helper_classes

Module Contents

Classes

DatasetTriple	An abstract class representing a Dataset.
HeadAndRelationBatchLoader	An abstract class representing a Dataset.
Reproduce

Attributes

seed

helper_classes.seed = 1

class helper_classes.DatasetTriple(data)

Bases: torch.utils.data.Dataset

An abstract class representing a Dataset.

All datasets that represent a map from keys to data samples should subclass it. All subclasses should overwrite __getitem__(), supporting fetching a data sample for a given key. Subclasses could also optionally overwrite __len__(), which is expected to return the size of the dataset by many Sampler implementations and the default options of DataLoader.

Note

DataLoader by default constructs a index sampler that yields integral indices. To make it work with a map-style dataset with non-integral indices/keys, a custom sampler must be provided.

__len__()

__getitem__(idx)

class helper_classes.HeadAndRelationBatchLoader(er_vocab, num_e)

Bases: torch.utils.data.Dataset

An abstract class representing a Dataset.

All datasets that represent a map from keys to data samples should subclass it. All subclasses should overwrite __getitem__(), supporting fetching a data sample for a given key. Subclasses could also optionally overwrite __len__(), which is expected to return the size of the dataset by many Sampler implementations and the default options of DataLoader.

Note

DataLoader by default constructs a index sampler that yields integral indices. To make it work with a map-style dataset with non-integral indices/keys, a custom sampler must be provided.

__len__()

__getitem__(idx)

class helper_classes.Reproduce

static get_er_vocab(data)

static get_head_tail_vocab(data)

get_data_idxs(data)

get_batch_1_to_N(er_vocab, er_vocab_pairs, idx)

evaluate_link_prediction(model, data, per_rel_flag_=True)

reproduce(model_path, data_path, model_name, per_rel_flag_=False, tail_pred_constraint=False, out_of_vocab_flag=False)

get_embeddings(model_path, data_path, model_name, per_rel_flag_=False, tail_pred_constraint=False, out_of_vocab_flag=False)

load_model(model_path, model_name)

reproduce_ensemble(model, data_path, per_rel_flag_=False, tail_pred_constraint=False, out_of_vocab_flag=False)

per_rel_flag_ reports link prediction results per relations. flag_of_removal -> removes triples from testing split containing entities that did not occur during training at testing time.

lp_based_on_head_and_tail_entity_rankings-> computes rank of missing entities based on head and tail entity.