def main():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset',
type=str,
default=BEAUTY,
help='One of {BEAUTY, CELL, CD, CLOTH}.')
args = parser.parse_args()
# Create AmazonDataset instance for dataset.
# ========== BEGIN ========== #
print('Load', args.dataset, 'dataset from file...')
if not os.path.isdir(
TMP_DIR[args.dataset]
): #if the required temp file doesn't exist, create it
os.makedirs(TMP_DIR[args.dataset]) #TMP_DIR is a dict from utils.py
dataset = AmazonDataset(DATASET_DIR[args.dataset])
save_dataset(args.dataset,
dataset) #pickle.dump dataset to file, no return value
# Generate knowledge graph instance.
# ========== BEGIN ========== #
print('Create', args.dataset, 'knowledge graph from dataset...')
dataset = load_dataset(args.dataset)
kg = KnowledgeGraph(dataset)
kg.compute_degrees()
save_kg(args.dataset, kg) #uses pickle.dump
# =========== END =========== #
# Genereate train/test labels.
# ========== BEGIN ========== #
print('Generate', args.dataset, 'train/test labels.')
generate_labels(args.dataset, 'train')
generate_labels(args.dataset, 'test')
def main():
parser = argparse.ArgumentParser()
args = parser.parse_args()
args.dataset = config["dataset"]
# Create AmazonDataset instance for dataset.
# ========== BEGIN ========== #
print("Load", args.dataset, "dataset from file...")
if not os.path.isdir(TMP_DIR[args.dataset]):
os.makedirs(TMP_DIR[args.dataset])
dataset = AmazonDataset(DATASET_DIR[args.dataset])
save_dataset(args.dataset, dataset)
# Generate knowledge graph instance.
# ========== BEGIN ========== #
print("Create", args.dataset, "knowledge graph from dataset...")
dataset = load_dataset(args.dataset)
kg = KnowledgeGraph(dataset)
kg.compute_degrees()
save_kg(args.dataset, kg)
# =========== END =========== #
# Genereate train/test labels.
# ========== BEGIN ========== #
print("Generate", args.dataset, "train/test labels.")
generate_labels(args.dataset, "train")
generate_labels(args.dataset, "test")
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--dataset',
type=str,
default=BEAUTY,
help='One of {BEAUTY, CELL, CD, CLOTH}.')
args = parser.parse_args()
# Create AmazonDataset instance for dataset.
# ========== BEGIN ========== #
print('Load', args.dataset, 'dataset from file...')
if not os.path.isdir(TMP_DIR[args.dataset]):
os.makedirs(TMP_DIR[args.dataset])
dataset = AmazonDataset(DATASET_DIR[args.dataset])
save_dataset(args.dataset, dataset)
# Generate knowledge graph instance.
# ========== BEGIN ========== #
print('Create', args.dataset, 'knowledge graph from dataset...')
dataset = load_dataset(args.dataset)
kg = KnowledgeGraph(dataset)
kg.compute_degrees()
save_kg(args.dataset, kg)
# =========== END =========== #
# Generate train/test labels.
# ========== BEGIN ========== #
print('Generate', args.dataset, 'train/test labels.')
generate_labels(args.dataset, 'train')
generate_labels(args.dataset, 'test')