Add GMLVQ examples

examples/gmlvq_iris.py

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+"""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

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+"""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)