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train_batch_multiRank_inductive_newscheme.py
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train_batch_multiRank_inductive_newscheme.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
from utils import *
from models import GCN, MLP, GCN_APPRO
# 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_appr', 'Model string.') # 'gcn', 'gcn_appr'
flags.DEFINE_float('learning_rate', 0.01, 'Initial learning rate.')
flags.DEFINE_integer('epochs', 300, 'Number of epochs to train.')
flags.DEFINE_integer('hidden1', 16, 'Number of units in hidden layer 1.')
flags.DEFINE_float('dropout', 0.5, '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.')
rank1 = 300
rank0 = 300
# Load data
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, rank0):
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]
# adj_val = adj[val_index, :][:, val_index]
# val_mask = val_mask[val_index]
# y_val = y_val[val_index]
# test_index = np.where(test_mask)[0]
# adj_test = adj[test_index, :][:, test_index]
# test_mask = test_mask[test_index]
# y_test = y_test[test_index]
numNode_train = adj_train.shape[0]
# print("numNode", numNode)
# Some preprocessing
features = nontuple_preprocess_features(features).todense()
train_features = features[train_index]
if FLAGS.model == 'gcn_appr':
normADJ_train = nontuple_preprocess_adj(adj_train)
normADJ = nontuple_preprocess_adj(adj)
# normADJ_val = nontuple_preprocess_adj(adj_val)
# normADJ_test = nontuple_preprocess_adj(adj_test)
num_supports = 2
model_func = GCN_APPRO
else:
raise ValueError('Invalid argument for model: ' + str(FLAGS.model))
# Define placeholders
placeholders = {
'support': [tf.sparse_placeholder(tf.float32) for _ in range(num_supports)],
'features': tf.placeholder(tf.float32, shape=(None, features.shape[1])),
'labels': tf.placeholder(tf.float32, shape=(None, y_train.shape[1])),
'labels_mask': tf.placeholder(tf.int32),
'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, mask, placeholders):
t_test = time.time()
feed_dict_val = construct_feed_dict(features, support, labels, mask, 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())
cost_val = []
p0 = column_prop(normADJ_train)
p1 = mix_prop(normADJ_train, features[train_index, :])
testSupport = [sparse_to_tuple(normADJ), sparse_to_tuple(normADJ)]
# valSupport = [sparse_to_tuple(normADJ_val), sparse_to_tuple(normADJ_val)]
# testSupport = [sparse_to_tuple(normADJ_test), sparse_to_tuple(normADJ_test)]
t = time.time()
# Train model
for epoch in range(FLAGS.epochs):
n = 0
for batch in iterate_minibatches_listinputs([normADJ_train, y_train, train_mask], batchsize=50, shuffle=True):
[normADJ_batch, y_train_batch, train_mask_batch] = batch
if sum(train_mask_batch) < 1:
continue
# p1 = column_prop(normADJ_batch)
q1 = np.random.choice(np.arange(numNode_train), rank1, p=p0) # top layer
q0 = np.random.choice(np.arange(numNode_train), rank0, p=p0) # bottom layer
support1 = sparse_to_tuple(normADJ_batch[:, q1].dot(sp.diags(1.0 / (p1[q1] * rank1))))
support0 = sparse_to_tuple(normADJ_train[q1, :][:, q0])
# support1 = sparse_to_tuple(normADJ_batch)
# support0 = sparse_to_tuple(normADJ[:, q0])
features_inputs = sp.diags(1.0 / (p1[q0] * rank0)).dot(train_features[q0, :]) # selected nodes for approximation
# Construct feed dictionary
feed_dict = construct_feed_dict(features_inputs, [support0, support1], y_train_batch, train_mask_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)
# Validation
cost, acc, duration = evaluate(features, testSupport, y_val, val_mask, placeholders)
cost_val.append(cost)
# # 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=", "{:.5f}".format(time.time() - t))
if epoch > FLAGS.early_stopping and cost_val[-1] > np.mean(cost_val[-(FLAGS.early_stopping + 1):-1]):
# print("Early stopping...")
break
train_duration = time.time() - t
# Testing
test_cost, test_acc, test_duration = evaluate(features, testSupport, y_test, test_mask,
placeholders)
print("rank1 = {}".format(rank1), "rank0 = {}".format(rank0), "cost=", "{:.5f}".format(test_cost),
"accuracy=", "{:.5f}".format(test_acc), "training time per epoch=", "{:.5f}".format(train_duration/epoch))
if __name__=="__main__":
print("DATASET:", FLAGS.dataset)
for k in range(100, 1000, 200):
main(k, k)