def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
print('Starting', format_filename())
if FLAGS.load_strategy == 'schur':
adjacency, features, labels, label_mask = load_npz_to_sparse_graph(
FLAGS.graph_path)
elif FLAGS.load_strategy == 'kipf':
adjacency, features, labels, label_mask = load_kipf_data(
*os.path.split(FLAGS.graph_path))
else:
raise Exception('Unknown loading strategy!')
n_nodes = adjacency.shape[0]
feature_size = features.shape[1]
architecture = [int(x) for x in FLAGS.architecture.strip('[]').split('_')]
architecture.append(FLAGS.n_clusters)
graph_clean = scipy_to_tf(adjacency)
graph_clean_normalized = scipy_to_tf(
normalize_graph(adjacency.copy(), normalized=True))
input_features = tf.keras.layers.Input(shape=(feature_size, ))
input_graph = tf.keras.layers.Input((n_nodes, ), sparse=True)
input_adjacency = tf.keras.layers.Input((n_nodes, ), sparse=True)
model = gcn_modularity(
[input_features, input_graph, input_adjacency],
architecture,
dropout_rate=FLAGS.dropout_rate,
orthogonality_regularization=FLAGS.orthogonality_regularization,
cluster_size_regularization=FLAGS.cluster_size_regularization)
def grad(model, inputs):
with tf.GradientTape() as tape:
_ = model(inputs, training=True)
loss_value = sum(model.losses)
return model.losses, tape.gradient(loss_value,
model.trainable_variables)
optimizer = tf.keras.optimizers.Adam(FLAGS.learning_rate)
model.compile(optimizer, None)
for epoch in range(FLAGS.n_epochs):
loss_values, grads = grad(
model, [features, graph_clean_normalized, graph_clean])
optimizer.apply_gradients(zip(grads, model.trainable_variables))
print(f'epoch {epoch}, losses: ' + ' '.join(
[f'{loss_value.numpy():.4f}' for loss_value in loss_values]))
_, assignments = model([features, graph_clean_normalized, graph_clean],
training=False)
assignments = assignments.numpy()
clusters = assignments.argmax(axis=1)
print('Conductance:', conductance(adjacency, clusters))
print('Modularity:', modularity(adjacency, clusters))
print(
'NMI:',
normalized_mutual_info_score(labels,
clusters[label_mask],
average_method='arithmetic'))
print('Precision:', precision(labels, clusters[label_mask]))
print('Recall:', recall(labels, clusters[label_mask]))
with open(format_filename(), 'w') as out_file:
print('Conductance:', conductance(adjacency, clusters), file=out_file)
print('Modularity:', modularity(adjacency, clusters), file=out_file)
print('NMI:',
normalized_mutual_info_score(labels,
clusters[label_mask],
average_method='arithmetic'),
file=out_file)
print('Precision:',
precision(labels, clusters[label_mask]),
file=out_file)
print('Recall:', recall(labels, clusters[label_mask]), file=out_file)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
print('Starting', format_filename())
if FLAGS.load_strategy == 'schur':
adjacency, features, labels, label_mask = load_npz_to_sparse_graph(
FLAGS.graph_path)
elif FLAGS.load_strategy == 'kipf':
adjacency, features, labels, label_mask = load_kipf_data(
*os.path.split(FLAGS.graph_path))
else:
raise Exception('Unknown loading strategy!')
n_nodes = adjacency.shape[0]
feature_size = features.shape[1]
architecture = [int(x) for x in FLAGS.architecture.strip('[]').split('_')]
graph_clean_normalized = scipy_to_tf(
normalize_graph(adjacency.copy(), normalized=True))
input_features = tf.keras.layers.Input(shape=(feature_size,))
input_features_corrupted = tf.keras.layers.Input(shape=(feature_size,))
input_graph = tf.keras.layers.Input((n_nodes,), sparse=True)
encoder = [GCN(512) for size in architecture]
model = deep_graph_infomax(
[input_features, input_features_corrupted, input_graph], encoder)
def loss(model, x, y, training):
_, y_ = model(x, training=training)
return loss_object(y_true=y, y_pred=y_)
def grad(model, inputs, targets):
with tf.GradientTape() as tape:
loss_value = loss(model, inputs, targets, training=True)
for loss_internal in model.losses:
loss_value += loss_internal
return loss_value, tape.gradient(loss_value, model.trainable_variables)
loss_object = tf.keras.losses.BinaryCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam(FLAGS.learning_rate)
patience = 20
best_loss = 999
patience_counter = 0
for epoch in range(FLAGS.n_epochs):
features_corr = features.copy()
pseudolabels = tf.concat([tf.zeros([n_nodes, 1]), tf.ones([n_nodes, 1])], 0)
features_corr = features_corr.copy()
np.random.shuffle(features_corr)
loss_value, grads = grad(model,
[features, features_corr, graph_clean_normalized],
pseudolabels)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
loss_value = loss_value.numpy()
print(epoch, loss_value)
if loss_value > best_loss:
patience_counter += 1
if patience_counter == patience:
break
else:
best_loss = loss_value
patience_counter = 0
representations = model([features, features, graph_clean_normalized],
training=False)[0].numpy()
clf = KMeans(n_clusters=FLAGS.n_clusters)
clf.fit(representations)
clusters = clf.labels_
print('Conductance:', conductance(adjacency, clusters))
print('Modularity:', modularity(adjacency, clusters))
print(
'NMI:',
normalized_mutual_info_score(
labels, clusters[label_mask], average_method='arithmetic'))
print('Precision:', precision(labels, clusters[label_mask]))
print('Recall:', recall(labels, clusters[label_mask]))
with open(format_filename(), 'w') as out_file:
print('Conductance:', conductance(adjacency, clusters), file=out_file)
print('Modularity:', modularity(adjacency, clusters), file=out_file)
print(
'NMI:',
normalized_mutual_info_score(
labels, clusters[label_mask], average_method='arithmetic'),
file=out_file)
print('Precision:', precision(labels, clusters[label_mask]), file=out_file)
print('Recall:', recall(labels, clusters[label_mask]), file=out_file)