fix: update scikit-learn dependency

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.7.6
+current_version = 0.7.4
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)

.github/workflows/pythonapp.yml

@@ -12,36 +12,36 @@ jobs:
   style:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python 3.11
-        uses: actions/setup-python@v4
+    - uses: actions/checkout@v2
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v2
       with:
-          python-version: "3.11"
+        python-version: "3.10"
     - 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", "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"
-          - 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 +56,11 @@ jobs:
     needs: compatibility
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+    - uses: actions/checkout@v2
     - name: Set up Python 3.10
-        uses: actions/setup-python@v4
+      uses: actions/setup-python@v2
       with:
-          python-version: "3.11"
+        python-version: "3.10"
     - 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
 

docs/source/conf.py

@@ -23,7 +23,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "0.7.6"
+release = "0.7.4"
 
 # -- General configuration ---------------------------------------------------
 

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__ = "0.7.4"
 
 __all_core__ = [
     "competitions",

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/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/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/utils/utils.py

@@ -5,7 +5,6 @@ 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(
-        user_distribution,
-        clabels: Optional[Iterable[int]] = None) -> Dict[int, int]:
+def parse_distribution(user_distribution,
+                       clabels: Iterable[int] = None) -> Dict[int, int]:
     """Parse user-provided distribution.
 
     Return a dictionary with integer keys that represent the class labels and

setup.py

@@ -15,13 +15,13 @@ 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",
-    "torchvision",
-    "numpy",
+    "torch>=1.3.1",
+    "torchvision>=0.7.4",
+    "numpy>=1.9.1",
     "scikit-learn",
     "matplotlib",
 ]
@@ -51,7 +51,7 @@ ALL = DATASETS + DEV + DOCS + EXAMPLES + TESTS
 
 setup(
     name="prototorch",
-    version="0.7.6",
+    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=">=3.8",
+    python_requires=">=3.7",
     install_requires=INSTALL_REQUIRES,
     extras_require={
         "datasets": DATASETS,
@@ -85,10 +85,10 @@ 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_datasets.py

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