import json
from dicee.executer import Execute, ContinuousExecute
import argparse
[docs]
def get_default_arguments(description=None):
""" Extends pytorch_lightning Trainer's arguments with ours """
# From "pytorch-lightning==1.6.4" to "lightning>=2.1.3", 'Trainer' has no attribute 'add_argparse_args'
# parser = pl.Trainer.add_argparse_args(argparse.ArgumentParser(add_help=False))
parser = argparse.ArgumentParser(add_help=False)
# Default Trainer param https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html#methods
# Knowledge graph related arguments
parser.add_argument("--dataset_dir", type=str, default=None,
help="The path of a folder containing train.txt, and/or valid.txt and/or test.txt"
",e.g., KGs/UMLS")
parser.add_argument("--sparql_endpoint", type=str, default=None,
help="An endpoint of a triple store, e.g. 'http://localhost:3030/mutagenesis/'. ")
parser.add_argument("--path_single_kg", type=str, default=None,
help="Path of a file corresponding to the input knowledge graph")
# Saved files related arguments
parser.add_argument("--path_to_store_single_run", type=str, default=None,
help="A single directory created that contains related data about embeddings.")
parser.add_argument("--storage_path", type=str, default='Experiments',
help="A directory named with time of execution under --storage_path "
"that contains related data about embeddings.")
parser.add_argument("--save_embeddings_as_csv", action="store_true",
help="A flag for saving embeddings in csv file.")
parser.add_argument("--backend", type=str, default="polars",
choices=["pandas", "polars", "rdflib"],
help='Backend for loading, preprocessing, indexing input knowledge graph.')
# Model related arguments
parser.add_argument("--model", type=str,
default="Keci",
choices=["ComplEx", "Keci", "ConEx", "AConEx", "ConvQ", "AConvQ", "ConvO", "AConvO", "QMult",
"OMult", "Shallom", "DistMult", "TransE", "DualE",
"BytE",
"Pykeen_MuRE", "Pykeen_QuatE", "Pykeen_DistMult", "Pykeen_BoxE", "Pykeen_CP",
"Pykeen_HolE", "Pykeen_ProjE", "Pykeen_RotatE",
"Pykeen_TransE", "Pykeen_TransF", "Pykeen_TransH",
"Pykeen_TransR", "Pykeen_TuckER", "Pykeen_ComplEx","LFMult", "DeCaL"],
help="Available knowledge graph embedding models. "
"To use other knowledge graph embedding models available in python, e.g.,"
"**Pykeen_BoxE** and add this into choices")
parser.add_argument('--optim', type=str, default='Adam',
help='An optimizer',
choices=['Adam', 'AdamW', 'SGD',"NAdam", "Adagrad", "ASGD"])
parser.add_argument('--embedding_dim', type=int, default=32,
help='Number of dimensions for an embedding vector. ')
parser.add_argument("--num_epochs", type=int, default=10, help='Number of epochs for training. ')
parser.add_argument('--batch_size', type=int, default=32,
help='Mini batch size. If None, automatic batch finder is applied')
parser.add_argument("--lr", type=float, default=0.1)
parser.add_argument('--callbacks', type=json.loads,
default={},
help='{"PPE":{ "last_percent_to_consider": 10}}'
'"Perturb": {"level": "out", "ratio": 0.2, "method": "RN", "scaler": 0.3}')
parser.add_argument("--trainer", type=str, default='PL',
choices=['torchCPUTrainer', 'PL', 'torchDDP'],
help='PL (pytorch lightning trainer), torchDDP (custom ddp), torchCPUTrainer (custom cpu only)')
parser.add_argument('--scoring_technique', default="AllvsAll",
help="Training technique for knowledge graph embedding model",
choices=["AllvsAll", "KvsAll", "1vsAll", "NegSample", "1vsSample", "KvsSample"])
parser.add_argument('--neg_ratio', type=int, default=10,
help='The number of negative triples generated per positive triple.')
parser.add_argument('--weight_decay', type=float, default=0.0, help='L2 penalty e.g.(0.00001)')
parser.add_argument('--input_dropout_rate', type=float, default=0.0)
parser.add_argument('--hidden_dropout_rate', type=float, default=0.0)
parser.add_argument("--feature_map_dropout_rate", type=float, default=0.0)
parser.add_argument("--normalization", type=str, default="None",
choices=["LayerNorm", "BatchNorm1d", None],
help="Normalization technique")
parser.add_argument("--init_param", type=str, default=None, choices=["xavier_normal", None],
help="Initialization technique")
parser.add_argument("--gradient_accumulation_steps", type=int, default=0,
help="e.g. gradient_accumulation_steps=2 "
"implies that gradients are accumulated at every second mini-batch")
parser.add_argument("--label_smoothing_rate", type=float, default=0.0, help='None for not using it.')
parser.add_argument("--kernel_size", type=int, default=3,
help="Square kernel size for convolution based models.")
parser.add_argument("--num_of_output_channels", type=int, default=2,
help="# of output channels in convolution")
parser.add_argument("--num_core", type=int, default=0,
help='Number of cores to be used. 0 implies using single CPU')
parser.add_argument("--random_seed", type=int, default=1,
help='Seed for all, see pl seed_everything().')
parser.add_argument('--p', type=int, default=0,
help='P for Clifford Algebra')
parser.add_argument('--q', type=int, default=1,
help='Q for Clifford Algebra')
parser.add_argument('--pykeen_model_kwargs', type=json.loads, default={})
# Evaluation Related
parser.add_argument('--num_folds_for_cv', type=int, default=0,
help='Number of folds in k-fold cross validation.'
'If >2 ,no evaluation scenario is applied implies no evaluation.')
parser.add_argument("--eval_model", type=str, default="train_val_test",
choices=["None", "train", "train_val", "train_val_test", "test"],
help='Evaluating link prediction performance on data splits. ')
parser.add_argument("--save_model_at_every_epoch", type=int, default=None,
help='At every X number of epochs model will be saved. If None, we save 4 times.')
# Continual Learning
parser.add_argument("--continual_learning", type=str, default=None,
help="The path of a folder containing a pretrained model and configurations")
parser.add_argument("--sample_triples_ratio", type=float, default=None, help='Sample input data.')
parser.add_argument("--read_only_few", type=int, default=None,
help='READ only first N triples. If 0, read all.')
parser.add_argument("--add_noise_rate", type=float, default=0.0,
help='Add x % of noisy triples into training dataset.')
# WIP
parser.add_argument('--r', type=int, default=0,
help='R for Clifford Algebra')
parser.add_argument('--block_size', type=int, default=8,
help='Block size for BytE')
parser.add_argument("--byte_pair_encoding",
action="store_true",
help="Currently only avail. for KGE implemented within dice-embeddings.")
parser.add_argument("--adaptive_swa",
action="store_true",
help="Adaptive stochastic weight averaging")
parser.add_argument("--swa",
action="store_true",
help="Stochastic weight averaging")
parser.add_argument('--degree', type=int, default=0,
help='degree for polynomial embeddings')
if description is None:
return parser.parse_args()
return parser.parse_args(description)
[docs]
def main():
args = get_default_arguments()
if args.continual_learning:
ContinuousExecute(args).continual_start()
else:
Execute(get_default_arguments()).start()
if __name__ == '__main__':
main()