Switch linting to ruff

pyt-team · Jan 31, 2024 · 0b95211 · 0b95211
1 parent 161df86
commit 0b95211
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diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
@@ -1,36 +1,14 @@
-name: "Lint"
+name: Linting
 
 on:
   push:
-    branches: [main,github-actions-test]
+    branches: [ main,github-actions-test ]
   pull_request:
-    branches: [main]
+    branches: [ main ]
 
 jobs:
-  black-isort-flake8:
-
-    runs-on: ${{matrix.os}}
-    strategy:
-      matrix:
-        os: [ubuntu-latest]
-        python-version: [3.11.3]
-      fail-fast: false
-
+  ruff:
+    runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
-      - name: Linting
-        uses: actions/setup-python@v4
-        with:
-          python-version: ${{matrix.python-version}}
-          cache: "pip"
-          cache-dependency-path: "pyproject.toml"
-
-      - name: Install main package [pip]
-        run:  |
-          pip install -e .[dev]
-      - name : linting [black, isort, flake8]
-        run: |
-          black . --check
-          isort --profile black --check .
-          flake8 .
-
+      - uses: chartboost/ruff-action@v1
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -1,11 +1,11 @@
 default_language_version:
-  python: python3.11
+  python: python3.10
 
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.0.1
     hooks:
-      - id: check-byte-order-marker
+      - id: fix-byte-order-marker
       - id: check-case-conflict
       - id: check-merge-conflict
       - id: check-yaml
@@ -18,25 +18,16 @@ repos:
       - id: trailing-whitespace
       - id: requirements-txt-fixer
 
-  - repo: https://github.com/psf/black
-    rev: 23.10.1
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.1.14
     hooks:
-      - id: black-jupyter
+      - id: ruff
+        types_or: [ python, pyi, jupyter ]
+        args: [ --fix ]
+      - id: ruff-format
+        types_or: [ python, pyi, jupyter ]
 
-  - repo: https://github.com/pycqa/isort
-    rev: 5.12.0
+  - repo: https://github.com/numpy/numpydoc
+    rev: v1.6.0
     hooks:
-      - id : isort
-        args : ["--profile=black", "--filter-files"]
-
-  - repo: https://github.com/asottile/blacken-docs
-    rev: 1.13.0
-    hooks:
-    -   id: blacken-docs
-        additional_dependencies: [black==23.3.0]
-
-  - repo: https://github.com/pycqa/flake8
-    rev: 6.0.0
-    hooks:
-    -   id: flake8
-        additional_dependencies: [flake8-docstrings, Flake8-pyproject]
+      - id: numpydoc-validation
diff --git a/pyproject.toml b/pyproject.toml
@@ -40,6 +40,7 @@ dependencies=[
 
 [project.optional-dependencies]
 doc = [
+    "hypernetx",
     "jupyter",
     "nbsphinx",
     "nbsphinx_link",
@@ -49,13 +50,8 @@ doc = [
     "pydata-sphinx-theme"
 ]
 lint = [
-    "black < 24.0",
-    "black[jupyter]",
-    "flake8",
-    "flake8-docstrings",
-    "Flake8-pyproject",
-    "isort",
-    "pre-commit"
+    "pre-commit",
+    "ruff"
 ]
 test = [
     "pytest",
@@ -74,6 +70,30 @@ all = ["TopoModelX[dev, doc]"]
 homepage="https://github.com/pyt-team/TopoModelX"
 repository="https://github.com/pyt-team/TopoModelX"
 
+[tool.ruff]
+target-version = "py310"
+extend-include = ["*.ipynb"]
+
+[tool.ruff.format]
+docstring-code-format = true
+
+[tool.ruff.lint]
+select = [
+    "F", # pyflakes errors
+    "E", # code style
+    "W", # warnings
+    "I", # import order
+    "UP", # pyupgrade rules
+    "SIM", # code simplifications
+]
+ignore = ["E501"] # line too long
+
+[tool.ruff.lint.pydocstyle]
+convention = "numpy"
+
+[tool.ruff.per-file-ignores]
+"__init__.py" = ["F403"]
+
 [tool.setuptools.dynamic]
 version = {attr = "topomodelx.__version__"}
 
@@ -98,26 +118,11 @@ ignore_missing_imports = true
 [tool.pytest.ini_options]
 addopts = "--capture=no"
 
-[tool.black]
-line-length = 88
-
-[tool.isort]
-line_length = 88
-multi_line_output = 3
-include_trailing_comma = true
-skip = [".gitignore", "__init__.py"]
-
-[tool.flake8]
-max-line-length = 88
-application_import_names = "topomodelx"
-docstring-convention = "numpy"
-exclude = [
-    "topomodelx/__init__.py",
-    "docs/conf.py"
-]
-
-import_order_style = "smarkets"
-extend-ignore = ["E501", "E203"]
-per-file-ignores = [
-    "*/__init__.py: D104, F401",
+[tool.numpydoc_validation]
+checks = [
+    "all",
+    "GL01",
+    "ES01",
+    "EX01",
+    "SA01"
 ]
diff --git a/topomodelx/__init__.py b/topomodelx/__init__.py
@@ -1,5 +1,5 @@
 __version__ = "0.0.1"
 
+from .base import *
 from .nn import *
 from .utils import *
-from .base import *
diff --git a/topomodelx/nn/__init__.py b/topomodelx/nn/__init__.py
@@ -1,21 +1,20 @@
 from .cell.can_layer import (
+    CANLayer,
     LiftLayer,
-    MultiHeadLiftLayer,
-    PoolLayer,
     MultiHeadCellAttention,
     MultiHeadCellAttention_v2,
-    CANLayer,
+    MultiHeadLiftLayer,
+    PoolLayer,
 )
 from .cell.ccxn_layer import CCXNLayer
 from .cell.cwn_layer import (
     CWNLayer,
+    _CWNDefaultAggregate,
     _CWNDefaultFirstConv,
     _CWNDefaultSecondConv,
-    _CWNDefaultAggregate,
     _CWNDefaultUpdate,
 )
 
-
 __all__ = [
     "LiftLayer",
     "MultiHeadLiftLayer",

diff --git a/topomodelx/nn/cell/can_layer.py b/topomodelx/nn/cell/can_layer.py
@@ -1,12 +1,12 @@
 """Cell Attention Network layer."""
 
-from typing import Callable, Literal
+from collections.abc import Callable
+from typing import Literal
 
 import torch
 from torch import nn, topk
-from torch.nn import Linear, Parameter
+from torch.nn import Linear, Parameter, init
 from torch.nn import functional as F
-from torch.nn import init
 
 from topomodelx.base.aggregation import Aggregation
 from topomodelx.base.message_passing import MessagePassing
@@ -112,7 +112,7 @@ def __init__(
         signal_lift_activation: Callable,
         signal_lift_dropout: float,
     ) -> None:
-        super(LiftLayer, self).__init__()
+        super().__init__()
 
         self.in_channels_0 = in_channels_0
         self.att_parameter = nn.Parameter(torch.empty(size=(2 * in_channels_0, heads)))
@@ -201,7 +201,7 @@ def __init__(
         signal_lift_dropout: float = 0.0,
         signal_lift_readout: str = "cat",
     ) -> None:
-        super(MultiHeadLiftLayer, self).__init__()
+        super().__init__()
 
         assert heads > 0, ValueError("Number of heads must be > 0")
         assert signal_lift_readout in [
@@ -305,7 +305,7 @@ def __init__(
         signal_pool_activation: Callable,
         readout: bool = True,
     ) -> None:
-        super(PoolLayer, self).__init__()
+        super().__init__()
 
         self.k_pool = k_pool
         self.in_channels_0 = in_channels_0
@@ -322,7 +322,9 @@ def reset_parameters(self) -> None:
         gain = init.calculate_gain("relu")
         init.xavier_uniform_(self.att_pool.data, gain=gain)
 
-    def forward(self, x_0, lower_neighborhood, upper_neighborhood) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:  # type: ignore[override]
+    def forward(  # type: ignore[override]
+        self, x_0, lower_neighborhood, upper_neighborhood
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         r"""Forward pass.
 
         Parameters

diff --git a/topomodelx/nn/combinatorial/hmc_layer.py b/topomodelx/nn/combinatorial/hmc_layer.py
@@ -809,7 +809,7 @@ def __init__(
         update_func_aggregation=None,
         initialization="xavier_uniform",
     ):
-        super(HMCLayer, self).__init__()
+        super().__init__()
         super().__init__()
 
         assert (

diff --git a/topomodelx/nn/hypergraph/allset_layer.py b/topomodelx/nn/hypergraph/allset_layer.py
@@ -215,7 +215,7 @@ def __init__(
         mlp_dropout: float = 0.0,
         mlp_norm=None,
     ) -> None:
-        super(AllSetBlock, self).__init__()
+        super().__init__()
 
         self.dropout = dropout
         if mlp_num_layers > 0:

diff --git a/topomodelx/nn/hypergraph/unigcn_layer.py b/topomodelx/nn/hypergraph/unigcn_layer.py
@@ -44,7 +44,7 @@ def __init__(
     ) -> None:
         super().__init__()
 
-        with_linear_transform = False if in_channels == hidden_channels else True
+        with_linear_transform = in_channels != hidden_channels
         self.conv_level1_0_to_1 = Conv(
             in_channels=in_channels,
             out_channels=hidden_channels,

diff --git a/topomodelx/nn/hypergraph/unisage_layer.py b/topomodelx/nn/hypergraph/unisage_layer.py
@@ -76,14 +76,14 @@ def __init__(
         self.vertex2edge = Conv(
             in_channels=hidden_channels,
             out_channels=hidden_channels,
-            aggr_norm=False if self.e_aggr == "sum" else True,
+            aggr_norm=self.e_aggr != "sum",
             with_linear_transform=False,
         )
 
         self.edge2vertex = Conv(
             in_channels=hidden_channels,
             out_channels=hidden_channels,
-            aggr_norm=False if self.v_aggr == "sum" else True,
+            aggr_norm=self.v_aggr != "sum",
             with_linear_transform=False,
         )
 

diff --git a/tutorials/cell/can_train.ipynb b/tutorials/cell/can_train.ipynb
@@ -113,16 +113,12 @@
    },
    "outputs": [],
    "source": [
-    "import random\n",
-    "\n",
     "import numpy as np\n",
     "import torch\n",
     "from sklearn.model_selection import train_test_split\n",
     "from toponetx.classes.cell_complex import CellComplex\n",
     "from torch_geometric.datasets import TUDataset\n",
     "from torch_geometric.utils.convert import to_networkx\n",
-    "import torch.nn.functional as F\n",
-    "\n",
     "\n",
     "from topomodelx.nn.cell.can import CAN\n",
     "from topomodelx.utils.sparse import from_sparse\n",
@@ -263,7 +259,7 @@
     "    try:\n",
     "        upper_neighborhood_t = cell_complex.up_laplacian_matrix(rank=1)\n",
     "        upper_neighborhood_t = from_sparse(upper_neighborhood_t)\n",
-    "    except:\n",
+    "    except ValueError:\n",
     "        upper_neighborhood_t = np.zeros(\n",
     "            (lower_neighborhood_t.shape[0], lower_neighborhood_t.shape[0])\n",
     "        )\n",
@@ -482,9 +478,10 @@
     "        x_0 = x_0.float().to(device)\n",
     "        x_1, y = x_1.float().to(device), torch.tensor(y, dtype=torch.long).to(device)\n",
     "        adjacency = adjacency.float().to(device)\n",
-    "        lower_neighborhood, upper_neighborhood = lower_neighborhood.float().to(\n",
-    "            device\n",
-    "        ), upper_neighborhood.float().to(device)\n",
+    "        lower_neighborhood, upper_neighborhood = (\n",
+    "            lower_neighborhood.float().to(device),\n",
+    "            upper_neighborhood.float().to(device),\n",
+    "        )\n",
     "        opt.zero_grad()\n",
     "        y_hat = model(x_0, x_1, adjacency, lower_neighborhood, upper_neighborhood)\n",
     "        loss = crit(y_hat, y)\n",
@@ -511,13 +508,15 @@
     "                y_test,\n",
     "            ):\n",
     "                x_0 = x_0.float().to(device)\n",
-    "                x_1, y = x_1.float().to(device), torch.tensor(y, dtype=torch.long).to(\n",
-    "                    device\n",
+    "                x_1, y = (\n",
+    "                    x_1.float().to(device),\n",
+    "                    torch.tensor(y, dtype=torch.long).to(device),\n",
     "                )\n",
     "                adjacency = adjacency.float().to(device)\n",
-    "                lower_neighborhood, upper_neighborhood = lower_neighborhood.float().to(\n",
-    "                    device\n",
-    "                ), upper_neighborhood.float().to(device)\n",
+    "                lower_neighborhood, upper_neighborhood = (\n",
+    "                    lower_neighborhood.float().to(device),\n",
+    "                    upper_neighborhood.float().to(device),\n",
+    "                )\n",
     "                y_hat = model(\n",
     "                    x_0, x_1, adjacency, lower_neighborhood, upper_neighborhood\n",
     "                )\n",