masks -> ParameterList(requires_grad=False)

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.6.0
+current_version = 0.7.1
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)
@@ -8,6 +8,6 @@ message = build: bump version {current_version} → {new_version}
 
 [bumpversion:file:pyproject.toml]
 
-[bumpversion:file:./prototorch/models/__init__.py]
+[bumpversion:file:./src/prototorch/models/__init__.py]
 
 [bumpversion:file:./docs/source/conf.py]

.github/workflows/pythonapp.yml

@@ -65,9 +65,9 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install .[all]
-          pip install wheel
+          pip install build
       - name: Build package
-        run: python setup.py sdist bdist_wheel
+        run: python -m build . -C verbose
       - name: Publish a Python distribution to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1
         with:

docs/source/conf.py

@@ -23,7 +23,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "0.6.0"
+release = "0.7.1"
 
 # -- General configuration ---------------------------------------------------
 

examples/cbc_iris.py

@@ -1,5 +1,4 @@
 """CBC example using the Iris dataset."""
-
 import argparse
 import warnings
 

pyproject.toml

@@ -1,7 +1,7 @@
 
 [project]
 name = "prototorch-models"
-version = "0.6.0"
+version = "0.7.1"
 description = "Pre-packaged prototype-based machine learning models using ProtoTorch and PyTorch-Lightning."
 authors = [
     { name = "Jensun Ravichandran", email = "jjensun@gmail.com" },
@@ -64,9 +64,6 @@ all = [
     "ipykernel",
 ]
 
-[project.entry-points."prototorch.plugins"]
-models = "prototorch.models"
-
 [build-system]
 requires = ["setuptools>=61", "wheel"]
 build-backend = "setuptools.build_meta"
@@ -91,6 +88,3 @@ line_length = 79
 [tool.mypy]
 explicit_package_bases = true
 namespace_packages = true
-
-[tool.setuptools]
-py-modules = ["prototorch"]

src/prototorch/models/__init__.py

@@ -36,4 +36,4 @@ from .unsupervised import (
 )
 from .vis import *
 
-__version__ = "0.6.0"
+__version__ = "0.7.1"

src/prototorch/models/abstract.py

@@ -2,7 +2,6 @@
 
 import logging
 
-import prototorch
 import pytorch_lightning as pl
 import torch
 import torch.nn.functional as F
@@ -228,7 +227,7 @@ class NonGradientMixin(ProtoTorchMixin):
         super().__init__(*args, **kwargs)
         self.automatic_optimization = False
 
-    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
+    def training_step(self, train_batch, batch_idx):
         raise NotImplementedError
 
 

src/prototorch/models/cbc.py

@@ -44,7 +44,7 @@ class CBC(SiameseGLVQ):
         probs = self.competition_layer(detections, reasonings)
         return probs
 
-    def shared_step(self, batch, batch_idx, optimizer_idx=None):
+    def shared_step(self, batch, batch_idx):
         x, y = batch
         y_pred = self(x)
         num_classes = self.num_classes
@@ -52,8 +52,8 @@ class CBC(SiameseGLVQ):
         loss = self.loss(y_pred, y_true).mean()
         return y_pred, loss
 
-    def training_step(self, batch, batch_idx, optimizer_idx=None):
+    def training_step(self, batch, batch_idx):
-        y_pred, train_loss = self.shared_step(batch, batch_idx, optimizer_idx)
+        y_pred, train_loss = self.shared_step(batch, batch_idx)
         preds = torch.argmax(y_pred, dim=1)
         accuracy = torchmetrics.functional.accuracy(
             preds.int(),

src/prototorch/models/glvq.py

@@ -1,13 +1,15 @@
 """Models based on the GLVQ framework."""
 
 import torch
+from numpy.typing import NDArray
 from prototorch.core.competitions import wtac
 from prototorch.core.distances import (
+    ML_omega_distance,
     lomega_distance,
     omega_distance,
     squared_euclidean_distance,
 )
-from prototorch.core.initializers import EyeLinearTransformInitializer
+from prototorch.core.initializers import LLTI, EyeLinearTransformInitializer
 from prototorch.core.losses import (
     GLVQLoss,
     lvq1_loss,
@@ -15,7 +17,7 @@ from prototorch.core.losses import (
 )
 from prototorch.core.transforms import LinearTransform
 from prototorch.nn.wrappers import LambdaLayer, LossLayer
-from torch.nn.parameter import Parameter
+from torch.nn import Parameter, ParameterList
 
 from .abstract import ImagePrototypesMixin, SupervisedPrototypeModel
 from .extras import ltangent_distance, orthogonalization
@@ -45,36 +47,38 @@ class GLVQ(SupervisedPrototypeModel):
 
     def initialize_prototype_win_ratios(self):
         self.register_buffer(
-            "prototype_win_ratios",
+            "prototype_win_ratios", torch.zeros(self.num_prototypes, device=self.device)
-            torch.zeros(self.num_prototypes, device=self.device))
+        )
 
     def on_train_epoch_start(self):
         self.initialize_prototype_win_ratios()
 
     def log_prototype_win_ratios(self, distances):
         batch_size = len(distances)
-        prototype_wc = torch.zeros(self.num_prototypes,
+        prototype_wc = torch.zeros(
-                                   dtype=torch.long,
+            self.num_prototypes, dtype=torch.long, device=self.device
-                                   device=self.device)
+        )
-        wi, wc = torch.unique(distances.min(dim=-1).indices,
+        wi, wc = torch.unique(
-                              sorted=True,
+            distances.min(dim=-1).indices, sorted=True, return_counts=True
-                              return_counts=True)
+        )
         prototype_wc[wi] = wc
         prototype_wr = prototype_wc / batch_size
-        self.prototype_win_ratios = torch.vstack([
+        self.prototype_win_ratios = torch.vstack(
+            [
                 self.prototype_win_ratios,
                 prototype_wr,
-        ])
+            ]
+        )
 
-    def shared_step(self, batch, batch_idx, optimizer_idx=None):
+    def shared_step(self, batch, batch_idx):
         x, y = batch
         out = self.compute_distances(x)
         _, plabels = self.proto_layer()
         loss = self.loss(out, y, plabels)
         return out, loss
 
-    def training_step(self, batch, batch_idx, optimizer_idx=None):
+    def training_step(self, batch, batch_idx):
-        out, train_loss = self.shared_step(batch, batch_idx, optimizer_idx)
+        out, train_loss = self.shared_step(batch, batch_idx)
         self.log_prototype_win_ratios(out)
         self.log("train_loss", train_loss)
         self.log_acc(out, batch[-1], tag="train_acc")
@@ -99,10 +103,6 @@ class GLVQ(SupervisedPrototypeModel):
             test_loss += batch_loss.item()
         self.log("test_loss", test_loss)
 
-    # TODO
-    # def predict_step(self, batch, batch_idx, dataloader_idx=None):
-    #     pass
-
 
 class SiameseGLVQ(GLVQ):
     """GLVQ in a Siamese setting.
@@ -113,11 +113,9 @@ class SiameseGLVQ(GLVQ):
 
     """
 
-    def __init__(self,
+    def __init__(
-                 hparams,
+        self, hparams, backbone=torch.nn.Identity(), both_path_gradients=False, **kwargs
-                 backbone=torch.nn.Identity(),
+    ):
-                 both_path_gradients=False,
-                 **kwargs):
         distance_fn = kwargs.pop("distance_fn", squared_euclidean_distance)
         super().__init__(hparams, distance_fn=distance_fn, **kwargs)
         self.backbone = backbone
@@ -179,6 +177,7 @@ class GRLVQ(SiameseGLVQ):
     TODO Make a RelevanceLayer. `bb_lr` is ignored otherwise.
 
     """
+
     _relevances: torch.Tensor
 
     def __init__(self, hparams, **kwargs):
@@ -189,8 +188,7 @@ class GRLVQ(SiameseGLVQ):
         self.register_parameter("_relevances", Parameter(relevances))
 
         # Override the backbone
-        self.backbone = LambdaLayer(self._apply_relevances,
+        self.backbone = LambdaLayer(self._apply_relevances, name="relevance scaling")
-                                    name="relevance scaling")
 
     def _apply_relevances(self, x):
         return x @ torch.diag(self._relevances)
@@ -214,8 +212,9 @@ class SiameseGMLVQ(SiameseGLVQ):
         super().__init__(hparams, **kwargs)
 
         # Override the backbone
-        omega_initializer = kwargs.get("omega_initializer",
+        omega_initializer = kwargs.get(
-                                       EyeLinearTransformInitializer())
+            "omega_initializer", EyeLinearTransformInitializer()
+        )
         self.backbone = LinearTransform(
             self.hparams["input_dim"],
             self.hparams["latent_dim"],
@@ -233,6 +232,49 @@ class SiameseGMLVQ(SiameseGLVQ):
         return lam.detach().cpu()
 
 
+class GMLMLVQ(GLVQ):
+    """Generalized Multi-Layer Matrix Learning Vector Quantization.
+    Masks are applied to the omega layers to achieve sparsity and constrain
+    learning to certain items of each omega.
+
+    Implemented as a regular GLVQ network that simply uses a different distance
+    function. This makes it easier to implement a localized variant.
+    """
+
+    # Parameters
+    _omegas: list[torch.Tensor]
+    masks: list[torch.Tensor]
+
+    def __init__(self, hparams, **kwargs):
+        distance_fn = kwargs.pop("distance_fn", ML_omega_distance)
+        super().__init__(hparams, distance_fn=distance_fn, **kwargs)
+
+        # Additional parameters
+        self._masks = ParameterList(
+            [Parameter(mask, requires_grad=False) for mask in kwargs.get("masks")]
+        )
+        self._omegas = ParameterList([LLTI(mask).generate(1, 1) for mask in self._masks])
+
+    @property
+    def omega_matrices(self):
+        return [_omega.detach().cpu() for _omega in self._omegas]
+
+    @property
+    def lambda_matrix(self):
+        # TODO update to respective lambda calculation rules.
+        omega = self._omega.detach()  # (input_dim, latent_dim)
+        lam = omega @ omega.T
+        return lam.detach().cpu()
+
+    def compute_distances(self, x):
+        protos, _ = self.proto_layer()
+        distances = self.distance_layer(x, protos, self._omegas, self._masks)
+        return distances
+
+    def extra_repr(self):
+        return f"(omegas): (shapes: {[tuple(_omega.shape) for _omega in self._omegas]})"
+
+
 class GMLVQ(GLVQ):
     """Generalized Matrix Learning Vector Quantization.
 
@@ -249,10 +291,12 @@ class GMLVQ(GLVQ):
         super().__init__(hparams, distance_fn=distance_fn, **kwargs)
 
         # Additional parameters
-        omega_initializer = kwargs.get("omega_initializer",
+        omega_initializer = kwargs.get(
-                                       EyeLinearTransformInitializer())
+            "omega_initializer", EyeLinearTransformInitializer()
-        omega = omega_initializer.generate(self.hparams["input_dim"],
+        )
-                                           self.hparams["latent_dim"])
+        omega = omega_initializer.generate(
+            self.hparams["input_dim"], self.hparams["latent_dim"]
+        )
         self.register_parameter("_omega", Parameter(omega))
 
     @property

src/prototorch/models/knn.py

@@ -34,7 +34,7 @@ class KNN(SupervisedPrototypeModel):
             labels_initializer=LiteralLabelsInitializer(targets))
         self.competition_layer = KNNC(k=self.hparams.k)
 
-    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
+    def training_step(self, train_batch, batch_idx):
         return 1  # skip training step
 
     def on_train_batch_start(self, train_batch, batch_idx):

src/prototorch/models/lvq.py

@@ -13,7 +13,7 @@ from .glvq import GLVQ
 class LVQ1(NonGradientMixin, GLVQ):
     """Learning Vector Quantization 1."""
 
-    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
+    def training_step(self, train_batch, batch_idx):
         protos, plables = self.proto_layer()
         x, y = train_batch
         dis = self.compute_distances(x)
@@ -43,7 +43,7 @@ class LVQ1(NonGradientMixin, GLVQ):
 class LVQ21(NonGradientMixin, GLVQ):
     """Learning Vector Quantization 2.1."""
 
-    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
+    def training_step(self, train_batch, batch_idx):
         protos, plabels = self.proto_layer()
 
         x, y = train_batch
@@ -100,7 +100,7 @@ class MedianLVQ(NonGradientMixin, GLVQ):
         lower_bound = (gamma * f.log()).sum()
         return lower_bound
 
-    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
+    def training_step(self, train_batch, batch_idx):
         protos, plabels = self.proto_layer()
 
         x, y = train_batch

src/prototorch/models/probabilistic.py

@@ -21,7 +21,7 @@ class CELVQ(GLVQ):
         # Loss
         self.loss = torch.nn.CrossEntropyLoss()
 
-    def shared_step(self, batch, batch_idx, optimizer_idx=None):
+    def shared_step(self, batch, batch_idx):
         x, y = batch
         out = self.compute_distances(x)  # [None, num_protos]
         _, plabels = self.proto_layer()
@@ -63,7 +63,7 @@ class ProbabilisticLVQ(GLVQ):
         prediction[confidence < self.rejection_confidence] = -1
         return prediction
 
-    def training_step(self, batch, batch_idx, optimizer_idx=None):
+    def training_step(self, batch, batch_idx):
         x, y = batch
         out = self.forward(x)
         _, plabels = self.proto_layer()
@@ -123,7 +123,7 @@ class PLVQ(ProbabilisticLVQ, SiameseGMLVQ):
         self.loss = torch.nn.KLDivLoss()
 
     # FIXME
-    # def training_step(self, batch, batch_idx, optimizer_idx=None):
+    # def training_step(self, batch, batch_idx):
     #     x, y = batch
     #     y_pred = self(x)
     #     batch_loss = self.loss(y_pred, y)

tests/test_models.py

@@ -1,195 +1,193 @@
 """prototorch.models test suite."""
 
-import prototorch as pt
+import prototorch.models
-import pytest
-import torch
 
 
 def test_glvq_model_build():
-    model = pt.models.GLVQ(
+    model = prototorch.models.GLVQ(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_glvq1_model_build():
-    model = pt.models.GLVQ1(
+    model = prototorch.models.GLVQ1(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_glvq21_model_build():
-    model = pt.models.GLVQ1(
+    model = prototorch.models.GLVQ1(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_gmlvq_model_build():
-    model = pt.models.GMLVQ(
+    model = prototorch.models.GMLVQ(
         {
             "distribution": (3, 2),
             "input_dim": 2,
             "latent_dim": 2,
         },
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_grlvq_model_build():
-    model = pt.models.GRLVQ(
+    model = prototorch.models.GRLVQ(
         {
             "distribution": (3, 2),
             "input_dim": 2,
         },
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_gtlvq_model_build():
-    model = pt.models.GTLVQ(
+    model = prototorch.models.GTLVQ(
         {
             "distribution": (3, 2),
             "input_dim": 4,
             "latent_dim": 2,
         },
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_lgmlvq_model_build():
-    model = pt.models.LGMLVQ(
+    model = prototorch.models.LGMLVQ(
         {
             "distribution": (3, 2),
             "input_dim": 4,
             "latent_dim": 2,
         },
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_image_glvq_model_build():
-    model = pt.models.ImageGLVQ(
+    model = prototorch.models.ImageGLVQ(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(16),
+        prototypes_initializer=prototorch.initializers.RNCI(16),
     )
 
 
 def test_image_gmlvq_model_build():
-    model = pt.models.ImageGMLVQ(
+    model = prototorch.models.ImageGMLVQ(
         {
             "distribution": (3, 2),
             "input_dim": 16,
             "latent_dim": 2,
         },
-        prototypes_initializer=pt.initializers.RNCI(16),
+        prototypes_initializer=prototorch.initializers.RNCI(16),
     )
 
 
 def test_image_gtlvq_model_build():
-    model = pt.models.ImageGMLVQ(
+    model = prototorch.models.ImageGMLVQ(
         {
             "distribution": (3, 2),
             "input_dim": 16,
             "latent_dim": 2,
         },
-        prototypes_initializer=pt.initializers.RNCI(16),
+        prototypes_initializer=prototorch.initializers.RNCI(16),
     )
 
 
 def test_siamese_glvq_model_build():
-    model = pt.models.SiameseGLVQ(
+    model = prototorch.models.SiameseGLVQ(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(4),
+        prototypes_initializer=prototorch.initializers.RNCI(4),
     )
 
 
 def test_siamese_gmlvq_model_build():
-    model = pt.models.SiameseGMLVQ(
+    model = prototorch.models.SiameseGMLVQ(
         {
             "distribution": (3, 2),
             "input_dim": 4,
             "latent_dim": 2,
         },
-        prototypes_initializer=pt.initializers.RNCI(4),
+        prototypes_initializer=prototorch.initializers.RNCI(4),
     )
 
 
 def test_siamese_gtlvq_model_build():
-    model = pt.models.SiameseGTLVQ(
+    model = prototorch.models.SiameseGTLVQ(
         {
             "distribution": (3, 2),
             "input_dim": 4,
             "latent_dim": 2,
         },
-        prototypes_initializer=pt.initializers.RNCI(4),
+        prototypes_initializer=prototorch.initializers.RNCI(4),
     )
 
 
 def test_knn_model_build():
-    train_ds = pt.datasets.Iris(dims=[0, 2])
+    train_ds = prototorch.datasets.Iris(dims=[0, 2])
-    model = pt.models.KNN(dict(k=3), data=train_ds)
+    model = prototorch.models.KNN(dict(k=3), data=train_ds)
 
 
 def test_lvq1_model_build():
-    model = pt.models.LVQ1(
+    model = prototorch.models.LVQ1(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_lvq21_model_build():
-    model = pt.models.LVQ21(
+    model = prototorch.models.LVQ21(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_median_lvq_model_build():
-    model = pt.models.MedianLVQ(
+    model = prototorch.models.MedianLVQ(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_celvq_model_build():
-    model = pt.models.CELVQ(
+    model = prototorch.models.CELVQ(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_rslvq_model_build():
-    model = pt.models.RSLVQ(
+    model = prototorch.models.RSLVQ(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_slvq_model_build():
-    model = pt.models.SLVQ(
+    model = prototorch.models.SLVQ(
         {"distribution": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_growing_neural_gas_model_build():
-    model = pt.models.GrowingNeuralGas(
+    model = prototorch.models.GrowingNeuralGas(
         {"num_prototypes": 5},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_kohonen_som_model_build():
-    model = pt.models.KohonenSOM(
+    model = prototorch.models.KohonenSOM(
         {"shape": (3, 2)},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )
 
 
 def test_neural_gas_model_build():
-    model = pt.models.NeuralGas(
+    model = prototorch.models.NeuralGas(
         {"num_prototypes": 5},
-        prototypes_initializer=pt.initializers.RNCI(2),
+        prototypes_initializer=prototorch.initializers.RNCI(2),
     )