Bump version: 0.4.3 → 0.4.4

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.4.2
+current_version = 0.4.4
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)

docs/source/conf.py

@@ -23,7 +23,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "0.4.2"
+release = "0.4.4"
 
 # -- General configuration ---------------------------------------------------
 

prototorch/__init__.py

@@ -1,7 +1,7 @@
 """ProtoTorch package."""
 
 # Core Setup
-__version__ = "0.4.2"
+__version__ = "0.4.4"
 
 __all_core__ = [
     "datasets",

prototorch/components/components.py

@@ -67,8 +67,9 @@ class LabeledComponents(Components):
                  *,
                  initialized_components=None):
         if initialized_components is not None:
-            super().__init__(initialized_components=initialized_components[0])
-            self._labels = initialized_components[1]
+            components, component_labels = initialized_components
+            super().__init__(initialized_components=components)
+            self._labels = component_labels
         else:
             self._initialize_labels(distribution)
             super().__init__(number_of_components=len(self._labels),

prototorch/datasets/__init__.py

@@ -1,11 +1,6 @@
 """ProtoTorch datasets."""
 
 from .abstract import NumpyDataset
+from .iris import Iris
 from .spiral import Spiral
 from .tecator import Tecator
-
-__all__ = [
-    "NumpyDataset",
-    "Spiral",
-    "Tecator",
-]

prototorch/datasets/iris.py

@@ -0,0 +1,15 @@
+"""Thin wrapper for the Iris classification dataset from sklearn.
+
+URL:
+    https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html
+
+"""
+
+from prototorch.datasets.abstract import NumpyDataset
+from sklearn.datasets import load_iris
+
+
+class Iris(NumpyDataset):
+    def __init__(self):
+        x, y = load_iris(return_X_y=True)
+        super().__init__(x, y)

prototorch/functions/competitions.py

@@ -3,7 +3,6 @@
 import torch
 
 
-# @torch.jit.script
 def stratified_min(distances, labels):
     clabels = torch.unique(labels, dim=0)
     nclasses = clabels.size()[0]
@@ -31,15 +30,15 @@ def stratified_min(distances, labels):
     return winning_distances.T  # return with `batch_size` first
 
 
-# @torch.jit.script
 def wtac(distances, labels):
     winning_indices = torch.min(distances, dim=1).indices
     winning_labels = labels[winning_indices].squeeze()
     return winning_labels
 
 
-# @torch.jit.script
-def knnc(distances, labels, k):
-    winning_indices = torch.topk(-distances, k=k.item(), dim=1).indices
-    winning_labels = labels[winning_indices].squeeze()
+def knnc(distances, labels, k=1):
+    winning_indices = torch.topk(-distances, k=k, dim=1).indices
+    # winning_labels = torch.mode(labels[winning_indices].squeeze(),
+    #                             dim=1).values
+    winning_labels = torch.mode(labels[winning_indices], dim=1).values
     return winning_labels

setup.py

@@ -23,6 +23,7 @@ INSTALL_REQUIRES = [
 ]
 DATASETS = [
     "requests",
+    "sklearn",
     "tqdm",
 ]
 DEV = ["bumpversion"]
@@ -42,7 +43,7 @@ ALL = DATASETS + DEV + DOCS + EXAMPLES + TESTS
 
 setup(
     name="prototorch",
-    version="0.4.2",
+    version="0.4.4",
     description="Highly extensible, GPU-supported "
     "Learning Vector Quantization (LVQ) toolbox "
     "built using PyTorch and its nn API.",

tests/test_functions.py

@@ -4,7 +4,6 @@ import unittest
 
 import numpy as np
 import torch
-
 from prototorch.functions import (activations, competitions, distances,
                                   initializers, losses)
 
@@ -139,7 +138,7 @@ class TestCompetitions(unittest.TestCase):
     def test_knnc_k1(self):
         d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
         labels = torch.tensor([0, 1, 2, 3])
-        actual = competitions.knnc(d, labels, k=torch.tensor([1]))
+        actual = competitions.knnc(d, labels, k=1)
         desired = torch.tensor([2, 0])
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,