Add knn

prototorch/models/__init__.py

@@ -1,8 +1,10 @@
 from importlib.metadata import PackageNotFoundError, version
 
 from .cbc import CBC
-from .glvq import GLVQ, GMLVQ, GRLVQ, LVQMLN, ImageGLVQ, SiameseGLVQ, LVQ1, LVQ21
+from .glvq import (GLVQ, GMLVQ, GRLVQ, LVQ1, LVQ21, LVQMLN, ImageGLVQ,
+                   SiameseGLVQ)
+from .knn import KNN
 from .neural_gas import NeuralGas
 from .vis import *
 
-__version__ = "0.1.6"
+__version__ = "0.1.6"

prototorch/models/knn.py

@@ -0,0 +1,62 @@
+"""The popular K-Nearest-Neighbors classification algorithm."""
+
+import warnings
+
+import torch
+import torchmetrics
+from prototorch.components import LabeledComponents
+from prototorch.components.initializers import parse_init_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_init_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().cpu()
+
+    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.numpy()
+
+    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