class_docstr: AutoNE HPOptimizer
  The Implementation of "AutoNE: Hyperparameter Optimization for Massive Network Embedding"(KDD 2019).
  See https://github.com/tadpole/AutoNE for more information
  
  Attributes
  ----------
  max_evals : int
      The max rounds of evaluating HPs
  subgraphs : int
      The number of subgraphs
  sub_evals : int
      The number of evaluation times on each subgraph
  sample_batch_size, sample_walk_length : int
      Using for sampling subgraph, see torch_geometric.data.GraphSAINRandomWalkSampler
code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
      self.max_evals = kwargs.get("max_evals", 100)
      self.subgraphs = kwargs.get("subgraphs", 2)
      self.sub_evals = kwargs.get("sub_evals", 2)
      self.sample_batch_size = kwargs.get("sample_batch_size", 150)
      self.sample_walk_length = kwargs.get("sample_walk_length", 100)
  
file_path: AutoGL/autogl/module/hpo/autone.py
imports: import time
  import numpy
  from tqdm import trange
  from .None import register_hpo
  from .base import BaseHPOptimizer, TimeTooLimitedError
  import random
  from .autone_file import utils
  from ..feature import NetLSD
  from autogl.backend import DependentBackend
label_desc: Use image node_id 1 to create a new AutoNE object from inherited base classes: BaseHPOptimizer with example: obj = AutoNE()