utils.py

import argparse
import numpy as np
import torch
from time import perf_counter
import networkx as nx
import random
from sklearn.metrics import roc_auc_score, average_precision_score, auc, precision_recall_curve
import scipy.sparse as sp


def aucPerformance(y_true, y_pred):
    y_true = y_true.flatten()
    y_pred = y_pred.flatten()
    roc_auc = roc_auc_score(y_true, y_pred)
    precision, recall, _ = precision_recall_curve(y_true, y_pred)
    auc_pr = auc(recall, precision)
    ap = average_precision_score(y_true, y_pred)
    return roc_auc, auc_pr, ap


def sgc_precompute(features, adj, degree):
    # compute S^K
    for i in range(degree):
        features = torch.spmm(adj, features)
    return features


def normalize_adjacency(adj):
    adj = adj + sp.eye(adj.shape[0])
    adj = sp.coo_matrix(adj)
    row_sum = np.array(adj.sum(1))
    d_inv_sqrt = np.power(row_sum, -0.5).flatten()
    d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.
    d_mat_inv_sqrt = sp.diags(d_inv_sqrt)
    return d_mat_inv_sqrt.dot(adj).dot(d_mat_inv_sqrt).tocoo()


def normalize_feature(feature):
    # Row-wise normalization of sparse feature matrix
    rowsum = np.array(feature.sum(1))
    r_inv = np.power(rowsum, -1).flatten()
    r_inv[np.isinf(r_inv)] = 0.
    r_mat_inv = sp.diags(r_inv)
    mx = r_mat_inv.dot(feature)
    return mx