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param_mincut.py
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param_mincut.py
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# coding=utf-8
# Copyright 2024 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TODO(tsitsulin): add headers, tests, and improve style."""
import os
from absl import app
from absl import flags
from sklearn.metrics import normalized_mutual_info_score
import tensorflow.compat.v2 as tf
from graph_embedding.dmon.models.gcn_mincut import gcn_mincut
from graph_embedding.dmon.utilities.graph import load_kipf_data
from graph_embedding.dmon.utilities.graph import load_npz_to_sparse_graph
from graph_embedding.dmon.utilities.graph import normalize_graph
from graph_embedding.dmon.utilities.graph import scipy_to_tf
from graph_embedding.dmon.utilities.metrics import conductance
from graph_embedding.dmon.utilities.metrics import modularity
from graph_embedding.dmon.utilities.metrics import precision
from graph_embedding.dmon.utilities.metrics import recall
tf.compat.v1.enable_v2_behavior()
FLAGS = flags.FLAGS
flags.DEFINE_string('graph_path', None, 'Input graph path')
flags.DEFINE_string('output_path', None, 'Output results path')
flags.DEFINE_string('architecture', None, 'Network architecture')
flags.DEFINE_string('load_strategy', 'schur', 'Graph format')
flags.DEFINE_string('postfix', '', 'File postfix')
flags.DEFINE_float(
'orthogonality_regularization',
1,
'Orthogonality regularization parameter',
lower_bound=0)
flags.DEFINE_float(
'cluster_size_regularization',
0,
'Cluster size regularization parameter',
lower_bound=0)
flags.DEFINE_float(
'dropout_rate',
0,
'Orthogonality regularization parameter',
lower_bound=0,
upper_bound=1)
flags.DEFINE_integer('n_clusters', 16, 'Number of clusters', lower_bound=0)
flags.DEFINE_integer('n_epochs', 1000, 'Number of epochs', lower_bound=0)
flags.DEFINE_float('learning_rate', 0.001, 'Learning rate', lower_bound=0)
def format_filename():
graph_name = os.path.split(FLAGS.graph_path)[1]
architecture_str = FLAGS.architecture.strip('[]')
return (f'{FLAGS.output_path}/{graph_name}-'
f'nclusters-{FLAGS.n_clusters}-'
f'architecture-{architecture_str}-'
f'ortho-{FLAGS.orthogonality_regularization}-'
f'clustersize-{FLAGS.cluster_size_regularization}-'
f'dropout-{FLAGS.dropout_rate}-'
f'lr-{FLAGS.learning_rate}-'
f'epochs-{FLAGS.n_epochs}'
f'postfix-{FLAGS.postfix}'
'.txt')
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_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)
model = gcn_mincut(
[input_features, input_graph], 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])
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], 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)
if __name__ == '__main__':
app.run(main)