feat: gtlvq with examples

examples/gtlvq_moons.py

@@ -0,0 +1,130 @@
+"""Localized-GMLVQ example using the Moons dataset."""
+
+import argparse
+
+import prototorch as pt
+import pytorch_lightning as pl
+import torch
+
+if __name__ == "__main__":
+    # Command-line arguments
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
+    # Reproducibility
+    pl.utilities.seed.seed_everything(seed=2)
+
+    # Dataset
+    train_ds = pt.datasets.Moons(num_samples=300, noise=0.2, seed=42)
+
+    # Dataloaders
+    train_loader = torch.utils.data.DataLoader(train_ds,
+                                               batch_size=256,
+                                               shuffle=True)
+
+    # Hyperparameters
+    hparams = dict(distribution=[1, 3], input_dim=2, latent_dim=2)
+
+    # Initialize the model
+    model = pt.models.GTLVQ(
+        hparams,
+        prototypes_initializer=pt.initializers.SMCI(train_ds),
+        omega_initializer=-pt.initializers.PCALinearTransformInitializer(
+            train_ds))
+
+    # Compute intermediate input and output sizes
+    model.example_input_array = torch.zeros(4, 2)
+
+    # Summary
+    print(model)
+
+    # Callbacks
+    vis = pt.models.VisGLVQ2D(data=train_ds)
+    es = pl.callbacks.EarlyStopping(
+        monitor="train_acc",
+        min_delta=0.001,
+        patience=20,
+        mode="max",
+        verbose=False,
+        check_on_train_epoch_end=True,
+    )
+
+    # Setup trainer
+    trainer = pl.Trainer.from_argparse_args(
+        args,
+        callbacks=[
+            vis,
+            es,
+        ],
+        weights_summary="full",
+        accelerator="ddp",
+    )
+
+    # Training loop
+    trainer.fit(model, train_loader)
+"""Localized-GMLVQ example using the Moons dataset."""
+
+import argparse
+
+import prototorch as pt
+import pytorch_lightning as pl
+import torch
+
+if __name__ == "__main__":
+    # Command-line arguments
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
+    # Reproducibility
+    pl.utilities.seed.seed_everything(seed=2)
+
+    # Dataset
+    train_ds = pt.datasets.Moons(num_samples=300, noise=0.2, seed=42)
+
+    # Dataloaders
+    train_loader = torch.utils.data.DataLoader(train_ds,
+                                               batch_size=256,
+                                               shuffle=True)
+
+    # Hyperparameters
+    hparams = dict(distribution=[1, 3], input_dim=2, latent_dim=2)
+
+    # Initialize the model
+    model = pt.models.GTLVQ(
+        hparams,
+        prototypes_initializer=pt.initializers.SMCI(train_ds),
+        omega_initializer=-pt.initializers.PCALinearTransformInitializer(
+            train_ds))
+
+    # Compute intermediate input and output sizes
+    model.example_input_array = torch.zeros(4, 2)
+
+    # Summary
+    print(model)
+
+    # Callbacks
+    vis = pt.models.VisGLVQ2D(data=train_ds)
+    es = pl.callbacks.EarlyStopping(
+        monitor="train_acc",
+        min_delta=0.001,
+        patience=20,
+        mode="max",
+        verbose=False,
+        check_on_train_epoch_end=True,
+    )
+
+    # Setup trainer
+    trainer = pl.Trainer.from_argparse_args(
+        args,
+        callbacks=[
+            vis,
+            es,
+        ],
+        weights_summary="full",
+        accelerator="ddp",
+    )
+
+    # Training loop
+    trainer.fit(model, train_loader)

prototorch/models/__init__.py

@@ -8,6 +8,7 @@ from .glvq import (
     GLVQ21,
     GMLVQ,
     GRLVQ,
+    GTLVQ,
     LGMLVQ,
     LVQMLN,
     ImageGLVQ,

prototorch/models/extras.py

@@ -15,6 +15,44 @@ def rank_scaled_gaussian(distances, lambd):
     return torch.exp(-torch.exp(-ranks / lambd) * distances)
 
 
+def orthogonalization(tensors):
+    """Orthogonalization via polar decomposition """
+    u, _, v = torch.svd(tensors, compute_uv=True)
+    u_shape = tuple(list(u.shape))
+    v_shape = tuple(list(v.shape))
+
+    # reshape to (num x N x M)
+    u = torch.reshape(u, (-1, u_shape[-2], u_shape[-1]))
+    v = torch.reshape(v, (-1, v_shape[-2], v_shape[-1]))
+
+    out = u @ v.permute([0, 2, 1])
+
+    out = torch.reshape(out, u_shape[:-1] + (v_shape[-2], ))
+
+    return out
+
+
+def ltangent_distance(x, y, omegas):
+    r"""Localized Tangent distance.
+    Compute Orthogonal Complement: math:`\bm P_k = \bm I - \Omega_k \Omega_k^T`
+    Compute Tangent Distance: math:`{\| \bm P \bm x - \bm P_k \bm y_k \|}_2`
+
+    :param `torch.tensor` omegas: Three dimensional matrix
+    :rtype: `torch.tensor`
+    """
+    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
+    p = torch.eye(omegas.shape[-2], device=omegas.device) - torch.bmm(
+        omegas, omegas.permute([0, 2, 1]))
+    projected_x = x @ p
+    projected_y = torch.diagonal(y @ p).T
+    expanded_y = torch.unsqueeze(projected_y, dim=1)
+    batchwise_difference = expanded_y - projected_x
+    differences_squared = batchwise_difference**2
+    distances = torch.sqrt(torch.sum(differences_squared, dim=2))
+    distances = distances.permute(1, 0)
+    return distances
+
+
 class GaussianPrior(torch.nn.Module):
     def __init__(self, variance):
         super().__init__()

prototorch/models/glvq.py

@@ -10,6 +10,7 @@ from ..core.losses import GLVQLoss, lvq1_loss, lvq21_loss
 from ..core.transforms import LinearTransform
 from ..nn.wrappers import LambdaLayer, LossLayer
 from .abstract import ImagePrototypesMixin, SupervisedPrototypeModel
+from .extras import ltangent_distance, orthogonalization
 
 
 class GLVQ(SupervisedPrototypeModel):
@@ -282,6 +283,30 @@ class LGMLVQ(GMLVQ):
         self.register_parameter("_omega", Parameter(omega))
 
 
+class GTLVQ(LGMLVQ):
+    """Localized and Generalized Matrix Learning Vector Quantization."""
+    def __init__(self, hparams, **kwargs):
+        distance_fn = kwargs.pop("distance_fn", ltangent_distance)
+        super().__init__(hparams, distance_fn=distance_fn, **kwargs)
+
+        omega_initializer = kwargs.get("omega_initializer")
+        omega = omega_initializer.generate(self.hparams.input_dim,
+                                           self.hparams.latent_dim)
+
+        # Re-register `_omega` to override the one from the super class.
+        omega = torch.rand(
+            self.num_prototypes,
+            self.hparams.input_dim,
+            self.hparams.latent_dim,
+            device=self.device,
+        )
+        self.register_parameter("_omega", Parameter(omega))
+
+    def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
+        with torch.no_grad():
+            self._omega.copy_(orthogonalization(self._omega))
+
+
 class GLVQ1(GLVQ):
     """Generalized Learning Vector Quantization 1."""
     def __init__(self, hparams, **kwargs):