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constructor.py
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constructor.py
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import tensorflow as tf
import numpy as np
from model import ARGA, Discriminator
from optimizer import OptimizerAE
import scipy.sparse as sp
import inspect
from scipy.sparse import csr_matrix
from scipy.stats import norm
from preprocessing import preprocess_graph, sparse_to_tuple, construct_feed_dict
flags = tf.app.flags
FLAGS = flags.FLAGS
def get_placeholder(adj):
placeholders = {
'features': tf.sparse_placeholder(tf.float32),
'adj': tf.sparse_placeholder(tf.float32),
'adj_orig': tf.sparse_placeholder(tf.float32),
'dropout': tf.placeholder_with_default(0., shape=()),
'real_distribution': tf.placeholder(dtype=tf.float32, shape=[adj.shape[0], FLAGS.hidden2],
name='real_distribution')
}
return placeholders
def get_model(model_str, placeholders, num_features, num_nodes, features_nonzero):
discriminator = Discriminator()
d_real = discriminator.construct(placeholders['real_distribution'])
model = None
if model_str == 'arga_ae':
model = ARGA(placeholders, num_features, features_nonzero)
elif model_str == 'arga_vae':
model = ARVGA(placeholders, num_features, num_nodes, features_nonzero)
return d_real, discriminator, model
def format_data_new(adj, features):
# Store original adjacency matrix (without diagonal entries) for later
adj_orig = adj
adj_orig = adj_orig - sp.dia_matrix((adj_orig.diagonal()[np.newaxis, :], [0]), shape=adj_orig.shape)
adj_orig.eliminate_zeros()
# Some preprocessing
adj_norm = preprocess_graph(adj)
num_nodes = adj.shape[0]
features = sparse_to_tuple(features.tocoo())
num_features = features[2][1]
features_nonzero = features[1].shape[0]
pos_weight = float(adj.shape[0] * adj.shape[0] - adj.sum()) / adj.sum()
norm = adj.shape[0] * adj.shape[0] / float((adj.shape[0] * adj.shape[0] - adj.sum()) * 2)
adj_label = adj + sp.eye(adj.shape[0])
adj_label = sparse_to_tuple(adj_label)
values = [adj, num_features, num_nodes, features_nonzero, pos_weight, norm, adj_norm, adj_label, features, adj_orig]
keys = ['adj', 'num_features', 'num_nodes', 'features_nonzero', 'pos_weight', 'norm', 'adj_norm', 'adj_label', 'features', 'adj_orig']
feas = {}
feas = dict(zip(keys, values))
return feas
def get_optimizer(model_str, model, discriminator, placeholders, pos_weight, norm, d_real,num_nodes):
if model_str == 'arga_ae':
d_fake = discriminator.construct(model.embeddings, reuse=True)
opt = OptimizerAE(preds=model.reconstructions,
labels=tf.reshape(tf.sparse_tensor_to_dense(placeholders['adj_orig'],
validate_indices=False), [-1]),
pos_weight=pos_weight,
norm=norm,
d_real=d_real,
d_fake=discriminator.construct(model.embeddings, reuse=True))
elif model_str == 'arga_vae':
opt = OptimizerVAE(preds=model.reconstructions,
labels=tf.reshape(tf.sparse_tensor_to_dense(placeholders['adj_orig'],
validate_indices=False), [-1]),
model=model, num_nodes=num_nodes,
pos_weight=pos_weight,
norm=norm,
d_real=d_real,
d_fake=discriminator.construct(model.embeddings, reuse=True))
return opt
def update(model, opt, sess, adj_norm, adj_label, features, placeholders, adj, prior):
# Construct feed dictionary
feed_dict = construct_feed_dict(adj_norm, adj_label, features, placeholders)
feed_dict.update({placeholders['dropout']: FLAGS.dropout})
feed_dict.update({placeholders['dropout']: 0})
emb = sess.run(model.z_mean, feed_dict=feed_dict)
#sns.set()
featureAverage = np.mean(prior, axis=1)
(mu, sigma) = norm.fit(featureAverage)
z_real_dist = np.random.normal(mu, sigma, (adj.shape[0], FLAGS.hidden2))
z_real_dist_prior = np.random.normal(mu, sigma, (adj.shape[0], FLAGS.hidden2))
feed_dict.update({placeholders['real_distribution']: z_real_dist_prior})
for j in range(5):
_, reconstruct_loss = sess.run([opt.opt_op, opt.cost], feed_dict=feed_dict)
d_loss, _ = sess.run([opt.dc_loss, opt.discriminator_optimizer], feed_dict=feed_dict)
g_loss, _ = sess.run([opt.generator_loss, opt.generator_optimizer], feed_dict=feed_dict)
avg_cost = reconstruct_loss
return emb, avg_cost
def retrieve_name(var):
callers_local_vars = inspect.currentframe().f_back.f_locals.items()
return [var_name for var_name, var_val in callers_local_vars if var_val is var][0]