feat: distribute GMLVQ into mixins

examples/y_architecture_example.py

@@ -2,12 +2,12 @@ import prototorch as pt
 import pytorch_lightning as pl
 import torchmetrics
 from prototorch.core import SMCI
-from prototorch.models.y_arch.callbacks import (
+from prototorch.y_arch.callbacks import (
     LogTorchmetricCallback,
     PlotLambdaMatrixToTensorboard,
     VisGMLVQ2D,
 )
-from prototorch.models.y_arch.library.gmlvq import GMLVQ
+from prototorch.y_arch.library.gmlvq import GMLVQ
 from pytorch_lightning.callbacks import EarlyStopping
 from torch.utils.data import DataLoader
 
@@ -39,8 +39,7 @@ if __name__ == "__main__":
 
     # Define Hyperparameters
     hyperparameters = GMLVQ.HyperParameters(
-        lr=0.1,
-        backbone_lr=5,
+        lr=dict(components_layer=0.1, _omega=0),
         input_dim=4,
         distribution=dict(
             num_classes=3,

prototorch/models/y_arch/architectures/comparison.py

@@ -1,41 +0,0 @@
-from dataclasses import dataclass, field
-from typing import Callable
-
-from prototorch.core.distances import euclidean_distance
-from prototorch.models.y_arch.architectures.base import BaseYArchitecture
-from prototorch.nn.wrappers import LambdaLayer
-
-
-class SimpleComparisonMixin(BaseYArchitecture):
-    """
-    Simple Comparison
-
-    A comparison layer that only uses the positions of the components and the batch for dissimilarity computation.
-    """
-
-    # HyperParameters
-    # ----------------------------------------------------------------------------------------------------
-    @dataclass
-    class HyperParameters(BaseYArchitecture.HyperParameters):
-        """
-        comparison_fn: The comparison / dissimilarity function to use. Default: euclidean_distance.
-        comparison_args: Keyword arguments for the comparison function. Default: {}.
-        """
-        comparison_fn: Callable = euclidean_distance
-        comparison_args: dict = field(default_factory=lambda: dict())
-
-    # Steps
-    # ----------------------------------------------------------------------------------------------------
-    def init_comparison(self, hparams: HyperParameters):
-        self.comparison_layer = LambdaLayer(fn=hparams.comparison_fn,
-                                            **hparams.comparison_args)
-
-    def comparison(self, batch, components):
-        comp_tensor, _ = components
-        batch_tensor, _ = batch
-
-        comp_tensor = comp_tensor.unsqueeze(1)
-
-        distances = self.comparison_layer(batch_tensor, comp_tensor)
-
-        return distances

prototorch/models/y_arch/architectures/optimization.py

@@ -1,36 +0,0 @@
-from dataclasses import dataclass
-from typing import Type
-
-import torch
-from prototorch.models.y_arch import BaseYArchitecture
-
-
-class SingleLearningRateMixin(BaseYArchitecture):
-    """
-    Single Learning Rate
-
-    All parameters are updated with a single learning rate.
-    """
-
-    # HyperParameters
-    # ----------------------------------------------------------------------------------------------------
-    @dataclass
-    class HyperParameters(BaseYArchitecture.HyperParameters):
-        """
-        lr: The learning rate. Default: 0.1.
-        optimizer: The optimizer to use. Default: torch.optim.Adam.
-        """
-        lr: float = 0.1
-        optimizer: Type[torch.optim.Optimizer] = torch.optim.Adam
-
-    # Steps
-    # ----------------------------------------------------------------------------------------------------
-    def __init__(self, hparams: HyperParameters) -> None:
-        super().__init__(hparams)
-        self.lr = hparams.lr
-        self.optimizer = hparams.optimizer
-
-    # Hooks
-    # ----------------------------------------------------------------------------------------------------
-    def configure_optimizers(self):
-        return self.optimizer(self.parameters(), lr=self.lr)  # type: ignore

prototorch/y_arch/__init__.py

@@ -1,14 +1,22 @@
 from .architectures.base import BaseYArchitecture
-from .architectures.comparison import SimpleComparisonMixin
+from .architectures.comparison import (
+    OmegaComparisonMixin,
+    SimpleComparisonMixin,
+)
 from .architectures.competition import WTACompetitionMixin
 from .architectures.components import SupervisedArchitecture
 from .architectures.loss import GLVQLossMixin
-from .architectures.optimization import SingleLearningRateMixin
+from .architectures.optimization import (
+    MultipleLearningRateMixin,
+    SingleLearningRateMixin,
+)
 
 __all__ = [
     'BaseYArchitecture',
+    "OmegaComparisonMixin",
     "SimpleComparisonMixin",
     "SingleLearningRateMixin",
+    "MultipleLearningRateMixin",
     "SupervisedArchitecture",
     "WTACompetitionMixin",
     "GLVQLossMixin",

prototorch/y_arch/architectures/comparison.py

@@ -1,86 +1,50 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, field
-from typing import Callable
+from typing import Callable, Dict
 
 import torch
-from prototorch.core.distances import omega_distance
+from prototorch.core.distances import euclidean_distance
 from prototorch.core.initializers import (
     AbstractLinearTransformInitializer,
     EyeLinearTransformInitializer,
 )
-from prototorch.models.y_arch import (
-    GLVQLossMixin,
-    SimpleComparisonMixin,
-    SupervisedArchitecture,
-    WTACompetitionMixin,
-)
 from prototorch.nn.wrappers import LambdaLayer
+from prototorch.y_arch.architectures.base import BaseYArchitecture
+from torch import Tensor
 from torch.nn.parameter import Parameter
 
 
-class GMLVQ(
-        SupervisedArchitecture,
-        SimpleComparisonMixin,
-        GLVQLossMixin,
-        WTACompetitionMixin,
-):
+class SimpleComparisonMixin(BaseYArchitecture):
     """
-    Generalized Matrix Learning Vector Quantization (GMLVQ)
+    Simple Comparison
 
-    A GMLVQ architecture that uses the winner-take-all strategy and the GLVQ loss.
+    A comparison layer that only uses the positions of the components and the batch for dissimilarity computation.
     """
 
-    _omega: torch.Tensor
-
     # HyperParameters
     # ----------------------------------------------------------------------------------------------------
     @dataclass
-    class HyperParameters(
-            SimpleComparisonMixin.HyperParameters,
-            GLVQLossMixin.HyperParameters,
-            WTACompetitionMixin.HyperParameters,
-            SupervisedArchitecture.HyperParameters,
-    ):
+    class HyperParameters(BaseYArchitecture.HyperParameters):
         """
-        comparison_fn: The comparison / dissimilarity function to use. Override Default: omega_distance.
-        comparison_args: Keyword arguments for the comparison function. Override Default: {}.
-        input_dim: Necessary Field: The dimensionality of the input.
-        latent_dim: The dimensionality of the latent space. Default: 2.
-        omega_initializer: The initializer to use for the omega matrix. Default: EyeLinearTransformInitializer.
+        comparison_fn: The comparison / dissimilarity function to use. Default: euclidean_distance.
+        comparison_args: Keyword arguments for the comparison function. Default: {}.
         """
-        backbone_lr: float = 0.1
-        lr: float = 0.1
-        comparison_fn: Callable = omega_distance
+        comparison_fn: Callable = euclidean_distance
         comparison_args: dict = field(default_factory=lambda: dict())
-        input_dim: int | None = None
-        latent_dim: int = 2
-        omega_initializer: type[
-            AbstractLinearTransformInitializer] = EyeLinearTransformInitializer
 
-        optimizer: type[torch.optim.Optimizer] = torch.optim.Adam
+        comparison_parameters: dict = field(default_factory=lambda: dict())
 
     # Steps
     # ----------------------------------------------------------------------------------------------------
-    def __init__(self, hparams) -> None:
-        super().__init__(hparams)
-        self.lr = hparams.lr
-        self.backbone_lr = hparams.backbone_lr
-        self.optimizer = hparams.optimizer
-
-    def init_comparison(self, hparams: HyperParameters) -> None:
-        if hparams.input_dim is None:
-            raise ValueError("input_dim must be specified.")
-        omega = hparams.omega_initializer().generate(
-            hparams.input_dim,
-            hparams.latent_dim,
-        )
-        self.register_parameter("_omega", Parameter(omega))
+    def init_comparison(self, hparams: HyperParameters):
         self.comparison_layer = LambdaLayer(
             fn=hparams.comparison_fn,
             **hparams.comparison_args,
         )
 
+        self.comparison_kwargs: dict[str, Tensor] = dict()
+
     def comparison(self, batch, components):
         comp_tensor, _ = components
         batch_tensor, _ = batch
@@ -90,21 +54,50 @@ class GMLVQ(
         distances = self.comparison_layer(
             batch_tensor,
             comp_tensor,
-            self._omega,
+            **self.comparison_kwargs,
         )
 
         return distances
 
-    def configure_optimizers(self):
-        proto_opt = self.optimizer(
-            self.components_layer.parameters(),
-            lr=self.lr,
+
+class OmegaComparisonMixin(SimpleComparisonMixin):
+    """
+    Omega Comparison
+
+    A comparison layer that uses the positions of the components and the batch for dissimilarity computation.
+    """
+
+    _omega: torch.Tensor
+
+    # HyperParameters
+    # ----------------------------------------------------------------------------------------------------
+    @dataclass
+    class HyperParameters(SimpleComparisonMixin.HyperParameters):
+        """
+        input_dim: Necessary Field: The dimensionality of the input.
+        latent_dim: The dimensionality of the latent space. Default: 2.
+        omega_initializer: The initializer to use for the omega matrix. Default: EyeLinearTransformInitializer.
+        """
+        input_dim: int | None = None
+        latent_dim: int = 2
+        omega_initializer: type[
+            AbstractLinearTransformInitializer] = EyeLinearTransformInitializer
+
+    # Steps
+    # ----------------------------------------------------------------------------------------------------
+    def init_comparison(self, hparams: HyperParameters) -> None:
+        super().init_comparison(hparams)
+
+        # Initialize the omega matrix
+        if hparams.input_dim is None:
+            raise ValueError("input_dim must be specified.")
+        else:
+            omega = hparams.omega_initializer().generate(
+                hparams.input_dim,
+                hparams.latent_dim,
             )
-        omega_opt = self.optimizer(
-            [self._omega],
-            lr=self.backbone_lr,
-        )
-        return [proto_opt, omega_opt]
+            self.register_parameter("_omega", Parameter(omega))
+            self.comparison_kwargs = dict(omega=self._omega)
 
     # Properties
     # ----------------------------------------------------------------------------------------------------

prototorch/y_arch/architectures/competition.py

@@ -1,7 +1,7 @@
 from dataclasses import dataclass
 
 from prototorch.core.competitions import WTAC
-from prototorch.models.y_arch.architectures.base import BaseYArchitecture
+from prototorch.y_arch.architectures.base import BaseYArchitecture
 
 
 class WTACompetitionMixin(BaseYArchitecture):

prototorch/y_arch/architectures/components.py

@@ -5,7 +5,7 @@ from prototorch.core.initializers import (
     AbstractComponentsInitializer,
     LabelsInitializer,
 )
-from prototorch.models.y_arch import BaseYArchitecture
+from prototorch.y_arch import BaseYArchitecture
 
 
 class SupervisedArchitecture(BaseYArchitecture):

prototorch/y_arch/architectures/loss.py

@@ -1,7 +1,7 @@
 from dataclasses import dataclass, field
 
 from prototorch.core.losses import GLVQLoss
-from prototorch.models.y_arch.architectures.base import BaseYArchitecture
+from prototorch.y_arch.architectures.base import BaseYArchitecture
 
 
 class GLVQLossMixin(BaseYArchitecture):

prototorch/y_arch/architectures/optimization.py

@@ -0,0 +1,86 @@
+from dataclasses import dataclass, field
+from typing import Type
+
+import torch
+from prototorch.y_arch import BaseYArchitecture
+from torch.nn.parameter import Parameter
+
+
+class SingleLearningRateMixin(BaseYArchitecture):
+    """
+    Single Learning Rate
+
+    All parameters are updated with a single learning rate.
+    """
+
+    # HyperParameters
+    # ----------------------------------------------------------------------------------------------------
+    @dataclass
+    class HyperParameters(BaseYArchitecture.HyperParameters):
+        """
+        lr: The learning rate. Default: 0.1.
+        optimizer: The optimizer to use. Default: torch.optim.Adam.
+        """
+        lr: float = 0.1
+        optimizer: Type[torch.optim.Optimizer] = torch.optim.Adam
+
+    # Steps
+    # ----------------------------------------------------------------------------------------------------
+    def __init__(self, hparams: HyperParameters) -> None:
+        super().__init__(hparams)
+        self.lr = hparams.lr
+        self.optimizer = hparams.optimizer
+
+    # Hooks
+    # ----------------------------------------------------------------------------------------------------
+    def configure_optimizers(self):
+        return self.optimizer(self.parameters(), lr=self.lr)  # type: ignore
+
+
+class MultipleLearningRateMixin(BaseYArchitecture):
+    """
+    Multiple Learning Rates
+
+    Define Different Learning Rates for different parameters.
+    """
+
+    # HyperParameters
+    # ----------------------------------------------------------------------------------------------------
+    @dataclass
+    class HyperParameters(BaseYArchitecture.HyperParameters):
+        """
+        lr: The learning rate. Default: 0.1.
+        optimizer: The optimizer to use. Default: torch.optim.Adam.
+        """
+        lr: dict = field(default_factory=lambda: dict())
+        optimizer: Type[torch.optim.Optimizer] = torch.optim.Adam
+
+    # Steps
+    # ----------------------------------------------------------------------------------------------------
+    def __init__(self, hparams: HyperParameters) -> None:
+        super().__init__(hparams)
+        self.lr = hparams.lr
+        self.optimizer = hparams.optimizer
+
+    # Hooks
+    # ----------------------------------------------------------------------------------------------------
+    def configure_optimizers(self):
+        optimizers = []
+        for name, lr in self.lr.items():
+            if not hasattr(self, name):
+                raise ValueError(f"{name} is not a parameter of {self}")
+            else:
+                model_part = getattr(self, name)
+                if isinstance(model_part, Parameter):
+                    optimizers.append(
+                        self.optimizer(
+                            [model_part],
+                            lr=lr,  # type: ignore
+                        ))
+                elif hasattr(model_part, "parameters"):
+                    optimizers.append(
+                        self.optimizer(
+                            model_part.parameters(),
+                            lr=lr,  # type: ignore
+                        ))
+        return optimizers

prototorch/y_arch/callbacks.py

@@ -7,9 +7,9 @@ import torch
 import torchmetrics
 from matplotlib import pyplot as plt
 from prototorch.models.vis import Vis2DAbstract
-from prototorch.models.y_arch.architectures.base import BaseYArchitecture
-from prototorch.models.y_arch.library.gmlvq import GMLVQ
 from prototorch.utils.utils import mesh2d
+from prototorch.y_arch.architectures.base import BaseYArchitecture
+from prototorch.y_arch.library.gmlvq import GMLVQ
 from pytorch_lightning.loggers import TensorBoardLogger
 
 DIVERGING_COLOR_MAPS = [

prototorch/y_arch/library/glvq.py

@@ -1,12 +1,12 @@
 from dataclasses import dataclass
 
-from prototorch.models.y_arch import (
+from prototorch.y_arch import (
     SimpleComparisonMixin,
     SingleLearningRateMixin,
     SupervisedArchitecture,
     WTACompetitionMixin,
 )
-from prototorch.models.y_arch.architectures.loss import GLVQLossMixin
+from prototorch.y_arch.architectures.loss import GLVQLossMixin
 
 
 class GLVQ(

prototorch/y_arch/library/gmlvq.py

@@ -0,0 +1,50 @@
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Callable
+
+import torch
+from prototorch.core.distances import omega_distance
+from prototorch.y_arch import (
+    GLVQLossMixin,
+    MultipleLearningRateMixin,
+    OmegaComparisonMixin,
+    SupervisedArchitecture,
+    WTACompetitionMixin,
+)
+
+
+class GMLVQ(
+        SupervisedArchitecture,
+        OmegaComparisonMixin,
+        GLVQLossMixin,
+        WTACompetitionMixin,
+        MultipleLearningRateMixin,
+):
+    """
+    Generalized Matrix Learning Vector Quantization (GMLVQ)
+
+    A GMLVQ architecture that uses the winner-take-all strategy and the GLVQ loss.
+    """
+    # HyperParameters
+    # ----------------------------------------------------------------------------------------------------
+    @dataclass
+    class HyperParameters(
+            MultipleLearningRateMixin.HyperParameters,
+            OmegaComparisonMixin.HyperParameters,
+            GLVQLossMixin.HyperParameters,
+            WTACompetitionMixin.HyperParameters,
+            SupervisedArchitecture.HyperParameters,
+    ):
+        """
+        comparison_fn: The comparison / dissimilarity function to use. Override Default: omega_distance.
+        comparison_args: Keyword arguments for the comparison function. Override Default: {}.
+        """
+        comparison_fn: Callable = omega_distance
+        comparison_args: dict = field(default_factory=lambda: dict())
+        optimizer: type[torch.optim.Optimizer] = torch.optim.Adam
+
+        lr: dict = field(default_factory=lambda: dict(
+            components_layer=0.1,
+            _omega=0.5,
+        ))