Added Vis for GMLVQ with more then 2 dims using PCA (#11)

* Added Vis for GMLVQ with more then 2 dims using PCA

* Added initialization possibility to GMlVQ with PCA and one example with omega init + PCA vis of 3 dims

* test(githooks): Add githooks for automatic commit checks

Co-authored-by: staps@hs-mittweida.de <staps@hs-mittweida.de>
Co-authored-by: Alexander Engelsberger <alexanderengelsberger@gmail.com>

examples/gmlvq_iris.py

@@ -0,0 +1,59 @@
+"""GLVQ example using the Iris dataset."""
+
+import argparse
+
+import prototorch as pt
+import pytorch_lightning as pl
+import torch
+from torch.optim.lr_scheduler import ExponentialLR
+
+if __name__ == "__main__":
+    # Command-line arguments
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
+    # Dataset
+    train_ds = pt.datasets.Iris()
+
+    # Dataloaders
+    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=64)
+
+    # Hyperparameters
+    hparams = dict(
+        input_dim=4,
+        latent_dim=3,
+        distribution={
+            "num_classes": 3,
+            "prototypes_per_class": 2
+        },
+        proto_lr=0.0005,
+        bb_lr=0.0005,
+    )
+
+    # Initialize the model
+    model = pt.models.GMLVQ(
+        hparams,
+        optimizer=torch.optim.Adam,
+        prototype_initializer=pt.components.SSI(train_ds),
+        lr_scheduler=ExponentialLR,
+        lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
+        omega_initializer=pt.components.PCA(train_ds.data)
+    )
+
+    # Compute intermediate input and output sizes
+    #model.example_input_array = torch.zeros(4, 2)
+
+    # Callbacks
+    vis = pt.models.VisGMLVQ2D(data=train_ds, border=0.1)
+
+    # Setup trainer
+    trainer = pl.Trainer.from_argparse_args(
+        args,
+        callbacks=[vis],
+        weights_summary="full",
+        accelerator="ddp",
+    )
+
+    # Training loop
+    trainer.fit(model, train_loader)

prototorch/models/glvq.py

@@ -7,6 +7,7 @@ from prototorch.functions.distances import (lomega_distance, omega_distance,
                                             squared_euclidean_distance)
 from prototorch.functions.helper import get_flat
 from prototorch.functions.losses import glvq_loss, lvq1_loss, lvq21_loss
+from prototorch.components import LinearMapping
 from prototorch.modules import LambdaLayer, LossLayer
 from torch.nn.parameter import Parameter
 
@@ -239,10 +240,17 @@ class GMLVQ(GLVQ):
         super().__init__(hparams, distance_fn=distance_fn, **kwargs)
 
         # Additional parameters
-        omega = torch.randn(self.hparams.input_dim,
-                            self.hparams.latent_dim,
-                            device=self.device)
-        self.register_parameter("_omega", Parameter(omega))
+        omega_initializer = kwargs.get("omega_initializer", None)
+        initialized_omega = kwargs.get("initialized_omega", None)
+        if omega_initializer is not None or initialized_omega is not None:
+            self.omega_layer = LinearMapping(
+                mapping_shape=(self.hparams.input_dim, self.hparams.latent_dim),
+                initializer=omega_initializer,
+                initialized_linearmapping=initialized_omega,
+            )
+
+        self.register_parameter("_omega", Parameter(self.omega_layer.mapping))
+        self.backbone = LambdaLayer(lambda x: x @ self._omega, name = "omega matrix")
        
     @property
     def omega_matrix(self):
@@ -256,6 +264,24 @@ class GMLVQ(GLVQ):
     def extra_repr(self):
         return f"(omega): (shape: {tuple(self._omega.shape)})"
 
+    def predict_latent(self, x, map_protos=True):
+        """Predict `x` assuming it is already embedded in the latent space.
+
+        Only the prototypes are embedded in the latent space using the
+        backbone.
+ 
+        """
+        self.eval()
+        with torch.no_grad():
+            protos, plabels = self.proto_layer()
+            if map_protos:
+                protos = self.backbone(protos)
+            d = squared_euclidean_distance(x, protos)
+            y_pred = wtac(d, plabels)
+        return y_pred
+
+
+
 
 class LGMLVQ(GMLVQ):
     """Localized and Generalized Matrix Learning Vector Quantization."""

prototorch/models/vis.py

@@ -83,7 +83,13 @@ class Vis2DAbstract(pl.Callback):
         mesh_input = np.c_[xx.ravel(), yy.ravel()]
         return mesh_input, xx, yy
 
-    def plot_data(self, ax, x, y):
+    def perform_pca_2D(self, data):
+        (_, eigVal, eigVec) = torch.pca_lowrank(data, q=2)
+        return data @ eigVec
+
+    def plot_data(self, ax, x, y, pca=False):
+        if pca:
+            x = self.perform_pca_2D(x)
         ax.scatter(
             x[:, 0],
             x[:, 1],
@@ -94,7 +100,9 @@ class Vis2DAbstract(pl.Callback):
             s=30,
         )
 
-    def plot_protos(self, ax, protos, plabels):
+    def plot_protos(self, ax, protos, plabels, pca=False):
+        if pca:
+            protos = self.perform_pca_2D(protos)
         ax.scatter(
             protos[:, 0],
             protos[:, 1],
@@ -186,6 +194,50 @@ class VisSiameseGLVQ2D(Vis2DAbstract):
         self.log_and_display(trainer, pl_module)
 
 
+class VisGMLVQ2D(Vis2DAbstract):
+    def __init__(self, *args, map_protos=True, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.map_protos = map_protos
+
+    def on_epoch_end(self, trainer, pl_module):
+        if not self.precheck(trainer):
+            return True
+
+        protos = pl_module.prototypes
+        plabels = pl_module.prototype_labels
+        x_train, y_train = self.x_train, self.y_train
+        device = pl_module.device
+        with torch.no_grad():
+            x_train = pl_module.backbone(torch.Tensor(x_train).to(device))
+            x_train = x_train.cpu().detach()
+        if self.map_protos:
+            with torch.no_grad():
+                protos = pl_module.backbone(torch.Tensor(protos).to(device))
+                protos = protos.cpu().detach()
+        ax = self.setup_ax()
+        if x_train.shape[1] > 2:
+            self.plot_data(ax, x_train, y_train, pca=True)
+        else:
+            self.plot_data(ax, x_train, y_train, pca=False)
+        if self.show_protos:
+            if protos.shape[1] > 2:
+                self.plot_protos(ax, protos, plabels, pca=True)
+            else:
+                self.plot_protos(ax, protos, plabels, pca=False)
+        ### something to work on: meshgrid with pca
+        #    x = np.vstack((x_train, protos))
+        #    mesh_input, xx, yy = self.get_mesh_input(x)
+        #else:
+        #    mesh_input, xx, yy = self.get_mesh_input(x_train)
+        #_components = pl_module.proto_layer._components
+        #mesh_input = torch.Tensor(mesh_input).type_as(_components)
+        #y_pred = pl_module.predict_latent(mesh_input,
+        #                                  map_protos=self.map_protos)
+        #y_pred = y_pred.cpu().reshape(xx.shape)
+        #ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
+        self.log_and_display(trainer, pl_module)
+
+
 class VisCBC2D(Vis2DAbstract):
     def on_epoch_end(self, trainer, pl_module):
         if not self.precheck(trainer):