Update Documentation

Clean up project

prototorch/models/__init__.py

@@ -3,8 +3,7 @@ from importlib.metadata import PackageNotFoundError, version
 from .cbc import CBC, ImageCBC
 from .glvq import (GLVQ, GLVQ1, GLVQ21, GMLVQ, GRLVQ, LVQ1, LVQ21, LVQMLN,
                    ImageGLVQ, ImageGMLVQ, SiameseGLVQ)
-from .knn import KNN
-from .neural_gas import NeuralGas
+from .unsupervised import KNN, NeuralGas
 from .vis import *
 
 __version__ = "0.1.7"

prototorch/models/callbacks.py

@@ -1,14 +0,0 @@
-"""Callbacks for Pytorch Lighning Modules"""
-
-import pytorch_lightning as pl
-import torch
-
-
-class StopOnNaN(pl.Callback):
-    def __init__(self, param):
-        super().__init__()
-        self.param = param
-
-    def on_epoch_end(self, trainer, pl_module, logs={}):
-        if torch.isnan(self.param).any():
-            raise ValueError("NaN encountered. Stopping.")

prototorch/models/glvq.py

@@ -1,3 +1,4 @@
+"""Models based on the GLVQ Framework"""
 import torch
 import torchmetrics
 from prototorch.components import LabeledComponents
@@ -6,15 +7,8 @@ from prototorch.functions.competitions import wtac
 from prototorch.functions.distances import (euclidean_distance, omega_distance,
                                             sed)
 from prototorch.functions.helper import get_flat
-from prototorch.functions.losses import (_get_dp_dm, _get_matcher, glvq_loss,
-                                         lvq1_loss, lvq21_loss)
-
-from .abstract import AbstractPrototypeModel, PrototypeImageModel
-
-
-class GLVQ(AbstractPrototypeModel):
-    """Generalized Learning Vector Quantization."""
-
+from prototorch.functions.losses import (_get_dp_dm, glvq_loss, lvq1_loss,
+                                         lvq21_loss)
 
 from .abstract import AbstractPrototypeModel, PrototypeImageModel
 
@@ -192,11 +186,14 @@ class GRLVQ(SiameseGLVQ):
         self.relevances = torch.nn.parameter.Parameter(
             torch.ones(self.hparams.input_dim))
 
+        # Overwrite backbone
+        self.backbone = self._backbone
+
     @property
     def relevance_profile(self):
         return self.relevances.detach().cpu()
 
-    def backbone(self, x):
+    def _backbone(self, x):
         """Namespace hook for the visualization callbacks to work."""
         return x @ torch.diag(self.relevances)
 
@@ -262,6 +259,7 @@ class LVQMLN(SiameseGLVQ):
 
 
 class NonGradientGLVQ(GLVQ):
+    """Abstract Model for Models that do not use gradients in their update phase."""
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.automatic_optimization = False
@@ -271,6 +269,7 @@ class NonGradientGLVQ(GLVQ):
 
 
 class LVQ1(NonGradientGLVQ):
+    """Learning Vector Quantization 1."""
     def training_step(self, train_batch, batch_idx, optimizer_idx=None):
         protos = self.proto_layer.components
         plabels = self.proto_layer.component_labels
@@ -299,6 +298,7 @@ class LVQ1(NonGradientGLVQ):
 
 
 class LVQ21(NonGradientGLVQ):
+    """Learning Vector Quantization 2.1."""
     def training_step(self, train_batch, batch_idx, optimizer_idx=None):
         protos = self.proto_layer.components
         plabels = self.proto_layer.component_labels
@@ -311,8 +311,7 @@ class LVQ21(NonGradientGLVQ):
             xi = xi.view(1, -1)
             yi = yi.view(1, )
             d = self(xi)
-            preds = wtac(d, plabels)
-            (dp, wp), (dn, wn) = _get_dp_dm(d, yi, plabels, with_indices=True)
+            (_, wp), (_, wn) = _get_dp_dm(d, yi, plabels, with_indices=True)
             shiftp = xi - protos[wp]
             shiftn = protos[wn] - xi
             updated_protos = protos + 0.0
@@ -328,11 +327,11 @@ class LVQ21(NonGradientGLVQ):
 
 
 class MedianLVQ(NonGradientGLVQ):
-    ...
+    """Median LVQ"""
 
 
 class GLVQ1(GLVQ):
-    """Learning Vector Quantization 1."""
+    """Generalized Learning Vector Quantization 1."""
     def __init__(self, hparams, **kwargs):
         super().__init__(hparams, **kwargs)
         self.loss = lvq1_loss
@@ -340,7 +339,7 @@ class GLVQ1(GLVQ):
 
 
 class GLVQ21(GLVQ):
-    """Learning Vector Quantization 2.1."""
+    """Generalized Learning Vector Quantization 2.1."""
     def __init__(self, hparams, **kwargs):
         super().__init__(hparams, **kwargs)
         self.loss = lvq21_loss
@@ -354,7 +353,6 @@ class ImageGLVQ(PrototypeImageModel, GLVQ):
     after updates.
 
     """
-    pass
 
 
 class ImageGMLVQ(PrototypeImageModel, GMLVQ):
@@ -364,4 +362,3 @@ class ImageGMLVQ(PrototypeImageModel, GMLVQ):
     after updates.
 
     """
-    pass

prototorch/models/knn.py

@@ -1,62 +0,0 @@
-"""The popular K-Nearest-Neighbors classification algorithm."""
-
-import warnings
-
-import torch
-import torchmetrics
-from prototorch.components import LabeledComponents
-from prototorch.components.initializers import parse_data_arg
-from prototorch.functions.competitions import knnc
-from prototorch.functions.distances import euclidean_distance
-
-from .abstract import AbstractPrototypeModel
-
-
-class KNN(AbstractPrototypeModel):
-    """K-Nearest-Neighbors classification algorithm."""
-    def __init__(self, hparams, **kwargs):
-        super().__init__()
-
-        self.save_hyperparameters(hparams)
-
-        # Default Values
-        self.hparams.setdefault("k", 1)
-        self.hparams.setdefault("distance", euclidean_distance)
-
-        data = kwargs.get("data")
-        x_train, y_train = parse_data_arg(data)
-
-        self.proto_layer = LabeledComponents(initialized_components=(x_train,
-                                                                     y_train))
-
-        self.train_acc = torchmetrics.Accuracy()
-
-    @property
-    def prototype_labels(self):
-        return self.proto_layer.component_labels.detach()
-
-    def forward(self, x):
-        protos, _ = self.proto_layer()
-        dis = self.hparams.distance(x, protos)
-        return dis
-
-    def predict(self, x):
-        # model.eval()  # ?!
-        with torch.no_grad():
-            d = self(x)
-            plabels = self.proto_layer.component_labels
-            y_pred = knnc(d, plabels, k=self.hparams.k)
-        return y_pred
-
-    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
-        return 1
-
-    def on_train_batch_start(self,
-                             train_batch,
-                             batch_idx,
-                             dataloader_idx=None):
-        warnings.warn("k-NN has no training, skipping!")
-        return -1
-
-    def configure_optimizers(self):
-        return None

prototorch/models/unsupervised.py

@@ -1,7 +1,13 @@
+"""Unsupervised prototype learning algorithms."""
+
+import warnings
+
 import torch
-from prototorch.components import Components
+import torchmetrics
+from prototorch.components import Components, LabeledComponents
 from prototorch.components import initializers as cinit
-from prototorch.components.initializers import ZerosInitializer
+from prototorch.components.initializers import ZerosInitializer, parse_data_arg
+from prototorch.functions.competitions import knnc
 from prototorch.functions.distances import euclidean_distance
 from prototorch.modules.losses import NeuralGasEnergy
 
@@ -36,6 +42,56 @@ class ConnectionTopology(torch.nn.Module):
         return f"agelimit: {self.agelimit}"
 
 
+class KNN(AbstractPrototypeModel):
+    """K-Nearest-Neighbors classification algorithm."""
+    def __init__(self, hparams, **kwargs):
+        super().__init__()
+
+        self.save_hyperparameters(hparams)
+
+        # Default Values
+        self.hparams.setdefault("k", 1)
+        self.hparams.setdefault("distance", euclidean_distance)
+
+        data = kwargs.get("data")
+        x_train, y_train = parse_data_arg(data)
+
+        self.proto_layer = LabeledComponents(initialized_components=(x_train,
+                                                                     y_train))
+
+        self.train_acc = torchmetrics.Accuracy()
+
+    @property
+    def prototype_labels(self):
+        return self.proto_layer.component_labels.detach()
+
+    def forward(self, x):
+        protos, _ = self.proto_layer()
+        dis = self.hparams.distance(x, protos)
+        return dis
+
+    def predict(self, x):
+        # model.eval()  # ?!
+        with torch.no_grad():
+            d = self(x)
+            plabels = self.proto_layer.component_labels
+            y_pred = knnc(d, plabels, k=self.hparams.k)
+        return y_pred
+
+    def training_step(self, train_batch, batch_idx, optimizer_idx=None):
+        return 1
+
+    def on_train_batch_start(self,
+                             train_batch,
+                             batch_idx,
+                             dataloader_idx=None):
+        warnings.warn("k-NN has no training, skipping!")
+        return -1
+
+    def configure_optimizers(self):
+        return None
+
+
 class NeuralGas(AbstractPrototypeModel):
     def __init__(self, hparams, **kwargs):
         super().__init__()