chore: rename clc-lc to proto-Y-architecture

prototorch/models/proto_y_architecture/base.py

@@ -20,7 +20,7 @@ import torch
 from torchmetrics import Accuracy, Metric
 
 
-class CLCCScheme(pl.LightningModule):
+class BaseYArchitecture(pl.LightningModule):
 
     @dataclass
     class HyperParameters:

prototorch/models/proto_y_architecture/callbacks.py

@@ -1,20 +1,12 @@
 from typing import Optional, Type
 
 import numpy as np
-import prototorch as pt
 import pytorch_lightning as pl
 import torch
 import torchmetrics
-from prototorch.core import SMCI
-from prototorch.models.clcc.clcc_glvq import GLVQ
-from prototorch.models.clcc.clcc_scheme import CLCCScheme
+from prototorch.models.proto_y_architecture.base import BaseYArchitecture
 from prototorch.models.vis import Vis2DAbstract
 from prototorch.utils.utils import mesh2d
-from pytorch_lightning.callbacks import EarlyStopping
-from torch.utils.data import DataLoader
-
-# NEW STUFF
-# ##############################################################################
 
 
 class LogTorchmetricCallback(pl.Callback):
@@ -34,7 +26,7 @@ class LogTorchmetricCallback(pl.Callback):
     def setup(
         self,
         trainer: pl.Trainer,
-        pl_module: CLCCScheme,
+        pl_module: BaseYArchitecture,
         stage: Optional[str] = None,
     ) -> None:
         if self.on == "prediction":
@@ -69,65 +61,3 @@ class VisGLVQ2D(Vis2DAbstract):
         y_pred = pl_module.predict(mesh_input)
         y_pred = y_pred.cpu().reshape(xx.shape)
         ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
-
-
-# TODO: Pruning
-
-# ##############################################################################
-
-if __name__ == "__main__":
-    # Dataset
-    train_ds = pt.datasets.Iris(dims=[0, 2])
-    train_ds.targets[train_ds.targets == 2.0] = 1.0
-    # Dataloaders
-    train_loader = DataLoader(
-        train_ds,
-        batch_size=64,
-        num_workers=0,
-        shuffle=True,
-    )
-
-    components_initializer = SMCI(train_ds)
-    #components_initializer = RandomNormalCompInitializer(2)
-
-    hyperparameters = GLVQ.HyperParameters(
-        lr=0.5,
-        distribution=dict(
-            num_classes=2,
-            per_class=1,
-        ),
-        component_initializer=components_initializer,
-    )
-
-    model = GLVQ(hyperparameters)
-
-    print(model)
-
-    # Callbacks
-    vis = VisGLVQ2D(data=train_ds)
-    recall = LogTorchmetricCallback(
-        'recall',
-        torchmetrics.Recall,
-        num_classes=2,
-    )
-
-    es = EarlyStopping(
-        monitor="recall",
-        min_delta=0.001,
-        patience=15,
-        mode="max",
-        check_on_train_epoch_end=True,
-    )
-
-    # Train
-    trainer = pl.Trainer(
-        callbacks=[
-            vis,
-            recall,
-            es,
-        ],
-        gpus=0,
-        max_epochs=200,
-        log_every_n_steps=1,
-    )
-    trainer.fit(model, train_loader)

prototorch/models/proto_y_architecture/glvq.py

@@ -10,11 +10,11 @@ from prototorch.core.initializers import (
     LabelsInitializer,
 )
 from prototorch.core.losses import GLVQLoss
-from prototorch.models.clcc.clcc_scheme import CLCCScheme
+from prototorch.models.proto_y_architecture.base import BaseYArchitecture
 from prototorch.nn.wrappers import LambdaLayer
 
 
-class SupervisedScheme(CLCCScheme):
+class SupervisedScheme(BaseYArchitecture):
 
     @dataclass
     class HyperParameters:

prototorch/models/proto_y_architecture/y_architecture_example.py

@@ -0,0 +1,89 @@
+import prototorch as pt
+import pytorch_lightning as pl
+import torchmetrics
+from prototorch.core import SMCI
+from prototorch.models.proto_y_architecture.callbacks import (
+    LogTorchmetricCallback,
+    VisGLVQ2D,
+)
+from prototorch.models.proto_y_architecture.glvq import GLVQ
+from pytorch_lightning.callbacks import EarlyStopping
+from torch.utils.data import DataLoader
+
+# ##############################################################################
+
+if __name__ == "__main__":
+
+    # ------------------------------------------------------------
+    # DATA
+    # ------------------------------------------------------------
+
+    # Dataset
+    train_ds = pt.datasets.Iris(dims=[0, 2])
+    train_ds.targets[train_ds.targets == 2.0] = 1.0
+
+    # Dataloader
+    train_loader = DataLoader(
+        train_ds,
+        batch_size=64,
+        num_workers=0,
+        shuffle=True,
+    )
+
+    # ------------------------------------------------------------
+    # HYPERPARAMETERS
+    # ------------------------------------------------------------
+
+    # Select Initializer
+    components_initializer = SMCI(train_ds)
+
+    # Define Hyperparameters
+    hyperparameters = GLVQ.HyperParameters(
+        lr=0.5,
+        distribution=dict(
+            num_classes=2,
+            per_class=1,
+        ),
+        component_initializer=components_initializer,
+    )
+
+    # Create Model
+    model = GLVQ(hyperparameters)
+    print(model)
+
+    # ------------------------------------------------------------
+    # TRAINING
+    # ------------------------------------------------------------
+
+    # Controlling Callbacks
+    stopping_criterion = LogTorchmetricCallback(
+        'recall',
+        torchmetrics.Recall,
+        num_classes=2,
+    )
+
+    es = EarlyStopping(
+        monitor=stopping_criterion.name,
+        min_delta=0.001,
+        patience=15,
+        mode="max",
+        check_on_train_epoch_end=True,
+    )
+
+    # Visualization Callback
+    vis = VisGLVQ2D(data=train_ds)
+
+    # Define trainer
+    trainer = pl.Trainer(
+        callbacks=[
+            vis,
+            stopping_criterion,
+            es,
+        ],
+        gpus=0,
+        max_epochs=200,
+        log_every_n_steps=1,
+    )
+
+    # Train
+    trainer.fit(model, train_loader)