dicee.models.complex

Classes

ConEx	Convolutional ComplEx Knowledge Graph Embeddings
AConEx	Additive Convolutional ComplEx Knowledge Graph Embeddings
ComplEx	Base class for all Knowledge Graph Embedding models.

Module Contents

class dicee.models.complex.ConEx(args)

Bases: dicee.models.base_model.BaseKGE

Convolutional ComplEx Knowledge Graph Embeddings

name = 'ConEx'

conv2d

fc_num_input

fc1

norm_fc1

bn_conv2d

feature_map_dropout

residual_convolution(C_1: Tuple[torch.Tensor, torch.Tensor], C_2: Tuple[torch.Tensor, torch.Tensor]) → torch.FloatTensor

Compute residual score of two complex-valued embeddings. :param C_1: a tuple of two pytorch tensors that corresponds complex-valued embeddings :param C_2: a tuple of two pytorch tensors that corresponds complex-valued embeddings :return:

forward_k_vs_all(x: torch.Tensor) → torch.FloatTensor

Score a (head, relation) batch against every entity.

Sub-classes must override this method. The default implementation raises ValueError to make missing overrides obvious at runtime.

Returns:

Shape (batch_size, num_entities) score matrix.

Return type:

torch.FloatTensor

forward_triples(x: torch.Tensor) → torch.FloatTensor

Score a batch of (head, relation, tail) index triples.

Parameters:

x (torch.LongTensor) – Shape (batch_size, 3) integer tensor where each row is [head_idx, relation_idx, tail_idx].

Returns:

Shape (batch_size,) triple scores.

Return type:

torch.FloatTensor

forward_k_vs_sample(x: torch.Tensor, target_entity_idx: torch.Tensor)

Score a (head, relation) batch against a sampled subset of entities.

Used by KvsSample and 1vsSample datasets. Sub-classes that support sample-based labelling must override this method.

Returns:

Shape (batch_size, k) score matrix where k is the number of sampled target entities.

Return type:

torch.FloatTensor

class dicee.models.complex.AConEx(args)

Bases: dicee.models.base_model.BaseKGE

Additive Convolutional ComplEx Knowledge Graph Embeddings

name = 'AConEx'

conv2d

fc_num_input

fc1

norm_fc1

bn_conv2d

feature_map_dropout

residual_convolution(C_1: Tuple[torch.Tensor, torch.Tensor], C_2: Tuple[torch.Tensor, torch.Tensor]) → torch.FloatTensor

Compute residual score of two complex-valued embeddings. :param C_1: a tuple of two pytorch tensors that corresponds complex-valued embeddings :param C_2: a tuple of two pytorch tensors that corresponds complex-valued embeddings :return:

forward_k_vs_all(x: torch.Tensor) → torch.FloatTensor

Score a (head, relation) batch against every entity.

Sub-classes must override this method. The default implementation raises ValueError to make missing overrides obvious at runtime.

Returns:

Shape (batch_size, num_entities) score matrix.

Return type:

torch.FloatTensor

forward_triples(x: torch.Tensor) → torch.FloatTensor

Score a batch of (head, relation, tail) index triples.

Parameters:

x (torch.LongTensor) – Shape (batch_size, 3) integer tensor where each row is [head_idx, relation_idx, tail_idx].

Returns:

Shape (batch_size,) triple scores.

Return type:

torch.FloatTensor

forward_k_vs_sample(x: torch.Tensor, target_entity_idx: torch.Tensor)

Score a (head, relation) batch against a sampled subset of entities.

Used by KvsSample and 1vsSample datasets. Sub-classes that support sample-based labelling must override this method.

Returns:

Shape (batch_size, k) score matrix where k is the number of sampled target entities.

Return type:

torch.FloatTensor

class dicee.models.complex.ComplEx(args)

Bases: dicee.models.base_model.BaseKGE

Base class for all Knowledge Graph Embedding models.

Inherits the Lightning training loop from BaseKGELightning and adds the embedding tables, normalisation / dropout layers, and the routing logic that dispatches forward() calls to the appropriate scoring method.

Sub-classes must implement at minimum:

• forward_triples() — score a batch of (h, r, t) triples.

• forward_k_vs_all() — score a (h, r) batch against every entity.

Parameters:

args (dict) – Flat configuration dictionary produced by vars(argparse.Namespace). Required keys: embedding_dim, num_entities, num_relations, learning_rate (or lr), optim, scoring_technique.

name = 'ComplEx'

static score(head_ent_emb: torch.FloatTensor, rel_ent_emb: torch.FloatTensor, tail_ent_emb: torch.FloatTensor)

static k_vs_all_score(emb_h: torch.FloatTensor, emb_r: torch.FloatTensor, emb_E: torch.FloatTensor)

Parameters:

• emb_h

• emb_r

• emb_E

forward_k_vs_all(x: torch.LongTensor) → torch.FloatTensor

Score a (head, relation) batch against every entity.

Sub-classes must override this method. The default implementation raises ValueError to make missing overrides obvious at runtime.

Returns:

Shape (batch_size, num_entities) score matrix.

Return type:

torch.FloatTensor

forward_k_vs_sample(x: torch.LongTensor, target_entity_idx: torch.LongTensor)

Score a (head, relation) batch against a sampled subset of entities.

Used by KvsSample and 1vsSample datasets. Sub-classes that support sample-based labelling must override this method.

Returns:

Shape (batch_size, k) score matrix where k is the number of sampled target entities.

Return type:

torch.FloatTensor