snippets for TextGCN blog

dataset.py

import scipy.sparse as sp
import numpy as np
 
def init_node_feats(self, type, device):
    if type == 'one_hot_init':
        num_nodes = self.graph.shape[0]
        identity = sp.identity(num_nodes)
        ind0, ind1, values = sp.find(identity)
        inds = np.stack((ind0, ind1), axis=0)
        self.node_feats = torch.sparse_coo_tensor(inds, values, device=device, dtype=torch.float)

model_text_gcn.py

def forward(self, x, edge_index, edge_weight=None):
    """ 
    for the PyTorch Geometric GCN implementation 
    see https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/nn/conv/gcn_conv.html#GCNConv
    """
    if x.is_sparse:
        x = torch.sparse.mm(x, self.weight)
    else:
        x = torch.matmul(x, self.weight)
 
    if not self.cached or self.cached_result is None:
        edge_index, norm = GCNConv.norm(edge_index, x.size(0), edge_weight,
                                        self.improved, x.dtype)
        self.cached_result = edge_index, norm
 
    edge_index, norm = self.cached_result
    return self.propagate(edge_index, x=x, norm=norm)

prep_data.py

string = re.sub(r"http[s]?\:\/\/.[a-zA-Z0-9\.\/\_?=%&#\-\+!]+", " ", string)
string = re.sub(r"[^A-Za-z0-9()_+,!?:\'\`]", " ", string)  # replace all non alpha numeric characters