2 years ago · e881dd753b
--- a/.idea/.gitignore
+++ b/.idea/.gitignore
@@ -0,0 +1,3 @@

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 /workspace.xml
--- a/.idea/GraphRNN.iml
+++ b/.idea/GraphRNN.iml
@@ -0,0 +1,11 @@
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--- a/.idea/inspectionProfiles/profiles_settings.xml
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
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--- a/.idea/misc.xml
+++ b/.idea/misc.xml
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--- a/.idea/modules.xml
+++ b/.idea/modules.xml
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--- a/.idea/remote-mappings.xml
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--- a/2019-10-06).xml
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 </project>
--- a/GraphCompletion/graph_completion_test.py
+++ b/GraphCompletion/graph_completion_test.py
@@ -0,0 +1,164 @@
 from sklearn.metrics import mean_absolute_error
 import sys
 sys.path.append('../')
 from train import *
 from args import Args
 from GraphCompletion.graph_completion_with_training import graph_show
 from GraphCompletion.graph_show import graph_save


 def test_completion(x_batch, y_len_unsorted, args, rnn, output, test_batch_size=32, sample_time=1):
    rnn.hidden = rnn.init_hidden(1)
    rnn.eval()
    output.eval()

    # generate graphs
    max_num_node = int(args.max_num_node)
    y_pred = Variable(
        torch.zeros(test_batch_size, max_num_node, args.max_prev_node)).cuda()  # normalized prediction score
    y_pred_long = Variable(torch.zeros(test_batch_size, max_num_node, args.max_prev_node)).cuda()  # discrete prediction
    x_step = Variable(torch.ones(1, 1, args.max_prev_node)).cuda()

    # number_of_missing_nodes = args.number_of_missing_nodes
    number_of_missing_nodes = 2
    for j in range(test_batch_size):
        incomplete_graph_size = y_len_unsorted.data[j] - number_of_missing_nodes
        for i in range(y_len_unsorted.data[j]-1):
            h = rnn(x_step)
            y_pred_step = output(h)
            y_pred[j:j+1, i:i + 1, :] = F.sigmoid(y_pred_step)
            if(i<incomplete_graph_size):
                x_step = (x_batch[j:j+1, i+1:i+2, :]).cuda()
            else:
                x_step = sample_sigmoid(y_pred_step, sample=True, sample_time=sample_time)
            y_pred_long[j:j+1, i:i + 1, :] = x_step
            rnn.hidden = Variable(rnn.hidden.data).cuda()
    y_pred_long_data = y_pred_long.data.long()
    adj_true_list = []
    graph_true_list = []
    adj_incomplete_list = []
    graph_incomplete_list = []
    adj_pred_list = []
    graph_pred_list = []
    for i in range(test_batch_size):
        adj_true = decode_adj(x_batch[i, 1:y_len_unsorted.data[i], :].cpu().numpy())
        adj_true_list.append(adj_true)
        graph_true_list.append(nx.from_numpy_matrix(adj_true))
        adj_incomplete = decode_adj(x_batch[i, 1:y_len_unsorted.data[i] - number_of_missing_nodes, :].cpu().numpy())
        adj_incomplete_list.append(adj_incomplete)
        graph_incomplete_list.append(nx.from_numpy_matrix(adj_incomplete))
        adj_pred = decode_adj(y_pred_long_data[i, 0:y_len_unsorted.data[i]-1, :].cpu().numpy())
        adj_pred_list.append(adj_pred)
        graph_pred_list.append(nx.from_numpy_matrix(adj_pred))
    return adj_true_list, graph_true_list, adj_incomplete_list, graph_incomplete_list, adj_pred_list, graph_pred_list


 if __name__ == '__main__':
    # All necessary arguments are defined in args.py
    args = Args()
    os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda)
    # check if necessary directories exist
    if not os.path.isdir(args.model_save_path):
        os.makedirs(args.model_save_path)
    if not os.path.isdir(args.graph_save_path):
        os.makedirs(args.graph_save_path)
    if not os.path.isdir(args.figure_save_path):
        os.makedirs(args.figure_save_path)
    if not os.path.isdir(args.timing_save_path):
        os.makedirs(args.timing_save_path)
    if not os.path.isdir(args.figure_prediction_save_path):
        os.makedirs(args.figure_prediction_save_path)
    if not os.path.isdir(args.nll_save_path):
        os.makedirs(args.nll_save_path)

    graphs = create_graphs.create(args)

    # split datasets
    random.seed(123)
    shuffle(graphs)
    graphs_len = len(graphs)

    args.max_num_node = max([graphs[i].number_of_nodes() for i in range(len(graphs))])
    max_num_edge = max([graphs[i].number_of_edges() for i in range(len(graphs))])
    min_num_edge = min([graphs[i].number_of_edges() for i in range(len(graphs))])

    # show graphs statistics
    print('total graph num: {}'.format(len(graphs)))
    print('max number node: {}'.format(args.max_num_node))
    print('max/min number edge: {}; {}'.format(max_num_edge, min_num_edge))
    print('max previous node: {}'.format(args.max_prev_node))

    if 'nobfs' in args.note:
        args.max_prev_node = args.max_num_node-1
    dataset = Graph_sequence_sampler_pytorch_nobfs_for_completion(graphs,
                                                 max_num_node=args.max_num_node)
    sample_strategy = torch.utils.data.sampler.WeightedRandomSampler([1.0 / len(dataset) for i in range(len(dataset))],
                                                                     num_samples=args.batch_size * args.batch_ratio,
                                                                     replacement=True)
    dataset_loader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, num_workers=args.num_workers,
                                                 sampler=sample_strategy)

    rnn_GraphRNN = GRU_plain(input_size=args.max_prev_node, embedding_size=args.embedding_size_rnn,
                    hidden_size=args.hidden_size_rnn, num_layers=args.num_layers, has_input=True,
                    has_output=False).cuda()
    output_GraphRNN = MLP_plain(h_size=args.hidden_size_rnn, embedding_size=args.embedding_size_output,
                       y_size=args.max_prev_node).cuda()
    fname = args.model_save_path + args.fname_GraphRNN + 'lstm_' + str(args.load_epoch) + '.dat'
    rnn_GraphRNN.load_state_dict(torch.load(fname))
    fname = args.model_save_path + args.fname_GraphRNN + 'output_' + str(args.load_epoch) + '.dat'
    output_GraphRNN.load_state_dict(torch.load(fname))

    # ******************************************************************
    rnn_GraphCompletion = GRU_plain(input_size=args.max_prev_node, embedding_size=args.embedding_size_rnn,
                    hidden_size=args.hidden_size_rnn, num_layers=args.num_layers, has_input=True,
                    has_output=False).cuda()
    output_GraphCompletion = MLP_plain(h_size=args.hidden_size_rnn, embedding_size=args.embedding_size_output,
                       y_size=args.max_prev_node).cuda()

    fname = args.model_save_path + args.fname + 'lstm_' + args.graph_completion_string + str(args.epochs) + '.dat'
    rnn_GraphCompletion.load_state_dict(torch.load(fname))
    fname = args.model_save_path + args.fname + 'output_' + args.graph_completion_string + str(args.epochs) + '.dat'
    output_GraphCompletion.load_state_dict(torch.load(fname))
    # ******************************************************************
    MAE_GraphRNN = []
    MAE_GraphCompletion = []
    for batch_idx, data in enumerate(dataset_loader):
        if True:
            x_unsorted = data['x'].float()
            y_unsorted = data['y'].float()
            y_len_unsorted = data['len']
            # *********************************
            adj_true_list, graph_true_list, adj_incomplete_list, graph_incomplete_list, adj_pred_list, graph_pred_list\
                = test_completion(x_unsorted, y_len_unsorted, args, rnn_GraphRNN, output_GraphRNN)
            adj_true_list, graph_true_list, adj_incomplete_list, graph_incomplete_list, adj_pred_list_completion_model\
                , graph_pred_list_completion_model \
                = test_completion(x_unsorted, y_len_unsorted, args, rnn_GraphCompletion, output_GraphCompletion)
            mae = np.sum(np.absolute((adj_pred_list[0].astype("float") - adj_true_list[0].astype("float"))))
            # print("adj_true: ")
            # print(adj_true_list[0])
            # graph_show(nx.from_numpy_matrix(adj_true_list[0]),"adj_true" )
            # graph_show(nx.from_numpy_matrix(adj_incomplete_list[0]), "adj_incomplete")
            # print("my error")
            # print(mae)
            # print(mean_absolute_error(adj_pred_list[0], adj_true_list[0]))
            # print("adj_pred_list:")
            # print(adj_pred_list[0])
            # graph_show(nx.from_numpy_matrix(adj_pred_list[0]), "pred_true")

            for i in range(len(graph_true_list)):
                # graph_save(graph_true_list[i], graph_incomplete_list[i],
                #            graph_pred_list[i], graph_pred_list_completion_model[i], i, args.graph_save_path)
                mae = mean_absolute_error(adj_pred_list[i], adj_true_list[i])
                MAE_GraphRNN.append(mae)
                mae = mean_absolute_error(adj_pred_list_completion_model[i], adj_true_list[i])
                MAE_GraphCompletion.append(mae)
            # *********************************
            # print(MAE_GraphCompletion)
            # G_pred_step = test_mlp(x_unsorted, y_len_unsorted, epoch, args, rnn_for_graph_completion,
            #                        output_for_graph_completion)
            # nx.write_gpickle(G_pred_step, "completed_graphs_with_training.dat")
    print("MAE_GraphRNN:")
    print(np.mean(MAE_GraphRNN))
    # print(MAE_GraphRNN)
    print("MAE_GraphCompletion:")
    print(np.mean(MAE_GraphCompletion))
--- a/GraphCompletion/graph_completion_test_without_training.py
+++ b/GraphCompletion/graph_completion_test_without_training.py
@@ -0,0 +1,213 @@
 import torch
 import torch.nn.functional as F
 from torch.autograd import Variable
 from sklearn.metrics import mean_absolute_error
 import sys
 sys.path.append('../')
 from data import *
 from train import *
 from args import Args


 def test_mlp(x_batch, y_len_unsorted, epoch, args, rnn, output, test_batch_size=32, save_histogram=False,sample_time=1):
    rnn.hidden = rnn.init_hidden(1)
    rnn.eval()
    output.eval()

    # generate graphs
    max_num_node = int(args.max_num_node)
    y_pred = Variable(
        torch.zeros(test_batch_size, max_num_node, args.max_prev_node)).cuda()  # normalized prediction score
    y_pred_long = Variable(torch.zeros(test_batch_size, max_num_node, args.max_prev_node)).cuda()  # discrete prediction
    x_step = Variable(torch.ones(1, 1, args.max_prev_node)).cuda()

    incompleteness_ratio = 0.96

    for j in range(test_batch_size):
        incomplete_graph_size = int(int(y_len_unsorted.data[j]) * incompleteness_ratio)
        # print(y_len_unsorted.data[j])
        for i in range(y_len_unsorted.data[j]-1):
            h = rnn(x_step)
            y_pred_step = output(h)
            y_pred[j:j+1, i:i + 1, :] = F.sigmoid(y_pred_step)
            if (i<incomplete_graph_size):
                x_step = (x_batch[j:j+1, i+1:i+2, :]).cuda()
            else:
                x_step = sample_sigmoid(y_pred_step, sample=True, sample_time=sample_time)
            y_pred_long[j:j+1, i:i + 1, :] = x_step
            rnn.hidden = Variable(rnn.hidden.data).cuda()
    y_pred_long_data = y_pred_long.data.long()

    G_pred_list = []
    adj_true_list = []
    adj_pred_list = []
    for i in range(test_batch_size):
        adj_pred = decode_adj(y_pred_long_data[i].cpu().numpy())
        adj_pred_list.append(adj_pred)
        adj_true_list.append(decode_adj(x_batch[i].cpu().numpy()))
        G_pred = get_graph(adj_pred)  # get a graph from zero-padded adj
        G_pred_list.append(G_pred)
    return G_pred_list, adj_true_list, adj_pred_list


 def save_graph(graph, name):
    adj_pred = decode_adj(graph.cpu().numpy())
    G_pred = get_graph(adj_pred)  # get a graph from zero-padded adj
    G = np.asarray(nx.to_numpy_matrix(G_pred))
    np.savetxt(name + '.txt', G, fmt='%d')


 def data_to_graph_converter(data):
    G_list = []
    for i in range(len(data)):
        x = data[i].numpy()
        x = x.astype(int)
        adj_pred = decode_adj(x)
        G = get_graph(adj_pred) # get a graph from zero-padded adj
        G_list.append(G)
    return G_list


 def get_incomplete_graph(x_batch, y_len_unsorted, incompleteness_ratio = 0.96):
    batch_size = len(x_batch)
    max_prev_node = len(x_batch[0][0])
    max_incomplete_num_node = int(int(max(y_len_unsorted)) * incompleteness_ratio)
    incomplete_graph = Variable(torch.zeros(batch_size, max_incomplete_num_node, max_prev_node))
    for i in range(len(y_len_unsorted)):
        incomplete_graph_size = int(int(y_len_unsorted.data[i])*incompleteness_ratio)
        incomplete_graph[i] = torch.cat((x_batch[i,:incomplete_graph_size],
                                         torch.zeros([max_incomplete_num_node - incomplete_graph_size,
                                                      max_prev_node])), dim=0)
    return incomplete_graph


 if __name__ == '__main__':
    # All necessary arguments are defined in args.py
    args = Args()
    os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda)
    print('CUDA', args.cuda)
    print('File name prefix', args.fname)
    # check if necessary directories exist
    if not os.path.isdir(args.model_save_path):
        os.makedirs(args.model_save_path)
    if not os.path.isdir(args.graph_save_path):
        os.makedirs(args.graph_save_path)
    if not os.path.isdir(args.figure_save_path):
        os.makedirs(args.figure_save_path)
    if not os.path.isdir(args.timing_save_path):
        os.makedirs(args.timing_save_path)
    if not os.path.isdir(args.figure_prediction_save_path):
        os.makedirs(args.figure_prediction_save_path)
    if not os.path.isdir(args.nll_save_path):
        os.makedirs(args.nll_save_path)

    graphs = create_graphs.create(args)

    # split datasets
    random.seed(123)
    shuffle(graphs)
    graphs_len = len(graphs)
    graphs_test = graphs[int(0.8 * graphs_len):]
    graphs_train = graphs[0:int(0.8 * graphs_len)]
    graphs_validate = graphs[0:int(0.2 * graphs_len)]

    graph_validate_len = 0
    for graph in graphs_validate:
        graph_validate_len += graph.number_of_nodes()
    graph_validate_len /= len(graphs_validate)
    print('graph_validate_len', graph_validate_len)

    graph_test_len = 0
    for graph in graphs_test:
        graph_test_len += graph.number_of_nodes()
    graph_test_len /= len(graphs_test)
    print('graph_test_len', graph_test_len)

    args.max_num_node = max([graphs[i].number_of_nodes() for i in range(len(graphs))])
    max_num_edge = max([graphs[i].number_of_edges() for i in range(len(graphs))])
    min_num_edge = min([graphs[i].number_of_edges() for i in range(len(graphs))])

    # args.max_num_node = 2000
    # show graphs statistics
    print('total graph num: {}, training set: {}'.format(len(graphs), len(graphs_train)))
    print('max number node: {}'.format(args.max_num_node))
    print('max/min number edge: {}; {}'.format(max_num_edge, min_num_edge))
    print('max previous node: {}'.format(args.max_prev_node))

    # save ground truth graphs
    ## To get train and test set, after loading you need to manually slice
    save_graph_list(graphs, args.graph_save_path + args.fname_train + '0.dat')
    save_graph_list(graphs, args.graph_save_path + args.fname_test + '0.dat')
    print('train and test graphs saved at: ', args.graph_save_path + args.fname_test + '0.dat')

    if 'nobfs' in args.note:
        args.max_prev_node = args.max_num_node-1
    dataset = Graph_sequence_sampler_pytorch_nobfs_for_completion(graphs_train,
                                                 max_num_node=args.max_num_node)
    # dataset = Graph_sequence_sampler_pytorch(graphs_train, max_prev_node=args.max_prev_node,
    #                                              max_num_node=args.max_num_node)
    sample_strategy = torch.utils.data.sampler.WeightedRandomSampler([1.0 / len(dataset) for i in range(len(dataset))],
                                                                     num_samples=args.batch_size * args.batch_ratio,
                                                                     replacement=True)
    dataset_loader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, num_workers=args.num_workers,
                                                 sampler=sample_strategy)

    rnn = GRU_plain(input_size=args.max_prev_node, embedding_size=args.embedding_size_rnn,
                    hidden_size=args.hidden_size_rnn, num_layers=args.num_layers, has_input=True,
                    has_output=False).cuda()
    output = MLP_plain(h_size=args.hidden_size_rnn, embedding_size=args.embedding_size_output,
                       y_size=args.max_prev_node).cuda()
    name = "GraphRNN_MLP" + '_' + args.graph_type + '_' + str(args.num_layers) + '_' + str(args.hidden_size_rnn) + '_'
    fname = args.model_save_path + name + 'lstm_' + str(args.load_epoch) + '.dat'
    rnn.load_state_dict(torch.load(fname))
    fname = args.model_save_path + name + 'output_' + str(args.load_epoch) + '.dat'
    output.load_state_dict(torch.load(fname))

    # ******************************************************************
    rnn_for_graph_completion = GRU_plain(input_size=args.max_prev_node, embedding_size=args.embedding_size_rnn,
                    hidden_size=args.hidden_size_rnn, num_layers=args.num_layers, has_input=True,
                    has_output=False).cuda()
    output_for_graph_completion = MLP_plain(h_size=args.hidden_size_rnn, embedding_size=args.embedding_size_output,
                       y_size=args.max_prev_node).cuda()

    graph_completion_string = 'graph_completion_one_node_'

    # fname = args.model_save_path + args.fname + 'lstm_' + graph_completion_string + str(args.load_epoch) + '.dat'
    # rnn_for_graph_completion.load_state_dict(torch.load(fname))
    # fname = args.model_save_path + args.fname + 'output_' + graph_completion_string + str(args.load_epoch) + '.dat'
    # output_for_graph_completion.load_state_dict(torch.load(fname))
    # ******************************************************************

    args.lr = 0.00001
    epoch = args.load_epoch
    print('model loaded!, lr: {}'.format(args.lr))

    for batch_idx, data in enumerate(dataset_loader):
        if batch_idx==0:
            rnn.zero_grad()
            output.zero_grad()
            x_unsorted = data['x'].float()
            G = data_to_graph_converter(x_unsorted[:,1:,:])
            nx.write_gpickle(G, "main_graphs.dat")
            y_unsorted = data['y'].float()
            y_len_unsorted = data['len']
            # *********************************
            G = get_incomplete_graph(x_unsorted, y_len_unsorted)
            G = data_to_graph_converter(G[:, 1:, :])
            nx.write_gpickle(G, "incomplete_graphs.dat")
            # *********************************
            G_pred_step, adj_true_list, adj_pred_list = test_mlp(x_unsorted, y_len_unsorted, epoch, args, rnn, output)
            nx.write_gpickle(G_pred_step, "completed_graphs.dat")
            mae = np.sum(np.absolute((adj_pred_list[0].astype("float") - adj_true_list[0].astype("float"))))
            print("adj_true: ")
            print(adj_true_list[0])

            print("my err")
            print(mae)
            print(mean_absolute_error(adj_pred_list[0], adj_true_list[0]))
            print("adj_pred_list:")
            print(adj_pred_list[0])
            # *********************************
            # G_pred_step = test_mlp(x_unsorted, y_len_unsorted, epoch, args, rnn_for_graph_completion,
            #                        output_for_graph_completion)
            # nx.write_gpickle(G_pred_step, "completed_graphs_with_training.dat")
--- a/GraphCompletion/graph_completion_with_training.py
+++ b/GraphCompletion/graph_completion_with_training.py
@@ -0,0 +1,675 @@
 import torch
 import torch.nn.functional as F
 from torch.autograd import Variable
 import sys
 sys.path.append('../')
 from train import *
 from args import Args
 from torch import optim
 from torch.optim.lr_scheduler import MultiStepLR
 from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence
 from torch.nn.init import *
 import matplotlib.pyplot as plt
 import networkx as nx
 from tensorboard_logger import configure, log_value
 from baselines.graphvae.graphvae_model import GraphVAE
 import itertools

 device = torch.device("cuda:0")


 def plot_grad_flow(named_parameters):
    ave_grads = []
    layers = []
    for n, p in named_parameters:
        if(p.requires_grad) and ("bias" not in n):
            layers.append(n)
            print(p.grad)
            print("**********************************")
            print(p.grad.abs())
            print("**********************************")
            print(p.grad.abs().mean())
            print("**********************************")
            ave_grads.append(p.grad.abs().mean())
    plt.plot(ave_grads, alpha=0.3, color="b")
    plt.hlines(0, 0, len(ave_grads)+1, linewidth=1, color="k" )
    plt.xticks(range(0,len(ave_grads), 1), layers, rotation="vertical")
    plt.xlim(xmin=0, xmax=len(ave_grads))
    plt.xlabel("Layers")
    plt.ylabel("average gradient")
    plt.title("Gradient flow")
    plt.grid(True)
    plt.show()


 def graph_show(G, title):
    pos = nx.spring_layout(G, scale=2)
    nx.draw(G, pos, font_size=8)
    fig = plt.gcf()
    fig.canvas.set_window_title(title)
    plt.show()


 def decode_adj(adj_output):
    '''
        recover to adj from adj_output
        note: here adj_output have shape (n-1)*m
    '''
    max_prev_node = adj_output.shape[1]
    adj = np.zeros((adj_output.shape[0], adj_output.shape[0]))
    for i in range(adj_output.shape[0]):
        input_start = max(0, i - max_prev_node + 1)
        input_end = i + 1
        output_start = max_prev_node + max(0, i - max_prev_node + 1) - (i + 1)
        output_end = max_prev_node
        adj[i, input_start:input_end] = adj_output[i,::-1][output_start:output_end] # reverse order
    adj_full = np.zeros((adj_output.shape[0]+1, adj_output.shape[0]+1))
    n = adj_full.shape[0]
    adj_full[1:n, 0:n-1] = np.tril(adj, 0)
    adj_full = adj_full + adj_full.T

    return adj_full


 def tensor_to_graph_converter(tensor, title):
    adj_matrix = decode_adj(tensor.numpy())
    G = nx.from_numpy_matrix(adj_matrix)
    graph_show(G, title)
    return


 def get_indices(number_of_missing_nodes, row_size):
    # print("$$$$$$$$")
    # print(row_size)
    row_index = []
    column_index = []
    start_index = 0
    for i in range(number_of_missing_nodes):
        for j in range(row_size):
            row_index.append(i)
            column_index.append(j + start_index)
        start_index+=1
    return row_index, column_index


 def GraphRNN_loss2(true_adj_batch, predicted_adj_batch, y_pred, y, batch_size, number_of_missing_nodes):
    row_list = list(range(number_of_missing_nodes))
    permutation_indices = list(itertools.permutations(row_list))
    loss_sum = 0
    for data_index in range(batch_size):
        row_size = len(true_adj_batch[data_index])-number_of_missing_nodes
        row_index, column_index = get_indices(number_of_missing_nodes, row_size)
        # print("true_adj_batch: ")
        # print(true_adj_batch[data_index])
        # print("y: ")
        # print(y[data_index])
        # print("predicted_adj_batch: ")
        # print(predicted_adj_batch[data_index])
        # print("y_pred:")
        # print(y_pred[data_index]+0)
        # print("selected_y_pred:")
        # print(y_pred[data_index][row_index, column_index])
        # print("selected_y:")
        # print(y[data_index][row_index, column_index])
        loss_list = []
        for row_order in permutation_indices:
            permuted_y_pred = y_pred[data_index][list(row_order)]
            loss_list.append(F.binary_cross_entropy(permuted_y_pred[row_index, column_index],
                                                    y[data_index][row_index, column_index]))
        loss_sum+=min(loss_list)
    loss_sum/=batch_size
    return loss_sum


 def GraphRNN_loss(true_adj_batch, predicted_adj_batch, batch_size, number_of_missing_nodes):
    # loss_sum = 0
    for data_index in range(batch_size):
        loss_sum= F.binary_cross_entropy(predicted_adj_batch[data_index],
                                          true_adj_batch[data_index])
        break
    # loss_sum/=batch_size
    # loss_sum.requires_grad = True
    return loss_sum


 def matrix_completion_loss(true_adj_batch, predicted_adj_batch, batch_size, number_of_missing_nodes):
    # graph_show(nx.from_numpy_matrix(true_adj[0]), "true_adj")
    # graph_show(nx.from_numpy_matrix(predicted_adj[0]), "predicted_adj")
    loss_sum=F.binary_cross_entropy(torch.from_numpy(predicted_adj_batch[0]),
                                                torch.from_numpy(true_adj_batch[0]))

    # loss_sum = 0
    # for data_index in range(batch_size):
    #     # print("########## data_len: ")
    #     # print(data_len[data_index].item())
    #     # print("^^^^   data_index:   " + str(data_index))
    #     adj_matrix = true_adj_batch[data_index]
    #     predicted_adj_matrix = predicted_adj_batch[data_index]
    #     number_of_nodes = np.shape(adj_matrix)[1]
    #     # loss_sum+=F.binary_cross_entropy(torch.from_numpy(predicted_adj_matrix),
    #     #                                             torch.from_numpy(adj_matrix))
    #     sub_matrix = adj_matrix[number_of_nodes - number_of_missing_nodes:][:,
    #                  :number_of_nodes - number_of_missing_nodes]
    #     predicted_sub_matrix = predicted_adj_matrix[number_of_nodes - number_of_missing_nodes:][:,
    #                            :number_of_nodes - number_of_missing_nodes]
    #     min_permutation_loss = float("inf")
    #     row_list = list(range(number_of_missing_nodes))
    #     permutation_indices = list(itertools.permutations(row_list))
    #     number_of_rows = np.shape(predicted_sub_matrix)[0]
    #     number_of_columns = np.shape(predicted_sub_matrix)[1]
    #     matrix_size = number_of_rows*number_of_columns
    #     # print(str(number_of_rows) + " ### " + str(number_of_columns) + " @@@ " + str(matrix_size))
    #
    #     for row_order in permutation_indices:
    #         permuted_sub_matrix = sub_matrix[list(row_order)]
    #         # print("*****   permuted_sub_matrix ")
    #         # print(permuted_sub_matrix)
    #         # print("*****   predicted_sub_matrix ")
    #         # print(predicted_sub_matrix)
    #         adj_recon_loss = F.binary_cross_entropy(torch.from_numpy(predicted_sub_matrix),
    #                                                 torch.from_numpy(permuted_sub_matrix))
    #         # print(adj_recon_loss)
    #         min_permutation_loss = np.minimum(min_permutation_loss, adj_recon_loss.item())
    #     # print(adj_recon_loss)
    #     loss_sum += min_permutation_loss/matrix_size
    # # print("^^^^^  ")
    # # print(loss_sum)
    # loss_sum = torch.autograd.Variable(torch.from_numpy(np.array(loss_sum)))
    # # print(loss_sum)
    # loss_sum.requires_grad = True
    # print(loss_sum)
    # print(loss_sum/batch_size)
    return loss_sum/batch_size


 def graph_matching_loss(true_adj_batch, predicted_adj_batch, data_len, batch_size):
    # graph_show(nx.from_numpy_matrix(true_adj[0]), "true_adj")
    # graph_show(nx.from_numpy_matrix(predicted_adj[0]), "predicted_adj")
    loss_sum = 0
    for data_index in range(batch_size):
        features = torch.zeros(data_len[data_index])
        number_of_nodes = data_len[data_index].item()
        # print("########## data_len: ")
        # print(data_len[data_index].item())
        if data_len[data_index].item()>14:
            continue
        graphvae = GraphVAE(1, 64, 256, data_len[data_index].item())
        # print("^^^^   data_index:   " + str(data_index))
        current_true_adj = true_adj_batch[data_index]
        current_predicted_adj = predicted_adj_batch[data_index]
        current_features = features
        S = graphvae.edge_similarity_matrix(current_true_adj, current_predicted_adj, current_features, current_features,
                                    graphvae.deg_feature_similarity)
        init_corr = 1 / number_of_nodes
        init_assignment = torch.ones(number_of_nodes, number_of_nodes) * init_corr
        assignment = graphvae.mpm(init_assignment, S)
        row_ind, col_ind = scipy.optimize.linear_sum_assignment(-assignment.numpy())
        permuted_adj = graphvae.permute_adj(torch.from_numpy(current_true_adj).float(), row_ind, col_ind)
        # print("*****   permuted_adj ")
        # print(permuted_adj)
        # print("*****   current_predicted_adj ")
        # print(torch.from_numpy(current_predicted_adj).float())
        adj_recon_loss = graphvae.adj_recon_loss(permuted_adj, torch.from_numpy(current_predicted_adj).float())
        # print(adj_recon_loss)
        loss_sum += adj_recon_loss.data
    loss_sum.requires_grad = True
    # print(loss_sum/batch_size)
    # print("^^^^^  ")
    # print(loss_sum)
    return loss_sum/batch_size


 def make_batch_of_matrices(initial_y, initial_y_len, y_train, y_pred, batch_size):
    true_adj_batch = []
    predicted_adj_batch = []
    for data_index in range(batch_size):
        a = initial_y[data_index][:initial_y_len[data_index] - 1].cpu()
        b = y_train[data_index]
        true_adj = torch.cat((a, b), dim=0)
        true_adj = decode_adj(true_adj.numpy())
        true_adj_tensor = torch.from_numpy(true_adj + np.identity(len(true_adj[0]))).float().to(device)
        true_adj_batch.append(true_adj_tensor)
        b = y_pred[data_index].data
        # print("bbbbbbbbbbbbbbbbbbbbbbb     :")
        # print(b)
        # print("b.data   :")
        # print(b.data)
        # print("**********************************************************************************")
        predicted_adj = torch.cat((a, b.cpu()), dim=0)
        predicted_adj = decode_adj(predicted_adj.numpy())
        predicted_adj_tensor = torch.from_numpy(predicted_adj + np.identity(len(predicted_adj[0]))).float().to(device)
        predicted_adj_tensor.requires_grad = True
        # predicted_adj_tensor = predicted_adj_tensor + 1
        # print("***********   predicted_adj_tensor   *******")
        # print(predicted_adj_tensor+0)
        predicted_adj_batch.append(predicted_adj_tensor+0)
    return true_adj_batch, predicted_adj_batch


 def train_mlp_epoch_for_completion(epoch, args, rnn, output, rnn_for_initialization,
                                   output_for_initialization, data_loader,
                                   optimizer_rnn, optimizer_output,
                                   scheduler_rnn, scheduler_output, number_of_missing_nodes):
    rnn.train()
    output.train()
    loss_sum = 0
    for batch_idx, data in enumerate(data_loader):
        rnn.zero_grad()
        output.zero_grad()
        x_unsorted = data['x'].float()
        y_unsorted = data['y'].float()
        data_len = y_len_unsorted = data['len']

        # data_index = 0
        # b = y_unsorted[data_index][:y_len_unsorted[data_index] - 1].cpu()
        # tensor_to_graph_converter(b, "Complete Graph")
        # ****
        x_unsorted, y_unsorted, y_len_unsorted, hidden, initial_y, initial_y_len = \
            data_initializer_for_training(x_unsorted, y_unsorted, y_len_unsorted,
                                          args, rnn_for_initialization, output_for_initialization,
                                          number_of_missing_nodes, False, 0.96)
        # ____________________________________________________________________________________________
        # x_unsorted1, y_unsorted1, y_len_unsorted1, hidden, initial_y1, initial_y_len1 = \
        #     data_initializer_for_training(x_unsorted, y_unsorted, y_len_unsorted,
        #                                   args, rnn_for_initialization, output_for_initialization,
        #                                   number_of_missing_nodes, False, 0.96)
        # ____________________________________________________________________________________________
        # a = initial_y[data_index][:initial_y_len[data_index]-1].cpu()
        # tensor_to_graph_converter(a, "Incomplete Graph")

        # b = y_unsorted[data_index][:-1]
        # recons = torch.cat((a, b), dim=0)
        # tensor_to_graph_converter(recons, "Merged Complete Graph")
        # ****
        y_len_max = max(y_len_unsorted)
        x_unsorted = x_unsorted[:, 0:y_len_max, :]
        y_unsorted = y_unsorted[:, 0:y_len_max, :]
        # initialize lstm hidden state according to batch size
        # rnn.hidden = rnn.init_hidden(batch_size=x_unsorted.size(0))
        # ****
        rnn.hidden = hidden
        # ****

        # sort input
        y_len,sort_index = torch.sort(y_len_unsorted,0,descending=True)
        y_len = y_len.numpy().tolist()
        x = torch.index_select(x_unsorted,0,sort_index)
        y = torch.index_select(y_unsorted,0,sort_index)
        x = Variable(x).cuda()
        y = Variable(y).cuda()

        h = rnn(x, pack=True, input_len=y_len)
        y_pred = output(h)
        y_pred = F.sigmoid(y_pred)

        # clean
        y_pred = pack_padded_sequence(y_pred, y_len, batch_first=True)
        y_pred = pad_packed_sequence(y_pred, batch_first=True)[0]
        # use cross entropy loss
        # print("y_unsorted: ")
        # print(y_unsorted[0])
        # print("y: ")
        # print(y[0].cpu())
        true_adj_batch, predicted_adj_batch = make_batch_of_matrices(initial_y, initial_y_len, y.cpu(),y_pred, len(y_pred))
        # graph_show(nx.from_numpy_matrix(true_adj_batch[data_index]), "Complete2")
        # import time
        # start = time.time()
        # print("@@@@@@@@@@@@@@@@@@@@@@@@@")
        # print(args.loss)
        if args.loss == "graph_matching_loss":
            print(graph_matching_loss)
            # loss = graph_matching_loss(true_adj_batch, predicted_adj_batch, data_len, len(data_len))
        elif args.loss == "matrix_completion_loss":
            print(matrix_completion_loss)
            # loss = matrix_completion_loss(true_adj_batch, predicted_adj_batch, len(data_len),
            #                               args.number_of_missing_nodes)
        elif args.loss =="GraphRNN_loss":
            # print("GraphRNN_loss")
            # print(y)
            # print(y_pred)
            # loss = F.binary_cross_entropy(torch.from_numpy(predicted_adj_batch[0]), torch.from_numpy(true_adj_batch[0]))
            # loss.requires_grad = True
            # print("true_adj_batch: ")
            # print(true_adj_batch[0])
            # print("y: ")
            # print(y[0])
            # print("predicted_adj_batch: ")
            # print(predicted_adj_batch[0])
            # print("y_pred:")
            # print(y_pred[0]+0)
            # print("**************************************************************")
            # loss = GraphRNN_loss(true_adj_batch, predicted_adj_batch, len(data_len), args.number_of_missing_nodes);
            # loss = binary_cross_entropy_weight(predicted_adj_batch[0], true_adj_batch[0])
            # print("****   GraphRNN_loss: ")
            # print(loss)
            # row_index = [0, 2]
            # col_index = [1, 3]
            # print("**************************")
            # print(y_pred[0][row_index, col_index])
            # print(np.shape(y_pred[0])[1])
            loss = GraphRNN_loss2(true_adj_batch, predicted_adj_batch, y_pred, y, len(y_pred), args.number_of_missing_nodes)
            # loss = binary_cross_entropy_weight(y_pred[0][row_index, col_index], y[0][row_index, col_index])
            # print("****   main_loss: ")
            # print(loss)
        # print("*** epoch : " + str(epoch) + "*** loss: " + str(loss.data))
        # break
        # end = time.time()
        # print(end - start)
        # print("y_pred")
        # print(y_pred[0])
        # print("y")
        # print(y[0])
        # print("***************************************************")
        # loss = binary_cross_entropy_weight(y_pred, y)
        loss.backward()
        # update deterministic and lstm
        optimizer_output.step()
        optimizer_rnn.step()
        scheduler_output.step()
        scheduler_rnn.step()
        # for p in rnn.parameters():
            # print(p)
            # print(p.grad)
            # if p.grad is not None:
            #     print("________________________________________________________")
            #     print(p.grad.data)
        # plot_grad_flow(rnn.named_parameters())


        if epoch % args.epochs_log==0 and batch_idx==0: # only output first batch's statistics
            print('Epoch: {}/{}, train loss: {:.6f}, graph type: {}, num_layer: {}, hidden: {}'.format(
                epoch, args.epochs,loss.data, args.graph_type, args.num_layers, args.hidden_size_rnn))

        log_value(args.loss + args.fname + args.training_mode + "_ "
                                                              "number_of_missing_nodes_" +
                  str(number_of_missing_nodes), loss.data, epoch * args.batch_ratio + batch_idx)
        loss_sum += loss.data
    return loss_sum/(batch_idx+1)


 def train_for_completion(args, dataset_train, rnn, output, rnn_for_initialization,output_for_initialization):
    # check if load existing model
    if args.load:
        fname = args.model_save_path + args.fname + 'lstm_' + args.graph_completion_string + str(args.load_epoch) + '.dat'
        rnn.load_state_dict(torch.load(fname))
        fname = args.model_save_path + args.fname + 'output_' + args.graph_completion_string + str(args.load_epoch) + '.dat'
        output.load_state_dict(torch.load(fname))

        args.lr = 0.00001
        epoch = args.load_epoch
        print('model loaded!, lr: {}'.format(args.lr))
    else:
        epoch = 1

    # initialize optimizer
    optimizer_rnn = optim.Adam(list(rnn.parameters()), lr=args.lr)
    optimizer_output = optim.Adam(list(output.parameters()), lr=args.lr)

    scheduler_rnn = MultiStepLR(optimizer_rnn, milestones=args.milestones, gamma=args.lr_rate)
    scheduler_output = MultiStepLR(optimizer_output, milestones=args.milestones, gamma=args.lr_rate)