Merge branch 'main' into line

Author: gbg141

.github/workflows/lint.yml

@@ -11,4 +11,6 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
-      - uses: chartboost/ruff-action@v1
+      - uses: chartboost/ruff-action@v1
+        with:
+          src: './modules'

.github/workflows/test_codebase.yml

@@ -27,34 +27,31 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest]
-        python-version: ["3.10", "3.11"]
-        torch-version: [2.0.1]
-        include:
-          - torch-version: 2.0.1
+        python-version: [3.11.3]
 
     steps:
-      - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+      - uses: actions/checkout@v4
+      - name: Set up Python ${{matrix.python-version}}
+        uses: actions/setup-python@v5
         with:
-          python-version: ${{ matrix.python-version }}
-          cache: "pip"
-          cache-dependency-path: '**/pyproject.toml'
+          python-version: ${{matrix.python-version}}
 
-      - name: Install PyTorch ${{ matrix.torch-version }}+cpu
-        run: |
-          pip install --upgrade pip setuptools wheel
-          pip install torch==${{ matrix.torch-version}} --extra-index-url https://download.pytorch.org/whl/cpu
-          pip install torch-scatter -f https://data.pyg.org/whl/torch-${{ matrix.torch-version }}+cpu.html
-          pip install torch-sparse -f https://data.pyg.org/whl/torch-${{ matrix.torch-version }}+cpu.html
-          pip install torch-cluster -f https://data.pyg.org/whl/torch-${{ matrix.torch-version }}+cpu.html
-          pip show pip
-      - name: Install main package
+      - uses: actions/cache@v4
+        with:
+          path: ~/.cache/pip
+          key: ${{matrix.os}}-${{matrix.python-version}}-${{ hashFiles('pyproject.toml') }}
+
+      - name: Install dependencies
         run: |
-          pip install -e .[all]
-      - name: Run tests for codebase [pytest]
+          python -m pip install --upgrade pip
+          pip install pytest
+          if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+          source env_setup.sh
+
+      - name: Test with pytest
         run: |
           pytest -n 2 --cov --cov-report=xml:coverage.xml test/transforms/feature_liftings test/transforms/liftings
+          pytest test/tutorials/test_tutorials.py
       - name: Upload coverage
         uses: codecov/codecov-action@v3
         with:

.github/workflows/test_tutorials.yml

@@ -17,25 +17,12 @@ repos:
       - id: trailing-whitespace
       - id: requirements-txt-fixer
 
-  - repo: https://github.com/psf/black
-    rev: 23.3.0
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.4.4
     hooks:
-      - id: black-jupyter
+      - id: ruff-format
 
-  - repo: https://github.com/pycqa/isort
-    rev: 5.12.0
-    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
+  # - repo: https://github.com/numpy/numpydoc
+  #   rev: v1.6.0
   #   hooks:
-  #   -   id: flake8
-  #       additional_dependencies: [flake8-docstrings, Flake8-pyproject]
+  #     - id: numpydoc-validation

.pre-commit-config.yaml

@@ -0,0 +1,6 @@
+transform_type: 'lifting'
+transform_name: "SimplicialGraphInducedLifting"
+complex_dim: 3
+preserve_edge_attr: False
+signed: True
+feature_lifting: ProjectionSum

configs/transforms/liftings/graph2simplicial/graph_induced_lifting.yaml

@@ -0,0 +1,7 @@
+transform_type: 'lifting'
+transform_name: "SimplicialVietorisRipsLifting"
+complex_dim: 3
+preserve_edge_attr: False
+signed: True
+distance_threshold: 2.0
+feature_lifting: ProjectionSum

configs/transforms/liftings/graph2simplicial/vietoris_rips_lifting.yaml

@@ -0,0 +1,15 @@
+#!/bin/bash -l
+pip install --upgrade pip
+pip install -e '.[all]'
+
+# Note that not all combinations of torch and CUDA are available
+# See https://github.com/pyg-team/pyg-lib to check the configuration that works for you
+TORCH="2.3.0"   # available options: 1.12.0, 1.13.0, 2.0.0, 2.1.0, 2.2.0, or 2.3.0
+CUDA="cpu"    # if available, select the CUDA version suitable for your system
+                # available options: cpu, cu102, cu113, cu116, cu117, cu118, or cu121
+pip install torch==${TORCH} --extra-index-url https://download.pytorch.org/whl/${CUDA}
+pip install pyg-lib torch-scatter torch-sparse torch-cluster -f https://data.pyg.org/whl/torch-${TORCH}+${CUDA}.html
+
+#pytest
+
+pre-commit install

env_setup.sh

@@ -6,7 +6,9 @@
 from omegaconf import DictConfig
 
 from modules.data.load.base import AbstractLoader
-from modules.data.utils.concat2geometric_dataset import ConcatToGeometricDataset
+from modules.data.utils.concat2geometric_dataset import (
+    ConcatToGeometricDataset,
+)
 from modules.data.utils.custom_dataset import CustomDataset
 from modules.data.utils.utils import (
     load_cell_complex_dataset,
@@ -45,7 +47,9 @@ def load(self) -> torch_geometric.data.Dataset:
         root_folder = rootutils.find_root()
         root_data_dir = os.path.join(root_folder, self.parameters["data_dir"])
 
-        self.data_dir = os.path.join(root_data_dir, self.parameters["data_name"])
+        self.data_dir = os.path.join(
+            root_data_dir, self.parameters["data_name"]
+        )
         if (
             self.parameters.data_name.lower() in ["cora", "citeseer", "pubmed"]
             and self.parameters.data_type == "cocitation"

modules/data/load/loaders.py

@@ -8,23 +8,33 @@
     OneHotDegreeFeatures,
 )
 from modules.transforms.feature_liftings.feature_liftings import ProjectionSum
-from modules.transforms.liftings.graph2cell.cycle_lifting import CellCycleLifting
+from modules.transforms.liftings.graph2cell.cycle_lifting import (
+    CellCycleLifting,
+)
 from modules.transforms.liftings.graph2hypergraph.knn_lifting import (
     HypergraphKNNLifting,
 )
 from modules.transforms.liftings.graph2simplicial.clique_lifting import (
     SimplicialCliqueLifting,
 )
+from modules.transforms.liftings.graph2simplicial.graph_induced_lifting import (
+    SimplicialGraphInducedLifting,
+)
 from modules.transforms.liftings.graph2simplicial.line_lifting import (
     SimplicialLineLifting,
 )
+from modules.transforms.liftings.graph2simplicial.vietoris_rips_lifting import (
+    SimplicialVietorisRipsLifting,
+)
 
 TRANSFORMS = {
     # Graph -> Hypergraph
     "HypergraphKNNLifting": HypergraphKNNLifting,
     # Graph -> Simplicial Complex
     "SimplicialCliqueLifting": SimplicialCliqueLifting,
     "SimplicialLineLifting": SimplicialLineLifting,
+    "SimplicialVietorisRipsLifting": SimplicialVietorisRipsLifting,
+    "SimplicialGraphInducedLifting": SimplicialGraphInducedLifting,
     # Graph -> Cell Complex
     "CellCycleLifting": CellCycleLifting,
     # Feature Liftings
@@ -56,10 +66,14 @@ def __init__(self, transform_name, **kwargs):
         self.parameters = kwargs
 
         self.transform = (
-            TRANSFORMS[transform_name](**kwargs) if transform_name is not None else None
+            TRANSFORMS[transform_name](**kwargs)
+            if transform_name is not None
+            else None
         )
 
-    def forward(self, data: torch_geometric.data.Data) -> torch_geometric.data.Data:
+    def forward(
+        self, data: torch_geometric.data.Data
+    ) -> torch_geometric.data.Data:
         """Forward pass of the lifting.
 
         Parameters

modules/transforms/data_transform.py

@@ -1,7 +1,9 @@
 import torch
 import torch_geometric
 
-from modules.transforms.liftings.graph2hypergraph.base import Graph2HypergraphLifting
+from modules.transforms.liftings.graph2hypergraph.base import (
+    Graph2HypergraphLifting,
+)
 
 
 class HypergraphKNNLifting(Graph2HypergraphLifting):
@@ -45,22 +47,30 @@ def lift_topology(self, data: torch_geometric.data.Data) -> dict:
         if self.loop:
             for i in range(num_nodes):
                 if not torch.any(
-                    torch.all(data_lifted.edge_index == torch.tensor([[i, i]]).T, dim=0)
+                    torch.all(
+                        data_lifted.edge_index == torch.tensor([[i, i]]).T,
+                        dim=0,
+                    )
                 ):
                     connected_nodes = data_lifted.edge_index[
                         0, data_lifted.edge_index[1] == i
                     ]
                     dists = torch.sqrt(
                         torch.sum(
-                            (data.pos[connected_nodes] - data.pos[i].unsqueeze(0) ** 2),
+                            (
+                                data.pos[connected_nodes]
+                                - data.pos[i].unsqueeze(0) ** 2
+                            ),
                             dim=1,
                         )
                     )
                     furthest = torch.argmax(dists)
                     idx = torch.where(
                         torch.all(
                             data_lifted.edge_index
-                            == torch.tensor([[connected_nodes[furthest], i]]).T,
+                            == torch.tensor(
+                                [[connected_nodes[furthest], i]]
+                            ).T,
                             dim=0,
                         )
                     )[0]