[FEATURE] Add PLVQ model

README.md

@@ -31,7 +31,9 @@ be available for use in your Python environment as `prototorch.models`.
 - Learning Vector Quantization Multi-Layer Network (LVQMLN)
 - Siamese GLVQ
 - Cross-Entropy Learning Vector Quantization (CELVQ)
+- Soft Learning Vector Quantization (SLVQ)
 - Robust Soft Learning Vector Quantization (RSLVQ)
+- Probabilistic Learning Vector Quantization (PLVQ)
 
 ### Other
 
@@ -49,7 +51,6 @@ be available for use in your Python environment as `prototorch.models`.
 
 - Median-LVQ
 - Generalized Tangent Learning Vector Quantization (GTLVQ)
-- Probabilistic Learning Vector Quantization (PLVQ)
 - Self-Incremental Learning Vector Quantization (SILVQ)
 
 ## Development setup

docs/source/custom.rst

@@ -2,8 +2,6 @@
 
 Abstract Models
 ========================================
-.. autoclass:: prototorch.models.abstract.AbstractPrototypeModel
+.. automodule:: prototorch.models.abstract
    :members:
-
-.. autoclass:: prototorch.models.abstract.PrototypeImageModel
-   :members:
+   :undoc-members:

docs/source/library.rst

@@ -8,7 +8,7 @@ Models
 
 Unsupervised Methods
 -----------------------------------------
-.. autoclass:: prototorch.models.unsupervised.KNN
+.. autoclass:: prototorch.models.knn.KNN
    :members:
 
 .. autoclass:: prototorch.models.unsupervised.NeuralGas
@@ -80,9 +80,11 @@ Every prototypes is a center of a gaussian distribution of its class, generating
 .. autoclass:: prototorch.models.probabilistic.RSLVQ
    :members:
 
-Missing:
+:cite:t:`villmann2018` proposed two changes to RSLVQ: First incooperate the winning rank into the prior probability calculation.
+And second use divergence as loss function.
 
-- PLVQ
+.. autoclass:: prototorch.models.probabilistic.PLVQ
+   :members:
 
 Classification by Component
 --------------------------------------------

docs/source/refs.bib

@@ -60,3 +60,14 @@
     doi = {10.1162/neco.2009.11-08-908},
 }
 
+@InProceedings{villmann2018,
+    author="Villmann, Andrea
+    and Kaden, Marika
+    and Saralajew, Sascha
+    and Villmann, Thomas",
+    title="Probabilistic Learning Vector Quantization with Cross-Entropy for Probabilistic Class Assignments in Classification Learning",
+    booktitle="Artificial Intelligence and Soft Computing",
+    year="2018",
+    publisher="Springer International Publishing",
+}
+

examples/rslvq_iris.py

@@ -5,6 +5,7 @@ import argparse
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from torchvision.transforms import Lambda
 
 if __name__ == "__main__":
     # Command-line arguments
@@ -24,12 +25,15 @@ if __name__ == "__main__":
     # Hyperparameters
     hparams = dict(
         distribution=[2, 2, 3],
-        lr=0.05,
-        variance=0.1,
+        proto_lr=0.05,
+        lambd=0.1,
+        input_dim=2,
+        latent_dim=2,
+        bb_lr=0.01,
     )
 
     # Initialize the model
-    model = pt.models.probabilistic.RSLVQ(
+    model = pt.models.probabilistic.PLVQ(
         hparams,
         optimizer=torch.optim.Adam,
         # prototype_initializer=pt.components.SMI(train_ds),
@@ -45,7 +49,7 @@ if __name__ == "__main__":
     print(model)
 
     # Callbacks
-    vis = pt.models.VisGLVQ2D(data=train_ds)
+    vis = pt.models.VisSiameseGLVQ2D(data=train_ds)
 
     # Setup trainer
     trainer = pl.Trainer.from_argparse_args(

prototorch/models/__init__.py

@@ -4,22 +4,11 @@ from importlib.metadata import PackageNotFoundError, version
 
 from .callbacks import PrototypeConvergence, PruneLoserPrototypes
 from .cbc import CBC, ImageCBC
-from .glvq import (
-    GLVQ,
-    GLVQ1,
-    GLVQ21,
-    GMLVQ,
-    GRLVQ,
-    LGMLVQ,
-    LVQMLN,
-    ImageGLVQ,
-    ImageGMLVQ,
-    SiameseGLVQ,
-    SiameseGMLVQ,
-)
+from .glvq import (GLVQ, GLVQ1, GLVQ21, GMLVQ, GRLVQ, LGMLVQ, LVQMLN,
+                   ImageGLVQ, ImageGMLVQ, SiameseGLVQ, SiameseGMLVQ)
 from .knn import KNN
 from .lvq import LVQ1, LVQ21, MedianLVQ
-from .probabilistic import CELVQ, RSLVQ, SLVQ
+from .probabilistic import CELVQ, PLVQ, RSLVQ, SLVQ
 from .unsupervised import GrowingNeuralGas, HeskesSOM, KohonenSOM, NeuralGas
 from .vis import *
 

prototorch/models/probabilistic.py

@@ -2,11 +2,13 @@
 
 import torch
 from prototorch.functions.losses import nllr_loss, rslvq_loss
-from prototorch.functions.pooling import stratified_min_pooling, stratified_sum_pooling
-from prototorch.functions.transforms import gaussian
+from prototorch.functions.pooling import (stratified_min_pooling,
+                                          stratified_sum_pooling)
+from prototorch.functions.transforms import (GaussianPrior,
+                                             RankScaledGaussianPrior)
 from prototorch.modules import LambdaLayer, LossLayer
 
-from .glvq import GLVQ
+from .glvq import GLVQ, SiameseGMLVQ
 
 
 class CELVQ(GLVQ):
@@ -32,13 +34,12 @@ class ProbabilisticLVQ(GLVQ):
     def __init__(self, hparams, rejection_confidence=0.0, **kwargs):
         super().__init__(hparams, **kwargs)
 
-        self.conditional_distribution = gaussian
+        self.conditional_distribution = None
         self.rejection_confidence = rejection_confidence
 
     def forward(self, x):
         distances = self.compute_distances(x)
-        conditional = self.conditional_distribution(distances,
-                                                    self.hparams.variance)
+        conditional = self.conditional_distribution(distances)
         prior = (1. / self.num_prototypes) * torch.ones(self.num_prototypes,
                                                         device=self.device)
         posterior = conditional * prior
@@ -66,6 +67,7 @@ class SLVQ(ProbabilisticLVQ):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.loss = LossLayer(nllr_loss)
+        self.conditional_distribution = GaussianPrior(self.hparams.variance)
 
 
 class RSLVQ(ProbabilisticLVQ):
@@ -73,3 +75,25 @@ class RSLVQ(ProbabilisticLVQ):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.loss = LossLayer(rslvq_loss)
+        self.conditional_distribution = GaussianPrior(self.hparams.variance)
+
+
+class PLVQ(ProbabilisticLVQ, SiameseGMLVQ):
+    """Probabilistic Learning Vector Quantization.
+
+    TODO: Use Backbone LVQ instead
+    """
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.conditional_distribution = RankScaledGaussianPrior(
+            self.hparams.lambd)
+        self.loss = torch.nn.KLDivLoss()
+
+    def training_step(self, batch, batch_idx, optimizer_idx=None):
+        x, y = batch
+        out = self.forward(x)
+        y_dist = torch.nn.functional.one_hot(
+            y.long(), num_classes=self.num_classes).float()
+        batch_loss = self.loss(out, y_dist)
+        loss = batch_loss.sum(dim=0)
+        return loss