Rename rslvq example

examples/rslvq_iris.py

@@ -13,6 +13,9 @@ if __name__ == "__main__":
     parser = pl.Trainer.add_argparse_args(parser)
     args = parser.parse_args()
 
+    # Reproducibility
+    pl.utilities.seed.seed_everything(seed=42)
+
     # Dataset
     train_ds = pt.datasets.Iris(dims=[0, 2])
 
@@ -20,20 +23,17 @@ if __name__ == "__main__":
     train_loader = torch.utils.data.DataLoader(train_ds, batch_size=64)
 
     # Hyperparameters
-    num_classes = 3
-    prototypes_per_class = 2
     hparams = dict(
-        distribution=(num_classes, prototypes_per_class),
+        distribution=[2, 2, 3],
         lr=0.05,
-        variance=1.0,
+        variance=0.3,
     )
 
     # Initialize the model
-    model = pt.models.probabilistic.LikelihoodRatioLVQ(
+    model = pt.models.probabilistic.RSLVQ(
         hparams,
         optimizer=torch.optim.Adam,
-        # prototype_initializer=pt.components.UniformInitializer(2),
-        prototype_initializer=pt.components.SMI(train_ds),
+        prototype_initializer=pt.components.SSI(train_ds, noise=0.2),
     )
 
     print(model)
@@ -45,6 +45,9 @@ if __name__ == "__main__":
     trainer = pl.Trainer.from_argparse_args(
         args,
         callbacks=[vis],
+        terminate_on_nan=True,
+        weights_summary=None,
+        # accelerator="ddp",
     )
 
     # Training loop

prototorch/models/__init__.py

@@ -1,6 +1,6 @@
 from importlib.metadata import PackageNotFoundError, version
 
-from . import probabilistic
+from .probabilistic import LikelihoodRatioLVQ, RSLVQ
 from .cbc import CBC, ImageCBC
 from .glvq import (CELVQ, GLVQ, GLVQ1, GLVQ21, GMLVQ, GRLVQ, LVQMLN, ImageGLVQ,
                    ImageGMLVQ, SiameseGLVQ)

prototorch/models/probabilistic.py

@@ -2,15 +2,14 @@
 
 import torch
 from prototorch.functions.competitions import stratified_sum
-from prototorch.functions.losses import (log_likelihood_ratio_loss,
-                                         robust_soft_loss)
+from prototorch.functions.losses import log_likelihood_ratio_loss, robust_soft_loss
 from prototorch.functions.transform import gaussian
 
 from .glvq import GLVQ
 
 
 class ProbabilisticLVQ(GLVQ):
-    def __init__(self, hparams, rejection_confidence=1.0, **kwargs):
+    def __init__(self, hparams, rejection_confidence=0.0, **kwargs):
         super().__init__(hparams, **kwargs)
 
         self.conditional_distribution = gaussian
@@ -45,19 +44,14 @@ class ProbabilisticLVQ(GLVQ):
 
 
 class LikelihoodRatioLVQ(ProbabilisticLVQ):
-    """Learning Vector Quantization based on Likelihood Ratios
-    """
+    """Learning Vector Quantization based on Likelihood Ratios."""
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.loss_fn = log_likelihood_ratio_loss
 
 
 class RSLVQ(ProbabilisticLVQ):
-    """Learning Vector Quantization based on Likelihood Ratios
-    """
+    """Robust Soft Learning Vector Quantization."""
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.loss_fn = robust_soft_loss
-
-
-__all__ = ["LikelihoodRatioLVQ", "RSLVQ"]