[FEATURE] Add warm-starting example

examples/warm_starting.py

@@ -0,0 +1,84 @@
+"""Warm-starting GLVQ with prototypes from Growing Neural Gas."""
+
+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()
+
+    # Prepare the data
+    train_ds = pt.datasets.Iris(dims=[0, 2])
+    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=64)
+
+    # Initialize the gng
+    gng = pt.models.GrowingNeuralGas(
+        hparams=dict(num_prototypes=5, insert_freq=2, lr=0.1),
+        prototypes_initializer=pt.initializers.ZCI(2),
+        lr_scheduler=ExponentialLR,
+        lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
+    )
+
+    # Callbacks
+    es = pl.callbacks.EarlyStopping(
+        monitor="loss",
+        min_delta=0.001,
+        patience=20,
+        mode="min",
+        verbose=False,
+        check_on_train_epoch_end=True,
+    )
+
+    # Setup trainer for GNG
+    trainer = pl.Trainer(
+        max_epochs=200,
+        callbacks=[es],
+        weights_summary=None,
+    )
+
+    # Training loop
+    trainer.fit(gng, train_loader)
+
+    # Hyperparameters
+    hparams = dict(
+        distribution=[],
+        lr=0.01,
+    )
+
+    # Warm-start prototypes
+    knn = pt.models.KNN(dict(k=1), data=train_ds)
+    prototypes = gng.prototypes
+    plabels = knn.predict(prototypes)
+
+    # Initialize the model
+    model = pt.models.GLVQ(
+        hparams,
+        optimizer=torch.optim.Adam,
+        prototypes_initializer=pt.initializers.LCI(prototypes),
+        labels_initializer=pt.initializers.LLI(plabels),
+        lr_scheduler=ExponentialLR,
+        lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
+    )
+
+    # Compute intermediate input and output sizes
+    model.example_input_array = torch.zeros(4, 2)
+
+    # Callbacks
+    vis = pt.models.VisGLVQ2D(data=train_ds)
+
+    # 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/abstract.py

@@ -9,6 +9,7 @@ import torchmetrics
 from ..core.competitions import WTAC
 from ..core.components import Components, LabeledComponents
 from ..core.distances import euclidean_distance
+from ..core.initializers import LabelsInitializer
 from ..core.pooling import stratified_min_pooling
 from ..nn.wrappers import LambdaLayer
 
@@ -111,10 +112,13 @@ class SupervisedPrototypeModel(PrototypeModel):
 
         # Layers
         prototypes_initializer = kwargs.get("prototypes_initializer", None)
+        labels_initializer = kwargs.get("labels_initializer",
+                                        LabelsInitializer())
         if prototypes_initializer is not None:
             self.proto_layer = LabeledComponents(
                 distribution=self.hparams.distribution,
                 components_initializer=prototypes_initializer,
+                labels_initializer=labels_initializer,
             )
         self.competition_layer = WTAC()
 

prototorch/models/callbacks.py

@@ -118,6 +118,7 @@ class GNGCallback(pl.Callback):
 
             # Add component
             pl_module.proto_layer.add_components(
+                None,
                 initializer=LiteralCompInitializer(new_component.unsqueeze(0)))
 
             # Adjust Topology