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