utils.expand_dims_tile(
utils.expand_dims_tile(batch_adj + np.eye(batch_adj.shape[0]), 0,
n_ξ_samples), 0, 1),
utils.expand_dims_tile(labels[final_nodes], 1, n_ξ_samples),
]
# ### TRAIN ###
steps_per_epoch = int(np.ceil(len(labels) / seeds_per_batch))
history = vae.fit_generator_feed(
batching.batches(vae,
adj,
labels,
target_func,
seeds_per_batch,
max_walk_length,
p=p,
q=q,
neighbour_samples=neighbour_samples),
steps_per_epoch=steps_per_epoch,
epochs=n_epochs,
check_array_lengths=False,
shuffle=False,
verbose=0,
callbacks=[
# keras.callbacks.TensorBoard(),
#ae.ModelBatchCheckpoint(MODEL_DATA + '.batch-checkpoint.epoch={epoch:05d}-batch={batch:05d}-loss={loss:.2f}.h5',
# monitor='loss', period=50,
# #save_best_only=True
# ),
def train(max_walk_length, p, q, run):
# ### DEFINE TRAINING DATA ###
g = nx.planted_partition_graph(n_communities, community_size, p_in, p_out)
labels = np.zeros((n_communities * community_size, n_communities),
dtype=np.float32)
for c in range(n_communities):
labels[range(c * community_size, (c + 1) * community_size), c] = 1
# features = (np.random.random((n_communities * community_size, n_communities))
# .astype(np.float32))
features = (labels + np.abs(
np.random.normal(loc=0.0,
scale=features_noise_scale,
size=(n_communities * community_size,
n_communities)).astype(np.float32)))
# ## Update model parameters ##
dim_data = n_communities
dims = (dim_data, dim_l1, dim_ξ)
DATA_PARAMETERS = ('n_communities={n_communities}'
'-community_size={community_size}'
'-p_in={p_in}'
'-p_out={p_out}'
'-fns={features_noise_scale}').format(
n_communities=n_communities,
community_size=community_size,
p_in=p_in,
p_out=p_out,
features_noise_scale=features_noise_scale)
VAE_PARAMETERS = ('orth'
'-adj_scaling'
'-n_ξ_samples={n_ξ_samples}'
'-dims={dims}'
'-bias={use_bias}').format(n_ξ_samples=n_ξ_samples,
dims=dims,
use_bias=use_bias)
TRAINING_PARAMETERS = ('spb={seeds_per_batch}'
'-WL={max_walk_length}'
'-p={p}'
'-q={q}'
'-ns={neighbour_samples}'
'-n_epochs={n_epochs}'
'-run={run}').format(
seeds_per_batch=seeds_per_batch,
max_walk_length=max_walk_length,
p=p,
q=q,
neighbour_samples=neighbour_samples,
n_epochs=n_epochs,
run=run)
MODEL_DATA = os.path.join(
MODEL_PATH,
DATA_PARAMETERS + '---' + VAE_PARAMETERS + '---' + TRAINING_PARAMETERS)
MODEL_RESULTS = MODEL_DATA + '.results.pkl'
if os.path.exists(MODEL_RESULTS):
warnings.warn('"{}" already exist, skipping.'.format(MODEL_RESULTS))
return
# ### BUILD THE VAE ###
adj = nx.adjacency_matrix(g).astype(np.float32)
def build_q(dims, use_bias=False):
dim_data, dim_l1, dim_ξ = dims
q_input = keras.layers.Input(shape=(dim_data, ), name='q_input')
# CANDO: change activation
q_layer1_placeholders, q_layer1 = ae.gc_layer_with_placeholders(
dim_l1, 'q_layer1', {
'use_bias': use_bias,
'activation': 'relu'
}, q_input)
q_μ_flat_placeholders, q_μ_flat = ae.gc_layer_with_placeholders(
dim_ξ, 'q_mu_flat', {
'use_bias': use_bias,
'gather_mask': True
}, q_layer1)
q_logD_flat_placeholders, q_logD_flat = ae.gc_layer_with_placeholders(
dim_ξ, 'q_logD_flat', {
'use_bias': use_bias,
'gather_mask': True
}, q_layer1)
q_μlogD_flat = keras.layers.Concatenate(name='q_mulogD_flat')(
[q_μ_flat, q_logD_flat])
q_model = ae.Model(
inputs=([q_input] + q_layer1_placeholders + q_μ_flat_placeholders +
q_logD_flat_placeholders),
outputs=q_μlogD_flat)
return q_model, ('OrthogonalGaussian', )
q_model, q_codecs = build_q(dims, use_bias=use_bias)
def p_builder(p_input):
# CANDO: change activation
p_layer1 = keras.layers.Dense(dim_l1,
use_bias=use_bias,
activation='relu',
kernel_regularizer='l2',
bias_regularizer='l2',
name='p_layer1')(p_input)
p_adj = layers.Bilinear(0,
use_bias=use_bias,
kernel_regularizer='l2',
bias_regularizer='l2',
name='p_adj')([p_layer1, p_layer1])
p_v = keras.layers.Dense(dim_data,
use_bias=use_bias,
kernel_regularizer='l2',
bias_regularizer='l2',
name='p_v')(p_layer1)
return ([p_adj,
p_v], ('SigmoidBernoulliScaledAdjacency', 'SigmoidBernoulli'))
vae, vae_codecs = ae.build_vae(
(q_model, q_codecs),
p_builder,
n_ξ_samples,
[
1.0, # q loss
1.0, # p adj loss
1.0, # p v loss
],
)
# ### DEFINE TRAINING OBJECTIVES ###
def target_func(batch_adj, required_nodes, final_nodes):
return [
np.zeros(1), # ignored
utils.expand_dims_tile(
utils.expand_dims_tile(batch_adj + np.eye(batch_adj.shape[0]),
0, n_ξ_samples), 0, 1),
utils.expand_dims_tile(labels[final_nodes], 1, n_ξ_samples),
]
# ### TRAIN ###
steps_per_epoch = int(np.ceil(len(features) / seeds_per_batch))
history = vae.fit_generator_feed(
batching.batches(vae,
adj,
labels,
target_func,
seeds_per_batch,
max_walk_length,
p=p,
q=q,
neighbour_samples=neighbour_samples),
steps_per_epoch=steps_per_epoch,
epochs=n_epochs,
check_array_lengths=False,
shuffle=False,
verbose=0,
callbacks=[
# keras.callbacks.TensorBoard(),
ae.ModelBatchCheckpoint(MODEL_DATA + '.batch-checkpoint.h5',
monitor='loss',
period=10,
save_best_only=True),
keras.callbacks.ModelCheckpoint(MODEL_DATA +
'.epoch-checkpoint.h5',
monitor='loss',
period=10,
save_best_only=True),
TQDMCallback(),
])
# ### SAVE HISTORY ###
x, _, feeds = next(
batching.batches(vae,
adj,
features,
target_func,
adj.shape[0],
1,
p=1,
q=1,
neighbour_samples=None))
embeddings, adj_pred, features_pred = vae.predict_on_fed_batch(x,
feeds=feeds)
with open(MODEL_RESULTS, 'wb') as outfile:
pickle.dump(
{
'history': history.history,
'labels': labels,
'features': features,
'adj': adj,
'embeddings': embeddings,
'adj_pred': adj_pred,
'features_pred': features_pred
}, outfile)