def test_text_vectorization():
mongo_dataset = MongoHC('hc', 're0')
data = [d for d in mongo_dataset.get_all(order_by='id_doc')]
text = [d['text'] for d in data[1:2]]
tfidf_vectorizer = TfidfVectorizer(max_df=1,
max_features=200000,
min_df=1,
stop_words='english',
strip_accents='unicode',
use_idf=True,
ngram_range=(1, 1),
norm='l2')
tfidf_matrix = tfidf_vectorizer.fit_transform(text)
print tfidf_vectorizer.get_feature_names()
print tfidf_matrix.data
indices = np.argsort(tfidf_vectorizer.idf_)[::-1]
print indices
features = tfidf_vectorizer.get_feature_names()
top_n = 5
top_features = [features[i] for i in indices[:top_n]]
print len(features)
print tfidf_matrix.shape
print top_features
def test(db, dataset):
mongo = MongoHC(db, dataset)
docs = mongo.get_element_by_id(1114)
docs = [docs]
for doc in docs[:1]:
logfun.info('#' * 80)
logfun.info('Scanning documents: %(id_doc)s' % doc)
logfun.info('#' * 80)
#try:
entitySet, annotationsSorted, response = getAnnotation(doc['text'])
'''doc['abstracts'] = []
for e in entitySet:
logfun.info('Extracting abstract for entity %s' % e)
abstract = get_abstract(e)
if abstract:
doc['abstracts'].append(abstract)
else:
logfun.warning('Abstract not found!')
logfun.info('-' * 80)
doc['entity_set'] = list(entitySet)'''
pp.pprint(response)
def test(db, dataset):
mongo = MongoHC(db, dataset)
docs = mongo.get_element_by_id(1114)
docs = [docs]
for doc in docs[:1]:
logfun.info('#' * 80)
logfun.info('Scanning documents: %(id_doc)s' % doc)
logfun.info('#' * 80)
#try:
entitySet,annotationsSorted,response = getAnnotation(doc['text'])
'''doc['abstracts'] = []
for e in entitySet:
logfun.info('Extracting abstract for entity %s' % e)
abstract = get_abstract(e)
if abstract:
doc['abstracts'].append(abstract)
else:
logfun.warning('Abstract not found!')
logfun.info('-' * 80)
doc['entity_set'] = list(entitySet)'''
pp.pprint(response)
def test_text_vectorization():
mongo_dataset = MongoHC("hc", "re0")
data = [d for d in mongo_dataset.get_all(order_by="id_doc")]
text = [d["text"] for d in data[1:2]]
tfidf_vectorizer = TfidfVectorizer(
max_df=1,
max_features=200000,
min_df=1,
stop_words="english",
strip_accents="unicode",
use_idf=True,
ngram_range=(1, 1),
norm="l2",
)
tfidf_matrix = tfidf_vectorizer.fit_transform(text)
print tfidf_vectorizer.get_feature_names()
print tfidf_matrix.data
indices = np.argsort(tfidf_vectorizer.idf_)[::-1]
print indices
features = tfidf_vectorizer.get_feature_names()
top_n = 5
top_features = [features[i] for i in indices[:top_n]]
print len(features)
print tfidf_matrix.shape
print top_features
def extract_entity(db, dataset):
mongo_from = MongoHC(db, dataset + '_for_alchemy')
mongo_to = MongoHC(db, dataset)
docs = mongo_from.get_all(order_by='id_doc')
docs = [doc for doc in docs]
for doc in docs[:]:
logfun.info('#' * 80)
logfun.info('Scanning documents: %(id_doc)s' % doc)
logfun.info('#' * 80)
try:
entitySet, annotationsSorted, response = getAnnotation(doc['text'])
doc['abstracts'] = []
doc['alchemy_response'] = response
for e in entitySet:
logfun.info('Extracting abstract for entity %s' % e)
abstract = get_abstract(e)
if abstract:
doc['abstracts'].append(abstract)
else:
logfun.warning('Abstract not found!')
logfun.info('-' * 80)
doc['entity_set'] = list(entitySet)
mongo_to.save_document(doc)
mongo_from.remove_document_by_id(doc['id_doc'])
except Exception, e:
logfun.error("Something awful happened!")
logfun.error(e)
logfun.error(sys.exc_info()[2])
def extract_abstract_dandelion(db, dataset):
mongo = MongoHC(db, dataset)
mongo_dbpedia = MongoHC(db, 'dbpedia')
docs = [doc for doc in mongo.get_all(order_by='id_doc')]
for doc in docs:
try:
entities = [
e['lod']['dbpedia'] for e in doc['dandelion']['annotations']
]
for e in entities:
if mongo_dbpedia.get_element_by_mongo_id(e):
logfun.info('Entities already in database')
continue
dbpedia = {}
logfun.info('Extracting abstract for entity %s' % e)
abstract = get_abstract(e)
if abstract:
dbpedia['_id'] = e
dbpedia['abstract'] = abstract
mongo_dbpedia.save_document(dbpedia)
else:
logfun.warning('Abstract not found!')
logfun.info('-' * 80)
except Exception, e:
logfun.error("Something awful happened!")
logfun.error(e)
logfun.error(sys.exc_info()[2])
def extract_alchemy(db, dataset):
mongo = MongoHC(db, dataset)
docs = [doc for doc in mongo.get_doc_with_no_key('alchemy_response')]
for doc in docs:
try:
entitySet,annotationsSorted,response = getAnnotation(doc['text'])
doc['alchemy_response'] = response
mongo.save_document(doc)
except Exception, e:
logfun.error("Something awful happened!")
logfun.error(e)
logfun.error(sys.exc_info()[2])
def extract_alchemy(db, dataset):
mongo = MongoHC(db, dataset)
docs = [doc for doc in mongo.get_doc_with_no_key('alchemy_response')]
for doc in docs:
try:
entitySet, annotationsSorted, response = getAnnotation(doc['text'])
doc['alchemy_response'] = response
mongo.save_document(doc)
except Exception, e:
logfun.error("Something awful happened!")
logfun.error(e)
logfun.error(sys.exc_info()[2])
def extract_dandelion(db, dataset):
mongo = MongoHC(db, dataset)
docs = [doc for doc in mongo.get_doc_with_no_key('dandelion',
order_by='id_doc')]
for doc in docs:
try:
dan = get_entities_from_dandelion(doc['text'])
logfun.info(dan['timestamp'])
doc['dandelion'] = dan
mongo.save_document(doc)
except Exception, e:
logfun.error(traceback.format_exc())
def test_fabio(db,
dataset,
gamma=0.5,
ranking_metric='pr',
lsa=False,
save=False):
mongo_result = MongoHC(db, 'test_fabio')
result = clf.cluster_fabio(db,
dataset,
gamma=gamma,
ranking_metric=ranking_metric,
with_lsa=lsa)
if save:
mongo_result.save_document(result)
pp.pprint(result)
def extract_dandelion(db, dataset):
mongo = MongoHC(db, dataset)
docs = [
doc
for doc in mongo.get_doc_with_no_key('dandelion', order_by='id_doc')
]
for doc in docs:
try:
dan = get_entities_from_dandelion(doc['text'])
logfun.info(dan['timestamp'])
doc['dandelion'] = dan
mongo.save_document(doc)
except Exception, e:
logfun.error(traceback.format_exc())
def extract_entity(db, dataset):
mongo_from = MongoHC(db, dataset + '_for_alchemy')
mongo_to = MongoHC(db, dataset)
docs = mongo_from.get_all(order_by='id_doc')
docs = [doc for doc in docs]
for doc in docs[:]:
logfun.info('#' * 80)
logfun.info('Scanning documents: %(id_doc)s' % doc)
logfun.info('#' * 80)
try:
entitySet,annotationsSorted,response = getAnnotation(doc['text'])
doc['abstracts'] = []
doc['alchemy_response'] = response
for e in entitySet:
logfun.info('Extracting abstract for entity %s' % e)
abstract = get_abstract(e)
if abstract:
doc['abstracts'].append(abstract)
else:
logfun.warning('Abstract not found!')
logfun.info('-' * 80)
doc['entity_set'] = list(entitySet)
mongo_to.save_document(doc)
mongo_from.remove_document_by_id(doc['id_doc'])
except Exception, e:
logfun.error("Something awful happened!")
logfun.error(e)
logfun.error(sys.exc_info()[2])
def entities_distribution(db, dataset):
mongo = MongoHC(db, dataset)
data = [doc for doc in mongo.get_all(order_by='id_doc')]
entities = set()
for d in data:
for e in d['alchemy_response']['entities']:
entities.add(e['text'])
entities_dict = {e: 0 for i, e in enumerate(entities)}
for d in data:
for e in d['alchemy_response']['entities']:
entities_dict[e['text']] += 1
return entities_dict, entities
def entities_distribution(db, dataset):
mongo = MongoHC(db, dataset)
data = [doc for doc in mongo.get_all(order_by='id_doc')]
entities = set()
for d in data:
for e in d['alchemy_response']['entities']:
entities.add(e['text'])
entities_dict = {e: 0 for i, e in enumerate(entities)}
for d in data:
for e in d['alchemy_response']['entities']:
entities_dict[e['text']] += 1
return entities_dict, entities
def extract_abstract(db, dataset):
mongo = MongoHC(db, dataset)
docs = [doc for doc in mongo.get_empty_abstract()]
for doc in docs:
try:
for e in doc['entity_set']:
logfun.info('Extracting abstract for entity %s' % e)
abstract = get_abstract(e)
if abstract:
doc['abstracts'].append(abstract)
else:
logfun.warning('Abstract not found!')
logfun.info('-' * 80)
mongo.save_document(doc)
except Exception, e:
logfun.error("Something awful happened!")
logfun.error(e)
logfun.error(sys.exc_info()[2])
def extract_abstract(db, dataset):
mongo = MongoHC(db, dataset)
docs = [doc for doc in mongo.get_empty_abstract()]
for doc in docs:
try:
for e in doc['entity_set']:
logfun.info('Extracting abstract for entity %s' % e)
abstract = get_abstract(e)
if abstract:
doc['abstracts'].append(abstract)
else:
logfun.warning('Abstract not found!')
logfun.info('-' * 80)
mongo.save_document(doc)
except Exception, e:
logfun.error("Something awful happened!")
logfun.error(e)
logfun.error(sys.exc_info()[2])
def extract_abstract_dandelion(db, dataset):
mongo = MongoHC(db, dataset)
mongo_dbpedia = MongoHC(db, 'dbpedia')
docs = [doc for doc in mongo.get_all(order_by='id_doc')]
for doc in docs:
try:
entities = [e['lod']['dbpedia'] for e in doc['dandelion']['annotations']]
for e in entities:
if mongo_dbpedia.get_element_by_mongo_id(e):
logfun.info('Entities already in database')
continue
dbpedia = {}
logfun.info('Extracting abstract for entity %s' % e)
abstract = get_abstract(e)
if abstract:
dbpedia['_id'] = e
dbpedia['abstract'] = abstract
mongo_dbpedia.save_document(dbpedia)
else:
logfun.warning('Abstract not found!')
logfun.info('-' * 80)
except Exception, e:
logfun.error("Something awful happened!")
logfun.error(e)
logfun.error(sys.exc_info()[2])
__author__ = 'biagio'
from mongo_hc import MongoHC
import classifier as clf
import pprint as pp
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from text_utils import TextUtils
import argparse
mongo = MongoHC('hc', 'test_new')
def first_test_re0():
for i, g in enumerate(np.arange(0.1, 1, 0.01)):
result = clf.cluster_alchemy('re1', gamma=g, filter=True)
pp.pprint(result)
result['n_attempt'] = i + 1
result['test'] = 'fourth'
mongo.save_document(result)
def test_without_entity():
result = clf.cluster_alchemy('re1', gamma=1)
pp.pprint(result)
result['test'] = 'baseline'
mongo.save_document(result)
def test_bow():
result = clf.scipy_algo('re0')
def test_fabio(db, dataset, gamma=0.5, ranking_metric="pr", lsa=False, save=False):
mongo_result = MongoHC(db, "test_fabio")
result = clf.cluster_fabio(db, dataset, gamma=gamma, ranking_metric=ranking_metric, with_lsa=lsa)
if save:
mongo_result.save_document(result)
pp.pprint(result)