decagon-pytorch/src/decagon_pytorch/data.py

from collections import defaultdict
from .decode import BilinearDecoder
from .weights import init_glorot


class NodeType(object):
    def __init__(self, name, count):
        self.name = name
        self.count = count


class RelationType(object):
    def __init__(self, name, node_type_row, node_type_column,
        adjacency_matrix):
        self.name = name
        self.node_type_row = node_type_row
        self.node_type_column = node_type_column
        self.adjacency_matrix = adjacency_matrix

    def get_adjacency_matrix(node_type_row, node_type_column):
        if self.node_type_row == node_type_row and \
            self.node_type_column == node_type_column:
            return self.adjacency_matrix

        elif self.node_type_row == node_type_column and \
            self.node_type_column == node_type_row:
            return self.adjacency_matrix.transpose(0, 1)

        else:
            raise ValueError('Specified row/column types do not correspond to this relation')


class Data(object):
    def __init__(self):
        self.node_types = []
        self.relation_types = defaultdict(list)
        # self.decoder_types = defaultdict(lambda: BilinearDecoder)
        # self.latent_node = []

    def add_node_type(self, name, count): # , latent_length):
        self.node_types.append(NodeType(name, count))
        # self.latent_node.append(init_glorot(count, latent_length))

    def add_relation_type(self, name, node_type_row, node_type_column, adjacency_matrix):
        n = len(self.node_types)
        if node_type_row >= n or node_type_column >= n:
            raise ValueError('Node type index out of bounds, add node type first')
        key = (node_type_row, node_type_column)
        self.relation_types[key].append(RelationType(name, node_type_row, node_type_column, adjacency_matrix))
        # _ = self.decoder_types[(node_type_row, node_type_column)]

    #def set_decoder_type(self, node_type_row, node_type_column, decoder_class):
    #    if (node_type_row, node_type_column) not in self.decoder_types:
    #        raise ValueError('Relation type not found, add relation first')
    #    self.decoder_types[(node_type_row, node_type_column)] = decoder_class

    def get_adjacency_matrices(self, node_type_row, node_type_column):
        # rels = list(filter(lambda a: a[0] == node_type_row and a[1] == node_type_column), self.relation_types)
        key = (node_type_row, node_type_column)
        if key not in self.relation_types:
            raise ValueError('Relation type not found')
        rels = self.relation_types[key]
        rels = list(map(lambda a: a.adjacency_matrix, rels))
        return rels


    def __repr__(self):
        n = len(self.node_types)
        if n == 0:
            return 'Empty GNN Data'
        s = ''
        s += 'GNN Data with:\n'
        s += '- ' + str(n) + ' node type(s):\n'
        for nt in self.node_types:
            s += '  - ' + nt.name + '\n'
        if len(self.relation_types) == 0:
            s += '- No relation types\n'
            return s.strip()
        n = sum(map(len, self.relation_types))
        s += '- ' + str(n) + ' relation type(s):\n'
        for i in range(n):
            for j in range(n):
                key = (i, j)
                if key not in self.relation_types:
                    continue
                rels = self.relation_types[key]
                # rels = list(filter(lambda a: a[0] == i and a[1] == j, self.relation_types))
                #if len(rels) == 0:
                #    continue
                # dir = '<->' if i == j else '->'
                dir = '--'
                s += '  - ' + self.node_types[i].name + ' ' + dir + ' ' + self.node_types[j].name + ':\n'
                #' (' + self.decoder_types[(i, j)].__name__ + '):\n'
                for r in rels:
                    s += '    - ' + r.name + '\n'
        return s.strip()