def get_docs_stats():
logging.root.setLevel(logging.ERROR)
acumm_stats_before = [0 for _ in voz.Document.get_stats_labels()]
acumm_stats_after = [0 for _ in voz.Document.get_stats_labels()]
acumm_count = 0
logging.basicConfig(level=logging.WARNING)
file_path = settings.STY_FILE_PATH
for sty_file in settings.STY_FILES:
acumm_count +=1
logger.info("Processing %s" % sty_file)
doc = styhelper.create_document_from_sty_file(file_path+sty_file)
doc_stats = doc.get_stats()
for i in xrange(len(acumm_stats_before)):
acumm_stats_before[i]+=doc_stats[i]
quoted_speech_file = sty_file.split()[0] + "/sentences.tsv.csv"
quotedspeechhelper.annotate_sentences(doc, file_path + quoted_speech_file)
quotedspeechhelper.clean_quoted_speech_from_document(doc)
doc_stats = doc.get_stats()
for i in xrange(len(acumm_stats_before)):
acumm_stats_after[i]+=doc_stats[i]
#break
print "Counts"
#print voz.Document.format_stats(acumm_stats_before)
print voz.Document.format_stats(acumm_stats_after)
print "Averages"
for i in xrange(len(acumm_stats_before)):
#acumm_stats_before[i]=1.0*acumm_stats_before[i]/acumm_count
acumm_stats_after[i]=1.0*acumm_stats_after[i]/acumm_count
#print voz.Document.format_stats(acumm_stats_before)
print voz.Document.format_stats(acumm_stats_after)
def generate_filtered_text_files():
"""
Generate files to be processed by parsers.
ClearNLP:
source /Users/josepvalls/soft/clearnlp/setup_classpath.sh
java -Xmx5g -XX:+UseConcMarkSweepGC edu.emory.clir.clearnlp.bin.NLPDecode -mode ner -c config_decode_ner.xml -i /Users/josepvalls/voz2/stories/dialog_filtered -ie txt
ClearNLP Coref
java /Users/josepvalls/Dropbox/projects/clearnlp/src/main/java/edu/drexel/valls
Stanford CoreNLP
use CoreNLP server cache
Open NLP
sh /Users/josepvalls/Dropbox/projects/coref-opennlp/Coref/run.cmd
"""
logging.basicConfig(level=logging.DEBUG)
file_path = settings.STY_FILE_PATH
for sty_file in settings.STY_FILES:
logger.info("Processing %s" % sty_file)
quoted_speech_file = sty_file.split()[0] + "/sentences.csv"
doc = styhelper.create_document_from_sty_file(file_path + sty_file)
quotedspeechhelper.annotate_sentences(doc,
file_path + quoted_speech_file)
sentences = []
for sentence in doc.sentences:
if sentence.is_normal():
sentences.append(sentence.get_text() + '\n')
file_name = settings.STORY_TXT_PATH + str(doc.id) + '.txt'
logger.info("Writing %d sentences to %s" % (len(sentences), file_name))
with open(file_name, 'w') as f:
f.writelines(sentences)
def get_docs_stats(feature_group,feature_distribution):
tsv = None
arff = None
idxlst = ''
#logging.root.setLevel(logging.ERROR)
file_path = settings.STY_FILE_PATH
documents = []
#for sty_file in []:
for sty_file in settings.STY_FILES[14:]:
#for sty_file in settings.STY_FILES:
#for sty_file in ['03 - Bukhtan Bukhtanovich.sty']:
try:
0/0
doc = voz.create_document_from_jsonpickle_file('/Users/josepvalls/temp/voz2/'+sty_file+'.json')
logger.info("Loading JSON %s" % sty_file)
except:
logger.info("Processing %s" % sty_file)
quoted_speech_file = sty_file.split()[0] + "/sentences.csv"
quoted_speech_file = "all_sentences.tsv"
doc = styhelper.create_document_from_sty_file(file_path+sty_file)
if DO_REMOVE_DIALOG:
quotedspeechhelper.annotate_sentences(doc, file_path + quoted_speech_file, format='tsv', single_sentences_file_story_id=doc.id)
quotedspeechhelper.clean_quoted_speech_from_document(doc)
doc.serialize_to_file(TEMP_CACHE_PATH+sty_file+'.json',use_deep_copy=True)
# print util.string_as_print(doc.id,doc.properties.get('afanasev_new',doc.id),doc.properties.get('afanasev_old',doc.id), doc.narrative.format_summary())
documents.append(doc)
if False and not DO_USE_OLD_AUTO_DATA_INSTEAD_OF_STY_GT:
for document_id in [1001,1002,1003,1004,2001]:
#for document_id in [1004]:
documents.append(oldannotationhelper.load_old_annotations_into_document(document_id))
for doc in documents:
import narrativehelper
narrativehelper.VERB_FEATURES = feature_group
narrativehelper.DO_COMPUTE_ROLE_DISTRIBUTION = feature_distribution
narrativehelper.DO_USE_OLD_AUTO_DATA_INSTEAD_OF_STY_GT = DO_USE_OLD_AUTO_DATA_INSTEAD_OF_STY_GT
assert isinstance(doc,voz.Document)
doc.narrative.filter_non_actual_default = DO_FILTER_NONACTUAL
doc.narrative.compute_features()
print doc.id,"Narrative: ",doc.narrative.format(options={'one_liner':True,'use_function_group':True})
continue
print sum([i.tokens_count for i in doc.narrative.function_list])
if DO_WRITE_FILES:
for _ in doc.narrative.functions():
idxlst += "%d\n" % doc.id
if not tsv:
tsv = doc.narrative.format_tsv()
arff = doc.narrative.format_arff()
idxlst
else:
tsv += doc.narrative.format_tsv(False)
arff += doc.narrative.format_arff(False)
if DO_PRINT_TO_SCREEN:
#print doc.id
for function in doc.narrative.functions():
print doc.id,function.get_feature_vector()
if DO_WRITE_FILES:
open('tool_corpus_functions_summary/story_indices%s%s.txt' % (('_filtered' if DO_FILTER_NONACTUAL else ''),('_nodiag' if DO_REMOVE_DIALOG else '')),'w').write(idxlst)
open('tool_corpus_functions_summary/tool_corpus_functions_summary_%d_%s%s%s%s.tsv' % (feature_group,'dist' if feature_distribution else 'abs','_filtered' if DO_FILTER_NONACTUAL else '','_auto' if DO_USE_OLD_AUTO_DATA_INSTEAD_OF_STY_GT else '','_nodiag' if DO_REMOVE_DIALOG else ''), 'w').write(tsv)
open('tool_corpus_functions_summary/tool_corpus_functions_summary_%d_%s%s%s%s.arff' % (feature_group,'dist' if feature_distribution else 'abs','_filtered' if DO_FILTER_NONACTUAL else '','_auto' if DO_USE_OLD_AUTO_DATA_INSTEAD_OF_STY_GT else '','_nodiag' if DO_REMOVE_DIALOG else ''), 'w').write(arff)
def get_stats_docs_verbs():
docs = []
for sty_file in settings.STY_FILES:
doc = styhelper.create_document_from_sty_file(settings.STY_FILE_PATH + sty_file)
quotedspeechhelper.annotate_sentences(doc, settings.STORY_ALL_SENTENCES, format='tsv',
single_sentences_file_story_id=doc.id)
docs.append(doc)
print sum([len(i.get_all_verbs()) for i in docs])
print sum([sum([len([k for k in j._objects if k and k.is_independent]) + len(
[k for k in j._subjects if k and k.is_independent]) for j in i.get_all_verbs()]) for i in docs])
def generate_filtered_entity_file():
logging.basicConfig(level=logging.DEBUG)
file_path = settings.STY_FILE_PATH
mentions = []
for sty_file in settings.STY_FILES[2:3]:
logger.info("Processing %s" % sty_file)
quoted_speech_file = sty_file.split()[0] + "/sentences.csv"
doc = styhelper.create_document_from_sty_file(file_path + sty_file)
quotedspeechhelper.annotate_sentences(doc,
file_path + quoted_speech_file)
for sentence in doc.sentences:
assert (isinstance(sentence, voz.Sentence))
if sentence.annotations.is_normal():
for mention in sentence.mentions:
mentions.append(mention.get_text().lower() + '\n')
file_name = '/Users/josepvalls/voz2/stories/finlayson-entities.txt'
logger.info("Writing %d mentions to %s" % (len(mentions), file_name))
with open(file_name, 'w') as f:
f.writelines(mentions)
def main_print_stats():
len_quotes = 0
len_sentences = 0
len_verbs = 0
len_mentions = 0
len_pp = 0
len_pn = 0
len_tokens = 0
len_tokens_in_quotes = 0
for story_file in settings.STY_FILES:
print story_file
doc = styhelper.create_document_from_sty_file(settings.STY_FILE_PATH +
story_file)
#styhelper.fix_sty_annotations(doc)
quotedspeechhelper.annotate_sentences(
doc,
settings.STORY_ALL_SENTENCES,
single_sentences_file_story_id=doc.id)
output_tuple = tokenize_document(doc)
output, quotes, mentions, verbs = output_tuple
print tokenized_string_to_string(output, 1)
len_quotes += len(quotes)
len_verbs += len(verbs)
len_mentions += len(mentions)
len_sentences += len(doc.sentences)
len_pp += len(
[i for i in mentions if [j for j in i.tokens if j.pos == 'PRP']])
len_pn += len(
[i for i in mentions if [j for j in i.tokens if j.pos == 'NNP']])
len_tokens += len(doc.get_text())
len_tokens_in_quotes += sum([q.offset_end - q.offset for q in quotes])
print 'TOTAL NUM QUOTES\t', len_quotes
print 'TOTAL NUM SENT\t', len_sentences
print 'TOTAL NUM VERBS\t', len_verbs
print 'TOTAL NUM MENT\t', len_mentions
print 'TOTAL NUM PP\t', len_pp
print 'TOTAL NUM PN\t', len_pn
print 'TOTAL NUM chars\t', len_tokens
print 'TOTAL NUM chars in quotes\t', len_tokens_in_quotes
def create_document_using_stanford_from_filtered_sty_file(sty_file):
import styhelper, quotedspeechhelper, entitymanager
stats_not_found = 0
stats_ambiguous = 0
stats_match_ok = 0
logger.info("Processing %s" % sty_file)
doc = styhelper.create_document_from_sty_file(sty_file)
quotedspeechhelper.annotate_sentences(
doc,
settings.STORY_ALL_SENTENCES,
format='tsv',
single_sentences_file_story_id=doc.id)
text = "\n".join([
sentence.get_text() for sentence in doc.sentences
if sentence.annotations.is_normal()
])
doc_new = create_document_from_raw_text(text, {'story_id': doc.id + 1000})
assert len([
sentence for sentence in doc.sentences
if sentence.annotations.is_normal()
]) == len(
doc_new.sentences
), "Sentence length mismatch between annotated and processed document"
fixed_annotation_file = settings.STORY_ANNOTATION_FIXES + '%d.tsv' % doc_new.id
if not os.path.isfile(fixed_annotation_file):
# Dump data for fixing
f_fixes = open(fixed_annotation_file, 'w')
for sentence in [
sentence for sentence in doc.sentences
if sentence.annotations.is_normal()
]:
mentions_check = [
i for i in sentence.mentions
if len([j for j in i.tokens if j.pos != 'DT']) > 1
]
mentions_check = sorted(mentions_check,
key=lambda i:
(len(i.child_mentions) * 100 - i.id),
reverse=True)
while mentions_check:
mention = mentions_check.pop(0)
assert isinstance(mention, entitymanager.Mention)
f_data = mention.get_text() + "\t" + str(
mention.get_taxonomy(
entitymanager.TaxonomyContainer.TAXONOMY_ENTITY_TYPES)
) + ' ' + str(
mention.get_taxonomy(
entitymanager.TaxonomyContainer.
TAXONOMY_CHARACTER_6ROLES)) + ' ' + str(
mention.get_coref_group_id())
f_data = "\t%d\t%d\t%s\n" % (doc_new.id, mention.id, f_data)
f_fixes.write(f_data)
for mention_ in mention.child_mentions:
f_data = mention_.get_text() + "\t" + str(
mention_.get_taxonomy(
entitymanager.TaxonomyContainer.
TAXONOMY_ENTITY_TYPES)) + ' ' + str(
mention_.get_taxonomy(
entitymanager.TaxonomyContainer.
TAXONOMY_CHARACTER_6ROLES)) + str(
mention.get_coref_group_id())
f_data = "\t%d\t%d\t - %s\n" % (doc_new.id, mention_.id,
f_data)
f_fixes.write(f_data)
try:
mentions_check.remove(mention_)
except:
pass
f_fixes.close()
# Annotate
fixed_annotation_file_extra = settings.STORY_ANNOTATION_FIXES + '%d-extra.tsv' % doc_new.id
if not os.path.isfile(fixed_annotation_file_extra):
f_fixes = open(fixed_annotation_file_extra, 'w')
else:
f_fixes = None
for sentence_ref, sentence in zip([
sentence
for sentence in doc.sentences if sentence.annotations.is_normal()
], doc_new.sentences):
assert isinstance(sentence, voz.Sentence)
for mention in sentence.mentions:
if not mention.is_independent: continue
assert isinstance(mention, entitymanager.Mention)
tokens_ref = [sentence_ref.tokens[i.idx] for i in mention.tokens]
mentions_ref = set(
filter(None, [
sentence_ref._parent_document.get_mention_by_token_id(i.id)
for i in tokens_ref
]))
if not mentions_ref:
logger.warning("UNABLE TO FIND ANNOTATION FOR MENTION %s" %
mention.get_text())
if f_fixes:
f_fixes.write(
"%d\tMISS\t%s\t%s\n" %
(mention.id, mention.get_text(), str(mention)))
stats_not_found += 1
continue
elif not len(mentions_ref) == 1:
logger.warning("AMBIGUOUS ANNOTATION FOR MENTION")
stats_ambiguous += 1
mentions_ref = sorted(mentions_ref,
key=lambda i: len(i.tokens))
for i in mentions_ref:
if mention_ref.get_taxonomy(
entitymanager.TaxonomyContainer.
TAXONOMY_CHARACTER_6ROLES):
mention_ref = i
break
if f_fixes:
f_fixes.write("%d\tAMBG\t%s\t%s\t%s\n" %
(mention.id, mention.get_text(),
[str(i)
for i in mentions_ref], mention_ref))
else:
mention_ref = mentions_ref.pop()
stats_match_ok += 1
if len(
mention_ref.get_taxonomy(entitymanager.TaxonomyContainer.
TAXONOMY_ENTITY_TYPES)) > 1:
logger.info(
util.string_as_print(
"POTENTIALLY IGNORE", mention_ref,
mention_ref.get_taxonomy(
entitymanager.TaxonomyContainer.
TAXONOMY_ENTITY_TYPES)))
mention.annotations.split_ignore = True
mention.annotations.coref = mention_ref.get_coref_group_id()
mention.annotations.type = \
(mention_ref.get_taxonomy(entitymanager.TaxonomyContainer.TAXONOMY_ENTITY_TYPES) or ['NA'])[0]
mention.annotations.role = \
(mention_ref.get_taxonomy(entitymanager.TaxonomyContainer.TAXONOMY_CHARACTER_6ROLES) or ['NA'])[0]
sentence.annotations.verbs = sentence_ref.verbs
if f_fixes:
f_fixes.close()
#print stats_not_found, stats_ambiguous, stats_match_ok
return doc_new
def get_verbs():
logging.root.setLevel(logging.ERROR)
file_path = settings.STY_FILE_PATH
verbs = []
frames = []
functions = collections.defaultdict(list)
import verbmanager
mapper = verbmanager.VerbMapper(verbmanager.VerbMapper.MODE_FRAMENET_TEXT)
for sty_file in settings.STY_FILES:
try:
0 / 0
doc = voz.create_document_from_jsonpickle_file(
'/Users/josepvalls/temp/voz2/' + sty_file + '.json')
logger.info("Loading JSON %s" % sty_file)
except:
logger.info("Processing %s" % sty_file)
quoted_speech_file = sty_file.split()[0] + "/sentences.csv"
doc = styhelper.create_document_from_sty_file(file_path + sty_file)
assert isinstance(doc, voz.Document)
if DO_REMOVE_DIALOG:
quotedspeechhelper.annotate_sentences(
doc, file_path + quoted_speech_file)
quotedspeechhelper.clean_quoted_speech_from_document(doc)
doc.serialize_to_file('/Users/josepvalls/temp/voz2/' + sty_file +
'.json',
use_deep_copy=True)
#print len(doc.get_all_tokens())
logger.info(
util.string_as_print(doc.id,
doc.properties.get('afanasev_new', doc.id),
doc.properties.get('afanasev_old', doc.id),
doc.narrative.format_summary()))
assert isinstance(doc, voz.Document)
doc.narrative.compute_features()
print sum([
f.tokens_count
for f in doc.narrative.functions(filter_non_actual=False)
])
continue
for f in doc.narrative.functions:
assert isinstance(f, voz.narrativehelper.NarrativeFunction)
#functions[f.function_group].extend([i.token.lemma for i in f._verbs])
functions[f.function_group].extend([
mapper.map(i.token.lemma, fallback=False)
for i in doc.get_all_verbs()
])
verbs.extend([i.token.text for i in doc.get_all_verbs()])
#frames.update([i.frame for i in doc.get_all_verbs()])
#frames.extend(filter(None,[mapper.map(i.token.lemma,fallback=False) for i in doc.get_all_verbs()]))
frames.extend([
mapper.map(i.token.lemma, fallback=False)
for i in doc.get_all_verbs()
])
#break
sys.exit()
roots = util.flatten(
util.flatten([[i.root_hypernyms() for i in wn.synsets(verb, 'v')]
for verb in verbs]))
print len(verbs), len(set(verbs))
print len(frames), len(set(frames))
print len(roots)
print collections.Counter(roots).most_common()
print collections.Counter(frames).most_common()
print collections.Counter(verbs).most_common()
pprint.pprint(functions)
vozbase.serialize_to_file([verbs, frames, functions],
'/Users/josepvalls/temp/voz2/verbs.json', False,
False)
mapper.save_cache()
def make_coreferences():
documents = []
file_path = settings.STY_FILE_PATH
vars_names = ['p1','r1','f1','p0','r0','f0','length','length**2','count1','count0','mentions_characters','char_uniq','coref_groups','c/gr','gr/c','eval']
num_vars = len(vars_names)
matrices_to_compute = ['OLD_STANFORD_COREF','OLD_NAME_COREF']+['OLD_RESTRICTION','OLD_TYPE']+['OLD_IDX']
matrices_to_merge = ['OLD_STANFORD_COREF','OLD_NAME_COREF']+['OLD_RESTRICTION','OLD_TYPE']
# OLD_ROLE_PRED1
# OLD_ROLE_GT
#matrices_to_print = matrices_to_compute+["AGGREGATED"]
matrices_to_print = ["AGGREGATED"]
cumulative = dict([(i,[0.0]*num_vars) for i in matrices_to_compute+["AGGREGATED"]])
for sty_file in settings.STY_FILES:
logger.info("Processing %s" % sty_file)
quoted_speech_file = sty_file.split()[0]+"/sentences.csv"
doc = styhelper.create_document_from_sty_file(file_path+sty_file)
assert isinstance(doc,voz.Document)
quotedspeechhelper.annotate_sentences(doc, file_path + quoted_speech_file)
quotedspeechhelper.clean_quoted_speech_from_document(doc)
doc.coreference_aux[voz.entitymanager.TaggableContainer.TAG_CHARACTER_SYMBOL] = doc.coreference
mentions = doc.get_all_mentions()
mentions = [i for i in mentions if i.is_independent]
mentions = [i for i in mentions if 'CH' in i.get_taxonomy(voz.entitymanager.TaxonomyContainer.TAXONOMY_NONCHARACTER)]
'''# create stanford, name, roles coref
for coref_key in matrices_to_eval:
coref_ids = sorted([i for i in set(util.flatten([mention.get_tag(coref_key) for mention in mentions]))])
print "mentions, coref_ids",len(mentions),len(coref_ids),coref_ids
doc.coreference_aux[coref_key] = voz.entitymanager.Coreference(doc)
for coref_id in coref_ids:
mentions_coref = [i for i in mentions if coref_id in i.get_tag(coref_key)]
doc.coreference_aux[coref_key].create_coref_group_and_entity_from_mentions(doc.get_symbol_id(coref_id,'COREF_SYMBOL'),coref_id,mentions_coref)
# eval coref
print voz.Document.format_stats(doc.get_stats())'''
# eval the individual matrices and compute their table for aggregation later
tables_to_merge = []
table_gt_temp = None
for coref_key in matrices_to_compute:
print coref_key
table,individual = voz.entitymanager.Coreference.eval_prf(coref_key,mentions)
if table_gt_temp is None:
table_gt_temp = table
if coref_key in matrices_to_merge:
tables_to_merge.append((table,individual))
for i in xrange(num_vars):
cumulative[coref_key][i]+=individual[i]
# aggregate the tables and evaluate aggregation
coref_key = "AGGREGATED"
merge_matrices(mentions,tables_to_merge,table_gt_temp)
table,individual = voz.entitymanager.Coreference.eval_prf(coref_key,mentions)
for i in xrange(num_vars):
cumulative[coref_key][i]+=individual[i]
#break # sty_file
for j in matrices_to_print:
for i in xrange(num_vars):
cumulative[j][i]=cumulative[j][i]/cumulative[j][7]
print 'CUMMULATIVE OVER STORIES'
for j in matrices_to_print:
print j
for i in xrange(num_vars):
print "%s\t%f" % (vars_names[i],cumulative[j][i])
for i in xrange(num_vars-3,num_vars):
print "%s\t%f" % (vars_names[i],cumulative[j][i]/15.0)
avg = 1.0 * (cumulative[j][2]*cumulative[j][8]+cumulative[j][3]*cumulative[j][9])/(cumulative[j][8]+cumulative[j][9])
print "error\t%f\t%f" % (avg,1-avg)
