def main():
emb = n2v.Node2VecPathSnapEmbGensim()
graphs = [gc.Graph.init_facebook_wosn_2009_snowball_sampled_2000()]
feature_type = ft.FeatureType.DIFF_BIN_WITH_DIM
diff_type = dt.DiffType.DIFFERENCE
num_bins = 10
for graph in graphs:
# base_dir = "/run/user/1002/gvfs/sftp:host=alpha/home/mellers/"
save_info = sl.MemoryAccess(graph=str(graph),
embedding_type=str(emb),
num_iterations=30,
diff_type=diff_type,
use_remote_data=False)
classifier = [ # KNeighborsClassifier(),
# SVC(kernel="linear", probability=True),
# DecisionTreeClassifier(),
# RandomForestClassifier(),
# AdaBoostClassifier(),
# GaussianNB(),
MLPClassifier(max_iter=100000, hidden_layer_sizes=(
100,
100,
))
]
test(save_info=save_info,
graph=graph,
feature_type=feature_type,
num_of_bins=num_bins,
classifier=classifier)
def main():
graph = gc.Graph.init_karate_club_graph()
graph = gc.Graph.init_sampled_aps_pacs052030()
embedding_function = n2v.Node2VecPathSnapEmbGensim()
num_of_bins = 10
num_iterations = 30
# nodes_to_predict = list(graph.nodes)
save_info = sl.MemoryAccess(graph=str(graph),
embedding_type=str(embedding_function),
num_iterations=num_iterations)
for i in range(34):
removed_nodes = [i]
diff = save_info.load_diff_matrix(removed_nodes)
create_feature_from_diff_bins_with_dim(diff=diff,
removed_nodes=removed_nodes,
original_graph=graph,
num_of_bins=num_of_bins,
save_info=save_info,
save=True,
check_for_existing=False)
for j in range(34):
if i != j:
removed_nodes = [i, j]
diff = save_info.load_diff_matrix(removed_nodes)
create_feature_from_diff_bins_with_dim(
diff=diff,
removed_nodes=removed_nodes,
original_graph=graph,
num_of_bins=num_of_bins,
save_info=save_info,
save=True,
check_for_existing=False)
logger=logger)
# exp num used training graphs
logger.info('kp1 exp 2: different number of training graphs')
jobs.run_experiments(
graphs=[gc.Graph.init_barabasi_m5_n1000()],
embeddings=[gem.GEM_embedding.init_hope(),
gem.GEM_embedding.init_sdne()],
list_num_tr_graphs=[1, 2, 5, 10, 20],
logger=logger)
# exp num iterations
logger.info('kp1 exp 3: number of embeddings on same graph')
jobs.run_experiments(graphs=[gc.Graph.init_barabasi_m5_n1000()],
embeddings=[
n2v.Node2VecPathSnapEmbGensim(),
line.Line(),
gem.GEM_embedding.init_hope(),
gem.GEM_embedding.init_sdne()
],
list_num_iter=[1, 2, 5, 10],
logger=logger)
logger.info('kp1 exp 4: all graphs')
jobs.run_experiments(graphs=gc.Graph.init_all_barabasi_graphs(),
embeddings=[
n2v.Node2VecPathSnapEmbGensim(),
line.Line(),
gem.GEM_embedding.init_hope(),
gem.GEM_embedding.init_sdne()
],
import sys
sys.path.insert(0, "..")
import jobs.all_exp as jobs
import graphs.graph_class as gc
import embeddings.line as line
import embeddings.node2vec_c_path_gensim_emb as n2v
import embeddings.GEM_embeddings as gem
import features.diff_type as dt
jobs.run_experiments(graphs=[gc.Graph.init_karate_club_graph()],
embeddings=[gem.GEM_embedding.init_hope(8), n2v.Node2VecPathSnapEmbGensim(), line.Line(),
gem.GEM_embedding.init_sdne()], list_num_tr_graphs=[1], num_test_eval=1)
# exp num used training graphs
logger.info('kp0 exp 2: different number of training graphs')
jobs.run_experiments(
graphs=[gc.Graph.init_facebook_wosn_2009_snowball_sampled_2000()],
embeddings=[gem.GEM_embedding.init_hope(),
gem.GEM_embedding.init_sdne()],
list_num_tr_graphs=[1, 2, 5, 10],
logger=logger)
# exp num iterations
logger.info('kp0 exp 3: different num iterations')
jobs.run_experiments(
graphs=[gc.Graph.init_facebook_wosn_2009_snowball_sampled_2000()],
embeddings=[
n2v.Node2VecPathSnapEmbGensim(),
line.Line(),
gem.GEM_embedding.init_hope(),
gem.GEM_embedding.init_sdne()
],
list_num_iter=[1, 2, 5, 10],
logger=logger)
logger.info('kp0 exp 4: all graphs')
jobs.run_experiments(graphs=gc.Graph.init_all_but_barabasi(),
embeddings=[
n2v.Node2VecPathSnapEmbGensim(),
line.Line(),
gem.GEM_embedding.init_hope(),
gem.GEM_embedding.init_sdne()
],