diff --git a/.gitignore b/.gitignore index 15201acc11..b48ac441a9 100644 --- a/.gitignore +++ b/.gitignore @@ -166,6 +166,24 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ +.idea/ # PyPI configuration file .pypirc + +# GEDLIB build artifacts +gedlib/build/ +gedlib/cmake-build-debug/ +gedlib/lib/ +gedlib/bin/ +gedlib/docs/ +gedlib/median/bin/ +gedlib/median/output/ +gedlib/tests/*/bin/ +gedlib/tests/*/output/ +gedlib/ext/.INSTALLED +gedlib/ext/**/build/ +gedlib/ext/**/lib/*.dylib +gedlib/ext/**/lib/*.so +gedlib/ext/**/bin/ +gedlib/ext/libsvm.3.22/*.o diff --git a/Makefile b/Makefile new file mode 100644 index 0000000000..3302165754 --- /dev/null +++ b/Makefile @@ -0,0 +1,100 @@ +SHELL := /bin/bash +.SHELLFLAGS := -eu -o pipefail -c + +POETRY ?= poetry +PYTHON ?= python +POETRY_RUN ?= $(POETRY) run +RUN_PY ?= $(POETRY_RUN) $(PYTHON) +CMAKE ?= cmake +GEDLIB_BUILD_DIR ?= gedlib/build +GEDLIB_LIB ?= gxl +GXL_DATASET ?= aids +GXL_OPTIONS := aids imdb proteins +DATA_ROOT ?= data +TUD_ROOT ?= $(DATA_ROOT)/_tud +DATASETS ?= AIDS IMDB-BINARY PROTEINS + +.PHONY: help gedlib-install gedlib-configure gedlib-build gedlib-all gedlib-test \ + convert-gxl convert-json convert-txt lower-bound lower-bound-validate \ + exact-ged gedlib-run gedlib-edit-path simgnn-edit-path apply-edit-path \ + gedlib-validate-path simgnn-validate-path generate-gxl-collection \ + generate-json-pairs simgnn-train simgnn-eval test-python install-datasets + +help: ## Show available make targets + @printf "Available targets:\\n" + @grep -E '^[a-zA-Z0-9_.-]+:.*?##' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?##"} {printf " %-24s %s\n", $$1, $$2}' + +gedlib-install: ## Run GEDLIB install script with cleanup and docs (set GEDLIB_LIB=gxl|... as needed) + cd gedlib && $(PYTHON) install.py --clean --doc --lib $(GEDLIB_LIB) + +gedlib-configure: ## Configure GEDLIB CMake build directory + $(CMAKE) -S gedlib -B $(GEDLIB_BUILD_DIR) + +gedlib-build: gedlib-configure ## Build GEDLIB via CMake + $(CMAKE) --build $(GEDLIB_BUILD_DIR) + +gedlib-all: ## Install and build GEDLIB + $(MAKE) gedlib-install + $(MAKE) gedlib-build + +gedlib-test: ## Run GEDLIB ctest suite after building + cd $(GEDLIB_BUILD_DIR) && ctest + +convert-gxl: ## Convert a dataset to GXL/XML for GEDLIB (set GXL_DATASET=$(GXL_OPTIONS)) + cd src/converters/gxl_xml && \ + if [ ! -f preprocess_$(GXL_DATASET).py ]; then \ + echo "Unknown dataset '$(GXL_DATASET)'. Choose from: $(GXL_OPTIONS)"; \ + exit 1; \ + fi; \ + $(RUN_PY) preprocess_$(GXL_DATASET).py + +convert-json: ## Convert datasets to JSON pairs for SimGNN + cd src/converters/json && $(RUN_PY) preprocess_all.py + +convert-txt: ## Convert datasets to TXT graph pairs + cd src/converters/txt && $(RUN_PY) preprocess_all.py + +lower-bound: ## Estimate lower bounds for graph pairs + cd heuristics && $(RUN_PY) estimate_lower_bound.py + +lower-bound-validate: ## Validate lower bound estimations + cd heuristics && $(RUN_PY) validate_lower_bounds.py + +exact-ged: ## Compute exact GED using AStar-BMao parser + cd src/c++_parsers && $(RUN_PY) astar_exact_ged.py + +gedlib-run: ## Run GEDLIB parser for approximate GED + cd src/c++_parsers && $(RUN_PY) gedlib_parser.py + +gedlib-edit-path: ## Extract GEDLIB edit paths + cd src/c++_parsers && $(RUN_PY) gedlib_edit_path.py + +simgnn-edit-path: ## Extract SimGNN edit paths + cd SimGNN/src && $(RUN_PY) simgnn_extract_edit_path.py + +apply-edit-path: ## Apply edit paths to simulate edits + cd src/edit_path_test && $(RUN_PY) apply_edit_path.py + +gedlib-validate-path: ## Validate GEDLIB edit paths + cd src/edit_path_test/test && $(RUN_PY) gedlib_validate_edit_path.py + +simgnn-validate-path: ## Validate SimGNN edit paths + cd SimGNN/src && $(RUN_PY) simgnn_validate_edit_path.py + +generate-gxl-collection: ## Generate synthetic GXL collection for edit-path testing + cd src/edit_path_test/generate_synthetic_graphs && $(RUN_PY) generate_gxl_collection.py + +generate-json-pairs: ## Generate synthetic JSON pairs for edit-path testing + cd src/edit_path_test/generate_synthetic_graphs && $(RUN_PY) generate_json_pairs.py + +simgnn-train: ## Train SimGNN model + cd SimGNN/src && $(RUN_PY) main.py + +simgnn-eval: ## Evaluate SimGNN model + cd SimGNN/src && $(RUN_PY) simgnn_evaluate.py + +test-python: ## Run pytest suite + $(RUN_PY) -m pytest + +install-datasets: ## Download TU datasets (any torch_geometric TUDataset name) into data/ + $(RUN_PY) scripts/install_datasets.py --datasets $(DATASETS) --download-root $(TUD_ROOT) --target-root $(DATA_ROOT) diff --git a/README.md b/README.md index 43018e8b5c..6d580327db 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ git clone https://github.com/michaelflppv/ged-approximation.git cd ged-approximation ``` -## **πŸ“Œ Important Notice: Using Precompiled Data** +## **Important Notice: Using Precompiled Data** This repository includes **precompiled datasets** and large files (e.g., GXL/XML files, JSON graph pairs, and pre-trained models). To ensure these files are correctly downloaded, [Git LFS (Large File Storage)](https://git-lfs.github.com/) must be installed. 1. Download and install [Git LFS](https://git-lfs.github.com/). 2. Run the setup command: @@ -36,7 +36,7 @@ git lfs pull ## **πŸ“‚ Project Structure** ``` πŸ“¦ ged-approximation -β”œβ”€β”€ πŸ“œ README.md # Project documentation +β”œβ”€β”€ README.md # Project documentation β”œβ”€β”€ πŸ“‚ data/ # Raw graph datasets (AIDS, IMDB, etc.) β”‚ β”œβ”€β”€ πŸ“‚ AIDS/ β”‚ β”œβ”€β”€ πŸ“‚ IMDB-BINARY/ @@ -57,8 +57,8 @@ git lfs pull β”‚ └── πŸ“‚ label_diversity/ # Label diversity stats β”œβ”€β”€ πŸ“‚ heuristics/ # Heuristic lower bound estimations β”‚ β”œβ”€β”€ πŸ“‚ plots/ # Visualizations of lower bounds -β”‚ β”œβ”€β”€ πŸ“œ estimate_lower_bound.py -β”‚ └── πŸ“œ validate_lower_bounds.py +β”‚ β”œβ”€β”€ estimate_lower_bound.py +β”‚ └── validate_lower_bounds.py β”œβ”€β”€ πŸ“‚ SimGNN/ # Neural GED model (SimGNN) β”‚ β”œβ”€β”€ πŸ“‚ assets/ β”‚ β”œβ”€β”€ πŸ“‚ dataset/ # Train/test data in JSON format @@ -92,24 +92,24 @@ git lfs pull β”‚ β”‚ └── generate_json_pairs.py β”‚ β”œβ”€β”€ πŸ“‚ test/ # Edit path validation utilities β”‚ β”‚ └── gedlib_validate_edit_path.py # Validate GEDLIB paths -β”‚ └── πŸ“œ apply_edit_path.py # Apply and simulate edit path execution +β”‚ └── apply_edit_path.py # Apply and simulate edit path execution β”œβ”€β”€ πŸ“‚ helper_functions/ # Miscellaneous utility scripts -β”‚ └── πŸ“œ label_diversity_calculator.py # Computes label diversity in datasets +β”‚ └── label_diversity_calculator.py # Computes label diversity in datasets β”œβ”€β”€ πŸ“‚ gedlib/ # GEDLIB C++ source and interface β”‚ β”œβ”€β”€ πŸ“‚ src/, include/, lib/ # C++ logic and libraries -β”‚ β”œβ”€β”€ πŸ“œ main.cpp, CMakeLists.txt # Entry and build files -β”‚ └── πŸ“œ install.py # Installation script +β”‚ β”œβ”€β”€ main.cpp, CMakeLists.txt # Entry and build files +β”‚ └── install.py # Installation script β”œβ”€β”€ πŸ“‚ median/ # Placeholder (possibly for GED median) β”œβ”€β”€ πŸ“‚ tests/ # Unit and functional tests β”œβ”€β”€ πŸ“‚ venv/ # Python virtual environment (optional) -└── πŸ“œ LICENSE, .gitignore, ... # Meta files +└── LICENSE, .gitignore, ... # Meta files ``` --- -## **πŸš€ Installation & Setup** -### **1️⃣ Install Dependencies** +## **Installation & Setup** +### **1. Install Dependencies** #### 1.1 System & OS Dependencies - **Linux**: Ubuntu 18.04+ / Debian / Fedora (recommended) @@ -118,37 +118,42 @@ git lfs pull #### 1.2 Language & Runtime Versions -- Python β‰₯ 3.8 (recommended 3.9) +- Python 3.9–3.11 (dependencies pinned via Poetry) - C++17‑compatible compiler (e.g. GCC β‰₯ 7, Clang β‰₯ 5, MSVC β‰₯ 2017) -The required Python packages are listed in the `requirements.txt` file. You can install them using the following command: +Python dependencies are managed with Poetry. After installing [Poetry](https://python-poetry.org/) and Git LFS, set up the environment with: ```bash -pip install -r requirements.txt +poetry install +# optional: enter the virtualenv +poetry shell ``` +All project commands can be run from the repository root via the `Makefile`; see `make help` for a full list. The make targets already wrap `poetry run`, so install dependencies once and then use `make ` for workflows. #### 1.3 Build Tools - **CMake**: Required for building the C++ components. Install it from [CMake](https://cmake.org/download/). - **Doxygen**: Required for generating documentation. Install it from [Doxygen](https://www.doxygen.nl/download.html). - **OpenMP**: Required for parallel processing. Ensure your compiler supports [OpenMP](https://www.openmp.org/). +- **macOS**: Install `libomp` so headers and libs are available to CMake: +```bash +brew install libomp +``` Find more information on how to install these tools in [GEDLIB](https://github.com/dbblumenthal/gedlib). -### **2️⃣ Clone & Compile GEDLIB** +### **2. Clone & Compile GEDLIB** This repository partially relies on GEDLIB for GED computation. The required repository and its external libraries should already be installed within this project. If not, refer to the [GEDLIB](https://github.com/dbblumenthal/gedlib) for more information. -To compile the C++ code, navigate to the `gedlib` directory and run: +To compile the C++ code from the project root, use: ```bash -cd gedlib -python install.py --clean --doc --lib gxl -mkdir build && cd build -cmake .. -make +make gedlib-all # install GEDLIB deps and build via CMake +make gedlib-test # optional: run ctest after build ``` +Set `GEDLIB_LIB` if you need a different GEDLIB library target (default: `gxl`). My repostory called **[mixup](https://github.com/michaelflppv/mixup.git)** contains a backup copy of GEDLIB with the source code, required to compile this project. -### **3️⃣ Set Up External Dependencies** +### **3. Set Up External Dependencies** This project also relies on the **[Graph Edit Distance (GED) repository by Lijun Chang](https://github.com/LijunChang/Graph_Edit_Distance.git)** for **exact GED computation**. To use this repository: @@ -158,76 +163,104 @@ To use this repository: cd Graph_Edit_Distance ``` 2. **Follow the build instructions** provided in the [repository]((https://github.com/LijunChang/Graph_Edit_Distance.git)) to compile and set up the exact GED computation framework. +### **4. Download TU datasets** + +Use the make target to download TU-format datasets (any name supported by `torch_geometric.datasets.TUDataset`) and stage the raw files under `data/`: +```bash +make install-datasets DATASETS="AIDS IMDB-BINARY PROTEINS MUTAG ENZYMES NCI1" +``` + +Set `DATASETS` to any compatible TU dataset names; `DATA_ROOT` and `TUD_ROOT` can be overridden if you want custom locations. If the list is long, pass them space-separated as shown above. + --- -## **πŸ§ͺ Run Experiments** -### 1️⃣ Data Conversion -To convert datasets into the required formats, follow these steps: -- Navigate to the [src/converters](https://github.com/michaelflppv/ged-approximation/tree/main/src/converters) directory. -- Run the appropriate conversion script for your dataset. For example, to convert the AIDS dataset to GXL: - - Choose [gxl_xml]() directory. - - Select the appropriate script (e.g., `preprocess_aids.py`) or specify the dataset name in the script (e.g., `preprocess_all.py`). - - Run the script: +## **Run Experiments** +All commands below run from the repository root via `make`. + +### 1. Data Conversion +- Convert to GXL/XML for GEDLIB (choose dataset with `GXL_DATASET=aids|imdb|proteins`): ```bash - python preprocess_aids.py - ``` -### 2️⃣ Lower Bound Estimation -To estimate lower bounds for the graph pairs: -- Navigate to [heuristics](https://github.com/michaelflppv/ged-approximation/tree/main/heuristics) and run: - ```bash - python estimate_lower_bound.py - ``` -- The results will be saved in the `results/lower_bound` directory. + make convert-gxl GXL_DATASET=aids + ``` +- Convert all datasets to JSON pairs for SimGNN: + ```bash + make convert-json + ``` +- Convert datasets to TXT graph pairs: + ```bash + make convert-txt + ``` -### 3️⃣ Exact GED Computation -To compute the exact GED using the AStar-BMao algorithm: -- Set up the environment as described in the **Installation & Setup** section. -- Navigate to [src/c++_parsers](https://github.com/michaelflppv/ged-approximation/blob/main/src/c%2B%2B_parsers). -- Run the AStar-BMao script: - ```bash - python astar_exact_ged.py - ``` -- If you want, you can adjust the amount of threads and graph pairs used for the computation in the script. -- The results will be saved in the `results/exact_ged` directory. - -### 4️⃣ GEDLIB Computation -To compute an approximate GED using any algorithm available in the GEDLIB: -- Set up the environment as described in the **Installation & Setup** section. -- Navigate to [src/c++_parsers](https://github.com/michaelflppv/ged-approximation/blob/main/src/c%2B%2B_parsers). -- Select the appropriate script (e.g., `gedlib_parser.py`) and the algorithm you want to use. -- For changing the algorithm, you can modify the `command = [GED_EXECUTABLE, dataset_path, preprocessed_xml, "IPFP"]` line in the script. -- Run the script: - ```bash - python gedlib_parser.py - ``` +### 2. Lower Bound Estimation +- Estimate lower bounds: + ```bash + make lower-bound + ``` +- Validate lower bound estimations: + ```bash + make lower-bound-validate + ``` +Results are written to `results/lower_bound`. -### 5️⃣ SimGNN Training & Evaluation -To train the model, navigate to [SimGNN/src](https://github.com/michaelflppv/ged-approximation/tree/main/SimGNN/src) and run `main.py`: -```bash -python main.py -``` -For more information and hyperparameter settings, refer to original [SimGNN repository](https://github.com/benedekrozemberczki/SimGNN). +### 3. Exact GED Computation +- Run the AStar-BMao exact GED computation: + ```bash + make exact-ged + ``` +Adjust threads/pair counts inside `src/c++_parsers/astar_exact_ged.py` if needed. Outputs land in `results/exact_ged`. -To test the model, navigate to [SimGNN/src](https://github.com/michaelflppv/ged-approximation/tree/main/SimGNN/src) and run: -```bash -python simgnn_evaluate.py -``` -To test the model on a specific dataset, you can modify the paths in the script. The results will be saved in the `results/simgnn` directory. +### 4. GEDLIB Computation +- Run GEDLIB parser for approximate GED (ensure GEDLIB is built first): + ```bash + make gedlib-run + ``` +Modify the algorithm in `src/c++_parsers/gedlib_parser.py` (the `command` list) to switch methods. + +### 5. SimGNN Training & Evaluation +- Train SimGNN: + ```bash + make simgnn-train + ``` +- Evaluate SimGNN: + ```bash + make simgnn-eval + ``` +Adjust dataset/model paths inside `SimGNN/src` scripts as needed. Results are saved under `results/simgnn`. -### 6️⃣ Edit Path Extraction & Validation -The repository includes tools for extracting and validating edit paths using GEDLIB algorithms and SimGNN. To extract edit paths: -- To extract edit path for a pair of graphs, navigate either to [src/c++_parsers](https://github.com/michaelflppv/ged-approximation/blob/main/src/c%2B%2B_parsers) or [SimGNN/src](https://github.com/michaelflppv/ged-approximation/tree/main/SimGNN/src) for GEDLIB and SimGNN respectively. - - Run the appropriate script (e.g., `gedlib_edit_path.py` or `simgnn_extract_edit_path.py`) and specify the graph pair and dataset path. -- To validate or apply edit paths, navigate to [src/edit_path_test](https://github.com/michaelflppv/ged-approximation/tree/main/src/edit_path_test) and choose an appropriate script (e.g., `gedlib_validate_edit_path.py` or `apply_edit_path.py`). Modify the paths in the script to point to the edit path files and dataset. -- To validate the edit paths for SimGNN, navigate to [simgnn_validate_edit_path.py](https://github.com/michaelflppv/ged-approximation/blob/main/SimGNN/src/simgnn_validate_edit_path.py) and follow the same steps. +### 6. Edit Path Extraction & Validation +- Extract GEDLIB edit paths: + ```bash + make gedlib-edit-path + ``` +- Extract SimGNN edit paths: + ```bash + make simgnn-edit-path + ``` +- Apply edit paths to simulate edits: + ```bash + make apply-edit-path + ``` +- Validate GEDLIB edit paths: + ```bash + make gedlib-validate-path + ``` +- Validate SimGNN edit paths: + ```bash + make simgnn-validate-path + ``` +- Generate synthetic data for edit-path testing (optional): + ```bash + make generate-gxl-collection + make generate-json-pairs + ``` -### 7️⃣ Results Analysis & Visualization +### 7. Results Analysis & Visualization This repository includes Jupyter Notebooks for analyzing and visualizing the results of the experiments. To explore the results: - Navigate to [notebooks](https://github.com/michaelflppv/ged-approximation/tree/main/src/analysis/notebooks). - Open the desired notebook (e.g., `lower_bound_analysis.ipynb`, `plot_analysis.ipynb`, or `statistics_analysis.ipynb`) and run the cells to visualize the results. --- -## **πŸ—ƒοΈ Datasets** +## **Datasets** The repository includes several datasets for benchmarking the GED algorithms. The datasets are stored in the `data` directory and include: - **AIDS**: A dataset of molecular graphs. @@ -238,7 +271,7 @@ These datasets were taken from TUDataset, which is a collection of benchmark dat --- -## **πŸ“œ Citation & References** +## **Citation & References** If you use this code in your work, please cite: ``` @misc{Filippov2025, @@ -262,5 +295,5 @@ The SimGNN implementation is based on the [original repository](https://github.c --- -## **πŸ“¬ Contact** +## **Contact** For questions, create an issue or reach out via email. diff --git a/gedlib/CMakeLists.txt b/gedlib/CMakeLists.txt index 12a1e3faa0..6c6af02152 100644 --- a/gedlib/CMakeLists.txt +++ b/gedlib/CMakeLists.txt @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:f24ff9955a4fe9045b7a3a370609db2c70038de08ee57ff96c2d683d4438cbaa -size 5130 +oid sha256:98030d7d4326324dbad5b26b04e04603d0531061598c23e59a188278fa00c4a6 +size 5550 diff --git a/gedlib/ext/eigen.3.3.4/Eigen/src/Core/Transpositions.h b/gedlib/ext/eigen.3.3.4/Eigen/src/Core/Transpositions.h index 19c17bb4a6..ea1f0ebb4b 100644 --- a/gedlib/ext/eigen.3.3.4/Eigen/src/Core/Transpositions.h +++ b/gedlib/ext/eigen.3.3.4/Eigen/src/Core/Transpositions.h @@ -384,7 +384,9 @@ class Transpose > const Product operator*(const MatrixBase& matrix, const Transpose& trt) { - return Product(matrix.derived(), trt.derived()); + // The transposed transposition specialization does not expose derived(), + // so forward the object itself instead of calling a non-existent member. + return Product(matrix.derived(), trt); } /** \returns the \a matrix with the inverse transpositions applied to the rows. diff --git a/gedlib/ext/fann.2.2.0/CMakeLists.txt b/gedlib/ext/fann.2.2.0/CMakeLists.txt index 79f8a725ba..a29b8c66fc 100644 --- a/gedlib/ext/fann.2.2.0/CMakeLists.txt +++ b/gedlib/ext/fann.2.2.0/CMakeLists.txt @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:7ee114928867de4ff1a6107c7815170140165e51aaf887be12f9f59e30465165 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