/
batching_bce.py
67 lines (60 loc) · 3.11 KB
/
batching_bce.py
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import tensorflow as tf
import numpy as np
def replace_val(n_values, last_idx, role_val, arity, new_facts_indexes, new_facts_values, whole_train_facts):
"""
Replace values randomly to get negative samples
"""
rmd_dict = role_val
for cur_idx in range(last_idx):
role_ind = (np.random.randint(np.iinfo(np.int32).max) % arity) * 2
tmp_role = new_facts_indexes[last_idx + cur_idx, role_ind]
tmp_len = len(rmd_dict[tmp_role])
rdm_w = np.random.randint(0, tmp_len) # [low,high)
# Sample a random value
times = 1
tmp_array = new_facts_indexes[last_idx + cur_idx]
tmp_array[role_ind+1] = rmd_dict[tmp_role][rdm_w]
while (tuple(tmp_array) in whole_train_facts):
if (tmp_len == 1) or (times > 2*tmp_len) or (times > 100):
tmp_array[role_ind+1] = np.random.randint(0, n_values)
else:
rdm_w = np.random.randint(0, tmp_len)
tmp_array[role_ind+1] = rmd_dict[tmp_role][rdm_w]
times = times + 1
new_facts_indexes[last_idx + cur_idx, role_ind+1] = tmp_array[role_ind+1]
new_facts_values[last_idx + cur_idx] = [0]
def replace_role(n_roles, last_idx, arity, new_facts_indexes, new_facts_values, whole_train_facts):
"""
Replace roles randomly to get negative samples
"""
rdm_ws = np.random.randint(0, n_roles, last_idx)
for cur_idx in range(last_idx):
role_ind = (np.random.randint(np.iinfo(np.int32).max) % arity) * 2
# Sample a random role
tmp_array = new_facts_indexes[last_idx + cur_idx]
tmp_array[role_ind] = rdm_ws[cur_idx]
while (tuple(tmp_array) in whole_train_facts):
tmp_array[role_ind] = np.random.randint(0, n_roles)
new_facts_indexes[last_idx + cur_idx, role_ind] = tmp_array[role_ind]
new_facts_values[last_idx + cur_idx] = [0]
def Batch_Loader(train_batch_indexes, train_batch_values, values_indexes, roles_indexes, role_val, batch_size, arity, whole_train_facts):
new_facts_indexes = np.empty((batch_size*2, 2*arity)).astype(np.int32)
new_facts_values = np.empty((batch_size*2, 1)).astype(np.float32)
idxs = np.random.randint(0, len(train_batch_values), batch_size)
new_facts_indexes[:batch_size, :] = train_batch_indexes[idxs, :]
new_facts_values[:batch_size] = train_batch_values[idxs, :]
last_idx = batch_size
# Copy everyting in advance
new_facts_indexes[last_idx:(last_idx*2), :] = np.tile(
new_facts_indexes[:last_idx, :], (1, 1))
new_facts_values[last_idx:(last_idx*2)] = np.tile(
new_facts_values[:last_idx], (1, 1))
n_values = len(values_indexes)
n_roles = len(roles_indexes)
val_role = np.random.randint(np.iinfo(np.int32).max) % (n_values+n_roles)
if val_role < n_values: # 0~(n_values-1)
replace_val(n_values, last_idx, role_val, arity, new_facts_indexes, new_facts_values, whole_train_facts)
else:
replace_role(n_roles, last_idx, arity, new_facts_indexes, new_facts_values, whole_train_facts)
last_idx += batch_size
return new_facts_indexes[:last_idx, :], new_facts_values[:last_idx]