class_docstr: Auto Link Predictor.
  
  Used to automatically solve the link prediction problems.
  
  Parameters
  ----------
  feature_module: autogl.module.feature.BaseFeatureEngineer or str or None
      The (name of) auto feature engineer used to process the given dataset. Default ``deepgl``.
      Disable feature engineer by setting it to ``None``.
  
  graph_models: list of autogl.module.model.BaseModel or list of str
      The (name of) models to be optimized as backbone. Default ``['gat', 'gcn']``.
  
  hpo_module: autogl.module.hpo.BaseHPOptimizer or str or None
      The (name of) hpo module used to search for best hyper parameters. Default ``anneal``.
      Disable hpo by setting it to ``None``.
  
  ensemble_module: autogl.module.ensemble.BaseEnsembler or str or None
      The (name of) ensemble module used to ensemble the multi-models found. Default ``voting``.
      Disable ensemble by setting it to ``None``.
  
  max_evals: int (Optional)
      If given, will set the number eval times the hpo module will use.
      Only be effective when hpo_module is ``str``. Default ``None``.
  
  default_trainer: str (Optional)
      The (name of) the trainer used in this solver. Default to ``NodeClassificationFull``.
  
  trainer_hp_space: list of dict (Optional)
      trainer hp space or list of trainer hp spaces configuration.
      If a single trainer hp is given, will specify the hp space of trainer for every model.
      If a list of trainer hp is given, will specify every model with corrsponding
      trainer hp space.
      Default ``None``.
  
  model_hp_spaces: list of list of dict (Optional)
      model hp space configuration.
      If given, will specify every hp space of every passed model. Default ``None``.
      If the encoder(-decoder) is passed, the space should be a dict containing keys "encoder"
      and "decoder", specifying the detailed encoder decoder hp spaces.
  
  size: int (Optional)
      The max models ensemble module will use. Default ``None``.
  
  device: torch.device or str
      The device where model will be running on. If set to ``auto``, will use gpu when available.
      You can also specify the device by directly giving ``gpu`` or ``cuda:0``, etc.
      Default ``auto``.
docstr: Fit current solver on given dataset.
  
  Parameters
  ----------
  dataset: torch_geometric.data.dataset.Dataset
      The dataset needed to fit on. This dataset must have only one graph.
  
  time_limit: int
      The time limit of the whole fit process (in seconds). If set below 0,
      will ignore time limit. Default ``-1``.
  
  inplace: bool
      Whether we process the given dataset in inplace manner. Default ``False``.
      Set it to True if you want to save memory by modifying the given dataset directly.
  
  train_split: float or int (Optional)
      The train ratio (in ``float``) or number (in ``int``) of dataset. If you want to
      use default train/val/test split in dataset, please set this to ``None``.
      Default ``None``.
  
  val_split: float or int (Optional)
      The validation ratio (in ``float``) or number (in ``int``) of dataset. If you want
      to use default train/val/test split in dataset, please set this to ``None``.
      Default ``None``.
  
  evaluation_method: (list of) str or autogl.module.train.evaluation
      A (list of) evaluation method for current solver. If ``infer``, will automatically
      determine. Default ``infer``.
  
  seed: int (Optional)
      The random seed. If set to ``None``, will run everything at random.
      Default ``None``.
  
  Returns
  -------
  self: autogl.solver.AutoNodeClassifier
      A reference of current solver.
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code: def fit(
      self,
      dataset,
      time_limit=-1,
      inplace=False,
      train_split=None,
      val_split=None,
      evaluation_method="infer",
      seed=None,
  ) -> "AutoLinkPredictor":
      """
      Fit current solver on given dataset.
  
      Parameters
      ----------
      dataset: torch_geometric.data.dataset.Dataset
          The dataset needed to fit on. This dataset must have only one graph.
  
      time_limit: int
          The time limit of the whole fit process (in seconds). If set below 0,
          will ignore time limit. Default ``-1``.
  
      inplace: bool
          Whether we process the given dataset in inplace manner. Default ``False``.
          Set it to True if you want to save memory by modifying the given dataset directly.
  
      train_split: float or int (Optional)
          The train ratio (in ``float``) or number (in ``int``) of dataset. If you want to
          use default train/val/test split in dataset, please set this to ``None``.
          Default ``None``.
  
      val_split: float or int (Optional)
          The validation ratio (in ``float``) or number (in ``int``) of dataset. If you want
          to use default train/val/test split in dataset, please set this to ``None``.
          Default ``None``.
  
      evaluation_method: (list of) str or autogl.module.train.evaluation
          A (list of) evaluation method for current solver. If ``infer``, will automatically
          determine. Default ``infer``.
  
      seed: int (Optional)
          The random seed. If set to ``None``, will run everything at random.
          Default ``None``.
  
      Returns
      -------
      self: autogl.solver.AutoNodeClassifier
          A reference of current solver.
      """
      set_seed(seed)
  
      if time_limit < 0:
          time_limit = 3600 * 24
      time_begin = time.time()
  
      # initialize leaderboard
      if evaluation_method == "infer":
          if hasattr(dataset, "metric"):
              evaluation_method = [dataset.metric]
          else:
              num_of_label = dataset.num_classes
              if num_of_label == 2:
                  evaluation_method = ["auc"]
              else:
                  evaluation_method = ["acc"]
      assert isinstance(evaluation_method, list)
      evaluator_list = get_feval(evaluation_method)
  
      self.leaderboard = LeaderBoard(
          [e.get_eval_name() for e in evaluator_list],
          {
              e.get_eval_name(): e.is_higher_better()
              for e in evaluator_list
          },
      )
  
      graph_data = dataset[0]  # get_graph_from_dataset(dataset)
  
      # set up the dataset
      if train_split is not None and val_split is not None:
          dataset = utils.split_edges(dataset, train_split, val_split)
          graph_data = dataset[0]
      else:
          if BACKEND == "pyg":
              assert all(
                  [
                      hasattr(graph_data, f"{name}")
                      for name in [
                          "train_pos_edge_index",
                          "train_neg_adj_mask",
                          "val_pos_edge_index",
                          "val_neg_edge_index",
                          "test_pos_edge_index",
                          "test_neg_edge_index",
                      ]
                  ]
              ), (
                  "The dataset has no default train/val split! Please manually pass "
                  "train and val ratio."
              )
          elif BACKEND == "dgl":
              assert len(graph_data) in [5, 7], (
                  "The dataset has no default train/val split! Please manually pass "
                  "train and val ratio."
              )
  
          LOGGER.info(
              "Use the default train/val/test ratio in given dataset"
          )
  
      # feature engineering
      if self.feature_module is not None:
          if BACKEND == "pyg":
              dataset = self.feature_module.fit_transform(
                  dataset, inplace=inplace
              )
          else:
              _dataset = self.feature_module.fit_transform(
                  [g[0] for g in dataset], inplace=inplace
              )
              dataset = [
                  [_d, *d[1:]] for _d, d in zip(_dataset, dataset)
              ]
  
      self.dataset = dataset
  
      # check whether the dataset has features.
      # currently we only support graph classification with features.
  
      if BACKEND == "dgl":
          feat = get_graph_node_features(graph_data[0])
      else:
          feat = get_graph_node_features(graph_data)
      assert feat is not None, (
          "Does not support fit on non node-feature dataset!"
          " Please add node features to dataset or specify feature engineers that generate"
          " node features."
      )
  
      # TODO: how can we get num_features?
      num_features = feat.size(-1)
  
      # initialize graph networks
      self._init_graph_module(
          self.gml,
          num_features=num_features,
          feval=evaluator_list,
          device=self.runtime_device,
          loss="binary_cross_entropy_with_logits"
          if not hasattr(dataset, "loss")
          else dataset.loss,
      )
  
      # train the models and tune hpo
      result_valid = []
      names = []
      for idx, model in enumerate(self.graph_model_list):
          time_for_each_model = (
              time_limit - time.time() + time_begin
          ) / (len(self.graph_model_list) - idx)
          if self.hpo_module is None:
              model.initialize()
              model.train(self._compose_dataset(self.dataset), True)
              optimized = model
          else:
              optimized, _ = self.hpo_module.optimize(
                  trainer=model,
                  dataset=self._compose_dataset(self.dataset),
                  time_limit=time_for_each_model,
              )
          # to save memory, all the trainer derived will be mapped to cpu
          optimized.to(torch.device("cpu"))
          name = str(optimized) + "_idx%d" % (idx)
          names.append(name)
          performance_on_valid, _ = optimized.get_valid_score(
              return_major=False
          )
          result_valid.append(
              self._to_prob(
                  optimized.get_valid_predict_proba().cpu().numpy()
              )
          )
          self.leaderboard.insert_model_performance(
              name,
              dict(
                  zip(
                      [e.get_eval_name() for e in evaluator_list],
                      performance_on_valid,
                  )
              ),
          )
          self.trained_models[name] = optimized
  
      # fit the ensemble model
      if self.ensemble_module is not None:
          if BACKEND == "pyg":
              pos_edge_index, neg_edge_index = (
                  self.dataset[0].val_pos_edge_index,
                  self.dataset[0].val_neg_edge_index,
              )
          elif BACKEND == "dgl":
              pos_edge_index, neg_edge_index = torch.stack(
                  self.dataset[0][3].edges()
              ), torch.stack(self.dataset[0][4].edges())
          E = pos_edge_index.size(1) + neg_edge_index.size(1)
          link_labels = torch.zeros(E, dtype=torch.float)
          link_labels[: pos_edge_index.size(1)] = 1.0
  
          performance = self.ensemble_module.fit(
              result_valid,
              link_labels.detach().cpu().numpy(),
              names,
              evaluator_list,
              n_classes=2,
          )
          self.leaderboard.insert_model_performance(
              "ensemble",
              dict(
                  zip(
                      [e.get_eval_name() for e in evaluator_list],
                      performance,
                  )
              ),
          )
  
      return self
  
imports: import logging
  import time
  import json
  from copy import deepcopy
  from typing import Sequence
  import torch
  import numpy
  import yaml
  from .base import BaseClassifier
  from ..base import _parse_hp_space, _initialize_single_model, _parse_model_hp
  from ...module.feature import FEATURE_DICT
  from ...module.train import TRAINER_DICT, BaseLinkPredictionTrainer
  from ...module.train import get_feval
  from ..utils import LeaderBoard, get_graph_node_features, convert_dataset, set_seed
  from ...datasets import utils
  from ..utils import get_logger
  from ...backend import DependentBackend
label_desc: Use image node_id 5 for calling the AutoLinkPredictor obj's underlying member method code with example usage: obj.fit(dataset, time_limit, inplace, train_split, val_split, evaluation_method, seed) and returns: self