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))
def train(self, train_data, test_data=None):
print("Training model...")
timer = Timer()
timer.tic()
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], ))])
placeholders = self._construct_placeholders(num_classes)
minibatch = NodeMinibatchIterator(G,
id_map,
placeholders,
class_map,
num_classes,
batch_size=self.batch_size,
max_degree=self.max_degree)
adj_info_ph = tf.compat.v1.placeholder(tf.int32,
shape=minibatch.adj.shape)
adj_info = tf.Variable(adj_info_ph, trainable=False, name="adj_info")
model = self._create_model(num_classes, placeholders, features,
adj_info, minibatch)
config = tf.compat.v1.ConfigProto(
log_device_placement=self.log_device_placement)
config.gpu_options.allow_growth = True
config.allow_soft_placement = True
# Initialize session
sess = tf.compat.v1.Session(config=config)
merged = tf.compat.v1.summary.merge_all()
# summary_writer = tf.summary.FileWriter(self._log_dir(), sess.graph)
# Initialize model saver
saver = tf.compat.v1.train.Saver(max_to_keep=self.epochs)
# Init variables
sess.run(tf.compat.v1.global_variables_initializer(),
feed_dict={adj_info_ph: minibatch.adj})
# Train model
total_steps = 0
avg_time = 0.0
epoch_val_costs = []
train_losses = []
validation_losses = []
train_adj_info = tf.compat.v1.assign(adj_info, minibatch.adj)
val_adj_info = tf.compat.v1.assign(adj_info, minibatch.test_adj)
for epoch in range(self.epochs):
minibatch.shuffle()
iter = 0
print('Epoch: %04d' % (epoch))
epoch_val_costs.append(0)
train_loss_epoch = []
validation_loss_epoch = []
while not minibatch.end():
# Construct feed dictionary
feed_dict, labels = minibatch.next_minibatch_feed_dict()
feed_dict.update({placeholders['dropout']: self.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]
train_loss_epoch.append(train_cost)
if iter % self.validate_iter == 0:
# Validation
sess.run(val_adj_info.op)
if self.validate_batch_size == -1:
val_cost, val_f1_mic, val_f1_mac, duration = self._incremental_evaluate(
sess, model, minibatch, self.batch_size)
else:
val_cost, val_f1_mic, val_f1_mac, duration = self._evaluate(
sess, model, minibatch, self.validate_batch_size)
sess.run(train_adj_info.op)
epoch_val_costs[-1] += val_cost
validation_loss_epoch.append(val_cost)
# if total_steps % self.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 % self.print_every == 0:
train_f1_mic, train_f1_mac = self._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 > self.max_total_steps:
break
# Keep track of train and validation losses per epoch
train_losses.append(sum(train_loss_epoch) / len(train_loss_epoch))
validation_losses.append(
sum(validation_loss_epoch) / len(validation_loss_epoch))
# If the epoch has the lowest validation loss so far
if validation_losses[-1] == min(validation_losses):
print(
"Minimum validation loss so far ({}) at epoch {}.".format(
validation_losses[-1], epoch))
# Save model at each epoch
print("Saving model at epoch {}.".format(epoch))
saver.save(sess, os.path.join(self._log_dir(), "model.ckpt"))
if total_steps > self.max_total_steps:
break
print("Optimization Finished!")
training_time = timer.toc()
self._plot_losses(train_losses, validation_losses)
self._print_stats(train_losses, validation_losses, training_time)
sess.run(val_adj_info.op)
val_cost, val_f1_mic, val_f1_mac, duration = self._incremental_evaluate(
sess, model, minibatch, self.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(self._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))