <EOL> return zip ( tokens , tags ) <EOL> return tags </s>
