def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
print('Bröther may i have some self-lööps')
n_nodes = FLAGS.n_nodes
n_clusters = FLAGS.n_clusters
train_size = FLAGS.train_size
batch_size = FLAGS.batch_size
data_clean, data_dirty, labels = line_gaussians(n_nodes, n_clusters)
graph_clean = construct_knn_graph(data_clean)
n_neighbors = [15, 10] # TODO(tsitsulin): move to FLAGS.
total_matrix_size = 1 + np.cumprod(n_neighbors).sum()
train_mask = np.zeros(n_nodes, dtype=np.bool)
train_mask[np.random.choice(np.arange(n_nodes),
int(n_nodes * train_size),
replace=False)] = True
test_mask = ~train_mask
print(f'Data shape: {data_clean.shape}, graph shape: {graph_clean.shape}')
print(f'Train size: {train_mask.sum()}, test size: {test_mask.sum()}')
input_features = tf.keras.layers.Input(shape=(
total_matrix_size,
2,
))
input_graph = tf.keras.layers.Input((
total_matrix_size,
total_matrix_size,
))
output = multilayer_gcn([input_features, input_graph],
[64, 32, n_clusters])
model = tf.keras.Model(inputs=[input_features, input_graph],
outputs=output[:, 0, :])
model.compile(
optimizer=tf.keras.optimizers.Adam(FLAGS.learning_rate),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
for epoch in range(FLAGS.n_epochs):
subgraph_mat, features_mat, node_ids, _ = random_batch(
graph_clean, data_dirty, batch_size, n_neighbors)
model.fit([features_mat, subgraph_mat],
labels[node_ids],
batch_size,
shuffle=False)
subgraph_mat, features_mat, _ = make_batch(graph_clean, data_dirty,
np.arange(n_nodes)[test_mask],
n_neighbors)
clusters = model([features_mat, subgraph_mat]).numpy().argmax(axis=1)
print(
'NMI:',
normalized_mutual_info_score(labels[test_mask],
clusters,
average_method='arithmetic'))
print('Accuracy:', accuracy_score(labels[test_mask], clusters))
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
print('Bröther may i have some self-lööps')
n_nodes = FLAGS.n_nodes
n_clusters = FLAGS.n_clusters
train_size = FLAGS.train_size
data_clean, data_dirty, labels = line_gaussians(n_nodes, n_clusters)
graph_clean = construct_knn_graph(data_clean).todense().A1.reshape(
n_nodes, n_nodes)
train_mask = np.zeros(n_nodes, dtype=np.bool)
train_mask[np.random.choice(np.arange(n_nodes),
int(n_nodes * train_size),
replace=False)] = True
test_mask = ~train_mask
print(f'Data shape: {data_clean.shape}, graph shape: {graph_clean.shape}')
print(f'Train size: {train_mask.sum()}, test size: {test_mask.sum()}')
input_features = tf.keras.layers.Input(shape=(2, ))
input_graph = tf.keras.layers.Input((n_nodes, ))
output = multilayer_gcn([input_features, input_graph],
[64, 32, n_clusters])
model = tf.keras.Model(inputs=[input_features, input_graph],
outputs=output)
model.compile(
optimizer=tf.keras.optimizers.Adam(FLAGS.learning_rate),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
for epoch in range(FLAGS.n_epochs):
model.fit([data_dirty, graph_clean],
labels,
n_nodes,
shuffle=False,
sample_weight=train_mask)
clusters = model([data_dirty,
graph_clean]).numpy().argmax(axis=1)[test_mask]
print(
'NMI:',
normalized_mutual_info_score(labels[test_mask],
clusters,
average_method='arithmetic'))
print('Accuracy:', accuracy_score(labels[test_mask], clusters))