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util.py
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/
util.py
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import datetime
import os
import pickle
#from tensorflow.keras import backend as K
import numpy as np
import time
import bz2
import re
triple = 3
import datetime
import logging
import os
import time
def create_experiment_folder(folder_name='Experiments'):
directory = os.getcwd() + '/' + folder_name + '/'
folder_name = str(datetime.datetime.now())
path_of_folder = directory + folder_name
os.makedirs(path_of_folder)
return path_of_folder, path_of_folder[:path_of_folder.rfind('/')]
def create_logger(*, name, p):
logger = logging.getLogger(name)
logger.setLevel(logging.INFO)
# create file handler which logs even debug messages
fh = logging.FileHandler(p + '/info.log')
fh.setLevel(logging.INFO)
# create console handler with a higher log level
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
# create formatter and add it to the handlers
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
ch.setFormatter(formatter)
fh.setFormatter(formatter)
# add the handlers to logger
logger.addHandler(ch)
logger.addHandler(fh)
return logger
def performance_debugger(func_name):
def function_name_decoratir(func):
def debug(*args, **kwargs):
long_string = ''
starT = time.time()
print('\n\n######', func_name, ' starts ######')
r = func(*args, **kwargs)
print(func_name, ' took ', time.time() - starT, ' seconds\n')
long_string += str(func_name) + ' took:' + str(time.time() - starT) + ' seconds'
return r
return debug
return function_name_decoratir
def pairwise_iteration(it):
it = iter(it)
while True:
yield next(it), next(it)
def get_path_knowledge_graphs(path: str):
"""
:param path: str represents path of a KB or path of folder containg KBs
:return:
"""
KGs = list()
if os.path.isfile(path):
KGs.append(path)
else:
for root, dir, files in os.walk(path):
for file in files:
print(file)
if '.nq' in file or '.nt' in file or 'ttl' in file:
KGs.append(path + '/' + file)
if len(KGs) == 0:
print(path + ' is not a path for a file or a folder containing any .nq or .nt formatted files')
exit(1)
return KGs
def file_type(f_name):
if f_name[-4:] == '.bz2':
reader = bz2.open(f_name, "rt")
return reader
return open(f_name, "r")
def create_experiment_folder():
directory = os.getcwd() + '/Experiments/'
folder_name = str(datetime.datetime.now())
path_of_folder = directory + folder_name
os.makedirs(path_of_folder)
return path_of_folder, path_of_folder[:path_of_folder.rfind('/')]
def serializer(*, object_: object, path: str, serialized_name: str):
with open(path + '/' + serialized_name + ".p", "wb") as f:
pickle.dump(object_, f)
f.close()
def deserializer(*, path: str, serialized_name: str):
with open(path + "/" + serialized_name + ".p", "rb") as f:
obj_ = pickle.load(f)
f.close()
return obj_
def generator_of_reader(bound, knowledge_graphs, rdf_decomposer):
for f_name in knowledge_graphs:
reader = file_type(f_name)
total_sentence = 0
for sentence in reader:
# Ignore Literals
if '"' in sentence or "'" in sentence or '# started' in sentence:
continue
if total_sentence == bound: break
total_sentence += 1
try:
s, p, o, flag = rdf_decomposer(sentence)
# <..> <..> <..>
if flag != triple:
print(sentence, '+', flag)
print('exitting')
exit(1)
continue
except ValueError:
print('value error')
exit(1)
yield s, p, o
reader.close()
@performance_debugger('Training')
def learn(model, storage_path, x, y, batch_size=10000, epochs=1):
history = model.fit(x, y, batch_size=batch_size, epochs=epochs, use_multiprocessing=True)
return model,history
def recall_m(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def precision_m(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
def f1_m(y_true, y_pred):
precision = precision_m(y_true, y_pred)
recall = recall_m(y_true, y_pred)
return 2 * ((precision * recall) / (precision + recall + K.epsilon()))
def decompose_rdf(sentence):
flag = 0
#components = re.findall('<(.+?)>', sentence)
components = sentence.split() # re.findall('<(.+?)>', sentence)
if len(components) == 2:
s, p = components
remaining_sentence = sentence[sentence.index(p) + len(p) + 2:]
literal = remaining_sentence[:-1]
o = literal
flag = 2
elif len(components) == 4:
del components[-1]
s, p, o = components
flag = 4
elif len(components) == 3:
s, p, o = components
flag = 3
elif len(components) > 4:
s = components[0]
p = components[1]
remaining_sentence = sentence[sentence.index(p) + len(p) + 2:]
literal = remaining_sentence[:remaining_sentence.index(' <http://')]
o = literal
else:
## This means that literal contained in RDF triple contains < > symbol
raise ValueError()
o = re.sub("\s+", "", o)
s = re.sub("\s+", "", s)
p = re.sub("\s+", "", p)
if flag != 3:
print(components)
print(len(components))
print('here')
exit(1)
return s, p, o, flag
def construct_subject_object_inverted_index(path, path_to_deseralize):
s_o_inverted_index = dict()
predicate_mapper = dict()
vocab = dict()
num_of_rdf = 0
num_of_invaid_rdf_triples = 0
# Construct s_o_inverted_index
with open(path, 'r') as reader:
for sentence in reader:
num_of_rdf += 1
try:
# components = re.findall('<(.+?)>', sentence)
# s, p, o = components[0], components[1], components[2]
s, p, o = sentence.split() # components[0], components[1], components[2]
except:
num_of_invaid_rdf_triples += 1
continue
# mapping from string to vocabulary
vocab.setdefault(s, len(vocab))
# vocab.setdefault(p, len(vocab))
vocab.setdefault(o, len(vocab))
predicate_mapper.setdefault(p, len(predicate_mapper))
s_o_inverted_index.setdefault((vocab[s], vocab[o]), []).append(predicate_mapper[p])
print('Number of RDF triples processed:', num_of_rdf)
print('Number of invalid RDF triples:', num_of_invaid_rdf_triples)
print('Number of entities: ', len(vocab))
print('Number of predicates: ', len(predicate_mapper))
num_of_entities = len(vocab)
serializer(object_=vocab, path=path_to_deseralize, serialized_name='vocabulary')
del vocab
return s_o_inverted_index, num_of_entities, predicate_mapper
def construct_dataset_for_relation_prediction(embeddings, kg_path, storage_path):
s_o_inverted_index, num_of_resources, predicate_mapper = construct_subject_object_inverted_index(kg_path,
storage_path)
vocab = deserializer(path=storage_path, serialized_name='vocabulary')
inverse_vocab = np.array(list(vocab.keys()))
# inverse_predicate_mapper = np.array(list(predicate_mapper.keys()))
num_of_predicates = len(predicate_mapper) # correspond to number of labels as well
del vocab, predicate_mapper
X = []
y_row = [] # construct representation.
y_col = []
for ith, t in enumerate(s_o_inverted_index.items()):
i_s_o, i_predicates = t
i_s, i_o = i_s_o
for i_p in i_predicates: # predicates have own indexes
y_row.append(ith)
y_col.append(i_p)
emb_s=embeddings.loc[inverse_vocab[i_s]]
emb_o=embeddings.loc[inverse_vocab[i_o]]
x=emb_s.append(emb_o)
X.append(x)
del s_o_inverted_index
X = np.array(X)
y = csr_matrix((np.ones(len(y_row)), (np.array(y_row), np.array(y_col))),
shape=(len(X), num_of_predicates), dtype=np.uint16)
return X, y
def eval_h_at_N_sparse(path, model, inverse_output_mapper, logger, vocab=None, embeddings=None):
hit_at_1 = []
hit_at_3 = []
hit_at_5 = []
hit_at_10 = []
logger.info('Evaluation starts on {0}'.format(path))
num_of_entitiy_not_seen_training = 0
with open(path, 'r') as reader:
for index, sentence in enumerate(reader):
# components = re.findall('<(.+?)>', sentence)
# s, p, o = components[0], components[1], components[2]
s, p, o = sentence.split()
if vocab:
try:
i_s, i_o = vocab[s], vocab[o]
input_ = np.array([i_s, i_o]).reshape(1, 2)
except:
num_of_entitiy_not_seen_training += 1
continue
else:
try:
input_ = embeddings.loc[s].append(embeddings.loc[o]).to_numpy()
except:
num_of_entitiy_not_seen_training += 1
continue
input_ = input_.reshape(1, len(input_))
predictions = model.predict(input_)[0]
idx_of_tops_ = predictions.argsort()[::-1] # Total time complexity: |G^Testing| |Relations|
h1 = inverse_output_mapper[idx_of_tops_[0]]
h3 = inverse_output_mapper[idx_of_tops_[:2]]
h5 = inverse_output_mapper[idx_of_tops_[:4]]
h10 = inverse_output_mapper[idx_of_tops_[:9]]
hit_at_1.append(1 if p in h1 else 0)
hit_at_3.append(1 if p in h3 else 0)
hit_at_5.append(1 if p in h5 else 0)
hit_at_10.append(1 if p in h10 else 0)
if index % 500 == 0:
if index > 0:
logger.info('###')
logger.info('{0}th test triple: HIT@1 {1}'.format(index, stats.describe(hit_at_1)))
logger.info('{0}th test triple: HIT@3 {1}'.format(index, stats.describe(hit_at_3)))
logger.info('{0}th test triple: HIT@5 {1}'.format(index, stats.describe(hit_at_5)))
logger.info('{0}th test triple: HIT@10 {1}'.format(index, stats.describe(hit_at_10)))
logger.info('###')
logger.info('Number of triples in testing:{0}'.format(index + 1))
return stats.describe(hit_at_1), stats.describe(hit_at_3), stats.describe(hit_at_5), stats.describe(hit_at_10)