.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.7.6
+current_version = 0.7.2
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)

.github/workflows/pythonapp.yml

@@ -6,42 +6,40 @@ name: tests
 on:
   push:
   pull_request:
-    branches: [master]
+    branches: [ master ]
 
 jobs:
   style:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+    - uses: actions/checkout@v2
-      - name: Set up Python 3.11
+    - name: Set up Python 3.9
-        uses: actions/setup-python@v4
+      uses: actions/setup-python@v2
       with:
-          python-version: "3.11"
+        python-version: 3.9
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
         pip install .[all]
-      - uses: pre-commit/action@v3.0.0
+    - uses: pre-commit/action@v2.0.3
   compatibility:
     needs: style
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.7", "3.8", "3.9"]
         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"
-          - os: windows-latest
-            python-version: "3.10"
 
     runs-on: ${{ matrix.os }}
     steps:
-      - uses: actions/checkout@v3
+    - uses: actions/checkout@v2
     - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+      uses: actions/setup-python@v2
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
@@ -56,11 +54,11 @@ jobs:
     needs: compatibility
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+    - uses: actions/checkout@v2
-      - name: Set up Python 3.10
+    - name: Set up Python 3.9
-        uses: actions/setup-python@v4
+      uses: actions/setup-python@v2
       with:
-          python-version: "3.11"
+        python-version: "3.9"
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip

.pre-commit-config.yaml

@@ -3,7 +3,7 @@
 
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.4.0
+  rev: v4.1.0
   hooks:
   - id: trailing-whitespace
   - id: end-of-file-fixer
@@ -13,17 +13,17 @@ repos:
   - id: check-case-conflict
 
 - repo: https://github.com/myint/autoflake
-  rev: v2.1.1
+  rev: v1.4
   hooks:
   - id: autoflake
 
 - repo: http://github.com/PyCQA/isort
-  rev: 5.12.0
+  rev: 5.10.1
   hooks:
   - id: isort
 
 - repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v1.3.0
+  rev: v0.931
   hooks:
   - id: mypy
     files: prototorch
@@ -35,14 +35,14 @@ repos:
   - id: yapf
 
 - repo: https://github.com/pre-commit/pygrep-hooks
-  rev: v1.10.0
+  rev: v1.9.0
   hooks:
   - id: python-use-type-annotations
   - id: python-no-log-warn
   - id: python-check-blanket-noqa
 
 - repo: https://github.com/asottile/pyupgrade
-  rev: v3.7.0
+  rev: v2.31.0
   hooks:
   - id: pyupgrade
 

LICENSE

@@ -1,7 +1,6 @@
 MIT License
 
-Copyright (c) 2020 Saxon Institute for Computational Intelligence and Machine
+Copyright (c) 2020 si-cim
-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 = "0.7.6"
+release = "0.7.2"
 
 # -- General configuration ---------------------------------------------------
 
@@ -120,7 +120,7 @@ html_css_files = [
 # -- Options for HTMLHelp output ------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = "prototorchdoc"
+htmlhelp_basename = "protoflowdoc"
 
 # -- Options for LaTeX output ---------------------------------------------
 

examples/cbc_iris.py

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

examples/gmlvq.py

@@ -1,76 +0,0 @@
-"""ProtoTorch GMLVQ example using Iris data."""
-
-import torch
-
-import prototorch as pt
-
-
-class GMLVQ(torch.nn.Module):
-    """
-    Implementation of Generalized Matrix Learning Vector Quantization.
-    """
-
-    def __init__(self, data, **kwargs):
-        super().__init__(**kwargs)
-
-        self.components_layer = pt.components.LabeledComponents(
-            distribution=[1, 1, 1],
-            components_initializer=pt.initializers.SMCI(data, noise=0.1),
-        )
-
-        self.backbone = pt.transforms.Omega(
-            len(data[0][0]),
-            len(data[0][0]),
-            pt.initializers.RandomLinearTransformInitializer(),
-        )
-
-    def forward(self, data):
-        """
-        Forward function that returns a tuple of dissimilarities and label information.
-        Feed into GLVQLoss to get a complete GMLVQ model.
-        """
-        components, label = self.components_layer()
-
-        latent_x = self.backbone(data)
-        latent_components = self.backbone(components)
-
-        distance = pt.distances.squared_euclidean_distance(
-            latent_x, latent_components)
-
-        return distance, label
-
-    def predict(self, data):
-        """
-        The GMLVQ has a modified prediction step, where a competition layer is applied.
-        """
-        components, label = self.components_layer()
-        distance = pt.distances.squared_euclidean_distance(data, components)
-        winning_label = pt.competitions.wtac(distance, label)
-        return winning_label
-
-
-if __name__ == "__main__":
-    train_ds = pt.datasets.Iris()
-
-    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)
-
-    model = GMLVQ(train_ds)
-    optimizer = torch.optim.Adam(model.parameters(), lr=0.05)
-    criterion = pt.losses.GLVQLoss()
-
-    for epoch in range(200):
-        correct = 0.0
-        for x, y in train_loader:
-            d, labels = model(x)
-            loss = criterion(d, y, labels).mean(0)
-
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-
-            with torch.no_grad():
-                y_pred = model.predict(x)
-                correct += (y_pred == y).float().sum(0)
-
-        acc = 100 * correct / len(train_ds)
-        print(f"Epoch: {epoch} Accuracy: {acc:05.02f}%")

prototorch/__init__.py

@@ -17,7 +17,7 @@ from .core import similarities  # noqa: F401
 from .core import transforms  # noqa: F401
 
 # Core Setup
-__version__ = "0.7.6"
+__version__ = "0.7.2"
 
 __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) + 1e-8)
+    probs = numerator / (pk + nk).sum(1)
     return probs
 
 

prototorch/core/distances.py

@@ -11,7 +11,7 @@ def squared_euclidean_distance(x, y):
     **Alias:**
     ``prototorch.functions.distances.sed``
     """
-    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
+    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
     expanded_x = x.unsqueeze(dim=1)
     batchwise_difference = y - expanded_x
     differences_raised = torch.pow(batchwise_difference, 2)
@@ -27,14 +27,14 @@ def euclidean_distance(x, y):
     :returns: Distance Tensor of shape :math:`X \times Y`
     :rtype: `torch.tensor`
     """
-    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
+    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
     distances_raised = squared_euclidean_distance(x, y)
     distances = torch.sqrt(distances_raised)
     return distances
 
 
 def euclidean_distance_v2(x, y):
-    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
+    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
     diff = y - x.unsqueeze(1)
     pairwise_distances = (diff @ diff.permute((0, 2, 1))).sqrt()
     # Passing `dim1=-2` and `dim2=-1` to `diagonal()` takes the
@@ -54,7 +54,7 @@ def lpnorm_distance(x, y, p):
 
     :param p: p parameter of the lp norm
     """
-    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
+    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
     distances = torch.cdist(x, y, p=p)
     return distances
 
@@ -66,7 +66,7 @@ def omega_distance(x, y, omega):
 
     :param `torch.tensor` omega: Two dimensional matrix
     """
-    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
+    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
     projected_x = x @ omega
     projected_y = y @ omega
     distances = squared_euclidean_distance(projected_x, projected_y)
@@ -80,7 +80,7 @@ def lomega_distance(x, y, omegas):
 
     :param `torch.tensor` omegas: Three dimensional matrix
     """
-    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
+    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
     projected_x = x @ omegas
     projected_y = torch.diagonal(y @ omegas).T
     expanded_y = torch.unsqueeze(projected_y, dim=1)

prototorch/core/initializers.py

@@ -32,12 +32,6 @@ 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)
@@ -237,8 +231,6 @@ 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,
@@ -465,15 +457,7 @@ class OnesLinearTransformInitializer(AbstractLinearTransformInitializer):
         return self.generate_end_hook(weights)
 
 
-class RandomLinearTransformInitializer(AbstractLinearTransformInitializer):
+class EyeTransformInitializer(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):
@@ -512,13 +496,6 @@ 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
@@ -547,9 +524,7 @@ RRI = RandomReasoningsInitializer
 ZRI = ZerosReasoningsInitializer
 
 # Aliases - Transforms
-ELTI = Eye = EyeLinearTransformInitializer
+Eye = EyeTransformInitializer
 OLTI = OnesLinearTransformInitializer
-RLTI = RandomLinearTransformInitializer
 ZLTI = ZerosLinearTransformInitializer
 PCALTI = PCALinearTransformInitializer
-LLTI = LiteralLinearTransformInitializer

prototorch/core/losses.py

@@ -107,24 +107,14 @@ def margin_loss(y_pred, y_true, margin=0.3):
 
 class GLVQLoss(torch.nn.Module):
 
-    def __init__(self,
+    def __init__(self, margin=0.0, transfer_fn="identity", beta=10, **kwargs):
-                 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, plabels)
+        mu = glvq_loss(outputs, targets, prototype_labels=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/similarities.py

@@ -21,7 +21,7 @@ def cosine_similarity(x, y):
     Expected dimension of x is 2.
     Expected dimension of y is 2.
     """
-    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
+    x, y = [arr.view(arr.size(0), -1) for arr in (x, y)]
     norm_x = x.pow(2).sum(1).sqrt()
     norm_y = y.pow(2).sum(1).sqrt()
     norm_mat = norm_x.unsqueeze(-1) @ norm_y.unsqueeze(-1).T

prototorch/core/transforms.py

@@ -5,7 +5,7 @@ from torch.nn.parameter import Parameter
 
 from .initializers import (
     AbstractLinearTransformInitializer,
-    EyeLinearTransformInitializer,
+    EyeTransformInitializer,
 )
 
 
@@ -16,7 +16,7 @@ class LinearTransform(torch.nn.Module):
         in_dim: int,
         out_dim: int,
         initializer:
-        AbstractLinearTransformInitializer = EyeLinearTransformInitializer()):
+        AbstractLinearTransformInitializer = EyeTransformInitializer()):
         super().__init__()
         self.set_weights(in_dim, out_dim, initializer)
 
@@ -32,15 +32,12 @@ class LinearTransform(torch.nn.Module):
         in_dim: int,
         out_dim: int,
         initializer:
-        AbstractLinearTransformInitializer = EyeLinearTransformInitializer()):
+        AbstractLinearTransformInitializer = EyeTransformInitializer()):
         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/abstract.py

@@ -20,7 +20,7 @@ class Dataset(torch.utils.data.Dataset):
     _repr_indent = 2
 
     def __init__(self, root):
-        if isinstance(root, str):
+        if isinstance(root, torch._six.string_classes):
             root = os.path.expanduser(root)
         self.root = root
 

prototorch/datasets/sklearn.py

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

prototorch/datasets/tecator.py

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

prototorch/utils/__init__.py

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

prototorch/utils/colors.py

@@ -1,13 +1,4 @@
-"""ProtoTorch color utilities"""
+"""ProtoFlow 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):
@@ -22,39 +13,3 @@ 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

@@ -1,11 +1,10 @@
-"""ProtoTorch utilities"""
+"""ProtoFlow utilities"""
 
 import warnings
 from typing import (
     Dict,
     Iterable,
     List,
-    Optional,
     Union,
 )
 
@@ -19,7 +18,7 @@ def generate_mesh(
     maxima: torch.TensorType,
     border: float = 1.0,
     resolution: int = 100,
-    device: Optional[torch.device] = None,
+    device: torch.device = None,
 ):
     # Apply Border
     ptp = maxima - minima
@@ -56,15 +55,14 @@ def mesh2d(x=None, border: float = 1.0, resolution: int = 100):
 
 
 def distribution_from_list(list_dist: List[int],
-                           clabels: Optional[Iterable[int]] = None):
+                           clabels: Iterable[int] = None):
     clabels = clabels or list(range(len(list_dist)))
     distribution = dict(zip(clabels, list_dist))
     return distribution
 
 
-def parse_distribution(
+def parse_distribution(user_distribution,
-        user_distribution,
+                       clabels: Iterable[int] = None) -> Dict[int, int]:
-        clabels: Optional[Iterable[int]] = None) -> Dict[int, int]:
     """Parse user-provided distribution.
 
     Return a dictionary with integer keys that represent the class labels and

setup.py

@@ -15,15 +15,14 @@ from setuptools import find_packages, setup
 PROJECT_URL = "https://github.com/si-cim/prototorch"
 DOWNLOAD_URL = "https://github.com/si-cim/prototorch.git"
 
-with open("README.md", encoding="utf-8") as fh:
+with open("README.md", "r") as fh:
     long_description = fh.read()
 
 INSTALL_REQUIRES = [
-    "torch>=2.0.0",
+    "torch>=1.3.1",
-    "torchvision",
+    "torchvision>=0.7.2",
-    "numpy",
+    "numpy>=1.9.1",
-    "scikit-learn",
+    "sklearn",
-    "matplotlib",
 ]
 DATASETS = [
     "requests",
@@ -41,6 +40,7 @@ DOCS = [
     "sphinx-autodoc-typehints",
 ]
 EXAMPLES = [
+    "matplotlib",
     "torchinfo",
 ]
 TESTS = [
@@ -51,7 +51,7 @@ ALL = DATASETS + DEV + DOCS + EXAMPLES + TESTS
 
 setup(
     name="prototorch",
-    version="0.7.6",
+    version="0.7.2",
     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=">=3.8",
+    python_requires=">=3.7,<3.10",
     install_requires=INSTALL_REQUIRES,
     extras_require={
         "datasets": DATASETS,
@@ -85,10 +85,9 @@ setup(
         "License :: OSI Approved :: MIT License",
         "Operating System :: OS Independent",
         "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.7",
         "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",
-        "Programming Language :: Python :: 3.10",
-        "Programming Language :: Python :: 3.11",
     ],
     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.EyeLinearTransformInitializer()
+    t = pt.initializers.EyeTransformInitializer()
     I = t.generate(3, 3)
     assert torch.allclose(I, torch.eye(3))
 
 
 def test_eye_transform_init_narrow():
-    t = pt.initializers.EyeLinearTransformInitializer()
+    t = pt.initializers.EyeTransformInitializer()
     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.EyeLinearTransformInitializer()
+    t = pt.initializers.EyeTransformInitializer()
     actual = t.generate(2, 3)
     desired = torch.Tensor([[1, 0, 0], [0, 1, 0]])
     assert torch.allclose(actual, desired)

tests/test_datasets.py

@@ -1,6 +1,7 @@
 """ProtoTorch datasets test suite"""
 
 import os
+import shutil
 import unittest
 
 import numpy as np