def learn(config, data):
start = timeit.default_timer()
train(config, data)
end = timeit.default_timer()
print('Time : {0}'.format(end - start))
if __name__ == '__main__':
start = timeit.default_timer()
path = "/Users/siddharthashankardas/Purdue/Dataset/Karate/"
from GNN_configuration import getSettings, load_data_DGL
load_config = {
"input_path": path,
"labeled_only": True,
"dataset_name": "karate"
}
data = load_data_DGL(load_config)
print(data)
gnn_settings = getSettings(load_config['dataset_name'])
gnn_settings['output_path'] = path
train(gnn_settings, data)
#load_saved(gnn_settings,data)
end = timeit.default_timer()
print('Time : {0}'.format(end - start))
def learn():
if (LEARNING_data_config['algorithm'] == 'GCN_DGL'):
from GNN.GCN_DGL.GCN_DGL_main import learn
from GNN_configuration import getSettings, load_data_DGL
if LEARNING_data_config["dataset_name"] == "karate":
LEARNING_data_config["vectorize"] = ""
LEARNING_data_config['input_path'] = dataset_info[LEARNING_data_config[
'dataset_name']]['path'] + LEARNING_data_config["vectorize"] + "/"
data = load_data_DGL(LEARNING_data_config)
gnn_settings = getSettings(LEARNING_data_config['dataset_name'], data)
gnn_settings['output_path'] = dataset_info[
LEARNING_data_config['dataset_name']]['output_path']
learn(gnn_settings, data)
elif (LEARNING_data_config['algorithm'] == 'GSAGE_DGL'):
from GNN.GSAGE_DGL.GSAGE_DGL_main import learn
from GNN_configuration import getSettings, load_data_DGL
if LEARNING_data_config["dataset_name"] == "karate":
LEARNING_data_config["vectorize"] = ""
LEARNING_data_config['input_path'] = dataset_info[LEARNING_data_config['dataset_name']]['path'] + \
LEARNING_data_config["vectorize"] + "/"
data = load_data_DGL(LEARNING_data_config)
gnn_settings = getSettings(LEARNING_data_config['dataset_name'])
gnn_settings['output_path'] = dataset_info[
LEARNING_data_config['dataset_name']]['output_path']
learn(gnn_settings, data)
elif (LEARNING_data_config['algorithm'] == 'GAT_DGL'):
from GNN.GAT_DGL.GAT_DGL_main import learn
from GNN_configuration import getSettings, load_data_DGL
if LEARNING_data_config["dataset_name"] == "karate":
LEARNING_data_config["vectorize"] = ""
LEARNING_data_config['input_path'] = dataset_info[LEARNING_data_config['dataset_name']]['path'] + \
LEARNING_data_config["vectorize"] + "/"
data = load_data_DGL(LEARNING_data_config)
gnn_settings = getSettings(LEARNING_data_config['dataset_name'])
gnn_settings['output_path'] = dataset_info[
LEARNING_data_config['dataset_name']]['output_path']
learn(gnn_settings, data)
elif (LEARNING_data_config['algorithm'] == 'FC'):
from GNN.FNN_PT_TF_Keras.FC import learn
from GNN_configuration import getSettings, load_data_DGL
if LEARNING_data_config["dataset_name"] == "karate":
LEARNING_data_config["vectorize"] = ""
LEARNING_data_config['input_path'] = dataset_info[LEARNING_data_config['dataset_name']]['path'] + \
LEARNING_data_config["vectorize"] + "/"
data = load_data_DGL(LEARNING_data_config)
gnn_settings = getSettings(LEARNING_data_config['dataset_name'])
gnn_settings['output_path'] = dataset_info[
LEARNING_data_config['dataset_name']]['output_path']
learn(gnn_settings, data)
else:
print("this GNN is not incorporated yet")
sys.exit(0)
return