decagon-pytorch/src/decagon_pytorch/decode/pairwise.py

#
# Copyright (C) Stanislaw Adaszewski, 2020
# License: GPLv3
#


import torch
from ..weights import init_glorot
from ..dropout import dropout


class DEDICOMDecoder(torch.nn.Module):
    """DEDICOM Tensor Factorization Decoder model layer for link prediction."""
    def __init__(self, input_dim, num_relation_types, drop_prob=0.,
        activation=torch.sigmoid, **kwargs):

        super().__init__(**kwargs)
        self.input_dim = input_dim
        self.num_relation_types = num_relation_types
        self.drop_prob = drop_prob
        self.activation = activation

        self.global_interaction = init_glorot(input_dim, input_dim)
        self.local_variation = [
            torch.flatten(init_glorot(input_dim, 1)) \
                for _ in range(num_relation_types)
        ]

    def forward(self, inputs_row, inputs_col):
        outputs = []
        for k in range(self.num_relation_types):
            inputs_row = dropout(inputs_row, 1.-self.drop_prob)
            inputs_col = dropout(inputs_col, 1.-self.drop_prob)

            relation = torch.diag(self.local_variation[k])

            product1 = torch.mm(inputs_row, relation)
            product2 = torch.mm(product1, self.global_interaction)
            product3 = torch.mm(product2, relation)
            rec = torch.mm(product3, torch.transpose(inputs_col, 0, 1))
            outputs.append(self.activation(rec))
        return outputs


class DistMultDecoder(torch.nn.Module):
    """DEDICOM Tensor Factorization Decoder model layer for link prediction."""
    def __init__(self, input_dim, num_relation_types, drop_prob=0.,
        activation=torch.sigmoid, **kwargs):

        super().__init__(**kwargs)
        self.input_dim = input_dim
        self.num_relation_types = num_relation_types
        self.drop_prob = drop_prob
        self.activation = activation

        self.relation = [
            torch.flatten(init_glorot(input_dim, 1)) \
                for _ in range(num_relation_types)
        ]

    def forward(self, inputs_row, inputs_col):
        outputs = []
        for k in range(self.num_relation_types):
            inputs_row = dropout(inputs_row, 1.-self.drop_prob)
            inputs_col = dropout(inputs_col, 1.-self.drop_prob)

            relation = torch.diag(self.relation[k])

            intermediate_product = torch.mm(inputs_row, relation)
            rec = torch.mm(intermediate_product, torch.transpose(inputs_col, 0, 1))
            outputs.append(self.activation(rec))
        return outputs


class BilinearDecoder(torch.nn.Module):
    """DEDICOM Tensor Factorization Decoder model layer for link prediction."""
    def __init__(self, input_dim, num_relation_types, drop_prob=0.,
        activation=torch.sigmoid, **kwargs):

        super().__init__(**kwargs)
        self.input_dim = input_dim
        self.num_relation_types = num_relation_types
        self.drop_prob = drop_prob
        self.activation = activation

        self.relation = [
            init_glorot(input_dim, input_dim) \
                for _ in range(num_relation_types)
        ]

    def forward(self, inputs_row, inputs_col):
        outputs = []
        for k in range(self.num_relation_types):
            inputs_row = dropout(inputs_row, 1.-self.drop_prob)
            inputs_col = dropout(inputs_col, 1.-self.drop_prob)

            intermediate_product = torch.mm(inputs_row, self.relation[k])
            rec = torch.mm(intermediate_product, torch.transpose(inputs_col, 0, 1))
            outputs.append(self.activation(rec))
        return outputs


class InnerProductDecoder(torch.nn.Module):
    """DEDICOM Tensor Factorization Decoder model layer for link prediction."""
    def __init__(self, input_dim, num_relation_types, drop_prob=0.,
        activation=torch.sigmoid, **kwargs):

        super().__init__(**kwargs)
        self.input_dim = input_dim
        self.num_relation_types = num_relation_types
        self.drop_prob = drop_prob
        self.activation = activation


    def forward(self, inputs_row, inputs_col):
        outputs = []
        for k in range(self.num_relation_types):
            inputs_row = dropout(inputs_row, 1.-self.drop_prob)
            inputs_col = dropout(inputs_col, 1.-self.drop_prob)

            rec = torch.mm(inputs_row, torch.transpose(inputs_col, 0, 1))
            outputs.append(self.activation(rec))
        return outputs