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pubmed_Mix_uniform.py
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from __future__ import division
from __future__ import print_function
import time
import tensorflow as tf
import scipy.sparse as sp
import os
from utils import *
from models import GCN_APPRO_Mix
# Set random seed
seed = 123
np.random.seed(seed)
tf.set_random_seed(seed)
# Settings
flags = tf.app.flags
FLAGS = flags.FLAGS
flags.DEFINE_string('dataset', 'pubmed', 'Dataset string.') # 'cora', 'citeseer', 'pubmed'
flags.DEFINE_string('model', 'gcn_mix', 'Model string.') # 'gcn_mix', 'gcn_appr'
flags.DEFINE_float('learning_rate', 0.001, 'Initial learning rate.')
flags.DEFINE_integer('epochs', 200, 'Number of epochs to train.')
flags.DEFINE_integer('hidden1', 16, 'Number of units in hidden layer 1.')
flags.DEFINE_float('dropout', 0.0, 'Dropout rate (1 - keep probability).')
flags.DEFINE_float('weight_decay', 5e-4, 'Weight for L2 loss on embedding matrix.')
flags.DEFINE_integer('early_stopping', 30, 'Tolerance for early stopping (# of epochs).')
flags.DEFINE_integer('max_degree', 3, 'Maximum Chebyshev polynomial degree.')
def construct_feeddict_forMixlayers(AXfeatures, support, labels, placeholders):
feed_dict = dict()
feed_dict.update({placeholders['labels']: labels})
feed_dict.update({placeholders['AXfeatures']: AXfeatures})
feed_dict.update({placeholders['support']: support})
feed_dict.update({placeholders['num_features_nonzero']: AXfeatures[1].shape})
return feed_dict
def iterate_minibatches_listinputs(inputs, batchsize, shuffle=False):
assert inputs is not None
numSamples = inputs[0].shape[0]
if shuffle:
indices = np.arange(numSamples)
np.random.shuffle(indices)
for start_idx in range(0, numSamples - batchsize + 1, batchsize):
if shuffle:
excerpt = indices[start_idx:start_idx + batchsize]
else:
excerpt = slice(start_idx, start_idx + batchsize)
yield [input[excerpt] for input in inputs]
def main(rank1):
adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask = load_data(FLAGS.dataset)
train_index = np.where(train_mask)[0]
adj_train = adj[train_index, :][:, train_index]
train_mask = train_mask[train_index]
y_train = y_train[train_index]
val_index = np.where(val_mask)[0]
y_val = y_val[val_index]
test_index = np.where(test_mask)[0]
y_test = y_test[test_index]
train_val_index = np.concatenate([train_index, val_index],axis=0)
train_test_idnex = np.concatenate([train_index, test_index],axis=0)
numNode_train = adj_train.shape[0]
# print("numNode", numNode)
if FLAGS.model == 'gcn_mix':
normADJ_train = nontuple_preprocess_adj(adj_train)
# normADJ = nontuple_preprocess_adj(adj)
normADJ_val = nontuple_preprocess_adj(adj[train_val_index,:][:,train_val_index])
normADJ_test = nontuple_preprocess_adj(adj[train_test_idnex,:][:,train_test_idnex])
num_supports = 2
model_func = GCN_APPRO_Mix
else:
raise ValueError('Invalid argument for model: ' + str(FLAGS.model))
# Some preprocessing
features = nontuple_preprocess_features(features).todense()
train_features = normADJ_train.dot(features[train_index])
val_features = normADJ_val.dot(features[train_val_index])
test_features = normADJ_test.dot(features[train_test_idnex])
nonzero_feature_number = len(np.nonzero(features)[0])
nonzero_feature_number_train = len(np.nonzero(train_features)[0])
# Define placeholders
placeholders = {
'support': tf.sparse_placeholder(tf.float32) ,
'AXfeatures': tf.placeholder(tf.float32, shape=(None, features.shape[1])),
'labels': tf.placeholder(tf.float32, shape=(None, y_train.shape[1])),
'dropout': tf.placeholder_with_default(0., shape=()),
'num_features_nonzero': tf.placeholder(tf.int32) # helper variable for sparse dropout
}
# Create model
model = model_func(placeholders, input_dim=features.shape[-1], logging=True)
# Initialize session
sess = tf.Session()
# Define model evaluation function
def evaluate(features, support, labels, placeholders):
t_test = time.time()
feed_dict_val = construct_feeddict_forMixlayers(features, support, labels, placeholders)
outs_val = sess.run([model.loss, model.accuracy], feed_dict=feed_dict_val)
return outs_val[0], outs_val[1], (time.time() - t_test)
# Init variables
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
cost_val = []
p0 = column_prop(normADJ_train)
# testSupport = [sparse_to_tuple(normADJ), sparse_to_tuple(normADJ)]
valSupport = sparse_to_tuple(normADJ_val[len(train_index):, :])
testSupport = sparse_to_tuple(normADJ_test[len(train_index):, :])
t
76D4
= time.time()
maxACC = 0.0
# Train model
for epoch in range(FLAGS.epochs):
t1 = time.time()
n = 0
for batch in iterate_minibatches_listinputs([normADJ_train, y_train], batchsize=1024, shuffle=True):
[normADJ_batch, y_train_batch] = batch
p1 = column_prop(normADJ_batch)
if rank1 is None:
support1 = sparse_to_tuple(normADJ_batch)
features_inputs = train_features
else:
distr = np.nonzero(np.sum(normADJ_batch, axis=0))[1]
if rank1 > len(distr):
q1 = distr
else:
q1 = np.random.choice(distr, rank1, replace=False) # top layer
# q1 = np.random.choice(np.arange(numNode_train), rank1) # top layer
support1 = sparse_to_tuple(normADJ_batch[:, q1] * numNode_train / len(q1))
features_inputs = train_features[q1, :] # selected nodes for approximation
# Construct feed dictionary
feed_dict = construct_feeddict_forMixlayers(features_inputs, support1, y_train_batch,
placeholders)
feed_dict.update({placeholders['dropout']: FLAGS.dropout})
# Training step
outs = sess.run([model.opt_op, model.loss, model.accuracy], feed_dict=feed_dict)
n = n +1
# Validation
cost, acc, duration = evaluate(val_features, valSupport, y_val, placeholders)
cost_val.append(cost)
# if epoch > 50 and acc>maxACC:
# maxACC = acc
# save_path = saver.save(sess, "tmp/tmp_MixModel.ckpt")
# Print results
# print("Epoch:", '%04d' % (epoch + 1), "train_loss=", "{:.5f}".format(outs[1]),
# "train_acc=", "{:.5f}".format(outs[2]), "val_loss=", "{:.5f}".format(cost),
# "val_acc=", "{:.5f}".format(acc), "time per batch=", "{:.5f}".format((time.time() - t1)/n))
if epoch > FLAGS.early_stopping and np.mean(cost_val[-2:]) > np.mean(cost_val[-(FLAGS.early_stopping + 1):-1]):
# print("Early stopping...")
break
train_duration = time.time() - t
# Testing
# if os.path.exists("tmp/pubmed_MixModel.ckpt"):
# saver.restore(sess, "tmp/pubmed_MixModel.ckpt")
test_cost, test_acc, test_duration = evaluate(test_features, testSupport, y_test,
placeholders)
print("rank1 = {}".format(rank1), "cost=", "{:.5f}".format(test_cost),
"accuracy=", "{:.5f}".format(test_acc), "training time per epoch=", "{:.5f}".format(train_duration/(epoch+1)),
"test time=", "{:.5f}".format(test_duration))
if __name__=="__main__":
print("DATASET:", FLAGS.dataset)
main(5)
# for k in [25, 50, 100, 200, 400]:
# main(k)