def train(train_data):
G = train_data[0]
features = train_data[1]
labels = train_data[2]
train_nodes = train_data[3]
test_nodes = train_data[4]
val_nodes = train_data[5]
num_classes = 2
if not features is None:
# pad with dummy zero vector
features = np.vstack([features, np.zeros((features.shape[1], ))])
placeholders = construct_placeholders(num_classes)
minibatch = NodeMinibatchIterator(G,
placeholders,
labels,
train_nodes,
test_nodes,
val_nodes,
num_classes,
batch_size=FLAGS.batch_size,
max_degree=FLAGS.max_degree)
adj_info_ph = tf.placeholder(tf.int32, shape=minibatch.adj.shape)
adj_info = tf.Variable(adj_info_ph, trainable=False, name="adj_info")
if FLAGS.model == 'graphsage_mean':
# Create model
sampler = UniformNeighborSampler(adj_info)
if FLAGS.samples_3 != 0:
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2),
SAGEInfo("node", sampler, FLAGS.samples_3, FLAGS.dim_2)
]
elif FLAGS.samples_2 != 0:
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
else:
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1)
]
model = SupervisedGraphsage(num_classes,
placeholders,
features,
adj_info,
minibatch.deg,
layer_infos,
model_size=FLAGS.model_size,
sigmoid_loss=FLAGS.sigmoid,
identity_dim=FLAGS.identity_dim,
logging=True)
config = tf.ConfigProto(log_device_placement=FLAGS.log_device_placement)
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
# Initialize session
sess = tf.Session(config=config)
merged = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(results_folder, sess.graph)
# Init variables
sess.run(tf.global_variables_initializer(),
feed_dict={adj_info_ph: minibatch.adj})
# Train model
total_steps = 0
avg_time = 0.0
epoch_val_costs = []
train_adj_info = tf.assign(adj_info, minibatch.adj)
val_adj_info = tf.assign(adj_info, minibatch.test_adj)
for epoch in range(FLAGS.epochs):
minibatch.shuffle()
iter = 0
print('Epoch: %04d' % (epoch + 1))
epoch_val_costs.append(0)
while not minibatch.end():
# Construct feed dictionary
feed_dict, labels, batch = minibatch.next_minibatch_feed_dict()
feed_dict.update({placeholders['dropout']: FLAGS.dropout})
t = time.time()
# Training step
outs = sess.run([merged, model.opt_op, model.loss, model.preds],
feed_dict=feed_dict)
train_cost = outs[2]
if iter % FLAGS.validate_iter == 0:
# Validation
sess.run(val_adj_info.op)
if FLAGS.validate_batch_size == -1:
val_cost, acc, prec, rec, f1_score, conf_mat = incremental_evaluate(
sess, model, minibatch, FLAGS.batch_size)
else:
val_cost, acc, prec, rec, f1_score, conf_mat, fpr, tpr, thresholds = evaluate(
sess, model, minibatch, FLAGS.validate_batch_size)
sess.run(train_adj_info.op)
epoch_val_costs[-1] += val_cost
if total_steps % FLAGS.print_every == 0:
summary_writer.add_summary(outs[0], total_steps)
# Print results
avg_time = (avg_time * total_steps + time.time() -
t) / (total_steps + 1)
if total_steps % FLAGS.print_every == 0:
train_acc, train_prec, train_rec, train_f1_score, train_conf_mat, fpr, tpr, thresholds = eval(
labels, outs[-1])
print("Iter:", '%04d' % iter, "train_loss=",
"{:.5f}".format(train_cost), "train_accuracy=",
"{:.5f}".format(train_acc), "train_precision=",
"{:.5f}".format(train_prec), "train_recall=",
"{:.5f}".format(train_rec), "train_f1_score=",
"{:.5f}".format(train_f1_score))
with open(results_folder + "/train_stats.txt", "a") as fp:
fp.write(
"Iter:{:d} loss={:.5f} acc={:.5f} prec={:.5f} rec={:.5f} f1={:.5f} tp={:d} fp={:d} fn={:d} tn={:d}\n"
.format(iter, train_cost, train_acc, train_prec,
train_rec, train_f1_score,
train_conf_mat[0][0], train_conf_mat[0][1],
train_conf_mat[1][0], train_conf_mat[1][1]))
iter += 1
total_steps += 1
if total_steps > int(FLAGS.max_total_steps):
break
if total_steps > int(FLAGS.max_total_steps):
break
print("Optimization Finished!")
sess.run(val_adj_info.op)
val_cost, val_acc, val_prec, val_rec, val_f1_score, val_conf_mat = incremental_evaluate(
sess, model, minibatch, FLAGS.batch_size)
print("Full validation stats:", "val_cost=", "{:.5f}".format(val_cost),
"val_acc=", "{:.5f}".format(val_acc), "val_prec=",
"{:.5f}".format(val_prec), "val_rec=", "{:.5f}".format(val_rec),
"val_f1_score=", "{:.5f}".format(val_f1_score), "val_conf_mat=",
val_conf_mat)
with open(results_folder + "/val_stats.txt", "a") as fp:
fp.write(
"loss={:.5f} acc={:.5f} prec={:.5f} rec={:.5f} f1={:.5f} tp={:d} fp={:d} fn={:d} tn={:d} time=={:s}\n"
.format(val_cost, val_acc, val_prec, val_rec, val_f1_score,
val_conf_mat[0][0], val_conf_mat[0][1], val_conf_mat[1][0],
val_conf_mat[1][1], current_time))
print("Writing test set stats to file")
test_cost, test_acc, test_prec, test_rec, test_f1_score, test_conf_mat = incremental_evaluate(
sess, model, minibatch, FLAGS.batch_size, test=True)
print("Full test stats:", "test_cost=", "{:.5f}".format(test_cost),
"test_acc=", "{:.5f}".format(test_acc), "test_prec=",
"{:.5f}".format(test_prec), "test_rec=", "{:.5f}".format(test_rec),
"test_f1_score=", "{:.5f}".format(test_f1_score), "test_conf_mat=",
test_conf_mat)
with open(results_folder + "/test_stats.txt", "a") as fp:
fp.write(
"loss={:.5f} acc={:.5f} prec={:.5f} rec={:.5f} f1={:.5f} tp={:d} fp={:d} fn={:d} tn={:d} time=={:s}\n"
.format(test_cost, test_acc, test_prec, test_rec, test_f1_score,
test_conf_mat[0][0], test_conf_mat[0][1],
test_conf_mat[1][0], test_conf_mat[1][1], current_time))
def train(train_data, test_data=None):
G = train_data[0]
features = train_data[1]
id_map = train_data[2]
class_map = train_data[4]
if isinstance(list(class_map.values())[0], list):
num_classes = len(list(class_map.values())[0])
else:
num_classes = len(set(class_map.values()))
if not features is None:
# pad with dummy zero vector
features = np.vstack([features, np.zeros((features.shape[1], ))])
context_pairs = train_data[3] if FLAGS.random_context else None
placeholders = construct_placeholders(num_classes)
minibatch = NodeMinibatchIterator(G,
id_map,
placeholders,
class_map,
num_classes,
batch_size=FLAGS.batch_size,
max_degree=FLAGS.max_degree,
context_pairs=context_pairs)
adj_info_ph = tf.placeholder(tf.int32, shape=minibatch.adj.shape)
adj_info = tf.Variable(adj_info_ph, trainable=False, name="adj_info")
if FLAGS.model == 'graphsage_mean':
# Create model
sampler = UniformNeighborSampler(adj_info)
if FLAGS.samples_3 != 0:
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2),
SAGEInfo("node", sampler, FLAGS.samples_3, FLAGS.dim_2)
]
elif FLAGS.samples_2 != 0:
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
else:
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1)
]
model = SupervisedGraphsage(num_classes,
placeholders,
features,
adj_info,
minibatch.deg,
layer_infos,
model_size=FLAGS.model_size,
sigmoid_loss=FLAGS.sigmoid,
identity_dim=FLAGS.identity_dim,
logging=True)
elif FLAGS.model == 'gcn':
# Create model
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, 2 * FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, 2 * FLAGS.dim_2)
]
model = SupervisedGraphsage(num_classes,
placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
aggregator_type="gcn",
model_size=FLAGS.model_size,
concat=False,
sigmoid_loss=FLAGS.sigmoid,
identity_dim=FLAGS.identity_dim,
logging=True)
elif FLAGS.model == 'graphsage_seq':
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
model = SupervisedGraphsage(num_classes,
placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
aggregator_type="seq",
model_size=FLAGS.model_size,
sigmoid_loss=FLAGS.sigmoid,
identity_dim=FLAGS.identity_dim,
logging=True)
elif FLAGS.model == 'graphsage_maxpool':
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
model = SupervisedGraphsage(num_classes,
placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
aggregator_type="maxpool",
model_size=FLAGS.model_size,
sigmoid_loss=FLAGS.sigmoid,
identity_dim=FLAGS.identity_dim,
logging=True)
elif FLAGS.model == 'graphsage_meanpool':
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
model = SupervisedGraphsage(num_classes,
placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
aggregator_type="meanpool",
model_size=FLAGS.model_size,
sigmoid_loss=FLAGS.sigmoid,
identity_dim=FLAGS.identity_dim,
logging=True)
else:
raise Exception('Error: model name unrecognized.')
config = tf.ConfigProto(log_device_placement=FLAGS.log_device_placement)
config.gpu_options.allow_growth = True
#config.gpu_options.per_process_gpu_memory_fraction = GPU_MEM_FRACTION
config.allow_soft_placement = True
# Initialize session
sess = tf.Session(config=config)
merged = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(log_dir(), sess.graph)
# Init variables
sess.run(tf.global_variables_initializer(),
feed_dict={adj_info_ph: minibatch.adj})
# Train model
total_steps = 0
avg_time = 0.0
epoch_val_costs = []
train_adj_info = tf.assign(adj_info, minibatch.adj)
val_adj_info = tf.assign(adj_info, minibatch.test_adj)
for epoch in range(FLAGS.epochs):
minibatch.shuffle()
iter = 0
print('Epoch: %04d' % (epoch + 1))
epoch_val_costs.append(0)
while not minibatch.end():
# Construct feed dictionary
feed_dict, labels = minibatch.next_minibatch_feed_dict()
feed_dict.update({placeholders['dropout']: FLAGS.dropout})
t = time.time()
# Training step
outs = sess.run([merged, model.opt_op, model.loss, model.preds],
feed_dict=feed_dict)
train_cost = outs[2]
if iter % FLAGS.validate_iter == 0:
# Validation
sess.run(val_adj_info.op)
if FLAGS.validate_batch_size == -1:
val_cost, val_f1_mic, val_f1_mac, duration = incremental_evaluate(
sess, model, minibatch, FLAGS.batch_size)
else:
val_cost, val_f1_mic, val_f1_mac, duration = evaluate(
sess, model, minibatch, FLAGS.validate_batch_size)
sess.run(train_adj_info.op)
epoch_val_costs[-1] += val_cost
if total_steps % FLAGS.print_every == 0:
summary_writer.add_summary(outs[0], total_steps)
# Print results
avg_time = (avg_time * total_steps + time.time() -
t) / (total_steps + 1)
if total_steps % FLAGS.print_every == 0:
train_f1_mic, train_f1_mac = calc_f1(labels, outs[-1])
print("Iter:", '%04d' % iter, "train_loss=",
"{:.5f}".format(train_cost), "train_f1_mic=",
"{:.5f}".format(train_f1_mic), "train_f1_mac=",
"{:.5f}".format(train_f1_mac), "val_loss=",
"{:.5f}".format(val_cost), "val_f1_mic=",
"{:.5f}".format(val_f1_mic), "val_f1_mac=",
"{:.5f}".format(val_f1_mac), "time=",
"{:.5f}".format(avg_time))
iter += 1
total_steps += 1
if total_steps > FLAGS.max_total_steps:
break
if total_steps > FLAGS.max_total_steps:
break
print("Optimization Finished!")
sess.run(val_adj_info.op)
val_cost, val_f1_mic, val_f1_mac, duration = incremental_evaluate(
sess, model, minibatch, FLAGS.batch_size)
print("Full validation stats:", "loss=", "{:.5f}".format(val_cost),
"f1_micro=", "{:.5f}".format(val_f1_mic), "f1_macro=",
"{:.5f}".format(val_f1_mac), "time=", "{:.5f}".format(duration))
with open(log_dir() + "val_stats.txt", "w") as fp:
fp.write(
"loss={:.5f} f1_micro={:.5f} f1_macro={:.5f} time={:.5f}".format(
val_cost, val_f1_mic, val_f1_mac, duration))
print("Writing test set stats to file (don't peak!)")
val_cost, val_f1_mic, val_f1_mac, duration = incremental_evaluate(
sess, model, minibatch, FLAGS.batch_size, test=True)
with open(log_dir() + "test_stats.txt", "w") as fp:
fp.write("loss={:.5f} f1_micro={:.5f} f1_macro={:.5f}".format(
val_cost, val_f1_mic, val_f1_mac))
dicoIdMap[node] = compteur
compteur += 1
id_map = dicoIdMap
minibatch = EdgeMinibatchIterator(G,
edgelist,
test_edgelist,
id_map,
batch_size=100,
max_degree=3)
sampler = UniformNeighborSampler(minibatch.adj)
layer_infos = [
SAGEInfo("node", sampler, 3, 128),
SAGEInfo("node", sampler, 3, 256)
]
features = []
maximum = -1
"""
for node in G.nodes:
text_embed = dicoEmbT[str(node)]
maximum = max(tf.convert_to_tensor(text_embed.detach().numpy()).shape[1], maximum)
print("MAXIMUM", maximum)
for node in G.nodes:
def _create_model(self, num_classes, placeholders, features, adj_info,
minibatch):
if self.model_name == 'graphsage_mean':
# Create model
sampler = UniformNeighborSampler(adj_info)
if self.samples_3 != 0:
layer_infos = [
SAGEInfo("node", sampler, self.samples_1, self.dim_1),
SAGEInfo("node", sampler, self.samples_2, self.dim_2),
SAGEInfo("node", sampler, self.samples_3, self.dim_2)
]
elif self.samples_2 != 0:
layer_infos = [
SAGEInfo("node", sampler, self.samples_1, self.dim_1),
SAGEInfo("node", sampler, self.samples_2, self.dim_2)
]
else:
layer_infos = [
SAGEInfo("node", sampler, self.samples_1, self.dim_1)
]
model = SupervisedGraphsage(num_classes,
placeholders,
features,
adj_info,
minibatch.deg,
layer_infos,
weight_decay=self.weight_decay,
learning_rate=self.learning_rate,
model_size=self.model_size,
sigmoid_loss=self.sigmoid,
identity_dim=self.identity_dim,
logging=True)
elif self.model_name == 'gcn':
# Create model
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, self.samples_1, 2 * self.dim_1),
SAGEInfo("node", sampler, self.samples_2, 2 * self.dim_2)
]
model = SupervisedGraphsage(num_classes,
placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
weight_decay=self.weight_decay,
learning_rate=self.learning_rate,
aggregator_type="gcn",
model_size=self.model_size,
concat=False,
sigmoid_loss=self.sigmoid,
identity_dim=self.identity_dim,
logging=True)
elif self.model_name == 'graphsage_seq':
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, self.samples_1, self.dim_1),
SAGEInfo("node", sampler, self.samples_2, self.dim_2)
]
model = SupervisedGraphsage(num_classes,
placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
weight_decay=self.weight_decay,
learning_rate=self.learning_rate,
aggregator_type="seq",
model_size=self.model_size,
sigmoid_loss=self.sigmoid,
identity_dim=self.identity_dim,
logging=True)
elif self.model_name == 'graphsage_maxpool':
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, self.samples_1, self.dim_1),
SAGEInfo("node", sampler, self.samples_2, self.dim_2)
]
model = SupervisedGraphsage(num_classes,
placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
weight_decay=self.weight_decay,
learning_rate=self.learning_rate,
aggregator_type="maxpool",
model_size=self.model_size,
sigmoid_loss=self.sigmoid,
identity_dim=self.identity_dim,
logging=True)
elif self.model_name == 'graphsage_meanpool':
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, self.samples_1, self.dim_1),
SAGEInfo("node", sampler, self.samples_2, self.dim_2)
]
model = SupervisedGraphsage(num_classes,
placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
weight_decay=self.weight_decay,
learning_rate=self.learning_rate,
aggregator_type="meanpool",
model_size=self.model_size,
sigmoid_loss=self.sigmoid,
identity_dim=self.identity_dim,
logging=True)
else:
raise Exception('Error: model name unrecognized.')
return model
def train(train_data, test_data=None):
# adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask
G = train_data[0]
features = train_data[1]
if not features is None:
# pad with dummy zero vector
features = np.vstack([features, np.zeros((features.shape[1], ))])
placeholders = construct_placeholders()
minibatch = EdgeMinibatchIterator(G,
placeholders,
batch_size=FLAGS.batch_size,
max_degree=FLAGS.max_degree,
num_neg_samples=FLAGS.neg_sample_size)
adj_info_ph = tf.placeholder(tf.int32, shape=minibatch.adj.shape)
adj_info = tf.Variable(adj_info_ph, trainable=False, name="adj_info")
if FLAGS.model == 'graphsage_mean':
# Create model
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
model = SampleAndAggregate(placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
model_size=FLAGS.model_size,
identity_dim=FLAGS.identity_dim,
logging=True)
elif FLAGS.model == 'gcn':
# Create model
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, 2 * FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, 2 * FLAGS.dim_2)
]
model = SampleAndAggregate(placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
aggregator_type="gcn",
model_size=FLAGS.model_size,
identity_dim=FLAGS.identity_dim,
concat=False,
logging=True)
elif FLAGS.model == 'graphsage_seq':
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
model = SampleAndAggregate(placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
identity_dim=FLAGS.identity_dim,
aggregator_type="seq",
model_size=FLAGS.model_size,
logging=True)
elif FLAGS.model == 'graphsage_maxpool':
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
model = SampleAndAggregate(placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
aggregator_type="maxpool",
model_size=FLAGS.model_size,
identity_dim=FLAGS.identity_dim,
logging=True)
elif FLAGS.model == 'graphsage_meanpool':
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
model = SampleAndAggregate(placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
aggregator_type="meanpool",
model_size=FLAGS.model_size,
identity_dim=FLAGS.identity_dim,
logging=True)
else:
raise Exception('Error: model name unrecognized.')
config = tf.ConfigProto(log_device_placement=FLAGS.log_device_placement)
config.gpu_options.allow_growth = True
#config.gpu_options.per_process_gpu_memory_fraction = GPU_MEM_FRACTION
config.allow_soft_placement = True
# Initialize session
sess = tf.Session(config=config)
merged = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(log_dir(), sess.graph)
# Init variables
sess.run(tf.global_variables_initializer(),
feed_dict={adj_info_ph: minibatch.adj})
# Train model
train_shadow_mrr = None
shadow_mrr = None
total_steps = 0
avg_time = 0.0
epoch_val_costs = []
train_adj_info = tf.assign(adj_info, minibatch.adj)
for epoch in range(FLAGS.epochs):
minibatch.shuffle()
iter = 0
print('Epoch: %04d' % (epoch + 1))
epoch_val_costs.append(0)
while not minibatch.end():
# Construct feed dictionary
feed_dict = minibatch.next_minibatch_feed_dict()
feed_dict.update({placeholders['dropout']: FLAGS.dropout})
t = time.time()
# Training step
outs = sess.run([
merged, model.opt_op, model.loss, model.ranks, model.aff_all,
model.mrr, model.outputs1
],
feed_dict=feed_dict)
train_cost = outs[2]
train_mrr = outs[5]
if train_shadow_mrr is None:
train_shadow_mrr = train_mrr #
else:
train_shadow_mrr -= (1 - 0.99) * (train_shadow_mrr - train_mrr)
# Print results
avg_time = (avg_time * total_steps + time.time() -
t) / (total_steps + 1)
if total_steps % FLAGS.print_every == 0:
print("Iter:", '%04d' % iter, "train_loss=",
"{:.5f}".format(train_cost), "train_mrr=",
"{:.5f}".format(train_mrr), "train_mrr_ema=",
"{:.5f}".format(train_shadow_mrr))
iter += 1
total_steps += 1
if total_steps > FLAGS.max_total_steps:
break
if total_steps > FLAGS.max_total_steps:
break
print("Optimization Finished!")
if FLAGS.save_embeddings:
save_val_embeddings(sess, model, minibatch, FLAGS.validate_batch_size,
log_dir())
def train(train_data, test_data=None):
G = train_data[0]
features = train_data[1]
id_map = train_data[2]
if not features is None:
# pad with dummy zero vector
features = np.vstack([features, np.zeros((features.shape[1], ))])
context_pairs = train_data[3] if FLAGS.random_context else None
placeholders = construct_placeholders()
minibatch = EdgeMinibatchIterator(G,
id_map,
placeholders,
batch_size=FLAGS.batch_size,
max_degree=FLAGS.max_degree,
num_neg_samples=FLAGS.neg_sample_size,
context_pairs=context_pairs)
adj_info_ph = tf.placeholder(tf.int32, shape=minibatch.adj.shape)
adj_info = tf.Variable(adj_info_ph, trainable=False, name="adj_info")
if FLAGS.model == 'graphsage_mean':
# Create model
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
model = SampleAndAggregate(placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
model_size=FLAGS.model_size,
identity_dim=FLAGS.identity_dim,
logging=True)
elif FLAGS.model == 'gcn':
# Create model
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, 2 * FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, 2 * FLAGS.dim_2)
]
model = SampleAndAggregate(placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
aggregator_type="gcn",
model_size=FLAGS.model_size,
identity_dim=FLAGS.identity_dim,
concat=False,
logging=True)
elif FLAGS.model == 'graphsage_seq':
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
model = SampleAndAggregate(placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
identity_dim=FLAGS.identity_dim,
aggregator_type="seq",
model_size=FLAGS.model_size,
logging=True)
elif FLAGS.model == 'graphsage_maxpool':
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
model = SampleAndAggregate(placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
aggregator_type="maxpool",
model_size=FLAGS.model_size,
identity_dim=FLAGS.identity_dim,
logging=True)
elif FLAGS.model == 'graphsage_meanpool':
sampler = UniformNeighborSampler(adj_info)
layer_infos = [
SAGEInfo("node", sampler, FLAGS.samples_1, FLAGS.dim_1),
SAGEInfo("node", sampler, FLAGS.samples_2, FLAGS.dim_2)
]
model = SampleAndAggregate(placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
aggregator_type="meanpool",
model_size=FLAGS.model_size,
identity_dim=FLAGS.identity_dim,
logging=True)
elif FLAGS.model == 'n2v':
model = Node2VecModel(
placeholders,
features.shape[0],
minibatch.deg,
#2x because graphsage uses concat
nodevec_dim=2 * FLAGS.dim_1,
lr=FLAGS.learning_rate)
else:
raise Exception('Error: model name unrecognized.')
config = tf.ConfigProto(log_device_placement=FLAGS.log_device_placement)
config.gpu_options.allow_growth = True
#config.gpu_options.per_process_gpu_memory_fraction = GPU_MEM_FRACTION
config.allow_soft_placement = True
# Initialize session
sess = tf.Session(config=config)
merged = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(log_dir(), sess.graph)
# Init variables
sess.run(tf.global_variables_initializer(),
feed_dict={adj_info_ph: minibatch.adj})
# Train model
train_shadow_mrr = None
shadow_mrr = None
total_steps = 0
avg_time = 0.0
epoch_val_costs = []
train_adj_info = tf.assign(adj_info, minibatch.adj)
val_adj_info = tf.assign(adj_info, minibatch.test_adj)
for epoch in range(FLAGS.epochs):
minibatch.shuffle()
iter = 0
print('Epoch: %04d' % (epoch + 1))
epoch_val_costs.append(0)
while not minibatch.end():
# Construct feed dictionary
feed_dict = minibatch.next_minibatch_feed_dict()
feed_dict.update({placeholders['dropout']: FLAGS.dropout})
t = time.time()
# Training step
outs = sess.run([
merged, model.opt_op, model.loss, model.ranks, model.aff_all,
model.mrr, model.outputs1
],
feed_dict=feed_dict)
train_cost = outs[2]
train_mrr = outs[5]
if train_shadow_mrr is None:
train_shadow_mrr = train_mrr #
else:
train_shadow_mrr -= (1 - 0.99) * (train_shadow_mrr - train_mrr)
if iter % FLAGS.validate_iter == 0:
# Validation
sess.run(val_adj_info.op)
val_cost, ranks, val_mrr, duration = evaluate(
sess, model, minibatch, size=FLAGS.validate_batch_size)
sess.run(train_adj_info.op)
epoch_val_costs[-1] += val_cost
if shadow_mrr is None:
shadow_mrr = val_mrr
else:
shadow_mrr -= (1 - 0.99) * (shadow_mrr - val_mrr)
if total_steps % FLAGS.print_every == 0:
summary_writer.add_summary(outs[0], total_steps)
# Print results
avg_time = (avg_time * total_steps + time.time() -
t) / (total_steps + 1)
if total_steps % FLAGS.print_every == 0:
print(
"Iter:",
'%04d' % iter,
"train_loss=",
"{:.5f}".format(train_cost),
"train_mrr=",
"{:.5f}".format(train_mrr),
"train_mrr_ema=",
"{:.5f}".format(
train_shadow_mrr), # exponential moving average
"val_loss=",
"{:.5f}".format(val_cost),
"val_mrr=",
"{:.5f}".format(val_mrr),
"val_mrr_ema=",
"{:.5f}".format(shadow_mrr), # exponential moving average
"time=",
"{:.5f}".format(avg_time))
iter += 1
total_steps += 1
if total_steps > FLAGS.max_total_steps:
break
if total_steps > FLAGS.max_total_steps:
break
print("Optimization Finished!")
if FLAGS.save_embeddings:
sess.run(val_adj_info.op)
save_val_embeddings(sess, model, minibatch, FLAGS.validate_batch_size,
log_dir())
if FLAGS.model == "n2v":
# stopping the gradient for the already trained nodes
train_ids = tf.constant(
[[id_map[n]] for n in G.nodes_iter()
if not G.node[n]['val'] and not G.node[n]['test']],
dtype=tf.int32)
test_ids = tf.constant([[id_map[n]] for n in G.nodes_iter()
if G.node[n]['val'] or G.node[n]['test']],
dtype=tf.int32)
update_nodes = tf.nn.embedding_lookup(model.context_embeds,
tf.squeeze(test_ids))
no_update_nodes = tf.nn.embedding_lookup(model.context_embeds,
tf.squeeze(train_ids))
update_nodes = tf.scatter_nd(test_ids, update_nodes,
tf.shape(model.context_embeds))
no_update_nodes = tf.stop_gradient(
tf.scatter_nd(train_ids, no_update_nodes,
tf.shape(model.context_embeds)))
model.context_embeds = update_nodes + no_update_nodes
sess.run(model.context_embeds)
# run random walks
from graphsage.utils import run_random_walks
nodes = [
n for n in G.nodes_iter()
if G.node[n]["val"] or G.node[n]["test"]
]
start_time = time.time()
pairs = run_random_walks(G, nodes, num_walks=50)
walk_time = time.time() - start_time
test_minibatch = EdgeMinibatchIterator(
G,
id_map,
placeholders,
batch_size=FLAGS.batch_size,
max_degree=FLAGS.max_degree,
num_neg_samples=FLAGS.neg_sample_size,
context_pairs=pairs,
n2v_retrain=True,
fixed_n2v=True)
start_time = time.time()
print("Doing test training for n2v.")
test_steps = 0
for epoch in range(FLAGS.n2v_test_epochs):
test_minibatch.shuffle()
while not test_minibatch.end():
feed_dict = test_minibatch.next_minibatch_feed_dict()
feed_dict.update({placeholders['dropout']: FLAGS.dropout})
outs = sess.run([
model.opt_op, model.loss, model.ranks, model.aff_all,
model.mrr, model.outputs1
],
feed_dict=feed_dict)
if test_steps % FLAGS.print_every == 0:
print("Iter:", '%04d' % test_steps, "train_loss=",
"{:.5f}".format(outs[1]), "train_mrr=",
"{:.5f}".format(outs[-2]))
test_steps += 1
train_time = time.time() - start_time
save_val_embeddings(sess,
model,
minibatch,
FLAGS.validate_batch_size,
log_dir(),
mod="-test")
print("Total time: ", train_time + walk_time)
print("Walk time: ", walk_time)
print("Train time: ", train_time)
def _create_model(self, sampler_name, placeholders, features, adj_info,
minibatch):
if self.model_name == 'mean_concat':
# Create model
sampler = self._create_sampler(sampler_name, adj_info, features)
layer_infos = [
SAGEInfo("node", sampler, self.samples_1, self.dim_1),
SAGEInfo("node", sampler, self.samples_2, self.dim_2)
]
model = SampleAndAggregate(placeholders,
features,
adj_info,
minibatch.deg,
concat=True,
layer_infos=layer_infos,
weight_decay=self.weight_decay,
learning_rate=self.learning_rate,
neg_sample_size=self.neg_sample_size,
batch_size=self.batch_size,
model_size=self.model_size,
identity_dim=self.identity_dim,
logging=True)
elif self.model_name == 'mean_add':
# Create model
sampler = self._create_sampler(sampler_name, adj_info, features)
layer_infos = [
SAGEInfo("node", sampler, self.samples_1, self.dim_1),
SAGEInfo("node", sampler, self.samples_2, self.dim_2)
]
model = SampleAndAggregate(placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
weight_decay=self.weight_decay,
learning_rate=self.learning_rate,
neg_sample_size=self.neg_sample_size,
batch_size=self.batch_size,
concat=False,
model_size=self.model_size,
identity_dim=self.identity_dim,
logging=True)
elif self.model_name == 'gcn':
# Create model
sampler = self._create_sampler(sampler_name, adj_info, features)
layer_infos = [
SAGEInfo("node", sampler, self.samples_1, 2 * self.dim_1),
SAGEInfo("node", sampler, self.samples_2, 2 * self.dim_2)
]
model = SampleAndAggregate(placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
weight_decay=self.weight_decay,
learning_rate=self.learning_rate,
neg_sample_size=self.neg_sample_size,
batch_size=self.batch_size,
aggregator_type="gcn",
model_size=self.model_size,
identity_dim=self.identity_dim,
concat=False,
logging=True)
elif self.model_name == 'graphsage_seq':
# Create model
sampler = self._create_sampler(sampler_name, adj_info, features)
layer_infos = [
SAGEInfo("node", sampler, self.samples_1, self.dim_1),
SAGEInfo("node", sampler, self.samples_2, self.dim_2)
]
model = SampleAndAggregate(placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
weight_decay=self.weight_decay,
learning_rate=self.learning_rate,
neg_sample_size=self.neg_sample_size,
batch_size=self.batch_size,
identity_dim=self.identity_dim,
aggregator_type="seq",
model_size=self.model_size,
logging=True)
elif self.model_name == 'graphsage_maxpool':
# Create model
sampler = self._create_sampler(sampler_name, adj_info, features)
layer_infos = [
SAGEInfo("node", sampler, self.samples_1, self.dim_1),
SAGEInfo("node", sampler, self.samples_2, self.dim_2)
]
model = SampleAndAggregate(placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
weight_decay=self.weight_decay,
learning_rate=self.learning_rate,
neg_sample_size=self.neg_sample_size,
batch_size=self.batch_size,
aggregator_type="maxpool",
model_size=self.model_size,
identity_dim=self.identity_dim,
logging=True)
elif self.model_name == 'graphsage_meanpool':
# Create model
sampler = self._create_sampler(sampler_name, adj_info, features)
layer_infos = [
SAGEInfo("node", sampler, self.samples_1, self.dim_1),
SAGEInfo("node", sampler, self.samples_2, self.dim_2)
]
model = SampleAndAggregate(placeholders,
features,
adj_info,
minibatch.deg,
layer_infos=layer_infos,
weight_decay=self.weight_decay,
learning_rate=self.learning_rate,
neg_sample_size=self.neg_sample_size,
batch_size=self.batch_size,
aggregator_type="meanpool",
model_size=self.model_size,
identity_dim=self.identity_dim,
logging=True)
else:
raise Exception('Error: model name unrecognized.')
return model