train.py 文件源码

def get_roc_score(edges_pos, edges_neg, emb=None):
    if emb is None:
        feed_dict.update({placeholders['dropout']: 0})
        emb = sess.run(model.z_mean, feed_dict=feed_dict)

    def sigmoid(x):
        return 1 / (1 + np.exp(-x))

    # Predict on test set of edges
    adj_rec = np.dot(emb, emb.T)
    preds = []
    pos = []
    for e in edges_pos:
        preds.append(sigmoid(adj_rec[e[0], e[1]]))
        pos.append(adj_orig[e[0], e[1]])

    preds_neg = []
    neg = []
    for e in edges_neg:
        preds_neg.append(sigmoid(adj_rec[e[0], e[1]]))
        neg.append(adj_orig[e[0], e[1]])

    preds_all = np.hstack([preds, preds_neg])
    labels_all = np.hstack([np.ones(len(preds)), np.zeros(len(preds))])
    roc_score = roc_auc_score(labels_all, preds_all)
    ap_score = average_precision_score(labels_all, preds_all)

    return roc_score, ap_score