Add GRLVQ with examples.

examples/grlvq_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 sklearn.datasets import load_iris
+from torch.utils.data import DataLoader
+
+from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
+from prototorch.models.glvq import GRLVQ
+
+from sklearn.preprocessing import StandardScaler
+
+
+class PrintRelevanceCallback(pl.Callback):
+    def on_epoch_end(self, trainer, pl_module: GRLVQ):
+        print(pl_module.relevance_profile)
+
+
+if __name__ == "__main__":
+    # Dataset
+    x_train, y_train = load_iris(return_X_y=True)
+    x_train = x_train[:, [0, 2]]
+    scaler = StandardScaler()
+    scaler.fit(x_train)
+    x_train = scaler.transform(x_train)
+    train_ds = NumpyDataset(x_train, y_train)
+
+    # Dataloaders
+    train_loader = DataLoader(train_ds,
+                              num_workers=0,
+                              batch_size=50,
+                              shuffle=True)
+
+    # Hyperparameters
+    hparams = dict(
+        nclasses=3,
+        prototypes_per_class=1,
+        #prototype_initializer=cinit.SMI(torch.Tensor(x_train),
+        #                                torch.Tensor(y_train)),
+        prototype_initializer=cinit.UniformInitializer(2),
+        input_dim=x_train.shape[1],
+        lr=0.1,
+        #transfer_function="sigmoid_beta",
+    )
+
+    # Initialize the model
+    model = GRLVQ(hparams)
+
+    # Model summary
+    print(model)
+
+    # Callbacks
+    vis = VisSiameseGLVQ2D(x_train, y_train)
+    debug = PrintRelevanceCallback()
+
+    # Setup trainer
+    trainer = pl.Trainer(max_epochs=200, callbacks=[vis, debug])
+
+    # Training loop
+    trainer.fit(model, train_loader)

examples/grlvq_spiral.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 sklearn.datasets import load_iris
+from torch.utils.data import DataLoader
+
+from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
+from prototorch.models.glvq import GRLVQ
+
+from sklearn.preprocessing import StandardScaler
+
+from prototorch.datasets.spiral import make_spiral
+
+
+class PrintRelevanceCallback(pl.Callback):
+    def on_epoch_end(self, trainer, pl_module: GRLVQ):
+        print(pl_module.relevance_profile)
+
+
+if __name__ == "__main__":
+    # Dataset
+    x_train, y_train = make_spiral(n_samples=1000, noise=0.3)
+    train_ds = NumpyDataset(x_train, y_train)
+
+    # Dataloaders
+    train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
+
+    # Hyperparameters
+    hparams = dict(
+        nclasses=2,
+        prototypes_per_class=20,
+        prototype_initializer=cinit.SSI(torch.Tensor(x_train),
+                                        torch.Tensor(y_train)),
+        #prototype_initializer=cinit.UniformInitializer(2),
+        input_dim=x_train.shape[1],
+        lr=0.1,
+        #transfer_function="sigmoid_beta",
+    )
+
+    # Initialize the model
+    model = GRLVQ(hparams)
+
+    # Model summary
+    print(model)
+
+    # Callbacks
+    vis = VisSiameseGLVQ2D(x_train, y_train)
+    debug = PrintRelevanceCallback()
+
+    # Setup trainer
+    trainer = pl.Trainer(max_epochs=200, callbacks=[vis, debug])
+
+    # Training loop
+    trainer.fit(model, train_loader)