DGCN-PyTorch/data_loader.py

import scipy.io as sio
import numpy as np
import torch

class DataLoader:
    """Data Loader class"""
    def __init__(self, S_train, S_val, S_test, data_dir, mat_file_path):
        self.S_train = S_train
        self.S_val = S_val
        self.S_test = S_test
        self.data_root_dir = data_dir
        self.mat_file_path = mat_file_path

        self.saved_content = sio.loadmat(self.data_root_dir + self.mat_file_path)
        self.T = np.max(self.saved_content["A_labels_subs"][0, :]) + 1
        print("Number of total graphs: {}".format(self.T))
        self.N = max(np.max(self.saved_content["A_labels_subs"][1, :]),
                     np.max(self.saved_content["A_labels_subs"][2, :])) + 1
        print("Number of nodes in each graph: {}".format(self.N))

        self.A_size = torch.Size([self.T, self.N, self.N])  # size of the adjacency matrix
        self.C_size = torch.Size([self.T, self.N, self.N])  # similar to the adjacency matrix, C tensor has TxNxN shape
        # labels of the edges
        self.A_labels = torch.sparse.FloatTensor(torch.tensor(self.saved_content["A_labels_subs"], dtype=torch.long),
                                                 torch.squeeze(torch.tensor(self.saved_content["A_labels_vals"])),
                                                 self.A_size).coalesce()

        # Laplacian Transformed of adjacency matrix
        self.C = torch.sparse.FloatTensor(torch.tensor(self.saved_content["C_subs"], dtype=torch.long),
                                          torch.squeeze(torch.tensor(self.saved_content["C_vals"])),
                                          self.C_size).coalesce()

        # adjacency matrix
        self.A = torch.sparse.FloatTensor(self.A_labels._indices(),
                                          torch.ones(self.A_labels._values().shape),
                                          self.A_size).coalesce()
        # create node features
        self.X = self.create_node_features()

    def split_data(self):
        C_train = []
        for j in range(self.S_train):
            idx = self.C._indices()[0] == j
            C_train.append(torch.sparse.FloatTensor(self.C._indices()[1:3, idx],
                                                    self.C._values()[idx]))

        C_val = []
        for j in range(self.S_train, self.S_train + self.S_val):
            idx = self.C._indices()[0] == j
            C_val.append(torch.sparse.FloatTensor(self.C._indices()[1:3, idx],
                                                    self.C._values()[idx]))

        C_test = []
        for j in range(self.S_train+self.S_test, self.S_train + self.S_val + self.S_test):
            idx = self.C._indices()[0] == j
            C_test.append(torch.sparse.FloatTensor(self.C._indices()[1:3, idx],
                                                  self.C._values()[idx]))

        C = {'C_train': C_train,
                 'C_val': C_val,
                 'C_test': C_test}

        X_train = self.X[0:self.S_train].double()
        X_val = self.X[self.S_train:self.S_train + self.S_val].double()
        X_test = self.X[self.S_train + self.S_val:].double()

        data = {'X_train' : X_train,
                'X_val': X_val,
                'X_test': X_test}


        return data, C

    def get_edges_and_labels(self):
        # training
        subs_train = self.A_labels._indices()[0] < self.S_train
        edges_train = self.A_labels._indices()[:, subs_train]
        labels_train = torch.sign(self.A_labels._values()[subs_train])
        target_train = (labels_train != -1).long()    # element = 0 if class = -1; and 1 if class is 0 or +1

        # validation
        subs_val = (self.A_labels._indices()[0] >= self.S_train) & (self.A_labels._indices()[0] < self.S_train + self.S_val)
        edges_val = self.A_labels._indices()[:, subs_val]
        edges_val[0] -= self.S_train
        labels_val = torch.sign(self.A_labels._values()[subs_val])
        target_val = (labels_val != -1).long()

        # Testing
        subs_test = (self.A_labels._indices()[0] >= self.S_train + self.S_val)
        edges_test = self.A_labels._indices()[:, subs_test]
        edges_test[0] -= (self.S_train + self.S_val)
        labels_test = torch.sign(self.A_labels._values()[subs_test])
        target_test = (labels_test != -1).long()

        targets = {'target_train': target_train,
                   'target_val': target_val,
                   'target_test': target_test}

        edges = {'edges_train': edges_train,
                 'edges_val': edges_val,
                 'edges_test': edges_test}

        return targets, edges

    def create_node_features(self):
        X = torch.zeros(self.A.shape[0], self.A.shape[1], 2)
        X[:, :, 0] = torch.sparse.sum(self.A, 1).to_dense()   # number of outgoing edges
        X[:, :, 1] = torch.sparse.sum(self.A, 2).to_dense()  # number of in coming edges

        return X

    def load_data(self):
        print("Loading the data...")
        data, C = self.split_data()
        targets, edges = self.get_edges_and_labels()
        print("======================")
        return data, C, targets, edges