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sample_links.py
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import numpy as np
import random
def sample_change_link(dataset,reduced_dataset,start_step,end_step):
np.random.seed(42)
random.seed(42)
positive_samples = []
negative_samples = []
for i in range(start_step-1,end_step):
graph=dataset[i]
cur_graph=reduced_dataset[i]
for e in cur_graph.edges():
e0_index = dataset.vertex2index[e[0]]
e1_index = dataset.vertex2index[e[1]]
positive_samples.append([i, e0_index, e1_index])
for _ in range(1):
for i, source_index, target_index in positive_samples:
graph = dataset[i]
source = dataset.vertices[source_index]
target = dataset.vertices[target_index]
while True:
if random.randrange(2) == 0: # replace source
new_source_index = random.randrange(len(dataset.vertices))
new_source = dataset.vertices[new_source_index]
if not graph.has_edge(new_source, target):
negative_samples.append([i, new_source_index, target_index])
break
else: # replace target
new_target_index = random.randrange(len(dataset.vertices))
new_target = dataset.vertices[new_target_index]
if not graph.has_edge(new_target, source):
negative_samples.append([i, source_index, new_target_index])
break
positive_samples = np.array(positive_samples)
negative_samples = np.array(negative_samples)
samples = np.concatenate((positive_samples, negative_samples))
positive_labels = np.ones(positive_samples.shape[0])
negative_labels = np.zeros(negative_samples.shape[0])
labels = np.concatenate((positive_labels, negative_labels))
return samples, labels
def sample_change_link_hard(dataset,reduced_dataset,start_step,end_step):
np.random.seed(42)
random.seed(42)
positive_samples = []
negative_samples = []
for i in range(start_step-1,end_step):
graph=dataset[i]
cur_graph=reduced_dataset[i]
for e in cur_graph.edges():
e0_index = dataset.vertex2index[e[0]]
e1_index = dataset.vertex2index[e[1]]
positive_samples.append([i, e0_index, e1_index])
for j in range(i,17):
next_reduced_graph=reduced_dataset[j+1]
for e in next_reduced_graph.edges():
e0_index = dataset.vertex2index[e[0]]
e1_index = dataset.vertex2index[e[1]]
if len(negative_samples)>=len(positive_samples):
break
else:
negative_samples.append([i, e0_index, e1_index])
while len(negative_samples)< len(positive_samples):
new_source_index = random.randrange(len(dataset.vertices))
new_target_index = random.randrange(len(dataset.vertices))
new_source = dataset.vertices[new_source_index]
new_target = dataset.vertices[new_target_index]
if new_source_index==new_target_index:
continue
if not graph.has_edge(new_target, new_source):
negative_samples.append([i, new_source_index, new_target_index])
positive_samples = np.array(positive_samples)
negative_samples = np.array(negative_samples)
samples = np.concatenate((positive_samples, negative_samples))
positive_labels = np.ones(positive_samples.shape[0])
negative_labels = np.zeros(negative_samples.shape[0])
labels = np.concatenate((positive_labels, negative_labels))
return samples, labels,len(samples)
def sample_all_link(dataset,start_step,end_step):
np.random.seed(42)
random.seed(42)
positive_samples = []
negative_samples = []
for i in range(start_step-1,end_step):
graph=dataset[i]
for e in graph.edges():
e0_index = dataset.vertex2index[e[0]]
e1_index = dataset.vertex2index[e[1]]
positive_samples.append([i, e0_index, e1_index])
for _ in range(1):
for i, source_index, target_index in positive_samples:
graph = dataset[i]
source = dataset.vertices[source_index]
target = dataset.vertices[target_index]
while True:
if random.randrange(2) == 0: # replace source
new_source_index = random.randrange(len(dataset.vertices))
new_source = dataset.vertices[new_source_index]
if not graph.has_edge(new_source, target):
negative_samples.append([i, new_source_index, target_index])
break
else: # replace target
new_target_index = random.randrange(len(dataset.vertices))
new_target = dataset.vertices[new_target_index]
if not graph.has_edge(new_target, source):
negative_samples.append([i, source_index, new_target_index])
break
#print("positive:",len(positive_samples))
#print("negative:",len(negative_samples))
positive_samples = np.array(positive_samples)
negative_samples = np.array(negative_samples)
samples = np.concatenate((positive_samples, negative_samples))
positive_labels = np.ones(positive_samples.shape[0])
negative_labels = np.zeros(negative_samples.shape[0])
labels = np.concatenate((positive_labels, negative_labels))
return samples, labels
def sample_all_link_hard(dataset,reduced_dataset,start_step,end_step):
np.random.seed(42)
random.seed(42)
positive_samples = []
negative_samples = []
for i in range(start_step-1,end_step):
graph=dataset[i]
for e in graph.edges():
e0_index = dataset.vertex2index[e[0]]
e1_index = dataset.vertex2index[e[1]]
positive_samples.append([i, e0_index, e1_index])
next_reduced_graph=reduced_dataset[i+1]
for e in next_reduced_graph.edges():
e0_index = dataset.vertex2index[e[0]]
e1_index = dataset.vertex2index[e[1]]
if len(negative_samples)>=len(positive_samples):
break
else:
negative_samples.append([i, e0_index, e1_index])
while len(negative_samples)< len(positive_samples):
new_source_index = random.randrange(len(dataset.vertices))
new_target_index = random.randrange(len(dataset.vertices))
new_source = dataset.vertices[new_source_index]
new_target = dataset.vertices[new_target_index]
if new_source_index==new_target_index:
continue
if not graph.has_edge(new_target, new_source):
negative_samples.append([i, new_source_index, new_target_index])
positive_samples = np.array(positive_samples)
negative_samples = np.array(negative_samples)
samples = np.concatenate((positive_samples, negative_samples))
positive_labels = np.ones(positive_samples.shape[0])
negative_labels = np.zeros(negative_samples.shape[0])
labels = np.concatenate((positive_labels, negative_labels))
return samples, labels