dataset.py

import torch
import numpy as np
import time

from config import *
from scipy.sparse import *

class HCSPMM_dataset(torch.nn.Module):
    """
    data loading for more graphs
    """
    def __init__(self, path, dim, num_class, load_from_txt=True, verbose=False):
        super(HCSPMM_dataset, self).__init__()

        self.nodes = set()

        self.load_from_txt = load_from_txt
        self.num_nodes = 0
        self.num_features = dim 
        self.num_classes = num_class
        self.edge_index = None
        
        self.reorder_flag = False
        self.verbose_flag = verbose

        self.avg_degree = -1
        self.avg_edgeSpan = -1

        self.init_edges(path)
        self.init_embedding(dim)
        self.init_labels(num_class)

        train = 1
        val = 0.3
        test = 0.1
        self.train_mask = [1] * int(self.num_nodes * train) + [0] * (self.num_nodes  - int(self.num_nodes * train))
        self.val_mask = [1] * int(self.num_nodes * val)+ [0] * (self.num_nodes  - int(self.num_nodes * val))
        self.test_mask = [1] * int(self.num_nodes * test) + [0] * (self.num_nodes  - int(self.num_nodes * test))
        self.train_mask = torch.BoolTensor(self.train_mask).cuda()
        self.val_mask = torch.BoolTensor(self.val_mask).cuda()
        self.test_mask = torch.BoolTensor(self.test_mask).cuda()

    def init_edges(self, path):

        # loading from a txt graph file
        if self.load_from_txt:
            fp = open(path, "r")
            src_li = []
            dst_li = []
            start = time.perf_counter()
            for line in fp:
                dst, src = line.strip('\n').split(",")
                src, dst = int(src) - 1, int(dst) - 1
                src_li.append(src)
                dst_li.append(dst)
                self.nodes.add(src)
                self.nodes.add(dst)
            
            self.num_edges = len(src_li)
            self.num_nodes = max(self.nodes) + 1
            self.edge_index = np.stack([src_li, dst_li])

            dur = time.perf_counter() - start
            if self.verbose_flag:
                print("# Loading (txt) {:.3f}s ".format(dur))

        # loading from a .npz graph file
        else: 
            if not path.endswith('.npz'):
                raise ValueError("graph file must be a .npz file")

            start = time.perf_counter()
            graph_obj = np.load(path)
            src_li = graph_obj['src_li']
            dst_li = graph_obj['dst_li']

            self.num_nodes = graph_obj['num_nodes']
            self.num_edges = len(src_li)
            self.edge_index = np.stack([src_li, dst_li])
            dur = time.perf_counter() - start
            if self.verbose_flag:
                print("# Loading (npz)(s): {:.3f}".format(dur))
        
        self.avg_degree = self.num_edges / self.num_nodes
        self.avg_edgeSpan = np.mean(np.abs(np.subtract(src_li, dst_li)))

        if self.verbose_flag:
            print('# nodes: {}'.format(self.num_nodes))
            print("# avg_degree: {:.2f}".format(self.avg_degree))
            print("# avg_edgeSpan: {}".format(int(self.avg_edgeSpan)))

        # Build graph CSR.
        val = [1] * self.num_edges
        start = time.perf_counter()
        scipy_coo = coo_matrix((val, self.edge_index), shape=(self.num_nodes, self.num_nodes))
        scipy_csr = scipy_coo.tocsr()
        build_csr = time.perf_counter() - start

        if self.verbose_flag:
            print("# Build CSR (s): {:.3f}".format(build_csr))

        self.column_index = torch.IntTensor(scipy_csr.indices)
        self.row_pointers = torch.IntTensor(scipy_csr.indptr)

        # Get degrees array.
        degrees = (self.row_pointers[1:] - self.row_pointers[:-1]).tolist()
        self.degrees = torch.sqrt(torch.FloatTensor(list(map(func, degrees)))).cuda()

    def init_embedding(self, dim):
        '''
        Generate node embedding for nodes.
        Called from __init__.
        '''
        self.x = torch.randn(self.num_nodes, dim).cuda()
    
    def init_labels(self, num_class):
        '''
        Generate the node label.
        Called from __init__.
        '''
        self.y = torch.ones(self.num_nodes).long().cuda()