dmlc--dgl
Tong He
9c41c22d7b
* add hilander model implementation draft * use focal loss * fix * change data root * add necessary scripts * update download links * update * update example table * fix * update readme with numbers * add empty folder * only eval at the end * set up hilander * inform results may fluctuate * address comments Co-authored-by: sneakerkg <xiaotj1990327@gmail.com> Co-authored-by: Ubuntu <ubuntu@ip-172-31-19-212.us-east-2.compute.internal>
88 行
2.3 KiB
Python
88 行
2.3 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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This file re-uses implementation from https://github.com/yl-1993/learn-to-cluster
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"""
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import os
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import time
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import json
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import pickle
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import random
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import numpy as np
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class TextColors:
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HEADER = '\033[35m'
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OKBLUE = '\033[34m'
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OKGREEN = '\033[32m'
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WARNING = '\033[33m'
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FATAL = '\033[31m'
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ENDC = '\033[0m'
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BOLD = '\033[1m'
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UNDERLINE = '\033[4m'
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class Timer():
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def __init__(self, name='task', verbose=True):
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self.name = name
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self.verbose = verbose
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def __enter__(self):
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self.start = time.time()
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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if self.verbose:
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print('[Time] {} consumes {:.4f} s'.format(
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self.name,
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time.time() - self.start))
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return exc_type is None
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def set_random_seed(seed, cuda=False):
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import torch
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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if cuda:
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torch.cuda.manual_seed_all(seed)
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def l2norm(vec):
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vec /= np.linalg.norm(vec, axis=1).reshape(-1, 1)
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return vec
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def is_l2norm(features, size):
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rand_i = random.choice(range(size))
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norm_ = np.dot(features[rand_i, :], features[rand_i, :])
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return abs(norm_ - 1) < 1e-6
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def is_spmat_eq(a, b):
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return (a != b).nnz == 0
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def aggregate(features, adj, times):
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dtype = features.dtype
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for i in range(times):
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features = adj * features
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return features.astype(dtype)
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def mkdir_if_no_exists(path, subdirs=[''], is_folder=False):
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if path == '':
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return
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for sd in subdirs:
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if sd != '' or is_folder:
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d = os.path.dirname(os.path.join(path, sd))
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else:
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d = os.path.dirname(path)
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if not os.path.exists(d):
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os.makedirs(d)
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def stop_iterating(current_l, total_l, early_stop, num_edges_add_this_level, num_edges_add_last_level, knn_k):
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# Stopping rule 1: run all levels
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if current_l == total_l - 1:
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return True
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# Stopping rule 2: no new edges
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if num_edges_add_this_level == 0:
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return True
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# Stopping rule 3: early stopping, two levels start to produce similar numbers of edges
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if early_stop and float(num_edges_add_last_level) / num_edges_add_this_level < knn_k - 1:
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return True
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return False
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