text: import json
  import torch
  
  from base.base_dataset import BaseADDataset
  from networks.main import build_network, build_autoencoder
  from optim.deepSVDD_trainer import DeepSVDDTrainer
  from optim.ae_trainer import AETrainer
  
  
  class DeepSVDD(object):
      """A class for the Deep SVDD method.
  
      Attributes:
          objective: A string specifying the Deep SVDD objective (either 'one-class' or 'soft-boundary').
          nu: Deep SVDD hyperparameter nu (must be 0 < nu <= 1).
          R: Hypersphere radius R.
          c: Hypersphere center c.
          net_name: A string indicating the name of the neural network to use.
          net: The neural network \phi.
          ae_net: The autoencoder network corresponding to \phi for network weights pretraining.
          trainer: DeepSVDDTrainer to train a Deep SVDD model.
          optimizer_name: A string indicating the optimizer to use for training the Deep SVDD network.
          ae_trainer: AETrainer to train an autoencoder in pretraining.
          ae_optimizer_name: A string indicating the optimizer to use for pretraining the autoencoder.
          results: A dictionary to save the results.
      """
  
      def __init__(self, objective: str = 'one-class', nu: float = 0.1):
          """Inits DeepSVDD with one of the two objectives and hyperparameter nu."""
  
          assert objective in ('one-class', 'soft-boundary'), "Objective must be either 'one-class' or 'soft-boundary'."
          self.objective = objective
          assert (0 < nu) & (nu <= 1), "For hyperparameter nu, it must hold: 0 < nu <= 1."
          self.nu = nu
          self.R = 0.0  # hypersphere radius R
          self.c = None  # hypersphere center c
  
          self.net_name = None
          self.net = None  # neural network \phi
  
          self.trainer = None
          self.optimizer_name = None
  
          self.ae_net = None  # autoencoder network for pretraining
          self.ae_trainer = None
          self.ae_optimizer_name = None
  
          self.results = {
              'train_time': None,
              'test_auc': None,
              'test_time': None,
              'test_scores': None,
          }
  
      def set_network(self, net_name):
          """Builds the neural network \\phi."""
          self.net_name = net_name
          self.net = build_network(net_name)
  
      def train(self, dataset: BaseADDataset, optimizer_name: str = 'adam', lr: float = 0.001, n_epochs: int = 50,
                lr_milestones: tuple = (), batch_size: int = 128, weight_decay: float = 1e-6, device: str = 'cuda',
                n_jobs_dataloader: int = 0):
          """Trains the Deep SVDD model on the training data."""
  
          self.optimizer_name = optimizer_name
          self.trainer = DeepSVDDTrainer(self.objective, self.R, self.c, self.nu, optimizer_name, lr=lr,
                                         n_epochs=n_epochs, lr_milestones=lr_milestones, batch_size=batch_size,
                                         weight_decay=weight_decay, device=device, n_jobs_dataloader=n_jobs_dataloader)
          # Get the model
          self.net = self.trainer.train(dataset, self.net)
          self.R = float(self.trainer.R.cpu().data.numpy())  # get float
          self.c = self.trainer.c.cpu().data.numpy().tolist()  # get list
          self.results['train_time'] = self.trainer.train_time
  
      def test(self, dataset: BaseADDataset, device: str = 'cuda', n_jobs_dataloader: int = 0):
          """Tests the Deep SVDD model on the test data."""
  
          if self.trainer is None:
              self.trainer = DeepSVDDTrainer(self.objective, self.R, self.c, self.nu,
                                             device=device, n_jobs_dataloader=n_jobs_dataloader)
  
          self.trainer.test(dataset, self.net)
          # Get results
          self.results['test_auc'] = self.trainer.test_auc
          self.results['test_time'] = self.trainer.test_time
          self.results['test_scores'] = self.trainer.test_scores
  
      def pretrain(self, dataset: BaseADDataset, optimizer_name: str = 'adam', lr: float = 0.001, n_epochs: int = 100,
                   lr_milestones: tuple = (), batch_size: int = 128, weight_decay: float = 1e-6, device: str = 'cuda',
                   n_jobs_dataloader: int = 0):
          """Pretrains the weights for the Deep SVDD network \phi via autoencoder."""
  
          self.ae_net = build_autoencoder(self.net_name)
          self.ae_optimizer_name = optimizer_name
          self.ae_trainer = AETrainer(optimizer_name, lr=lr, n_epochs=n_epochs, lr_milestones=lr_milestones,
                                      batch_size=batch_size, weight_decay=weight_decay, device=device,
                                      n_jobs_dataloader=n_jobs_dataloader)
          self.ae_net = self.ae_trainer.train(dataset, self.ae_net)
          self.ae_trainer.test(dataset, self.ae_net)
          self.init_network_weights_from_pretraining()
  
      def init_network_weights_from_pretraining(self):
          """Initialize the Deep SVDD network weights from the encoder weights of the pretraining autoencoder."""
  
          net_dict = self.net.state_dict()
          ae_net_dict = self.ae_net.state_dict()
  
          # Filter out decoder network keys
          ae_net_dict = {k: v for k, v in ae_net_dict.items() if k in net_dict}
          # Overwrite values in the existing state_dict
          net_dict.update(ae_net_dict)
          # Load the new state_dict
          self.net.load_state_dict(net_dict)
  
      def save_model(self, export_model, save_ae=True):
          """Save Deep SVDD model to export_model."""
  
          net_dict = self.net.state_dict()
          ae_net_dict = self.ae_net.state_dict() if save_ae else None
  
          torch.save({'R': self.R,
                      'c': self.c,
                      'net_dict': net_dict,
                      'ae_net_dict': ae_net_dict}, export_model)
  
      def load_model(self, model_path, load_ae=False):
          """Load Deep SVDD model from model_path."""
  
          model_dict = torch.load(model_path)
  
          self.R = model_dict['R']
          self.c = model_dict['c']
          self.net.load_state_dict(model_dict['net_dict'])
          if load_ae:
              if self.ae_net is None:
                  self.ae_net = build_autoencoder(self.net_name)
              self.ae_net.load_state_dict(model_dict['ae_net_dict'])
  
      def save_results(self, export_json):
          """Save results dict to a JSON-file."""
          with open(export_json, 'w') as fp:
              json.dump(self.results, fp)
  