import argparse
[docs]
class Namespace(argparse.Namespace):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.dataset_dir: str = None
"The path of a folder containing train.txt, and/or valid.txt and/or test.txt"
self.save_embeddings_as_csv: bool = False
"Embeddings of entities and relations are stored into CSV files to facilitate easy usage."
self.storage_path: str = "Experiments"
"A directory named with time of execution under --storage_path that contains related data about embeddings."
self.path_to_store_single_run: str = None
"A single directory created that contains related data about embeddings."
self.path_single_kg = None
"Path of a file corresponding to the input knowledge graph"
self.sparql_endpoint = None
"An endpoint of a triple store."
self.model: str = "Keci"
"KGE model"
self.optim: str = 'Adam'
"Optimizer"
self.embedding_dim: int = 64
"Size of continuous vector representation of an entity/relation"
self.num_epochs: int = 150
"Number of pass over the training data"
self.batch_size: int = 1024
"Mini-batch size if it is None, an automatic batch finder technique applied"
self.lr: float = 0.1
"""Learning rate"""
self.add_noise_rate: float = None
"The ratio of added random triples into training dataset"
self.gpus = None
"""Number GPUs to be used during training"""
self.callbacks = dict()
"""Callbacks, e.g., {"PPE":{ "last_percent_to_consider": 10}}"""
self.backend: str = "pandas"
"""Backend to read, process, and index input knowledge graph. pandas, polars and rdflib available"""
self.trainer: str = 'torchCPUTrainer'
"""Trainer for knowledge graph embedding model"""
self.scoring_technique: str = 'KvsAll'
"""Scoring technique for knowledge graph embedding models"""
self.neg_ratio: int = 0
"""Negative ratio for a true triple in NegSample training_technique"""
self.weight_decay: float = 0.0
"""Weight decay for all trainable params"""
self.normalization: str = "None"
""" LayerNorm, BatchNorm1d, or None """
self.init_param: str = None
""" xavier_normal or None"""
self.gradient_accumulation_steps: int = 0
""" Not tested e"""
self.num_folds_for_cv: int = 0
""" Number of folds for CV"""
self.eval_model: str = "train_val_test"
""" Evaluate trained model choices:["None", "train", "train_val", "train_val_test", "test"]"""
self.save_model_at_every_epoch: int = None
""" Not tested """
self.label_smoothing_rate: float = 0.0
self.num_core: int = 0
"""Number of CPUs to be used in the mini-batch loading process"""
self.random_seed: int = 0
"Random Seed"
self.sample_triples_ratio: float = None
"""Read some triples that are uniformly at random sampled. Ratio being between 0 and 1"""
self.read_only_few: int = None
"""Read only first few triples """
self.pykeen_model_kwargs = dict()
"""Additional keyword arguments for pykeen models"""
# Below attributes can be given as model_kwargs argument
self.kernel_size: int = 3
"""Size of a square kernel in a convolution operation"""
self.num_of_output_channels: int = 32
"""Number of slices in the generated feature map by convolution."""
self.p: int = 0
"P parameter of Clifford Embeddings"
self.q: int = 1
"Q parameter of Clifford Embeddings"
self.input_dropout_rate: float = 0.0
"""Dropout rate on embeddings of input triples"""
self.hidden_dropout_rate: float = 0.0
"""Dropout rate on hidden representations of input triples"""
self.feature_map_dropout_rate: float = 0.0
"""Dropout rate on a feature map generated by a convolution operation"""
self.byte_pair_encoding: bool = False
"WIP: Byte pair encoding"
self.adaptive_swa: bool = False
"Adaptive stochastic weight averaging"
self.swa: bool = False
"Stochastic weight averaging"
self.block_size: int = None
"block size of LLM"
self.continual_learning=None
"Path of a pretrained model size of LLM"
[docs]
def __iter__(self):
# Iterate
for k, v in self.__dict__.items():
yield k, v