chore: minor changes and version updates

examples/cbc_iris.py

@@ -1,5 +1,7 @@
 """ProtoTorch CBC example using 2D Iris data."""
 
+import logging
+
 import torch
 from matplotlib import pyplot as plt
 
@@ -34,7 +36,7 @@ class VisCBC2D():
         self.resolution = 100
         self.cmap = "viridis"
 
-    def on_epoch_end(self):
+    def on_train_epoch_end(self):
         x_train, y_train = self.x_train, self.y_train
         _components = self.model.components_layer._components.detach()
         ax = self.fig.gca()
@@ -94,5 +96,5 @@ if __name__ == "__main__":
             correct += (y_pred.argmax(1) == y).float().sum(0)
 
         acc = 100 * correct / len(train_ds)
-        print(f"Epoch: {epoch} Accuracy: {acc:05.02f}%")
-        vis.on_epoch_end()
+        logging.info(f"Epoch: {epoch} Accuracy: {acc:05.02f}%")
+        vis.on_train_epoch_end()

prototorch/datasets/sklearn.py

@@ -5,8 +5,10 @@ URL:
 
 """
 
+from __future__ import annotations
+
 import warnings
-from typing import Sequence, Union
+from typing import Sequence
 
 from sklearn.datasets import (
     load_iris,
@@ -41,9 +43,9 @@ class Iris(NumpyDataset):
     :param dims: select a subset of dimensions
     """
 
-    def __init__(self, dims: Sequence[int] = None):
+    def __init__(self, dims: Sequence[int] | None = None):
         x, y = load_iris(return_X_y=True)
-        if dims:
+        if dims is not None:
             x = x[:, dims]
         super().__init__(x, y)
 
@@ -56,15 +58,19 @@ class Blobs(NumpyDataset):
 
     """
 
-    def __init__(self,
+    def __init__(
+        self,
         num_samples: int = 300,
         num_features: int = 2,
-                 seed: Union[None, int] = 0):
-        x, y = make_blobs(num_samples,
+        seed: None | int = 0,
+    ):
+        x, y = make_blobs(
+            num_samples,
             num_features,
             centers=None,
             random_state=seed,
-                          shuffle=False)
+            shuffle=False,
+        )
         super().__init__(x, y)
 
 
@@ -77,21 +83,24 @@ class Random(NumpyDataset):
     Note: n_classes * n_clusters_per_class <= 2**n_informative must satisfy.
     """
 
-    def __init__(self,
+    def __init__(
+        self,
         num_samples: int = 300,
         num_features: int = 2,
         num_classes: int = 2,
         num_clusters: int = 2,
-                 num_informative: Union[None, int] = None,
+        num_informative: None | int = None,
         separation: float = 1.0,
-                 seed: Union[None, int] = 0):
+        seed: None | int = 0,
+    ):
         if not num_informative:
             import math
             num_informative = math.ceil(math.log2(num_classes * num_clusters))
             if num_features < num_informative:
                 warnings.warn("Generating more features than requested.")
                 num_features = num_informative
-        x, y = make_classification(num_samples,
+        x, y = make_classification(
+            num_samples,
             num_features,
             n_informative=num_informative,
             n_redundant=0,
@@ -99,7 +108,8 @@ class Random(NumpyDataset):
             n_clusters_per_class=num_clusters,
             class_sep=separation,
             random_state=seed,
-                                   shuffle=False)
+            shuffle=False,
+        )
         super().__init__(x, y)
 
 
@@ -113,16 +123,20 @@ class Circles(NumpyDataset):
 
     """
 
-    def __init__(self,
+    def __init__(
+        self,
         num_samples: int = 300,
         noise: float = 0.3,
         factor: float = 0.8,
-                 seed: Union[None, int] = 0):
-        x, y = make_circles(num_samples,
+        seed: None | int = 0,
+    ):
+        x, y = make_circles(
+            num_samples,
             noise=noise,
             factor=factor,
             random_state=seed,
-                            shuffle=False)
+            shuffle=False,
+        )
         super().__init__(x, y)
 
 
@@ -136,12 +150,16 @@ class Moons(NumpyDataset):
 
     """
 
-    def __init__(self,
+    def __init__(
+        self,
         num_samples: int = 300,
         noise: float = 0.3,
-                 seed: Union[None, int] = 0):
-        x, y = make_moons(num_samples,
+        seed: None | int = 0,
+    ):
+        x, y = make_moons(
+            num_samples,
             noise=noise,
             random_state=seed,
-                          shuffle=False)
+            shuffle=False,
+        )
         super().__init__(x, y)

prototorch/datasets/tecator.py

@@ -36,6 +36,7 @@ Description:
     are determined by analytic chemistry.
 """
 
+import logging
 import os
 
 import numpy as np
@@ -81,13 +82,11 @@ class Tecator(ProtoDataset):
         if self._check_exists():
             return
 
-        if self.verbose:
-            print("Making directories...")
+        logging.debug("Making directories...")
         os.makedirs(self.raw_folder, exist_ok=True)
         os.makedirs(self.processed_folder, exist_ok=True)
 
-        if self.verbose:
-            print("Downloading...")
+        logging.debug("Downloading...")
         for fileid, md5 in self._resources:
             filename = "tecator.npz"
             download_file_from_google_drive(fileid,
@@ -95,8 +94,7 @@ class Tecator(ProtoDataset):
                                             filename=filename,
                                             md5=md5)
 
-        if self.verbose:
-            print("Processing...")
+        logging.debug("Processing...")
         with np.load(os.path.join(self.raw_folder, "tecator.npz"),
                      allow_pickle=False) as f:
             x_train, y_train = f["x_train"], f["y_train"]
@@ -117,5 +115,4 @@ class Tecator(ProtoDataset):
                   "wb") as f:
             torch.save(test_set, f)
 
-        if self.verbose:
-            print("Done!")
+        logging.debug("Done!")