chosen_edge = chosen_adj_list[chosen_edge]
edge_number = chosen_edge[0]
edges[i] = edge_number
other_vertex = chosen_edge[1]
picked[edge_number] = True
sample_counts[chosen_vertex] -= 1
sample_counts[other_vertex] -= 1
seen[other_vertex] = True
return edges
if "NegativeSampleRate" in general_settings:
ns = auxilliaries.NegativeSampler(
int(general_settings["NegativeSampleRate"]),
general_settings["EntityCount"])
ns.set_known_positives(train_triplets)
def t_func(x): # horrible hack!!!
arr = np.array(x)
if not encoder.needs_graph():
return ns.transform(arr)
else:
if "GraphBatchSize" in general_settings:
graph_batch_size = int(general_settings["GraphBatchSize"])
"""
n = np.zeros(100)
for i in range(100):
if i % 20 == 0:
print(i)
chosen_edge = chosen_adj_list[chosen_edge]
edge_number = chosen_edge[0]
edges[i] = edge_number
other_vertex = chosen_edge[1]
picked[edge_number] = True
sample_counts[chosen_vertex] -= 1
sample_counts[other_vertex] -= 1
seen[other_vertex] = True
return edges
if 'NegativeSampleRate' in general_settings:
ns = auxilliaries.NegativeSampler(
int(general_settings['NegativeSampleRate']),
general_settings['EntityCount'])
ns.set_known_positives(train_triplets)
def t_func(x): #horrible hack!!!
arr = np.array(x)
if not encoder.needs_graph():
return ns.transform(arr)
else:
if 'GraphBatchSize' in general_settings:
graph_batch_size = int(general_settings['GraphBatchSize'])
'''
n = np.zeros(100)
for i in range(100):
if i % 20 == 0:
print(i)
np.arange(chosen_adj_list.shape[0]))
chosen_edge = chosen_adj_list[chosen_edge]
edge_number = chosen_edge[0]
edges[i] = edge_number
other_vertex = chosen_edge[1]
picked[edge_number] = True
sample_counts[chosen_vertex] -= 1
sample_counts[other_vertex] -= 1
seen[other_vertex] = True
return edges
if 'NegativeSampleRate' in general_settings:
ns = auxilliaries.NegativeSampler(
float(general_settings['NegativeSampleRate']),
general_settings['EntityCount'], entities.values())
ns.set_positives(train_triplets)
def t_func(x):
arr = np.array(x)
if not encoder.needs_graph():
return ns.transform(arr)
else:
graph_batch_size = int(general_settings['GraphBatchSize'])
graph_batch_ids = sample_edge_neighborhood(
arr, graph_batch_size)
graph_batch = train_triplets[graph_batch_ids]
# Apply dropouts:
graph_percentage = float(general_settings['GraphSplitSize'])