import random
from collections import defaultdict
import copy
import networkx as nx
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from gensim.models.keyedvectors import Vocab
from six import iteritems
from sklearn.metrics import auc, f1_score, precision_recall_curve, roc_auc_score
from tqdm import tqdm
from cogdl import options
from cogdl.datasets import build_dataset
from cogdl.models import build_model
from . import BaseTask, register_task
[docs]def get_score(embs, node1, node2):
vector1 = embs[int(node1)]
vector2 = embs[int(node2)]
return np.dot(vector1, vector2) / (
np.linalg.norm(vector1) * np.linalg.norm(vector2)
)
[docs]def evaluate(embs, true_edges, false_edges):
true_list = list()
prediction_list = list()
for edge in true_edges:
true_list.append(1)
prediction_list.append(get_score(embs, edge[0], edge[1]))
for edge in false_edges:
true_list.append(0)
prediction_list.append(get_score(embs, edge[0], edge[1]))
sorted_pred = prediction_list[:]
sorted_pred.sort()
threshold = sorted_pred[-len(true_edges)]
y_pred = np.zeros(len(prediction_list), dtype=np.int32)
for i in range(len(prediction_list)):
if prediction_list[i] >= threshold:
y_pred[i] = 1
y_true = np.array(true_list)
y_scores = np.array(prediction_list)
ps, rs, _ = precision_recall_curve(y_true, y_scores)
return roc_auc_score(y_true, y_scores), f1_score(y_true, y_pred), auc(rs, ps)
[docs]@register_task("multiplex_link_prediction")
class MultiplexLinkPrediction(BaseTask):
@staticmethod
[docs] def add_args(parser):
"""Add task-specific arguments to the parser."""
# fmt: off
parser.add_argument("--hidden-size", type=int, default=200)
parser.add_argument("--negative-ratio", type=int, default=5)
parser.add_argument("--eval-type", type=str, default='all', nargs='+')
# fmt: on
def __init__(self, args):
super(MultiplexLinkPrediction, self).__init__(args)
dataset = build_dataset(args)
data = dataset[0]
self.data = data
if hasattr(dataset, "num_features"):
args.num_features = dataset.num_features
model = build_model(args)
self.model = model
self.patience = args.patience
self.max_epoch = args.max_epoch
self.eval_type = args.eval_type
[docs] def train(self):
total_roc_auc, total_f1_score, total_pr_auc = [], [], []
if hasattr(self.model, "multiplicity"):
all_embs = self.model.train(self.data.train_data)
for key in self.data.train_data.keys():
if self.eval_type == "all" or key in self.eval_type:
embs = dict()
if not hasattr(self.model, "multiplicity"):
G = nx.Graph()
G.add_edges_from(self.data.train_data[key])
embeddings = self.model.train(G)
for vid, node in enumerate(G.nodes()):
embs[node] = embeddings[vid]
else:
embs = all_embs[key]
roc_auc, f1_score, pr_auc = evaluate(
embs, self.data.test_data[key][0], self.data.test_data[key][1]
)
total_roc_auc.append(roc_auc)
total_f1_score.append(f1_score)
total_pr_auc.append(pr_auc)
assert len(total_roc_auc) > 0
roc_auc, f1_score, pr_auc = (
np.mean(total_roc_auc),
np.mean(total_f1_score),
np.mean(total_pr_auc),
)
print(
f"Test ROC-AUC = {roc_auc:.4f}, F1 = {f1_score:.4f}, PR-AUC = {pr_auc:.4f}"
)
return dict(ROC_AUC=roc_auc, PR_AUC=pr_auc, F1=f1_score)