<jupyter_start><jupyter_text>Test on new division of dataset<jupyter_code>!python train.py --task to_follow2 --dataset ltc --model prone --sep_point 4
## if (t==time+1 or (time==-1 and t==3)) and u in nodes and v in nodes:
#   if stamp>t
!python train.py --task to_follow2 --dataset ltc --model prone --sep_point 3<jupyter_output>Namespace(cpu=False, dataset=['ltc'], device_id=[0], hidden_size=128, lr=0.01, max_epoch=10000, model=['prone'], mu=0.2, negative_ratio=5, patience=100, save_dir='.', seed=[1], sep_point=3, step=5, task='to_follow2', theta=0.5, weight_decay=0.0005)
num_workers 1
3
62510
210711
91 91
number of nodes(now): 62510
number of edges(now): 200251
number of nodes(future): 62510
number of edges(future): 200342
begin training for embs
neg 0.014776468276977539
svd sparse 5.1247855374335116e-05
sparsesvd time 3.8726449012756348
Chebyshev Series -----------------
Bessell time 2 0.3583204746246338
Bessell time 3 0.5069105625152588
Bessell time 4 0.6574187278747559
densesvd time 0.25953078269958496
sparse NE time 3.911468982696533
spectral Pro time 0.9924051761627197
Test ROC-AUC = 0.5720, F1 = 0.5604, PR-AUC = 0.5651
| Variant          | ROC_AUC       | PR_AUC        | F1            |
|------------------|---------------|---------------|---------------|
| ('ltc', 'prone') | 0.5720±0.0000 | 0.5651±0.00[...]
{'fraction_non_alphanumeric': 0.18277153558052434, 'fraction_numerical': 0.20224719101123595, 'mean_word_length': 3.3376623376623376, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '336134', 'license': 'MIT', 'n_tokens_mistral': 672, 'n_tokens_neox': 500, 'n_words': 132, 'path': 'Debug.ipynb', 'repo_name': 'zxhhh97/ABot', 'size': '1335'}