edges=edges,
iter=iter_node,
chunksize=batch_size)
cont_learner.train(model,
paths=graph_utils.combine_files_iter(walk_files),
total_nodes=context_total_path,
alpha=alpha,
chunksize=batch_size)
com_learner.fit(model, reg_covar=reg_covar, n_init=10)
com_learner.train(G.nodes(), model, beta, chunksize=batch_size, iter=iter_com)
log.info('time: %.2fs' % (timeit.default_timer() - start_time))
node_color = plot_utils.graph_plot(G,
graph_name=input_file,
show=False)
plot_utils.node_space_plot_2D_elipsoid(model.node_embedding, node_color,
means=model.centroid,
covariances=model.covariance_mat,
show=True)
# io_utils.save_embedding(model.node_embedding, model.vocab,
# file_name="{}_alpha-{}_beta-".format(output_file,
# alpha,
# beta))
import numpy as np
input_file = 'karate'
node_embedding = io_utils.load_embedding(
path='../data',
file_name="{}_my_ComE_l1-0_l2-0_ds-0_it-0".format(input_file),
ext=".txt")
g = mixture.GaussianMixture(n_components=2,
reg_covar=0.000001,
covariance_type='full',
n_init=5)
g.fit(node_embedding)
centroid = np.float32(g.means_)
covariance_mat = np.float32(g.covariances_)
G = graph_utils.load_adjacencylist(
path_join("../data/", input_file, input_file + '.adjlist'), True)
node_color = plot_utils.graph_plot(G=G,
show=False,
graph_name="karate",
node_position_file=True,
node_position_path='../data')
plot_utils.node_space_plot_2D_elipsoid(node_embedding,
means=centroid,
covariances=covariance_mat,
color_values=node_color,
grid=False,
show=True)
node_classification)
print(
f"===NMI=== for type={come_model_type} with d={representation_size} and K={k}: ",
nmi)
else:
print(
f"===NMI=== for type={come_model_type} with d={representation_size} and K={k} could not be computed"
)
# ### plotting
plot_name = f"{come_model_type}_d{representation_size}_k{k}"
if should_plot:
# graph_plot
plot_utils.graph_plot(G,
labels=node_classification,
plot_name=plot_name,
path=f"./plots/{output_file}",
save=True,
show_labels=False)
# node_space_plot_2D
plot_utils.node_space_plot_2d(model.node_embedding,
labels=node_classification,
plot_name=plot_name,
path=f"./plots/{output_file}",
save=True)
# node_space_plot_2d_ellipsoid
plot_utils.node_space_plot_2d_ellipsoid(
model.node_embedding,
labels=node_classification,
means=com_learner.g_mixture.means_,
covariances=com_learner.g_mixture.covariances_,
plot_name=plot_name,