feat: add simple test step

examples/knn_iris.py

@@ -6,6 +6,7 @@ import prototorch as pt
 import pytorch_lightning as pl
 import torch
 from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
 
 if __name__ == "__main__":
     # Command-line arguments
@@ -14,12 +15,20 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     # Dataset
-    x_train, y_train = load_iris(return_X_y=True)
-    x_train = x_train[:, [0, 2]]
-    train_ds = pt.datasets.NumpyDataset(x_train, y_train)
+    X, y = load_iris(return_X_y=True)
+    X = X[:, [0, 2]]
+
+    X_train, X_test, y_train, y_test = train_test_split(X,
+                                                        y,
+                                                        test_size=0.5,
+                                                        random_state=42)
+
+    train_ds = pt.datasets.NumpyDataset(X_train, y_train)
+    test_ds = pt.datasets.NumpyDataset(X_test, y_test)
 
     # Dataloaders
-    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=150)
+    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=16)
+    test_loader = torch.utils.data.DataLoader(test_ds, batch_size=16)
 
     # Hyperparameters
     hparams = dict(k=5)
@@ -35,7 +44,7 @@ if __name__ == "__main__":
 
     # Callbacks
     vis = pt.models.VisGLVQ2D(
-        data=(x_train, y_train),
+        data=(X_train, y_train),
         resolution=200,
         block=True,
     )
@@ -53,5 +62,8 @@ if __name__ == "__main__":
     trainer.fit(model, train_loader)
 
     # Recall
-    y_pred = model.predict(torch.tensor(x_train))
+    y_pred = model.predict(torch.tensor(X_train))
     print(y_pred)
+
+    # Test
+    trainer.test(model, dataloaders=test_loader)

prototorch/models/abstract.py

@@ -162,6 +162,14 @@ class SupervisedPrototypeModel(PrototypeModel):
                  prog_bar=True,
                  logger=True)
 
+    def test_step(self, batch, batch_idx):
+        x, targets = batch
+
+        preds = self.predict(x)
+        accuracy = torchmetrics.functional.accuracy(preds.int(), targets.int())
+
+        self.log("test_acc", accuracy)
+
 
 class ProtoTorchMixin(object):
     """All mixins are ProtoTorchMixins."""