Add GMLVQ examples

README.md

@@ -46,13 +46,13 @@ To assist in the development process, you may also find it useful to install
 - GLVQ
 - Siamese GLVQ
 - Neural Gas
+- GMLVQ
+- Limited-Rank GMLVQ
 
 ## Work in Progress
 
 - CBC
 - LVQMLN
-- GMLVQ
-- Limited-Rank GMLVQ
 
 ## Planned models
 
@@ -62,3 +62,4 @@ To assist in the development process, you may also find it useful to install
 - PLVQ
 - SILVQ
 - KNN
+- LVQ1

examples/gmlvq_iris.py

@@ -0,0 +1,47 @@
+"""GMLVQ example using all four dimensions of the Iris dataset."""
+
+import pytorch_lightning as pl
+import torch
+from prototorch.components import initializers as cinit
+from prototorch.datasets.abstract import NumpyDataset
+from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
+from prototorch.models.glvq import GMLVQ
+from sklearn.datasets import load_iris
+from torch.utils.data import DataLoader
+
+if __name__ == "__main__":
+    # Dataset
+    x_train, y_train = load_iris(return_X_y=True)
+    train_ds = NumpyDataset(x_train, y_train)
+
+    # Dataloaders
+    train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
+
+    # Hyperparameters
+    hparams = dict(
+        nclasses=3,
+        prototypes_per_class=1,
+        prototype_initializer=cinit.SMI(torch.Tensor(x_train),
+                                        torch.Tensor(y_train)),
+        input_dim=x_train.shape[1],
+        latent_dim=2,
+        lr=0.01,
+    )
+
+    # Initialize the model
+    model = GMLVQ(hparams)
+
+    # Model summary
+    print(model)
+
+    # Callbacks
+    vis = VisSiameseGLVQ2D(x_train, y_train)
+
+    # Namespace hook for the visualization to work
+    model.backbone = model.omega_layer
+
+    # Setup trainer
+    trainer = pl.Trainer(max_epochs=100, callbacks=[vis])
+
+    # Training loop
+    trainer.fit(model, train_loader)

examples/gmlvq_tecator.py

@@ -0,0 +1,47 @@
+"""GMLVQ example using the Tecator dataset."""
+
+import pytorch_lightning as pl
+import torch
+from prototorch.components import initializers as cinit
+from prototorch.datasets.tecator import Tecator
+from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
+from prototorch.models.glvq import GMLVQ
+from torch.utils.data import DataLoader
+
+if __name__ == "__main__":
+    # Dataset
+    train_ds = Tecator(root="./datasets/", train=True)
+
+    # Dataloaders
+    train_loader = DataLoader(train_ds, num_workers=0, batch_size=32)
+
+    # Grab the full dataset to warm-start prototypes
+    x, y = next(iter(DataLoader(train_ds, batch_size=len(train_ds))))
+
+    # Hyperparameters
+    hparams = dict(
+        nclasses=2,
+        prototypes_per_class=2,
+        prototype_initializer=cinit.SMI(x, y),
+        input_dim=x.shape[1],
+        latent_dim=2,
+        lr=0.01,
+    )
+
+    # Initialize the model
+    model = GMLVQ(hparams)
+
+    # Model summary
+    print(model)
+
+    # Callbacks
+    vis = VisSiameseGLVQ2D(x, y)
+
+    # Namespace hook for the visualization to work
+    model.backbone = model.omega_layer
+
+    # Setup trainer
+    trainer = pl.Trainer(max_epochs=100, callbacks=[vis])
+
+    # Training loop
+    trainer.fit(model, train_loader)

prototorch/models/glvq.py

@@ -94,11 +94,13 @@ class SiameseGLVQ(GLVQ):
                  hparams,
                  backbone_module=torch.nn.Identity,
                  backbone_params={},
+                 sync=True,
                  **kwargs):
         super().__init__(hparams, **kwargs)
         self.backbone = backbone_module(**backbone_params)
         self.backbone_dependent = backbone_module(
             **backbone_params).requires_grad_(False)
+        self.sync = sync
 
     def sync_backbones(self):
         master_state = self.backbone.state_dict()
@@ -117,6 +119,7 @@ class SiameseGLVQ(GLVQ):
             return proto_opt
 
     def forward(self, x):
+        if self.sync:
             self.sync_backbones()
         protos, _ = self.proto_layer()
         latent_x = self.backbone(x)
@@ -145,7 +148,7 @@ class GMLVQ(GLVQ):
     def __init__(self, hparams, **kwargs):
         super().__init__(hparams, **kwargs)
         self.omega_layer = torch.nn.Linear(self.hparams.input_dim,
-                                           self.latent_dim,
+                                           self.hparams.latent_dim,
                                            bias=False)
 
     def forward(self, x):
@@ -155,6 +158,21 @@ class GMLVQ(GLVQ):
         dis = squared_euclidean_distance(latent_x, latent_protos)
         return dis
 
+    def predict_latent(self, x):
+        """Predict `x` assuming it is already embedded in the latent space.
+
+        Only the prototypes are embedded in the latent space using the
+        backbone.
+
+        """
+        # model.eval()  # ?!
+        with torch.no_grad():
+            protos, plabels = self.proto_layer()
+            latent_protos = self.omega_layer(protos)
+            d = squared_euclidean_distance(x, latent_protos)
+            y_pred = wtac(d, plabels)
+        return y_pred.numpy()
+
 
 class LVQMLN(GLVQ):
     """Learning Vector Quantization Multi-Layer Network.