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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, ARVGA, Discriminator, Discriminator2
from optimizer import OptimizerAE, OptimizerVAE, OptimizerAE2
import scipy.sparse as sp
import inspect
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'),
'real_dist_TV': tf.placeholder(dtype=tf.float32, shape=[adj.shape[0]-1, 595], name='real_dist_TV'),
'fake_dist_for_d2' : tf.placeholder(dtype=tf.float32, name="fake_dist_for_d2")
}
return placeholders
def get_model_2(model_str, placeholders, num_features, num_nodes, features_nonzero):
discriminator2 = Discriminator2()
d_real_TV = discriminator2.construct(placeholders['real_dist_TV'])
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_TV, discriminator2, model
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_2(model_str, model, discriminator, discriminator2, placeholders, pos_weight, norm, d_real_TV, num_nodes):
if model_str == 'arga_ae':
d_fake = discriminator2.construct(placeholders['fake_dist_for_d2'], reuse=True)
firstZ = discriminator.construct(model.embeddings, reuse=True)
opt = OptimizerAE2(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_TV,
d_fake=d_fake,
firstZ=firstZ)
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_TV,
d_fake=d_fake)
return opt
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=d_fake)
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
#emb, avg_cost = update(ae_model, opt, sess, feas['adj_norm'], feas['adj_label'], feas['features'], placeholders, feas['adj'])
def update(model, opt, sess, adj_norm, adj_label, features, placeholders, adj, prior, hiddenSIMLR, new_fake_d):
# 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)
# prior can be (1) features or (2) original_train_TV for the second discriminator
featureAverage = np.mean(prior, axis=1)
(mu, sigma) = norm.fit(featureAverage)
if(hiddenSIMLR == "No_hidden_SIMLR"):
z_real_dist_prior = np.random.normal(mu, sigma, (adj.shape[0], FLAGS.hidden2))
feed_dict.update({placeholders['real_distribution']: z_real_dist_prior})
else:
z_real_dist_prior = np.random.normal(mu, sigma, (prior.shape[0], 595))
feed_dict.update({placeholders['real_dist_TV']: z_real_dist_prior})
feed_dict.update({placeholders['fake_dist_for_d2']: new_fake_d})
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]