dicee.config
============

.. py:module:: dicee.config


Classes
-------

.. autoapisummary::

   dicee.config.Namespace


Module Contents
---------------

.. py:class:: Namespace(**kwargs)

   Bases: :py:obj:`argparse.Namespace`


   Simple object for storing attributes.

   Implements equality by attribute names and values, and provides a simple
   string representation.


   .. py:attribute:: dataset_dir
      :type:  str
      :value: None


      The path of a folder containing train.txt, and/or valid.txt and/or test.txt


   .. py:attribute:: save_embeddings_as_csv
      :type:  bool
      :value: False


      Embeddings of entities and relations are stored into CSV files to facilitate easy usage.


   .. py:attribute:: storage_path
      :type:  str
      :value: 'Experiments'


      A directory named with time of execution under --storage_path that contains related data about embeddings.


   .. py:attribute:: path_to_store_single_run
      :type:  str
      :value: None


      A single directory created that contains related data about embeddings.


   .. py:attribute:: path_single_kg
      :value: None


      Path of a file corresponding to the input knowledge graph


   .. py:attribute:: sparql_endpoint
      :value: None


      An endpoint of a triple store.


   .. py:attribute:: model
      :type:  str
      :value: 'Keci'


      KGE model


   .. py:attribute:: optim
      :type:  str
      :value: 'Adam'


      Optimizer


   .. py:attribute:: embedding_dim
      :type:  int
      :value: 64


      Size of continuous vector representation of an entity/relation


   .. py:attribute:: num_epochs
      :type:  int
      :value: 150


      Number of pass over the training data


   .. py:attribute:: batch_size
      :type:  int
      :value: 1024


      Mini-batch size if it is None, an automatic batch finder technique applied


   .. py:attribute:: lr
      :type:  float
      :value: 0.1


      Learning rate


   .. py:attribute:: add_noise_rate
      :type:  float
      :value: None


      The ratio of added random triples into training dataset


   .. py:attribute:: gpus
      :value: None


      Number GPUs to be used during training


   .. py:attribute:: callbacks

      10}}

      :type: Callbacks, e.g., {"PPE"

      :type: { "last_percent_to_consider"


   .. py:attribute:: backend
      :type:  str
      :value: 'pandas'


      Backend to read, process, and index input knowledge graph. pandas, polars and rdflib available


   .. py:attribute:: separator
      :type:  str
      :value: '\\s+'


      separator for extracting head, relation and tail from a triple


   .. py:attribute:: trainer
      :type:  str
      :value: 'torchCPUTrainer'


      Trainer for knowledge graph embedding model


   .. py:attribute:: scoring_technique
      :type:  str
      :value: 'KvsAll'


      Scoring technique for knowledge graph embedding models


   .. py:attribute:: neg_ratio
      :type:  int
      :value: 0


      Negative ratio for a true triple in NegSample training_technique


   .. py:attribute:: weight_decay
      :type:  float
      :value: 0.0


      Weight decay for all trainable params


   .. py:attribute:: normalization
      :type:  str
      :value: 'None'


      LayerNorm, BatchNorm1d, or None


   .. py:attribute:: init_param
      :type:  str
      :value: None


      xavier_normal or None


   .. py:attribute:: gradient_accumulation_steps
      :type:  int
      :value: 0


      Not tested e


   .. py:attribute:: num_folds_for_cv
      :type:  int
      :value: 0


      Number of folds for CV


   .. py:attribute:: eval_model
      :type:  str
      :value: 'train_val_test'


      ["None", "train", "train_val", "train_val_test", "test"]

      :type: Evaluate trained model choices


   .. py:attribute:: save_model_at_every_epoch
      :type:  int
      :value: None


      Not tested


   .. py:attribute:: label_smoothing_rate
      :type:  float
      :value: 0.0



   .. py:attribute:: num_core
      :type:  int
      :value: 0


      Number of CPUs to be used in the mini-batch loading process


   .. py:attribute:: random_seed
      :type:  int
      :value: 0


      Random Seed


   .. py:attribute:: sample_triples_ratio
      :type:  float
      :value: None


      Read some triples that are uniformly at random sampled. Ratio being between 0 and 1


   .. py:attribute:: read_only_few
      :type:  int
      :value: None


      Read only first few triples


   .. py:attribute:: pykeen_model_kwargs

      Additional keyword arguments for pykeen models


   .. py:attribute:: kernel_size
      :type:  int
      :value: 3


      Size of a square kernel in a convolution operation


   .. py:attribute:: num_of_output_channels
      :type:  int
      :value: 32


      Number of slices in the generated feature map by convolution.


   .. py:attribute:: p
      :type:  int
      :value: 0


      P parameter of Clifford Embeddings


   .. py:attribute:: q
      :type:  int
      :value: 1


      Q parameter of Clifford Embeddings


   .. py:attribute:: input_dropout_rate
      :type:  float
      :value: 0.0


      Dropout rate on embeddings of input triples


   .. py:attribute:: hidden_dropout_rate
      :type:  float
      :value: 0.0


      Dropout rate on hidden representations of  input triples


   .. py:attribute:: feature_map_dropout_rate
      :type:  float
      :value: 0.0


      Dropout rate on a feature map generated by a convolution operation


   .. py:attribute:: byte_pair_encoding
      :type:  bool
      :value: False


      Byte pair encoding

      :type: WIP


   .. py:attribute:: adaptive_swa
      :type:  bool
      :value: False


      Adaptive stochastic weight averaging


   .. py:attribute:: swa
      :type:  bool
      :value: False


      Stochastic weight averaging


   .. py:attribute:: block_size
      :type:  int
      :value: None


      block size of LLM


   .. py:attribute:: continual_learning
      :value: None


      Path of a pretrained model size of LLM


   .. py:attribute:: auto_batch_finding
      :value: False


      A flag for using auto batch finding


   .. py:method:: __iter__()