build: bump version 0.7.3 → 0.7.4

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.7.2
+current_version = 0.7.4
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)

.github/workflows/pythonapp.yml

@@ -13,10 +13,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v2
-    - name: Set up Python 3.9
+    - name: Set up Python 3.10
       uses: actions/setup-python@v2
       with:
-        python-version: 3.9
+        python-version: "3.10"
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
@@ -27,13 +27,15 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.7", "3.8", "3.9"]
+        python-version: ["3.7", "3.8", "3.9", "3.10"]
         os: [ubuntu-latest, windows-latest]
         exclude:
         - os: windows-latest
           python-version: "3.7"
         - os: windows-latest
           python-version: "3.8"
+        - os: windows-latest
+          python-version: "3.9"
 
     runs-on: ${{ matrix.os }}
     steps:
@@ -55,10 +57,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@v2
-    - name: Set up Python 3.9
+    - name: Set up Python 3.10
       uses: actions/setup-python@v2
       with:
-        python-version: "3.9"
+        python-version: "3.10"
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

LICENSE

@@ -1,6 +1,7 @@
 MIT License
 
-Copyright (c) 2020 si-cim
+Copyright (c) 2020 Saxon Institute for Computational Intelligence and Machine
+Learning (SICIM)
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

docs/source/conf.py

@@ -23,7 +23,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "0.7.2"
+release = "0.7.4"
 
 # -- General configuration ---------------------------------------------------
 
@@ -120,7 +120,7 @@ html_css_files = [
 # -- Options for HTMLHelp output ------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = "protoflowdoc"
+htmlhelp_basename = "prototorchdoc"
 
 # -- Options for LaTeX output ---------------------------------------------
 

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/__init__.py

@@ -17,7 +17,7 @@ from .core import similarities  # noqa: F401
 from .core import transforms  # noqa: F401
 
 # Core Setup
-__version__ = "0.7.2"
+__version__ = "0.7.4"
 
 __all_core__ = [
     "competitions",

prototorch/core/competitions.py

@@ -38,7 +38,7 @@ def cbcc(detections: torch.Tensor, reasonings: torch.Tensor):
     pk = A
     nk = (1 - A) * B
     numerator = (detections @ (pk - nk).T) + nk.sum(1)
-    probs = numerator / (pk + nk).sum(1)
+    probs = numerator / ((pk + nk).sum(1) + 1e-8)
     return probs
 
 

prototorch/core/initializers.py

@@ -32,6 +32,12 @@ class LiteralCompInitializer(AbstractComponentsInitializer):
 
     def generate(self, num_components: int = 0):
         """Ignore `num_components` and simply return `self.components`."""
+        provided_num_components = len(self.components)
+        if provided_num_components != num_components:
+            wmsg = f"The number of components ({provided_num_components}) " \
+                f"provided to {self.__class__.__name__} " \
+                f"does not match the expected number ({num_components})."
+            warnings.warn(wmsg)
         if not isinstance(self.components, torch.Tensor):
             wmsg = f"Converting components to {torch.Tensor}..."
             warnings.warn(wmsg)
@@ -231,6 +237,8 @@ class AbstractStratifiedCompInitializer(AbstractClassAwareCompInitializer):
         components = torch.tensor([])
         for k, v in distribution.items():
             stratified_data = self.data[self.targets == k]
+            if len(stratified_data) == 0:
+                raise ValueError(f"No data available for class {k}.")
             initializer = self.subinit_type(
                 stratified_data,
                 noise=self.noise,
@@ -457,7 +465,15 @@ class OnesLinearTransformInitializer(AbstractLinearTransformInitializer):
         return self.generate_end_hook(weights)
 
 
-class EyeTransformInitializer(AbstractLinearTransformInitializer):
+class RandomLinearTransformInitializer(AbstractLinearTransformInitializer):
+    """Initialize a matrix with random values."""
+
+    def generate(self, in_dim: int, out_dim: int):
+        weights = torch.rand(in_dim, out_dim)
+        return self.generate_end_hook(weights)
+
+
+class EyeLinearTransformInitializer(AbstractLinearTransformInitializer):
     """Initialize a matrix with the largest possible identity matrix."""
 
     def generate(self, in_dim: int, out_dim: int):
@@ -496,6 +512,13 @@ class PCALinearTransformInitializer(AbstractDataAwareLTInitializer):
         return self.generate_end_hook(weights)
 
 
+class LiteralLinearTransformInitializer(AbstractDataAwareLTInitializer):
+    """'Generate' the provided weights."""
+
+    def generate(self, in_dim: int, out_dim: int):
+        return self.generate_end_hook(self.data)
+
+
 # Aliases - Components
 CACI = ClassAwareCompInitializer
 DACI = DataAwareCompInitializer
@@ -524,7 +547,9 @@ RRI = RandomReasoningsInitializer
 ZRI = ZerosReasoningsInitializer
 
 # Aliases - Transforms
-Eye = EyeTransformInitializer
+ELTI = Eye = EyeLinearTransformInitializer
 OLTI = OnesLinearTransformInitializer
+RLTI = RandomLinearTransformInitializer
 ZLTI = ZerosLinearTransformInitializer
 PCALTI = PCALinearTransformInitializer
+LLTI = LiteralLinearTransformInitializer

prototorch/core/losses.py

@@ -107,14 +107,24 @@ def margin_loss(y_pred, y_true, margin=0.3):
 
 class GLVQLoss(torch.nn.Module):
 
-    def __init__(self, margin=0.0, transfer_fn="identity", beta=10, **kwargs):
+    def __init__(self,
+                 margin=0.0,
+                 transfer_fn="identity",
+                 beta=10,
+                 add_dp=False,
+                 **kwargs):
         super().__init__(**kwargs)
         self.margin = margin
         self.transfer_fn = get_activation(transfer_fn)
         self.beta = torch.tensor(beta)
+        self.add_dp = add_dp
 
     def forward(self, outputs, targets, plabels):
-        mu = glvq_loss(outputs, targets, prototype_labels=plabels)
+        # mu = glvq_loss(outputs, targets, plabels)
+        dp, dm = _get_dp_dm(outputs, targets, plabels)
+        mu = (dp - dm) / (dp + dm)
+        if self.add_dp:
+            mu = mu + dp
         batch_loss = self.transfer_fn(mu + self.margin, beta=self.beta)
         return batch_loss.sum()
 

prototorch/core/transforms.py

@@ -5,7 +5,7 @@ from torch.nn.parameter import Parameter
 
 from .initializers import (
     AbstractLinearTransformInitializer,
-    EyeTransformInitializer,
+    EyeLinearTransformInitializer,
 )
 
 
@@ -16,7 +16,7 @@ class LinearTransform(torch.nn.Module):
         in_dim: int,
         out_dim: int,
         initializer:
-        AbstractLinearTransformInitializer = EyeTransformInitializer()):
+        AbstractLinearTransformInitializer = EyeLinearTransformInitializer()):
         super().__init__()
         self.set_weights(in_dim, out_dim, initializer)
 
@@ -32,12 +32,15 @@ class LinearTransform(torch.nn.Module):
         in_dim: int,
         out_dim: int,
         initializer:
-        AbstractLinearTransformInitializer = EyeTransformInitializer()):
+        AbstractLinearTransformInitializer = EyeLinearTransformInitializer()):
         weights = initializer.generate(in_dim, out_dim)
         self._register_weights(weights)
 
     def forward(self, x):
-        return x @ self.weights
+        return x @ self._weights
+
+    def extra_repr(self):
+        return f"weights: (shape: {tuple(self._weights.shape)})"
 
 
 # Aliases

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,
-                 num_samples: int = 300,
-                 num_features: int = 2,
-                 seed: Union[None, int] = 0):
-        x, y = make_blobs(num_samples,
-                          num_features,
-                          centers=None,
-                          random_state=seed,
-                          shuffle=False)
+    def __init__(
+        self,
+        num_samples: int = 300,
+        num_features: int = 2,
+        seed: None | int = 0,
+    ):
+        x, y = make_blobs(
+            num_samples,
+            num_features,
+            centers=None,
+            random_state=seed,
+            shuffle=False,
+        )
         super().__init__(x, y)
 
 
@@ -77,29 +83,33 @@ class Random(NumpyDataset):
     Note: n_classes * n_clusters_per_class <= 2**n_informative must satisfy.
     """
 
-    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,
-                 separation: float = 1.0,
-                 seed: Union[None, int] = 0):
+    def __init__(
+        self,
+        num_samples: int = 300,
+        num_features: int = 2,
+        num_classes: int = 2,
+        num_clusters: int = 2,
+        num_informative: None | int = None,
+        separation: float = 1.0,
+        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,
-                                   num_features,
-                                   n_informative=num_informative,
-                                   n_redundant=0,
-                                   n_classes=num_classes,
-                                   n_clusters_per_class=num_clusters,
-                                   class_sep=separation,
-                                   random_state=seed,
-                                   shuffle=False)
+        x, y = make_classification(
+            num_samples,
+            num_features,
+            n_informative=num_informative,
+            n_redundant=0,
+            n_classes=num_classes,
+            n_clusters_per_class=num_clusters,
+            class_sep=separation,
+            random_state=seed,
+            shuffle=False,
+        )
         super().__init__(x, y)
 
 
@@ -113,16 +123,20 @@ class Circles(NumpyDataset):
 
     """
 
-    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,
-                            noise=noise,
-                            factor=factor,
-                            random_state=seed,
-                            shuffle=False)
+    def __init__(
+        self,
+        num_samples: int = 300,
+        noise: float = 0.3,
+        factor: float = 0.8,
+        seed: None | int = 0,
+    ):
+        x, y = make_circles(
+            num_samples,
+            noise=noise,
+            factor=factor,
+            random_state=seed,
+            shuffle=False,
+        )
         super().__init__(x, y)
 
 
@@ -136,12 +150,16 @@ class Moons(NumpyDataset):
 
     """
 
-    def __init__(self,
-                 num_samples: int = 300,
-                 noise: float = 0.3,
-                 seed: Union[None, int] = 0):
-        x, y = make_moons(num_samples,
-                          noise=noise,
-                          random_state=seed,
-                          shuffle=False)
+    def __init__(
+        self,
+        num_samples: int = 300,
+        noise: float = 0.3,
+        seed: None | int = 0,
+    ):
+        x, y = make_moons(
+            num_samples,
+            noise=noise,
+            random_state=seed,
+            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!")

prototorch/utils/__init__.py

@@ -1,6 +1,11 @@
-"""ProtoFlow utils module"""
+"""ProtoTorch utils module"""
 
-from .colors import hex_to_rgb, rgb_to_hex
+from .colors import (
+    get_colors,
+    get_legend_handles,
+    hex_to_rgb,
+    rgb_to_hex,
+)
 from .utils import (
     mesh2d,
     parse_data_arg,

prototorch/utils/colors.py

@@ -1,4 +1,13 @@
-"""ProtoFlow color utilities"""
+"""ProtoTorch color utilities"""
+
+import matplotlib.lines as mlines
+import torch
+from matplotlib import cm
+from matplotlib.colors import (
+    Normalize,
+    to_hex,
+    to_rgb,
+)
 
 
 def hex_to_rgb(hex_values):
@@ -13,3 +22,39 @@ def rgb_to_hex(rgb_values):
     for v in rgb_values:
         c = "%02x%02x%02x" % tuple(v)
         yield c
+
+
+def get_colors(vmax, vmin=0, cmap="viridis"):
+    cmap = cm.get_cmap(cmap)
+    colornorm = Normalize(vmin=vmin, vmax=vmax)
+    colors = dict()
+    for c in range(vmin, vmax + 1):
+        colors[c] = to_hex(cmap(colornorm(c)))
+    return colors
+
+
+def get_legend_handles(colors, labels, marker="dots", zero_indexed=False):
+    handles = list()
+    for color, label in zip(colors.values(), labels):
+        if marker == "dots":
+            handle = mlines.Line2D(
+                xdata=[],
+                ydata=[],
+                label=label,
+                color="white",
+                markerfacecolor=color,
+                marker="o",
+                markersize=10,
+                markeredgecolor="k",
+            )
+        else:
+            handle = mlines.Line2D(
+                xdata=[],
+                ydata=[],
+                label=label,
+                color=color,
+                marker="",
+                markersize=15,
+            )
+        handles.append(handle)
+    return handles

prototorch/utils/utils.py

@@ -1,4 +1,4 @@
-"""ProtoFlow utilities"""
+"""ProtoTorch utilities"""
 
 import warnings
 from typing import (

setup.py

@@ -20,9 +20,10 @@ with open("README.md", "r") as fh:
 
 INSTALL_REQUIRES = [
     "torch>=1.3.1",
-    "torchvision>=0.7.2",
+    "torchvision>=0.7.4",
     "numpy>=1.9.1",
     "sklearn",
+    "matplotlib",
 ]
 DATASETS = [
     "requests",
@@ -40,7 +41,6 @@ DOCS = [
     "sphinx-autodoc-typehints",
 ]
 EXAMPLES = [
-    "matplotlib",
     "torchinfo",
 ]
 TESTS = [
@@ -51,7 +51,7 @@ ALL = DATASETS + DEV + DOCS + EXAMPLES + TESTS
 
 setup(
     name="prototorch",
-    version="0.7.2",
+    version="0.7.4",
     description="Highly extensible, GPU-supported "
     "Learning Vector Quantization (LVQ) toolbox "
     "built using PyTorch and its nn API.",
@@ -62,7 +62,7 @@ setup(
     url=PROJECT_URL,
     download_url=DOWNLOAD_URL,
     license="MIT",
-    python_requires=">=3.7,<3.10",
+    python_requires=">=3.7",
     install_requires=INSTALL_REQUIRES,
     extras_require={
         "datasets": DATASETS,
@@ -88,6 +88,7 @@ setup(
         "Programming Language :: Python :: 3.7",
         "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
     ],
     packages=find_packages(),
     zip_safe=False,

tests/test_core.py

@@ -245,20 +245,20 @@ def test_random_reasonings_init_channels_not_first():
 
 # Transform initializers
 def test_eye_transform_init_square():
-    t = pt.initializers.EyeTransformInitializer()
+    t = pt.initializers.EyeLinearTransformInitializer()
     I = t.generate(3, 3)
     assert torch.allclose(I, torch.eye(3))
 
 
 def test_eye_transform_init_narrow():
-    t = pt.initializers.EyeTransformInitializer()
+    t = pt.initializers.EyeLinearTransformInitializer()
     actual = t.generate(3, 2)
     desired = torch.Tensor([[1, 0], [0, 1], [0, 0]])
     assert torch.allclose(actual, desired)
 
 
 def test_eye_transform_init_wide():
-    t = pt.initializers.EyeTransformInitializer()
+    t = pt.initializers.EyeLinearTransformInitializer()
     actual = t.generate(2, 3)
     desired = torch.Tensor([[1, 0, 0], [0, 1, 0]])
     assert torch.allclose(actual, desired)