def generate_karate_embedding():
import visualisation
y_path = '../../local_resources/karate/y.p'
targets = utils.read_pickle(y_path)
y = np.array(targets['cat'])
log_path = '../../local_resources/tf_logs/run4/'
walk_path = '../../local_resources/karate/walks_n1_l10.csv'
size = 2 # dimensionality of the embedding
params = Params(walk_path, batch_size=4, embedding_size=size, neg_samples=5, skip_window=5, num_pairs=1500,
statistics_interval=0.1,
initial_learning_rate=1.0, save_path=log_path, epochs=10, concurrent_steps=1)
path = '../../local_resources/karate/embeddings/tf_Win_polar' + '_' + utils.get_timestamp() + '.csv'
embedding_in, embedding_out = HE.main(params)
visualisation.plot_poincare_embedding(embedding_in, y,
'../../results/karate/figs/poincare_polar_Win' + '_' + utils.get_timestamp() + '.pdf')
visualisation.plot_poincare_embedding(embedding_out, y,
'../../results/karate/figs/poincare_polar_Wout' + '_' + utils.get_timestamp() + '.pdf')
df_in = pd.DataFrame(data=embedding_in, index=range(embedding_in.shape[0]))
df_in.to_csv(path, sep=',')
df_out = pd.DataFrame(data=embedding_out, index=range(embedding_out.shape[0]))
df_out.to_csv(
'../../local_resources/karate/embeddings/tf_Wout_polar' + '_' + utils.get_timestamp() + '.csv',
sep=',')
return path
def generate_political_blogs_embedding():
import visualisation
s = datetime.datetime.now()
y_path = '../../local_resources/political_blogs/y.p'
y = utils.read_pickle(y_path)
log_path = '../../local_resources/tf_logs/polblogs/'
walk_path = '../../local_resources/political_blogs/walks_n1_l10.csv'
size = 2 # dimensionality of the embedding
params = Params(walk_path, batch_size=4, embedding_size=size, neg_samples=5, skip_window=5, num_pairs=1500,
statistics_interval=10.0,
initial_learning_rate=1.0, save_path=log_path, epochs=5, concurrent_steps=4)
path = '../../local_resources/political_blogs/embeddings/Win' + '_' + utils.get_timestamp() + '.csv'
embedding_in, embedding_out = HCE.main(params)
visualisation.plot_poincare_embedding(embedding_in, y,
'../../results/political_blogs/figs/poincare_polar_Win' + '_' + utils.get_timestamp() + '.pdf')
visualisation.plot_poincare_embedding(embedding_out, y,
'../../results/political_blogs/figs/poincare_polar_Wout' + '_' + utils.get_timestamp() + '.pdf')
df_in = pd.DataFrame(data=embedding_in, index=np.arange(embedding_in.shape[0]))
df_in.to_csv(path, sep=',')
df_out = pd.DataFrame(data=embedding_out, index=np.arange(embedding_out.shape[0]))
df_out.to_csv(
'../../local_resources/political_blogs/embeddings/Wout' + '_' + utils.get_timestamp() + '.csv',
sep=',')
print('political blogs sample generated in: ', datetime.datetime.now() - s)
political_blogs_scenario(path)
return path
def generate_blogcatalog_cartesian_embedding():
import visualisation
s = datetime.datetime.now()
y_path = '../../local_resources/blogcatalog/y.p'
y = utils.read_pickle(y_path)
log_path = '../../local_resources/tf_logs/blogcatalog_cartesian/final_throw1'
walk_path = '../../local_resources/blogcatalog/p025_q025_d128_walks.csv'
size = 128 # dimensionality of the embedding
params = Params(walk_path, batch_size=4, embedding_size=size, neg_samples=5, skip_window=5, num_pairs=1500,
statistics_interval=10,
initial_learning_rate=0.2, save_path=log_path, epochs=5, concurrent_steps=12)
path = '../../local_resources/blogcatalog/embeddings/Win_cartesian' + '_' + utils.get_timestamp() + '.csv'
embedding_in, embedding_out = HCE.main(params)
visualisation.plot_poincare_embedding(embedding_in, y,
'../../results/blogcatalog/figs/poincare_Win_cartesian' + '_' + utils.get_timestamp() + '.pdf')
visualisation.plot_poincare_embedding(embedding_out, y,
'../../results/blogcatalog/figs/poincare_Wout_cartesian' + '_' + utils.get_timestamp() + '.pdf')
df_in = pd.DataFrame(data=embedding_in, index=np.arange(embedding_in.shape[0]))
df_in.to_csv(path, sep=',')
df_out = pd.DataFrame(data=embedding_out, index=np.arange(embedding_out.shape[0]))
df_out.to_csv(
'../../local_resources/blogcatalog/embeddings/Wout_cartesian' + '_' + utils.get_timestamp() + '.csv',
sep=',')
print('blogcatalog cartesian embedding generated in: ', datetime.datetime.now() - s)
return path
def batch_size_scenario():
"""
Generate embeddings using different batch sizes for the ~1000 vertex polblogs network
:return:
"""
import visualisation
s = datetime.datetime.now()
y_path = '../../local_resources/political_blogs/y.p'
x_path = '../../local_resources/political_blogs/X.p'
y = utils.read_pickle(y_path)
log_path = '../../local_resources/tf_logs/polblogs/'
walk_path = '../../local_resources/political_blogs/walks_n1_l10.csv'
size = 2 # dimensionality of the embedding
batch_sizes = [1, 2, 4, 8, 16, 32, 64, 128]
embeddings = []
for batch_size in batch_sizes:
params = Params(walk_path, batch_size=batch_size, embedding_size=size, neg_samples=5, skip_window=5,
num_pairs=1500,
statistics_interval=10.0,
initial_learning_rate=0.1, save_path=log_path, epochs=5, concurrent_steps=4)
path = '../../local_resources/political_blogs/embeddings/Win_batch_{}_{}.csv'.format(
batch_size, utils.get_timestamp())
embedding_in, embedding_out = HCE.main(params)
visualisation.plot_poincare_embedding(embedding_in, y,
'../../results/political_blogs/figs/poincare_polar_Win_batch_{}_{}.pdf'.format(
batch_size, utils.get_timestamp()))
visualisation.plot_poincare_embedding(embedding_out, y,
'../../results/political_blogs/figs/poincare_polar_Wout_batch_{}_{}.pdf'.format(
batch_size, utils.get_timestamp()))
df_in = pd.DataFrame(data=embedding_in, index=np.arange(embedding_in.shape[0]))
df_in.to_csv(path, sep=',')
df_out = pd.DataFrame(data=embedding_out, index=np.arange(embedding_out.shape[0]))
df_out.to_csv(
'../../local_resources/political_blogs/embeddings/Wout_batch_{}_{}.csv'.format(
batch_size, utils.get_timestamp()),
sep=',')
print('political blogs embedding generated in: ', datetime.datetime.now() - s)
embeddings.append(embedding_in)
x, y = utils.read_data(x_path, y_path, threshold=0)
names = [[str(batch_size)] for batch_size in batch_sizes]
n_folds = 10
results = run_detectors.run_all_datasets(embeddings, y, names, classifiers, n_folds)
all_results = utils.merge_results(results, n_folds)
results, tests = utils.stats_test(all_results)
tests[0].to_csv('../../results/political_blogs/batch_size_pvalues' + utils.get_timestamp() + '.csv')
tests[1].to_csv('../../results/political_blogs/batch_size_pvalues' + utils.get_timestamp() + '.csv')
print('macro', results[0])
print('micro', results[1])
macro_path = '../../results/political_blogs/batch_size_macro' + utils.get_timestamp() + '.csv'
micro_path = '../../results/political_blogs/batch_size_micro' + utils.get_timestamp() + '.csv'
results[0].to_csv(macro_path, index=True)
results[1].to_csv(micro_path, index=True)
return path
def simulated_tree_scenario(branching_factor, levels):
import visualisation
folder = '../../local_resources/simulated_trees'
deepwalk_path = '../../local_resources/simulated_trees/deepwalk_z{}_l{}.emd'.format(
branching_factor, levels)
walk_path = '../../local_resources/simulated_trees/walks_long_z{}_l{}.emd'.format(
branching_factor, levels)
emb_path = create_adj_mat(folder, branching_factor, levels)
generate_simulated_tree(emb_path, walk_path, deepwalk_path)
deepwalk_emd = pd.read_csv(deepwalk_path,
header=None,
index_col=0,
skiprows=1,
sep=" ")
s = datetime.datetime.now()
# y_path = '../../local_resources/blogcatalog_121_sample/y.p'
# y = utils.read_pickle(y_path)
y = generate_y(branching_factor, levels)
log_path = '../../local_resources/tf_logs/sim_tree/'
# walk_path = '../../local_resources/simulated_trees/walks.csv'
size = 2 # dimensionality of the embedding
params = Params(walk_path,
batch_size=4,
embedding_size=size,
neg_samples=5,
skip_window=5,
num_pairs=1500,
statistics_interval=0.1,
initial_learning_rate=1.0,
save_path=log_path,
epochs=20,
concurrent_steps=4)
path = '../../local_resources/simulated_trees/embeddings/Win' + '_' + utils.get_timestamp(
) + '.csv'
embedding_in, embedding_out = HE.main(params)
visualisation.plot_deepwalk_embedding(
deepwalk_emd.values, y,
'../../results/simulated_trees/figs/deepwalk_z{}_l{}_{}.pdf'.format(
branching_factor, levels, utils.get_timestamp()))
visualisation.plot_poincare_embedding(
embedding_in, y,
'../../results/simulated_trees/figs/hyp_z{}_l{}_{}.pdf'.format(
branching_factor, levels, utils.get_timestamp()))
df_in = pd.DataFrame(data=embedding_in,
index=np.arange(embedding_in.shape[0]))
df_in.to_csv(path, sep=',')
return path
def run_embedding(folder, learning_rate, run_scenario=True, module=HE):
"""
Generate an embeddings for a given graph
:param folder: the name of the folder and also the graph
:param run_scenario: True if cv results are required
:param module: An alias for the module containing the specific embedding
:return: the path to the embedding
"""
import visualisation
s = datetime.datetime.now()
y_path = '../../local_resources/{}/y.p'.format(folder)
targets = utils.read_pickle(y_path)
y = np.array(targets['cat'])
log_path = '../../local_resources/tf_logs/run1/'
walk_path = '../../local_resources/{}/walks_n1_l10.csv'.format(folder)
size = 4 # dimensionality of the embedding
params = Params(walk_path,
batch_size=4,
embedding_size=size,
neg_samples=5,
skip_window=5,
num_pairs=1500,
statistics_interval=10.0,
initial_learning_rate=learning_rate,
save_path=log_path,
epochs=5,
concurrent_steps=4)
path = '../../local_resources/{0}/embeddings/Win_{1}.csv'.format(
folder, utils.get_timestamp())
embedding_in, embedding_out = module.main(params)
visualisation.plot_poincare_embedding(
embedding_in, y,
'../../results/all/embedding_figs/{}_Win_{}.pdf'.format(
folder, utils.get_timestamp()))
visualisation.plot_poincare_embedding(
embedding_out, y,
'../../results/all/embedding_figs/{}_Wout_{}.pdf'.format(
folder, utils.get_timestamp()))
df_in = pd.DataFrame(data=embedding_in,
index=np.arange(embedding_in.shape[0]))
df_in.to_csv(path, sep=',')
df_out = pd.DataFrame(data=embedding_out,
index=np.arange(embedding_out.shape[0]))
df_out.to_csv('../../local_resources/{0}/embeddings/Wout_{1}.csv'.format(
folder, utils.get_timestamp()),
sep=',')
print('{} embedding generated in: '.format(folder),
datetime.datetime.now() - s)
if run_scenario:
MLD.run_scenario(folder, path)
return path
