added deepsad base code

Deep-SAD-PyTorch/src/baselines/SemiDGM.py

@@ -0,0 +1,128 @@
+import json
+import torch
+
+from base.base_dataset import BaseADDataset
+from networks.main import build_network, build_autoencoder
+from optim import SemiDeepGenerativeTrainer, VAETrainer
+
+
+class SemiDeepGenerativeModel(object):
+    """A class for the Semi-Supervised Deep Generative model (M1+M2 model).
+
+    Paper: Kingma et al. (2014). Semi-supervised learning with deep generative models. In NIPS (pp. 3581-3589).
+    Link: https://papers.nips.cc/paper/5352-semi-supervised-learning-with-deep-generative-models.pdf
+
+    Attributes:
+        net_name: A string indicating the name of the neural network to use.
+        net: The neural network.
+        trainer: SemiDeepGenerativeTrainer to train a Semi-Supervised Deep Generative model.
+        optimizer_name: A string indicating the optimizer to use for training.
+        results: A dictionary to save the results.
+    """
+
+    def __init__(self, alpha: float = 0.1):
+        """Inits SemiDeepGenerativeModel."""
+
+        self.alpha = alpha
+
+        self.net_name = None
+        self.net = None
+
+        self.trainer = None
+        self.optimizer_name = None
+
+        self.vae_net = None  # variational autoencoder network for pretraining
+        self.vae_trainer = None
+        self.vae_optimizer_name = None
+
+        self.results = {
+            'train_time': None,
+            'test_auc': None,
+            'test_time': None,
+            'test_scores': None,
+        }
+
+        self.vae_results = {
+            'train_time': None,
+            'test_auc': None,
+            'test_time': None
+        }
+
+    def set_vae(self, net_name):
+        """Builds the variational autoencoder network for pretraining."""
+        self.net_name = net_name
+        self.vae_net = build_autoencoder(self.net_name)  # VAE for pretraining
+
+    def set_network(self, net_name):
+        """Builds the neural network."""
+        self.net_name = net_name
+        self.net = build_network(net_name, ae_net=self.vae_net)  # full M1+M2 model
+
+    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 Semi-Supervised Deep Generative model on the training data."""
+
+        self.optimizer_name = optimizer_name
+
+        self.trainer = SemiDeepGenerativeTrainer(alpha=self.alpha, optimizer_name=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.net = self.trainer.train(dataset, self.net)
+        self.results['train_time'] = self.trainer.train_time
+
+    def test(self, dataset: BaseADDataset, device: str = 'cuda', n_jobs_dataloader: int = 0):
+        """Tests the Semi-Supervised Deep Generative model on the test data."""
+
+        if self.trainer is None:
+            self.trainer = SemiDeepGenerativeTrainer(alpha=self.alpha, 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 a variational autoencoder (M1) for the Semi-Supervised Deep Generative model."""
+
+        # Train
+        self.vae_optimizer_name = optimizer_name
+        self.vae_trainer = VAETrainer(optimizer_name=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.vae_net = self.vae_trainer.train(dataset, self.vae_net)
+        # Get train results
+        self.vae_results['train_time'] = self.vae_trainer.train_time
+
+        # Test
+        self.vae_trainer.test(dataset, self.vae_net)
+        # Get test results
+        self.vae_results['test_auc'] = self.vae_trainer.test_auc
+        self.vae_results['test_time'] = self.vae_trainer.test_time
+
+    def save_model(self, export_model):
+        """Save a Semi-Supervised Deep Generative model to export_model."""
+
+        net_dict = self.net.state_dict()
+        torch.save({'net_dict': net_dict}, export_model)
+
+    def load_model(self, model_path):
+        """Load a Semi-Supervised Deep Generative model from model_path."""
+
+        model_dict = torch.load(model_path)
+        self.net.load_state_dict(model_dict['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)
+
+    def save_vae_results(self, export_json):
+        """Save variational autoencoder results dict to a JSON-file."""
+        with open(export_json, 'w') as fp:
+            json.dump(self.vae_results, fp)