update

Deep-SAD-PyTorch/src/datasets/main.py

@@ -21,6 +21,7 @@ def load_dataset(
     k_fold_num: int = None,
     num_known_normal: int = 0,
     num_known_outlier: int = 0,
+    split: float = 0.7,
 ):
     """Loads the dataset."""
 
@@ -49,6 +50,7 @@ def load_dataset(
             k_fold_num=k_fold_num,
             num_known_normal=num_known_normal,
             num_known_outlier=num_known_outlier,
+            split=split,
         )
 
     if dataset_name == "subtersplit":

Deep-SAD-PyTorch/src/main.py

@@ -152,6 +152,12 @@ from utils.visualization.plot_images_grid import plot_images_grid
     default=0.001,
     help="Initial learning rate for Deep SAD network training. Default=0.001",
 )
+@click.option(
+    "--train_test_split",
+    type=float,
+    default=0.7,
+    help="Ratio of training data in the train-test split (default: 0.7).",
+)
 @click.option("--n_epochs", type=int, default=50, help="Number of epochs to train.")
 @click.option(
     "--lr_milestone",
@@ -307,6 +313,7 @@ def main(
     seed,
     optimizer_name,
     lr,
+    train_test_split,
     n_epochs,
     lr_milestone,
     batch_size,
@@ -416,6 +423,7 @@ def main(
             k_fold_num=k_fold_num,
             num_known_normal=num_known_normal,
             num_known_outlier=num_known_outlier,
+            split=train_test_split,
         )
         # Log random sample of known anomaly classes if more than 1 class
         if n_known_outlier_classes > 1:
@@ -694,6 +702,7 @@ def main(
             ratio_pollution,
             random_state=np.random.RandomState(cfg.settings["seed"]),
             k_fold_num=k_fold_num,
+            split=train_test_split,
         )
 
         # Set up k-fold passes
@@ -804,12 +813,14 @@ def main(
             k_fold_num=cfg.settings["k_fold_num"],
             num_known_normal=cfg.settings["num_known_normal"],
             num_known_outlier=cfg.settings["num_known_outlier"],
+            split=train_test_split,
         )
 
         train_passes = (
             range(cfg.settings["k_fold_num"]) if cfg.settings["k_fold"] else [None]
         )
 
+        retest_autoencoder = False
         retest_isoforest = True
         retest_ocsvm = True
         retest_deepsad = True
@@ -865,6 +876,25 @@ def main(
                     k_fold_idx=fold_idx,
                 )
 
+            if retest_autoencoder:
+                # Initialize DeepSAD model and set neural network phi
+                deepSAD = DeepSAD(cfg.settings["latent_space_dim"], cfg.settings["eta"])
+                deepSAD.set_network(cfg.settings["net_name"])
+                deepSAD.load_model(
+                    model_path=ae_model_path, load_ae=True, map_location=device
+                )
+                logger.info("Loading model from %s." % load_model)
+                # Save pretraining results
+                if fold_idx is None:
+                    deepSAD.save_ae_results(
+                        export_pkl=load_model / "results_ae_retest.pkl"
+                    )
+                else:
+                    deepSAD.save_ae_results(
+                        export_pkl=load_model / f"results_ae_retest_{fold_idx}.pkl"
+                    )
+                del deepSAD
+
             # Initialize DeepSAD model and set neural network phi
             if retest_deepsad:
                 deepSAD = DeepSAD(cfg.settings["latent_space_dim"], cfg.settings["eta"])