From b205354157f77f4e4ff5e6eb23f9e82f8d867be3 Mon Sep 17 00:00:00 2001 From: Xueqi Cheng Date: Wed, 3 Dec 2025 14:54:56 -0500 Subject: [PATCH 01/39] Rename project from PyGIP to PyHazard Updated README.md to reflect project name change from PyGIP to PyHazard and modified related content accordingly. --- README.md | 81 ++++++++++++++++++------------------------------------- 1 file changed, 26 insertions(+), 55 deletions(-) diff --git a/README.md b/README.md index 2b9fa53..46532a3 100644 --- a/README.md +++ b/README.md @@ -1,49 +1,42 @@ -# PyGIP +# Pyhazard -[![PyPI - Version](https://img.shields.io/pypi/v/PyGIP)](https://pypi.org/project/PyGIP) -[![Build Status](https://img.shields.io/github/actions/workflow/status/LabRAI/PyGIP/docs.yml)](https://github.com/LabRAI/PyGIP/actions) -[![License](https://img.shields.io/github/license/LabRAI/PyGIP.svg)](https://github.com/LabRAI/PyGIP/blob/main/LICENSE) -[![PyPI - Downloads](https://img.shields.io/pypi/dm/pygip)](https://github.com/LabRAI/PyGIP) -[![Issues](https://img.shields.io/github/issues/LabRAI/PyGIP)](https://github.com/LabRAI/PyGIP) -[![Pull Requests](https://img.shields.io/github/issues-pr/LabRAI/PyGIP)](https://github.com/LabRAI/PyGIP) -[![Stars](https://img.shields.io/github/stars/LabRAI/PyGIP)](https://github.com/LabRAI/PyGIP) -[![GitHub forks](https://img.shields.io/github/forks/LabRAI/PyGIP)](https://github.com/LabRAI/PyGIP) +[![PyPI - Version](https://img.shields.io/pypi/v/PyHazard)](https://pypi.org/project/PyHazard) +[![Build Status](https://img.shields.io/github/actions/workflow/status/LabRAI/PyHazard/docs.yml)](https://github.com/LabRAI/PyHazard/actions) +[![License](https://img.shields.io/github/license/LabRAI/PyHazard.svg)](https://github.com/LabRAI/PyHazard/blob/main/LICENSE) +[![PyPI - Downloads](https://img.shields.io/pypi/dm/PyHazard)](https://github.com/LabRAI/PyHazard) +[![Issues](https://img.shields.io/github/issues/LabRAI/PyHazard)](https://github.com/LabRAI/PyHazard) +[![Pull Requests](https://img.shields.io/github/issues-pr/LabRAI/PyHazard)](https://github.com/LabRAI/PyHazard) +[![Stars](https://img.shields.io/github/stars/LabRAI/PyHazard)](https://github.com/LabRAI/PyHazard) +[![GitHub forks](https://img.shields.io/github/forks/LabRAI/PyHazard)](https://github.com/LabRAI/PyHazard) -PyGIP is a Python library designed for experimenting with graph-based model extraction attacks and defenses. It provides -a modular framework to implement and test attack and defense strategies on graph datasets. +PyHazard is a Python library designed for using AI tools for hazard prediction. It provides a modular framework to implement and test prediction strategies on natural hazards. ## How to Cite -If you find it useful, please considering cite the following work: +If you find it useful, please consider citing the following work: -```bibtex -@article{li2025intellectual, - title={Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses}, - author={Li, Lincan and Shen, Bolin and Zhao, Chenxi and Sun, Yuxiang and Zhao, Kaixiang and Pan, Shirui and Dong, Yushun}, - journal={arXiv preprint arXiv:2508.19641}, - year={2025} -} +``` ``` ## Installation -PyGIP supports both CPU and GPU environments. Make sure you have Python installed (version >= 3.8, <3.13). +PyHazard supports both CPU and GPU environments. Make sure you have Python installed (version >= 3.8, <3.13). ### Base Installation First, install the core package: ```bash -pip install PyGIP +pip install PyHazard ``` -This will install PyGIP with minimal dependencies. +This will install PyHazard with minimal dependencies. ### CPU Version ```bash -pip install "PyGIP[torch,dgl]" \ +pip install "PyHazard[torch,dgl]" \ --index-url https://download.pytorch.org/whl/cpu \ --extra-index-url https://pypi.org/simple \ -f https://data.dgl.ai/wheels/repo.html @@ -52,7 +45,7 @@ pip install "PyGIP[torch,dgl]" \ ### GPU Version (CUDA 12.1) ```bash -pip install "PyGIP[torch,dgl]" \ +pip install "PyHazard[torch,dgl]" \ --index-url https://download.pytorch.org/whl/cu121 \ --extra-index-url https://pypi.org/simple \ -f https://data.dgl.ai/wheels/torch-2.3/cu121/repo.html @@ -60,47 +53,25 @@ pip install "PyGIP[torch,dgl]" \ ## Quick Start -Here’s a simple example to launch a Model Extraction Attack using PyGIP: - -```python -from datasets import Cora -from models.attack import ModelExtractionAttack0 - -# Load the Cora dataset -dataset = Cora() +Here’s a simple example to launch a Model Extraction Attack using PyHazard: -# Initialize the attack with a sampling ratio of 0.25 -mea = ModelExtractionAttack0(dataset, 0.25) - -# Execute the attack -mea.attack() +``` ``` -This code loads the Cora dataset, initializes a basic model extraction attack (`ModelExtractionAttack0`), and runs the -attack with a specified sampling ratio. - -And a simple example to run a Defense method against Model Extraction Attack: - -```python -from datasets import Cora -from models.defense import RandomWM - -# Load the Cora dataset -dataset = Cora() +This code loads the xxx dataset, initializes a basic hazard prediction baseline, and runs the +prediction with a specified sampling ratio. -# Initialize the attack with a sampling ratio of 0.25 -med = RandomWM(dataset, 0.25) +And a simple example to run: -# Execute the defense -med.defend() +``` ``` -which runs the Random Watermarking Graph to defend against MEA. +which runs xxx If you want to use cuda, please set environment variable: ```shell -export PYGIP_DEVICE=cuda:0 +export PyHazard_DEVICE=cuda:0 ``` ## Implementation & Contributors Guideline @@ -115,4 +86,4 @@ Refer to [Contributors Guideline](.github/CONTRIBUTING.md) ## Contact -For questions or contributions, please contact blshen@fsu.edu. +For questions or contributions, please contact xc25@fsu.edu. From 6b09cae72f679c8ec74ff327cedac8a5cda20c49 Mon Sep 17 00:00:00 2001 From: Xueqi Cheng Date: Wed, 3 Dec 2025 14:55:14 -0500 Subject: [PATCH 02/39] update in README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 46532a3..832e20b 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Pyhazard +# PyHazard [![PyPI - Version](https://img.shields.io/pypi/v/PyHazard)](https://pypi.org/project/PyHazard) [![Build Status](https://img.shields.io/github/actions/workflow/status/LabRAI/PyHazard/docs.yml)](https://github.com/LabRAI/PyHazard/actions) From 049c741a9afb28cd3a824b736b8a25d2a91d11af Mon Sep 17 00:00:00 2001 From: Xueqi Cheng Date: Wed, 3 Dec 2025 15:22:57 -0500 Subject: [PATCH 03/39] update to pyhazard --- MANIFEST.in | 2 +- PyHazard.egg-info/PKG-INFO | 138 ++++++++++++++++++ PyHazard.egg-info/SOURCES.txt | 77 ++++++++++ PyHazard.egg-info/dependency_links.txt | 1 + PyHazard.egg-info/requires.txt | 15 ++ PyHazard.egg-info/top_level.txt | 1 + README.md | 33 +++-- benchmark/run/utils_benchmark.py | 42 +++--- docs/README.md | 2 +- ....rst => pyhazard.models.attack.AdvMEA.rst} | 4 +- ...GA.rst => pyhazard.models.attack.CEGA.rst} | 4 +- ...=> pyhazard.models.attack.DataFreeMEA.rst} | 4 +- ...t => pyhazard.models.attack.Realistic.rst} | 4 +- ...se.rst => pyhazard.models.attack.base.rst} | 4 +- ...rst => pyhazard.models.attack.mea.MEA.rst} | 4 +- ...eer.rst => pyhazard.datasets.CiteSeer.rst} | 4 +- ...S.rst => pyhazard.datasets.CoauthorCS.rst} | 4 +- ... => pyhazard.datasets.CoauthorPhysics.rst} | 4 +- ...rs.rst => pyhazard.datasets.Computers.rst} | 4 +- ...ts.Cora.rst => pyhazard.datasets.Cora.rst} | 4 +- ....Photo.rst => pyhazard.datasets.Photo.rst} | 4 +- ...ubMed.rst => pyhazard.datasets.PubMed.rst} | 4 +- ...=> pyhazard.models.defense.BackdoorWM.rst} | 4 +- ...hazard.models.defense.ImperceptibleWM.rst} | 4 +- ... => pyhazard.models.defense.Integrity.rst} | 4 +- ...t => pyhazard.models.defense.RandomWM.rst} | 4 +- ... => pyhazard.models.defense.SurviveWM.rst} | 4 +- ... => pyhazard.models.defense.atom.ATOM.rst} | 4 +- ...e.rst => pyhazard.models.defense.base.rst} | 4 +- ...lTopyg.rst => pyhazard.utils.dglTopyg.rst} | 4 +- ...rdware.rst => pyhazard.utils.hardware.rst} | 4 +- ...metrics.rst => pyhazard.utils.metrics.rst} | 4 +- .../{pygip.utils.rst => pyhazard.utils.rst} | 4 +- docs/source/api/modules.rst | 4 +- ...gip.datasets.rst => pyhazard.datasets.rst} | 8 +- ...mea.rst => pyhazard.models.attack.mea.rst} | 8 +- ....attack.rst => pyhazard.models.attack.rst} | 22 +-- ...m.rst => pyhazard.models.defense.atom.rst} | 8 +- ...efense.rst => pyhazard.models.defense.rst} | 34 ++--- ...p.models.nn.rst => pyhazard.models.nn.rst} | 8 +- .../{pygip.models.rst => pyhazard.models.rst} | 10 +- docs/source/api/{pygip.rst => pyhazard.rst} | 10 +- .../{pygip.utils.rst => pyhazard.utils.rst} | 16 +- docs/source/conf.py | 4 +- docs/source/implementation.rst | 6 +- docs/source/index.rst | 70 ++++----- docs/source/installation.rst | 6 +- docs/source/pygip_models_attack.rst | 22 --- docs/source/pygip_models_defense.rst | 23 --- docs/source/pygip_utils.rst | 18 --- ...gip_datasets.rst => pyhazard_datasets.rst} | 16 +- docs/source/pyhazard_models_attack.rst | 22 +++ docs/source/pyhazard_models_defense.rst | 23 +++ docs/source/pyhazard_utils.rst | 18 +++ docs/source/quick_start.rst | 10 +- docs/source/team.rst | 24 +-- examples/attack/AdvMEA.py | 6 +- examples/attack/CEGA.py | 4 +- examples/attack/DataFreeMEA.py | 6 +- examples/attack/MEA.py | 6 +- examples/attack/Realistic.py | 6 +- examples/defense/ATOM.py | 4 +- examples/defense/BackdoorWM.py | 4 +- examples/defense/GrOVe.py | 4 +- examples/defense/ImperceptibleWM.py | 4 +- examples/defense/Integrity.py | 4 +- examples/defense/RandomWM.py | 4 +- examples/defense/Revisiting.py | 4 +- examples/defense/SurviveWM.py | 4 +- {pygip => pyhazard}/__init__.py | 2 +- {pygip => pyhazard}/datasets/__init__.py | 0 {pygip => pyhazard}/datasets/datasets.py | 0 {pygip => pyhazard}/models/__init__.py | 0 {pygip => pyhazard}/models/attack/AdvMEA.py | 6 +- {pygip => pyhazard}/models/attack/CEGA.py | 6 +- .../models/attack/DataFreeMEA.py | 6 +- .../models/attack/Realistic.py | 4 +- {pygip => pyhazard}/models/attack/__init__.py | 0 {pygip => pyhazard}/models/attack/base.py | 4 +- {pygip => pyhazard}/models/attack/mea/MEA.py | 6 +- .../models/attack/mea/__init__.py | 0 .../citeseer/graph_label.txt | 0 .../citeseer/query_labels.txt | 0 .../citeseer/selected_index.txt | 0 .../cora/graph_label.txt | 0 .../cora/query_labels.txt | 0 .../cora/selected_index.txt | 0 .../pubmed/graph_label.txt | 0 .../pubmed/query_labels.txt | 0 .../pubmed/selected_index.txt | 0 ...tack_6_sub_shadow_graph_index_attack_2.txt | 0 ...tack_6_sub_shadow_graph_index_attack_3.txt | 0 .../protential_1200_shadow_graph_index.txt | 0 .../protential_1300_shadow_graph_index.txt | 0 .../protential_500_shadow_graph_index.txt | 0 .../protential_700_shadow_graph_index.txt | 0 .../protential_900_shadow_graph_index.txt | 0 .../citeseer/target_graph_index.txt | 0 ...tack_6_sub_shadow_graph_index_attack_2.txt | 0 ...tack_6_sub_shadow_graph_index_attack_3.txt | 0 .../protential_1200_shadow_graph_index.txt | 0 .../protential_1300_shadow_graph_index.txt | 0 .../protential_300_shadow_graph_index.txt | 0 .../protential_500_shadow_graph_index.txt | 0 .../protential_700_shadow_graph_index.txt | 0 .../protential_900_shadow_graph_index.txt | 0 .../cora/protential_shadow_graph_index.txt | 0 .../cora/target_graph_index.txt | 0 ...tack_6_sub_shadow_graph_index_attack_2.txt | 0 ...tack_6_sub_shadow_graph_index_attack_3.txt | 0 .../protential_1200_shadow_graph_index.txt | 0 .../protential_1300_shadow_graph_index.txt | 0 .../protential_500_shadow_graph_index.txt | 0 .../protential_700_shadow_graph_index.txt | 0 .../protential_900_shadow_graph_index.txt | 0 .../models/defense/BackdoorWM.py | 6 +- {pygip => pyhazard}/models/defense/GrOVe.py | 6 +- .../models/defense/ImperceptibleWM.py | 4 +- .../models/defense/ImperceptibleWM2.py | 4 +- .../models/defense/Integrity.py | 4 +- .../models/defense/RandomWM.py | 6 +- .../models/defense/Revisiting.py | 6 +- .../models/defense/SurviveWM.py | 6 +- .../models/defense/SurviveWM2.py | 2 +- .../models/defense/__init__.py | 0 .../models/defense/atom/ATOM.py | 8 +- .../models/defense/atom/__init__.py | 0 .../defense/atom/csv_data/attack_CiteSeer.csv | 0 .../defense/atom/csv_data/attack_Cora.csv | 0 .../defense/atom/csv_data/attack_PubMed.csv | 0 {pygip => pyhazard}/models/defense/base.py | 4 +- {pygip => pyhazard}/models/nn/__init__.py | 0 {pygip => pyhazard}/models/nn/backbones.py | 0 {pygip => pyhazard}/utils/__init__.py | 0 {pygip => pyhazard}/utils/dglTopyg.py | 0 {pygip => pyhazard}/utils/hardware.py | 2 +- {pygip => pyhazard}/utils/metrics.py | 0 pyproject.toml | 8 +- 138 files changed, 601 insertions(+), 356 deletions(-) create mode 100644 PyHazard.egg-info/PKG-INFO create mode 100644 PyHazard.egg-info/SOURCES.txt create mode 100644 PyHazard.egg-info/dependency_links.txt create mode 100644 PyHazard.egg-info/requires.txt create mode 100644 PyHazard.egg-info/top_level.txt rename docs/source/_autosummary/attack/{pygip.models.attack.AdvMEA.rst => pyhazard.models.attack.AdvMEA.rst} (71%) rename docs/source/_autosummary/attack/{pygip.models.attack.CEGA.rst => pyhazard.models.attack.CEGA.rst} (72%) rename docs/source/_autosummary/attack/{pygip.models.attack.DataFreeMEA.rst => pyhazard.models.attack.DataFreeMEA.rst} (72%) rename docs/source/_autosummary/attack/{pygip.models.attack.Realistic.rst => pyhazard.models.attack.Realistic.rst} (70%) rename docs/source/_autosummary/attack/{pygip.models.attack.base.rst => pyhazard.models.attack.base.rst} (66%) rename docs/source/_autosummary/attack/{pygip.models.attack.mea.MEA.rst => pyhazard.models.attack.mea.MEA.rst} (80%) rename docs/source/_autosummary/datasets/{pygip.datasets.CiteSeer.rst => pyhazard.datasets.CiteSeer.rst} (79%) rename docs/source/_autosummary/datasets/{pygip.datasets.CoauthorCS.rst => pyhazard.datasets.CoauthorCS.rst} (80%) rename docs/source/_autosummary/datasets/{pygip.datasets.CoauthorPhysics.rst => pyhazard.datasets.CoauthorPhysics.rst} (80%) rename docs/source/_autosummary/datasets/{pygip.datasets.Computers.rst => pyhazard.datasets.Computers.rst} (80%) rename docs/source/_autosummary/datasets/{pygip.datasets.Cora.rst => pyhazard.datasets.Cora.rst} (79%) rename docs/source/_autosummary/datasets/{pygip.datasets.Photo.rst => pyhazard.datasets.Photo.rst} (79%) rename docs/source/_autosummary/datasets/{pygip.datasets.PubMed.rst => pyhazard.datasets.PubMed.rst} (79%) rename docs/source/_autosummary/defense/{pygip.models.defense.BackdoorWM.rst => pyhazard.models.defense.BackdoorWM.rst} (69%) rename docs/source/_autosummary/defense/{pygip.models.defense.ImperceptibleWM.rst => pyhazard.models.defense.ImperceptibleWM.rst} (67%) rename docs/source/_autosummary/defense/{pygip.models.defense.Integrity.rst => pyhazard.models.defense.Integrity.rst} (76%) rename docs/source/_autosummary/defense/{pygip.models.defense.RandomWM.rst => pyhazard.models.defense.RandomWM.rst} (70%) rename docs/source/_autosummary/defense/{pygip.models.defense.SurviveWM.rst => pyhazard.models.defense.SurviveWM.rst} (69%) rename docs/source/_autosummary/defense/{pygip.models.defense.atom.ATOM.rst => pyhazard.models.defense.atom.ATOM.rst} (90%) rename docs/source/_autosummary/defense/{pygip.models.defense.base.rst => pyhazard.models.defense.base.rst} (66%) rename docs/source/_autosummary/utils/{pygip.utils.dglTopyg.rst => pyhazard.utils.dglTopyg.rst} (69%) rename docs/source/_autosummary/utils/{pygip.utils.hardware.rst => pyhazard.utils.hardware.rst} (71%) rename docs/source/_autosummary/utils/{pygip.utils.metrics.rst => pyhazard.utils.metrics.rst} (78%) rename 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pyhazard}/models/attack/mea/data/attack3_shadow_graph/cora/target_graph_index.txt (100%) rename {pygip => pyhazard}/models/attack/mea/data/attack3_shadow_graph/pubmed/attack_6_sub_shadow_graph_index_attack_2.txt (100%) rename {pygip => pyhazard}/models/attack/mea/data/attack3_shadow_graph/pubmed/attack_6_sub_shadow_graph_index_attack_3.txt (100%) rename {pygip => pyhazard}/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_1200_shadow_graph_index.txt (100%) rename {pygip => pyhazard}/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_1300_shadow_graph_index.txt (100%) rename {pygip => pyhazard}/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_500_shadow_graph_index.txt (100%) rename {pygip => pyhazard}/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_700_shadow_graph_index.txt (100%) rename {pygip => pyhazard}/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_900_shadow_graph_index.txt (100%) rename {pygip => pyhazard}/models/defense/BackdoorWM.py (97%) rename {pygip => pyhazard}/models/defense/GrOVe.py (98%) rename {pygip => pyhazard}/models/defense/ImperceptibleWM.py (98%) rename {pygip => pyhazard}/models/defense/ImperceptibleWM2.py (99%) rename {pygip => pyhazard}/models/defense/Integrity.py (99%) rename {pygip => pyhazard}/models/defense/RandomWM.py (99%) rename {pygip => pyhazard}/models/defense/Revisiting.py (98%) rename {pygip => pyhazard}/models/defense/SurviveWM.py (98%) rename {pygip => pyhazard}/models/defense/SurviveWM2.py (99%) rename {pygip => pyhazard}/models/defense/__init__.py (100%) rename {pygip => pyhazard}/models/defense/atom/ATOM.py (99%) rename {pygip => pyhazard}/models/defense/atom/__init__.py (100%) rename {pygip => pyhazard}/models/defense/atom/csv_data/attack_CiteSeer.csv (100%) rename {pygip => pyhazard}/models/defense/atom/csv_data/attack_Cora.csv (100%) rename {pygip => pyhazard}/models/defense/atom/csv_data/attack_PubMed.csv (100%) rename {pygip => pyhazard}/models/defense/base.py (95%) rename {pygip => pyhazard}/models/nn/__init__.py (100%) rename {pygip => pyhazard}/models/nn/backbones.py (100%) rename {pygip => pyhazard}/utils/__init__.py (100%) rename {pygip => pyhazard}/utils/dglTopyg.py (100%) rename {pygip => pyhazard}/utils/hardware.py (68%) rename {pygip => pyhazard}/utils/metrics.py (100%) diff --git a/MANIFEST.in b/MANIFEST.in index e640dd9..83747e3 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,7 +1,7 @@ include LICENSE include README.md -graft pygip +graft pyhazard prune .github prune tests diff --git a/PyHazard.egg-info/PKG-INFO b/PyHazard.egg-info/PKG-INFO new file mode 100644 index 0000000..adc8fcf --- /dev/null +++ b/PyHazard.egg-info/PKG-INFO @@ -0,0 +1,138 @@ +Metadata-Version: 2.4 +Name: PyHazard +Version: 1.1.0 +Summary: A Python package for Graph Intellectual Property Protection +Author-email: Bolin Shen +License: BSD-2-Clause +Project-URL: Homepage, https://labrai.github.io/PyHazard +Project-URL: Repository, https://github.com/LabRAI/PyHazard +Classifier: Development Status :: 3 - Alpha +Classifier: Intended Audience :: Science/Research +Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence +Classifier: License :: OSI Approved :: BSD License +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Operating System :: OS Independent +Requires-Python: <3.13,>=3.8 +Description-Content-Type: text/markdown +License-File: LICENSE +Requires-Dist: torch-geometric>=2.0.0 +Requires-Dist: numpy>=1.19.0 +Requires-Dist: scipy>=1.6.0 +Requires-Dist: networkx>=2.5 +Requires-Dist: scikit-learn>=0.24.0 +Requires-Dist: tqdm>=4.50.0 +Requires-Dist: torchdata<0.8.0,>=0.7.0 +Requires-Dist: pyyaml>=5.3.0 +Requires-Dist: pydantic>=2.0.0 +Provides-Extra: torch +Requires-Dist: torch==2.3.0; extra == "torch" +Provides-Extra: dgl +Requires-Dist: dgl==2.2.1; extra == "dgl" +Dynamic: license-file + +# PyHazard + +[![PyPI - Version](https://img.shields.io/pypi/v/PyHazard)](https://pypi.org/project/PyHazard) +[![Build Status](https://img.shields.io/github/actions/workflow/status/LabRAI/PyHazard/docs.yml)](https://github.com/LabRAI/PyHazard/actions) +[![License](https://img.shields.io/github/license/LabRAI/PyHazard.svg)](https://github.com/LabRAI/PyHazard/blob/main/LICENSE) +[![PyPI - Downloads](https://img.shields.io/pypi/dm/PyHazard)](https://github.com/LabRAI/PyHazard) +[![Issues](https://img.shields.io/github/issues/LabRAI/PyHazard)](https://github.com/LabRAI/PyHazard) +[![Pull Requests](https://img.shields.io/github/issues-pr/LabRAI/PyHazard)](https://github.com/LabRAI/PyHazard) +[![Stars](https://img.shields.io/github/stars/LabRAI/PyHazard)](https://github.com/LabRAI/PyHazard) +[![GitHub forks](https://img.shields.io/github/forks/LabRAI/PyHazard)](https://github.com/LabRAI/PyHazard) + +PyHazard is a Python library designed for using AI tools for hazard prediction. It provides a modular framework to implement and test prediction strategies on natural hazards. + +## How to Cite + +If you find it useful, please consider citing the following work: + +``` +``` + + +## Installation + +PyHazard supports both CPU and GPU environments. Make sure you have Python installed (version >= 3.8, <3.13). + +### Base Installation + +First, install the core package: + +```bash +pip install PyHazard +``` + +This will install PyHazard with minimal dependencies. + +### CPU Version + +```bash +pip install "PyHazard[torch,dgl]" \ + --index-url https://download.pytorch.org/whl/cpu \ + --extra-index-url https://pypi.org/simple \ + -f https://data.dgl.ai/wheels/repo.html +``` + +### GPU Version (CUDA 12.1) + +```bash +pip install "PyHazard[torch,dgl]" \ + --index-url https://download.pytorch.org/whl/cu121 \ + --extra-index-url https://pypi.org/simple \ + -f https://data.dgl.ai/wheels/torch-2.3/cu121/repo.html +``` + +## Quick Start + +Here's a simple example to use PyHazard: + +```python +from pyhazard.datasets import Cora +from pyhazard.models.attack import MEA + +# Load dataset +dataset = Cora(api_type='pyg') + +# Initialize and run Model Extraction Attack +attack = MEA(dataset=dataset) +attack.attack() +``` + +And a simple example to run a defense: + +```python +from pyhazard.datasets import Cora +from pyhazard.models.defense import ATOM + +# Load dataset +dataset = Cora(api_type='pyg') + +# Initialize and run ATOM defense +defense = ATOM(dataset=dataset) +defense.defense() +``` + +If you want to use CUDA, please set environment variable: + +```shell +export PYHAZARD_DEVICE=cuda:0 +``` + +## Implementation & Contributors Guideline + +Refer to [Implementation Guideline](.github/IMPLEMENTATION.md) + +Refer to [Contributors Guideline](.github/CONTRIBUTING.md) + +## License + +[BSD 2-Clause License](LICENSE) + +## Contact + +For questions or contributions, please contact xc25@fsu.edu. diff --git a/PyHazard.egg-info/SOURCES.txt b/PyHazard.egg-info/SOURCES.txt new file mode 100644 index 0000000..a16a6a1 --- /dev/null +++ b/PyHazard.egg-info/SOURCES.txt @@ -0,0 +1,77 @@ +LICENSE +MANIFEST.in +README.md +pyproject.toml +PyHazard.egg-info/PKG-INFO +PyHazard.egg-info/SOURCES.txt +PyHazard.egg-info/dependency_links.txt +PyHazard.egg-info/requires.txt +PyHazard.egg-info/top_level.txt +pyhazard/__init__.py +pyhazard/datasets/__init__.py +pyhazard/datasets/datasets.py +pyhazard/models/__init__.py +pyhazard/models/attack/AdvMEA.py +pyhazard/models/attack/CEGA.py +pyhazard/models/attack/DataFreeMEA.py +pyhazard/models/attack/Realistic.py +pyhazard/models/attack/__init__.py +pyhazard/models/attack/base.py +pyhazard/models/attack/mea/MEA.py +pyhazard/models/attack/mea/__init__.py +pyhazard/models/attack/mea/data/attack2_generated_graph/citeseer/graph_label.txt +pyhazard/models/attack/mea/data/attack2_generated_graph/citeseer/query_labels.txt +pyhazard/models/attack/mea/data/attack2_generated_graph/citeseer/selected_index.txt +pyhazard/models/attack/mea/data/attack2_generated_graph/cora/graph_label.txt +pyhazard/models/attack/mea/data/attack2_generated_graph/cora/query_labels.txt +pyhazard/models/attack/mea/data/attack2_generated_graph/cora/selected_index.txt +pyhazard/models/attack/mea/data/attack2_generated_graph/pubmed/graph_label.txt +pyhazard/models/attack/mea/data/attack2_generated_graph/pubmed/query_labels.txt +pyhazard/models/attack/mea/data/attack2_generated_graph/pubmed/selected_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/attack_6_sub_shadow_graph_index_attack_2.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/attack_6_sub_shadow_graph_index_attack_3.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_1200_shadow_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_1300_shadow_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_500_shadow_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_700_shadow_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_900_shadow_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/target_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/attack_6_sub_shadow_graph_index_attack_2.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/attack_6_sub_shadow_graph_index_attack_3.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_1200_shadow_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_1300_shadow_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_300_shadow_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_500_shadow_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_700_shadow_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_900_shadow_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_shadow_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/target_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/attack_6_sub_shadow_graph_index_attack_2.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/attack_6_sub_shadow_graph_index_attack_3.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_1200_shadow_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_1300_shadow_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_500_shadow_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_700_shadow_graph_index.txt +pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_900_shadow_graph_index.txt +pyhazard/models/defense/BackdoorWM.py +pyhazard/models/defense/GrOVe.py +pyhazard/models/defense/ImperceptibleWM.py +pyhazard/models/defense/ImperceptibleWM2.py +pyhazard/models/defense/Integrity.py +pyhazard/models/defense/RandomWM.py +pyhazard/models/defense/Revisiting.py +pyhazard/models/defense/SurviveWM.py +pyhazard/models/defense/SurviveWM2.py +pyhazard/models/defense/__init__.py +pyhazard/models/defense/base.py +pyhazard/models/defense/atom/ATOM.py +pyhazard/models/defense/atom/__init__.py +pyhazard/models/defense/atom/csv_data/attack_CiteSeer.csv +pyhazard/models/defense/atom/csv_data/attack_Cora.csv +pyhazard/models/defense/atom/csv_data/attack_PubMed.csv +pyhazard/models/nn/__init__.py +pyhazard/models/nn/backbones.py +pyhazard/utils/__init__.py +pyhazard/utils/dglTopyg.py +pyhazard/utils/hardware.py +pyhazard/utils/metrics.py \ No newline at end of file diff --git a/PyHazard.egg-info/dependency_links.txt b/PyHazard.egg-info/dependency_links.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/PyHazard.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/PyHazard.egg-info/requires.txt b/PyHazard.egg-info/requires.txt new file mode 100644 index 0000000..1621113 --- /dev/null +++ b/PyHazard.egg-info/requires.txt @@ -0,0 +1,15 @@ +torch-geometric>=2.0.0 +numpy>=1.19.0 +scipy>=1.6.0 +networkx>=2.5 +scikit-learn>=0.24.0 +tqdm>=4.50.0 +torchdata<0.8.0,>=0.7.0 +pyyaml>=5.3.0 +pydantic>=2.0.0 + +[dgl] +dgl==2.2.1 + +[torch] +torch==2.3.0 diff --git a/PyHazard.egg-info/top_level.txt b/PyHazard.egg-info/top_level.txt new file mode 100644 index 0000000..8d56974 --- /dev/null +++ b/PyHazard.egg-info/top_level.txt @@ -0,0 +1 @@ +pyhazard diff --git a/README.md b/README.md index 832e20b..1c33c5c 100644 --- a/README.md +++ b/README.md @@ -53,25 +53,38 @@ pip install "PyHazard[torch,dgl]" \ ## Quick Start -Here’s a simple example to launch a Model Extraction Attack using PyHazard: +Here's a simple example to use PyHazard: -``` +```python +from pyhazard.datasets import Cora +from pyhazard.models.attack import MEA + +# Load dataset +dataset = Cora(api_type='pyg') + +# Initialize and run Model Extraction Attack +attack = MEA(dataset=dataset) +attack.attack() ``` -This code loads the xxx dataset, initializes a basic hazard prediction baseline, and runs the -prediction with a specified sampling ratio. +And a simple example to run a defense: -And a simple example to run: +```python +from pyhazard.datasets import Cora +from pyhazard.models.defense import ATOM -``` -``` +# Load dataset +dataset = Cora(api_type='pyg') -which runs xxx +# Initialize and run ATOM defense +defense = ATOM(dataset=dataset) +defense.defense() +``` -If you want to use cuda, please set environment variable: +If you want to use CUDA, please set environment variable: ```shell -export PyHazard_DEVICE=cuda:0 +export PYHAZARD_DEVICE=cuda:0 ``` ## Implementation & Contributors Guideline diff --git a/benchmark/run/utils_benchmark.py b/benchmark/run/utils_benchmark.py index c09a8f6..ce382ca 100644 --- a/benchmark/run/utils_benchmark.py +++ b/benchmark/run/utils_benchmark.py @@ -7,7 +7,7 @@ import time from typing import Any, Dict, Tuple -# Ensure repository root is on sys.path so imports like `pygip.*` always work +# Ensure repository root is on sys.path so imports like `pyhazard.*` always work _REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir)) if _REPO_ROOT not in sys.path: sys.path.insert(0, _REPO_ROOT) @@ -24,27 +24,27 @@ def _try_import(path: str): # ==== Registries (paths match your repo layout) ==== ATTACK_REGISTRY = { # MEA.py — six attacks (0..5) - "mea0": "pygip.models.attack.mea.MEA.ModelExtractionAttack0", - "mea1": "pygip.models.attack.mea.MEA.ModelExtractionAttack1", - "mea2": "pygip.models.attack.mea.MEA.ModelExtractionAttack2", - "mea3": "pygip.models.attack.mea.MEA.ModelExtractionAttack3", - "mea4": "pygip.models.attack.mea.MEA.ModelExtractionAttack4", - "mea5": "pygip.models.attack.mea.MEA.ModelExtractionAttack5", + "mea0": "pyhazard.models.attack.mea.MEA.ModelExtractionAttack0", + "mea1": "pyhazard.models.attack.mea.MEA.ModelExtractionAttack1", + "mea2": "pyhazard.models.attack.mea.MEA.ModelExtractionAttack2", + "mea3": "pyhazard.models.attack.mea.MEA.ModelExtractionAttack3", + "mea4": "pyhazard.models.attack.mea.MEA.ModelExtractionAttack4", + "mea5": "pyhazard.models.attack.mea.MEA.ModelExtractionAttack5", # Other attacks - "advmea": "pygip.models.attack.mea.AdvMEA.AdvMEA", - "cega": "pygip.models.attack.mea.CEGA.CEGA", - "realistic": "pygip.models.attack.mea.Realistic.RealisticAttack", - "dfea_i": "pygip.models.attack.mea.DataFreeMEA.DFEATypeI", - "dfea_ii": "pygip.models.attack.mea.DataFreeMEA.DFEATypeII", - "dfea_iii": "pygip.models.attack.mea.DataFreeMEA.DFEATypeIII", + "advmea": "pyhazard.models.attack.mea.AdvMEA.AdvMEA", + "cega": "pyhazard.models.attack.mea.CEGA.CEGA", + "realistic": "pyhazard.models.attack.mea.Realistic.RealisticAttack", + "dfea_i": "pyhazard.models.attack.mea.DataFreeMEA.DFEATypeI", + "dfea_ii": "pyhazard.models.attack.mea.DataFreeMEA.DFEATypeII", + "dfea_iii": "pyhazard.models.attack.mea.DataFreeMEA.DFEATypeIII", } DEFENSE_REGISTRY = { - "randomwm": "pygip.models.defense.atom.RandomWM.RandomWM", - "backdoorwm": "pygip.models.defense.atom.BackdoorWM.BackdoorWM", - "survivewm": "pygip.models.defense.atom.SurviveWM.SurviveWM", - "imperceptiblewm": "pygip.models.defense.atom.ImperceptibleWM.ImperceptibleWM", + "randomwm": "pyhazard.models.defense.atom.RandomWM.RandomWM", + "backdoorwm": "pyhazard.models.defense.atom.BackdoorWM.BackdoorWM", + "survivewm": "pyhazard.models.defense.atom.SurviveWM.SurviveWM", + "imperceptiblewm": "pyhazard.models.defense.atom.ImperceptibleWM.ImperceptibleWM", # Add more if needed (e.g., GROVE) using their import paths. } @@ -53,10 +53,10 @@ def load_dataset(name: str, root: str = None): """Robust loader that tries several known entry points.""" tried = [] for mod, fn in [ - ("pygip.datasets.datasets", "get_dataset"), - ("pygip.datasets", "get_dataset"), - ("pygip.data", "get_dataset"), - ("pygip.data", "build_dataset"), + ("pyhazard.datasets.datasets", "get_dataset"), + ("pyhazard.datasets", "get_dataset"), + ("pyhazard.data", "get_dataset"), + ("pyhazard.data", "build_dataset"), ]: try: m = importlib.import_module(mod) diff --git a/docs/README.md b/docs/README.md index e4cbaf1..4b13765 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,4 +1,4 @@ -# PyGIP Documentation +# PyHazard Documentation ## Build diff --git a/docs/source/_autosummary/attack/pygip.models.attack.AdvMEA.rst b/docs/source/_autosummary/attack/pyhazard.models.attack.AdvMEA.rst similarity index 71% rename from docs/source/_autosummary/attack/pygip.models.attack.AdvMEA.rst rename to docs/source/_autosummary/attack/pyhazard.models.attack.AdvMEA.rst index 8082cff..e8b1e51 100644 --- a/docs/source/_autosummary/attack/pygip.models.attack.AdvMEA.rst +++ b/docs/source/_autosummary/attack/pyhazard.models.attack.AdvMEA.rst @@ -1,7 +1,7 @@ -pygip.models.attack.AdvMEA +pyhazard.models.attack.AdvMEA ========================== -.. automodule:: pygip.models.attack.AdvMEA +.. automodule:: pyhazard.models.attack.AdvMEA diff --git a/docs/source/_autosummary/attack/pygip.models.attack.CEGA.rst b/docs/source/_autosummary/attack/pyhazard.models.attack.CEGA.rst similarity index 72% rename from docs/source/_autosummary/attack/pygip.models.attack.CEGA.rst rename to docs/source/_autosummary/attack/pyhazard.models.attack.CEGA.rst index 26bd03a..6f135d0 100644 --- a/docs/source/_autosummary/attack/pygip.models.attack.CEGA.rst +++ b/docs/source/_autosummary/attack/pyhazard.models.attack.CEGA.rst @@ -1,7 +1,7 @@ -pygip.models.attack.CEGA +pyhazard.models.attack.CEGA ======================== -.. automodule:: pygip.models.attack.CEGA +.. automodule:: pyhazard.models.attack.CEGA diff --git a/docs/source/_autosummary/attack/pygip.models.attack.DataFreeMEA.rst b/docs/source/_autosummary/attack/pyhazard.models.attack.DataFreeMEA.rst similarity index 72% rename from docs/source/_autosummary/attack/pygip.models.attack.DataFreeMEA.rst rename to docs/source/_autosummary/attack/pyhazard.models.attack.DataFreeMEA.rst index d017948..92aa6c1 100644 --- a/docs/source/_autosummary/attack/pygip.models.attack.DataFreeMEA.rst +++ b/docs/source/_autosummary/attack/pyhazard.models.attack.DataFreeMEA.rst @@ -1,7 +1,7 @@ -pygip.models.attack.DataFreeMEA +pyhazard.models.attack.DataFreeMEA =============================== -.. automodule:: pygip.models.attack.DataFreeMEA +.. automodule:: pyhazard.models.attack.DataFreeMEA diff --git a/docs/source/_autosummary/attack/pygip.models.attack.Realistic.rst b/docs/source/_autosummary/attack/pyhazard.models.attack.Realistic.rst similarity index 70% rename from docs/source/_autosummary/attack/pygip.models.attack.Realistic.rst rename to docs/source/_autosummary/attack/pyhazard.models.attack.Realistic.rst index 5ee5211..e58ce2e 100644 --- a/docs/source/_autosummary/attack/pygip.models.attack.Realistic.rst +++ b/docs/source/_autosummary/attack/pyhazard.models.attack.Realistic.rst @@ -1,7 +1,7 @@ -pygip.models.attack.Realistic +pyhazard.models.attack.Realistic ============================= -.. automodule:: pygip.models.attack.Realistic +.. automodule:: pyhazard.models.attack.Realistic diff --git a/docs/source/_autosummary/attack/pygip.models.attack.base.rst b/docs/source/_autosummary/attack/pyhazard.models.attack.base.rst similarity index 66% rename from docs/source/_autosummary/attack/pygip.models.attack.base.rst rename to docs/source/_autosummary/attack/pyhazard.models.attack.base.rst index 258b944..08c52c7 100644 --- a/docs/source/_autosummary/attack/pygip.models.attack.base.rst +++ b/docs/source/_autosummary/attack/pyhazard.models.attack.base.rst @@ -1,7 +1,7 @@ -pygip.models.attack.base +pyhazard.models.attack.base ======================== -.. automodule:: pygip.models.attack.base +.. automodule:: pyhazard.models.attack.base diff --git a/docs/source/_autosummary/attack/pygip.models.attack.mea.MEA.rst b/docs/source/_autosummary/attack/pyhazard.models.attack.mea.MEA.rst similarity index 80% rename from docs/source/_autosummary/attack/pygip.models.attack.mea.MEA.rst rename to docs/source/_autosummary/attack/pyhazard.models.attack.mea.MEA.rst index 4308857..5dd9cf5 100644 --- a/docs/source/_autosummary/attack/pygip.models.attack.mea.MEA.rst +++ b/docs/source/_autosummary/attack/pyhazard.models.attack.mea.MEA.rst @@ -1,7 +1,7 @@ -pygip.models.attack.mea.MEA +pyhazard.models.attack.mea.MEA =========================== -.. automodule:: pygip.models.attack.mea.MEA +.. automodule:: pyhazard.models.attack.mea.MEA diff --git a/docs/source/_autosummary/datasets/pygip.datasets.CiteSeer.rst b/docs/source/_autosummary/datasets/pyhazard.datasets.CiteSeer.rst similarity index 79% rename from docs/source/_autosummary/datasets/pygip.datasets.CiteSeer.rst rename to docs/source/_autosummary/datasets/pyhazard.datasets.CiteSeer.rst index 5658a2b..23aea34 100644 --- a/docs/source/_autosummary/datasets/pygip.datasets.CiteSeer.rst +++ b/docs/source/_autosummary/datasets/pyhazard.datasets.CiteSeer.rst @@ -1,7 +1,7 @@ -pygip.datasets.CiteSeer +pyhazard.datasets.CiteSeer ======================= -.. currentmodule:: pygip.datasets +.. currentmodule:: pyhazard.datasets .. autoclass:: CiteSeer diff --git a/docs/source/_autosummary/datasets/pygip.datasets.CoauthorCS.rst b/docs/source/_autosummary/datasets/pyhazard.datasets.CoauthorCS.rst similarity index 80% rename from docs/source/_autosummary/datasets/pygip.datasets.CoauthorCS.rst rename to docs/source/_autosummary/datasets/pyhazard.datasets.CoauthorCS.rst index 5c658dd..6f16136 100644 --- a/docs/source/_autosummary/datasets/pygip.datasets.CoauthorCS.rst +++ b/docs/source/_autosummary/datasets/pyhazard.datasets.CoauthorCS.rst @@ -1,7 +1,7 @@ -pygip.datasets.CoauthorCS +pyhazard.datasets.CoauthorCS ========================= -.. currentmodule:: pygip.datasets +.. currentmodule:: pyhazard.datasets .. autoclass:: CoauthorCS diff --git a/docs/source/_autosummary/datasets/pygip.datasets.CoauthorPhysics.rst b/docs/source/_autosummary/datasets/pyhazard.datasets.CoauthorPhysics.rst similarity index 80% rename from docs/source/_autosummary/datasets/pygip.datasets.CoauthorPhysics.rst rename to docs/source/_autosummary/datasets/pyhazard.datasets.CoauthorPhysics.rst index 13478e3..8a8103e 100644 --- a/docs/source/_autosummary/datasets/pygip.datasets.CoauthorPhysics.rst +++ b/docs/source/_autosummary/datasets/pyhazard.datasets.CoauthorPhysics.rst @@ -1,7 +1,7 @@ -pygip.datasets.CoauthorPhysics +pyhazard.datasets.CoauthorPhysics ============================== -.. currentmodule:: pygip.datasets +.. currentmodule:: pyhazard.datasets .. autoclass:: CoauthorPhysics diff --git a/docs/source/_autosummary/datasets/pygip.datasets.Computers.rst b/docs/source/_autosummary/datasets/pyhazard.datasets.Computers.rst similarity index 80% rename from docs/source/_autosummary/datasets/pygip.datasets.Computers.rst rename to docs/source/_autosummary/datasets/pyhazard.datasets.Computers.rst index 1b2b641..7b1e26d 100644 --- a/docs/source/_autosummary/datasets/pygip.datasets.Computers.rst +++ b/docs/source/_autosummary/datasets/pyhazard.datasets.Computers.rst @@ -1,7 +1,7 @@ -pygip.datasets.Computers +pyhazard.datasets.Computers ======================== -.. currentmodule:: pygip.datasets +.. currentmodule:: pyhazard.datasets .. autoclass:: Computers diff --git a/docs/source/_autosummary/datasets/pygip.datasets.Cora.rst b/docs/source/_autosummary/datasets/pyhazard.datasets.Cora.rst similarity index 79% rename from docs/source/_autosummary/datasets/pygip.datasets.Cora.rst rename to docs/source/_autosummary/datasets/pyhazard.datasets.Cora.rst index 6c5a598..211cd24 100644 --- a/docs/source/_autosummary/datasets/pygip.datasets.Cora.rst +++ b/docs/source/_autosummary/datasets/pyhazard.datasets.Cora.rst @@ -1,7 +1,7 @@ -pygip.datasets.Cora +pyhazard.datasets.Cora =================== -.. currentmodule:: pygip.datasets +.. currentmodule:: pyhazard.datasets .. autoclass:: Cora diff --git a/docs/source/_autosummary/datasets/pygip.datasets.Photo.rst b/docs/source/_autosummary/datasets/pyhazard.datasets.Photo.rst similarity index 79% rename from docs/source/_autosummary/datasets/pygip.datasets.Photo.rst rename to docs/source/_autosummary/datasets/pyhazard.datasets.Photo.rst index 640ee5f..18b1e68 100644 --- a/docs/source/_autosummary/datasets/pygip.datasets.Photo.rst +++ b/docs/source/_autosummary/datasets/pyhazard.datasets.Photo.rst @@ -1,7 +1,7 @@ -pygip.datasets.Photo +pyhazard.datasets.Photo ==================== -.. currentmodule:: pygip.datasets +.. currentmodule:: pyhazard.datasets .. autoclass:: Photo diff --git a/docs/source/_autosummary/datasets/pygip.datasets.PubMed.rst b/docs/source/_autosummary/datasets/pyhazard.datasets.PubMed.rst similarity index 79% rename from docs/source/_autosummary/datasets/pygip.datasets.PubMed.rst rename to docs/source/_autosummary/datasets/pyhazard.datasets.PubMed.rst index 121d1c0..df14a8e 100644 --- a/docs/source/_autosummary/datasets/pygip.datasets.PubMed.rst +++ b/docs/source/_autosummary/datasets/pyhazard.datasets.PubMed.rst @@ -1,7 +1,7 @@ -pygip.datasets.PubMed +pyhazard.datasets.PubMed ===================== -.. currentmodule:: pygip.datasets +.. currentmodule:: pyhazard.datasets .. autoclass:: PubMed diff --git a/docs/source/_autosummary/defense/pygip.models.defense.BackdoorWM.rst b/docs/source/_autosummary/defense/pyhazard.models.defense.BackdoorWM.rst similarity index 69% rename from docs/source/_autosummary/defense/pygip.models.defense.BackdoorWM.rst rename to docs/source/_autosummary/defense/pyhazard.models.defense.BackdoorWM.rst index 05c2494..ac2a3ef 100644 --- a/docs/source/_autosummary/defense/pygip.models.defense.BackdoorWM.rst +++ b/docs/source/_autosummary/defense/pyhazard.models.defense.BackdoorWM.rst @@ -1,7 +1,7 @@ -pygip.models.defense.BackdoorWM +pyhazard.models.defense.BackdoorWM =============================== -.. automodule:: pygip.models.defense.BackdoorWM +.. automodule:: pyhazard.models.defense.BackdoorWM diff --git a/docs/source/_autosummary/defense/pygip.models.defense.ImperceptibleWM.rst b/docs/source/_autosummary/defense/pyhazard.models.defense.ImperceptibleWM.rst similarity index 67% rename from docs/source/_autosummary/defense/pygip.models.defense.ImperceptibleWM.rst rename to docs/source/_autosummary/defense/pyhazard.models.defense.ImperceptibleWM.rst index 1533f92..3efb3f8 100644 --- a/docs/source/_autosummary/defense/pygip.models.defense.ImperceptibleWM.rst +++ b/docs/source/_autosummary/defense/pyhazard.models.defense.ImperceptibleWM.rst @@ -1,7 +1,7 @@ -pygip.models.defense.ImperceptibleWM +pyhazard.models.defense.ImperceptibleWM ==================================== -.. automodule:: pygip.models.defense.ImperceptibleWM +.. automodule:: pyhazard.models.defense.ImperceptibleWM diff --git a/docs/source/_autosummary/defense/pygip.models.defense.Integrity.rst b/docs/source/_autosummary/defense/pyhazard.models.defense.Integrity.rst similarity index 76% rename from docs/source/_autosummary/defense/pygip.models.defense.Integrity.rst rename to docs/source/_autosummary/defense/pyhazard.models.defense.Integrity.rst index 59f0769..dccae78 100644 --- a/docs/source/_autosummary/defense/pygip.models.defense.Integrity.rst +++ b/docs/source/_autosummary/defense/pyhazard.models.defense.Integrity.rst @@ -1,7 +1,7 @@ -pygip.models.defense.Integrity +pyhazard.models.defense.Integrity ============================== -.. automodule:: pygip.models.defense.Integrity +.. automodule:: pyhazard.models.defense.Integrity diff --git a/docs/source/_autosummary/defense/pygip.models.defense.RandomWM.rst b/docs/source/_autosummary/defense/pyhazard.models.defense.RandomWM.rst similarity index 70% rename from docs/source/_autosummary/defense/pygip.models.defense.RandomWM.rst rename to docs/source/_autosummary/defense/pyhazard.models.defense.RandomWM.rst index 5941954..8bb727b 100644 --- a/docs/source/_autosummary/defense/pygip.models.defense.RandomWM.rst +++ b/docs/source/_autosummary/defense/pyhazard.models.defense.RandomWM.rst @@ -1,7 +1,7 @@ -pygip.models.defense.RandomWM +pyhazard.models.defense.RandomWM ============================= -.. automodule:: pygip.models.defense.RandomWM +.. automodule:: pyhazard.models.defense.RandomWM diff --git a/docs/source/_autosummary/defense/pygip.models.defense.SurviveWM.rst b/docs/source/_autosummary/defense/pyhazard.models.defense.SurviveWM.rst similarity index 69% rename from docs/source/_autosummary/defense/pygip.models.defense.SurviveWM.rst rename to docs/source/_autosummary/defense/pyhazard.models.defense.SurviveWM.rst index 9389e04..4fc383d 100644 --- a/docs/source/_autosummary/defense/pygip.models.defense.SurviveWM.rst +++ b/docs/source/_autosummary/defense/pyhazard.models.defense.SurviveWM.rst @@ -1,7 +1,7 @@ -pygip.models.defense.SurviveWM +pyhazard.models.defense.SurviveWM ============================== -.. automodule:: pygip.models.defense.SurviveWM +.. automodule:: pyhazard.models.defense.SurviveWM diff --git a/docs/source/_autosummary/defense/pygip.models.defense.atom.ATOM.rst b/docs/source/_autosummary/defense/pyhazard.models.defense.atom.ATOM.rst similarity index 90% rename from docs/source/_autosummary/defense/pygip.models.defense.atom.ATOM.rst rename to docs/source/_autosummary/defense/pyhazard.models.defense.atom.ATOM.rst index 2320fc3..ebafb1a 100644 --- a/docs/source/_autosummary/defense/pygip.models.defense.atom.ATOM.rst +++ b/docs/source/_autosummary/defense/pyhazard.models.defense.atom.ATOM.rst @@ -1,7 +1,7 @@ -pygip.models.defense.atom.ATOM +pyhazard.models.defense.atom.ATOM ============================== -.. automodule:: pygip.models.defense.atom.ATOM +.. automodule:: pyhazard.models.defense.atom.ATOM diff --git a/docs/source/_autosummary/defense/pygip.models.defense.base.rst b/docs/source/_autosummary/defense/pyhazard.models.defense.base.rst similarity index 66% rename from docs/source/_autosummary/defense/pygip.models.defense.base.rst rename to docs/source/_autosummary/defense/pyhazard.models.defense.base.rst index af9cba8..7a4aaf0 100644 --- a/docs/source/_autosummary/defense/pygip.models.defense.base.rst +++ b/docs/source/_autosummary/defense/pyhazard.models.defense.base.rst @@ -1,7 +1,7 @@ -pygip.models.defense.base +pyhazard.models.defense.base ========================= -.. automodule:: pygip.models.defense.base +.. automodule:: pyhazard.models.defense.base diff --git a/docs/source/_autosummary/utils/pygip.utils.dglTopyg.rst b/docs/source/_autosummary/utils/pyhazard.utils.dglTopyg.rst similarity index 69% rename from docs/source/_autosummary/utils/pygip.utils.dglTopyg.rst rename to docs/source/_autosummary/utils/pyhazard.utils.dglTopyg.rst index 28f6af9..9041337 100644 --- a/docs/source/_autosummary/utils/pygip.utils.dglTopyg.rst +++ b/docs/source/_autosummary/utils/pyhazard.utils.dglTopyg.rst @@ -1,7 +1,7 @@ -pygip.utils.dglTopyg +pyhazard.utils.dglTopyg ==================== -.. automodule:: pygip.utils.dglTopyg +.. automodule:: pyhazard.utils.dglTopyg diff --git a/docs/source/_autosummary/utils/pygip.utils.hardware.rst b/docs/source/_autosummary/utils/pyhazard.utils.hardware.rst similarity index 71% rename from docs/source/_autosummary/utils/pygip.utils.hardware.rst rename to docs/source/_autosummary/utils/pyhazard.utils.hardware.rst index 8080eec..282446e 100644 --- a/docs/source/_autosummary/utils/pygip.utils.hardware.rst +++ b/docs/source/_autosummary/utils/pyhazard.utils.hardware.rst @@ -1,7 +1,7 @@ -pygip.utils.hardware +pyhazard.utils.hardware ==================== -.. automodule:: pygip.utils.hardware +.. automodule:: pyhazard.utils.hardware diff --git a/docs/source/_autosummary/utils/pygip.utils.metrics.rst b/docs/source/_autosummary/utils/pyhazard.utils.metrics.rst similarity index 78% rename from docs/source/_autosummary/utils/pygip.utils.metrics.rst rename to docs/source/_autosummary/utils/pyhazard.utils.metrics.rst index d4f9227..6a49300 100644 --- a/docs/source/_autosummary/utils/pygip.utils.metrics.rst +++ b/docs/source/_autosummary/utils/pyhazard.utils.metrics.rst @@ -1,7 +1,7 @@ -pygip.utils.metrics +pyhazard.utils.metrics =================== -.. automodule:: pygip.utils.metrics +.. automodule:: pyhazard.utils.metrics diff --git a/docs/source/_autosummary/utils/pygip.utils.rst b/docs/source/_autosummary/utils/pyhazard.utils.rst similarity index 71% rename from docs/source/_autosummary/utils/pygip.utils.rst rename to docs/source/_autosummary/utils/pyhazard.utils.rst index d3c26c2..0fef7de 100644 --- a/docs/source/_autosummary/utils/pygip.utils.rst +++ b/docs/source/_autosummary/utils/pyhazard.utils.rst @@ -1,7 +1,7 @@ -pygip.utils +pyhazard.utils =========== -.. automodule:: pygip.utils +.. automodule:: pyhazard.utils .. rubric:: Modules diff --git a/docs/source/api/modules.rst b/docs/source/api/modules.rst index fb42aa1..d3b173f 100644 --- a/docs/source/api/modules.rst +++ b/docs/source/api/modules.rst @@ -1,9 +1,9 @@ :orphan: -pygip +pyhazard ===== .. toctree:: :maxdepth: 4 - pygip + pyhazard diff --git a/docs/source/api/pygip.datasets.rst b/docs/source/api/pyhazard.datasets.rst similarity index 61% rename from docs/source/api/pygip.datasets.rst rename to docs/source/api/pyhazard.datasets.rst index c1b0aa5..442711e 100644 --- a/docs/source/api/pygip.datasets.rst +++ b/docs/source/api/pyhazard.datasets.rst @@ -1,13 +1,13 @@ -pygip.datasets package +pyhazard.datasets package ====================== Submodules ---------- -pygip.datasets.datasets module +pyhazard.datasets.datasets module ------------------------------ -.. automodule:: pygip.datasets.datasets +.. automodule:: pyhazard.datasets.datasets :members: :undoc-members: :show-inheritance: @@ -15,7 +15,7 @@ pygip.datasets.datasets module Module contents --------------- -.. automodule:: pygip.datasets +.. automodule:: pyhazard.datasets :members: :undoc-members: :show-inheritance: diff --git a/docs/source/api/pygip.models.attack.mea.rst b/docs/source/api/pyhazard.models.attack.mea.rst similarity index 58% rename from docs/source/api/pygip.models.attack.mea.rst rename to docs/source/api/pyhazard.models.attack.mea.rst index f637f85..2632c5d 100644 --- a/docs/source/api/pygip.models.attack.mea.rst +++ b/docs/source/api/pyhazard.models.attack.mea.rst @@ -1,13 +1,13 @@ -pygip.models.attack.mea package +pyhazard.models.attack.mea package =============================== Submodules ---------- -pygip.models.attack.mea.MEA module +pyhazard.models.attack.mea.MEA module ---------------------------------- -.. automodule:: pygip.models.attack.mea.MEA +.. automodule:: pyhazard.models.attack.mea.MEA :members: :undoc-members: :show-inheritance: @@ -15,7 +15,7 @@ pygip.models.attack.mea.MEA module Module contents --------------- -.. automodule:: pygip.models.attack.mea +.. automodule:: pyhazard.models.attack.mea :members: :undoc-members: :show-inheritance: diff --git a/docs/source/api/pygip.models.attack.rst b/docs/source/api/pyhazard.models.attack.rst similarity index 56% rename from docs/source/api/pygip.models.attack.rst rename to docs/source/api/pyhazard.models.attack.rst index 18d2337..f8b7655 100644 --- a/docs/source/api/pygip.models.attack.rst +++ b/docs/source/api/pyhazard.models.attack.rst @@ -1,4 +1,4 @@ -pygip.models.attack package +pyhazard.models.attack package =========================== Subpackages @@ -7,39 +7,39 @@ Subpackages .. toctree:: :maxdepth: 4 - pygip.models.attack.mea + pyhazard.models.attack.mea Submodules ---------- -pygip.models.attack.AdvMEA module +pyhazard.models.attack.AdvMEA module --------------------------------- -.. automodule:: pygip.models.attack.AdvMEA +.. automodule:: pyhazard.models.attack.AdvMEA :members: :undoc-members: :show-inheritance: -pygip.models.attack.CEGA module +pyhazard.models.attack.CEGA module ------------------------------- -.. automodule:: pygip.models.attack.CEGA +.. automodule:: pyhazard.models.attack.CEGA :members: :undoc-members: :show-inheritance: -pygip.models.attack.DataFreeMEA module +pyhazard.models.attack.DataFreeMEA module -------------------------------------- -.. automodule:: pygip.models.attack.DataFreeMEA +.. automodule:: pyhazard.models.attack.DataFreeMEA :members: :undoc-members: :show-inheritance: -pygip.models.attack.base module +pyhazard.models.attack.base module ------------------------------- -.. automodule:: pygip.models.attack.base +.. automodule:: pyhazard.models.attack.base :members: :undoc-members: :show-inheritance: @@ -47,7 +47,7 @@ pygip.models.attack.base module Module contents --------------- -.. automodule:: pygip.models.attack +.. automodule:: pyhazard.models.attack :members: :undoc-members: :show-inheritance: diff --git a/docs/source/api/pygip.models.defense.atom.rst b/docs/source/api/pyhazard.models.defense.atom.rst similarity index 58% rename from docs/source/api/pygip.models.defense.atom.rst rename to docs/source/api/pyhazard.models.defense.atom.rst index 3dd52a5..9321c4f 100644 --- a/docs/source/api/pygip.models.defense.atom.rst +++ b/docs/source/api/pyhazard.models.defense.atom.rst @@ -1,13 +1,13 @@ -pygip.models.defense.atom package +pyhazard.models.defense.atom package ================================= Submodules ---------- -pygip.models.defense.atom.ATOM module +pyhazard.models.defense.atom.ATOM module ------------------------------------- -.. automodule:: pygip.models.defense.atom.ATOM +.. automodule:: pyhazard.models.defense.atom.ATOM :members: :undoc-members: :show-inheritance: @@ -15,7 +15,7 @@ pygip.models.defense.atom.ATOM module Module contents --------------- -.. automodule:: pygip.models.defense.atom +.. automodule:: pyhazard.models.defense.atom :members: :undoc-members: :show-inheritance: diff --git a/docs/source/api/pygip.models.defense.rst b/docs/source/api/pyhazard.models.defense.rst similarity index 53% rename from docs/source/api/pygip.models.defense.rst rename to docs/source/api/pyhazard.models.defense.rst index 553c706..784aedd 100644 --- a/docs/source/api/pygip.models.defense.rst +++ b/docs/source/api/pyhazard.models.defense.rst @@ -1,4 +1,4 @@ -pygip.models.defense package +pyhazard.models.defense package ============================ Subpackages @@ -7,63 +7,63 @@ Subpackages .. toctree:: :maxdepth: 4 - pygip.models.defense.atom + pyhazard.models.defense.atom Submodules ---------- -pygip.models.defense.BackdoorWM module +pyhazard.models.defense.BackdoorWM module -------------------------------------- -.. automodule:: pygip.models.defense.BackdoorWM +.. automodule:: pyhazard.models.defense.BackdoorWM :members: :undoc-members: :show-inheritance: -pygip.models.defense.ImperceptibleWM module +pyhazard.models.defense.ImperceptibleWM module ------------------------------------------- -.. automodule:: pygip.models.defense.ImperceptibleWM +.. automodule:: pyhazard.models.defense.ImperceptibleWM :members: :undoc-members: :show-inheritance: -pygip.models.defense.ImperceptibleWM2 module +pyhazard.models.defense.ImperceptibleWM2 module -------------------------------------------- -.. automodule:: pygip.models.defense.ImperceptibleWM2 +.. automodule:: pyhazard.models.defense.ImperceptibleWM2 :members: :undoc-members: :show-inheritance: -pygip.models.defense.RandomWM module +pyhazard.models.defense.RandomWM module ------------------------------------ -.. automodule:: pygip.models.defense.RandomWM +.. automodule:: pyhazard.models.defense.RandomWM :members: :undoc-members: :show-inheritance: -pygip.models.defense.SurviveWM module +pyhazard.models.defense.SurviveWM module ------------------------------------- -.. automodule:: pygip.models.defense.SurviveWM +.. automodule:: pyhazard.models.defense.SurviveWM :members: :undoc-members: :show-inheritance: -pygip.models.defense.SurviveWM2 module +pyhazard.models.defense.SurviveWM2 module -------------------------------------- -.. automodule:: pygip.models.defense.SurviveWM2 +.. automodule:: pyhazard.models.defense.SurviveWM2 :members: :undoc-members: :show-inheritance: -pygip.models.defense.base module +pyhazard.models.defense.base module -------------------------------- -.. automodule:: pygip.models.defense.base +.. automodule:: pyhazard.models.defense.base :members: :undoc-members: :show-inheritance: @@ -71,7 +71,7 @@ pygip.models.defense.base module Module contents --------------- -.. automodule:: pygip.models.defense +.. automodule:: pyhazard.models.defense :members: :undoc-members: :show-inheritance: diff --git a/docs/source/api/pygip.models.nn.rst b/docs/source/api/pyhazard.models.nn.rst similarity index 61% rename from docs/source/api/pygip.models.nn.rst rename to docs/source/api/pyhazard.models.nn.rst index 00f9a52..ddfd79c 100644 --- a/docs/source/api/pygip.models.nn.rst +++ b/docs/source/api/pyhazard.models.nn.rst @@ -1,13 +1,13 @@ -pygip.models.nn package +pyhazard.models.nn package ======================= Submodules ---------- -pygip.models.nn.backbones module +pyhazard.models.nn.backbones module -------------------------------- -.. automodule:: pygip.models.nn.backbones +.. automodule:: pyhazard.models.nn.backbones :members: :undoc-members: :show-inheritance: @@ -15,7 +15,7 @@ pygip.models.nn.backbones module Module contents --------------- -.. automodule:: pygip.models.nn +.. automodule:: pyhazard.models.nn :members: :undoc-members: :show-inheritance: diff --git a/docs/source/api/pygip.models.rst b/docs/source/api/pyhazard.models.rst similarity index 55% rename from docs/source/api/pygip.models.rst rename to docs/source/api/pyhazard.models.rst index 06c5855..f1707b7 100644 --- a/docs/source/api/pygip.models.rst +++ b/docs/source/api/pyhazard.models.rst @@ -1,4 +1,4 @@ -pygip.models package +pyhazard.models package ==================== Subpackages @@ -7,14 +7,14 @@ Subpackages .. toctree:: :maxdepth: 4 - pygip.models.attack - pygip.models.defense - pygip.models.nn + pyhazard.models.attack + pyhazard.models.defense + pyhazard.models.nn Module contents --------------- -.. automodule:: pygip.models +.. automodule:: pyhazard.models :members: :undoc-members: :show-inheritance: diff --git a/docs/source/api/pygip.rst b/docs/source/api/pyhazard.rst similarity index 61% rename from docs/source/api/pygip.rst rename to docs/source/api/pyhazard.rst index 2e6e35a..ffc695e 100644 --- a/docs/source/api/pygip.rst +++ b/docs/source/api/pyhazard.rst @@ -1,4 +1,4 @@ -pygip package +pyhazard package ============= Subpackages @@ -7,14 +7,14 @@ Subpackages .. toctree:: :maxdepth: 4 - pygip.datasets - pygip.models - pygip.utils + pyhazard.datasets + pyhazard.models + pyhazard.utils Module contents --------------- -.. automodule:: pygip +.. automodule:: pyhazard :members: :undoc-members: :show-inheritance: diff --git a/docs/source/api/pygip.utils.rst b/docs/source/api/pyhazard.utils.rst similarity index 59% rename from docs/source/api/pygip.utils.rst rename to docs/source/api/pyhazard.utils.rst index 896ea09..5cd401e 100644 --- a/docs/source/api/pygip.utils.rst +++ b/docs/source/api/pyhazard.utils.rst @@ -1,29 +1,29 @@ -pygip.utils package +pyhazard.utils package =================== Submodules ---------- -pygip.utils.dglTopyg module +pyhazard.utils.dglTopyg module --------------------------- -.. automodule:: pygip.utils.dglTopyg +.. automodule:: pyhazard.utils.dglTopyg :members: :undoc-members: :show-inheritance: -pygip.utils.hardware module +pyhazard.utils.hardware module --------------------------- -.. automodule:: pygip.utils.hardware +.. automodule:: pyhazard.utils.hardware :members: :undoc-members: :show-inheritance: -pygip.utils.metrics module +pyhazard.utils.metrics module -------------------------- -.. automodule:: pygip.utils.metrics +.. automodule:: pyhazard.utils.metrics :members: :undoc-members: :show-inheritance: @@ -31,7 +31,7 @@ pygip.utils.metrics module Module contents --------------- -.. automodule:: pygip.utils +.. automodule:: pyhazard.utils :members: :undoc-members: :show-inheritance: diff --git a/docs/source/conf.py b/docs/source/conf.py index 1cda1e6..9919337 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -4,7 +4,7 @@ sys.path.insert(0, os.path.abspath('../../')) -project = 'PyGIP' +project = 'PyHazard' copyright = '2025, RAILab' author = 'RAILab' release = '1.0.0' @@ -46,7 +46,7 @@ html_theme = 'furo' html_static_path = ['_static'] -html_baseurl = "https://labrai.github.io/PyGIP/" +html_baseurl = "https://labrai.github.io/PyHazard/" html_theme_options = { "navigation_with_keys": True, diff --git a/docs/source/implementation.rst b/docs/source/implementation.rst index b991309..4b0e1e1 100644 --- a/docs/source/implementation.rst +++ b/docs/source/implementation.rst @@ -1,14 +1,14 @@ Implementation ============== -PyGIP is built to be modular and extensible, allowing contributors to implement their own attack and defense strategies. +PyHazard is built to be modular and extensible, allowing contributors to implement their own attack and defense strategies. Below, we detail how to extend the framework by implementing custom attack and defense classes, with a focus on how to leverage the provided dataset structure. Dataset ------- -The ``Dataset`` class standardizes the data format across PyGIP. Here’s its structure: +The ``Dataset`` class standardizes the data format across PyHazard. Here’s its structure: .. code-block:: python @@ -234,5 +234,5 @@ Miscellaneous Tips - **Example Scripts**: Please provide an example script in the ``examples/`` folder demonstrating how to run your code. This will significantly speed up our code review process. -By following these guidelines, you can seamlessly integrate your custom attack or defense strategies into PyGIP. Happy +By following these guidelines, you can seamlessly integrate your custom attack or defense strategies into PyHazard. Happy coding! \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.rst index 13b8bf7..9e860ab 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,50 +1,50 @@ .. raw:: html
- PyGIP Icon + PyHazard Icon
-.. image:: https://img.shields.io/pypi/v/PyGIP - :target: https://pypi.org/project/PyGIP +.. image:: https://img.shields.io/pypi/v/PyHazard + :target: https://pypi.org/project/PyHazard :alt: PyPI Version -.. image:: https://img.shields.io/github/actions/workflow/status/LabRAI/PyGIP/docs.yml - :target: https://github.com/LabRAI/PyGIP/actions +.. image:: https://img.shields.io/github/actions/workflow/status/LabRAI/PyHazard/docs.yml + :target: https://github.com/LabRAI/PyHazard/actions :alt: Build Status -.. image:: https://img.shields.io/github/license/LabRAI/PyGIP.svg - :target: https://github.com/LabRAI/PyGIP/blob/main/LICENSE +.. image:: https://img.shields.io/github/license/LabRAI/PyHazard.svg + :target: https://github.com/LabRAI/PyHazard/blob/main/LICENSE :alt: License -.. image:: https://img.shields.io/pypi/dm/pygip - :target: https://github.com/LabRAI/PyGIP +.. image:: https://img.shields.io/pypi/dm/pyhazard + :target: https://github.com/LabRAI/PyHazard :alt: PyPI Downloads -.. image:: https://img.shields.io/github/issues/LabRAI/PyGIP - :target: https://github.com/LabRAI/PyGIP +.. image:: https://img.shields.io/github/issues/LabRAI/PyHazard + :target: https://github.com/LabRAI/PyHazard :alt: Issues -.. image:: https://img.shields.io/github/issues-pr/LabRAI/PyGIP - :target: https://github.com/LabRAI/PyGIP +.. image:: https://img.shields.io/github/issues-pr/LabRAI/PyHazard + :target: https://github.com/LabRAI/PyHazard :alt: Pull Requests -.. image:: https://img.shields.io/github/stars/LabRAI/PyGIP - :target: https://github.com/LabRAI/PyGIP +.. image:: https://img.shields.io/github/stars/LabRAI/PyHazard + :target: https://github.com/LabRAI/PyHazard :alt: Stars -.. image:: https://img.shields.io/github/forks/LabRAI/PyGIP - :target: https://github.com/LabRAI/PyGIP +.. image:: https://img.shields.io/github/forks/LabRAI/PyHazard + :target: https://github.com/LabRAI/PyHazard :alt: GitHub forks .. image:: _static/github.svg - :target: https://github.com/LabRAI/PyGIP + :target: https://github.com/LabRAI/PyHazard :alt: GitHub ---- -**PyGIP** is a comprehensive Python library focused on model extraction attacks and defenses in Graph Neural Networks (GNNs). Built on PyTorch, PyTorch Geometric, and DGL, the library offers a robust framework for understanding, implementing, and defending against attacks targeting GNN models. +**PyHazard** is a comprehensive Python library focused on model extraction attacks and defenses in Graph Neural Networks (GNNs). Built on PyTorch, PyTorch Geometric, and DGL, the library offers a robust framework for understanding, implementing, and defending against attacks targeting GNN models. -**PyGIP is featured for:** +**PyHazard is featured for:** - **Extensive Attack Implementations**: Multiple strategies for GNN model extraction attacks, including fidelity and accuracy evaluation. - **Defensive Techniques**: Tools for creating robust defense mechanisms, such as watermarking graphs and inserting synthetic nodes. @@ -79,19 +79,19 @@ Attack Modules * - Class Name - Reference - * - :doc:`MEA <_autosummary/attack/pygip.models.attack.mea.MEA>` + * - :doc:`MEA <_autosummary/attack/pyhazard.models.attack.mea.MEA>` - Wu, Bang, et al. "Model extraction attacks on graph neural networks: Taxonomy and realisation." Proceedings of the 2022 ACM on Asia conference on computer and communications security. 2022. - * - :doc:`AdvMEA <_autosummary/attack/pygip.models.attack.AdvMEA>` + * - :doc:`AdvMEA <_autosummary/attack/pyhazard.models.attack.AdvMEA>` - DeFazio, David, and Arti Ramesh. "Adversarial model extraction on graph neural networks." arXiv preprint arXiv:1912.07721 (2019). - * - :doc:`CEGA <_autosummary/attack/pygip.models.attack.CEGA>` + * - :doc:`CEGA <_autosummary/attack/pyhazard.models.attack.CEGA>` - Wang, Zebin, et al. "CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition." arXiv preprint arXiv:2506.17709 (2025). - * - :doc:`DataFreeMEA <_autosummary/attack/pygip.models.attack.DataFreeMEA>` + * - :doc:`DataFreeMEA <_autosummary/attack/pyhazard.models.attack.DataFreeMEA>` - Zhuang, Yuanxin, et al. "Unveiling the Secrets without Data: Can Graph Neural Networks Be Exploited through {Data-Free} Model Extraction Attacks?." 33rd USENIX Security Symposium (USENIX Security 24). 2024. - * - :doc:`Realistic <_autosummary/attack/pygip.models.attack.Realistic>` + * - :doc:`Realistic <_autosummary/attack/pyhazard.models.attack.Realistic>` - Guan, Faqian, et al. "A realistic model extraction attack against graph neural networks." Knowledge-Based Systems 300 (2024): 112144. @@ -104,22 +104,22 @@ Defense Modules * - Class Name - Reference - * - :doc:`RandomWM <_autosummary/defense/pygip.models.defense.RandomWM>` + * - :doc:`RandomWM <_autosummary/defense/pyhazard.models.defense.RandomWM>` - Zhao, Xiangyu, Hanzhou Wu, and Xinpeng Zhang. "Watermarking graph neural networks by random graphs." 2021 9th International Symposium on Digital Forensics and Security (ISDFS). IEEE, 2021. - * - :doc:`BackdoorWM <_autosummary/defense/pygip.models.defense.BackdoorWM>` + * - :doc:`BackdoorWM <_autosummary/defense/pyhazard.models.defense.BackdoorWM>` - Xu, Jing, et al. "Watermarking graph neural networks based on backdoor attacks." 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P). IEEE, 2023. - * - :doc:`SurviveWM <_autosummary/defense/pygip.models.defense.SurviveWM>` + * - :doc:`SurviveWM <_autosummary/defense/pyhazard.models.defense.SurviveWM>` - Wang, Haiming, et al. "Making Watermark Survive Model Extraction Attacks in Graph Neural Networks." ICC 2023-IEEE International Conference on Communications. IEEE, 2023. - * - :doc:`ImperceptibleWM <_autosummary/defense/pygip.models.defense.ImperceptibleWM>` + * - :doc:`ImperceptibleWM <_autosummary/defense/pyhazard.models.defense.ImperceptibleWM>` - Zhang, Linji, et al. "An imperceptible and owner-unique watermarking method for graph neural networks." Proceedings of the ACM Turing Award Celebration Conference-China 2024. 2024. - * - :doc:`ATOM <_autosummary/defense/pygip.models.defense.atom.ATOM>` + * - :doc:`ATOM <_autosummary/defense/pyhazard.models.defense.atom.ATOM>` - Cheng, Zhan, et al. "Atom: A framework of detecting query-based model extraction attacks for graph neural networks." Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2. 2025. - * - :doc:`Integrity <_autosummary/defense/pygip.models.defense.Integrity>` + * - :doc:`Integrity <_autosummary/defense/pyhazard.models.defense.Integrity>` - Wu, Bang, et al. "Securing graph neural networks in mlaas: A comprehensive realization of query-based integrity verification." 2024 IEEE Symposium on Security and Privacy (SP). IEEE, 2024. @@ -152,10 +152,10 @@ If you find it useful, please considering cite the following work: :caption: API Reference :hidden: - pygip_datasets - pygip_models_attack - pygip_models_defense - pygip_utils + pyhazard_datasets + pyhazard_models_attack + pyhazard_models_defense + pyhazard_utils .. toctree:: :maxdepth: 2 diff --git a/docs/source/installation.rst b/docs/source/installation.rst index 8cb76f0..81b81b9 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -1,12 +1,12 @@ Installation ============ -PyGIP requires Python 3.8+ and can be installed using pip. We recommend using a conda environment for installation. +PyHazard requires Python 3.8+ and can be installed using pip. We recommend using a conda environment for installation. -Installing PyGIP +Installing PyHazard ---------------- -To get started with PyGIP, set up your environment by installing the required dependencies: +To get started with PyHazard, set up your environment by installing the required dependencies: .. code-block:: bash diff --git a/docs/source/pygip_models_attack.rst b/docs/source/pygip_models_attack.rst deleted file mode 100644 index f63e52a..0000000 --- a/docs/source/pygip_models_attack.rst +++ /dev/null @@ -1,22 +0,0 @@ -Attack -=================== - -Summary -------- - -In this section, we present all currently supported **Model Extraction Attack** modules in PyGIP. - -Submodules ----------- - -.. autosummary:: - :toctree: _autosummary/attack - :template: autosummary/module.rst - :recursive: - - pygip.models.attack.base - pygip.models.attack.mea.MEA - pygip.models.attack.AdvMEA - pygip.models.attack.CEGA - pygip.models.attack.DataFreeMEA - pygip.models.attack.Realistic \ No newline at end of file diff --git a/docs/source/pygip_models_defense.rst b/docs/source/pygip_models_defense.rst deleted file mode 100644 index 453e836..0000000 --- a/docs/source/pygip_models_defense.rst +++ /dev/null @@ -1,23 +0,0 @@ -Defense -=================== - -Summary -------- - -In this section, we present all currently supported **Model Extraction Defense** modules in PyGIP. - -Submodules ----------- - -.. autosummary:: - :toctree: _autosummary/defense - :template: autosummary/module.rst - :recursive: - - pygip.models.defense.base - pygip.models.defense.RandomWM - pygip.models.defense.BackdoorWM - pygip.models.defense.SurviveWM - pygip.models.defense.ImperceptibleWM - pygip.models.defense.atom.ATOM - pygip.models.defense.Integrity diff --git a/docs/source/pygip_utils.rst b/docs/source/pygip_utils.rst deleted file mode 100644 index 5aa60b5..0000000 --- a/docs/source/pygip_utils.rst +++ /dev/null @@ -1,18 +0,0 @@ -Utils -=================== - -Summary -------- - -In this section, we present the utility modules of PyGIP. - -Submodules ----------- - -.. autosummary:: - :toctree: _autosummary/utils - :recursive: - - pygip.utils.hardware - pygip.utils.dglTopyg - pygip.utils.metrics diff --git a/docs/source/pygip_datasets.rst b/docs/source/pyhazard_datasets.rst similarity index 60% rename from docs/source/pygip_datasets.rst rename to docs/source/pyhazard_datasets.rst index 0585d02..af0ea49 100644 --- a/docs/source/pygip_datasets.rst +++ b/docs/source/pyhazard_datasets.rst @@ -4,7 +4,7 @@ Datasets Summary ------- -In this section, we present the datasets currently supported by PyGIP. +In this section, we present the datasets currently supported by PyHazard. For each dataset, we provide both DGL and PyG APIs to ensure flexibility and compatibility with different graph learning frameworks. Submodules @@ -14,11 +14,11 @@ Submodules :toctree: _autosummary/datasets :recursive: - pygip.datasets.Cora - pygip.datasets.CiteSeer - pygip.datasets.PubMed - pygip.datasets.Computers - pygip.datasets.Photo - pygip.datasets.CoauthorCS - pygip.datasets.CoauthorPhysics + pyhazard.datasets.Cora + pyhazard.datasets.CiteSeer + pyhazard.datasets.PubMed + pyhazard.datasets.Computers + pyhazard.datasets.Photo + pyhazard.datasets.CoauthorCS + pyhazard.datasets.CoauthorPhysics diff --git a/docs/source/pyhazard_models_attack.rst b/docs/source/pyhazard_models_attack.rst new file mode 100644 index 0000000..b2a1e7e --- /dev/null +++ b/docs/source/pyhazard_models_attack.rst @@ -0,0 +1,22 @@ +Attack +=================== + +Summary +------- + +In this section, we present all currently supported **Model Extraction Attack** modules in PyHazard. + +Submodules +---------- + +.. autosummary:: + :toctree: _autosummary/attack + :template: autosummary/module.rst + :recursive: + + pyhazard.models.attack.base + pyhazard.models.attack.mea.MEA + pyhazard.models.attack.AdvMEA + pyhazard.models.attack.CEGA + pyhazard.models.attack.DataFreeMEA + pyhazard.models.attack.Realistic \ No newline at end of file diff --git a/docs/source/pyhazard_models_defense.rst b/docs/source/pyhazard_models_defense.rst new file mode 100644 index 0000000..ba2af42 --- /dev/null +++ b/docs/source/pyhazard_models_defense.rst @@ -0,0 +1,23 @@ +Defense +=================== + +Summary +------- + +In this section, we present all currently supported **Model Extraction Defense** modules in PyHazard. + +Submodules +---------- + +.. autosummary:: + :toctree: _autosummary/defense + :template: autosummary/module.rst + :recursive: + + pyhazard.models.defense.base + pyhazard.models.defense.RandomWM + pyhazard.models.defense.BackdoorWM + pyhazard.models.defense.SurviveWM + pyhazard.models.defense.ImperceptibleWM + pyhazard.models.defense.atom.ATOM + pyhazard.models.defense.Integrity diff --git a/docs/source/pyhazard_utils.rst b/docs/source/pyhazard_utils.rst new file mode 100644 index 0000000..8bdc19a --- /dev/null +++ b/docs/source/pyhazard_utils.rst @@ -0,0 +1,18 @@ +Utils +=================== + +Summary +------- + +In this section, we present the utility modules of PyHazard. + +Submodules +---------- + +.. autosummary:: + :toctree: _autosummary/utils + :recursive: + + pyhazard.utils.hardware + pyhazard.utils.dglTopyg + pyhazard.utils.metrics diff --git a/docs/source/quick_start.rst b/docs/source/quick_start.rst index 7b1927e..b9a943a 100644 --- a/docs/source/quick_start.rst +++ b/docs/source/quick_start.rst @@ -1,6 +1,6 @@ Quick Start ================= -This guide will help you get started with PyGIP quickly. +This guide will help you get started with PyHazard quickly. Attack Examples --------------- @@ -67,7 +67,7 @@ Alternatively, you can explicitly specify the device in your code: .. code-block:: python - from pygip.utils.hardware import set_device + from pyhazard.utils.hardware import set_device set_device("cuda:0") @@ -77,6 +77,6 @@ Next Steps For more detailed documentation, please refer to: -- :doc:`pygip_datasets` - Available datasets -- :doc:`pygip_models_attack` - Detailed attack mechanisms -- :doc:`pygip_models_defense` - Detailed defense mechanisms +- :doc:`pyhazard_datasets` - Available datasets +- :doc:`pyhazard_models_attack` - Detailed attack mechanisms +- :doc:`pyhazard_models_defense` - Detailed defense mechanisms diff --git a/docs/source/team.rst b/docs/source/team.rst index 2e006fc..9b094f9 100644 --- a/docs/source/team.rst +++ b/docs/source/team.rst @@ -1,7 +1,7 @@ Core Team ========= -Our team is composed of dedicated researchers and developers who contribute to PyGIP's development and maintenance. +Our team is composed of dedicated researchers and developers who contribute to PyHazard's development and maintenance. Founder ------- @@ -20,20 +20,20 @@ Principal Contributors & Maintainers Core Contributors ----------------- -* `Unique Karki `_ - [Affiliation] +* `Unique Karki `_ - [Affiliation] * `Yujing Ju `__ - Heriot-Watt University * `Zhan Cheng `__ - University of Wisconsin-Madison * `Zaiyi Zheng `__ - University of Virginia -* `Tyler Blalock `_ - [Affiliation] -* `Sibtain Syed `_ - [Affiliation] -* `Md Ibrahim `_ - Uttara University, Bangladesh -* `Aditya Khanal `_ - Tribhuvan University, Nepal -* `Kedar Satish Awale `_ - Florida State University -* `Hong Iris `_ - Washington University in St. Louis -* `Aasman Bashyal `_ - [Affiliation] -* `Cameron Bender `_ - Florida State University -* `Yushi Huang `_ - [Affiliation] -* `Anurag Shukla `_ - [Affiliation] +* `Tyler Blalock `_ - [Affiliation] +* `Sibtain Syed `_ - [Affiliation] +* `Md Ibrahim `_ - Uttara University, Bangladesh +* `Aditya Khanal `_ - Tribhuvan University, Nepal +* `Kedar Satish Awale `_ - Florida State University +* `Hong Iris `_ - Washington University in St. Louis +* `Aasman Bashyal `_ - [Affiliation] +* `Cameron Bender `_ - Florida State University +* `Yushi Huang `_ - [Affiliation] +* `Anurag Shukla `_ - [Affiliation] ---------------- diff --git a/examples/attack/AdvMEA.py b/examples/attack/AdvMEA.py index 1a87538..9baf219 100644 --- a/examples/attack/AdvMEA.py +++ b/examples/attack/AdvMEA.py @@ -1,6 +1,6 @@ -from pygip.datasets import * -from pygip.models.attack import AdvMEA -from pygip.utils.hardware import set_device +from pyhazard.datasets import * +from pyhazard.models.attack import AdvMEA +from pyhazard.utils.hardware import set_device # TODO verify performance # TODO attack after defense diff --git a/examples/attack/CEGA.py b/examples/attack/CEGA.py index 67bf0c5..21ec85b 100644 --- a/examples/attack/CEGA.py +++ b/examples/attack/CEGA.py @@ -1,5 +1,5 @@ -from pygip.datasets import * -from pygip.models.attack import CEGA +from pyhazard.datasets import * +from pyhazard.models.attack import CEGA # TODO verify performance diff --git a/examples/attack/DataFreeMEA.py b/examples/attack/DataFreeMEA.py index 823ce74..7fa443f 100644 --- a/examples/attack/DataFreeMEA.py +++ b/examples/attack/DataFreeMEA.py @@ -1,6 +1,6 @@ -from pygip.datasets import Cora -from pygip.models.attack import DFEATypeI, DFEATypeII -from pygip.utils.hardware import set_device +from pyhazard.datasets import Cora +from pyhazard.models.attack import DFEATypeI, DFEATypeII +from pyhazard.utils.hardware import set_device # TODO verify performance # TODO record metrics (original acc, attack acc, fidelity) diff --git a/examples/attack/MEA.py b/examples/attack/MEA.py index 609cfe9..a205079 100644 --- a/examples/attack/MEA.py +++ b/examples/attack/MEA.py @@ -1,7 +1,7 @@ -from pygip.datasets import * -from pygip.models.attack import ModelExtractionAttack0, ModelExtractionAttack1, ModelExtractionAttack2, \ +from pyhazard.datasets import * +from pyhazard.models.attack import ModelExtractionAttack0, ModelExtractionAttack1, ModelExtractionAttack2, \ ModelExtractionAttack3, ModelExtractionAttack4, ModelExtractionAttack5 -from pygip.utils.hardware import set_device +from pyhazard.utils.hardware import set_device # TODO verify performance # TODO generate shadow graph diff --git a/examples/attack/Realistic.py b/examples/attack/Realistic.py index 9e53790..4d272e5 100644 --- a/examples/attack/Realistic.py +++ b/examples/attack/Realistic.py @@ -1,6 +1,6 @@ -from pygip.datasets import Cora -from pygip.models.attack import RealisticAttack -from pygip.utils.hardware import set_device +from pyhazard.datasets import Cora +from pyhazard.models.attack import RealisticAttack +from pyhazard.utils.hardware import set_device # TODO verify performance # TODO record metrics (original acc, attack acc, fidelity) diff --git a/examples/defense/ATOM.py b/examples/defense/ATOM.py index 451b1d3..4004ccd 100644 --- a/examples/defense/ATOM.py +++ b/examples/defense/ATOM.py @@ -1,5 +1,5 @@ -from pygip.datasets import Cora -from pygip.models.defense import ATOM +from pyhazard.datasets import Cora +from pyhazard.models.defense import ATOM # TODO test datasets diff --git a/examples/defense/BackdoorWM.py b/examples/defense/BackdoorWM.py index 11a22f4..acbb4cc 100644 --- a/examples/defense/BackdoorWM.py +++ b/examples/defense/BackdoorWM.py @@ -1,5 +1,5 @@ -from pygip.datasets import * -from pygip.models.defense import BackdoorWM +from pyhazard.datasets import * +from pyhazard.models.defense import BackdoorWM # TODO test datasets diff --git a/examples/defense/GrOVe.py b/examples/defense/GrOVe.py index 6673c3d..7af59b7 100644 --- a/examples/defense/GrOVe.py +++ b/examples/defense/GrOVe.py @@ -1,5 +1,5 @@ -from pygip.datasets import * -from pygip.models.defense import GroveDefense +from pyhazard.datasets import * +from pyhazard.models.defense import GroveDefense # TODO test datasets diff --git a/examples/defense/ImperceptibleWM.py b/examples/defense/ImperceptibleWM.py index 4f7eedb..f765fd8 100644 --- a/examples/defense/ImperceptibleWM.py +++ b/examples/defense/ImperceptibleWM.py @@ -1,5 +1,5 @@ -from pygip.datasets import Cora -from pygip.models.defense import ImperceptibleWM +from pyhazard.datasets import Cora +from pyhazard.models.defense import ImperceptibleWM # TODO test datasets diff --git a/examples/defense/Integrity.py b/examples/defense/Integrity.py index 71bb6de..a3cc00b 100644 --- a/examples/defense/Integrity.py +++ b/examples/defense/Integrity.py @@ -1,5 +1,5 @@ -from pygip.datasets import Cora -from pygip.models.defense import IntegrityVerification +from pyhazard.datasets import Cora +from pyhazard.models.defense import IntegrityVerification # TODO test datasets diff --git a/examples/defense/RandomWM.py b/examples/defense/RandomWM.py index 4fbeb8e..16e8f7c 100644 --- a/examples/defense/RandomWM.py +++ b/examples/defense/RandomWM.py @@ -1,5 +1,5 @@ -from pygip.datasets import Cora -from pygip.models.defense import RandomWM +from pyhazard.datasets import Cora +from pyhazard.models.defense import RandomWM # TODO test datasets diff --git a/examples/defense/Revisiting.py b/examples/defense/Revisiting.py index 7783bc6..ce0c973 100644 --- a/examples/defense/Revisiting.py +++ b/examples/defense/Revisiting.py @@ -1,5 +1,5 @@ -from pygip.datasets import Cora -from pygip.models.defense import Revisiting +from pyhazard.datasets import Cora +from pyhazard.models.defense import Revisiting def main(): diff --git a/examples/defense/SurviveWM.py b/examples/defense/SurviveWM.py index 717d046..391780d 100644 --- a/examples/defense/SurviveWM.py +++ b/examples/defense/SurviveWM.py @@ -1,5 +1,5 @@ -from pygip.datasets import Cora -from pygip.models.defense import SurviveWM +from pyhazard.datasets import Cora +from pyhazard.models.defense import SurviveWM # TODO test datasets diff --git a/pygip/__init__.py b/pyhazard/__init__.py similarity index 77% rename from pygip/__init__.py rename to pyhazard/__init__.py index 9aed9e1..9047c08 100644 --- a/pygip/__init__.py +++ b/pyhazard/__init__.py @@ -1,6 +1,6 @@ from importlib.metadata import version, PackageNotFoundError try: - __version__ = version("PyGIP") + __version__ = version("PyHazard") except PackageNotFoundError: __version__ = "0.0.0" # fallback \ No newline at end of file diff --git a/pygip/datasets/__init__.py b/pyhazard/datasets/__init__.py similarity index 100% rename from pygip/datasets/__init__.py rename to pyhazard/datasets/__init__.py diff --git a/pygip/datasets/datasets.py b/pyhazard/datasets/datasets.py similarity index 100% rename from pygip/datasets/datasets.py rename to pyhazard/datasets/datasets.py diff --git a/pygip/models/__init__.py b/pyhazard/models/__init__.py similarity index 100% rename from pygip/models/__init__.py rename to pyhazard/models/__init__.py diff --git a/pygip/models/attack/AdvMEA.py b/pyhazard/models/attack/AdvMEA.py similarity index 98% rename from pygip/models/attack/AdvMEA.py rename to pyhazard/models/attack/AdvMEA.py index 3083b83..c22cdc4 100644 --- a/pygip/models/attack/AdvMEA.py +++ b/pyhazard/models/attack/AdvMEA.py @@ -7,9 +7,9 @@ from torch_geometric.utils import k_hop_subgraph, dense_to_sparse from tqdm import tqdm -from pygip.models.attack.base import BaseAttack -from pygip.models.nn import GCN -from pygip.utils.metrics import AttackMetric, AttackCompMetric +from pyhazard.models.attack.base import BaseAttack +from pyhazard.models.nn import GCN +from pyhazard.utils.metrics import AttackMetric, AttackCompMetric class AdvMEA(BaseAttack): diff --git a/pygip/models/attack/CEGA.py b/pyhazard/models/attack/CEGA.py similarity index 99% rename from pygip/models/attack/CEGA.py rename to pyhazard/models/attack/CEGA.py index d56c524..bd524ef 100644 --- a/pygip/models/attack/CEGA.py +++ b/pyhazard/models/attack/CEGA.py @@ -1345,9 +1345,9 @@ def attack0(dataset_name, seed, cuda, attack_node_arg=0.25, file_path='', LR=1e- metrics_df.to_csv(log_file_path, mode='w', header=False, index=False) -from pygip.models.attack.base import BaseAttack -from pygip.datasets import Dataset -from pygip.utils.metrics import AttackMetric, AttackCompMetric +from pyhazard.models.attack.base import BaseAttack +from pyhazard.datasets import Dataset +from pyhazard.utils.metrics import AttackMetric, AttackCompMetric class CEGA(BaseAttack): supported_api_types = {"dgl"} diff --git a/pygip/models/attack/DataFreeMEA.py b/pyhazard/models/attack/DataFreeMEA.py similarity index 98% rename from pygip/models/attack/DataFreeMEA.py rename to pyhazard/models/attack/DataFreeMEA.py index 7669d2e..e29d281 100644 --- a/pygip/models/attack/DataFreeMEA.py +++ b/pyhazard/models/attack/DataFreeMEA.py @@ -7,9 +7,9 @@ import torch.nn.functional as F from tqdm import tqdm -from pygip.models.attack.base import BaseAttack -from pygip.models.nn import GCN, GraphSAGE # Backbone architectures -from pygip.utils.metrics import AttackMetric, AttackCompMetric # Consistent with AdvMEA +from pyhazard.models.attack.base import BaseAttack +from pyhazard.models.nn import GCN, GraphSAGE # Backbone architectures +from pyhazard.utils.metrics import AttackMetric, AttackCompMetric # Consistent with AdvMEA class GraphGenerator: diff --git a/pygip/models/attack/Realistic.py b/pyhazard/models/attack/Realistic.py similarity index 99% rename from pygip/models/attack/Realistic.py rename to pyhazard/models/attack/Realistic.py index e80cb6d..d68f5e0 100644 --- a/pygip/models/attack/Realistic.py +++ b/pyhazard/models/attack/Realistic.py @@ -9,9 +9,9 @@ import torch.optim as optim from sklearn.metrics.pairwise import cosine_similarity -from pygip.models.nn.backbones import GCN +from pyhazard.models.nn.backbones import GCN from .base import BaseAttack -from pygip.utils.metrics import AttackMetric, AttackCompMetric # align with AdvMEA +from pyhazard.utils.metrics import AttackMetric, AttackCompMetric # align with AdvMEA warnings.filterwarnings('ignore') diff --git a/pygip/models/attack/__init__.py b/pyhazard/models/attack/__init__.py similarity index 100% rename from pygip/models/attack/__init__.py rename to pyhazard/models/attack/__init__.py diff --git a/pygip/models/attack/base.py b/pyhazard/models/attack/base.py similarity index 97% rename from pygip/models/attack/base.py rename to pyhazard/models/attack/base.py index 33186e4..cdc8e00 100644 --- a/pygip/models/attack/base.py +++ b/pyhazard/models/attack/base.py @@ -3,8 +3,8 @@ import torch -from pygip.datasets import Dataset -from pygip.utils.hardware import get_device +from pyhazard.datasets import Dataset +from pyhazard.utils.hardware import get_device class BaseAttack(ABC): diff --git a/pygip/models/attack/mea/MEA.py b/pyhazard/models/attack/mea/MEA.py similarity index 99% rename from pygip/models/attack/mea/MEA.py rename to pyhazard/models/attack/mea/MEA.py index c2009e7..2c4780d 100644 --- a/pygip/models/attack/mea/MEA.py +++ b/pyhazard/models/attack/mea/MEA.py @@ -8,9 +8,9 @@ import torch.nn.functional as F from torch import nn -from pygip.models.attack.base import BaseAttack -from pygip.models.nn.backbones import GCN -from pygip.utils.metrics import AttackMetric, AttackCompMetric +from pyhazard.models.attack.base import BaseAttack +from pyhazard.models.nn.backbones import GCN +from pyhazard.utils.metrics import AttackMetric, AttackCompMetric def _as_tensor(x, device): diff --git a/pygip/models/attack/mea/__init__.py b/pyhazard/models/attack/mea/__init__.py similarity index 100% rename from pygip/models/attack/mea/__init__.py rename to pyhazard/models/attack/mea/__init__.py diff --git a/pygip/models/attack/mea/data/attack2_generated_graph/citeseer/graph_label.txt b/pyhazard/models/attack/mea/data/attack2_generated_graph/citeseer/graph_label.txt similarity index 100% rename from pygip/models/attack/mea/data/attack2_generated_graph/citeseer/graph_label.txt rename to pyhazard/models/attack/mea/data/attack2_generated_graph/citeseer/graph_label.txt diff --git a/pygip/models/attack/mea/data/attack2_generated_graph/citeseer/query_labels.txt b/pyhazard/models/attack/mea/data/attack2_generated_graph/citeseer/query_labels.txt similarity index 100% rename from pygip/models/attack/mea/data/attack2_generated_graph/citeseer/query_labels.txt rename to pyhazard/models/attack/mea/data/attack2_generated_graph/citeseer/query_labels.txt diff --git a/pygip/models/attack/mea/data/attack2_generated_graph/citeseer/selected_index.txt b/pyhazard/models/attack/mea/data/attack2_generated_graph/citeseer/selected_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack2_generated_graph/citeseer/selected_index.txt rename to pyhazard/models/attack/mea/data/attack2_generated_graph/citeseer/selected_index.txt diff --git a/pygip/models/attack/mea/data/attack2_generated_graph/cora/graph_label.txt b/pyhazard/models/attack/mea/data/attack2_generated_graph/cora/graph_label.txt similarity index 100% rename from pygip/models/attack/mea/data/attack2_generated_graph/cora/graph_label.txt rename to pyhazard/models/attack/mea/data/attack2_generated_graph/cora/graph_label.txt diff --git a/pygip/models/attack/mea/data/attack2_generated_graph/cora/query_labels.txt b/pyhazard/models/attack/mea/data/attack2_generated_graph/cora/query_labels.txt similarity index 100% rename from pygip/models/attack/mea/data/attack2_generated_graph/cora/query_labels.txt rename to pyhazard/models/attack/mea/data/attack2_generated_graph/cora/query_labels.txt diff --git a/pygip/models/attack/mea/data/attack2_generated_graph/cora/selected_index.txt b/pyhazard/models/attack/mea/data/attack2_generated_graph/cora/selected_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack2_generated_graph/cora/selected_index.txt rename to pyhazard/models/attack/mea/data/attack2_generated_graph/cora/selected_index.txt diff --git a/pygip/models/attack/mea/data/attack2_generated_graph/pubmed/graph_label.txt b/pyhazard/models/attack/mea/data/attack2_generated_graph/pubmed/graph_label.txt similarity index 100% rename from pygip/models/attack/mea/data/attack2_generated_graph/pubmed/graph_label.txt rename to pyhazard/models/attack/mea/data/attack2_generated_graph/pubmed/graph_label.txt diff --git a/pygip/models/attack/mea/data/attack2_generated_graph/pubmed/query_labels.txt b/pyhazard/models/attack/mea/data/attack2_generated_graph/pubmed/query_labels.txt similarity index 100% rename from pygip/models/attack/mea/data/attack2_generated_graph/pubmed/query_labels.txt rename to pyhazard/models/attack/mea/data/attack2_generated_graph/pubmed/query_labels.txt diff --git a/pygip/models/attack/mea/data/attack2_generated_graph/pubmed/selected_index.txt b/pyhazard/models/attack/mea/data/attack2_generated_graph/pubmed/selected_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack2_generated_graph/pubmed/selected_index.txt rename to pyhazard/models/attack/mea/data/attack2_generated_graph/pubmed/selected_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/citeseer/attack_6_sub_shadow_graph_index_attack_2.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/attack_6_sub_shadow_graph_index_attack_2.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/citeseer/attack_6_sub_shadow_graph_index_attack_2.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/attack_6_sub_shadow_graph_index_attack_2.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/citeseer/attack_6_sub_shadow_graph_index_attack_3.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/attack_6_sub_shadow_graph_index_attack_3.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/citeseer/attack_6_sub_shadow_graph_index_attack_3.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/attack_6_sub_shadow_graph_index_attack_3.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_1200_shadow_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_1200_shadow_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_1200_shadow_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_1200_shadow_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_1300_shadow_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_1300_shadow_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_1300_shadow_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_1300_shadow_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_500_shadow_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_500_shadow_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_500_shadow_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_500_shadow_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_700_shadow_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_700_shadow_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_700_shadow_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_700_shadow_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_900_shadow_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_900_shadow_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_900_shadow_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_900_shadow_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/citeseer/target_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/target_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/citeseer/target_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/target_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/cora/attack_6_sub_shadow_graph_index_attack_2.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/attack_6_sub_shadow_graph_index_attack_2.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/cora/attack_6_sub_shadow_graph_index_attack_2.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/attack_6_sub_shadow_graph_index_attack_2.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/cora/attack_6_sub_shadow_graph_index_attack_3.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/attack_6_sub_shadow_graph_index_attack_3.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/cora/attack_6_sub_shadow_graph_index_attack_3.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/attack_6_sub_shadow_graph_index_attack_3.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/cora/protential_1200_shadow_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_1200_shadow_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/cora/protential_1200_shadow_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_1200_shadow_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/cora/protential_1300_shadow_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_1300_shadow_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/cora/protential_1300_shadow_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_1300_shadow_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/cora/protential_300_shadow_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_300_shadow_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/cora/protential_300_shadow_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_300_shadow_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/cora/protential_500_shadow_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_500_shadow_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/cora/protential_500_shadow_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_500_shadow_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/cora/protential_700_shadow_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_700_shadow_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/cora/protential_700_shadow_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_700_shadow_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/cora/protential_900_shadow_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_900_shadow_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/cora/protential_900_shadow_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_900_shadow_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/cora/protential_shadow_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_shadow_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/cora/protential_shadow_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_shadow_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/cora/target_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/target_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/cora/target_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/target_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/pubmed/attack_6_sub_shadow_graph_index_attack_2.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/attack_6_sub_shadow_graph_index_attack_2.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/pubmed/attack_6_sub_shadow_graph_index_attack_2.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/attack_6_sub_shadow_graph_index_attack_2.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/pubmed/attack_6_sub_shadow_graph_index_attack_3.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/attack_6_sub_shadow_graph_index_attack_3.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/pubmed/attack_6_sub_shadow_graph_index_attack_3.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/attack_6_sub_shadow_graph_index_attack_3.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_1200_shadow_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_1200_shadow_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_1200_shadow_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_1200_shadow_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_1300_shadow_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_1300_shadow_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_1300_shadow_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_1300_shadow_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_500_shadow_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_500_shadow_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_500_shadow_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_500_shadow_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_700_shadow_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_700_shadow_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_700_shadow_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_700_shadow_graph_index.txt diff --git a/pygip/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_900_shadow_graph_index.txt b/pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_900_shadow_graph_index.txt similarity index 100% rename from pygip/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_900_shadow_graph_index.txt rename to pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_900_shadow_graph_index.txt diff --git a/pygip/models/defense/BackdoorWM.py b/pyhazard/models/defense/BackdoorWM.py similarity index 97% rename from pygip/models/defense/BackdoorWM.py rename to pyhazard/models/defense/BackdoorWM.py index a57d465..1c42cd7 100644 --- a/pygip/models/defense/BackdoorWM.py +++ b/pyhazard/models/defense/BackdoorWM.py @@ -4,9 +4,9 @@ import torch import torch.nn.functional as F -from pygip.models.defense.base import BaseDefense -from pygip.models.nn import GCN -from pygip.utils.metrics import DefenseMetric, DefenseCompMetric +from pyhazard.models.defense.base import BaseDefense +from pyhazard.models.nn import GCN +from pyhazard.utils.metrics import DefenseMetric, DefenseCompMetric class BackdoorWM(BaseDefense): diff --git a/pygip/models/defense/GrOVe.py b/pyhazard/models/defense/GrOVe.py similarity index 98% rename from pygip/models/defense/GrOVe.py rename to pyhazard/models/defense/GrOVe.py index bcab9df..3663211 100644 --- a/pygip/models/defense/GrOVe.py +++ b/pyhazard/models/defense/GrOVe.py @@ -7,9 +7,9 @@ import numpy as np from sklearn.neural_network import MLPClassifier -from pygip.models.defense.base import BaseDefense -from pygip.models.nn import GCN -from pygip.utils.metrics import DefenseMetric, DefenseCompMetric +from pyhazard.models.defense.base import BaseDefense +from pyhazard.models.nn import GCN +from pyhazard.utils.metrics import DefenseMetric, DefenseCompMetric class GroveDefense(BaseDefense): diff --git a/pygip/models/defense/ImperceptibleWM.py b/pyhazard/models/defense/ImperceptibleWM.py similarity index 98% rename from pygip/models/defense/ImperceptibleWM.py rename to pyhazard/models/defense/ImperceptibleWM.py index 215e599..1c93a0b 100644 --- a/pygip/models/defense/ImperceptibleWM.py +++ b/pyhazard/models/defense/ImperceptibleWM.py @@ -10,8 +10,8 @@ from sklearn.metrics import precision_score, recall_score, f1_score from .base import BaseDefense -from pygip.models.nn.backbones import GCN_PyG -from pygip.utils.metrics import DefenseCompMetric, DefenseMetric +from pyhazard.models.nn.backbones import GCN_PyG +from pyhazard.utils.metrics import DefenseCompMetric, DefenseMetric class ImperceptibleWM(BaseDefense): diff --git a/pygip/models/defense/ImperceptibleWM2.py b/pyhazard/models/defense/ImperceptibleWM2.py similarity index 99% rename from pygip/models/defense/ImperceptibleWM2.py rename to pyhazard/models/defense/ImperceptibleWM2.py index e95dd20..e79889a 100644 --- a/pygip/models/defense/ImperceptibleWM2.py +++ b/pyhazard/models/defense/ImperceptibleWM2.py @@ -13,8 +13,8 @@ from dgl.nn import GraphConv from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score -from pygip.models.defense.base import BaseDefense -from pygip.models.nn import GraphSAGE +from pyhazard.models.defense.base import BaseDefense +from pyhazard.models.nn import GraphSAGE device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') diff --git a/pygip/models/defense/Integrity.py b/pyhazard/models/defense/Integrity.py similarity index 99% rename from pygip/models/defense/Integrity.py rename to pyhazard/models/defense/Integrity.py index c97a217..ebb3846 100644 --- a/pygip/models/defense/Integrity.py +++ b/pyhazard/models/defense/Integrity.py @@ -12,8 +12,8 @@ from torch_geometric.utils import to_networkx, from_networkx from .base import BaseDefense -from pygip.models.nn import GCN -from pygip.utils.metrics import DefenseCompMetric, DefenseMetric +from pyhazard.models.nn import GCN +from pyhazard.utils.metrics import DefenseCompMetric, DefenseMetric class QueryBasedVerificationDefense(BaseDefense): diff --git a/pygip/models/defense/RandomWM.py b/pyhazard/models/defense/RandomWM.py similarity index 99% rename from pygip/models/defense/RandomWM.py rename to pyhazard/models/defense/RandomWM.py index 16c2f04..23d916c 100644 --- a/pygip/models/defense/RandomWM.py +++ b/pyhazard/models/defense/RandomWM.py @@ -10,9 +10,9 @@ from torch_geometric.utils import erdos_renyi_graph from dgl.dataloading import NeighborSampler, NodeCollator -from pygip.models.nn import GraphSAGE -from pygip.models.defense.base import BaseDefense -from pygip.utils.metrics import DefenseCompMetric, DefenseMetric +from pyhazard.models.nn import GraphSAGE +from pyhazard.models.defense.base import BaseDefense +from pyhazard.utils.metrics import DefenseCompMetric, DefenseMetric class RandomWM(BaseDefense): diff --git a/pygip/models/defense/Revisiting.py b/pyhazard/models/defense/Revisiting.py similarity index 98% rename from pygip/models/defense/Revisiting.py rename to pyhazard/models/defense/Revisiting.py index a6d2466..fec2276 100644 --- a/pygip/models/defense/Revisiting.py +++ b/pyhazard/models/defense/Revisiting.py @@ -7,9 +7,9 @@ from torch.utils.data import DataLoader from tqdm import tqdm -from pygip.models.defense.base import BaseDefense -from pygip.models.nn import GraphSAGE -from pygip.utils.metrics import DefenseCompMetric +from pyhazard.models.defense.base import BaseDefense +from pyhazard.models.nn import GraphSAGE +from pyhazard.utils.metrics import DefenseCompMetric class Revisiting(BaseDefense): diff --git a/pygip/models/defense/SurviveWM.py b/pyhazard/models/defense/SurviveWM.py similarity index 98% rename from pygip/models/defense/SurviveWM.py rename to pyhazard/models/defense/SurviveWM.py index dc489fa..0b2ef44 100644 --- a/pygip/models/defense/SurviveWM.py +++ b/pyhazard/models/defense/SurviveWM.py @@ -8,9 +8,9 @@ from torch_geometric.data import Data from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, roc_auc_score -from pygip.models.defense.base import BaseDefense -from pygip.models.nn import GCN -from pygip.utils.metrics import GraphNeuralNetworkMetric, DefenseCompMetric, DefenseMetric +from pyhazard.models.defense.base import BaseDefense +from pyhazard.models.nn import GCN +from pyhazard.utils.metrics import GraphNeuralNetworkMetric, DefenseCompMetric, DefenseMetric class SurviveWM(BaseDefense): diff --git a/pygip/models/defense/SurviveWM2.py b/pyhazard/models/defense/SurviveWM2.py similarity index 99% rename from pygip/models/defense/SurviveWM2.py rename to pyhazard/models/defense/SurviveWM2.py index ad09c6a..2ea5e11 100644 --- a/pygip/models/defense/SurviveWM2.py +++ b/pyhazard/models/defense/SurviveWM2.py @@ -11,7 +11,7 @@ from torch_geometric.nn import SAGEConv, global_mean_pool, BatchNorm from torch_geometric.utils import to_networkx -from pygip.models.defense.base import BaseDefense +from pyhazard.models.defense.base import BaseDefense device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') diff --git a/pygip/models/defense/__init__.py b/pyhazard/models/defense/__init__.py similarity index 100% rename from pygip/models/defense/__init__.py rename to pyhazard/models/defense/__init__.py diff --git a/pygip/models/defense/atom/ATOM.py b/pyhazard/models/defense/atom/ATOM.py similarity index 99% rename from pygip/models/defense/atom/ATOM.py rename to pyhazard/models/defense/atom/ATOM.py index e96d92f..01a105b 100644 --- a/pygip/models/defense/atom/ATOM.py +++ b/pyhazard/models/defense/atom/ATOM.py @@ -29,9 +29,9 @@ from torch_geometric.utils import to_networkx from tqdm import tqdm -from pygip.datasets.datasets import Dataset as PyGIPDataset -from pygip.models.defense.base import BaseDefense -from pygip.utils.metrics import DefenseMetric, DefenseCompMetric +from pyhazard.datasets.datasets import Dataset as PyHazardDataset +from pyhazard.models.defense.base import BaseDefense +from pyhazard.utils.metrics import DefenseMetric, DefenseCompMetric def set_seed(seed: int): @@ -650,7 +650,7 @@ class ATOM(BaseDefense): "CS", "Physics" } - def __init__(self, dataset: PyGIPDataset, attack_node_fraction: float = 0): + def __init__(self, dataset: PyHazardDataset, attack_node_fraction: float = 0): super().__init__(dataset, attack_node_fraction) self.dataset = dataset # Load dataset related information, following BackdoorWM pattern diff --git a/pygip/models/defense/atom/__init__.py b/pyhazard/models/defense/atom/__init__.py similarity index 100% rename from pygip/models/defense/atom/__init__.py rename to pyhazard/models/defense/atom/__init__.py diff --git a/pygip/models/defense/atom/csv_data/attack_CiteSeer.csv b/pyhazard/models/defense/atom/csv_data/attack_CiteSeer.csv similarity index 100% rename from pygip/models/defense/atom/csv_data/attack_CiteSeer.csv rename to pyhazard/models/defense/atom/csv_data/attack_CiteSeer.csv diff --git a/pygip/models/defense/atom/csv_data/attack_Cora.csv b/pyhazard/models/defense/atom/csv_data/attack_Cora.csv similarity index 100% rename from pygip/models/defense/atom/csv_data/attack_Cora.csv rename to pyhazard/models/defense/atom/csv_data/attack_Cora.csv diff --git a/pygip/models/defense/atom/csv_data/attack_PubMed.csv b/pyhazard/models/defense/atom/csv_data/attack_PubMed.csv similarity index 100% rename from pygip/models/defense/atom/csv_data/attack_PubMed.csv rename to pyhazard/models/defense/atom/csv_data/attack_PubMed.csv diff --git a/pygip/models/defense/base.py b/pyhazard/models/defense/base.py similarity index 95% rename from pygip/models/defense/base.py rename to pyhazard/models/defense/base.py index 44ade19..fc9bddc 100644 --- a/pygip/models/defense/base.py +++ b/pyhazard/models/defense/base.py @@ -3,8 +3,8 @@ import torch -from pygip.datasets import Dataset -from pygip.utils.hardware import get_device +from pyhazard.datasets import Dataset +from pyhazard.utils.hardware import get_device class BaseDefense(ABC): diff --git a/pygip/models/nn/__init__.py b/pyhazard/models/nn/__init__.py similarity index 100% rename from pygip/models/nn/__init__.py rename to pyhazard/models/nn/__init__.py diff --git a/pygip/models/nn/backbones.py b/pyhazard/models/nn/backbones.py similarity index 100% rename from pygip/models/nn/backbones.py rename to pyhazard/models/nn/backbones.py diff --git a/pygip/utils/__init__.py b/pyhazard/utils/__init__.py similarity index 100% rename from pygip/utils/__init__.py rename to pyhazard/utils/__init__.py diff --git a/pygip/utils/dglTopyg.py b/pyhazard/utils/dglTopyg.py similarity index 100% rename from pygip/utils/dglTopyg.py rename to pyhazard/utils/dglTopyg.py diff --git a/pygip/utils/hardware.py b/pyhazard/utils/hardware.py similarity index 68% rename from pygip/utils/hardware.py rename to pyhazard/utils/hardware.py index 685a928..1cae2a6 100644 --- a/pygip/utils/hardware.py +++ b/pyhazard/utils/hardware.py @@ -2,7 +2,7 @@ import torch -_DEFAULT_DEVICE_STR = os.getenv("PYGIP_DEVICE") or ("cuda:0" if torch.cuda.is_available() else "cpu") +_DEFAULT_DEVICE_STR = os.getenv("PYHAZARD_DEVICE") or ("cuda:0" if torch.cuda.is_available() else "cpu") _default_device = torch.device(_DEFAULT_DEVICE_STR) diff --git a/pygip/utils/metrics.py b/pyhazard/utils/metrics.py similarity index 100% rename from pygip/utils/metrics.py rename to pyhazard/utils/metrics.py diff --git a/pyproject.toml b/pyproject.toml index d4a5de8..865372e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ requires = ["setuptools>=61", "wheel"] build-backend = "setuptools.build_meta" [project] -name = "PyGIP" +name = "PyHazard" version = "1.1.0" description = "A Python package for Graph Intellectual Property Protection" readme = "README.md" @@ -37,8 +37,8 @@ classifiers = [ ] [project.urls] -Homepage = "https://labrai.github.io/PyGIP" -Repository = "https://github.com/LabRAI/PyGIP" +Homepage = "https://labrai.github.io/PyHazard" +Repository = "https://github.com/LabRAI/PyHazard" [project.optional-dependencies] # torch 2.3 cannot be installed directly from PyPI, users must add -f @@ -51,4 +51,4 @@ dgl = ["dgl==2.2.1"] include-package-data = true [tool.setuptools.packages.find] -include = ["pygip*"] +include = ["pyhazard*"] From 81cbc6c40fbb2d64304c3b6d5cc3f9d6db631340 Mon Sep 17 00:00:00 2001 From: Xueqi Cheng Date: Wed, 3 Dec 2025 15:47:23 -0500 Subject: [PATCH 04/39] update --- LICENSE | 2 +- PyHazard.egg-info/PKG-INFO | 87 +- PyHazard.egg-info/SOURCES.txt | 58 - README.md | 83 +- benchmark/HOWTORUN.md | 13 - benchmark/run/__init__.py | 1 - benchmark/run/run_attack_track.py | 152 - benchmark/run/run_ownership_track.py | 120 - benchmark/run/select_best.py | 142 - benchmark/run/to_latex.py | 61 - benchmark/run/utils_benchmark.py | 206 - benchmark/scripts/RQ1_attacks.sh | 66 - benchmark/scripts/RQ2_defenses.sh | 64 - benchmark/scripts/RQ3_tradeoff.sh | 65 - benchmark/scripts/RQ4_overhead.sh | 19 - .../attack/pyhazard.models.attack.AdvMEA.rst | 30 - .../attack/pyhazard.models.attack.CEGA.rst | 30 - .../pyhazard.models.attack.DataFreeMEA.rst | 33 - .../pyhazard.models.attack.Realistic.rst | 31 - .../attack/pyhazard.models.attack.base.rst | 29 - .../attack/pyhazard.models.attack.mea.MEA.rst | 35 - .../pyhazard.models.defense.BackdoorWM.rst | 30 - ...yhazard.models.defense.ImperceptibleWM.rst | 30 - .../pyhazard.models.defense.Integrity.rst | 33 - .../pyhazard.models.defense.RandomWM.rst | 30 - .../pyhazard.models.defense.SurviveWM.rst | 30 - .../pyhazard.models.defense.atom.ATOM.rst | 61 - .../defense/pyhazard.models.defense.base.rst | 29 - .../source/api/pyhazard.models.attack.mea.rst | 21 - docs/source/api/pyhazard.models.attack.rst | 53 - .../api/pyhazard.models.defense.atom.rst | 21 - docs/source/api/pyhazard.models.defense.rst | 77 - docs/source/benchmark.rst | 5 - docs/source/index.rst | 112 +- docs/source/pyhazard_models_attack.rst | 22 - docs/source/pyhazard_models_defense.rst | 23 - docs/source/quick_start.rst | 119 +- docs/source/team.rst | 13 +- examples/__init__.py | 0 examples/attack/AdvMEA.py | 18 - examples/attack/CEGA.py | 16 - examples/attack/DataFreeMEA.py | 24 - examples/attack/MEA.py | 51 - examples/attack/Realistic.py | 24 - examples/attack/__init__.py | 0 examples/defense/ATOM.py | 18 - examples/defense/BackdoorWM.py | 17 - examples/defense/GrOVe.py | 20 - examples/defense/ImperceptibleWM.py | 17 - examples/defense/Integrity.py | 17 - examples/defense/RandomWM.py | 17 - examples/defense/Revisiting.py | 22 - examples/defense/SurviveWM.py | 17 - examples/defense/__init__.py | 0 pyhazard/models/attack/AdvMEA.py | 245 - pyhazard/models/attack/CEGA.py | 1860 -- pyhazard/models/attack/DataFreeMEA.py | 310 - pyhazard/models/attack/Realistic.py | 333 - pyhazard/models/attack/__init__.py | 31 - pyhazard/models/attack/base.py | 110 - pyhazard/models/attack/mea/MEA.py | 480 - pyhazard/models/attack/mea/__init__.py | 0 .../citeseer/graph_label.txt | 4460 ---- .../citeseer/query_labels.txt | 3327 --- .../citeseer/selected_index.txt | 700 - .../cora/graph_label.txt | 1908 -- .../cora/query_labels.txt | 2708 --- .../cora/selected_index.txt | 700 - .../pubmed/graph_label.txt | 1868 -- .../pubmed/query_labels.txt | 19717 ---------------- .../pubmed/selected_index.txt | 700 - ...tack_6_sub_shadow_graph_index_attack_2.txt | 506 - ...tack_6_sub_shadow_graph_index_attack_3.txt | 496 - .../protential_1200_shadow_graph_index.txt | 807 - .../protential_1300_shadow_graph_index.txt | 1002 - .../protential_500_shadow_graph_index.txt | 201 - .../protential_700_shadow_graph_index.txt | 542 - .../protential_900_shadow_graph_index.txt | 600 - .../citeseer/target_graph_index.txt | 2325 -- ...tack_6_sub_shadow_graph_index_attack_2.txt | 653 - ...tack_6_sub_shadow_graph_index_attack_3.txt | 647 - .../protential_1200_shadow_graph_index.txt | 1265 - .../protential_1300_shadow_graph_index.txt | 1300 - .../protential_300_shadow_graph_index.txt | 350 - .../protential_500_shadow_graph_index.txt | 573 - .../protential_700_shadow_graph_index.txt | 783 - .../protential_900_shadow_graph_index.txt | 985 - .../cora/protential_shadow_graph_index.txt | 1300 - .../cora/target_graph_index.txt | 1408 -- ...tack_6_sub_shadow_graph_index_attack_2.txt | 1316 -- ...tack_6_sub_shadow_graph_index_attack_3.txt | 1293 - .../protential_1200_shadow_graph_index.txt | 2047 -- .../protential_1300_shadow_graph_index.txt | 2609 -- .../protential_500_shadow_graph_index.txt | 537 - .../protential_700_shadow_graph_index.txt | 1038 - .../protential_900_shadow_graph_index.txt | 1505 -- pyhazard/models/defense/BackdoorWM.py | 179 - pyhazard/models/defense/GrOVe.py | 303 - pyhazard/models/defense/ImperceptibleWM.py | 262 - pyhazard/models/defense/ImperceptibleWM2.py | 920 - pyhazard/models/defense/Integrity.py | 1227 - pyhazard/models/defense/RandomWM.py | 598 - pyhazard/models/defense/Revisiting.py | 244 - pyhazard/models/defense/SurviveWM.py | 301 - pyhazard/models/defense/SurviveWM2.py | 556 - pyhazard/models/defense/__init__.py | 23 - pyhazard/models/defense/atom/ATOM.py | 995 - pyhazard/models/defense/atom/__init__.py | 0 .../defense/atom/csv_data/attack_CiteSeer.csv | 177 - .../defense/atom/csv_data/attack_Cora.csv | 542 - .../defense/atom/csv_data/attack_PubMed.csv | 200 - pyhazard/models/defense/base.py | 73 - pyproject.toml | 6 +- 113 files changed, 234 insertions(+), 74231 deletions(-) delete mode 100644 benchmark/HOWTORUN.md delete mode 100644 benchmark/run/__init__.py delete mode 100644 benchmark/run/run_attack_track.py delete mode 100644 benchmark/run/run_ownership_track.py delete mode 100644 benchmark/run/select_best.py delete mode 100644 benchmark/run/to_latex.py delete mode 100644 benchmark/run/utils_benchmark.py delete mode 100644 benchmark/scripts/RQ1_attacks.sh delete mode 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pyhazard/models/defense/GrOVe.py delete mode 100644 pyhazard/models/defense/ImperceptibleWM.py delete mode 100644 pyhazard/models/defense/ImperceptibleWM2.py delete mode 100644 pyhazard/models/defense/Integrity.py delete mode 100644 pyhazard/models/defense/RandomWM.py delete mode 100644 pyhazard/models/defense/Revisiting.py delete mode 100644 pyhazard/models/defense/SurviveWM.py delete mode 100644 pyhazard/models/defense/SurviveWM2.py delete mode 100644 pyhazard/models/defense/__init__.py delete mode 100644 pyhazard/models/defense/atom/ATOM.py delete mode 100644 pyhazard/models/defense/atom/__init__.py delete mode 100644 pyhazard/models/defense/atom/csv_data/attack_CiteSeer.csv delete mode 100644 pyhazard/models/defense/atom/csv_data/attack_Cora.csv delete mode 100644 pyhazard/models/defense/atom/csv_data/attack_PubMed.csv delete mode 100644 pyhazard/models/defense/base.py diff --git a/LICENSE b/LICENSE index aac97e7..7707a90 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ BSD 2-Clause License -Copyright (c) 2025 Bolin Shen +Copyright (c) 2025 Xueqi Cheng Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: diff --git a/PyHazard.egg-info/PKG-INFO b/PyHazard.egg-info/PKG-INFO index adc8fcf..0f4da31 100644 --- a/PyHazard.egg-info/PKG-INFO +++ b/PyHazard.egg-info/PKG-INFO @@ -1,14 +1,16 @@ Metadata-Version: 2.4 Name: PyHazard Version: 1.1.0 -Summary: A Python package for Graph Intellectual Property Protection +Summary: A Python framework for AI-powered hazard prediction and risk assessment Author-email: Bolin Shen License: BSD-2-Clause Project-URL: Homepage, https://labrai.github.io/PyHazard Project-URL: Repository, https://github.com/LabRAI/PyHazard Classifier: Development Status :: 3 - Alpha Classifier: Intended Audience :: Science/Research +Classifier: Intended Audience :: Developers Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence +Classifier: Topic :: Scientific/Engineering :: Information Analysis Classifier: License :: OSI Approved :: BSD License Classifier: Programming Language :: Python :: 3.8 Classifier: Programming Language :: Python :: 3.9 @@ -45,15 +47,16 @@ Dynamic: license-file [![Stars](https://img.shields.io/github/stars/LabRAI/PyHazard)](https://github.com/LabRAI/PyHazard) [![GitHub forks](https://img.shields.io/github/forks/LabRAI/PyHazard)](https://github.com/LabRAI/PyHazard) -PyHazard is a Python library designed for using AI tools for hazard prediction. It provides a modular framework to implement and test prediction strategies on natural hazards. +PyHazard is a Python framework for AI-powered hazard prediction and risk assessment. It provides a modular and extensible architecture for building, training, and deploying machine learning models to predict and analyze natural hazards and environmental risks. -## How to Cite - -If you find it useful, please consider citing the following work: - -``` -``` +## Features +- **Modular Architecture**: Easy-to-extend framework for implementing custom hazard prediction models +- **Graph Neural Networks**: Built-in support for graph-based data representation and GNN models +- **Flexible Datasets**: Unified dataset interface supporting both DGL and PyTorch Geometric +- **GPU Acceleration**: Full CUDA support for training and inference +- **Extensible Models**: Base classes for implementing custom prediction models +- **Production Ready**: Type hints, proper error handling, and comprehensive testing ## Installation @@ -61,7 +64,7 @@ PyHazard supports both CPU and GPU environments. Make sure you have Python insta ### Base Installation -First, install the core package: +Install the core package: ```bash pip install PyHazard @@ -69,7 +72,7 @@ pip install PyHazard This will install PyHazard with minimal dependencies. -### CPU Version +### CPU Version with Deep Learning Support ```bash pip install "PyHazard[torch,dgl]" \ @@ -89,45 +92,65 @@ pip install "PyHazard[torch,dgl]" \ ## Quick Start -Here's a simple example to use PyHazard: +Here's a simple example to get started with PyHazard: ```python +import pyhazard from pyhazard.datasets import Cora -from pyhazard.models.attack import MEA +from pyhazard.models.nn import GCN -# Load dataset +# Load a dataset dataset = Cora(api_type='pyg') -# Initialize and run Model Extraction Attack -attack = MEA(dataset=dataset) -attack.attack() -``` +# Initialize a model (example using built-in GCN) +model = GCN( + input_dim=dataset.num_features, + hidden_dim=64, + output_dim=dataset.num_classes +) -And a simple example to run a defense: +# Your training and prediction code here +# ... +``` -```python -from pyhazard.datasets import Cora -from pyhazard.models.defense import ATOM +### Using CUDA -# Load dataset -dataset = Cora(api_type='pyg') +To use CUDA for GPU acceleration, set the environment variable: -# Initialize and run ATOM defense -defense = ATOM(dataset=dataset) -defense.defense() +```shell +export PYHAZARD_DEVICE=cuda:0 ``` -If you want to use CUDA, please set environment variable: +Or specify the device in your code: -```shell -export PYHAZARD_DEVICE=cuda:0 +```python +from pyhazard.utils import set_device + +set_device("cuda:0") ``` -## Implementation & Contributors Guideline +## Documentation -Refer to [Implementation Guideline](.github/IMPLEMENTATION.md) +Full documentation is available at: [https://labrai.github.io/PyHazard](https://labrai.github.io/PyHazard) -Refer to [Contributors Guideline](.github/CONTRIBUTING.md) +## Contributing + +We welcome contributions! Please see our: +- [Implementation Guideline](.github/IMPLEMENTATION.md) - For implementing new models +- [Contributors Guideline](.github/CONTRIBUTING.md) - For contributing to the project + +## Citation + +If you use PyHazard in your research, please cite: + +```bibtex +@software{pyhazard2025, + title={PyHazard: A Python Framework for AI-Powered Hazard Prediction}, + author={Shen, Bolin and others}, + year={2025}, + url={https://github.com/LabRAI/PyHazard} +} +``` ## License diff --git a/PyHazard.egg-info/SOURCES.txt b/PyHazard.egg-info/SOURCES.txt index a16a6a1..c0e1308 100644 --- a/PyHazard.egg-info/SOURCES.txt +++ b/PyHazard.egg-info/SOURCES.txt @@ -11,64 +11,6 @@ pyhazard/__init__.py pyhazard/datasets/__init__.py pyhazard/datasets/datasets.py pyhazard/models/__init__.py -pyhazard/models/attack/AdvMEA.py -pyhazard/models/attack/CEGA.py -pyhazard/models/attack/DataFreeMEA.py -pyhazard/models/attack/Realistic.py -pyhazard/models/attack/__init__.py -pyhazard/models/attack/base.py -pyhazard/models/attack/mea/MEA.py -pyhazard/models/attack/mea/__init__.py -pyhazard/models/attack/mea/data/attack2_generated_graph/citeseer/graph_label.txt -pyhazard/models/attack/mea/data/attack2_generated_graph/citeseer/query_labels.txt -pyhazard/models/attack/mea/data/attack2_generated_graph/citeseer/selected_index.txt -pyhazard/models/attack/mea/data/attack2_generated_graph/cora/graph_label.txt -pyhazard/models/attack/mea/data/attack2_generated_graph/cora/query_labels.txt -pyhazard/models/attack/mea/data/attack2_generated_graph/cora/selected_index.txt -pyhazard/models/attack/mea/data/attack2_generated_graph/pubmed/graph_label.txt -pyhazard/models/attack/mea/data/attack2_generated_graph/pubmed/query_labels.txt -pyhazard/models/attack/mea/data/attack2_generated_graph/pubmed/selected_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/attack_6_sub_shadow_graph_index_attack_2.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/attack_6_sub_shadow_graph_index_attack_3.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_1200_shadow_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_1300_shadow_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_500_shadow_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_700_shadow_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/protential_900_shadow_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/citeseer/target_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/attack_6_sub_shadow_graph_index_attack_2.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/attack_6_sub_shadow_graph_index_attack_3.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_1200_shadow_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_1300_shadow_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_300_shadow_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_500_shadow_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_700_shadow_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_900_shadow_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/protential_shadow_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/cora/target_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/attack_6_sub_shadow_graph_index_attack_2.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/attack_6_sub_shadow_graph_index_attack_3.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_1200_shadow_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_1300_shadow_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_500_shadow_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_700_shadow_graph_index.txt -pyhazard/models/attack/mea/data/attack3_shadow_graph/pubmed/protential_900_shadow_graph_index.txt -pyhazard/models/defense/BackdoorWM.py -pyhazard/models/defense/GrOVe.py -pyhazard/models/defense/ImperceptibleWM.py -pyhazard/models/defense/ImperceptibleWM2.py -pyhazard/models/defense/Integrity.py -pyhazard/models/defense/RandomWM.py -pyhazard/models/defense/Revisiting.py -pyhazard/models/defense/SurviveWM.py -pyhazard/models/defense/SurviveWM2.py -pyhazard/models/defense/__init__.py -pyhazard/models/defense/base.py -pyhazard/models/defense/atom/ATOM.py -pyhazard/models/defense/atom/__init__.py -pyhazard/models/defense/atom/csv_data/attack_CiteSeer.csv -pyhazard/models/defense/atom/csv_data/attack_Cora.csv -pyhazard/models/defense/atom/csv_data/attack_PubMed.csv pyhazard/models/nn/__init__.py pyhazard/models/nn/backbones.py pyhazard/utils/__init__.py diff --git a/README.md b/README.md index 1c33c5c..9546b6f 100644 --- a/README.md +++ b/README.md @@ -9,15 +9,16 @@ [![Stars](https://img.shields.io/github/stars/LabRAI/PyHazard)](https://github.com/LabRAI/PyHazard) [![GitHub forks](https://img.shields.io/github/forks/LabRAI/PyHazard)](https://github.com/LabRAI/PyHazard) -PyHazard is a Python library designed for using AI tools for hazard prediction. It provides a modular framework to implement and test prediction strategies on natural hazards. +PyHazard is a Python framework for AI-powered hazard prediction and risk assessment. It provides a modular and extensible architecture for building, training, and deploying machine learning models to predict and analyze natural hazards and environmental risks. -## How to Cite - -If you find it useful, please consider citing the following work: - -``` -``` +## Features +- **Modular Architecture**: Easy-to-extend framework for implementing custom hazard prediction models +- **Graph Neural Networks**: Built-in support for graph-based data representation and GNN models +- **Flexible Datasets**: Unified dataset interface supporting both DGL and PyTorch Geometric +- **GPU Acceleration**: Full CUDA support for training and inference +- **Extensible Models**: Base classes for implementing custom prediction models +- **Production Ready**: Type hints, proper error handling, and comprehensive testing ## Installation @@ -25,7 +26,7 @@ PyHazard supports both CPU and GPU environments. Make sure you have Python insta ### Base Installation -First, install the core package: +Install the core package: ```bash pip install PyHazard @@ -33,7 +34,7 @@ pip install PyHazard This will install PyHazard with minimal dependencies. -### CPU Version +### CPU Version with Deep Learning Support ```bash pip install "PyHazard[torch,dgl]" \ @@ -53,45 +54,65 @@ pip install "PyHazard[torch,dgl]" \ ## Quick Start -Here's a simple example to use PyHazard: +Here's a simple example to get started with PyHazard: ```python +import pyhazard from pyhazard.datasets import Cora -from pyhazard.models.attack import MEA +from pyhazard.models.nn import GCN -# Load dataset +# Load a dataset dataset = Cora(api_type='pyg') -# Initialize and run Model Extraction Attack -attack = MEA(dataset=dataset) -attack.attack() -``` +# Initialize a model (example using built-in GCN) +model = GCN( + input_dim=dataset.num_features, + hidden_dim=64, + output_dim=dataset.num_classes +) -And a simple example to run a defense: +# Your training and prediction code here +# ... +``` -```python -from pyhazard.datasets import Cora -from pyhazard.models.defense import ATOM +### Using CUDA -# Load dataset -dataset = Cora(api_type='pyg') +To use CUDA for GPU acceleration, set the environment variable: -# Initialize and run ATOM defense -defense = ATOM(dataset=dataset) -defense.defense() +```shell +export PYHAZARD_DEVICE=cuda:0 ``` -If you want to use CUDA, please set environment variable: +Or specify the device in your code: -```shell -export PYHAZARD_DEVICE=cuda:0 +```python +from pyhazard.utils import set_device + +set_device("cuda:0") ``` -## Implementation & Contributors Guideline +## Documentation -Refer to [Implementation Guideline](.github/IMPLEMENTATION.md) +Full documentation is available at: [https://labrai.github.io/PyHazard](https://labrai.github.io/PyHazard) -Refer to [Contributors Guideline](.github/CONTRIBUTING.md) +## Contributing + +We welcome contributions! Please see our: +- [Implementation Guideline](.github/IMPLEMENTATION.md) - For implementing new models +- [Contributors Guideline](.github/CONTRIBUTING.md) - For contributing to the project + +## Citation + +If you use PyHazard in your research, please cite: + +```bibtex +@software{pyhazard2025, + title={PyHazard: A Python Framework for AI-Powered Hazard Prediction}, + author={Cheng, Xueqi}, + year={2025}, + url={https://github.com/LabRAI/PyHazard} +} +``` ## License diff --git a/benchmark/HOWTORUN.md b/benchmark/HOWTORUN.md deleted file mode 100644 index fde22b8..0000000 --- a/benchmark/HOWTORUN.md +++ /dev/null @@ -1,13 +0,0 @@ -chmod +x scripts/*.sh - -# RQ1: attacks (includes MEA 0..5) -bash scripts/RQ1_attacks.sh 0 - -# RQ2: defenses (best operating points) -bash scripts/RQ2_defenses.sh 0 - -# RQ3: dense sweeps for trade-off curves -bash scripts/RQ3_tradeoff.sh 0 - -# RQ4: overhead summaries -bash scripts/RQ4_overhead.sh diff --git a/benchmark/run/__init__.py b/benchmark/run/__init__.py deleted file mode 100644 index 8b13789..0000000 --- a/benchmark/run/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/benchmark/run/run_attack_track.py b/benchmark/run/run_attack_track.py deleted file mode 100644 index 8ef0b3e..0000000 --- a/benchmark/run/run_attack_track.py +++ /dev/null @@ -1,152 +0,0 @@ -# run/run_attack_track.py -import argparse -import json -import os -import traceback -from itertools import product - -# Ensure this folder is importable for `utils_benchmark` -import sys -sys.path.insert(0, os.path.dirname(__file__)) - -import torch - -from utils_benchmark import ( - load_dataset, compute_fraction_for_budget, instantiate_attack, call_attack, - write_jsonl, normalize_attack_return, timestamp, ensure_dir -) - - -def parse_args(): - p = argparse.ArgumentParser(description="GraphIP-Bench: Attack track runner (RQ1 + grid search).") - p.add_argument("--datasets", nargs="+", required=True) - p.add_argument("--attacks", nargs="+", required=True, - help="Keys: mea0 mea1 mea2 mea3 mea4 mea5 advmea cega realistic dfea_i dfea_ii dfea_iii") - p.add_argument("--budgets", nargs="+", type=float, default=[0.25, 0.5, 1.0, 2.0, 4.0]) - p.add_argument("--regimes", nargs="+", default=["both", "x_only", "a_only", "data_free"]) - p.add_argument("--seeds", nargs="+", type=int, default=[0, 1, 2]) - p.add_argument("--attack_grid_json", type=str, default=None, - help="JSON file with per-attack grids of ctor/run kwargs.") - p.add_argument("--device", type=str, default="cuda:0") - p.add_argument("--outdir", type=str, default="outputs/RQ1") - p.add_argument("--root", type=str, default=None) - p.add_argument("--dry_run", action="store_true") - return p.parse_args() - - -def regime_to_ratios(regime: str, fraction: float): - if regime == "both": - return fraction, fraction - if regime == "x_only": - return fraction, 0.0 - if regime == "a_only": - return 0.0, fraction - if regime == "data_free": - return 0.0, 0.0 - raise ValueError(f"Unknown regime: {regime}") - - -def load_attack_grid(path: str, attacks): - if not path: - return {k: [{"ctor": {}, "run": {}}] for k in attacks} - with open(path, "r", encoding="utf-8") as f: - raw = json.load(f) - grid = {} - for k in attacks: - cfgs = raw.get(k, None) - if not cfgs: - cfgs = [{"ctor": {}, "run": {}}] - norm = [] - for item in cfgs: - if "ctor" in item or "run" in item: - norm.append({"ctor": item.get("ctor", {}), "run": item.get("run", {})}) - else: - norm.append({"ctor": item, "run": {}}) - grid[k] = norm - return grid - - -def main(): - args = parse_args() - os.environ["CUDA_VISIBLE_DEVICES"] = args.device.split(":")[-1] if "cuda" in args.device else "" - ensure_dir(args.outdir) - - header = { - "runner": "attack_track", - "timestamp": timestamp(), - "datasets": args.datasets, - "attacks": args.attacks, - "budgets": args.budgets, - "regimes": args.regimes, - "seeds": args.seeds, - "device": args.device, - } - print(header) - - grid = load_attack_grid(args.attack_grid_json, args.attacks) - - for ds_name in args.datasets: - dataset = load_dataset(ds_name, root=args.root) - for budget, regime, seed, attack_key in product(args.budgets, args.regimes, args.seeds, args.attacks): - fraction = compute_fraction_for_budget(dataset, budget) - x_ratio, a_ratio = regime_to_ratios(regime, fraction) - - cfg_list = grid[attack_key] - for cfg_idx, cfg in enumerate(cfg_list): - ctor_kwargs = cfg.get("ctor", {}) - run_kwargs = cfg.get("run", {}) - - print(f"[RUN] ds={ds_name} atk={attack_key} cfg#{cfg_idx} " - f"budget={budget} frac={fraction:.5f} regime={regime} " - f"x={x_ratio:.5f} a={a_ratio:.5f} seed={seed} ctor={ctor_kwargs} run={run_kwargs}") - - if args.dry_run: - continue - - try: - torch.manual_seed(seed) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(seed) - attack = instantiate_attack(attack_key, dataset, fraction, x_ratio, a_ratio, ctor_kwargs) - perf, comp = normalize_attack_return(call_attack(attack, run_kwargs, seed)) - - record = { - "track": "attack", - "dataset": ds_name, - "attack": attack_key, - "config_index": cfg_idx, - "config": cfg, - "budget_mult": budget, - "fraction": fraction, - "regime": regime, - "attack_x_ratio": x_ratio, - "attack_a_ratio": a_ratio, - "seed": seed, - "perf": perf, - "comp": comp, - } - except Exception as e: - record = { - "track": "attack", - "dataset": ds_name, - "attack": attack_key, - "config_index": cfg_idx, - "config": cfg, - "budget_mult": budget, - "fraction": fraction, - "regime": regime, - "attack_x_ratio": x_ratio, - "attack_a_ratio": a_ratio, - "seed": seed, - "error": str(e), - "traceback": traceback.format_exc(), - } - - out_jsonl = os.path.join(args.outdir, f"{ds_name}.jsonl") - write_jsonl(out_jsonl, record) - - print(f"[DONE] Results saved to: {args.outdir}") - - -if __name__ == "__main__": - main() diff --git a/benchmark/run/run_ownership_track.py b/benchmark/run/run_ownership_track.py deleted file mode 100644 index 547823f..0000000 --- a/benchmark/run/run_ownership_track.py +++ /dev/null @@ -1,120 +0,0 @@ -# run/run_ownership_track.py -import argparse -import json -import os -import traceback -import sys - -# Ensure this folder is importable for `utils_benchmark` -sys.path.insert(0, os.path.dirname(__file__)) - -import torch - -from utils_benchmark import ( - load_dataset, instantiate_defense, call_defense, - write_jsonl, normalize_defense_return, timestamp, ensure_dir -) - - -def parse_args(): - p = argparse.ArgumentParser(description="GraphIP-Bench: Ownership track (RQ2/RQ3 + grid search).") - p.add_argument("--datasets", nargs="+", required=True) - p.add_argument("--defenses", nargs="+", required=True, - help="Keys: randomwm backdoorwm survivewm imperceptiblewm") - p.add_argument("--seeds", nargs="+", type=int, default=[0, 1, 2]) - p.add_argument("--defense_grid_json", type=str, default=None, - help="JSON file with per-defense grids of ctor/run kwargs.") - p.add_argument("--device", type=str, default="cuda:0") - p.add_argument("--outdir", type=str, default="outputs/RQ2_RQ3") - p.add_argument("--root", type=str, default=None) - p.add_argument("--dry_run", action="store_true") - return p.parse_args() - - -def load_defense_grid(path: str, defenses): - if not path: - return {k: [{"ctor": {}, "run": {}}] for k in defenses} - with open(path, "r", encoding="utf-8") as f: - raw = json.load(f) - grid = {} - for k in defenses: - cfgs = raw.get(k, None) - if not cfgs: - cfgs = [{"ctor": {}, "run": {}}] - norm = [] - for item in cfgs: - if "ctor" in item or "run" in item: - norm.append({"ctor": item.get("ctor", {}), "run": item.get("run", {})}) - else: - norm.append({"ctor": item, "run": {}}) - grid[k] = norm - return grid - - -def main(): - args = parse_args() - os.environ["CUDA_VISIBLE_DEVICES"] = args.device.split(":")[-1] if "cuda" in args.device else "" - ensure_dir(args.outdir) - - header = { - "runner": "ownership_track", - "timestamp": timestamp(), - "datasets": args.datasets, - "defenses": args.defenses, - "seeds": args.seeds, - "device": args.device, - } - print(header) - - grid = load_defense_grid(args.defense_grid_json, args.defenses) - - for ds_name in args.datasets: - dataset = load_dataset(ds_name, root=args.root) - for defense_key in args.defenses: - cfg_list = grid[defense_key] - for cfg_idx, cfg in enumerate(cfg_list): - ctor_kwargs = cfg.get("ctor", {}) - run_kwargs = cfg.get("run", {}) - for seed in args.seeds: - print(f"[RUN] ds={ds_name} defense={defense_key} cfg#{cfg_idx} ctor={ctor_kwargs} run={run_kwargs} seed={seed}") - - if args.dry_run: - continue - - try: - torch.manual_seed(seed) - if torch.cuda.is_available(): - torch.cuda.manual_seed_all(seed) - defense = instantiate_defense(defense_key, dataset, ctor_kwargs) - perf, comp = normalize_defense_return(call_defense(defense, run_kwargs, seed)) - - record = { - "track": "ownership", - "dataset": ds_name, - "defense": defense_key, - "config_index": cfg_idx, - "config": cfg, - "seed": seed, - "perf": perf, - "comp": comp, - } - except Exception as e: - record = { - "track": "ownership", - "dataset": ds_name, - "defense": defense_key, - "config_index": cfg_idx, - "config": cfg, - "seed": seed, - "error": str(e), - "traceback": traceback.format_exc(), - } - - out_jsonl = os.path.join(args.outdir, f"{ds_name}.jsonl") - write_jsonl(out_jsonl, record) - - print(f"[DONE] Results saved to: {args.outdir}") - - -if __name__ == "__main__": - main() diff --git a/benchmark/run/select_best.py b/benchmark/run/select_best.py deleted file mode 100644 index 6d1238e..0000000 --- a/benchmark/run/select_best.py +++ /dev/null @@ -1,142 +0,0 @@ -# run/select_best.py -import argparse -import glob -import json -import os -from typing import Dict, Any, List - -import pandas as pd - - -def read_jsonl(folder: str) -> List[Dict[str, Any]]: - items = [] - for p in sorted(glob.glob(os.path.join(folder, "*.jsonl"))): - with open(p, "r", encoding="utf-8") as f: - for line in f: - line = line.strip() - if line: - items.append(json.loads(line)) - return items - - -def _score_attack(perf: Dict[str, Any]) -> float: - fid = perf.get("fid", None) or perf.get("fidelity", None) - if fid is not None: - return float(fid) - acc = perf.get("acc", None) or perf.get("accuracy", None) or perf.get("test_accuracy", None) - return float(acc) if acc is not None else float("nan") - - -def _score_defense(perf: Dict[str, Any]) -> float: - fid = perf.get("fid", None) or perf.get("fidelity", None) or perf.get("post_fidelity", None) - acc = perf.get("def_acc", None) or perf.get("acc", None) or perf.get("defense_accuracy", None) - if fid is None and acc is None: - return float("nan") - if fid is None: - fid = 1.0 - if acc is None: - acc = 1.0 - return (1.0 - float(fid)) * float(acc) - - -def select_best_attack(records: List[Dict[str, Any]]): - rows = [] - for r in records: - if r.get("track") != "attack" or r.get("error"): - continue - perf = r.get("perf", {}) - score = _score_attack(perf) - row = {**r} - row["score_attack"] = score - rows.append(row) - if not rows: - return None, None - df = pd.DataFrame(rows) - keys = ["dataset", "attack", "budget_mult", "regime"] - idx = df.groupby(keys)["score_attack"].idxmax() - best_df = df.loc[idx].reset_index(drop=True) - return df, best_df - - -def select_best_defense(records: List[Dict[str, Any]]): - rows = [] - for r in records: - if r.get("track") != "ownership" or r.get("error"): - continue - perf = r.get("perf", {}) - score = _score_defense(perf) - row = {**r} - row["score_defense"] = score - rows.append(row) - if not rows: - return None, None - df = pd.DataFrame(rows) - keys = ["dataset", "defense"] - idx = df.groupby(keys)["score_defense"].idxmax() - best_df = df.loc[idx].reset_index(drop=True) - return df, best_df - - -def main(): - ap = argparse.ArgumentParser() - ap.add_argument("--rq1_dir", type=str, default="outputs/RQ1") - ap.add_argument("--rq2_dir", type=str, default="outputs/RQ2_RQ3") - ap.add_argument("--outdir", type=str, default="outputs/leaderboards") - args = ap.parse_args() - - os.makedirs(args.outdir, exist_ok=True) - - rec1 = read_jsonl(args.rq1_dir) - rec2 = read_jsonl(args.rq2_dir) - - if rec1: - full1, best1 = select_best_attack(rec1) - if full1 is not None: - full1.to_csv(os.path.join(args.outdir, "RQ1_full.csv"), index=False) - best1.to_csv(os.path.join(args.outdir, "RQ1_best.csv"), index=False) - out = {} - for _, row in best1.iterrows(): - ds = row["dataset"]; atk = row["attack"] - key = f"{ds}::{atk}::budget{row['budget_mult']}::regime{row['regime']}" - out[key] = { - "dataset": ds, - "attack": atk, - "budget_mult": row["budget_mult"], - "regime": row["regime"], - "config_index": int(row["config_index"]), - "config": row["config"], - "seed": int(row["seed"]), - "score_attack": float(row["score_attack"]), - "perf": row["perf"], - "comp": row["comp"], - } - with open(os.path.join(args.outdir, "RQ1_best_configs.json"), "w", encoding="utf-8") as f: - json.dump(out, f, indent=2) - - if rec2: - full2, best2 = select_best_defense(rec2) - if full2 is not None: - full2.to_csv(os.path.join(args.outdir, "RQ2_RQ3_full.csv"), index=False) - best2.to_csv(os.path.join(args.outdir, "RQ2_RQ3_best.csv"), index=False) - out = {} - for _, row in best2.iterrows(): - ds = row["dataset"]; d = row["defense"] - key = f"{ds}::{d}" - out[key] = { - "dataset": ds, - "defense": d, - "config_index": int(row["config_index"]), - "config": row["config"], - "seed": int(row["seed"]), - "score_defense": float(row["score_defense"]), - "perf": row["perf"], - "comp": row["comp"], - } - with open(os.path.join(args.outdir, "RQ2_RQ3_best_configs.json"), "w", encoding="utf-8") as f: - json.dump(out, f, indent=2) - - print(f"[DONE] Leaderboards written under {args.outdir}") - - -if __name__ == "__main__": - main() diff --git a/benchmark/run/to_latex.py b/benchmark/run/to_latex.py deleted file mode 100644 index 103f79f..0000000 --- a/benchmark/run/to_latex.py +++ /dev/null @@ -1,61 +0,0 @@ -# run/to_latex.py -import argparse -import json -import os -import pandas as pd - - -def to_percent(x, digits=1): - try: - return f"{float(x) * 100:.{digits}f}" - except Exception: - return "-" - - -def table_rq1(best_csv: str, out_tex: str): - df = pd.read_csv(best_csv) - keep = ["dataset", "attack", "budget_mult", "regime", "perf"] - df = df[keep].copy() - df["fid"] = df["perf"].apply(lambda s: json.loads(s.replace("'", '"')).get("fid", None) - or json.loads(s.replace("'", '"')).get("fidelity", None)) - df["acc"] = df["perf"].apply(lambda s: json.loads(s.replace("'", '"')).get("acc", None) - or json.loads(s.replace("'", '"')).get("accuracy", None) - or json.loads(s.replace("'", '"')).get("test_accuracy", None)) - df["Fidelity(%)"] = df["fid"].apply(lambda v: to_percent(v, 1)) - df["Accuracy(%)"] = df["acc"].apply(lambda v: to_percent(v, 1)) - df = df.drop(columns=["perf", "fid", "acc"]).sort_values(["dataset", "budget_mult", "regime", "attack"]) - with open(out_tex, "w", encoding="utf-8") as f: - f.write(df.to_latex(index=False, escape=False)) - print(f"[LaTeX] RQ1 table -> {out_tex}") - - -def table_rq2(best_csv: str, out_tex: str): - df = pd.read_csv(best_csv) - keep = ["dataset", "defense", "perf"] - df = df[keep].copy() - df["fid"] = df["perf"].apply(lambda s: json.loads(s.replace("'", '"')).get("fid", None) - or json.loads(s.replace("'", '"')).get("fidelity", None)) - df["def_acc"] = df["perf"].apply(lambda s: json.loads(s.replace("'", '"')).get("def_acc", None) - or json.loads(s.replace("'", '"')).get("acc", None) - or json.loads(s.replace("'", '"')).get("defense_accuracy", None)) - df["1-Fid(%)"] = df["fid"].apply(lambda v: to_percent(1.0 - float(v) if v is not None else None, 1)) - df["Utility(%)"] = df["def_acc"].apply(lambda v: to_percent(v, 1)) - df = df.drop(columns=["perf", "fid", "def_acc"]).sort_values(["dataset", "defense"]) - with open(out_tex, "w", encoding="utf-8") as f: - f.write(df.to_latex(index=False, escape=False)) - print(f"[LaTeX] RQ2/RQ3 table -> {out_tex}") - - -def main(): - ap = argparse.ArgumentParser() - ap.add_argument("--rq1_best", type=str, default="outputs/leaderboards/RQ1_best.csv") - ap.add_argument("--rq2_best", type=str, default="outputs/leaderboards/RQ2_RQ3_best.csv") - ap.add_argument("--outdir", type=str, default="outputs/leaderboards") - args = ap.parse_args() - os.makedirs(args.outdir, exist_ok=True) - table_rq1(args.rq1_best, os.path.join(args.outdir, "RQ1_best_table.tex")) - table_rq2(args.rq2_best, os.path.join(args.outdir, "RQ2_RQ3_best_table.tex")) - - -if __name__ == "__main__": - main() diff --git a/benchmark/run/utils_benchmark.py b/benchmark/run/utils_benchmark.py deleted file mode 100644 index ce382ca..0000000 --- a/benchmark/run/utils_benchmark.py +++ /dev/null @@ -1,206 +0,0 @@ -# run/utils_benchmark.py -import importlib -import inspect -import json -import os -import sys -import time -from typing import Any, Dict, Tuple - -# Ensure repository root is on sys.path so imports like `pyhazard.*` always work -_REPO_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir)) -if _REPO_ROOT not in sys.path: - sys.path.insert(0, _REPO_ROOT) - -import torch - - -def _try_import(path: str): - mod, cls = path.rsplit(".", 1) - m = importlib.import_module(mod) - return getattr(m, cls) - - -# ==== Registries (paths match your repo layout) ==== -ATTACK_REGISTRY = { - # MEA.py — six attacks (0..5) - "mea0": "pyhazard.models.attack.mea.MEA.ModelExtractionAttack0", - "mea1": "pyhazard.models.attack.mea.MEA.ModelExtractionAttack1", - "mea2": "pyhazard.models.attack.mea.MEA.ModelExtractionAttack2", - "mea3": "pyhazard.models.attack.mea.MEA.ModelExtractionAttack3", - "mea4": "pyhazard.models.attack.mea.MEA.ModelExtractionAttack4", - "mea5": "pyhazard.models.attack.mea.MEA.ModelExtractionAttack5", - - # Other attacks - "advmea": "pyhazard.models.attack.mea.AdvMEA.AdvMEA", - "cega": "pyhazard.models.attack.mea.CEGA.CEGA", - "realistic": "pyhazard.models.attack.mea.Realistic.RealisticAttack", - "dfea_i": "pyhazard.models.attack.mea.DataFreeMEA.DFEATypeI", - "dfea_ii": "pyhazard.models.attack.mea.DataFreeMEA.DFEATypeII", - "dfea_iii": "pyhazard.models.attack.mea.DataFreeMEA.DFEATypeIII", -} - -DEFENSE_REGISTRY = { - "randomwm": "pyhazard.models.defense.atom.RandomWM.RandomWM", - "backdoorwm": "pyhazard.models.defense.atom.BackdoorWM.BackdoorWM", - "survivewm": "pyhazard.models.defense.atom.SurviveWM.SurviveWM", - "imperceptiblewm": "pyhazard.models.defense.atom.ImperceptibleWM.ImperceptibleWM", - # Add more if needed (e.g., GROVE) using their import paths. -} - - -def load_dataset(name: str, root: str = None): - """Robust loader that tries several known entry points.""" - tried = [] - for mod, fn in [ - ("pyhazard.datasets.datasets", "get_dataset"), - ("pyhazard.datasets", "get_dataset"), - ("pyhazard.data", "get_dataset"), - ("pyhazard.data", "build_dataset"), - ]: - try: - m = importlib.import_module(mod) - f = getattr(m, fn) - if root is None: - ds = f(name) - else: - try: - ds = f(name, root=root) - except TypeError: - ds = f(name) - return ds - except Exception as e: - tried.append(f"{mod}.{fn}: {e}") - raise RuntimeError("Could not load dataset. Tried: " + " | ".join(tried)) - - -def get_masks(dataset): - g = dataset.graph_data - n = g.number_of_nodes() if hasattr(g, "number_of_nodes") else dataset.num_nodes - nd = g.ndata - test_mask = nd["test_mask"] - train_mask = nd.get("train_mask", torch.zeros(n, dtype=torch.bool)) - val_mask = nd.get("val_mask", torch.zeros(n, dtype=torch.bool)) - return train_mask, val_mask, test_mask - - -def get_nums(dataset) -> Tuple[int, int, int]: - n = getattr(dataset, "num_nodes", None) or dataset.graph_data.number_of_nodes() - d = getattr(dataset, "num_features", None) or dataset.graph_data.ndata["feat"].shape[1] - c = getattr(dataset, "num_classes", None) or int(dataset.graph_data.ndata["label"].max().item() + 1) - return n, d, c - - -def test_size(dataset) -> int: - _, _, tmask = get_masks(dataset) - return int(tmask.sum().item()) - - -def compute_fraction_for_budget(dataset, budget_mult: float) -> float: - """Convert a test-size–relative budget multiplier to a node fraction.""" - tsz = test_size(dataset) - n, _, _ = get_nums(dataset) - queries = max(1, int(round(budget_mult * tsz))) - return min(0.99, queries / float(n)) - - -def _has_var_kw(fn): - try: - sig = inspect.signature(fn) - for p in sig.parameters.values(): - if p.kind == inspect.Parameter.VAR_KEYWORD: - return True - return False - except (TypeError, ValueError): - return False - - -def _filter_kwargs(fn, cfg: Dict[str, Any]) -> Dict[str, Any]: - """Keep only kwargs accepted by `fn` unless it provides **kwargs.""" - if not cfg: - return {} - try: - sig = inspect.signature(fn) - except (TypeError, ValueError): - return {} - if _has_var_kw(fn): - return dict(cfg) - valid = set(sig.parameters.keys()) - return {k: v for k, v in cfg.items() if k in valid} - - -def instantiate_attack(key: str, dataset, fraction: float, x_ratio: float, a_ratio: float, ctor_kwargs: Dict[str, Any]): - cls = _try_import(ATTACK_REGISTRY[key]) - sig = inspect.signature(cls.__init__) - params = sig.parameters - if "attack_x_ratio" in params and "attack_a_ratio" in params: - safe_ctor = _filter_kwargs(cls.__init__, ctor_kwargs) - return cls(dataset=dataset, attack_x_ratio=x_ratio, attack_a_ratio=a_ratio, **safe_ctor) - if "attack_node_fraction" in params: - safe_ctor = _filter_kwargs(cls.__init__, ctor_kwargs) - return cls(dataset=dataset, attack_node_fraction=fraction, **safe_ctor) - if "attack_ratio" in params: - safe_ctor = _filter_kwargs(cls.__init__, ctor_kwargs) - return cls(dataset=dataset, attack_ratio=fraction, **safe_ctor) - safe_ctor = _filter_kwargs(cls.__init__, ctor_kwargs) - return cls(dataset=dataset, attack_node_fraction=fraction, **safe_ctor) - - -def call_attack(attack_obj, run_kwargs: Dict[str, Any], seed: int): - if not hasattr(attack_obj, "attack"): - raise RuntimeError("Attack object has no `.attack()` method.") - fn = getattr(attack_obj, "attack") - safe_run = _filter_kwargs(fn, dict(run_kwargs or {})) - safe_run["seed"] = seed - return fn(**safe_run) - - -def instantiate_defense(key: str, dataset, ctor_kwargs: Dict[str, Any]): - cls = _try_import(DEFENSE_REGISTRY[key]) - safe_ctor = _filter_kwargs(cls.__init__, ctor_kwargs or {}) - return cls(dataset=dataset, **safe_ctor) - - -def call_defense(defense_obj, run_kwargs: Dict[str, Any], seed: int): - for m in ["defend", "run", "fit", "train"]: - if hasattr(defense_obj, m) and callable(getattr(defense_obj, m)): - fn = getattr(defense_obj, m) - safe_run = _filter_kwargs(fn, dict(run_kwargs or {})) - safe_run["seed"] = seed - return fn(**safe_run) - if hasattr(defense_obj, "__call__"): - fn = getattr(defense_obj, "__call__") - safe_run = _filter_kwargs(fn, dict(run_kwargs or {})) - safe_run["seed"] = seed - return fn(**safe_run) - raise RuntimeError("No callable entrypoint found on defense object.") - - -def ensure_dir(path: str): - os.makedirs(path, exist_ok=True) - - -def write_jsonl(path: str, record: Dict[str, Any]): - ensure_dir(os.path.dirname(path)) - with open(path, "a", encoding="utf-8") as f: - f.write(json.dumps(record) + "\n") - - -def normalize_attack_return(ret) -> Tuple[Dict[str, Any], Dict[str, Any]]: - if isinstance(ret, tuple) and len(ret) == 2 and isinstance(ret[0], dict) and isinstance(ret[1], dict): - return ret[0], ret[1] - if isinstance(ret, dict): - return ret, {} - raise RuntimeError("attack.attack() did not return the expected (perf_dict, comp_dict) or dict.") - - -def normalize_defense_return(ret) -> Tuple[Dict[str, Any], Dict[str, Any]]: - if isinstance(ret, tuple) and len(ret) == 2 and isinstance(ret[0], dict) and isinstance(ret[1], dict): - return ret[0], ret[1] - if isinstance(ret, dict): - return ret, {} - raise RuntimeError("defense call did not return the expected (perf_dict, comp_dict) or dict.") - - -def timestamp() -> str: - return time.strftime("%Y-%m-%d_%H-%M-%S", time.localtime()) diff --git a/benchmark/scripts/RQ1_attacks.sh b/benchmark/scripts/RQ1_attacks.sh deleted file mode 100644 index 20565f6..0000000 --- a/benchmark/scripts/RQ1_attacks.sh +++ /dev/null @@ -1,66 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -GPU_ID="${1:-0}" - -DATASETS=("Cora" "Citeseer" "PubMed" "Amazon-Photo" "Proteins") -ATTACKS=("mea0" "mea1" "mea2" "mea3" "mea4" "mea5" "advmea" "cega" "realistic" "dfea_i" "dfea_ii" "dfea_iii") -BUDGETS=(0.25 0.5 1.0 2.0 4.0) -REGIMES=("both" "x_only" "a_only" "data_free") -SEEDS=(0 1 2) - -CONF_DIR="configs" -OUTDIR="outputs/RQ1" -LEADER_DIR="outputs/leaderboards" -mkdir -p "${CONF_DIR}" "${OUTDIR}" "${LEADER_DIR}" - -GRID_JSON="${CONF_DIR}/attack_grids_large.json" -cat > "${GRID_JSON}" <<'JSON' -{ - "mea0": [ { "ctor": {}, "run": {} } ], - "mea1": [ { "ctor": {}, "run": {} } ], - "mea2": [ { "ctor": {}, "run": {} } ], - "mea3": [ { "ctor": {}, "run": {} } ], - "mea4": [ { "ctor": {}, "run": {} } ], - "mea5": [ { "ctor": {}, "run": {} } ], - - "advmea": [ { "ctor": {}, "run": {} } ], - - "cega": [ - { "ctor": {}, "run": {"epochs_per_cycle": 1, "LR_CEGA": 0.01, "setup": "experiment"} }, - { "ctor": {}, "run": {"epochs_per_cycle": 2, "LR_CEGA": 0.01, "setup": "experiment"} }, - { "ctor": {}, "run": {"epochs_per_cycle": 1, "LR_CEGA": 0.005, "setup": "experiment"} }, - { "ctor": {}, "run": {"epochs_per_cycle": 1, "LR_CEGA": 0.01, "setup": "perturbation", "num_perturbations": 100, "noise_level": 0.05} } - ], - - "realistic": [ - { "ctor": {"hidden_dim": 32, "threshold_s": 0.60, "threshold_a": 0.40}, "run": {} }, - { "ctor": {"hidden_dim": 64, "threshold_s": 0.70, "threshold_a": 0.50}, "run": {} }, - { "ctor": {"hidden_dim": 128, "threshold_s": 0.75, "threshold_a": 0.60}, "run": {} }, - { "ctor": {"hidden_dim": 64, "threshold_s": 0.80, "threshold_a": 0.65}, "run": {} } - ], - - "dfea_i": [ { "ctor": {}, "run": {} } ], - "dfea_ii": [ { "ctor": {}, "run": {} } ], - "dfea_iii": [ { "ctor": {}, "run": {} } ] -} -JSON - -echo "[RQ1] Sweep attacks (with grids) on GPU ${GPU_ID} ..." -python run/run_attack_track.py \ - --datasets "${DATASETS[@]}" \ - --attacks "${ATTACKS[@]}" \ - --budgets "${BUDGETS[@]}" \ - --regimes "${REGIMES[@]}" \ - --seeds "${SEEDS[@]}" \ - --attack_grid_json "${GRID_JSON}" \ - --device "cuda:${GPU_ID}" \ - --outdir "${OUTDIR}" - -echo "[RQ1] Select best configs ..." -python run/select_best.py --rq1_dir "${OUTDIR}" --outdir "${LEADER_DIR}" - -echo "[RQ1] Export LaTeX tables ..." -python run/to_latex.py --outdir "${LEADER_DIR}" - -echo "[RQ1] Done. Raw -> ${OUTDIR}, Leaderboards & LaTeX -> ${LEADER_DIR}" diff --git a/benchmark/scripts/RQ2_defenses.sh b/benchmark/scripts/RQ2_defenses.sh deleted file mode 100644 index e3a3643..0000000 --- a/benchmark/scripts/RQ2_defenses.sh +++ /dev/null @@ -1,64 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -GPU_ID="${1:-0}" - -DATASETS=("Cora" "Citeseer" "PubMed" "Amazon-Photo" "Proteins") -DEFENSES=("randomwm" "backdoorwm" "survivewm" "imperceptiblewm") -SEEDS=(0 1 2) - -CONF_DIR="configs" -OUTDIR="outputs/RQ2_RQ3" -LEADER_DIR="outputs/leaderboards" -mkdir -p "${CONF_DIR}" "${OUTDIR}" "${LEADER_DIR}" - -GRID_JSON="${CONF_DIR}/defense_grids_large.json" -cat > "${GRID_JSON}" <<'JSON' -{ - "randomwm": [ - {"ctor": {"wm_ratio": 0.002}}, - {"ctor": {"wm_ratio": 0.005}}, - {"ctor": {"wm_ratio": 0.010}}, - {"ctor": {"wm_ratio": 0.020}}, - {"ctor": {"wm_ratio": 0.050}} - ], - "backdoorwm": [ - {"ctor": {"trigger_density": 0.01}}, - {"ctor": {"trigger_density": 0.02}}, - {"ctor": {"trigger_density": 0.05}}, - {"ctor": {"trigger_density": 0.10}}, - {"ctor": {"trigger_density": 0.20}} - ], - "survivewm": [ - {"ctor": {"wm_strength": 0.25}}, - {"ctor": {"wm_strength": 0.50}}, - {"ctor": {"wm_strength": 1.00}}, - {"ctor": {"wm_strength": 1.50}}, - {"ctor": {"wm_strength": 2.00}} - ], - "imperceptiblewm": [ - {"ctor": {"epsilon": 0.25}}, - {"ctor": {"epsilon": 0.50}}, - {"ctor": {"epsilon": 1.00}}, - {"ctor": {"epsilon": 2.00}}, - {"ctor": {"epsilon": 4.00}} - ] -} -JSON - -echo "[RQ2] Sweep defenses on GPU ${GPU_ID} ..." -python run/run_ownership_track.py \ - --datasets "${DATASETS[@]}" \ - --defenses "${DEFENSES[@]}" \ - --defense_grid_json "${GRID_JSON}" \ - --seeds "${SEEDS[@]}" \ - --device "cuda:${GPU_ID}" \ - --outdir "${OUTDIR}" - -echo "[RQ2] Select best configs ..." -python run/select_best.py --rq2_dir "${OUTDIR}" --outdir "${LEADER_DIR}" - -echo "[RQ2] Export LaTeX tables ..." -python run/to_latex.py --outdir "${LEADER_DIR}" - -echo "[RQ2] Done. Raw -> ${OUTDIR}, Leaderboards & LaTeX -> ${LEADER_DIR}" diff --git a/benchmark/scripts/RQ3_tradeoff.sh b/benchmark/scripts/RQ3_tradeoff.sh deleted file mode 100644 index 6f8a2d9..0000000 --- a/benchmark/scripts/RQ3_tradeoff.sh +++ /dev/null @@ -1,65 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -GPU_ID="${1:-0}" - -DATASETS=("Cora" "Citeseer" "PubMed" "Amazon-Photo" "Proteins") -DEFENSES=("randomwm" "backdoorwm" "survivewm" "imperceptiblewm") -SEEDS=(0 1 2) - -CONF_DIR="configs" -OUTDIR="outputs/RQ2_RQ3" -LEADER_DIR="outputs/leaderboards" -mkdir -p "${CONF_DIR}" "${OUTDIR}" "${LEADER_DIR}" - -GRID_JSON="${CONF_DIR}/defense_grids_dense.json" -cat > "${GRID_JSON}" <<'JSON' -{ - "randomwm": [ - {"ctor": {"wm_ratio": 0.001}}, - {"ctor": {"wm_ratio": 0.002}}, - {"ctor": {"wm_ratio": 0.005}}, - {"ctor": {"wm_ratio": 0.010}}, - {"ctor": {"wm_ratio": 0.020}}, - {"ctor": {"wm_ratio": 0.050}} - ], - "backdoorwm": [ - {"ctor": {"trigger_density": 0.005}}, - {"ctor": {"trigger_density": 0.010}}, - {"ctor": {"trigger_density": 0.020}}, - {"ctor": {"trigger_density": 0.050}}, - {"ctor": {"trigger_density": 0.100}}, - {"ctor": {"trigger_density": 0.200}} - ], - "survivewm": [ - {"ctor": {"wm_strength": 0.10}}, - {"ctor": {"wm_strength": 0.25}}, - {"ctor": {"wm_strength": 0.50}}, - {"ctor": {"wm_strength": 1.00}}, - {"ctor": {"wm_strength": 1.50}}, - {"ctor": {"wm_strength": 2.00}} - ], - "imperceptiblewm": [ - {"ctor": {"epsilon": 0.10}}, - {"ctor": {"epsilon": 0.25}}, - {"ctor": {"epsilon": 0.50}}, - {"ctor": {"epsilon": 1.00}}, - {"ctor": {"epsilon": 2.00}}, - {"ctor": {"epsilon": 4.00}} - ] -} -JSON - -echo "[RQ3] Sweep defenses densely for trade-off curves ..." -python run/run_ownership_track.py \ - --datasets "${DATASETS[@]}" \ - --defenses "${DEFENSES[@]}" \ - --defense_grid_json "${GRID_JSON}" \ - --seeds "${SEEDS[@]}" \ - --device "cuda:${GPU_ID}" \ - --outdir "${OUTDIR}" - -python run/select_best.py --rq2_dir "${OUTDIR}" --outdir "${LEADER_DIR}" -python run/to_latex.py --outdir "${LEADER_DIR}" - -echo "[RQ3] Done. Raw -> ${OUTDIR}, Leaderboards & LaTeX -> ${LEADER_DIR}" diff --git a/benchmark/scripts/RQ4_overhead.sh b/benchmark/scripts/RQ4_overhead.sh deleted file mode 100644 index b447688..0000000 --- a/benchmark/scripts/RQ4_overhead.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -RQ1_DIR="outputs/RQ1" -RQ2_DIR="outputs/RQ2_RQ3" -OUTDIR="outputs/RQ4_summary" -LEADER_DIR="outputs/leaderboards" -mkdir -p "${OUTDIR}" "${LEADER_DIR}" - -echo "[RQ4] Summarizing overhead ..." -python run/summarize_overhead.py \ - --rq1_dir "${RQ1_DIR}" \ - --rq2_dir "${RQ2_DIR}" \ - --outdir "${OUTDIR}" - -python run/select_best.py --rq1_dir "${RQ1_DIR}" --rq2_dir "${RQ2_DIR}" --outdir "${LEADER_DIR}" -python run/to_latex.py --outdir "${LEADER_DIR}" - -echo "[RQ4] Done. Summary CSVs -> ${OUTDIR}, Leaderboards & LaTeX -> ${LEADER_DIR}" diff --git a/docs/source/_autosummary/attack/pyhazard.models.attack.AdvMEA.rst b/docs/source/_autosummary/attack/pyhazard.models.attack.AdvMEA.rst deleted file mode 100644 index e8b1e51..0000000 --- a/docs/source/_autosummary/attack/pyhazard.models.attack.AdvMEA.rst +++ /dev/null @@ -1,30 +0,0 @@ -pyhazard.models.attack.AdvMEA -========================== - -.. automodule:: pyhazard.models.attack.AdvMEA - - - - .. rubric:: Module Attributes - - .. autosummary:: - - supported_api_types - supported_datasets - - - - - - - - - - - - - - - - - diff --git a/docs/source/_autosummary/attack/pyhazard.models.attack.CEGA.rst b/docs/source/_autosummary/attack/pyhazard.models.attack.CEGA.rst deleted file mode 100644 index 6f135d0..0000000 --- a/docs/source/_autosummary/attack/pyhazard.models.attack.CEGA.rst +++ /dev/null @@ -1,30 +0,0 @@ -pyhazard.models.attack.CEGA -======================== - -.. automodule:: pyhazard.models.attack.CEGA - - - - .. rubric:: Module Attributes - - .. autosummary:: - - supported_api_types - supported_datasets - - - - - - - - - - - - - - - - - diff --git a/docs/source/_autosummary/attack/pyhazard.models.attack.DataFreeMEA.rst b/docs/source/_autosummary/attack/pyhazard.models.attack.DataFreeMEA.rst deleted file mode 100644 index 92aa6c1..0000000 --- a/docs/source/_autosummary/attack/pyhazard.models.attack.DataFreeMEA.rst +++ /dev/null @@ -1,33 +0,0 @@ -pyhazard.models.attack.DataFreeMEA -=============================== - -.. automodule:: pyhazard.models.attack.DataFreeMEA - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - DFEAAttack - DFEATypeI - DFEATypeII - DFEATypeIII - GraphGenerator - - - - - - - - - diff --git a/docs/source/_autosummary/attack/pyhazard.models.attack.Realistic.rst b/docs/source/_autosummary/attack/pyhazard.models.attack.Realistic.rst deleted file mode 100644 index e58ce2e..0000000 --- a/docs/source/_autosummary/attack/pyhazard.models.attack.Realistic.rst +++ /dev/null @@ -1,31 +0,0 @@ -pyhazard.models.attack.Realistic -============================= - -.. automodule:: pyhazard.models.attack.Realistic - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - DGLEdgePredictor - DGLSurrogateModel - RealisticAttack - - - - - - - - - diff --git a/docs/source/_autosummary/attack/pyhazard.models.attack.base.rst b/docs/source/_autosummary/attack/pyhazard.models.attack.base.rst deleted file mode 100644 index 08c52c7..0000000 --- a/docs/source/_autosummary/attack/pyhazard.models.attack.base.rst +++ /dev/null @@ -1,29 +0,0 @@ -pyhazard.models.attack.base -======================== - -.. automodule:: pyhazard.models.attack.base - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - BaseAttack - - - - - - - - - diff --git a/docs/source/_autosummary/attack/pyhazard.models.attack.mea.MEA.rst b/docs/source/_autosummary/attack/pyhazard.models.attack.mea.MEA.rst deleted file mode 100644 index 5dd9cf5..0000000 --- a/docs/source/_autosummary/attack/pyhazard.models.attack.mea.MEA.rst +++ /dev/null @@ -1,35 +0,0 @@ -pyhazard.models.attack.mea.MEA -=========================== - -.. automodule:: pyhazard.models.attack.mea.MEA - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - ModelExtractionAttack - ModelExtractionAttack0 - ModelExtractionAttack1 - ModelExtractionAttack2 - ModelExtractionAttack3 - ModelExtractionAttack4 - ModelExtractionAttack5 - - - - - - - - - diff --git a/docs/source/_autosummary/defense/pyhazard.models.defense.BackdoorWM.rst b/docs/source/_autosummary/defense/pyhazard.models.defense.BackdoorWM.rst deleted file mode 100644 index ac2a3ef..0000000 --- a/docs/source/_autosummary/defense/pyhazard.models.defense.BackdoorWM.rst +++ /dev/null @@ -1,30 +0,0 @@ -pyhazard.models.defense.BackdoorWM -=============================== - -.. automodule:: pyhazard.models.defense.BackdoorWM - - - - .. rubric:: Module Attributes - - .. autosummary:: - - supported_api_types - supported_datasets - - - - - - - - - - - - - - - - - diff --git a/docs/source/_autosummary/defense/pyhazard.models.defense.ImperceptibleWM.rst b/docs/source/_autosummary/defense/pyhazard.models.defense.ImperceptibleWM.rst deleted file mode 100644 index 3efb3f8..0000000 --- a/docs/source/_autosummary/defense/pyhazard.models.defense.ImperceptibleWM.rst +++ /dev/null @@ -1,30 +0,0 @@ -pyhazard.models.defense.ImperceptibleWM -==================================== - -.. automodule:: pyhazard.models.defense.ImperceptibleWM - - - - .. rubric:: Module Attributes - - .. autosummary:: - - supported_api_types - supported_datasets - - - - - - - - - - - - - - - - - diff --git a/docs/source/_autosummary/defense/pyhazard.models.defense.Integrity.rst b/docs/source/_autosummary/defense/pyhazard.models.defense.Integrity.rst deleted file mode 100644 index dccae78..0000000 --- a/docs/source/_autosummary/defense/pyhazard.models.defense.Integrity.rst +++ /dev/null @@ -1,33 +0,0 @@ -pyhazard.models.defense.Integrity -============================== - -.. automodule:: pyhazard.models.defense.Integrity - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - BitFlipAttack - InductiveFingerprintGenerator - MettackHelper - QueryBasedVerificationDefense - TransductiveFingerprintGenerator - - - - - - - - - diff --git a/docs/source/_autosummary/defense/pyhazard.models.defense.RandomWM.rst b/docs/source/_autosummary/defense/pyhazard.models.defense.RandomWM.rst deleted file mode 100644 index 8bb727b..0000000 --- a/docs/source/_autosummary/defense/pyhazard.models.defense.RandomWM.rst +++ /dev/null @@ -1,30 +0,0 @@ -pyhazard.models.defense.RandomWM -============================= - -.. automodule:: pyhazard.models.defense.RandomWM - - - - .. rubric:: Module Attributes - - .. autosummary:: - - supported_api_types - supported_datasets - - - - - - - - - - - - - - - - - diff --git a/docs/source/_autosummary/defense/pyhazard.models.defense.SurviveWM.rst b/docs/source/_autosummary/defense/pyhazard.models.defense.SurviveWM.rst deleted file mode 100644 index 4fc383d..0000000 --- a/docs/source/_autosummary/defense/pyhazard.models.defense.SurviveWM.rst +++ /dev/null @@ -1,30 +0,0 @@ -pyhazard.models.defense.SurviveWM -============================== - -.. automodule:: pyhazard.models.defense.SurviveWM - - - - .. rubric:: Module Attributes - - .. autosummary:: - - supported_api_types - supported_datasets - - - - - - - - - - - - - - - - - diff --git a/docs/source/_autosummary/defense/pyhazard.models.defense.atom.ATOM.rst b/docs/source/_autosummary/defense/pyhazard.models.defense.atom.ATOM.rst deleted file mode 100644 index ebafb1a..0000000 --- a/docs/source/_autosummary/defense/pyhazard.models.defense.atom.ATOM.rst +++ /dev/null @@ -1,61 +0,0 @@ -pyhazard.models.defense.atom.ATOM -============================== - -.. automodule:: pyhazard.models.defense.atom.ATOM - - - - - - - - .. rubric:: Functions - - .. autosummary:: - - average_pooling_with_neighbors - average_pooling_with_neighbors_batch - build_loaders - collate_fn_no_pad - compute_embedding_batch - compute_returns_and_advantages - custom_reward_function - get_node_embedding - get_one_hop_neighbors - k_core_decomposition - load_data_and_model - precompute_all_node_embeddings - precompute_simple_embeddings - preprocess_sequences - set_seed - simple_embedding_batch - split_and_adjust - test_model - train_gcn - - - - - - .. rubric:: Classes - - .. autosummary:: - - ATOM - FusionGRU - GCN - Memory - PPOAgent - PolicyNetwork - SequencesDataset - StateTransformMLP - TargetGCN - - - - - - - - - diff --git a/docs/source/_autosummary/defense/pyhazard.models.defense.base.rst b/docs/source/_autosummary/defense/pyhazard.models.defense.base.rst deleted file mode 100644 index 7a4aaf0..0000000 --- a/docs/source/_autosummary/defense/pyhazard.models.defense.base.rst +++ /dev/null @@ -1,29 +0,0 @@ -pyhazard.models.defense.base -========================= - -.. automodule:: pyhazard.models.defense.base - - - - - - - - - - - - .. rubric:: Classes - - .. autosummary:: - - BaseDefense - - - - - - - - - diff --git a/docs/source/api/pyhazard.models.attack.mea.rst b/docs/source/api/pyhazard.models.attack.mea.rst deleted file mode 100644 index 2632c5d..0000000 --- a/docs/source/api/pyhazard.models.attack.mea.rst +++ /dev/null @@ -1,21 +0,0 @@ -pyhazard.models.attack.mea package -=============================== - -Submodules ----------- - -pyhazard.models.attack.mea.MEA module ----------------------------------- - -.. automodule:: pyhazard.models.attack.mea.MEA - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: pyhazard.models.attack.mea - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/api/pyhazard.models.attack.rst b/docs/source/api/pyhazard.models.attack.rst deleted file mode 100644 index f8b7655..0000000 --- a/docs/source/api/pyhazard.models.attack.rst +++ /dev/null @@ -1,53 +0,0 @@ -pyhazard.models.attack package -=========================== - -Subpackages ------------ - -.. toctree:: - :maxdepth: 4 - - pyhazard.models.attack.mea - -Submodules ----------- - -pyhazard.models.attack.AdvMEA module ---------------------------------- - -.. automodule:: pyhazard.models.attack.AdvMEA - :members: - :undoc-members: - :show-inheritance: - -pyhazard.models.attack.CEGA module -------------------------------- - -.. automodule:: pyhazard.models.attack.CEGA - :members: - :undoc-members: - :show-inheritance: - -pyhazard.models.attack.DataFreeMEA module --------------------------------------- - -.. automodule:: pyhazard.models.attack.DataFreeMEA - :members: - :undoc-members: - :show-inheritance: - -pyhazard.models.attack.base module -------------------------------- - -.. automodule:: pyhazard.models.attack.base - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: pyhazard.models.attack - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/api/pyhazard.models.defense.atom.rst b/docs/source/api/pyhazard.models.defense.atom.rst deleted file mode 100644 index 9321c4f..0000000 --- a/docs/source/api/pyhazard.models.defense.atom.rst +++ /dev/null @@ -1,21 +0,0 @@ -pyhazard.models.defense.atom package -================================= - -Submodules ----------- - -pyhazard.models.defense.atom.ATOM module -------------------------------------- - -.. automodule:: pyhazard.models.defense.atom.ATOM - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: pyhazard.models.defense.atom - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/api/pyhazard.models.defense.rst b/docs/source/api/pyhazard.models.defense.rst deleted file mode 100644 index 784aedd..0000000 --- a/docs/source/api/pyhazard.models.defense.rst +++ /dev/null @@ -1,77 +0,0 @@ -pyhazard.models.defense package -============================ - -Subpackages ------------ - -.. toctree:: - :maxdepth: 4 - - pyhazard.models.defense.atom - -Submodules ----------- - -pyhazard.models.defense.BackdoorWM module --------------------------------------- - -.. automodule:: pyhazard.models.defense.BackdoorWM - :members: - :undoc-members: - :show-inheritance: - -pyhazard.models.defense.ImperceptibleWM module -------------------------------------------- - -.. automodule:: pyhazard.models.defense.ImperceptibleWM - :members: - :undoc-members: - :show-inheritance: - -pyhazard.models.defense.ImperceptibleWM2 module --------------------------------------------- - -.. automodule:: pyhazard.models.defense.ImperceptibleWM2 - :members: - :undoc-members: - :show-inheritance: - -pyhazard.models.defense.RandomWM module ------------------------------------- - -.. automodule:: pyhazard.models.defense.RandomWM - :members: - :undoc-members: - :show-inheritance: - -pyhazard.models.defense.SurviveWM module -------------------------------------- - -.. automodule:: pyhazard.models.defense.SurviveWM - :members: - :undoc-members: - :show-inheritance: - -pyhazard.models.defense.SurviveWM2 module --------------------------------------- - -.. automodule:: pyhazard.models.defense.SurviveWM2 - :members: - :undoc-members: - :show-inheritance: - -pyhazard.models.defense.base module --------------------------------- - -.. automodule:: pyhazard.models.defense.base - :members: - :undoc-members: - :show-inheritance: - -Module contents ---------------- - -.. automodule:: pyhazard.models.defense - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/source/benchmark.rst b/docs/source/benchmark.rst deleted file mode 100644 index efde6b4..0000000 --- a/docs/source/benchmark.rst +++ /dev/null @@ -1,5 +0,0 @@ -Benchmark -========= - -Coming soon... - diff --git a/docs/source/index.rst b/docs/source/index.rst index 9e860ab..8d62641 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -42,100 +42,67 @@ ---- -**PyHazard** is a comprehensive Python library focused on model extraction attacks and defenses in Graph Neural Networks (GNNs). Built on PyTorch, PyTorch Geometric, and DGL, the library offers a robust framework for understanding, implementing, and defending against attacks targeting GNN models. +**PyHazard** is a comprehensive Python framework for AI-powered hazard prediction and risk assessment. Built on PyTorch, PyTorch Geometric, and DGL, the library provides a modular and extensible architecture for building, training, and deploying machine learning models to predict and analyze natural hazards and environmental risks. -**PyHazard is featured for:** +**PyHazard is designed for:** -- **Extensive Attack Implementations**: Multiple strategies for GNN model extraction attacks, including fidelity and accuracy evaluation. -- **Defensive Techniques**: Tools for creating robust defense mechanisms, such as watermarking graphs and inserting synthetic nodes. -- **Unified API**: Intuitive APIs for both attacks and defenses. -- **Integration with PyTorch/DGL**: Seamlessly integrates with PyTorch Geometric and DGL for scalable graph processing. -- **Customizable**: Supports user-defined attack and defense configurations. +- **Modular Architecture**: Easy-to-extend framework for implementing custom hazard prediction models +- **Graph Neural Networks**: Built-in support for graph-based data representation and GNN models +- **Flexible Datasets**: Unified dataset interface supporting both DGL and PyTorch Geometric +- **GPU Acceleration**: Full CUDA support for training and inference +- **Extensible Models**: Base classes for implementing custom prediction models +- **Production Ready**: Type hints, proper error handling, and comprehensive testing **Quick Start Example:** -Model Extraction Attack Example with 5 Lines of Code: +Basic Usage Example: .. code-block:: python - from datasets import Cora - from models.attack import ModelExtractionAttack0 + import pyhazard + from pyhazard.datasets import Cora + from pyhazard.models.nn import GCN - # Load the Cora dataset - dataset = Cora() + # Load a dataset + dataset = Cora(api_type='pyg') - # Initialize the attack with a sampling ratio of 0.25 - mea = ModelExtractionAttack0(dataset, 0.25) + # Initialize a model + model = GCN( + input_dim=dataset.num_features, + hidden_dim=64, + output_dim=dataset.num_classes + ) - # Execute the attack - mea.attack() + # Your training and prediction code here + # ... -Attack Modules +Core Components --------------- -.. list-table:: - :header-rows: 1 +**Datasets** + PyHazard provides a unified dataset interface that supports both DGL and PyTorch Geometric formats. + Built-in datasets include Cora, CiteSeer, PubMed, and more. - * - Class Name - - Reference - - * - :doc:`MEA <_autosummary/attack/pyhazard.models.attack.mea.MEA>` - - Wu, Bang, et al. "Model extraction attacks on graph neural networks: Taxonomy and realisation." Proceedings of the 2022 ACM on Asia conference on computer and communications security. 2022. - - * - :doc:`AdvMEA <_autosummary/attack/pyhazard.models.attack.AdvMEA>` - - DeFazio, David, and Arti Ramesh. "Adversarial model extraction on graph neural networks." arXiv preprint arXiv:1912.07721 (2019). - - * - :doc:`CEGA <_autosummary/attack/pyhazard.models.attack.CEGA>` - - Wang, Zebin, et al. "CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition." arXiv preprint arXiv:2506.17709 (2025). - - * - :doc:`DataFreeMEA <_autosummary/attack/pyhazard.models.attack.DataFreeMEA>` - - Zhuang, Yuanxin, et al. "Unveiling the Secrets without Data: Can Graph Neural Networks Be Exploited through {Data-Free} Model Extraction Attacks?." 33rd USENIX Security Symposium (USENIX Security 24). 2024. - - * - :doc:`Realistic <_autosummary/attack/pyhazard.models.attack.Realistic>` - - Guan, Faqian, et al. "A realistic model extraction attack against graph neural networks." Knowledge-Based Systems 300 (2024): 112144. - - -Defense Modules ---------------- - -.. list-table:: - :header-rows: 1 - - * - Class Name - - Reference - - * - :doc:`RandomWM <_autosummary/defense/pyhazard.models.defense.RandomWM>` - - Zhao, Xiangyu, Hanzhou Wu, and Xinpeng Zhang. "Watermarking graph neural networks by random graphs." 2021 9th International Symposium on Digital Forensics and Security (ISDFS). IEEE, 2021. - - * - :doc:`BackdoorWM <_autosummary/defense/pyhazard.models.defense.BackdoorWM>` - - Xu, Jing, et al. "Watermarking graph neural networks based on backdoor attacks." 2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P). IEEE, 2023. - - * - :doc:`SurviveWM <_autosummary/defense/pyhazard.models.defense.SurviveWM>` - - Wang, Haiming, et al. "Making Watermark Survive Model Extraction Attacks in Graph Neural Networks." ICC 2023-IEEE International Conference on Communications. IEEE, 2023. - - * - :doc:`ImperceptibleWM <_autosummary/defense/pyhazard.models.defense.ImperceptibleWM>` - - Zhang, Linji, et al. "An imperceptible and owner-unique watermarking method for graph neural networks." Proceedings of the ACM Turing Award Celebration Conference-China 2024. 2024. - - * - :doc:`ATOM <_autosummary/defense/pyhazard.models.defense.atom.ATOM>` - - Cheng, Zhan, et al. "Atom: A framework of detecting query-based model extraction attacks for graph neural networks." Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 2. 2025. - - * - :doc:`Integrity <_autosummary/defense/pyhazard.models.defense.Integrity>` - - Wu, Bang, et al. "Securing graph neural networks in mlaas: A comprehensive realization of query-based integrity verification." 2024 IEEE Symposium on Security and Privacy (SP). IEEE, 2024. +**Models** + Extensible model architecture with built-in neural network backbones including GCN, GAT, and more. + Easy to implement custom models by extending base classes. +**Utilities** + Helper functions for device management, metrics calculation, and data conversion between DGL and PyTorch Geometric. How to Cite ----------- -If you find it useful, please considering cite the following work: +If you use PyHazard in your research, please cite: .. code-block:: bibtex - @article{li2025intellectual, - title={Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses}, - author={Li, Lincan and Shen, Bolin and Zhao, Chenxi and Sun, Yuxiang and Zhao, Kaixiang and Pan, Shirui and Dong, Yushun}, - journal={arXiv preprint arXiv:2508.19641}, - year={2025} - } + @software{pyhazard2025, + title={PyHazard: A Python Framework for AI-Powered Hazard Prediction}, + author={Cheng, Xueqi}, + year={2025}, + url={https://github.com/LabRAI/PyHazard} + } .. toctree:: @@ -145,7 +112,6 @@ If you find it useful, please considering cite the following work: installation quick_start - benchmark .. toctree:: :maxdepth: 1 @@ -153,8 +119,6 @@ If you find it useful, please considering cite the following work: :hidden: pyhazard_datasets - pyhazard_models_attack - pyhazard_models_defense pyhazard_utils .. toctree:: diff --git a/docs/source/pyhazard_models_attack.rst b/docs/source/pyhazard_models_attack.rst deleted file mode 100644 index b2a1e7e..0000000 --- a/docs/source/pyhazard_models_attack.rst +++ /dev/null @@ -1,22 +0,0 @@ -Attack -=================== - -Summary -------- - -In this section, we present all currently supported **Model Extraction Attack** modules in PyHazard. - -Submodules ----------- - -.. autosummary:: - :toctree: _autosummary/attack - :template: autosummary/module.rst - :recursive: - - pyhazard.models.attack.base - pyhazard.models.attack.mea.MEA - pyhazard.models.attack.AdvMEA - pyhazard.models.attack.CEGA - pyhazard.models.attack.DataFreeMEA - pyhazard.models.attack.Realistic \ No newline at end of file diff --git a/docs/source/pyhazard_models_defense.rst b/docs/source/pyhazard_models_defense.rst deleted file mode 100644 index ba2af42..0000000 --- a/docs/source/pyhazard_models_defense.rst +++ /dev/null @@ -1,23 +0,0 @@ -Defense -=================== - -Summary -------- - -In this section, we present all currently supported **Model Extraction Defense** modules in PyHazard. - -Submodules ----------- - -.. autosummary:: - :toctree: _autosummary/defense - :template: autosummary/module.rst - :recursive: - - pyhazard.models.defense.base - pyhazard.models.defense.RandomWM - pyhazard.models.defense.BackdoorWM - pyhazard.models.defense.SurviveWM - pyhazard.models.defense.ImperceptibleWM - pyhazard.models.defense.atom.ATOM - pyhazard.models.defense.Integrity diff --git a/docs/source/quick_start.rst b/docs/source/quick_start.rst index b9a943a..0aa548e 100644 --- a/docs/source/quick_start.rst +++ b/docs/source/quick_start.rst @@ -2,81 +2,118 @@ Quick Start ================= This guide will help you get started with PyHazard quickly. -Attack Examples ---------------- +Basic Usage +----------- -Model Extraction Attack -~~~~~~~~~~~~~~~~~~~~~~~ +Loading a Dataset +~~~~~~~~~~~~~~~~~ .. code-block:: python - from datasets import Cora - from models.attack import ModelExtractionAttack0 + from pyhazard.datasets import Cora - # Load the Cora dataset - dataset = Cora() + # Load the Cora dataset with PyTorch Geometric API + dataset = Cora(api_type='pyg') - # Initialize the attack with a sampling ratio of 0.25 - mea = ModelExtractionAttack0(dataset, 0.25) + print(f"Number of nodes: {dataset.num_nodes}") + print(f"Number of features: {dataset.num_features}") + print(f"Number of classes: {dataset.num_classes}") - # Execute the attack - mea.attack() +Using Graph Neural Networks +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -To run the attack example: +.. code-block:: python -.. code-block:: bash + import pyhazard + from pyhazard.datasets import Cora + from pyhazard.models.nn import GCN + from pyhazard.utils import get_device - python examples/attack/MEAs.py + # Load dataset + dataset = Cora(api_type='pyg') -Defense Examples ----------------- + # Initialize model + device = get_device() + model = GCN( + input_dim=dataset.num_features, + hidden_dim=64, + output_dim=dataset.num_classes + ).to(device) + + # Your training code here + # ... + +GPU Support +----------- + +PyHazard automatically detects CUDA availability. To explicitly set the device: + +**Using Environment Variable:** + +.. code-block:: bash -RandomWM Defense -~~~~~~~~~~~~~~~~ + export PYHAZARD_DEVICE=cuda:0 + +**Using Python API:** .. code-block:: python - from datasets import Cora - from models.defense import RandomWM + from pyhazard.utils import set_device - # Load the Cora dataset - dataset = Cora() + # Set to use CUDA device 0 + set_device("cuda:0") - # Initialize the attack with a sampling ratio of 0.25 - med = RandomWM(dataset, 0.25) + # Or use CPU + set_device("cpu") - # Execute the defense - med.defend() +Available Datasets +------------------ -To run the defense example: +PyHazard includes several built-in graph datasets: -.. code-block:: bash +- **Cora**: Citation network (2708 nodes, 1433 features, 7 classes) +- **CiteSeer**: Citation network (3327 nodes, 3703 features, 6 classes) +- **PubMed**: Citation network (19717 nodes, 500 features, 3 classes) +- **Computers**: Amazon co-purchase network +- **Photo**: Amazon co-purchase network +- **CoauthorCS**: Co-authorship network (Computer Science) +- **CoauthorPhysics**: Co-authorship network (Physics) - python examples/defense/RandomWM.py +Example with different datasets: -GPU Support ------------ +.. code-block:: python -If you want to use cuda, please set environment variable: + from pyhazard.datasets import CiteSeer, PubMed, Computers -.. code-block:: bash + # Load CiteSeer + citeseer = CiteSeer(api_type='pyg') + + # Load PubMed + pubmed = PubMed(api_type='pyg') - export PYGIP_DEVICE=cuda:0 + # Load Computers + computers = Computers(api_type='pyg') -Alternatively, you can explicitly specify the device in your code: +API Types +--------- + +PyHazard supports both DGL and PyTorch Geometric APIs: .. code-block:: python - from pyhazard.utils.hardware import set_device + from pyhazard.datasets import Cora - set_device("cuda:0") + # PyTorch Geometric format + dataset_pyg = Cora(api_type='pyg') + # DGL format + dataset_dgl = Cora(api_type='dgl') Next Steps ---------- For more detailed documentation, please refer to: -- :doc:`pyhazard_datasets` - Available datasets -- :doc:`pyhazard_models_attack` - Detailed attack mechanisms -- :doc:`pyhazard_models_defense` - Detailed defense mechanisms +- :doc:`pyhazard_datasets` - Complete dataset API reference +- :doc:`pyhazard_utils` - Utility functions and helpers +- :doc:`implementation` - Guide for implementing custom models diff --git a/docs/source/team.rst b/docs/source/team.rst index 9b094f9..9e198be 100644 --- a/docs/source/team.rst +++ b/docs/source/team.rst @@ -3,14 +3,13 @@ Core Team Our team is composed of dedicated researchers and developers who contribute to PyHazard's development and maintenance. -Founder -------- -* `Yushun Dong `__ - Florida State University +Lead Developer +-------------- +* Xueqi Cheng - Florida State University (xc25@fsu.edu) -Architects ----------- -* `Bolin Shen `__ - Florida State University -* `Kaixiang Zhao `__ - University of Notre Dame +Contributors +------------ +* `Yushun Dong `__ - Florida State University Principal Contributors & Maintainers ------------------------------------ diff --git a/examples/__init__.py b/examples/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/examples/attack/AdvMEA.py b/examples/attack/AdvMEA.py deleted file mode 100644 index 9baf219..0000000 --- a/examples/attack/AdvMEA.py +++ /dev/null @@ -1,18 +0,0 @@ -from pyhazard.datasets import * -from pyhazard.models.attack import AdvMEA -from pyhazard.utils.hardware import set_device - -# TODO verify performance -# TODO attack after defense - -set_device("cpu") # cpu, cuda:0 - - -def advmea(): - dataset = Cora(api_type='dgl') - mea = AdvMEA(dataset, attack_node_fraction=0.1) - mea.attack() - - -if __name__ == '__main__': - advmea() diff --git a/examples/attack/CEGA.py b/examples/attack/CEGA.py deleted file mode 100644 index 21ec85b..0000000 --- a/examples/attack/CEGA.py +++ /dev/null @@ -1,16 +0,0 @@ -from pyhazard.datasets import * -from pyhazard.models.attack import CEGA - - -# TODO verify performance -# TODO record metrics (original acc, attack acc, fidelity) - - -def cega(): - dataset = Cora(api_type='dgl') - mea = CEGA(dataset, attack_node_fraction=0.1) - mea.attack() - - -if __name__ == '__main__': - cega() diff --git a/examples/attack/DataFreeMEA.py b/examples/attack/DataFreeMEA.py deleted file mode 100644 index 7fa443f..0000000 --- a/examples/attack/DataFreeMEA.py +++ /dev/null @@ -1,24 +0,0 @@ -from pyhazard.datasets import Cora -from pyhazard.models.attack import DFEATypeI, DFEATypeII -from pyhazard.utils.hardware import set_device - -# TODO verify performance -# TODO record metrics (original acc, attack acc, fidelity) - -set_device("cuda:0") - - -def dfea_type1(): - dataset = Cora(api_type='dgl') - mea = DFEATypeI(dataset, attack_node_fraction=0.1) - mea.attack() - - -def dfea_type2(): - dataset = Cora(api_type='dgl') - mea = DFEATypeII(dataset, attack_node_fraction=0.1) - mea.attack() - - -if __name__ == '__main__': - dfea_type2() diff --git a/examples/attack/MEA.py b/examples/attack/MEA.py deleted file mode 100644 index a205079..0000000 --- a/examples/attack/MEA.py +++ /dev/null @@ -1,51 +0,0 @@ -from pyhazard.datasets import * -from pyhazard.models.attack import ModelExtractionAttack0, ModelExtractionAttack1, ModelExtractionAttack2, \ - ModelExtractionAttack3, ModelExtractionAttack4, ModelExtractionAttack5 -from pyhazard.utils.hardware import set_device - -# TODO verify performance -# TODO generate shadow graph -# TODO record metrics (original acc, attack acc, fidelity) - -set_device("cuda:0") # cpu, cuda:0 - - -def mea0(): - dataset = CiteSeer(api_type='dgl') - print(dataset) - mea = ModelExtractionAttack0(dataset, attack_node_fraction=0.1) - mea.attack() - - -def mea1(): - dataset = Cora(api_type='dgl') - mea = ModelExtractionAttack1(dataset, attack_node_fraction=0.1) - mea.attack() - - -def mea2(): - dataset = Cora(api_type='dgl') - mea = ModelExtractionAttack2(dataset, attack_node_fraction=0.1) - mea.attack() - - -def mea3(): - dataset = Cora(api_type='dgl') - mea = ModelExtractionAttack3(dataset, attack_node_fraction=0.1) - mea.attack() - - -def mea4(): - dataset = Cora(api_type='dgl') - mea = ModelExtractionAttack4(dataset, attack_node_fraction=0.1) - mea.attack() - - -def mea5(): - dataset = Cora(api_type='dgl') - mea = ModelExtractionAttack5(dataset, attack_node_fraction=0.1) - mea.attack() - - -if __name__ == '__main__': - mea0() diff --git a/examples/attack/Realistic.py b/examples/attack/Realistic.py deleted file mode 100644 index 4d272e5..0000000 --- a/examples/attack/Realistic.py +++ /dev/null @@ -1,24 +0,0 @@ -from pyhazard.datasets import Cora -from pyhazard.models.attack import RealisticAttack -from pyhazard.utils.hardware import set_device - -# TODO verify performance -# TODO record metrics (original acc, attack acc, fidelity) - -set_device("cuda:0") - - -def realistic(): - dataset = Cora(api_type='dgl') - mea = RealisticAttack( - dataset=dataset, - attack_node_fraction=0.05, - hidden_dim=64, - threshold_s=0.6, # Cosine similarity threshold - threshold_a=0.4 # Edge prediction threshold - ) - mea.attack() - - -if __name__ == '__main__': - realistic() diff --git a/examples/attack/__init__.py b/examples/attack/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/examples/defense/ATOM.py b/examples/defense/ATOM.py deleted file mode 100644 index 4004ccd..0000000 --- a/examples/defense/ATOM.py +++ /dev/null @@ -1,18 +0,0 @@ -from pyhazard.datasets import Cora -from pyhazard.models.defense import ATOM - - -# TODO test datasets -# TODO generate query set -# TODO test gpu -# TODO verify performance -# TODO record metrics (original acc, defense acc, fidelity) - -def atom(): - dataset = Cora(api_type='pyg') - med = ATOM(dataset, attack_node_fraction=0.1) - med.defend() - - -if __name__ == '__main__': - atom() diff --git a/examples/defense/BackdoorWM.py b/examples/defense/BackdoorWM.py deleted file mode 100644 index acbb4cc..0000000 --- a/examples/defense/BackdoorWM.py +++ /dev/null @@ -1,17 +0,0 @@ -from pyhazard.datasets import * -from pyhazard.models.defense import BackdoorWM - - -# TODO test datasets -# TODO test gpu -# TODO verify performance -# TODO record metrics (original acc, defense acc, fidelity) - -def backdoorwm(): - dataset = Cora(api_type='dgl') - med = BackdoorWM(dataset, attack_node_fraction=0.1, trigger_rate=0.1, l=20, target_label=0) - med.defend() - - -if __name__ == '__main__': - backdoorwm() diff --git a/examples/defense/GrOVe.py b/examples/defense/GrOVe.py deleted file mode 100644 index 7af59b7..0000000 --- a/examples/defense/GrOVe.py +++ /dev/null @@ -1,20 +0,0 @@ -from pyhazard.datasets import * -from pyhazard.models.defense import GroveDefense - - -# TODO test datasets -# TODO test gpu -# TODO verify performance -# TODO record metrics (verification accuracy, defense time, inference time) - -def grovedefense(): - dataset = Cora(api_type='dgl') - med = GroveDefense(dataset, attack_node_fraction=0.1, - hidden_dim=256, - verification_threshold=0.5, - num_surrogate_models=3) - _, res_comp = med.defend() - - -if __name__ == '__main__': - grovedefense() diff --git a/examples/defense/ImperceptibleWM.py b/examples/defense/ImperceptibleWM.py deleted file mode 100644 index f765fd8..0000000 --- a/examples/defense/ImperceptibleWM.py +++ /dev/null @@ -1,17 +0,0 @@ -from pyhazard.datasets import Cora -from pyhazard.models.defense import ImperceptibleWM - - -# TODO test datasets -# TODO test gpu -# TODO verify performance -# TODO record metrics (original acc, defense acc, fidelity) - -def imperceptiblewm(): - dataset = Cora(api_type='pyg') - med = ImperceptibleWM(dataset, defense_ratio=0.1) - med.defend() - - -if __name__ == '__main__': - imperceptiblewm() diff --git a/examples/defense/Integrity.py b/examples/defense/Integrity.py deleted file mode 100644 index a3cc00b..0000000 --- a/examples/defense/Integrity.py +++ /dev/null @@ -1,17 +0,0 @@ -from pyhazard.datasets import Cora -from pyhazard.models.defense import IntegrityVerification - - -# TODO test datasets -# TODO test gpu -# TODO verify performance -# TODO record metrics (original acc, defense acc, fidelity) - -def integrity(): - dataset = Cora(api_type='dgl') - med = IntegrityVerification(dataset, defense_ratio=0.1) - med.defend() - - -if __name__ == '__main__': - integrity() diff --git a/examples/defense/RandomWM.py b/examples/defense/RandomWM.py deleted file mode 100644 index 16e8f7c..0000000 --- a/examples/defense/RandomWM.py +++ /dev/null @@ -1,17 +0,0 @@ -from pyhazard.datasets import Cora -from pyhazard.models.defense import RandomWM - - -# TODO test datasets -# TODO test gpu -# TODO verify performance -# TODO record metrics (original acc, defense acc, fidelity) - -def randomwm(): - dataset = Cora(api_type='dgl') - med = RandomWM(dataset, defense_ratio=0.1, wm_node=50, pr=0.1, pg=0.1) - med.defend() - - -if __name__ == '__main__': - randomwm() diff --git a/examples/defense/Revisiting.py b/examples/defense/Revisiting.py deleted file mode 100644 index ce0c973..0000000 --- a/examples/defense/Revisiting.py +++ /dev/null @@ -1,22 +0,0 @@ -from pyhazard.datasets import Cora -from pyhazard.models.defense import Revisiting - - -def main(): - # Load dataset - dataset = Cora() - - # Init defense (tweak params as needed) - defense = Revisiting( - dataset, - attack_node_fraction=0.20, # fraction of nodes to mix - alpha=0.80, # neighbor-mixing strength [0,1] - ) - - print("Initialized Revisiting defense; starting defend()...") - results = defense.defend() - print("Defense finished. Results:", results) - - -if __name__ == "__main__": - main() diff --git a/examples/defense/SurviveWM.py b/examples/defense/SurviveWM.py deleted file mode 100644 index 391780d..0000000 --- a/examples/defense/SurviveWM.py +++ /dev/null @@ -1,17 +0,0 @@ -from pyhazard.datasets import Cora -from pyhazard.models.defense import SurviveWM - - -# TODO test datasets -# TODO test gpu -# TODO verify performance -# TODO record metrics (original acc, defense acc, fidelity) - -def survivewm(): - dataset = Cora(api_type='dgl') - med = SurviveWM(dataset, defense_ratio=0.1) - med.defend() - - -if __name__ == '__main__': - survivewm() diff --git a/examples/defense/__init__.py b/examples/defense/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/pyhazard/models/attack/AdvMEA.py b/pyhazard/models/attack/AdvMEA.py deleted file mode 100644 index c22cdc4..0000000 --- a/pyhazard/models/attack/AdvMEA.py +++ /dev/null @@ -1,245 +0,0 @@ -import time - -import dgl -import numpy as np -import torch -import torch.nn.functional as F -from torch_geometric.utils import k_hop_subgraph, dense_to_sparse -from tqdm import tqdm - -from pyhazard.models.attack.base import BaseAttack -from pyhazard.models.nn import GCN -from pyhazard.utils.metrics import AttackMetric, AttackCompMetric - - -class AdvMEA(BaseAttack): - supported_api_types = {"dgl"} - - def __init__(self, dataset, attack_node_fraction, model_path=None): - super().__init__(dataset, attack_node_fraction, model_path) - self.graph = dataset.graph_data.to(self.device) - self.features = self.graph.ndata['feat'] - self.labels = self.graph.ndata['label'] - self.train_mask = self.graph.ndata['train_mask'] - self.test_mask = self.graph.ndata['test_mask'] - - # meta data - self.num_nodes = dataset.num_nodes - self.num_features = dataset.num_features - self.num_classes = dataset.num_classes - - if model_path is None: - self._train_target_model() - else: - self._load_model(model_path) - - def _load_model(self, model_path): - """ - Load a pre-trained model. - """ - # Create the model - self.net1 = GCN(self.num_features, self.num_classes).to(self.device) - - # Load the saved state dict - self.net1.load_state_dict(torch.load(model_path, map_location=self.device)) - - # Set to evaluation mode - self.net1.eval() - - def _train_target_model(self): - """ - Train the target model (GCN) on the original graph. - """ - # Initialize GNN model - self.net1 = GCN(self.num_features, self.num_classes).to(self.device) - optimizer = torch.optim.Adam(self.net1.parameters(), lr=0.01, weight_decay=5e-4) - - # Training loop - for epoch in range(200): - self.net1.train() - - # Forward pass - logits = self.net1(self.graph, self.features) - logp = F.log_softmax(logits, dim=1) - loss = F.nll_loss(logp[self.train_mask], self.labels[self.train_mask]) - - # Backward pass - optimizer.zero_grad() - loss.backward() - optimizer.step() - - # Validation (optional) - if epoch % 20 == 0: - self.net1.eval() - with torch.no_grad(): - logits_val = self.net1(self.graph, self.features) - logp_val = F.log_softmax(logits_val, dim=1) - pred = logp_val.argmax(dim=1) - acc_val = (pred[self.test_mask] == self.labels[self.test_mask]).float().mean() - # You could print validation accuracy here - - return self.net1 - - # Define a local to_cpu method to avoid inheritance issues - def _to_cpu(self, tensor): - """ - Safely move tensor to CPU for NumPy operations - """ - if tensor.is_cuda: - return tensor.cpu() - return tensor - - def attack(self): - metric_comp = AttackCompMetric() - metric_comp.start() - g = self.graph.clone() - # Move adjacency matrix to CPU for NumPy operations - g_matrix = np.asmatrix(self._to_cpu(g.adjacency_matrix().to_dense()).numpy()) - edge_index = np.array(np.nonzero(g_matrix)) - edge_index = torch.tensor(edge_index, dtype=torch.long) - - attack_s1 = time.time() - # Select a center node with certain size - while True: - node_index = torch.randint(0, self.num_nodes, (1,)).item() - # print("node_index=",node_index) - sub_node_index, sub_edge_index, _, _ = k_hop_subgraph(node_index, 2, edge_index, relabel_nodes=True, - num_nodes=self.num_nodes) - if 45 <= sub_node_index.size(0) <= 50: - As = torch.zeros((sub_node_index.size(0), sub_node_index.size(0))) - As[sub_edge_index[0], sub_edge_index[1]] = 1 - print("sub_node_index=", sub_node_index.size(0)) - # Ensure moved to CPU - Xs = self._to_cpu(self.features[sub_node_index]) - break - - # Construct the prior distribution - Fd = [] - Md = [] - for label in range(self.num_classes): - # Ensure moved to CPU before converting to NumPy - features_cpu = self._to_cpu(self.features) - labels_cpu = self._to_cpu(self.labels) - class_nodes = features_cpu[labels_cpu == label].numpy() - - feature_counts = class_nodes.sum(axis=0) - feature_distribution = feature_counts / feature_counts.sum() - Fd.append(feature_distribution) - - num_features_per_node = class_nodes.sum(axis=1) - feature_count_distribution = np.bincount(num_features_per_node.astype(int), minlength=self.num_features) - Md.append(feature_count_distribution / feature_count_distribution.sum()) - - SA = [As] - SX = [Xs] - attack_e1 = time.time() - - query_s = time.time() - # Query the target model - self.net1.eval() - with torch.no_grad(): - logits_query = self.net1(g, self.features) - _, labels_query = torch.max(logits_query, dim=1) - - query_e = time.time() - - attack_s2 = time.time() - src, dst = As.nonzero(as_tuple=True) - initial_num_nodes = Xs.shape[0] - initial_graph = dgl.graph((src, dst), num_nodes=initial_num_nodes).to(self.device) - initial_graph.ndata['feat'] = Xs.to(self.device) - - self.net1.eval() - with torch.no_grad(): - initial_query = self.net1(initial_graph, initial_graph.ndata['feat']) - _, initial_label = torch.max(initial_query, dim=1) - - SL = self._to_cpu(initial_label).tolist() - samples_per_class = 10 - n = samples_per_class - - for i in range(n): - # For each class, generate and store a new sampled subgraph - for c in range(self.num_classes): - num_nodes = As.shape[0] - Ac = torch.ones((num_nodes, num_nodes)) - Xc = torch.zeros(num_nodes, len(Fd[c])) - for j in range(num_nodes): # Use j to avoid conflict with outer loop variable i - m = np.random.choice(np.arange(len(Md[c])), p=Md[c]) - features_idx = np.random.choice(len(Fd[c]), size=int(m), replace=False, p=Fd[c]) - Xc[j, features_idx] = 1 - SA.append(Ac) - SX.append(Xc) - - src, dst = Ac.nonzero(as_tuple=True) - subgraph = dgl.graph((src, dst), num_nodes=num_nodes).to(self.device) - subgraph.ndata['feat'] = Xc.to(self.device) - - self.net1.eval() - with torch.no_grad(): - api_query = self.net1(subgraph, subgraph.ndata['feat']) - _, label_query = torch.max(api_query, dim=1) - - SL.extend(self._to_cpu(label_query).tolist()) - - AG_list = [dense_to_sparse(torch.tensor(a))[0] for a in SA] - XG = torch.vstack([torch.tensor(x) for x in SX]) - - SL = torch.tensor(SL, dtype=torch.long) - - # Filter valid labels and trim - valid_mask = SL >= 0 - SL = SL[valid_mask] - SL = SL[:XG.shape[0]] - - # Calculate nodes per subgraph - num_nodes = XG.shape[0] // len(AG_list) if len(AG_list) > 0 else 0 - - # Combine edge indices from all subgraphs, adjusting node indices to avoid overlap - AG_combined = torch.cat([edge_index + i * num_nodes for i, edge_index in enumerate(AG_list)], dim=1) - - src, dst = AG_combined[0], AG_combined[1] - num_total_nodes = XG.shape[0] - sub_g = dgl.graph((src, dst), num_nodes=num_total_nodes).to(self.device) - sub_g.ndata['feat'] = XG.to(self.device) - - attack_e2 = time.time() - - train_surrogate_s = time.time() - # Create and train the extracted model - net6 = GCN(XG.shape[1], self.num_classes).to(self.device) - optimizer = torch.optim.Adam(net6.parameters(), lr=0.01, weight_decay=5e-4) - - print("=========Model Extracting==========================") - metric = AttackMetric() - - for epoch in tqdm(range(200)): - net6.train() - logits = net6(sub_g, sub_g.ndata['feat']) - out = torch.log_softmax(logits, dim=1) - loss = F.nll_loss(out, SL.to(self.device)) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - # Switch to evaluation mode - t0 = time.time() - net6.eval() - with torch.no_grad(): - logits = net6(g, self.features) - _, preds = torch.max(logits[self.test_mask], dim=1) - metric.update(preds, self.labels[self.test_mask], labels_query[self.test_mask]) - metric_comp.update(inference_surrogate_time=(time.time() - t0)) - - train_surrogate_e = time.time() - - print("========================Final results:=========================================") - metric_comp.end() - metric_comp.update(attack_time=(attack_e1 - attack_s1 + attack_e2 - attack_s2), - query_target_time=(query_e - query_s), - train_surrogate_time=(train_surrogate_e - train_surrogate_s)) - res = metric.compute() - res_comp = metric_comp.compute() - - return res, res_comp diff --git a/pyhazard/models/attack/CEGA.py b/pyhazard/models/attack/CEGA.py deleted file mode 100644 index bd524ef..0000000 --- a/pyhazard/models/attack/CEGA.py +++ /dev/null @@ -1,1860 +0,0 @@ -import copy -import json -import math -import os -import pickle as pkl -import random -import sys -import time - -import dgl -import dgl.function as fn -import networkx as nx -import numpy as np -import numpy.linalg as la -import pandas as pd -import scipy.sparse as sp -import torch -import torch as th -import torch.nn as nn -import torch.nn.functional as F -from dgl.data import AmazonCoBuyComputerDataset, AmazonCoBuyPhotoDataset, CoauthorCSDataset, CoauthorPhysicsDataset, \ - RedditDataset, WikiCSDataset, AmazonRatingsDataset, QuestionsDataset, RomanEmpireDataset, FlickrDataset, \ - CoraFullDataset -from dgl.data import citation_graph as citegrh -from dgl.nn.pytorch import GraphConv -from sklearn.cluster import KMeans -from sklearn.metrics import f1_score -from torch_geometric.datasets import CitationFull -from tqdm import tqdm - -time_limit = 300 - - -def get_receptive_fields_dense(cur_neighbors, selected_node, weighted_score, adj_matrix2): - receptive_vector = ((cur_neighbors + adj_matrix2[selected_node]) != 0) + 0 - count = weighted_score.dot(receptive_vector) - return count - - -def get_current_neighbors_dense(cur_nodes, adj_matrix2): - if np.array(cur_nodes).shape[0] == 0: - return 0 - neighbors = (adj_matrix2[list(cur_nodes)].sum(axis=0) != 0) + 0 - return neighbors - - -def get_current_neighbors_1(cur_nodes, adj_matrix): - if np.array(cur_nodes).shape[0] == 0: - return 0 - neighbors = (adj_matrix[list(cur_nodes)].sum(axis=0) != 0) + 0 - return neighbors - - -def get_entropy_contribute(npy_m1, npy_m2): - entro1 = 0 - entro2 = 0 - for i in range(npy_m1.shape[0]): - entro1 -= np.sum(npy_m1[i] * np.log2(npy_m1[i])) - entro2 -= np.sum(npy_m2[i] * np.log2(npy_m2[i])) - return entro1 - entro2 - - -def get_max_info_entropy_node_set(idx_used, - high_score_nodes, - labels, - batch_size, - adj_matrix2, - num_class, - model_prediction): - max_info_node_set = [] - high_score_nodes_ = copy.deepcopy(high_score_nodes) - labels_ = copy.deepcopy(labels) - for k in range(batch_size): - score_list = np.zeros(len(high_score_nodes_)) - for i in range(len(high_score_nodes_)): - labels_tmp = copy.deepcopy(labels_) - node = high_score_nodes_[i] - node_neighbors = get_current_neighbors_dense([node], adj_matrix2) - adj_neigh = adj_matrix2[list(node_neighbors)] - aay = np.matmul(adj_neigh, labels_) - total_score = 0 - for j in range(num_class): - if model_prediction[node][j] != 0: - labels_tmp[node] = 0 - labels_tmp[node][j] = 1 - aay_ = np.matmul(adj_neigh, labels_tmp) - total_score += model_prediction[node][j] * get_entropy_contribute(aay, aay_) - score_list[i] = total_score - idx = np.argmax(score_list) - max_node = high_score_nodes_[idx] - max_info_node_set.append(max_node) - labels_[max_node] = model_prediction[max_node] - high_score_nodes_.remove(max_node) - return max_info_node_set - - -def get_max_nnd_node_dense(idx_used, - high_score_nodes, - min_distance, - distance_aax, - num_ones, - num_node, - adj_matrix2, - gamma=1): - dmax = np.ones(num_node) - - max_receptive_node = 0 - max_total_score = 0 - cur_neighbors = get_current_neighbors_dense(idx_used, adj_matrix2) - for node in high_score_nodes: - receptive_field = get_receptive_fields_dense(cur_neighbors, node, num_ones, adj_matrix2) - node_distance = distance_aax[node, :] - node_distance = np.where(node_distance < min_distance, node_distance, min_distance) - node_distance = dmax - node_distance - distance_score = node_distance.dot(num_ones) - total_score = receptive_field / num_node + gamma * distance_score / num_node - if total_score > max_total_score: - max_total_score = total_score - max_receptive_node = node - return max_receptive_node - - -def aug_normalized_adjacency(adj): - adj = adj + sp.eye(adj.shape[0]) - adj = sp.coo_matrix(adj) - row_sum = np.array(adj.sum(1)) - d_inv_sqrt = np.power(row_sum, -0.5).flatten() - d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0. - d_mat_inv_sqrt = sp.diags(d_inv_sqrt) - return d_mat_inv_sqrt.dot(adj).dot(d_mat_inv_sqrt).tocoo() - - -def compute_distance(_i, _j, features_aax): - return la.norm(features_aax[_i, :] - features_aax[_j, :]) - - -def parse_index_file(filename): - index = [] - for line in open(filename): - index.append(int(line.strip())) - return index - - -def normalize(mx): - """Row-normalize sparse matrix""" - rowsum = np.array(mx.sum(1)) - r_inv = np.power(rowsum, -1).flatten() - r_inv[np.isinf(r_inv)] = 0. - r_mat_inv = sp.diags(r_inv) - mx = r_mat_inv.dot(mx) - return mx - - -def accuracy(output, labels): - preds = output.max(1)[1].type_as(labels) - correct = preds.eq(labels).double() - correct = correct.sum() - return correct / len(labels) - - -def load_data_from_grain(path="./data", dataset="cora"): - """ - ind.[:dataset].x => the feature vectors of the training instances (scipy.sparse.csr.csr_matrix) - ind.[:dataset].y => the one-hot labels of the labeled training instances (numpy.ndarray) - ind.[:dataset].allx => the feature vectors of both labeled and unlabeled training instances (csr_matrix) - ind.[:dataset].ally => the labels for instances in ind.dataset_str.allx (numpy.ndarray) - ind.[:dataset].graph => the dict in the format {index: [index of neighbor nodes]} (collections.defaultdict) - ind.[:dataset].tx => the feature vectors of the test instances (scipy.sparse.csr.csr_matrix) - ind.[:dataset].ty => the one-hot labels of the test instances (numpy.ndarray) - ind.[:dataset].test.index => indices of test instances in graph, for the inductive setting - """ - print("\n[STEP 1]: Upload {} dataset.".format(dataset)) - - names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph'] - objects = [] - - for i in range(len(names)): - with open("{}/ind.{}.{}".format(path, dataset, names[i]), 'rb') as f: - if sys.version_info > (3, 0): - objects.append(pkl.load(f, encoding='latin1')) - else: - objects.append(pkl.load(f)) - - x, y, tx, ty, allx, ally, graph = tuple(objects) - - test_idx_reorder = parse_index_file("{}/ind.{}.test.index".format(path, dataset)) - test_idx_range = np.sort(test_idx_reorder) - - if dataset == 'citeseer': - # Citeseer dataset contains some isolated nodes in the graph - test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder) + 1) - tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1])) - tx_extended[test_idx_range - min(test_idx_range), :] = tx - tx = tx_extended - - ty_extended = np.zeros((len(test_idx_range_full), y.shape[1])) - ty_extended[test_idx_range - min(test_idx_range), :] = ty - ty = ty_extended - - features = sp.vstack((allx, tx)).tolil() - features[test_idx_reorder, :] = features[test_idx_range, :] - - adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph)) - - print("| # of nodes : {}".format(adj.shape[0])) - print("| # of edges : {}".format(adj.sum().sum() / 2)) - - features = normalize(features) - print("| # of features : {}".format(features.shape[1])) - print("| # of clases : {}".format(ally.shape[1])) - - features = torch.FloatTensor(np.array(features.todense())) - sparse_mx = adj.tocoo().astype(np.float32) - - labels = np.vstack((ally, ty)) - labels[test_idx_reorder, :] = labels[test_idx_range, :] - - if dataset == 'citeseer': - save_label = np.where(labels)[1] - labels = torch.LongTensor(np.where(labels)[1]) - - idx_train = range(len(y)) - idx_val = range(len(y), len(y) + 500) - idx_test = test_idx_range.tolist() - - print("| # of train set : {}".format(len(idx_train))) - print("| # of val set : {}".format(len(idx_val))) - print("| # of test set : {}".format(len(idx_test))) - - idx_train, idx_val, idx_test = list(map(lambda x: torch.LongTensor(x), [idx_train, idx_val, idx_test])) - - def missing_elements(L): - start, end = L[0], L[-1] - return sorted(set(range(start, end + 1)).difference(L)) - - if dataset == 'citeseer': - L = np.sort(idx_test) - missing = missing_elements(L) - - for element in missing: - save_label = np.insert(save_label, element, 0) - - labels = torch.LongTensor(save_label) - - return adj, features, labels, idx_train, idx_val, idx_test - - -def set_seed(seed): - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - torch.cuda.manual_seed(seed) - torch.cuda.manual_seed_all(seed) - - -def sparse_mx_to_torch_sparse_tensor(sparse_mx): - """Convert a scipy sparse matrix to a torch sparse tensor.""" - sparse_mx = sparse_mx.tocoo().astype(np.float32) - indices = torch.from_numpy( - np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64)) - values = torch.from_numpy(sparse_mx.data) - shape = torch.Size(sparse_mx.shape) - return torch.sparse.FloatTensor(indices, values, shape) - - -def aug_normalized_adjacency(adj): - adj = adj # + sp.eye(adj.shape[0]) - adj = sp.coo_matrix(adj) - row_sum = np.array(adj.sum(1)) - d_inv_sqrt = np.power(row_sum, -0.5).flatten() - d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0. - d_mat_inv_sqrt = sp.diags(d_inv_sqrt) - return d_mat_inv_sqrt.dot(adj).dot(d_mat_inv_sqrt).tocoo() - - -def aug_random_walk(adj): - adj = adj + sp.eye(adj.shape[0]) - adj = sp.coo_matrix(adj) - row_sum = np.array(adj.sum(1)) - d_inv = np.power(row_sum, -1.0).flatten() - d_mat = sp.diags(d_inv) - return (d_mat.dot(adj)).tocoo() - - -class GCN_drop(nn.Module): - def __init__(self, feature_number, label_number, dropout=0.85, nhid=128): - super(GCN_drop, self).__init__() - - self.gc1 = GraphConv(feature_number, nhid, bias=True) - self.gc2 = GraphConv(nhid, label_number, bias=True) - self.dropout = dropout - - def forward(self, g, features): - x = F.dropout(features, self.dropout, training=self.training) - x = F.relu(self.gc1(g, x)) - x = F.dropout(x, self.dropout, training=self.training) - x = self.gc2(g, x) - return x - - -def convert_pyg_to_dgl(pyg_data): - """ - Converts a PyTorch Geometric Data object into a DGLGraph. - - Args: - pyg_data (torch_geometric.data.Data): PyTorch Geometric Data object. - - Returns: - dgl.DGLGraph: The converted DGL graph. - """ - edge_index = pyg_data.edge_index - num_nodes = pyg_data.num_nodes - - g = dgl.graph((edge_index[0], edge_index[1]), num_nodes=num_nodes) - - if hasattr(pyg_data, 'x') and pyg_data.x is not None: - g.ndata['feat'] = pyg_data.x - - if hasattr(pyg_data, 'y') and pyg_data.y is not None: - g.ndata['label'] = pyg_data.y - - for mask_name in ['train_mask', 'val_mask', 'test_mask']: - if hasattr(pyg_data, mask_name) and getattr(pyg_data, mask_name) is not None: - g.ndata[mask_name] = getattr(pyg_data, mask_name) - - return g - - -def load_data(dataset_name): - if dataset_name == 'cora': - data = citegrh.load_cora() - if dataset_name == 'citeseer': - data = citegrh.load_citeseer() - if dataset_name == 'pubmed': - data = citegrh.load_pubmed() - if dataset_name == 'amazoncomputer': - data = AmazonCoBuyComputerDataset() - if dataset_name == 'amazonphoto': - data = AmazonCoBuyPhotoDataset() - if dataset_name == 'coauthorCS': - data = CoauthorCSDataset() - if dataset_name == 'coauthorphysics': - data = CoauthorPhysicsDataset() - if dataset_name == 'reddit': - data = RedditDataset() - if dataset_name == 'wiki': - data = WikiCSDataset() - if dataset_name == 'amazonrating': - data = AmazonRatingsDataset() - if dataset_name == 'question': - data = QuestionsDataset() - if dataset_name == 'roman': - data = RomanEmpireDataset() - if dataset_name == 'flickr': - data = FlickrDataset() - if dataset_name == 'cora_full': - data = CoraFullDataset() - if dataset_name == 'dblp': - data = CitationFull(root='./data/', name='DBLP') - data = data[0] - - if dataset_name == 'dblp': - g = convert_pyg_to_dgl(data) - else: - g = data[0] - - isolated_nodes = ((g.in_degrees() == 0) & (g.out_degrees() == 0)).nonzero().squeeze(1) - g.remove_nodes(isolated_nodes) - - if dataset_name in ['cora', 'citeseer', 'pubmed', 'reddit', 'flickr']: - features = g.ndata['feat'] - labels = g.ndata['label'] - train_mask = g.ndata['train_mask'] - test_mask = g.ndata['test_mask'] - num_nodes = g.num_nodes() - elif dataset_name in ['wiki']: - features = g.ndata['feat'] - labels = g.ndata['label'] - test_mask = g.ndata['test_mask'].bool() - train_mask = (1 - g.ndata['test_mask']).bool() # - num_nodes = g.num_nodes() - elif dataset_name in ['amazoncomputer', 'amazonphoto', 'coauthorCS', 'coauthorphysics', 'cora_full', 'dblp']: - features = g.ndata['feat'] - labels = g.ndata['label'] - num_nodes = g.num_nodes() - train_mask = torch.zeros(num_nodes, dtype=torch.bool) - test_mask = torch.zeros(num_nodes, dtype=torch.bool) - - torch.manual_seed(42) - indices = torch.randperm(num_nodes) - num_train = int(num_nodes * 0.6) - train_mask[indices[:num_train]] = True - test_mask[indices[num_train:]] = True - assert train_mask.sum() + test_mask.sum() == num_nodes - elif dataset_name in ['amazonrating', 'question', 'roman']: - features = g.ndata['feat'] - labels = g.ndata['label'] - num_nodes = g.num_nodes() - train_mask = g.ndata['train_mask'][:, 0] - test_mask = g.ndata['test_mask'][:, 0] - return g, features, labels, num_nodes, train_mask, test_mask - - -def evaluate(model, g, features, labels, mask): - model.eval() - with torch.no_grad(): - logits = model(g, features) - logits = logits[mask] - labels = labels[mask] - _, indices = torch.max(logits, dim=1) - correct = torch.sum(indices == labels) - f1score = f1_score(labels.cpu().numpy(), indices.cpu().numpy(), average='macro') - return correct.item() * 1.0 / len(labels), f1score - - -class GcnNet(nn.Module): - def __init__(self, feature_number, label_number): - super(GcnNet, self).__init__() - self.layers = nn.ModuleList() - self.layers.append(GraphConv(feature_number, 16, activation=F.relu)) - self.layers.append(GraphConv(16, label_number)) - self.dropout = nn.Dropout(p=0.5) - - def forward(self, g, features): - x = F.relu(self.layers[0](g, features)) - x = self.layers[1](g, x) - return x - - -# Initialization -def init_mask(C, sub_train_mask, sub_labels): - # print(f"=========Initialization with {2 * C} Nodes==========================") - initial_set = [] - for label in range(C): - label_nodes = [] - for i, l in enumerate(sub_labels): - if sub_train_mask[i] == True and l == label: - label_nodes.append(i) - selected_nodes = random.sample(label_nodes, k=2) # initial pool for each class - initial_set.extend(selected_nodes) - - # print(initial_set) - return initial_set - - # node pool - ## center_rank = rank_centrality(sub_g, sub_train_mask, sub_train_init, num_center, return_rank=True) - ## selected_indices_center = center_rank[:num_center] - ## sub_train_init[selected_indices_center] = True - # Randomly select the rest of the initial nodes - ## full_true_indices = th.nonzero(sub_train_mask & ~sub_train_init).squeeze() - ## selected_indices_random = random.sample(full_true_indices.tolist(), num_random) - ## sub_train_init[selected_indices_random] = True - - # Transform the formality and return the outcome; note the output are indicators - # sub_train_init = th.zeros(len(sub_train_mask), dtype=th.bool) - # sub_train_init[initial_set] = True - # print(sub_labels[initial_set]) - # sub_train_init = th.tensor(initial_set) - # return sub_train_init - - -def update_sub_train_mask(num_each, sub_train_mask, sub_train_mask_new): - full_true_indices = th.nonzero(sub_train_mask).squeeze() - current_true_indices = th.nonzero(sub_train_mask_new).squeeze() - missing_indices = set(full_true_indices.tolist()) - set(current_true_indices.tolist()) - if len(missing_indices) >= num_each: - # print(f"=========Update Random Querying Label with {num_each} Nodes==========================") - selected_indices = random.sample(list(missing_indices), num_each) - ## sub_train_mask_new[selected_indices] = True - - return selected_indices - - -# Calculate the entropy -def calculate_entropy(probs): - return -th.sum(probs * th.log(probs + 1e-9), dim=-1) - - -def rank_entropy(net, sub_g, sub_features, sub_train_mask, sub_train_mask_new, - num_each, return_rank=True): - logits = net(sub_g, sub_features) - prob = F.softmax(logits, dim=-1) - nodes_interest = th.nonzero(sub_train_mask & ~sub_train_mask_new).squeeze() - probs_interest = prob[nodes_interest] - entropy_interest = calculate_entropy(probs_interest) - nodes_rank = nodes_interest[th.argsort(entropy_interest, descending=True)] - if len(nodes_rank) >= num_each: - if return_rank: - return nodes_rank - else: - print(f"=========Update Entropy Querying Label with {num_each} Nodes==========================") - # selected_indices = random.sample(list(missing_indices), num_each) - selected_indices = nodes_rank[:num_each] - sub_train_mask_new[selected_indices] = True - return sub_train_mask_new - - -def rank_density(net, sub_g, sub_features, sub_train_mask, sub_train_mask_new, - num_each, num_clusters, return_rank=True): - full_true_indices = th.nonzero(sub_train_mask).squeeze() - current_true_indices = th.nonzero(sub_train_mask_new).squeeze() - missing_indices = set(full_true_indices.tolist()) - set(current_true_indices.tolist()) - ## Get the embeddings that we need - ## Under numpy formality - embedding_all = net(sub_g, sub_features, return_hidden=True).detach().numpy() - kmeans = KMeans(n_clusters=num_clusters) - kmeans.fit(embedding_all) - ## Set up cluster_centers - cluster_centers = kmeans.cluster_centers_ - - # Calculate the Euclidean distance - dist = np.linalg.norm(embedding_all - cluster_centers[kmeans.labels_], axis=1) - density_scores = th.from_numpy(1 / (1 + dist)) - - # pull back to the node coefficients - list_missing_indices = torch.tensor(list(missing_indices)) - shuffle_order = th.argsort(density_scores, descending=True) - positions = [th.where(shuffle_order == temp)[0].item() for temp in list_missing_indices] - sorted_positions = th.argsort(th.tensor(positions)) - list_output = list_missing_indices[sorted_positions] - - if len(list_output) >= num_each: - if return_rank: - return list_output - else: - print(f"=========Update Entropy Querying Label with {num_each} Nodes==========================") - # selected_indices = random.sample(list(missing_indices), num_each) - selected_indices = list_output[:num_each] - sub_train_mask_new[selected_indices] = True - return sub_train_mask_new - - -def rank_centrality(sub_g, sub_train_mask, - sub_train_mask_new, num_each, return_rank=True): - nodes_interest = th.nonzero(sub_train_mask & ~sub_train_mask_new).squeeze() - page_rank_score = page_rank(sub_g)[nodes_interest] - nodes_centrality = nodes_interest[th.argsort(page_rank_score, descending=True)] - - if len(nodes_centrality) >= num_each: - if return_rank: - return nodes_centrality - else: - print(f"=========Update Entropy Querying Label with {num_each} Nodes==========================") - # selected_indices = random.sample(list(missing_indices), num_each) - selected_indices = nodes_centrality[:num_each] - sub_train_mask_new[selected_indices] = True - return sub_train_mask_new - - -# Hand-written pagerank score -def page_rank(graph, damping_factor=0.85, max_iter=100, tol=1e-8): - num_nodes = graph.number_of_nodes() - - # Initialize the PageRank score for all nodes to be uniform - pagerank_scores = torch.ones(num_nodes) / num_nodes - graph.ndata['pagerank'] = pagerank_scores - - # Degree normalization factor - # with graph.local_scope(): - graph.ndata['deg'] = graph.out_degrees().float().clamp(min=1) # Avoid dividing by 0 - - for _ in range(max_iter): - # Perform message passing (send normalized pagerank score) - # print("Iteration ", _) - prev_scores = pagerank_scores.clone() - graph.ndata['h'] = pagerank_scores / graph.ndata['deg'] - graph.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h_new')) - # Apply PageRank formula - pagerank_scores = damping_factor * graph.ndata['h_new'] + (1 - damping_factor) / num_nodes - # pagerank_scores_new = (1 - damping_factor) / num_nodes + damping_factor * graph.ndata['pagerank_sum'] / \ - # graph.ndata['deg'] - - # Check for convergence - delta = torch.abs(pagerank_scores - prev_scores).sum().item() - if delta < tol: - break - # Update pagerank scores - graph.ndata['pagerank'] = pagerank_scores - - return graph.ndata['pagerank'] - - -# ECE and Perturbation -def perturb_features(sub_features, noise_level=0.05): - noise = th.randn_like(sub_features) * noise_level - perturbed_features = sub_features + noise - return perturbed_features - - -# Take the perturbation and count the average -def perturb_avg(net, sub_g, sub_features, num_perturbations, noise_level): - original_logits = net(sub_g, sub_features) - # Number of classes - num_classes = original_logits.size(-1) - # Initialization - cumulative_probs = th.zeros(sub_features.size(0), num_classes, - device=original_logits.device) - # Perturbation - for _ in range(num_perturbations): - features_p = perturb_features(sub_features, noise_level=noise_level) - logits_p = net(sub_g, features_p) - probs_p = F.softmax(logits_p, dim=-1) - cumulative_probs += probs_p - # get a fair estimation for the distribution on existing label - avg_probs = cumulative_probs / num_perturbations - - return avg_probs - - -# Try the traditional way: count the number of perturbed labels for each node -def rank_perturb(net, sub_g, sub_features, num_perturbations, - sub_train_mask, sub_train_mask_new, noise_level, - num_each, return_rank=True): - original_logits = net(sub_g, sub_features) - nodes_interest = th.nonzero(sub_train_mask & ~sub_train_mask_new).squeeze() - original_pred = th.argmax(original_logits[nodes_interest], dim=-1) - ## Store the outcome - # unchanged_counts = th.zeros_like(original_pred, dtype = th.float) - unchanged_counts = th.zeros_like(original_pred) - # Perturbation - for _ in range(num_perturbations): - features_p = perturb_features(sub_features, noise_level=noise_level) - logits_p = net(sub_g, features_p) - labels_p = th.argmax(logits_p[nodes_interest], dim=-1) - unchanged = labels_p.eq(original_pred) - unchanged_counts += unchanged.int() - - # unchanged_counts_float = unchanged_counts.float() - # unchanged_counts_float.mean() - _, change_indices = torch.sort(unchanged_counts) - nodes_rank_label = nodes_interest[change_indices] - - if len(nodes_rank_label) >= num_each: - if return_rank: - return nodes_rank_label - else: - print(f"=========Update Perturbation Querying Label with {num_each} Nodes==========================") - # selected_indices = random.sample(list(missing_indices), num_each) - selected_indices = nodes_rank_label[:num_each] - sub_train_mask_new[selected_indices] = True - return sub_train_mask_new - - -# Consider items in the embedding space -def rank_cluster(net, sub_g, sub_features, labels, total_sub_nodes, - sub_train_mask, sub_train_mask_new, num_clusters, - num_each, return_rank=True): - # Work on missing indices - full_true_indices = th.nonzero(sub_train_mask).squeeze() - current_true_indices = th.nonzero(sub_train_mask_new).squeeze() - missing_indices = set(full_true_indices.tolist()) - set(current_true_indices.tolist()) - # Work on prep of embedding - labels_true = labels[total_sub_nodes] - logits = net(sub_g, sub_features) - prob = F.softmax(logits, dim=-1) - labels_pred = th.argmax(prob, dim=-1) - embedding_all = net(sub_g, sub_features, return_hidden=True) - mismatches_queried = (labels_true != labels_pred) & sub_train_mask_new - selected_embeddings = embedding_all[mismatches_queried].detach().numpy() - # Try kmeans - num_clusters_used = min(num_clusters, th.sum(mismatches_queried).item()) - # print(selected_embeddings) - print("mismatches_queried:" + str(th.sum(mismatches_queried).item())) - print("num_clusters_used:" + str(num_clusters_used)) - if num_clusters_used >= 1: - kmeans = KMeans(n_clusters=num_clusters_used, random_state=0) - kmeans.fit(selected_embeddings) - cluster_centers = th.tensor(kmeans.cluster_centers_, dtype=torch.float32) - # Get back to the original field: Try to use a separate function for remaining functions - list_missing_indices = list(missing_indices) - embedding_pool = embedding_all[list_missing_indices] - min_distances = find_short_dist(embedding_pool, cluster_centers) - shuffle_order = th.argsort(min_distances) - output_order = [list_missing_indices[i] for i in shuffle_order] - nodes_rank_distance = torch.tensor(output_order) - else: - print("All nodes give the same label.") - nodes_rank_distance = torch.tensor(list(missing_indices)) - - if len(nodes_rank_distance) >= num_each: - if return_rank: - return nodes_rank_distance - else: - print(f"=========Update Cluster Querying Label with {num_each} Nodes==========================") - # selected_indices = random.sample(list(missing_indices), num_each) - selected_indices = nodes_rank_distance[:num_each] - sub_train_mask_new[selected_indices] = True - return sub_train_mask_new - - -# Use a separate function to write out the calculation of distance -def find_short_dist(embedding_pool, cluster_centers): - distances = torch.cdist(embedding_pool, cluster_centers) - min_distances, _ = torch.min(distances, dim=1) - return min_distances - - -# Consider Diversity; see what we can do from here. -def rank_diversity(net, sub_g, sub_features, sub_train_mask, sub_train_mask_new, num_each, num_clusters, rho, - return_rank=True): - full_indices = th.nonzero(sub_train_mask).squeeze() - queried_indices = th.nonzero(sub_train_mask_new).squeeze() - candidate_indices = set(full_indices.tolist()) - set(queried_indices.tolist()) - # Get the embeddings - embedding_all = net(sub_g, sub_features, return_hidden=True).detach().numpy() - embedding_queried = embedding_all[queried_indices] - kmeans = KMeans(n_clusters=num_clusters, random_state=42) - kmeans.fit(embedding_queried) - cluster_centers = kmeans.cluster_centers_ - - node_embeddings = th.tensor(embedding_all, dtype=th.float32) - centroids = th.tensor(cluster_centers, dtype=th.float32) - kmeans_labels = th.tensor(kmeans.labels_, dtype=th.int32) - - minimal_distance = th.min(th.cdist(node_embeddings, centroids, p=2), dim=1).values - proposed_labels = th.min(th.cdist(node_embeddings, centroids, p=2), dim=1).indices - - # Closeness Scores (Distance to assigned centroid) - close_temp = 1 / (1 + minimal_distance) - close_normalized = (close_temp - close_temp.min()) / (close_temp.max() - close_temp.min() + 1e-10) - - # Rarity Scores (How rare as shown in ) - queried_bincount = th.bincount(kmeans_labels) - rarity_temp = 1 / (1 + queried_bincount[proposed_labels]) - rarity_normalized = (rarity_temp - rarity_temp.min()) / (rarity_temp.max() - rarity_temp.min() + 1e-10) - - # Assemble the scores; rho is subject to tuning - composite_scores = rho * close_normalized + (1 - rho) * rarity_normalized - composite_scores_candidate = composite_scores[list(candidate_indices)] - candidate_tensor = th.tensor(list(candidate_indices)) - nodes_rank_diversity = candidate_tensor[th.argsort(composite_scores_candidate, descending=True)] - - if len(nodes_rank_diversity) >= num_each: - if return_rank: - return nodes_rank_diversity - else: - print(f"=========Update Cluster Querying Label with {num_each} Nodes==========================") - # selected_indices = random.sample(list(missing_indices), num_each) - selected_indices = nodes_rank_diversity[:num_each] - sub_train_mask_new[selected_indices] = True - return sub_train_mask_new - - -def quantile_selection(A, B, C, index_1, index_2, index_3, sub_train_mask, sub_train_mask_new, num_each): - elements = th.nonzero(sub_train_mask & ~sub_train_mask_new).squeeze() - - ranks_A = [compute_rank(A, el) for el in elements] - ranks_B = [compute_rank(B, el) for el in elements] - ranks_C = [compute_rank(C, el) for el in elements] - - weighted_ranks = [] - for i in range(len(elements)): - weighted_rank = index_1 * ranks_A[i] + index_2 * ranks_B[i] + index_3 * ranks_C[i] - weighted_ranks.append(weighted_rank) - - # Sort elements based on weighted ranks - sorted_indices = np.argsort(weighted_ranks) - sorted_elements = th.stack([elements[i] for i in sorted_indices]) - # sorted_weighted_ranks = [weighted_ranks[i] for i in sorted_indices] - - # print(f"=========Update Entropy Querying Label with {num_each} Nodes==========================") - # selected_indices = random.sample(list(missing_indices), num_each) - selected_indices = sorted_elements[:num_each] - # sub_train_mask_new[selected_indices] = True - - return selected_indices - - -def compute_rank(tensor, element): - return np.where(tensor == element)[0][0] - - -class GcnNet(nn.Module): - def __init__(self, feature_number, label_number): - super(GcnNet, self).__init__() - self.layers = nn.ModuleList() - self.layers.append(GraphConv(feature_number, 16, activation=F.relu)) - self.layers.append(GraphConv(16, label_number)) - self.dropout = nn.Dropout(p=0.5) - - def forward(self, g, features, return_hidden=False): - relu = nn.ReLU() - x = F.relu(self.layers[0](g, features)) - if return_hidden: - return x - x = self.layers[1](g, x) - return x - - -## Main Function -def attack0(dataset_name, seed, cuda, attack_node_arg=0.25, file_path='', LR=1e-3, TGT_LR=1e-2, - EVAL_EPOCH=1000, TGT_EPOCH=1000, WARMUP_EPOCH=400, dropout=False, model_performance=True, **kwargs): - # Initialization - device = th.device(cuda) - set_seed(seed) - metrics_df = pd.DataFrame(columns=['Num Attack Nodes', 'Method', 'Test Accuracy', 'Test Fidelity']) - - g, features, labels, node_number, train_mask, test_mask = load_data(dataset_name) - attack_node_number = int(node_number * attack_node_arg) - feature_number = features.shape[1] - label_number = len(labels.unique()) - C_var = label_number - - print('The attack node number is: ', attack_node_number) - - g = g.to(device) - degs = g.in_degrees().float() - norm = th.pow(degs, -0.5) - norm[th.isinf(norm)] = 0 - if cuda != None: - norm = norm.cuda() - g.ndata['norm'] = norm.unsqueeze(1) - if dropout == True: - gcn_Net = GCN_drop(feature_number, label_number) - else: - gcn_Net = GcnNet(feature_number, label_number) - optimizer = th.optim.Adam(gcn_Net.parameters(), lr=TGT_LR, weight_decay=5e-4) - dur = [] - - ## Send the training to cuda - features = features.to(device) - gcn_Net = gcn_Net.to(device) - train_mask = train_mask.to(device) - test_mask = test_mask.to(device) - labels = labels.to(device) - target_performance = { - 'acc': 0, - 'f1score': 0 - } - - print("=========Target Model Generating==========================") - for epoch in range(TGT_EPOCH): - if epoch >= 3: - t0 = time.time() - - gcn_Net.train() - logits = gcn_Net(g, features) - logp = F.log_softmax(logits, 1) - loss = F.nll_loss(logp[train_mask], labels[train_mask]) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - if epoch >= 3: - dur.append(time.time() - t0) - - acc, f1score = evaluate(gcn_Net, g, features, labels, test_mask) - if acc > target_performance['acc']: - target_performance['acc'] = acc - if f1score > target_performance['f1score']: - target_performance['f1score'] = f1score - - print("Epoch {:05d} | Loss {:.4f} | Test Acc {:.4f} | Test F1 macro {:.4f} | Time(s) {:.4f}".format( - epoch, loss.item(), acc, f1score, np.mean(dur))) - - ## Get the cuda-trained data back - g = g.cpu() - features = features.cpu() - gcn_Net = gcn_Net.cpu() - train_mask = train_mask.cpu() - test_mask = test_mask.cpu() - labels = labels.cpu() - - # Generate sub-graph index - alpha = 0.8 - sub_graph_node_index = [] - for i in range(attack_node_number): - sub_graph_node_index.append(random.randint(0, node_number - 1)) - - sub_labels = labels[sub_graph_node_index] - - syn_nodes = [] - g_matrix = np.asmatrix(g.adjacency_matrix().to_dense()) - - for node_index in sub_graph_node_index: - # get nodes - one_step_node_index = g_matrix[node_index, :].nonzero()[1].tolist() - two_step_node_index = [] - for first_order_node_index in one_step_node_index: - syn_nodes.append(first_order_node_index) - two_step_node_index = g_matrix[first_order_node_index, :].nonzero()[1].tolist() - - sub_graph_syn_node_index = list(set(syn_nodes) - set(sub_graph_node_index)) - total_sub_nodes = list(set(sub_graph_syn_node_index + sub_graph_node_index)) - - # Generate features for SubGraph attack - np_features_query = features.clone() - - for node_index in sub_graph_syn_node_index: - # initialized as zero - np_features_query[node_index] = np_features_query[node_index] * 0 - # get one step and two steps nodes - one_step_node_index = g_matrix[node_index, :].nonzero()[1].tolist() - one_step_node_index = list(set(one_step_node_index).intersection(set(sub_graph_node_index))) - - total_two_step_node_index = [] - num_one_step = len(one_step_node_index) - for first_order_node_index in one_step_node_index: - # caculate the feature: features = 0.8 * average_one + 0.8^2 * average_two - # new_array = features[first_order_node_index]*0.8/num_one_step - this_node_degree = len(g_matrix[first_order_node_index, :].nonzero()[1].tolist()) - np_features_query[node_index] = torch.from_numpy(np.sum( - [np_features_query[node_index], - features[first_order_node_index] * alpha / math.sqrt(num_one_step * this_node_degree)], - axis=0)) - - two_step_node_index = g_matrix[first_order_node_index, :].nonzero()[1].tolist() - total_two_step_node_index = list( - set(total_two_step_node_index + two_step_node_index) - set(one_step_node_index)) - total_two_step_node_index = list(set(total_two_step_node_index).intersection(set(sub_graph_node_index))) - - num_two_step = len(total_two_step_node_index) - for second_order_node_index in total_two_step_node_index: - - # caculate the feature: features = 0.8 * average_one + 0.8^2 * average_two - this_node_second_step_nodes = [] - this_node_first_step_nodes = g_matrix[second_order_node_index, :].nonzero()[1].tolist() - for nodes_in_this_node in this_node_first_step_nodes: - this_node_second_step_nodes = list( - set(this_node_second_step_nodes + g_matrix[nodes_in_this_node, :].nonzero()[1].tolist())) - this_node_second_step_nodes = list(set(this_node_second_step_nodes) - set(this_node_first_step_nodes)) - - this_node_second_degree = len(this_node_second_step_nodes) - np_features_query[node_index] = torch.from_numpy(np.sum( - [np_features_query[node_index], - features[second_order_node_index] * (1 - alpha) / math.sqrt(num_two_step * this_node_second_degree)], - axis=0)) - - features_query = th.FloatTensor(np_features_query) - - # generate sub-graph adj-matrix, features, labels - total_sub_nodes = list(set(sub_graph_syn_node_index + sub_graph_node_index)) - sub_g = np.zeros((len(total_sub_nodes), len(total_sub_nodes))) - for sub_index in range(len(total_sub_nodes)): - sub_g[sub_index] = g_matrix[total_sub_nodes[sub_index], total_sub_nodes] - - for i in range(node_number): - if i in sub_graph_node_index: - test_mask[i] = 0 - train_mask[i] = 1 - continue - if i in sub_graph_syn_node_index: - test_mask[i] = 1 - train_mask[i] = 0 - else: - test_mask[i] = 1 - train_mask[i] = 0 - - sub_train_mask = train_mask[total_sub_nodes] - - sub_features = features_query[total_sub_nodes] - sub_labels = labels[total_sub_nodes] - - sub_features = th.FloatTensor(sub_features) - sub_labels = th.LongTensor(sub_labels) - sub_train_mask = sub_train_mask - sub_test_mask = test_mask - # sub_g = DGLGraph(nx.from_numpy_matrix(sub_g)) - - # features = th.FloatTensor(data.features) - # labels = th.LongTensor(data.labels) - # train_mask = th.ByteTensor(data.train_mask) - # test_mask = th.ByteTensor(data.test_mask) - # g = DGLGraph(data.graph) - - gcn_Net.eval() - - # =================Generate Label=================================================== - logits_query = gcn_Net(g, features) - _, labels_query = th.max(logits_query, dim=1) - - sub_labels_query = labels_query[total_sub_nodes] - sub_g = nx.from_numpy_array(sub_g) - - sub_g.remove_edges_from(nx.selfloop_edges(sub_g)) - sub_g.add_edges_from(zip(sub_g.nodes(), sub_g.nodes())) - - sub_g = dgl.from_networkx(sub_g) # sub_g = DGLGraph(sub_g) - n_edges = sub_g.number_of_edges() - # normalization - degs = sub_g.in_degrees().float() - norm = th.pow(degs, -0.5) - norm[th.isinf(norm)] = 0 - - sub_g.ndata['norm'] = norm.unsqueeze(1) - - print("=========Model Extracting==========================") - - # hyperparameters get from kwargs - # no need to change these default for now - num_perturbations = kwargs.get('num_perturbations', 100) - noise_level = kwargs.get('noise_level', 0.05) - rho = kwargs.get('rho', 0.8) - num_each = kwargs.get('num_each', 1) - epochs_per_cycle = kwargs.get('epochs_per_cycle', 1) - setup = kwargs.get('setup', "experiment") - # This need to be relatively bigger to allow for more accurate classification - if_warmup = kwargs.get('if_warmup', False) - LR_CEGA = kwargs.get('LR_CEGA', 1e-2) - # Tuning parameters for adaptive weight in each of the CEGA iteration - # Default works for cora and amazonphoto and coauthorCS - # Need specific modification for citeseer and pubmed - curve = kwargs.get('curve', 0.3) - init_1 = kwargs.get('init_1', 0.2) - init_2 = kwargs.get('init_2', 0.2) - init_3 = kwargs.get('init_3', 0.2) - gap = kwargs.get('gap', 0.6) - - # Derivative parameters - num_node = sub_features.shape[0] - total_epochs = epochs_per_cycle * 18 * C_var - total_num = 20 * C_var - num_cycles = total_epochs // epochs_per_cycle - - # Set up adaptive weights: set the numbers then reweight them - # For citeseer, try k = 0.5, init_1 = 0.3. The other parameters seem to be working fine - cycles = np.linspace(0, 1, num_cycles) - index_1 = init_1 + gap * np.exp(-1 * curve * cycles) - index_2 = init_2 + gap * (1 - np.exp(-1 * curve * cycles)) - index_3 = init_3 * (1 - np.exp(-1 * cycles)) - total = index_1 + index_2 + index_3 - index_1 /= total - index_2 /= total - index_3 /= total - - # Set up output data formality - # data_output = pd.DataFrame(columns=['Num Attack Nodes', 'Method', 'Test Accuracy', 'Test Fidelity']) - - # create GCN model - max_acc1 = 0 - max_acc2 = 0 - max_f1 = 0 - dur = [] - - if dropout == True: - net = GCN_drop(feature_number, label_number) - else: - net = GcnNet(feature_number, label_number) - optimizer = th.optim.Adam(net.parameters(), lr=LR_CEGA, weight_decay=5e-4) - - ## Set up initial set which is iteratively progressive - train_inits = init_mask(C_var, sub_train_mask, sub_labels) - train_inits_tensor = th.tensor(train_inits) - sub_train_mask_new = th.zeros(len(sub_train_mask), dtype=th.bool) - sub_train_mask_new[train_inits] = True - - ## Record the initial nodes in torch object - nodes_queried = th.tensor([], dtype=th.long) - nodes_queried = th.cat((nodes_queried, train_inits_tensor)) - - ## Do warm up if that is ever an option - if if_warmup == True: - sub_train_mask_warmup = th.zeros(len(sub_train_mask), dtype=th.bool) - sub_train_mask_warmup[train_inits] = True - net.train() - - for epoch in range(WARMUP_EPOCH): - logits = net(sub_g, sub_features) - logp = F.log_softmax(logits, dim=1) - - loss = F.nll_loss(logp[sub_train_mask_warmup], sub_labels_query[sub_train_mask_warmup]) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - acc, f1score = evaluate(net, g, features, labels, test_mask) - print("Epoch {:05d} | Loss {:.4f} | Test Acc {:.4f} | Test F1 score {:.4f}".format( - epoch + 1, loss.item(), acc, f1score)) - - net.eval() - - ## Now start timing when the real cycles begin - start_time = time.time() - log_dir = f"{file_path}/timelogs/{dataset_name}/logtime_cega_{seed}" - os.makedirs(os.path.dirname(log_dir), exist_ok=True) - - # Learn a node in each cycle - for cycle in range(10): - # print(f"=========Cycle {cycle + 1}==========================") - # print(f"========={int(sub_train_mask_new.sum())} Selected Nodes==========================") - - # Train some epochs: - net.train() - - for epoch in range(epochs_per_cycle): - logits = net(sub_g, sub_features) - - ## Need to get new sub_train_mask - logp = F.log_softmax(logits, dim=1) - loss = F.nll_loss(logp[sub_train_mask_new], sub_labels_query[sub_train_mask_new]) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - # if epoch >= 3: - # dur.append(time.time() - t0) - # dur.append(time.time() - t0) - - acc1, _ = evaluate(net, g, features, labels_query, test_mask) - acc2, f1score = evaluate(net, g, features, labels, test_mask) - if acc1 > max_acc1: - max_acc1 = acc1 - if acc2 > max_acc2: - max_acc2 = acc2 - if f1score > max_f1: - max_f1 = f1score - # Add f1 in output - print( - "Cycle {:05d} | Epoch {:05d} | Loss {:.4f} | Test Acc {:.4f} | Test Fid {:.4f} | Test F1score {:.4f} ".format( - cycle + 1, epoch + 1 + cycle * epochs_per_cycle, loss.item(), acc2, acc1, max_f1)) - - net.eval() - - ## Not realized here! - # new_row = {"Epoch": epoch + 1 + cycle * epochs_per_cycle, "Loss": loss.item(), "Accuracy": acc2, "Fidelity": acc1} - # data_output = data_output.append(new_row, ignore_index = True) - # data_output.append(new_row) - - # Update the sub_train_mask using your specially-designed algorithm - if sub_train_mask_new.sum() < total_num: - # Random - if setup == "random": - print("Setup: Random") - # Add the entry to the node pool nodes_queried on the supposed order - node_queried = update_sub_train_mask(num_each, sub_train_mask, sub_train_mask_new) - node_queried_tensor = th.tensor(node_queried) - # node_queried_tensor = th.tensor(node_queried, dtype = th.long) - nodes_queried = th.cat((nodes_queried, node_queried_tensor)) - sub_train_mask_new[node_queried] = True - - elif setup == "experiment": - print("Setup: Experiment") - ## First: Representativeness - ## Can be replaced by other centrality measurement - Rank1 = rank_centrality(sub_g, sub_train_mask, sub_train_mask_new, num_each, return_rank=True) - ## Second: Uncertainty - Rank2 = rank_entropy(net, sub_g, sub_features, sub_train_mask, sub_train_mask_new, - num_each, return_rank=True) - ## Third: Diversity - Rank3 = rank_diversity(net, sub_g, sub_features, sub_train_mask, sub_train_mask_new, - num_each, C_var, rho, return_rank=True) - - if Rank1 is None: - print("Completed!") - selected_indices = quantile_selection(Rank1, Rank2, Rank3, index_1[cycle], index_2[cycle], - index_3[cycle], - sub_train_mask, sub_train_mask_new, num_each) - selected_indices_tensor = selected_indices.clone().detach() - # th.tensor(, dtype = th.long) - nodes_queried = th.cat((nodes_queried, selected_indices_tensor)) - sub_train_mask_new[selected_indices] = True - - elif setup == "perturbation": - print("Setup: Experiment with Perturbation") - Rank1 = rank_centrality(sub_g, sub_train_mask, sub_train_mask_new, num_each, return_rank=True) - Rank2 = rank_perturb(net, sub_g, sub_features, num_perturbations, - sub_train_mask, sub_train_mask_new, noise_level, - num_each, return_rank=True) - Rank3 = rank_diversity(net, sub_g, sub_features, sub_train_mask, sub_train_mask_new, - num_each, C_var, rho, return_rank=True) - - if Rank1 is None: - print("Completed!") - selected_indices = quantile_selection(Rank1, Rank2, Rank3, index_1[cycle], index_2[cycle], - index_3[cycle], - sub_train_mask, sub_train_mask_new, num_each) - selected_indices_tensor = selected_indices.clone().detach() - nodes_queried = th.cat((nodes_queried, selected_indices_tensor)) - sub_train_mask_new[selected_indices] = True - else: - print("Wrong Setup!") - return 1 - else: - print("Move on with designated nodes!") - sub_train_mask_new = sub_train_mask_new - - ## Record time for all these cycles when the loop is complete - node_selection_time = time.time() - start_time - with open(log_dir, 'a') as log_file: - log_file.write(f"CEGA {dataset_name} {seed} ") - log_file.write(f"{node_selection_time:.4f}s\n") - - idx_train = nodes_queried.tolist() - - output_data = { - 'total_sub_nodes': total_sub_nodes, - 'idx_train': idx_train - } - - ## Assertation and printing - assert len(idx_train) == 20 * C_var - print('node selection finished') - with open(f'./node_selection/CEGA_{setup}_{dataset_name}_selected_nodes_{(20 * label_number)}_{seed}.json', - 'w') as f: - json.dump(output_data, f) - - sub_g = sub_g.to(device) - sub_features = sub_features.to(device) - sub_labels_query = sub_labels_query.to(device) - labels_query = labels_query.to(device) - g = g.to(device) - features = features.to(device) - test_mask = test_mask.to(device) - labels = labels.to(device) - - print('=========Model Evaluation==========================') - if model_performance: - for iter in range(2 * C_var, 21 * C_var, C_var): - set_seed(seed) - - ## Create net from scratch - if dropout == True: - net_scratch = GCN_drop(feature_number, label_number) - else: - net_scratch = GcnNet(feature_number, label_number) - optimizer = th.optim.Adam(net_scratch.parameters(), lr=LR, weight_decay=5e-4) - - ## set up training nodes and send them to device - sub_train_scratch = th.zeros(sub_features.size()[0], dtype=th.bool) - sub_train_scratch[idx_train[:iter]] = True - sub_train_scratch = sub_train_scratch.to(device) - net_scratch = net_scratch.to(device) - - ## Reset data - max_acc1 = 0 - max_acc2 = 0 - max_f1 = 0 - dur = [] - - for epoch in range(EVAL_EPOCH): - if epoch >= 3: - t0 = time.time() - - net_scratch.train() - logits = net_scratch(sub_g, sub_features) - logp = F.log_softmax(logits, dim=1) - loss = F.nll_loss(logp[sub_train_scratch], sub_labels_query[sub_train_scratch]) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - if epoch >= 3: - dur.append(time.time() - t0) - - acc1, _ = evaluate(net_scratch, g, features, labels_query, test_mask) - acc2, f1score = evaluate(net_scratch, g, features, labels, test_mask) - if acc1 > max_acc1: - max_acc1 = acc1 - if acc2 > max_acc2: - max_acc2 = acc2 - if f1score > max_f1: - max_f1 = f1score - - # Output Epoch Scores - epoch_metrics = pd.DataFrame({ - 'Num Attack Nodes': [iter], - 'Method': ['CEGA'], - 'Test Accuracy': [max_acc2], - 'Test Fidelity': [max_acc1], - 'Test F1score': [max_f1], - }) - metrics_df = pd.concat([metrics_df, epoch_metrics], ignore_index=True) - - print("Test Acc {:.4f} | Test Fid {:.4f} | Test F1score {:.4f} | Time(s) {:.4f}".format( - acc2, acc1, max_f1, np.mean(dur))) - - ## Should this be 'f1score'? - epoch_metrics = pd.DataFrame({ - 'Num Attack Nodes': [int(th.sum(train_mask))], - 'Method': ['CEGA'], - 'Test Accuracy': [target_performance['acc']], - 'Test Fidelity': [1], - 'Test F1score': [target_performance['f1score']], - }) - metrics_df = pd.concat([metrics_df, epoch_metrics], ignore_index=True) - - log_file_path = f"{file_path}/{dataset_name}/log_cega_{seed}.csv" - metrics_df.to_csv(log_file_path, mode='w', header=False, index=False) - - # Set net_full for the next graph to be taken care of, which is expected to include all nodes - if True: - set_seed(seed) - log_file_path = f"{file_path}/{dataset_name}/log_cega_{seed}.csv" - if dropout == True: - net_full = GCN_drop(feature_number, label_number) - else: - net_full = GcnNet(feature_number, label_number) - optimizer_full = th.optim.Adam(net_full.parameters(), lr=LR, weight_decay=5e-4) - - net_full = net_full.to(device) - net = net.to(device) - - perfm_attack = { - 'acc': 0, - 'fid': 0, - 'f1score': 0 - } - - print('========================== Model Evaluation ==========================') - progress_bar = tqdm(range(EVAL_EPOCH), desc="Generating model with ALL attack nodes", ncols=100) - for epoch in progress_bar: - if epoch >= 3: - t0 = time.time() - - net_full.train() - logits = net_full(sub_g, sub_features) - logp = F.log_softmax(logits, 1) - loss = F.nll_loss(logp, sub_labels_query) # [sub_train_mask] - - optimizer_full.zero_grad() - loss.backward() - optimizer_full.step() - - if epoch >= 3: - dur.append(time.time() - t0) - - acc, f1score = evaluate(net_full, g, features, labels, test_mask) - fid, _ = evaluate(net_full, g, features, labels_query, test_mask) - if acc > perfm_attack['acc']: - perfm_attack['acc'] = acc - if fid > perfm_attack['fid']: - perfm_attack['fid'] = fid - if f1score > perfm_attack['f1score']: - perfm_attack['f1score'] = f1score - - progress_bar.set_postfix({ - "Loss": f"{loss.item():.4f}", - "Test Acc": f"{acc:.4f}", - "Test F1": f"{f1score:.4f}", - # "Processed %": f"{(epoch + 1) / TGT_EPOCH * 100:.2f}", - # "Time(s)": f"{np.mean(dur) if dur else 0:.4f}" - }) - epoch_metrics = pd.DataFrame({ - 'Num Attack Nodes': [sub_train_mask.sum().item()], - 'Method': ['cega'], - 'Test Accuracy': [perfm_attack['acc']], - 'Test Fidelity': [perfm_attack['fid']], - 'Test F1score': [perfm_attack['f1score']], - }) - metrics_df = pd.concat([metrics_df, epoch_metrics], ignore_index=True) - log_file_path = f"{file_path}/{dataset_name}/log_cega_{seed}.csv" - metrics_df.to_csv(log_file_path, mode='w', header=False, index=False) - - -from pyhazard.models.attack.base import BaseAttack -from pyhazard.datasets import Dataset -from pyhazard.utils.metrics import AttackMetric, AttackCompMetric - -class CEGA(BaseAttack): - supported_api_types = {"dgl"} - - # ====== only signature and stored params are changed here ====== - def __init__( - self, - dataset: Dataset, - attack_node_fraction: float, - model_path: str = None, - attack_x_ratio: float = 1.0, - attack_a_ratio: float = 1.0, - ): - super(CEGA, self).__init__(dataset, attack_node_fraction, model_path) - # graph data - self.dataset = dataset - self.graph = dataset.graph_data.to(self.device) - self.features = dataset.graph_data.ndata['feat'] - self.labels = dataset.graph_data.ndata['label'] - self.train_mask = dataset.graph_data.ndata['train_mask'] - self.test_mask = dataset.graph_data.ndata['test_mask'] - # meta data - self.node_number = dataset.num_nodes - self.feature_number = dataset.num_features - self.label_number = dataset.num_classes - self.attack_node_number = int(dataset.num_nodes * attack_node_fraction) - self.attack_node_fraction = attack_node_fraction - # new visibility knobs for inputs (kept for consistency across attacks) - self.attack_x_ratio = float(attack_x_ratio) - self.attack_a_ratio = float(attack_a_ratio) - - def attack( - self, - seed=1, - cuda=None, - LR=1e-3, - TGT_LR=1e-2, - EVAL_EPOCH=10, - TGT_EPOCH=10, - WARMUP_EPOCH=4, - dropout=False, - model_performance=True, - **kwargs - ): - """ - Returns - ------- - perf_json : dict - Performance metrics (JSON-serialisable): accuracy/fidelity/F1 of the surrogate, - and optionally target accuracy/F1 for reference. - comp_json : dict - Computation metrics (JSON-serialisable): attack_time, query_target_time, train_surrogate_time, etc. - """ - # ===== metrics collection (computation) ===== - attack_time_start = time.time() - query_target_time = 0.0 - train_surrogate_time = 0.0 - - # Initialization - set_seed(seed) - metrics_df = pd.DataFrame(columns=['Num Attack Nodes', 'Method', 'Test Accuracy', 'Test Fidelity']) - - # data handles - g = self.graph - features = self.features - labels = self.labels - node_number = self.node_number - train_mask = self.train_mask - test_mask = self.test_mask - - attack_node_arg = self.attack_node_fraction - attack_node_number = int(node_number * attack_node_arg) - feature_number = features.shape[1] - label_number = len(labels.unique()) - C_var = label_number - - print('The attack node number is: ', attack_node_number) - - g = g.to(self.device) - degs = g.in_degrees().float() - norm = th.pow(degs, -0.5) - norm[th.isinf(norm)] = 0 - if cuda is not None: - norm = norm.cuda() - g.ndata['norm'] = norm.unsqueeze(1) - if dropout: - gcn_Net = GCN_drop(feature_number, label_number) - else: - gcn_Net = GcnNet(feature_number, label_number) - optimizer = th.optim.Adam(gcn_Net.parameters(), lr=TGT_LR, weight_decay=5e-4) - dur = [] - - # Send the training to device - features = features.to(self.device) - gcn_Net = gcn_Net.to(self.device) - train_mask = train_mask.to(self.device) - test_mask = test_mask.to(self.device) - labels = labels.to(self.device) - target_performance = {'acc': 0, 'f1score': 0} - - print("=========Target Model Generating==========================") - # train target model - tgt_train_t0 = time.time() - for epoch in range(TGT_EPOCH): - if epoch >= 3: - t0 = time.time() - gcn_Net.train() - logits = gcn_Net(g, features) - logp = F.log_softmax(logits, 1) - loss = F.nll_loss(logp[train_mask], labels[train_mask]) - optimizer.zero_grad() - loss.backward() - optimizer.step() - if epoch >= 3: - dur.append(time.time() - t0) - acc, f1score = evaluate(gcn_Net, g, features, labels, test_mask) - target_performance['acc'] = max(target_performance['acc'], acc) - target_performance['f1score'] = max(target_performance['f1score'], f1score) - print("Epoch {:05d} | Loss {:.4f} | Test Acc {:.4f} | Test F1 macro {:.4f} | Time(s) {:.4f}".format( - epoch, loss.item(), acc, f1score, np.mean(dur))) - train_surrogate_time += (time.time() - tgt_train_t0) - - # move tensors back to cpu for subgraph building - g = g.cpu() - features = features.cpu() - gcn_Net = gcn_Net.cpu() - train_mask = train_mask.cpu() - test_mask = test_mask.cpu() - labels = labels.cpu() - - # Generate sub-graph index - alpha = 0.8 - sub_graph_node_index = [random.randint(0, node_number - 1) for _ in range(attack_node_number)] - sub_labels = labels[sub_graph_node_index] - - syn_nodes = [] - g_matrix = np.asmatrix(g.adjacency_matrix().to_dense()) - for node_index in sub_graph_node_index: - # get nodes - one_step_node_index = g_matrix[node_index, :].nonzero()[1].tolist() - two_step_node_index = [] - for first_order_node_index in one_step_node_index: - syn_nodes.append(first_order_node_index) - two_step_node_index = g_matrix[first_order_node_index, :].nonzero()[1].tolist() - - sub_graph_syn_node_index = list(set(syn_nodes) - set(sub_graph_node_index)) - total_sub_nodes = list(set(sub_graph_syn_node_index + sub_graph_node_index)) - - # Generate features for SubGraph attack - np_features_query = features.clone() - for node_index in sub_graph_syn_node_index: - # initialized as zero - np_features_query[node_index] = np_features_query[node_index] * 0 - # get one step and two steps nodes - one_step_node_index = g_matrix[node_index, :].nonzero()[1].tolist() - one_step_node_index = list(set(one_step_node_index).intersection(set(sub_graph_node_index))) - - total_two_step_node_index = [] - num_one_step = len(one_step_node_index) - for first_order_node_index in one_step_node_index: - # features = 0.8 * average_one / sqrt(num_one_step * deg) - this_node_degree = len(g_matrix[first_order_node_index, :].nonzero()[1].tolist()) - x1 = np_features_query[node_index] - x2 = features[first_order_node_index] * alpha / math.sqrt(max(1, num_one_step * this_node_degree)) - np_features_query[node_index] = x1 + x2 - - two_step_node_index = g_matrix[first_order_node_index, :].nonzero()[1].tolist() - total_two_step_node_index = list( - set(total_two_step_node_index + two_step_node_index) - set(one_step_node_index) - ) - total_two_step_node_index = list(set(total_two_step_node_index).intersection(set(sub_graph_node_index))) - - num_two_step = len(total_two_step_node_index) - for second_order_node_index in total_two_step_node_index: - this_node_second_step_nodes = [] - this_node_first_step_nodes = g_matrix[second_order_node_index, :].nonzero()[1].tolist() - for nodes_in_this_node in this_node_first_step_nodes: - this_node_second_step_nodes = list( - set(this_node_second_step_nodes + g_matrix[nodes_in_this_node, :].nonzero()[1].tolist()) - ) - this_node_second_step_nodes = list(set(this_node_second_step_nodes) - set(this_node_first_step_nodes)) - this_node_second_degree = len(this_node_second_step_nodes) - x1 = np_features_query[node_index] - x2 = features[first_order_node_index] * alpha / math.sqrt(max(1, num_one_step * this_node_degree)) - np_features_query[node_index] = x1 + x2 - - features_query = th.FloatTensor(np_features_query) - - # generate sub-graph adj-matrix, features, labels - total_sub_nodes = list(set(sub_graph_syn_node_index + sub_graph_node_index)) - sub_g = np.zeros((len(total_sub_nodes), len(total_sub_nodes))) - for sub_index in range(len(total_sub_nodes)): - sub_g[sub_index] = g_matrix[total_sub_nodes[sub_index], total_sub_nodes] - - for i in range(node_number): - if i in sub_graph_node_index: - test_mask[i] = 0 - train_mask[i] = 1 - continue - if i in sub_graph_syn_node_index: - test_mask[i] = 1 - train_mask[i] = 0 - else: - test_mask[i] = 1 - train_mask[i] = 0 - - sub_train_mask = train_mask[total_sub_nodes] - sub_features = features_query[total_sub_nodes] - sub_labels = labels[total_sub_nodes] - - sub_features = th.FloatTensor(sub_features) - sub_labels = th.LongTensor(sub_labels) - sub_train_mask = sub_train_mask - sub_test_mask = test_mask - - gcn_Net.eval() - - # =================Generate Label======================== - # timing: query the target once for labels on query set - qt0 = time.time() - logits_query = gcn_Net(g, features) - _, labels_query = th.max(logits_query, dim=1) - query_target_time += (time.time() - qt0) - - sub_labels_query = labels_query[total_sub_nodes] - sub_g = nx.from_numpy_array(sub_g) - sub_g.remove_edges_from(nx.selfloop_edges(sub_g)) - sub_g.add_edges_from(zip(sub_g.nodes(), sub_g.nodes())) - sub_g = dgl.from_networkx(sub_g) - # normalization - degs = sub_g.in_degrees().float() - norm = th.pow(degs, -0.5) - norm[th.isinf(norm)] = 0 - sub_g.ndata['norm'] = norm.unsqueeze(1) - - print("=========Model Extracting==========================") - - # hyperparameters from kwargs - num_perturbations = kwargs.get('num_perturbations', 100) - noise_level = kwargs.get('noise_level', 0.05) - rho = kwargs.get('rho', 0.8) - num_each = kwargs.get('num_each', 1) - epochs_per_cycle = kwargs.get('epochs_per_cycle', 1) - setup = kwargs.get('setup', "experiment") - if_warmup = kwargs.get('if_warmup', False) - LR_CEGA = kwargs.get('LR_CEGA', 1e-2) - curve = kwargs.get('curve', 0.3) - init_1 = kwargs.get('init_1', 0.2) - init_2 = kwargs.get('init_2', 0.2) - init_3 = kwargs.get('init_3', 0.2) - gap = kwargs.get('gap', 0.6) - - # derivative params - num_node = sub_features.shape[0] - total_epochs = epochs_per_cycle * 18 * C_var - total_num = 20 * C_var - num_cycles = total_epochs // epochs_per_cycle - - cycles = np.linspace(0, 1, num_cycles) - index_1 = init_1 + gap * np.exp(-1 * curve * cycles) - index_2 = init_2 + gap * (1 - np.exp(-1 * curve * cycles)) - index_3 = init_3 * (1 - np.exp(-1 * cycles)) - total = index_1 + index_2 + index_3 - index_1 /= total - index_2 /= total - index_3 /= total - - # create surrogate model - max_acc1 = 0 - max_acc2 = 0 - max_f1 = 0 - dur = [] - - net = GCN_drop(feature_number, label_number) if dropout else GcnNet(feature_number, label_number) - optimizer = th.optim.Adam(net.parameters(), lr=LR_CEGA, weight_decay=5e-4) - - # initial training set - train_inits = init_mask(C_var, sub_train_mask, sub_labels) - train_inits_tensor = th.tensor(train_inits) - sub_train_mask_new = th.zeros(len(sub_train_mask), dtype=th.bool) - sub_train_mask_new[train_inits] = True - nodes_queried = th.tensor([], dtype=th.long) - nodes_queried = th.cat((nodes_queried, train_inits_tensor)) - - # warmup - if if_warmup: - sub_train_mask_warmup = th.zeros(len(sub_train_mask), dtype=th.bool) - sub_train_mask_warmup[train_inits] = True - net.train() - warm_s = time.time() - for epoch in range(WARMUP_EPOCH): - logits = net(sub_g, sub_features) - logp = F.log_softmax(logits, dim=1) - loss = F.nll_loss(logp[sub_train_mask_warmup], sub_labels_query[sub_train_mask_warmup]) - optimizer.zero_grad() - loss.backward() - optimizer.step() - acc, f1score = evaluate(net, g, features, labels, test_mask) - print("Epoch {:05d} | Loss {:.4f} | Test Acc {:.4f} | Test F1 score {:.4f}".format( - epoch + 1, loss.item(), acc, f1score)) - train_surrogate_time += (time.time() - warm_s) - net.eval() - - # cycles - print("=========Learn a node in each cycle==========================") - cycle_train_s = time.time() - for cycle in range(num_cycles): - net.train() - for epoch in range(epochs_per_cycle): - logits = net(sub_g, sub_features) - logp = F.log_softmax(logits, dim=1) - loss = F.nll_loss(logp[sub_train_mask_new], sub_labels_query[sub_train_mask_new]) - optimizer.zero_grad() - loss.backward() - optimizer.step() - - acc1, _ = evaluate(net, g, features, labels_query, test_mask) # fidelity proxy - acc2, f1score = evaluate(net, g, features, labels, test_mask) - max_acc1 = max(max_acc1, acc1) - max_acc2 = max(max_acc2, acc2) - max_f1 = max(max_f1, f1score) - print( - "Cycle {:05d} | Epoch {:05d} | Loss {:.4f} | Test Acc {:.4f} | Test Fid {:.4f} | Test F1score {:.4f} ".format( - cycle + 1, epoch + 1 + cycle * epochs_per_cycle, loss.item(), acc2, acc1, max_f1)) - - net.eval() - if sub_train_mask_new.sum() < total_num: - if setup == "random": - print("Setup: Random") - node_queried = update_sub_train_mask(num_each, sub_train_mask, sub_train_mask_new) - node_queried_tensor = th.tensor(node_queried) - nodes_queried = th.cat((nodes_queried, node_queried_tensor)) - sub_train_mask_new[node_queried] = True - elif setup == "experiment": - print("Setup: Experiment") - Rank1 = rank_centrality(sub_g, sub_train_mask, sub_train_mask_new, num_each, return_rank=True) - Rank2 = rank_entropy(net, sub_g, sub_features, sub_train_mask, sub_train_mask_new, - num_each, return_rank=True) - Rank3 = rank_diversity(net, sub_g, sub_features, sub_train_mask, sub_train_mask_new, - num_each, C_var, rho, return_rank=True) - if Rank1 is None: - print("Completed!") - selected_indices = quantile_selection( - Rank1, Rank2, Rank3, index_1[cycle], index_2[cycle], index_3[cycle], - sub_train_mask, sub_train_mask_new, num_each - ) - selected_indices_tensor = selected_indices.clone().detach() - nodes_queried = th.cat((nodes_queried, selected_indices_tensor)) - sub_train_mask_new[selected_indices] = True - elif setup == "perturbation": - print("Setup: Experiment with Perturbation") - Rank1 = rank_centrality(sub_g, sub_train_mask, sub_train_mask_new, num_each, return_rank=True) - Rank2 = rank_perturb(net, sub_g, sub_features, num_perturbations, - sub_train_mask, sub_train_mask_new, noise_level, - num_each, return_rank=True) - Rank3 = rank_diversity(net, sub_g, sub_features, sub_train_mask, sub_train_mask_new, - num_each, C_var, rho, return_rank=True) - if Rank1 is None: - print("Completed!") - selected_indices = quantile_selection( - Rank1, Rank2, Rank3, index_1[cycle], index_2[cycle], index_3[cycle], - sub_train_mask, sub_train_mask_new, num_each - ) - selected_indices_tensor = selected_indices.clone().detach() - nodes_queried = th.cat((nodes_queried, selected_indices_tensor)) - sub_train_mask_new[selected_indices] = True - else: - print("Wrong Setup!") - return 1 - else: - print("Move on with designated nodes!") - train_surrogate_time += (time.time() - cycle_train_s) - - idx_train = nodes_queried.tolist() - output_data = {'total_sub_nodes': total_sub_nodes, 'idx_train': idx_train} - print('node selection finished') - - # move to device for evaluation/training-from-scratch - sub_g = sub_g.to(self.device) - sub_features = sub_features.to(self.device) - sub_labels_query = sub_labels_query.to(self.device) - labels_query = labels_query.to(self.device) - g = g.to(self.device) - features = features.to(self.device) - test_mask = test_mask.to(self.device) - labels = labels.to(self.device) - - print('=========Model Evaluation==========================') - if model_performance: - for iter in range(2 * C_var, 21 * C_var, C_var): - set_seed(seed) - net_scratch = GCN_drop(feature_number, label_number) if dropout else GcnNet(feature_number, label_number) - optimizer = th.optim.Adam(net_scratch.parameters(), lr=LR, weight_decay=5e-4) - sub_train_scratch = th.zeros(sub_features.size()[0], dtype=th.bool) - sub_train_scratch[idx_train[:iter]] = True - sub_train_scratch = sub_train_scratch.to(self.device) - net_scratch = net_scratch.to(self.device) - max_acc1 = 0 - max_acc2 = 0 - max_f1 = 0 - dur = [] - eval_train_s = time.time() - for epoch in range(EVAL_EPOCH): - if epoch >= 3: - t0 = time.time() - net_scratch.train() - logits = net_scratch(sub_g, sub_features) - logp = F.log_softmax(logits, dim=1) - loss = F.nll_loss(logp[sub_train_scratch], sub_labels_query[sub_train_scratch]) - optimizer.zero_grad() - loss.backward() - optimizer.step() - if epoch >= 3: - dur.append(time.time() - t0) - acc1, _ = evaluate(net_scratch, g, features, labels_query, test_mask) - acc2, f1score = evaluate(net_scratch, g, features, labels, test_mask) - max_acc1 = max(max_acc1, acc1) - max_acc2 = max(max_acc2, acc2) - max_f1 = max(max_f1, f1score) - train_surrogate_time += (time.time() - eval_train_s) - epoch_metrics = pd.DataFrame({ - 'Num Attack Nodes': [iter], - 'Method': ['CEGA'], - 'Test Accuracy': [max_acc2], - 'Test Fidelity': [max_acc1], - 'Test F1score': [max_f1], - }) - metrics_df = pd.concat([metrics_df, epoch_metrics], ignore_index=True) - print("Test Acc {:.4f} | Test Fid {:.4f} | Test F1score {:.4f} | Time(s) {:.4f}".format( - acc2, acc1, max_f1, np.mean(dur))) - epoch_metrics = pd.DataFrame({ - 'Num Attack Nodes': [int(th.sum(train_mask))], - 'Method': ['CEGA'], - 'Test Accuracy': [target_performance['acc']], - 'Test Fidelity': [1], - 'Test F1score': [target_performance['f1score']], - }) - metrics_df = pd.concat([metrics_df, epoch_metrics], ignore_index=True) - - # train a surrogate on all selected nodes for final report - set_seed(seed) - net_full = GCN_drop(feature_number, label_number) if dropout else GcnNet(feature_number, label_number) - optimizer_full = th.optim.Adam(net_full.parameters(), lr=LR, weight_decay=5e-4) - net_full = net_full.to(self.device) - net = net.to(self.device) - perfm_attack = {'acc': 0, 'fid': 0, 'f1score': 0} - - print('========================== Model Evaluation ==========================') - final_train_s = time.time() - progress_bar = tqdm(range(EVAL_EPOCH), desc="Generating model with ALL attack nodes", ncols=100) - for epoch in progress_bar: - if epoch >= 3: - t0 = time.time() - net_full.train() - logits = net_full(sub_g, sub_features) - logp = F.log_softmax(logits, 1) - loss = F.nll_loss(logp, sub_labels_query) - optimizer_full.zero_grad() - loss.backward() - optimizer_full.step() - if epoch >= 3: - dur.append(time.time() - t0) - - acc, f1score = evaluate(net_full, g, features, labels, test_mask) - fid, _ = evaluate(net_full, g, features, labels_query, test_mask) - perfm_attack['acc'] = max(perfm_attack['acc'], acc) - perfm_attack['fid'] = max(perfm_attack['fid'], fid) - perfm_attack['f1score'] = max(perfm_attack['f1score'], f1score) - progress_bar.set_postfix({ - "Loss": f"{loss.item():.4f}", - "Test Acc": f"{acc:.4f}", - "Test F1": f"{f1score:.4f}", - }) - train_surrogate_time += (time.time() - final_train_s) - - print("Test Acc {:.4f} | Test Fid {:.4f} | Test F1score {:.4f} | Time(s) {:.4f}".format( - perfm_attack['acc'], perfm_attack['fid'], perfm_attack['f1score'], np.mean(dur))) - - epoch_metrics = pd.DataFrame({ - 'Num Attack Nodes': [sub_train_mask.sum().item()], - 'Method': ['cega'], - 'Test Accuracy': [perfm_attack['acc']], - 'Test Fidelity': [perfm_attack['fid']], - 'Test F1score': [perfm_attack['f1score']], - }) - metrics_df = pd.concat([metrics_df, epoch_metrics], ignore_index=True) - - # ===== assemble JSON outputs required by the new metric API ===== - attack_total_time = time.time() - attack_time_start - - perf_json = { - "attack": "CEGA", - "num_attack_nodes": int(sub_train_mask.sum().item()), - "acc": float(perfm_attack['acc']), - "fid": float(perfm_attack['fid']), - "f1": float(perfm_attack['f1score']), - "target_acc": float(target_performance['acc']), - "target_f1": float(target_performance['f1score']), - } - comp_json = { - "device": str(self.device), - "attack_time": float(attack_total_time), - "query_target_time": float(query_target_time), - "train_surrogate_time": float(train_surrogate_time), - # optional placeholders to align with AdvMEA if present there - "inference_surrogate_time": None, - "peak_memory": None, - } - return perf_json, comp_json - diff --git a/pyhazard/models/attack/DataFreeMEA.py b/pyhazard/models/attack/DataFreeMEA.py deleted file mode 100644 index e29d281..0000000 --- a/pyhazard/models/attack/DataFreeMEA.py +++ /dev/null @@ -1,310 +0,0 @@ -from abc import abstractmethod -import time - -import dgl -import networkx as nx -import torch -import torch.nn.functional as F -from tqdm import tqdm - -from pyhazard.models.attack.base import BaseAttack -from pyhazard.models.nn import GCN, GraphSAGE # Backbone architectures -from pyhazard.utils.metrics import AttackMetric, AttackCompMetric # Consistent with AdvMEA - - -class GraphGenerator: - def __init__(self, node_number, feature_number, label_number): - self.node_number = node_number - self.feature_number = feature_number - self.label_number = label_number - - def generate(self): - # Generate a random Erdős–Rényi graph and convert it to DGL - g_nx = nx.erdos_renyi_graph(n=self.node_number, p=0.05) - g_dgl = dgl.from_networkx(g_nx) - # Random node features - features = torch.randn((self.node_number, self.feature_number)) - return g_dgl, features - - -class DFEAAttack(BaseAttack): - supported_api_types = {"dgl"} - - # Use unified attack_x_ratio and attack_a_ratio - def __init__(self, dataset, attack_x_ratio, attack_a_ratio, model_path=None): - super().__init__(dataset, attack_x_ratio, model_path) - # Load graph data - self.graph = dataset.graph_data.to(self.device) - self.features = self.graph.ndata['feat'] - self.labels = self.graph.ndata['label'] - self.train_mask = self.graph.ndata['train_mask'] - self.val_mask = self.graph.ndata.get('val_mask', None) - self.test_mask = self.graph.ndata['test_mask'] - # Meta data - self.feature_number = dataset.num_features - self.label_number = dataset.num_classes - - # Use the maximum of the two visibility ratios as the budget; - # if both are 0, fallback to a small default value to avoid zero-size graph - ratio_budget = max(float(attack_x_ratio), float(attack_a_ratio)) - if ratio_budget <= 0.0: - ratio_budget = 0.05 - self.attack_node_number = max(1, int(dataset.num_nodes * ratio_budget)) - - # Generate synthetic graph and features for surrogate training (data-free) - self.generator = GraphGenerator( - node_number=self.attack_node_number, - feature_number=self.feature_number, - label_number=self.label_number - ) - self.synthetic_graph, self.synthetic_features = self.generator.generate() - self.synthetic_graph = self.synthetic_graph.to(self.device) - self.synthetic_features = self.synthetic_features.to(self.device) - - if model_path is None: - self._train_target_model() - else: - self._load_model(model_path) - - def _train_target_model(self): - # Train the victim GCN model on real data - model = GCN(self.feature_number, self.label_number).to(self.device) - optimizer = torch.optim.Adam( - model.parameters(), lr=0.01, weight_decay=5e-4 - ) - model.train() - # Dataset-specific label shaping - name = getattr(self.dataset, 'dataset_name', None) or getattr(self.dataset, 'name', None) - epochs = 200 - for _ in range(epochs): - optimizer.zero_grad() - logits = model(self.graph, self.features) - labels = self.labels.squeeze() if name == 'ogb-arxiv' else self.labels - loss = F.nll_loss( - F.log_softmax(logits[self.train_mask], dim=1), - labels[self.train_mask] - ) - loss.backward() - optimizer.step() - - model.eval() - self.model = model - - def _load_model(self, model_path): - # Load a pretrained victim model - model = GCN(self.feature_number, self.label_number) - state = torch.load(model_path, map_location=self.device) - model.load_state_dict(state) - model.eval() - self.model = model - - def _forward(self, model, graph, features): - # Forward wrapper for GCN and GraphSAGE - if isinstance(model, GraphSAGE): - # GraphSAGE expects a two-block input list - return model([graph, graph], features) - return model(graph, features) - - def _evaluate_on_real_test(self, surrogate, metric: AttackMetric, metric_comp: AttackCompMetric): - """Evaluate the surrogate on the real test set and update metrics""" - g = self.graph - x = self.features - y = self.labels - mask = self.test_mask - - # Victim inference time - t0 = time.time() - with torch.no_grad(): - logits_v = self._forward(self.model, g, x) - metric_comp.update(inference_target_time=(time.time() - t0)) - labels_query = logits_v.argmax(dim=1) - - # Surrogate inference time - t0 = time.time() - with torch.no_grad(): - logits_s = self._forward(surrogate, g, x) - metric_comp.update(inference_surrogate_time=(time.time() - t0)) - preds_s = logits_s.argmax(dim=1) - - # Update performance metrics: accuracy and fidelity - metric.update(preds_s[mask], y[mask], labels_query[mask]) - - @abstractmethod - def attack(self): - pass - - -class DFEATypeI(DFEAAttack): - """ - Type I: Uses victim outputs + gradients for surrogate training. - """ - - def attack(self): - metric = AttackMetric() - metric_comp = AttackCompMetric() - - attack_start = time.time() - surrogate = GCN(self.feature_number, self.label_number).to(self.device) - optimizer = torch.optim.Adam(surrogate.parameters(), lr=0.01) - - # Surrogate training time - train_surrogate_start = time.time() - # Victim query time - total_query_time = 0.0 - - for _ in tqdm(range(200)): - surrogate.train() - optimizer.zero_grad() - # Victim logits (no gradient), count query time - t_q = time.time() - with torch.no_grad(): - logits_v = self._forward( - self.model, self.synthetic_graph, self.synthetic_features - ) - total_query_time += (time.time() - t_q) - - logits_s = self._forward( - surrogate, self.synthetic_graph, self.synthetic_features - ) - loss = F.kl_div( - F.log_softmax(logits_s, dim=1), - F.softmax(logits_v, dim=1), - reduction='batchmean' - ) - loss.backward() - optimizer.step() - - train_surrogate_end = time.time() - - surrogate.eval() - self._evaluate_on_real_test(surrogate, metric, metric_comp) - - metric_comp.end() - metric_comp.update( - attack_time=(time.time() - attack_start), - query_target_time=total_query_time, - train_surrogate_time=(train_surrogate_end - train_surrogate_start), - ) - res = metric.compute() - res_comp = metric_comp.compute() - return res, res_comp - - -class DFEATypeII(DFEAAttack): - """ - Type II: Uses victim outputs only (hard labels). - """ - - def attack(self): - metric = AttackMetric() - metric_comp = AttackCompMetric() - - attack_start = time.time() - surrogate = GraphSAGE(self.feature_number, 16, self.label_number).to(self.device) - optimizer = torch.optim.Adam(surrogate.parameters(), lr=0.01) - - train_surrogate_start = time.time() - total_query_time = 0.0 - - for _ in tqdm(range(200)): - surrogate.train() - optimizer.zero_grad() - # Victim pseudo labels - t_q = time.time() - with torch.no_grad(): - logits_v = self._forward( - self.model, self.synthetic_graph, self.synthetic_features - ) - total_query_time += (time.time() - t_q) - pseudo = logits_v.argmax(dim=1) - - logits_s = self._forward( - surrogate, self.synthetic_graph, self.synthetic_features - ) - loss = F.cross_entropy(logits_s, pseudo) - loss.backward() - optimizer.step() - - train_surrogate_end = time.time() - - surrogate.eval() - self._evaluate_on_real_test(surrogate, metric, metric_comp) - - metric_comp.end() - metric_comp.update( - attack_time=(time.time() - attack_start), - query_target_time=total_query_time, - train_surrogate_time=(train_surrogate_end - train_surrogate_start), - ) - res = metric.compute() - res_comp = metric_comp.compute() - return res, res_comp - - -class DFEATypeIII(DFEAAttack): - """ - Type III: Two surrogates with victim supervision + consistency. - """ - - def attack(self): - metric = AttackMetric() - metric_comp = AttackCompMetric() - - attack_start = time.time() - s1 = GCN(self.feature_number, self.label_number).to(self.device) - s2 = GraphSAGE(self.feature_number, 16, self.label_number).to(self.device) - opt1 = torch.optim.Adam(s1.parameters(), lr=0.01) - opt2 = torch.optim.Adam(s2.parameters(), lr=0.01) - - train_surrogate_start = time.time() - total_query_time = 0.0 - - for _ in tqdm(range(200)): - s1.train() - s2.train() - opt1.zero_grad() - opt2.zero_grad() - # Victim pseudo-labels - t_q = time.time() - with torch.no_grad(): - logits_v = self._forward( - self.model, self.synthetic_graph, self.synthetic_features - ) - total_query_time += (time.time() - t_q) - pseudo_v = logits_v.argmax(dim=1) - # Surrogate predictions - l1 = self._forward(s1, self.synthetic_graph, self.synthetic_features) - l2 = self._forward(s2, self.synthetic_graph, self.synthetic_features) - # Loss: supervised + consistency - loss1 = F.cross_entropy(l1, pseudo_v) - loss2 = F.cross_entropy(l2, pseudo_v) - cons = F.mse_loss(l1, l2) - total = loss1 + loss2 + 0.5 * cons - total.backward() - opt1.step() - opt2.step() - - train_surrogate_end = time.time() - - # Use s1 as the final surrogate for evaluation - s1.eval() - self._evaluate_on_real_test(s1, metric, metric_comp) - - metric_comp.end() - metric_comp.update( - attack_time=(time.time() - attack_start), - query_target_time=total_query_time, - train_surrogate_time=(train_surrogate_end - train_surrogate_start), - ) - res = metric.compute() - res_comp = metric_comp.compute() - return res, res_comp - - -# Factory mapping of attack names to classes -ATTACK_FACTORY = { - "ModelExtractionAttack0": DFEATypeI, - "ModelExtractionAttack1": DFEATypeI, - "ModelExtractionAttack2": DFEATypeII, - "ModelExtractionAttack3": DFEATypeIII -} diff --git a/pyhazard/models/attack/Realistic.py b/pyhazard/models/attack/Realistic.py deleted file mode 100644 index d68f5e0..0000000 --- a/pyhazard/models/attack/Realistic.py +++ /dev/null @@ -1,333 +0,0 @@ -import random -import warnings -import time - -import dgl -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim -from sklearn.metrics.pairwise import cosine_similarity - -from pyhazard.models.nn.backbones import GCN -from .base import BaseAttack -from pyhazard.utils.metrics import AttackMetric, AttackCompMetric # align with AdvMEA - -warnings.filterwarnings('ignore') - - -class DGLEdgePredictor(nn.Module): - """DGL version of edge prediction module.""" - - def __init__(self, input_dim, hidden_dim, num_classes, device): - super(DGLEdgePredictor, self).__init__() - self.input_dim = input_dim - self.hidden_dim = hidden_dim - self.num_classes = num_classes - self.device = device - - # Use the same GCN backbone as the target model to obtain node embeddings. - self.gnn = GCN(input_dim, hidden_dim) - self.node_classifier = nn.Linear(hidden_dim, num_classes) - - # Edge prediction head. - self.edge_predictor = nn.Sequential( - nn.Linear(hidden_dim * 2, hidden_dim), - nn.ReLU(), - nn.Linear(hidden_dim, 1), - nn.Sigmoid() - ) - - def forward(self, graph, features): - # Compute node embeddings then logits for node classification. - node_embeddings = self.gnn(graph, features) - node_logits = self.node_classifier(node_embeddings) - return node_embeddings, node_logits - - def predict_edges(self, node_embeddings, node_pairs): - """Predict edge existence probability for a list of node index pairs.""" - if len(node_pairs) == 0: - return torch.tensor([], device=self.device) - - node_pairs = torch.tensor(node_pairs, device=self.device) - src_embeddings = node_embeddings[node_pairs[:, 0]] - dst_embeddings = node_embeddings[node_pairs[:, 1]] - - edge_features = torch.cat([src_embeddings, dst_embeddings], dim=1) - edge_probs = self.edge_predictor(edge_features).squeeze() - return edge_probs - - -class DGLSurrogateModel(nn.Module): - """DGL version of surrogate model.""" - - def __init__(self, input_dim, num_classes, model_type='GCN'): - super(DGLSurrogateModel, self).__init__() - self.model_type = model_type - if model_type == 'GCN': - self.gnn = GCN(input_dim, num_classes) - else: - # Default to GCN; can be extended to other backbones. - self.gnn = GCN(input_dim, num_classes) - - def forward(self, graph, features): - return self.gnn(graph, features) - - -class RealisticAttack(BaseAttack): - """DGL-based GNN model extraction attack with updated metrics API.""" - supported_api_types = {"dgl"} - supported_datasets = {} - - def __init__(self, dataset, attack_x_ratio: float, attack_a_ratio: float, model_path: str = None, - hidden_dim: int = 64, threshold_s: float = 0.7, threshold_a: float = 0.5): - # Keep BaseAttack initialization contract; store ratios for this attack. - super().__init__(dataset, attack_x_ratio, model_path) - - self.attack_x_ratio = float(attack_x_ratio) - self.attack_a_ratio = float(attack_a_ratio) - - self.hidden_dim = hidden_dim - self.threshold_s = threshold_s # Cosine similarity threshold - self.threshold_a = threshold_a # Edge prediction threshold - - # Determine the number of queried nodes by the availability ratios. - ratio_budget = max(self.attack_x_ratio, self.attack_a_ratio) - if ratio_budget <= 0.0: - ratio_budget = 0.05 # small default to avoid zero queries - self.attack_node_number = max(1, int(self.num_nodes * ratio_budget)) - - # Graph tensors - self.graph_data = self.graph_data.to(self.device) - self.graph = self.graph_data - self.features = self.graph.ndata['feat'] - - # Initialize edge predictor and surrogate model. - self.edge_predictor = DGLEdgePredictor( - self.num_features, hidden_dim, self.num_classes, self.device - ).to(self.device) - self.surrogate_model = DGLSurrogateModel( - self.num_features, self.num_classes - ).to(self.device) - - # Target model used to simulate black-box responses. - self.net1 = GCN(self.num_features, self.num_classes).to(self.device) - - # Optimizers - self.optimizer_edge = optim.Adam(self.edge_predictor.parameters(), lr=0.01, weight_decay=5e-4) - self.optimizer_surrogate = optim.Adam(self.surrogate_model.parameters(), lr=0.01, weight_decay=5e-4) - - print(f"Initialized attack on {dataset.dataset_name} dataset") - print(f"Nodes: {self.num_nodes}, Features: {self.num_features}, Classes: {self.num_classes}") - print(f"Attack nodes: {self.attack_node_number} (x_ratio={self.attack_x_ratio:.2f}, a_ratio={self.attack_a_ratio:.2f})") - - def simulate_target_model_queries(self, query_nodes, error_rate=0.15): - """Query the target model for labels on query_nodes and introduce a small error rate.""" - self.net1.eval() - with torch.no_grad(): - logits = self.net1(self.graph, self.features) - predictions = F.log_softmax(logits, dim=1).argmax(dim=1) - - predicted_labels = predictions[query_nodes].clone() - - # Flip a portion of labels to simulate noise in responses. - num_errors = int(len(predicted_labels) * error_rate) - if num_errors > 0: - error_indices = random.sample(range(len(predicted_labels)), num_errors) - for idx in error_indices: - wrong_label = random.randint(0, self.num_classes - 1) - predicted_labels[idx] = wrong_label - return predicted_labels - - def compute_cosine_similarity(self, features): - """Compute cosine similarity of node features.""" - features_np = features.cpu().detach().numpy() - similarity_matrix = cosine_similarity(features_np) - return torch.tensor(similarity_matrix, dtype=torch.float32, device=self.device) - - def generate_candidate_edges(self, labeled_nodes, unlabeled_nodes): - """Generate candidate edges based on feature cosine similarity threshold.""" - similarity_matrix = self.compute_cosine_similarity(self.features) - candidate_edges = [] - for u_node in unlabeled_nodes: - for l_node in labeled_nodes: - if similarity_matrix[u_node, l_node] > self.threshold_s: - candidate_edges.append([u_node, l_node]) - print(f"Generated {len(candidate_edges)} candidate edges based on cosine similarity") - return candidate_edges - - def train_edge_predictor(self, labeled_nodes, predicted_labels, epochs=100): - """Train the auxiliary edge prediction model.""" - print("Training edge predictor...") - self.edge_predictor.train() - - # Create node labels tensor; only queried nodes are labeled. - train_labels = torch.full((self.num_nodes,), -1, dtype=torch.long, device=self.device) - train_labels[labeled_nodes] = predicted_labels - - for epoch in range(epochs): - self.optimizer_edge.zero_grad() - - # Forward pass through edge predictor - node_embeddings, node_logits = self.edge_predictor(self.graph, self.features) - - # Node classification loss (supervised on labeled nodes) - labeled_mask = train_labels != -1 - if labeled_mask.sum() > 0: - node_loss = F.cross_entropy(node_logits[labeled_mask], train_labels[labeled_mask]) - else: - node_loss = torch.tensor(0.0, device=self.device) - - # Positive and negative edge samples - src_nodes, dst_nodes = self.graph.edges() - positive_pairs = list(zip(src_nodes.cpu().numpy(), dst_nodes.cpu().numpy())) - - pos_edge_probs = self.edge_predictor.predict_edges(node_embeddings, positive_pairs) - pos_loss = -torch.log(pos_edge_probs + 1e-15).mean() - - negative_pairs = [] - num_neg_samples = min(len(positive_pairs), 1000) - for _ in range(num_neg_samples): - src = random.randint(0, self.num_nodes - 1) - dst = random.randint(0, self.num_nodes - 1) - if src != dst and not self.graph_data.has_edges_between(src, dst): - negative_pairs.append([src, dst]) - - if negative_pairs: - neg_edge_probs = self.edge_predictor.predict_edges(node_embeddings, negative_pairs) - neg_loss = -torch.log(1 - neg_edge_probs + 1e-15).mean() - else: - neg_loss = torch.tensor(0.0, device=self.device) - - total_loss = node_loss + 0.5 * (pos_loss + neg_loss) - total_loss.backward() - self.optimizer_edge.step() - - if epoch % 20 == 0: - print(f"Epoch {epoch:3d}: total={total_loss.item():.4f}, " - f"node={node_loss.item():.4f}, edge={(pos_loss + neg_loss).item():.4f}") - - def add_potential_edges(self, candidate_edges, labeled_nodes): - """Add potential edges whose predicted probability exceeds the threshold.""" - if not candidate_edges: - return self.graph - - print("Predicting edge weights and adding potential edges...") - self.edge_predictor.eval() - with torch.no_grad(): - node_embeddings, _ = self.edge_predictor(self.graph, self.features) - edge_probs = self.edge_predictor.predict_edges(node_embeddings, candidate_edges) - - selected_edges = [] - for i, (src, dst) in enumerate(candidate_edges): - if edge_probs[i] > self.threshold_a: - selected_edges.extend([(src, dst), (dst, src)]) # undirected - - print(f"Selected {len(selected_edges) // 2} potential edges to add") - if selected_edges: - enhanced_graph = dgl.add_edges( - self.graph, - [e[0] for e in selected_edges], - [e[1] for e in selected_edges] - ) - return enhanced_graph - else: - return self.graph - - def train_surrogate_model(self, enhanced_graph, labeled_nodes, predicted_labels, epochs=200): - """Train the surrogate model on queried nodes and pseudo labels.""" - print("Training surrogate model...") - self.surrogate_model.train() - - # Build training labels for queried nodes. - train_labels = torch.full((self.num_nodes,), -1, dtype=torch.long, device=self.device) - train_labels[labeled_nodes] = predicted_labels - labeled_mask = train_labels != -1 - - for epoch in range(epochs): - self.optimizer_surrogate.zero_grad() - logits = self.surrogate_model(enhanced_graph, self.features) - - if labeled_mask.sum() > 0: - logp = F.log_softmax(logits, dim=1) - loss = F.nll_loss(logp[labeled_mask], train_labels[labeled_mask]) - loss.backward() - self.optimizer_surrogate.step() - - if epoch % 50 == 0: - print(f"Surrogate epoch {epoch:3d}, loss={loss.item():.4f}") - - def _evaluate_and_update_metrics(self, enhanced_graph, metric: AttackMetric, metric_comp: AttackCompMetric): - """Evaluate surrogate against target on the real test set and update metric containers.""" - # Target inference - t0 = time.time() - with torch.no_grad(): - logits_target = self.net1(self.graph, self.features) - metric_comp.update(inference_target_time=(time.time() - t0)) - target_preds = F.log_softmax(logits_target, dim=1).argmax(dim=1) - - # Surrogate inference - t0 = time.time() - with torch.no_grad(): - logits_surrogate = self.surrogate_model(enhanced_graph, self.features) - metric_comp.update(inference_surrogate_time=(time.time() - t0)) - surrogate_preds = F.log_softmax(logits_surrogate, dim=1).argmax(dim=1) - - # Update performance metrics with ground truth and target predictions on test split. - mask = self.graph_data.ndata['test_mask'] - labels = self.graph_data.ndata['label'] - metric.update(surrogate_preds[mask], labels[mask], target_preds[mask]) - - def attack(self): - """Execute the attack and return two JSON-like dicts: performance and computation metrics.""" - metric = AttackMetric() - metric_comp = AttackCompMetric() - - print("=" * 60) - print("Starting GNN Model Extraction Attack (Realistic)") - print("=" * 60) - - attack_start = time.time() - - # Step 1: Randomly select query nodes according to the budget. - all_nodes = list(range(self.num_nodes)) - labeled_nodes = random.sample(all_nodes, self.attack_node_number) - unlabeled_nodes = [n for n in all_nodes if n not in labeled_nodes] - print(f"Selected {len(labeled_nodes)} nodes for querying") - - # Step 2: Query the target model once for pseudo labels. - t_q = time.time() - predicted_labels = self.simulate_target_model_queries(labeled_nodes) - query_time = time.time() - t_q - metric_comp.update(query_target_time=query_time) - print("Finished querying the target model") - - # Step 3: Generate candidate edges (feature similarity). - candidate_edges = self.generate_candidate_edges(labeled_nodes, unlabeled_nodes) - - # Step 4: Train the auxiliary edge predictor (included in total attack time). - self.train_edge_predictor(labeled_nodes, predicted_labels) - - # Step 5: Add potential edges to obtain an enhanced graph. - enhanced_graph = self.add_potential_edges(candidate_edges, labeled_nodes) - original_edges = self.graph_data.num_edges() - enhanced_edges = enhanced_graph.num_edges() - print(f"Enhanced graph: {original_edges} -> {enhanced_edges} edges (+{enhanced_edges - original_edges})") - - # Step 6: Train the surrogate model and record its training time. - t_train_surr = time.time() - self.train_surrogate_model(enhanced_graph, labeled_nodes, predicted_labels) - train_surrogate_time = time.time() - t_train_surr - metric_comp.update(train_surrogate_time=train_surrogate_time) - - # Step 7: One-shot evaluation and metrics update. - self._evaluate_and_update_metrics(enhanced_graph, metric, metric_comp) - - # Finalize computation stats. - metric_comp.end() - metric_comp.update(attack_time=(time.time() - attack_start)) - - # Return two JSON-like dicts as required by the new API. - res = metric.compute() - res_comp = metric_comp.compute() - return res, res_comp diff --git a/pyhazard/models/attack/__init__.py b/pyhazard/models/attack/__init__.py deleted file mode 100644 index 8cbeb84..0000000 --- a/pyhazard/models/attack/__init__.py +++ /dev/null @@ -1,31 +0,0 @@ -from .AdvMEA import AdvMEA -from .CEGA import CEGA -from .DataFreeMEA import ( - DFEATypeI, - DFEATypeII, - DFEATypeIII -) -from .mea.MEA import ( - ModelExtractionAttack0, - ModelExtractionAttack1, - ModelExtractionAttack2, - ModelExtractionAttack3, - ModelExtractionAttack4, - ModelExtractionAttack5 -) -from .Realistic import RealisticAttack - -__all__ = [ - 'AdvMEA', - 'CEGA', - 'RealisticAttack', - 'DFEATypeI', - 'DFEATypeII', - 'DFEATypeIII', - 'ModelExtractionAttack0', - 'ModelExtractionAttack1', - 'ModelExtractionAttack2', - 'ModelExtractionAttack3', - 'ModelExtractionAttack4', - 'ModelExtractionAttack5', -] diff --git a/pyhazard/models/attack/base.py b/pyhazard/models/attack/base.py deleted file mode 100644 index cdc8e00..0000000 --- a/pyhazard/models/attack/base.py +++ /dev/null @@ -1,110 +0,0 @@ -from abc import ABC, abstractmethod -from typing import Union, Optional - -import torch - -from pyhazard.datasets import Dataset -from pyhazard.utils.hardware import get_device - - -class BaseAttack(ABC): - """Abstract base class for attack models. - - This class provides a common interface for various attack strategies on graph-based - machine learning models. It handles device management, dataset loading, and - compatibility checks to ensure that the attack can be executed on the given - dataset and model API type. - - Attributes: - supported_api_types (set): A set of strings representing the supported API - types (e.g., 'pyg', 'dgl'). - supported_datasets (set): A set of strings representing the names of - supported dataset classes. - device (torch.device): The computing device (CPU or GPU) to be used for - the attack. - dataset (Dataset): The dataset object containing graph data and metadata. - graph_dataset: The raw graph dataset from the underlying library. - graph_data: The primary graph data structure. - num_nodes (int): The number of nodes in the graph. - num_features (int): The number of features per node. - num_classes (int): The number of classes for node classification. - attack_node_fraction (float, optional): The fraction of nodes to be - targeted by the attack. - model_path (str, optional): The path to a pre-trained target model. - """ - supported_api_types = set() - supported_datasets = set() - - def __init__(self, dataset: Dataset, attack_node_fraction: float = None, model_path: str = None, - device: Optional[Union[str, torch.device]] = None): - """Initializes the BaseAttack. - - Args: - dataset (Dataset): The dataset to be attacked. - attack_node_fraction (float, optional): The fraction of nodes to - target in the attack. Defaults to None. - model_path (str, optional): The path to a pre-trained model file. - Defaults to None. - device (Union[str, torch.device], optional): The device to run the - attack on. If None, it will be automatically selected. - Defaults to None. - """ - self.device = torch.device(device) if device else get_device() - print(f"Using device: {self.device}") - - # graph data - self.dataset = dataset - self.graph_dataset = dataset.graph_dataset - self.graph_data = dataset.graph_data - - # meta data - self.num_nodes = dataset.num_nodes - self.num_features = dataset.num_features - self.num_classes = dataset.num_classes - - # params - self.attack_node_fraction = attack_node_fraction - self.model_path = model_path - - self._check_dataset_compatibility() - - def _check_dataset_compatibility(self): - """Checks if the dataset is compatible with the attack. - - Raises: - ValueError: If the dataset's API type or class name is not in the - list of supported types. - """ - cls_name = self.dataset.__class__.__name__ - - if self.supported_api_types and self.dataset.api_type not in self.supported_api_types: - raise ValueError( - f"API type '{self.dataset.api_type}' is not supported. Supported: {self.supported_api_types}") - - if self.supported_datasets and cls_name not in self.supported_datasets: - raise ValueError(f"Dataset '{cls_name}' is not supported. Supported: {self.supported_datasets}") - - @abstractmethod - def attack(self): - """ - Execute the attack. - """ - raise NotImplementedError - - def _load_model(self, model_path): - """ - Load a pre-trained model. - """ - raise NotImplementedError - - def _train_target_model(self): - """ - Train the target model if not provided. - """ - raise NotImplementedError - - def _train_attack_model(self): - """ - Train the attack model. - """ - raise NotImplementedError diff --git a/pyhazard/models/attack/mea/MEA.py b/pyhazard/models/attack/mea/MEA.py deleted file mode 100644 index 2c4780d..0000000 --- a/pyhazard/models/attack/mea/MEA.py +++ /dev/null @@ -1,480 +0,0 @@ -import os -import random -import time -from typing import List, Tuple, Optional - -import dgl -import torch -import torch.nn.functional as F -from torch import nn - -from pyhazard.models.attack.base import BaseAttack -from pyhazard.models.nn.backbones import GCN -from pyhazard.utils.metrics import AttackMetric, AttackCompMetric - - -def _as_tensor(x, device): - if isinstance(x, torch.Tensor): - return x.to(device) - return torch.tensor(x, device=device) - - -def add_self_loops(g: dgl.DGLGraph) -> dgl.DGLGraph: - """Return a copy of g with self-loops added to every node.""" - num_nodes = g.number_of_nodes() - src = torch.arange(num_nodes) - dst = src.clone() - return dgl.add_edges(g, src, dst) - - -def subgraph_from_nodes(g: dgl.DGLGraph, node_idx: List[int]) -> dgl.DGLGraph: - """Induce a subgraph that contains only edges whose endpoints are both in node_idx.""" - sg = dgl.node_subgraph(g, node_idx) - sg = dgl.remove_self_loop(sg) - sg = dgl.add_self_loop(sg) - return sg - - -def erdos_renyi_graph(num_nodes: int, p: float = 0.05) -> dgl.DGLGraph: - import networkx as nx - g_nx = nx.erdos_renyi_graph(num_nodes, p) - g = dgl.from_networkx(g_nx) - g = add_self_loops(g) - return g - - -def random_shadow_indices(g: dgl.DGLGraph, k: int, extra: int = 2) -> Tuple[List[int], List[int]]: - """ - Heuristic shadow graph index generator. - Returns two sets: target_nodes (size k) and potential_nodes (neighbors around target nodes). - """ - num_nodes = g.number_of_nodes() - k = max(1, min(k, num_nodes)) - target_nodes = random.sample(range(num_nodes), k) - # collect neighbors up to 2 hops around the target nodes - neigh = set(target_nodes) - src, dst = g.edges() - src = src.tolist() - dst = dst.tolist() - adj = [[] for _ in range(num_nodes)] - for s, d in zip(src, dst): - adj[s].append(d) - adj[d].append(s) - for u in list(target_nodes): - for v in adj[u]: - neigh.add(v) - for w in adj[v]: - neigh.add(w) - # potential nodes are neighbors that are not target nodes - potential_nodes = list(sorted(set(neigh) - set(target_nodes))) - # if too many, sample a multiple of k - max_size = min(num_nodes, extra * k if extra * k > k else k) - if len(potential_nodes) > max_size: - potential_nodes = random.sample(potential_nodes, max_size) - return list(target_nodes), potential_nodes - - -def _safe_dir() -> str: - return os.path.dirname(os.path.abspath(__file__)) - - -def load_attack2_generated_graph(dataset_name: str, default_nodes: int) -> Tuple[ - dgl.DGLGraph, torch.Tensor, Optional[List[int]]]: - """ - Try to load an attack-2 pre-generated graph. If files are missing, fall back to - an on-the-fly Erdos–Rényi graph with random features. Returns (graph, features, selected_indices). - """ - base = os.path.join(_safe_dir(), "data", "attack2_generated_graph", dataset_name) - graph_label = os.path.join(base, "graph_label.txt") - selected_idx = os.path.join(base, "selected_index.txt") - if os.path.exists(graph_label): - # we only need the number of nodes; reconstruct a random graph and random features - try: - with open(graph_label, "r") as f: - n = sum(1 for _ in f) - num_nodes = max(1, n) - except Exception: - num_nodes = max(1, default_nodes) - g = erdos_renyi_graph(num_nodes, p=0.05) - return g, None, None - else: - g = erdos_renyi_graph(default_nodes, p=0.05) - return g, None, None - - -def load_attack3_shadow_indices(dataset_name: str, g: dgl.DGLGraph, k: int) -> Tuple[List[int], List[int]]: - """ - Try to load shadow graph indices from disk; if not found, generate heuristically. - """ - base = os.path.join(_safe_dir(), "data", "attack3_shadow_graph", dataset_name) - target_path = os.path.join(base, "target_graph_index.txt") - if os.path.exists(target_path): - try: - with open(target_path, "r") as f: - target_nodes = [int(x.strip()) for x in f if len(x.strip()) > 0] - except Exception: - target_nodes = None - else: - target_nodes = None - - potential_nodes = None - if os.path.isdir(base): - for fn in os.listdir(base): - if fn.startswith("protential") and fn.endswith(".txt"): - try: - with open(os.path.join(base, fn), "r") as f: - potential_nodes = [int(x.strip()) for x in f if len(x.strip()) > 0] - except Exception: - potential_nodes = None - break - - if target_nodes is None or potential_nodes is None: - t, p = random_shadow_indices(g, k) - return t, p - return target_nodes, potential_nodes - - -class _MEABase(BaseAttack): - """ - Base class for MEA family attacks. This class handles the target model training, - metric bookkeeping, and utility helpers. Subclasses only need to decide which - training indices and which graph to use for the surrogate. - """ - supported_api_types = {"dgl"} - - def __init__(self, dataset, attack_x_ratio: float, attack_a_ratio: float, model_path: Optional[str] = None): - super().__init__(dataset, attack_x_ratio, model_path) - - self.dataset = dataset - self.graph: dgl.DGLGraph = dataset.graph_data.to(self.device) - self.features: torch.Tensor = self.graph.ndata['feat'] - self.labels: torch.Tensor = dataset.graph_data.ndata['label'] - self.train_mask: torch.Tensor = dataset.graph_data.ndata['train_mask'] - self.test_mask: torch.Tensor = dataset.graph_data.ndata['test_mask'] - - self.num_nodes = dataset.num_nodes - self.num_features = dataset.num_features - self.num_classes = dataset.num_classes - - # budget based on the availability of features X and adjacency A - self.attack_x_ratio = float(attack_x_ratio) - self.attack_a_ratio = float(attack_a_ratio) - self.attack_node_num = max(1, int(self.num_nodes * max(self.attack_x_ratio, self.attack_a_ratio))) - - # target model - if model_path is None: - self._train_target_model() - else: - self._load_model(model_path) - - def _train_target_model(self): - self.net1 = GCN(self.num_features, self.num_classes).to(self.device) - opt = torch.optim.Adam(self.net1.parameters(), lr=0.01, weight_decay=5e-4) - - self.net1.train() - for _ in range(200): - opt.zero_grad() - logits = self.net1(self.graph, self.features) - logp = F.log_softmax(logits, dim=1) - loss = F.nll_loss(logp[self.train_mask], self.labels[self.train_mask]) - loss.backward() - opt.step() - self.net1.eval() - - def _load_model(self, model_path: str): - self.net1 = GCN(self.num_features, self.num_classes).to(self.device) - state = torch.load(model_path, map_location=self.device) - self.net1.load_state_dict(state) - self.net1.eval() - - # ---------- core utilities ---------- - def _query_target(self, g: dgl.DGLGraph, x: torch.Tensor) -> Tuple[torch.Tensor, float]: - start = time.time() - with torch.no_grad(): - logits = self.net1(g, x) - return logits, time.time() - start - - def _train_surrogate(self, g: dgl.DGLGraph, x: torch.Tensor, train_idx: torch.Tensor, y_train: torch.Tensor, - epochs: int = 200, lr: float = 0.01) -> Tuple[nn.Module, float]: - model = GCN(self.num_features, self.num_classes).to(self.device) - opt = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=5e-4) - - # boolean mask - if y_train.dim() > 1: - y_train = y_train.argmax(dim=1) - mask = torch.zeros(g.number_of_nodes(), dtype=torch.bool, device=self.device) - mask[train_idx] = True - - start = time.time() - for _ in range(epochs): - model.train() - opt.zero_grad() - logits = model(g, x) - logp = F.log_softmax(logits, dim=1) - loss = F.nll_loss(logp[mask], y_train[mask]) - loss.backward() - opt.step() - train_time = time.time() - start - model.eval() - return model, train_time - - def _compute_metrics(self, surrogate: nn.Module, metric: AttackMetric, metric_comp: AttackCompMetric): - # target inference - g = self.graph - x = self.features - y = self.labels - mask = self.test_mask - - t0 = time.time() - with torch.no_grad(): - logits_v = self.net1(g, x) - metric_comp.update(inference_target_time=time.time() - t0) - y_target = logits_v.argmax(dim=1) - - # surrogate inference - t0 = time.time() - with torch.no_grad(): - logits_s = surrogate(g, x) - metric_comp.update(inference_surrogate_time=time.time() - t0) - y_pred = logits_s.argmax(dim=1) - - metric.update(y_pred[mask], y[mask], y_target[mask]) - - # ---------- template method ---------- - def _attack_impl(self) -> Tuple[AttackMetric, AttackCompMetric]: - """ - Subclasses must implement this method to - 1) build a graph g_att and features x_att for training, - 2) pick a list of training indices idx_train of length attack_node_num, - 3) query the target for labels on idx_train and train a surrogate, - and then return filled metrics objects. - """ - raise NotImplementedError - - def attack(self, *args, **kwargs): - metric = AttackMetric() - metric_comp = AttackCompMetric() - start_all = time.time() - - # delegate to subclass implementation - metric, metric_comp = self._attack_impl() - - # finalize - metric_comp.update(attack_time=time.time() - start_all) - metric_comp.end() - - return metric.compute(), metric_comp.compute() - - -# ----------------------- concrete attacks ----------------------- - -class ModelExtractionAttack0(_MEABase): - """ - Attack-0: Random-node label-only extraction on the original graph. - """ - - def _attack_impl(self) -> Tuple[AttackMetric, AttackCompMetric]: - metric = AttackMetric() - metric_comp = AttackCompMetric() - - # sample nodes to query - idx_train = random.sample(range(self.num_nodes), self.attack_node_num) - idx_train_t = torch.tensor(idx_train, device=self.device) - - # query target - logits_v, q_time = self._query_target(self.graph, self.features) - y_pseudo = logits_v.argmax(dim=1) - metric_comp.update(query_target_time=q_time) - - # train surrogate on original graph but only using queried nodes - surrogate, t_train = self._train_surrogate(self.graph, self.features, idx_train_t, y_pseudo) - metric_comp.update(train_surrogate_time=t_train) - - # evaluate on real test set - self._compute_metrics(surrogate, metric, metric_comp) - return metric, metric_comp - - -class ModelExtractionAttack1(_MEABase): - """ - Attack-1: Degree-based sampling of query nodes on the original graph. - """ - - def _attack_impl(self) -> Tuple[AttackMetric, AttackCompMetric]: - metric = AttackMetric() - metric_comp = AttackCompMetric() - - g = self.graph - deg = g.in_degrees().cpu().tolist() - order = sorted(range(self.num_nodes), key=lambda i: deg[i], reverse=True) - idx_train = order[:self.attack_node_num] - idx_train_t = torch.tensor(idx_train, device=self.device) - - logits_v, q_time = self._query_target(self.graph, self.features) - y_pseudo = logits_v.argmax(dim=1) - metric_comp.update(query_target_time=q_time) - - surrogate, t_train = self._train_surrogate(self.graph, self.features, idx_train_t, y_pseudo) - metric_comp.update(train_surrogate_time=t_train) - - self._compute_metrics(surrogate, metric, metric_comp) - return metric, metric_comp - - -class ModelExtractionAttack2(_MEABase): - """ - Attack-2: Data-free extraction on a synthetic graph with random features. - """ - - def _attack_impl(self) -> Tuple[AttackMetric, AttackCompMetric]: - metric = AttackMetric() - metric_comp = AttackCompMetric() - - # build synthetic graph - syn_g, _, _ = load_attack2_generated_graph( - getattr(self.dataset, 'dataset_name', getattr(self.dataset, 'name', 'default')), - default_nodes=self.attack_node_num - ) - syn_g = syn_g.to(self.device) - syn_x = torch.randn(syn_g.number_of_nodes(), self.num_features, device=self.device) - - # query target on synthetic inputs - logits_v, q_time = self._query_target(syn_g, syn_x) - y_pseudo = logits_v.argmax(dim=1) - metric_comp.update(query_target_time=q_time) - - # use all nodes in synthetic graph for training - idx_train_t = torch.arange(syn_g.number_of_nodes(), device=self.device) - surrogate, t_train = self._train_surrogate(syn_g, syn_x, idx_train_t, y_pseudo) - metric_comp.update(train_surrogate_time=t_train) - - # evaluate on real test set - self._compute_metrics(surrogate, metric, metric_comp) - return metric, metric_comp - - -class ModelExtractionAttack3(_MEABase): - """ - Attack-3: Shadow-graph extraction. Train on a subgraph induced by a - set of target nodes and their neighbors (potential nodes). - """ - - def _attack_impl(self) -> Tuple[AttackMetric, AttackCompMetric]: - metric = AttackMetric() - metric_comp = AttackCompMetric() - - dataset_name = getattr(self.dataset, 'dataset_name', getattr(self.dataset, 'name', 'default')) - target_nodes, potential_nodes = load_attack3_shadow_indices(dataset_name, self.graph, self.attack_node_num) - - # training nodes are the union - idx_train = list(sorted(set(target_nodes) | set(potential_nodes))) - idx_train_t = torch.tensor(idx_train, device=self.device) - - sg = subgraph_from_nodes(self.graph, idx_train) - x_sg = self.features[idx_train_t] - - # map back to subgraph index for labels - logits_v_full, q_time = self._query_target(self.graph, self.features) - metric_comp.update(query_target_time=q_time) - y_pseudo_full = logits_v_full.argmax(dim=1) - y_pseudo = y_pseudo_full[idx_train_t] - - # train on the shadow subgraph - surrogate, t_train = self._train_surrogate(sg, x_sg, torch.arange(sg.number_of_nodes(), device=self.device), - y_pseudo) - metric_comp.update(train_surrogate_time=t_train) - - self._compute_metrics(surrogate, metric, metric_comp) - return metric, metric_comp - - -class ModelExtractionAttack4(_MEABase): - """ - Attack-4: Cosine-similarity neighbor expansion. Start from random seeds and - expand candidates by feature similarity to form the training subgraph. - """ - - def _attack_impl(self) -> Tuple[AttackMetric, AttackCompMetric]: - metric = AttackMetric() - metric_comp = AttackCompMetric() - - seeds = random.sample(range(self.num_nodes), max(1, self.attack_node_num // 4)) - # compute cosine similarity on CPU to save GPU memory - x = self.features.detach().cpu() - norm = x.norm(dim=1, keepdim=True) + 1e-12 - x_n = x / norm - sims = torch.mm(x_n, x_n.t()) - # choose top-k neighbors for each seed - cand = set(seeds) - for s in seeds: - topk = torch.topk(sims[s], k=min(self.num_nodes, self.attack_node_num)).indices.tolist() - cand.update(topk) - idx_train = list(sorted(cand))[:self.attack_node_num] - idx_train_t = torch.tensor(idx_train, device=self.device) - - # query target on original graph to get labels for these nodes - logits_v, q_time = self._query_target(self.graph, self.features) - metric_comp.update(query_target_time=q_time) - y_pseudo = logits_v.argmax(dim=1) - - sg = subgraph_from_nodes(self.graph, idx_train) - x_sg = self.features[idx_train_t] - surrogate, t_train = self._train_surrogate(sg, x_sg, torch.arange(sg.number_of_nodes(), device=self.device), - y_pseudo[idx_train_t]) - metric_comp.update(train_surrogate_time=t_train) - - self._compute_metrics(surrogate, metric, metric_comp) - return metric, metric_comp - - -class ModelExtractionAttack5(_MEABase): - """ - Attack-5: Variant of the shadow-graph attack that samples two candidate lists and - trains on their union. If attack_6 index files are present (historical name), - they will be used; otherwise we fall back to generated indices. - """ - - def _attack_impl(self) -> Tuple[AttackMetric, AttackCompMetric]: - metric = AttackMetric() - metric_comp = AttackCompMetric() - - dataset_name = getattr(self.dataset, 'dataset_name', getattr(self.dataset, 'name', 'default')) - base = os.path.join(_safe_dir(), "data", "attack3_shadow_graph", dataset_name) - f_a = os.path.join(base, "attack_6_sub_shadow_graph_index_attack_2.txt") - f_b = os.path.join(base, "attack_6_sub_shadow_graph_index_attack_3.txt") - - a_idx, b_idx = None, None - if os.path.exists(f_a): - try: - with open(f_a, "r") as f: - a_idx = [int(x.strip()) for x in f if len(x.strip()) > 0] - except Exception: - a_idx = None - if os.path.exists(f_b): - try: - with open(f_b, "r") as f: - b_idx = [int(x.strip()) for x in f if len(x.strip()) > 0] - except Exception: - b_idx = None - - if a_idx is None or b_idx is None: - t, p = random_shadow_indices(self.graph, self.attack_node_num, extra=3) - a_idx = t - b_idx = p - - idx_train = list(sorted(set(a_idx) | set(b_idx))) - idx_train = idx_train[:max(self.attack_node_num, len(idx_train))] - idx_train_t = torch.tensor(idx_train, device=self.device) - - logits_v, q_time = self._query_target(self.graph, self.features) - metric_comp.update(query_target_time=q_time) - y_pseudo = logits_v.argmax(dim=1) - - sg = subgraph_from_nodes(self.graph, idx_train) - x_sg = self.features[idx_train_t] - surrogate, t_train = self._train_surrogate(sg, x_sg, torch.arange(sg.number_of_nodes(), device=self.device), - y_pseudo[idx_train_t]) - metric_comp.update(train_surrogate_time=t_train) - - self._compute_metrics(surrogate, metric, metric_comp) - return metric, metric_comp diff --git a/pyhazard/models/attack/mea/__init__.py b/pyhazard/models/attack/mea/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git 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-8081 -16275 -16281 -16285 -16296 -16307 -16309 -16315 -8151 -8152 -8153 -8169 -16370 -16371 -8180 -16374 -16376 diff --git a/pyhazard/models/defense/BackdoorWM.py b/pyhazard/models/defense/BackdoorWM.py deleted file mode 100644 index 1c42cd7..0000000 --- a/pyhazard/models/defense/BackdoorWM.py +++ /dev/null @@ -1,179 +0,0 @@ -import random -from time import time - -import torch -import torch.nn.functional as F - -from pyhazard.models.defense.base import BaseDefense -from pyhazard.models.nn import GCN -from pyhazard.utils.metrics import DefenseMetric, DefenseCompMetric - - -class BackdoorWM(BaseDefense): - supported_api_types = {"dgl"} - - def __init__(self, dataset, attack_node_fraction, model_path=None, trigger_rate=0.01, l=20, target_label=0): - super().__init__(dataset, attack_node_fraction) - # load data - self.dataset = dataset - self.graph_dataset = dataset.graph_dataset - self.graph_data = dataset.graph_data.to(device=self.device) - self.model_path = model_path - self.features = self.graph_data.ndata['feat'] - self.labels = self.graph_data.ndata['label'] - self.train_mask = self.graph_data.ndata['train_mask'] - self.test_mask = self.graph_data.ndata['test_mask'] - - # load meta data - self.num_nodes = dataset.num_nodes - self.num_features = dataset.num_features - self.num_classes = dataset.num_classes - - # wm params - self.trigger_rate = trigger_rate - self.l = l - self.target_label = target_label - - def _load_model(self): - """ - Load a pre-trained model. - """ - assert self.model_path, "self.model_path should be defined" - - # Create the model - self.net1 = GCN(self.num_features, self.num_classes).to(self.device) - - # Load the saved state dict - self.net1.load_state_dict(torch.load(self.model_path, map_location=self.device)) - - # Set to evaluation mode - self.net1.eval() - - def inject_backdoor_trigger(self, data, trigger_rate=None, trigger_feat_val=0.99, l=None, target_label=None): - """Feature-based Trigger Injection""" - if trigger_rate is None: - trigger_rate = self.trigger_rate - if l is None: - l = self.l - if target_label is None: - target_label = self.target_label - - num_nodes = data.shape[0] - num_feats = data.shape[1] - num_trigger_nodes = int(trigger_rate * num_nodes) - - trigger_nodes = random.sample(range(num_nodes), num_trigger_nodes) - for node in trigger_nodes: - feature_indices = random.sample(range(num_feats), l) - data[node][feature_indices] = trigger_feat_val - return data, trigger_nodes - - def train_target_model(self, metric_comp: DefenseCompMetric): - """ - Train the target model with backdoor injection. - """ - # Initialize GNN model - self.net1 = GCN(self.num_features, self.num_classes).to(self.device) - optimizer = torch.optim.Adam(self.net1.parameters(), lr=0.01, weight_decay=5e-4) - - # Inject backdoor trigger - defense_s = time() - poisoned_features = self.features.clone() - poisoned_labels = self.labels.clone() - - poisoned_features_cpu = poisoned_features.cpu() - poisoned_features_cpu, trigger_nodes = self.inject_backdoor_trigger( - poisoned_features_cpu, - trigger_rate=self.trigger_rate, - l=self.l, - target_label=self.target_label - ) - poisoned_features = poisoned_features_cpu.to(self.device) - - # Modify labels for trigger nodes - for node in trigger_nodes: - poisoned_labels[node] = self.target_label - - self.trigger_nodes = trigger_nodes - self.poisoned_features = poisoned_features - self.poisoned_labels = poisoned_labels - defense_e = time() - - # Training loop - for epoch in range(200): - self.net1.train() - - # Forward pass - logits = self.net1(self.graph_data, poisoned_features) - logp = F.log_softmax(logits, dim=1) - loss = F.nll_loss(logp[self.train_mask], poisoned_labels[self.train_mask]) - - # Backward pass - optimizer.zero_grad() - loss.backward() - optimizer.step() - - # Validation (optional) - if epoch % 50 == 0: - self.net1.eval() - with torch.no_grad(): - logits_val = self.net1(self.graph_data, poisoned_features) - logp_val = F.log_softmax(logits_val, dim=1) - pred = logp_val.argmax(dim=1) - acc_val = (pred[self.test_mask] == poisoned_labels[self.test_mask]).float().mean() - print(f" Epoch {epoch}: training... Validation Accuracy: {acc_val.item():.4f}") - - metric_comp.update(defense_time=(defense_e - defense_s)) - - return self.net1 - - def verify_backdoor(self, model, trigger_nodes): - """Verify backdoor attack success rate""" - model.eval() - with torch.no_grad(): - out = model(self.graph_data, self.poisoned_features) - backdoor_preds = out.argmax(dim=1)[trigger_nodes] - # correct = (pred[trigger_nodes] == target_label).sum().item() - return backdoor_preds - - def evaluate_model(self, model, features): - """Evaluate model performance""" - model.eval() - with torch.no_grad(): - out = model(self.graph_data, features) - logits = out[self.test_mask] - preds = logits.argmax(dim=1).cpu() - - return preds - - def defend(self): - """ - Execute the backdoor watermark defense. - """ - metric_comp = DefenseCompMetric() - metric_comp.start() - print("====================Backdoor Watermark====================") - - # If model wasn't trained yet, train it - if not hasattr(self, 'net1'): - self.train_target_model(metric_comp) - - # Evaluate the backdoored model - preds = self.evaluate_model(self.net1, self.poisoned_features) - inference_s = time() - backdoor_preds = self.verify_backdoor(self.net1, self.trigger_nodes) - inference_e = time() - - # metric - metric = DefenseMetric() - metric.update(preds, self.poisoned_labels[self.test_mask]) - target = torch.full_like(backdoor_preds, fill_value=self.target_label) - metric.update_wm(backdoor_preds, target) - metric_comp.end() - - print("====================Final Results====================") - res = metric.compute() - metric_comp.update(inference_defense_time=(inference_e - inference_s)) - res_comp = metric_comp.compute() - - return res, res_comp diff --git a/pyhazard/models/defense/GrOVe.py b/pyhazard/models/defense/GrOVe.py deleted file mode 100644 index 3663211..0000000 --- a/pyhazard/models/defense/GrOVe.py +++ /dev/null @@ -1,303 +0,0 @@ - -import random -from time import time - -import torch -import torch.nn.functional as F -import numpy as np -from sklearn.neural_network import MLPClassifier - -from pyhazard.models.defense.base import BaseDefense -from pyhazard.models.nn import GCN -from pyhazard.utils.metrics import DefenseMetric, DefenseCompMetric - - -class GroveDefense(BaseDefense): - supported_api_types = {"dgl", "pyg"} - - def __init__(self, dataset, attack_node_fraction, model_path=None, - hidden_dim=256, verification_threshold=0.5, num_surrogate_models=3): - super().__init__(dataset, attack_node_fraction) - # load data - self.dataset = dataset - self.graph_dataset = dataset.graph_dataset - self.graph_data = dataset.graph_data.to(device=self.device) - self.model_path = model_path - self.features = self.graph_data.ndata['feat'] - self.labels = self.graph_data.ndata['label'] - self.train_mask = self.graph_data.ndata['train_mask'] - self.test_mask = self.graph_data.ndata['test_mask'] - - # load meta data - self.num_nodes = dataset.num_nodes - self.num_features = dataset.num_features - self.num_classes = dataset.num_classes - - # grove params - self.hidden_dim = hidden_dim - self.verification_threshold = verification_threshold - self.num_surrogate_models = num_surrogate_models - - # models - self.target_model = None - self.surrogate_models = [] - self.independent_models = [] - self.csim_classifier = None - - def _load_model(self): - """ - Load a pre-trained target model. - """ - if self.model_path: - # Create the model - self.target_model = GCN(self.num_features, self.num_classes).to(self.device) - # Load the saved state dict - self.target_model.load_state_dict(torch.load(self.model_path, map_location=self.device)) - else: - # Train new target model - self._train_target_model() - - # Set to evaluation mode - self.target_model.eval() - - def _train_target_model(self): - """ - Train the target model. - """ - self.target_model = GCN(self.num_features, self.num_classes).to(self.device) - optimizer = torch.optim.Adam(self.target_model.parameters(), lr=0.01, weight_decay=5e-4) - - for epoch in range(200): - self.target_model.train() - - # Forward pass - logits = self.target_model(self.graph_data, self.features) - logp = F.log_softmax(logits, dim=1) - loss = F.nll_loss(logp[self.train_mask], self.labels[self.train_mask]) - - # Backward pass - optimizer.zero_grad() - loss.backward() - optimizer.step() - - self.target_model.eval() - - def _train_surrogate_models(self): - """ - Train surrogate models to simulate model stealing attacks. - """ - print("Training surrogate models...") - - for i in range(self.num_surrogate_models): - # Create surrogate model with different initialization - torch.manual_seed(42 + i) - surrogate_model = GCN(self.num_features, self.num_classes).to(self.device) - optimizer = torch.optim.Adam(surrogate_model.parameters(), lr=0.01, weight_decay=5e-4) - - # Train with limited data (simulating stolen model scenario) - for epoch in range(100): - surrogate_model.train() - - logits = surrogate_model(self.graph_data, self.features) - logp = F.log_softmax(logits, dim=1) - loss = F.nll_loss(logp[self.train_mask], self.labels[self.train_mask]) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - surrogate_model.eval() - self.surrogate_models.append(surrogate_model) - - def _train_independent_models(self): - """ - Train independent models for comparison. - """ - print("Training independent models...") - - for i in range(self.num_surrogate_models): - # Create independent model with different random seed - torch.manual_seed(100 + i) - independent_model = GCN(self.num_features, self.num_classes).to(self.device) - optimizer = torch.optim.Adam(independent_model.parameters(), lr=0.01, weight_decay=5e-4) - - # Train independently - for epoch in range(150): - independent_model.train() - - logits = independent_model(self.graph_data, self.features) - logp = F.log_softmax(logits, dim=1) - loss = F.nll_loss(logp[self.train_mask], self.labels[self.train_mask]) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - independent_model.eval() - self.independent_models.append(independent_model) - - def _get_model_embeddings(self, model): - """ - Extract embeddings from a model. - """ - model.eval() - with torch.no_grad(): - # Get intermediate layer embeddings (before final classification) - embeddings = model.get_embeddings(self.graph_data, self.features) - return embeddings[self.test_mask] - - def _compute_distance_vectors(self, target_embeddings, suspect_embeddings): - """ - Compute element-wise squared distance vectors between embeddings. - """ - target_np = target_embeddings.detach().cpu().numpy() - suspect_np = suspect_embeddings.detach().cpu().numpy() - - # Ensure same shape - min_nodes = min(target_np.shape[0], suspect_np.shape[0]) - target_crop = target_np[:min_nodes] - suspect_crop = suspect_np[:min_nodes] - - # Compute element-wise squared distance - distance_vectors = (target_crop - suspect_crop) ** 2 - return distance_vectors - - def _train_csim_classifier(self): - """ - Train the CSim classifier for ownership verification. - """ - # Get target model embeddings - target_embeddings = self._get_model_embeddings(self.target_model) - - # Prepare training data - distance_vectors = [] - labels = [] - - # Positive samples: (target, surrogate) pairs - label 1 - for surrogate_model in self.surrogate_models: - surrogate_embeddings = self._get_model_embeddings(surrogate_model) - dist_vectors = self._compute_distance_vectors(target_embeddings, surrogate_embeddings) - distance_vectors.extend(dist_vectors) - labels.extend([1] * len(dist_vectors)) - - # Negative samples: (target, independent) pairs - label 0 - for independent_model in self.independent_models: - independent_embeddings = self._get_model_embeddings(independent_model) - dist_vectors = self._compute_distance_vectors(target_embeddings, independent_embeddings) - distance_vectors.extend(dist_vectors) - labels.extend([0] * len(dist_vectors)) - - X_train = np.array(distance_vectors) - y_train = np.array(labels) - - # Train MLP classifier - self.csim_classifier = MLPClassifier( - hidden_layer_sizes=(128,), - activation='relu', - random_state=42, - max_iter=1000 - ) - self.csim_classifier.fit(X_train, y_train) - - def verify_ownership(self, suspect_model): - """ - Verify if suspect model is a surrogate (stolen) or independent. - """ - target_embeddings = self._get_model_embeddings(self.target_model) - suspect_embeddings = self._get_model_embeddings(suspect_model) - - # Compute distance vectors - distance_vectors = self._compute_distance_vectors(target_embeddings, suspect_embeddings) - - # Predict using CSim classifier - predictions = self.csim_classifier.predict(distance_vectors) - probabilities = self.csim_classifier.predict_proba(distance_vectors) - - # Aggregate results - surrogate_probability = np.mean(probabilities[:, 1]) - is_surrogate = surrogate_probability > self.verification_threshold - - return { - 'is_surrogate': is_surrogate, - 'surrogate_probability': surrogate_probability, - 'confidence': abs(surrogate_probability - 0.5) * 2 - } - - def defend(self): - """ - Execute the Grove ownership verification defense. - """ - metric_comp = DefenseCompMetric() - metric_comp.start() - print("====================Grove Ownership Verification====================") - - # Load or train target model - if not hasattr(self, 'target_model') or self.target_model is None: - self._load_model() - - # Train surrogate and independent models - self._train_surrogate_models() - self._train_independent_models() - - # Train CSim classifier - defense_s = time() - self._train_csim_classifier() - defense_e = time() - - # Verify models - inference_s = time() - verification_results = [] - - # Test on surrogate models (should be detected as stolen) - for i, surrogate_model in enumerate(self.surrogate_models): - result = self.verify_ownership(surrogate_model) - result['model_type'] = 'surrogate' - result['expected_surrogate'] = True - verification_results.append(result) - - # Test on independent models (should be detected as independent) - for i, independent_model in enumerate(self.independent_models): - result = self.verify_ownership(independent_model) - result['model_type'] = 'independent' - result['expected_surrogate'] = False - verification_results.append(result) - - inference_e = time() - - # Calculate metrics - correct_predictions = sum( - 1 for result in verification_results - if result['is_surrogate'] == result['expected_surrogate'] - ) - total_predictions = len(verification_results) - accuracy = correct_predictions / total_predictions if total_predictions > 0 else 0 - - metric = DefenseMetric() - - # Pred and label for overall performance - dummy_preds = torch.tensor([1 if r['is_surrogate'] else 0 for r in verification_results]) - dummy_labels = torch.tensor([1 if r['expected_surrogate'] else 0 for r in verification_results]) - metric.update(dummy_preds, dummy_labels) - - wm_preds = torch.tensor([r['surrogate_probability'] for r in verification_results]) - wm_labels = torch.tensor([1.0 if r['expected_surrogate'] else 0.0 for r in verification_results]) - metric.update_wm(wm_preds, wm_labels) - - # Update computation metrics with recorded times - metric_comp.update(defense_time=(defense_e - defense_s)) - metric_comp.update(inference_defense_time=(inference_e - inference_s)) - metric_comp.end() - - print("====================Final Results====================") - print(f"Verification Accuracy: {accuracy:.4f}") - print(f"Correct Predictions: {correct_predictions}/{total_predictions}") - print(f"Defense Time: {defense_e - defense_s:.4f}s") - print(f"Inference Time: {inference_e - inference_s:.4f}s") - - res = metric.compute() - res['verification_accuracy'] = accuracy - res['correct_predictions'] = correct_predictions - res['total_predictions'] = total_predictions - res_comp = metric_comp.compute() - - return res, res_comp diff --git a/pyhazard/models/defense/ImperceptibleWM.py b/pyhazard/models/defense/ImperceptibleWM.py deleted file mode 100644 index 1c93a0b..0000000 --- a/pyhazard/models/defense/ImperceptibleWM.py +++ /dev/null @@ -1,262 +0,0 @@ -import copy -import time - -import torch -import torch.nn as nn -import torch.nn.functional as F -from tqdm import tqdm -from torch_geometric.nn import GCNConv -from torch_geometric.utils import to_dense_adj, dense_to_sparse -from sklearn.metrics import precision_score, recall_score, f1_score - -from .base import BaseDefense -from pyhazard.models.nn.backbones import GCN_PyG -from pyhazard.utils.metrics import DefenseCompMetric, DefenseMetric - - -class ImperceptibleWM(BaseDefense): - supported_api_types = {"pyg"} - - def __init__(self, dataset, defense_ratio=0.1, model_path=None): - super().__init__(dataset, defense_ratio) - # load data - self.model_trained = None - self.dataset = dataset - self.graph_dataset = dataset.graph_dataset - self.graph_data = dataset.graph_data.to(self.device) - self.defense_ratio = defense_ratio - self.num_triggers = int(dataset.num_nodes * defense_ratio) - self.model_path = model_path - - self.owner_id = torch.tensor([0.1, 0.3, 0.5, 0.7, 0.9], dtype=torch.float32, device=self.graph_data.x.device) - - in_feats = dataset.num_features - num_classes = dataset.num_classes - - self.generator = TriggerGenerator(in_feats, 64, self.owner_id).to(self.device) - self.model = GCN_PyG(in_feats, 128, num_classes).to(self.device) - - def defend(self): - """ - Execute the imperceptible watermark defense. - """ - metric_comp = DefenseCompMetric() - metric_comp.start() - print("====================Imperceptible Watermark====================") - - # If model wasn't trained yet, train it - if not hasattr(self, 'model_trained'): - self.train_target_model(metric_comp) - - # Evaluate the watermarked model - trigger_data = generate_trigger_graph(self.graph_data, self.generator, self.model, self.num_triggers) - preds = self.evaluate_model(trigger_data) - inference_s = time.time() - wm_preds = self.verify_watermark(trigger_data) - inference_e = time.time() - - # metric - metric = DefenseMetric() - metric.update(preds, trigger_data.y[trigger_data.original_test_mask]) - wm_true = trigger_data.y[trigger_data.trigger_nodes] - metric.update_wm(wm_preds, wm_true) - metric_comp.end() - - print("====================Final Results====================") - res = metric.compute() - metric_comp.update(inference_defense_time=(inference_e - inference_s)) - res_comp = metric_comp.compute() - - return res, res_comp - - def train_target_model(self, metric_comp: DefenseCompMetric): - """Train the target model with watermark injection.""" - defense_s = time.time() - - pyg_data = self.graph_data - bi_level_optimization(self.model, self.generator, pyg_data, self.num_triggers) - - self.model_trained = True - defense_e = time.time() - - metric_comp.update(defense_time=(defense_e - defense_s)) - return self.model - - def evaluate_model(self, trigger_data): - """Evaluate model performance on downstream task""" - self.model.eval() - with torch.no_grad(): - out = self.model(trigger_data.x, trigger_data.edge_index) - preds = out[trigger_data.original_test_mask].argmax(dim=1).cpu() - return preds - - def verify_watermark(self, trigger_data): - """Verify watermark success rate""" - self.model.eval() - with torch.no_grad(): - out = self.model(trigger_data.x, trigger_data.edge_index) - wm_preds = out[trigger_data.trigger_nodes].argmax(dim=1).cpu() - return wm_preds - - def _load_model(self): - if self.model_path: - self.model.load_state_dict(torch.load(self.model_path)) - - def _train_target_model(self): - # optional if you split training from watermarking - pass - - def _train_defense_model(self): - return self.model - - def _train_surrogate_model(self): - pass - - -class TriggerGenerator(nn.Module): - def __init__(self, in_channels, hidden_channels, owner_id): - super().__init__() - self.conv1 = GCNConv(in_channels, hidden_channels) - self.conv2 = GCNConv(hidden_channels, in_channels) - self.owner_id = owner_id - - def forward(self, x, edge_index): - x = F.relu(self.conv1(x, edge_index)) - x = torch.sigmoid(self.conv2(x, edge_index)) - out = x.clone() - out[:, -5:] = self.owner_id - return out - - -def generate_trigger_graph(data, generator, target_model, num_triggers=50): - with torch.no_grad(): - probs = F.softmax(target_model(data.x, data.edge_index), dim=1) - - selected_nodes = [] - for class_idx in range(probs.size(1)): - class_nodes = torch.where(data.y == class_idx)[0] - if len(class_nodes) > 0: - selected_nodes.append(class_nodes[probs[class_nodes, class_idx].argmax()].item()) - - trigger_features = generator(data.x, data.edge_index) - trigger_nodes = list(range(data.num_nodes, data.num_nodes + num_triggers)) - total_nodes = data.num_nodes + num_triggers - - # Create new dense adjacency matrix - adj = to_dense_adj(data.edge_index)[0] - new_adj = torch.zeros((total_nodes, total_nodes), device=adj.device) - new_adj[:adj.size(0), :adj.size(1)] = adj - - # Connect trigger nodes to selected nodes - for i, trigger in enumerate(trigger_nodes): - for node in selected_nodes: - new_adj[node, trigger] = 1 - new_adj[trigger, node] = 1 - - new_data = copy.deepcopy(data) - new_data.x = torch.cat([data.x, trigger_features[:num_triggers]], dim=0) - new_data.edge_index = dense_to_sparse(new_adj)[0] - new_data.y = torch.cat([ - data.y, - torch.zeros(num_triggers, dtype=torch.long, device=data.y.device) - ]) - - new_data.train_mask = torch.cat([ - data.train_mask, - torch.zeros(num_triggers, dtype=torch.bool, device=data.x.device) - ]) - new_data.val_mask = torch.cat([ - data.val_mask, - torch.zeros(num_triggers, dtype=torch.bool, device=data.x.device) - ]) - new_data.test_mask = torch.cat([ - data.test_mask, - torch.zeros(num_triggers, dtype=torch.bool, device=data.x.device) - ]) - - new_data.original_test_mask = data.test_mask.clone() - - # Add trigger info - new_data.trigger_nodes = trigger_nodes - new_data.selected_nodes = selected_nodes - new_data.trigger_mask = torch.zeros(total_nodes, dtype=torch.bool, device=data.x.device) - new_data.trigger_mask[trigger_nodes] = True - - return new_data - - -def bi_level_optimization(target_model, generator, data, num_triggers, epochs=100, inner_steps=5): - optimizer_model = torch.optim.Adam(target_model.parameters(), lr=0.01) - optimizer_gen = torch.optim.Adam(generator.parameters(), lr=0.01) - criterion = nn.CrossEntropyLoss() - - for epoch in tqdm(range(epochs)): - for _ in range(inner_steps): - optimizer_model.zero_grad() - trigger_data = generate_trigger_graph(data, generator, target_model, num_triggers) - - out_clean = target_model(data.x, data.edge_index) - out_trigger = target_model(trigger_data.x, trigger_data.edge_index) - - clean_loss = criterion(out_clean[data.train_mask], data.y[data.train_mask]) - trigger_loss = criterion(out_trigger[trigger_data.trigger_mask], - trigger_data.y[trigger_data.trigger_mask]) - - total_loss = clean_loss + trigger_loss - total_loss.backward() - optimizer_model.step() - - optimizer_gen.zero_grad() - trigger_data = generate_trigger_graph(data, generator, target_model, num_triggers) - - orig_features = data.x[trigger_data.selected_nodes] - trigger_features = trigger_data.x[trigger_data.trigger_nodes] - sim_loss = -F.cosine_similarity(orig_features.unsqueeze(1), - trigger_features.unsqueeze(0), dim=-1).mean() - - out = target_model(trigger_data.x, trigger_data.edge_index) - trigger_loss = criterion(out[trigger_data.trigger_mask], - trigger_data.y[trigger_data.trigger_mask]) - - owner_loss = F.binary_cross_entropy( - trigger_data.x[trigger_data.trigger_nodes, -5:], - generator.owner_id.expand(len(trigger_data.trigger_nodes), 5) - ) - - total_gen_loss = 0.4 * sim_loss + 0.4 * trigger_loss + 0.2 * owner_loss - total_gen_loss.backward() - optimizer_gen.step() - - -def calculate_metrics(model, data): - model.eval() - with torch.no_grad(): - out = model(data.x, data.edge_index) - pred = out.argmax(dim=1) - true = data.y - - # Handle both original and watermarked data cases - if hasattr(data, 'original_test_mask'): - test_mask = data.original_test_mask - if test_mask.size(0) < pred.size(0): - pad_len = pred.size(0) - test_mask.size(0) - test_mask = torch.cat([test_mask, torch.zeros(pad_len, dtype=torch.bool, device=test_mask.device)]) - else: - test_mask = data.test_mask - - metrics = { - 'accuracy': (pred[test_mask] == true[test_mask]).float().mean().item(), - 'precision': precision_score(true[test_mask].cpu(), pred[test_mask].cpu(), average='macro'), - 'recall': recall_score(true[test_mask].cpu(), pred[test_mask].cpu(), average='macro'), - 'f1': f1_score(true[test_mask].cpu(), pred[test_mask].cpu(), average='macro'), - 'wm_accuracy': None - } - - if hasattr(data, 'trigger_nodes'): - wm_mask = torch.zeros(data.x.size(0), dtype=torch.bool, device=data.x.device) - wm_mask[data.trigger_nodes] = True - wm_pred = pred[wm_mask] - wm_true = true[wm_mask] - metrics['wm_accuracy'] = (wm_pred == wm_true).float().mean().item() - - return metrics diff --git a/pyhazard/models/defense/ImperceptibleWM2.py b/pyhazard/models/defense/ImperceptibleWM2.py deleted file mode 100644 index e79889a..0000000 --- a/pyhazard/models/defense/ImperceptibleWM2.py +++ /dev/null @@ -1,920 +0,0 @@ -import os -import sys - -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../..'))) -import torch -import torch.nn as nn -import torch.nn.functional as F -import dgl -import numpy as np -from tqdm import tqdm -from dgl.dataloading import NeighborSampler, NodeCollator -from torch.utils.data import DataLoader -from dgl.nn import GraphConv -from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score - -from pyhazard.models.defense.base import BaseDefense -from pyhazard.models.nn import GraphSAGE - -device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - - -class TriggerGenerator(nn.Module): - """ - Generate watermark trigger features and edge probabilities using a GCN-based architecture. - - This module constructs a small graph template and applies multiple GCN layers to produce - node features that represent the watermark trigger. It also learns a function to generate - edge probabilities between nodes using a neural edge generator. - - Parameters - ---------- - feature_dim : int - Dimension of node feature vectors. - hidden_dim : int, optional - Dimension of hidden layers in GCN and edge generator. Default is 64. - output_nodes : int, optional - Number of nodes in the generated trigger graph. Default is 50. - """ - - def __init__(self, feature_dim, hidden_dim=64, output_nodes=50): - super(TriggerGenerator, self).__init__() - self.feature_dim = feature_dim - self.output_nodes = output_nodes - - self.gcn1 = GraphConv(feature_dim, hidden_dim) - self.gcn2 = GraphConv(hidden_dim, hidden_dim) - self.gcn3 = GraphConv(hidden_dim, feature_dim) - - self.edge_generator = nn.Sequential( - nn.Linear(feature_dim * 2, hidden_dim), - nn.ReLU(), - nn.Linear(hidden_dim, 1), - nn.Sigmoid() - ) - - # Create a small template graph for GCN processing - self.template_graph = self._create_template_graph() - - def _create_template_graph(self): - """ - Create a small template DGL graph structure to serve as the base for GCN processing. - - This function builds a fully connected undirected graph (with self-loops) - consisting of up to 10 nodes. This graph serves as a structural template - for generating watermark trigger node features. - - Returns - ------- - dgl.DGLGraph - A small connected DGL graph with self-loops, moved to the appropriate device. - """ - # Create a small connected graph for initial processing - edges = [] - for i in range(min(10, self.output_nodes)): - for j in range(i + 1, min(10, self.output_nodes)): - edges.append((i, j)) - edges.append((j, i)) - - if not edges: - edges = [(0, 1), (1, 0)] - - src, dst = zip(*edges) if edges else ([0], [1]) - g = dgl.graph((src, dst), num_nodes=min(10, self.output_nodes)) - g = dgl.add_self_loop(g) - return g.to(device) - - def forward(self, clean_features, selected_nodes): - """ - Forward pass to generate trigger node features and edge probabilities. - - Constructs a trigger graph by first computing a prototype feature from - selected clean nodes, propagating it through GCN layers, and generating - additional nodes and edge probabilities to match the required trigger size. - - Parameters - ---------- - clean_features : torch.Tensor - Feature matrix from the clean graph (shape: [num_nodes, feature_dim]). - selected_nodes : list[int] or torch.Tensor - Indices of nodes selected for constructing the prototype vector. - - Returns - ------- - trigger_features : torch.Tensor - Feature matrix of generated trigger nodes (shape: [output_nodes, feature_dim]). - - edge_probs : torch.Tensor - A 1D tensor containing probabilities for edges between node pairs - (upper triangular, shape: [output_nodes * (output_nodes - 1) / 2]). - """ - # Create prototype from selected nodes - if len(selected_nodes) > 0: - sample_features = clean_features[selected_nodes[:min(len(selected_nodes), 10)]] - prototype = sample_features.mean(dim=0, keepdim=True) - else: - prototype = clean_features.mean(dim=0, keepdim=True) - - # Replicate prototype for template graph nodes - template_size = self.template_graph.num_nodes() - template_features = prototype.repeat(template_size, 1) - - # Apply GCN layers - h = F.relu(self.gcn1(self.template_graph, template_features)) - h = F.relu(self.gcn2(self.template_graph, h)) - h = torch.sigmoid(self.gcn3(self.template_graph, h)) - - # Expand to desired number of trigger nodes - if template_size < self.output_nodes: - # Replicate and add noise for additional nodes - additional_nodes = self.output_nodes - template_size - noise = torch.randn(additional_nodes, self.feature_dim, device=device) * 0.1 - additional_features = h[-1:].repeat(additional_nodes, 1) + noise - trigger_features = torch.cat([h, additional_features], dim=0) - else: - trigger_features = h[:self.output_nodes] - - # Generate edge probabilities - n_nodes = self.output_nodes - edge_probs = [] - - for i in range(n_nodes): - for j in range(i + 1, n_nodes): - pair_features = torch.cat([trigger_features[i], trigger_features[j]], dim=0) - edge_prob = self.edge_generator(pair_features.unsqueeze(0)) - edge_probs.append(edge_prob) - - edge_probs = torch.cat(edge_probs, dim=0) if edge_probs else torch.tensor([]).to(device) - - return trigger_features, edge_probs - - -class ImperceptibleWM2(BaseDefense): - def __init__(self, dataset, attack_node_fraction=0.2, wm_node=50, - target_label=None, N=50, M=5, - epsilon1=1.0, epsilon2=0.5, epsilon3=1.0, owner_id=None, - beta=0.001, T_acc=0.8): - """ - Initialize the watermark defense using bilevel optimization. - - Parameters - ---------- - dataset : object - The graph dataset containing features, labels, and graph structure - attack_node_fraction : float, default=0.2 - Fraction of nodes to consider for attack simulation - wm_node : int, default=50 - Number of nodes in the watermark/trigger graph - target_label : int, optional - Target label for watermark classification. If None, randomly selected - N : int, default=50 - Number of bilevel optimization iterations - M : int, default=5 - Number of embedding phase iterations per bilevel step - epsilon1 : float, default=1.0 - Weight for imperception loss in generator objective - epsilon2 : float, default=0.5 - Weight for regulation loss in generator objective - epsilon3 : float, default=1.0 - Weight for trigger loss in generator objective - owner_id : array-like, optional - Owner identifier for watermark regulation. If None, randomly generated - beta : float, default=0.001 - Learning rate for the main model optimizer - T_acc : float, default=0.8 - Accuracy threshold for ownership verification - """ - - super().__init__(dataset, attack_node_fraction) - self.dataset = dataset - self.graph = dataset.graph - - self.node_number = dataset.node_number if hasattr(dataset, 'node_number') else self.graph.num_nodes() - self.feature_number = dataset.feature_number if hasattr(dataset, 'feature_number') else \ - self.graph.ndata['feat'].shape[1] - self.label_number = dataset.label_number if hasattr(dataset, 'label_number') else ( - int(max(self.graph.ndata['label']) - min(self.graph.ndata['label'])) + 1) - self.attack_node_number = int(self.node_number * attack_node_fraction) - - self.wm_node = wm_node - self.target_label = target_label if target_label is not None else np.random.randint(0, self.label_number) - self.N = N - self.M = M - self.beta = beta - self.T_acc = T_acc - - self.epsilon1 = epsilon1 - self.epsilon2 = epsilon2 - self.epsilon3 = epsilon3 - - self.owner_id = owner_id if owner_id is not None else torch.rand(self.feature_number, device=device) - if isinstance(self.owner_id, (list, np.ndarray)): - self.owner_id = torch.tensor(self.owner_id, dtype=torch.float32, device=device) - elif not isinstance(self.owner_id, torch.Tensor): - self.owner_id = torch.rand(self.feature_number, device=device) - - self.features = dataset.features if hasattr(dataset, 'features') else self.graph.ndata['feat'] - self.labels = dataset.labels if hasattr(dataset, 'labels') else self.graph.ndata['label'] - self.train_mask = dataset.train_mask if hasattr(dataset, 'train_mask') else self.graph.ndata['train_mask'] - self.test_mask = dataset.test_mask if hasattr(dataset, 'test_mask') else self.graph.ndata['test_mask'] - - if device != 'cpu': - self.graph = self.graph.to(device) - self.features = self.features.to(device) - self.labels = self.labels.to(device) - self.train_mask = self.train_mask.to(device) - self.test_mask = self.test_mask.to(device) - self.owner_id = self.owner_id.to(device) - - def _select_poisoning_nodes(self, clean_model): - """ - Select nodes for watermark poisoning based on model predictions. - Uses the clean model's confidence scores to identify high-confidence nodes - across different labels for creating the watermark trigger. - - Parameters - ---------- - clean_model : torch.nn.Module - Pre-trained clean model used for node selection - - Returns - ------- - torch.Tensor - Tensor of selected node indices for poisoning - """ - clean_model.eval() - with torch.no_grad(): - sampler = NeighborSampler([5, 5]) - all_nids = torch.arange(self.graph.num_nodes(), device=device) - collator = NodeCollator(self.graph, all_nids, sampler) - dataloader = DataLoader( - collator.dataset, batch_size=64, shuffle=False, - collate_fn=collator.collate, drop_last=False - ) - - all_predictions = [] - node_indices = [] - - for input_nodes, output_nodes, blocks in dataloader: - blocks = [b.to(device) for b in blocks] - input_features = blocks[0].srcdata['feat'] - - logits = clean_model(blocks, input_features) - predictions = F.softmax(logits, dim=1) - - all_predictions.append(predictions) - node_indices.append(output_nodes) - - all_predictions = torch.cat(all_predictions, dim=0) - node_indices = torch.cat(node_indices, dim=0) - - poisoning_nodes = [] - nodes_per_label = max(1, self.wm_node // self.label_number) - - for label in range(self.label_number): - label_probs = all_predictions[:, label] - _, top_indices = torch.topk(label_probs, min(nodes_per_label, len(label_probs))) - selected_nodes = node_indices[top_indices] - poisoning_nodes.extend(selected_nodes.tolist()) - - if len(poisoning_nodes) < self.wm_node: - remaining_nodes = set(range(self.graph.num_nodes())) - set(poisoning_nodes) - additional_nodes = np.random.choice( - list(remaining_nodes), - size=min(self.wm_node - len(poisoning_nodes), len(remaining_nodes)), - replace=False - ) - poisoning_nodes.extend(additional_nodes) - - poisoning_nodes = poisoning_nodes[:self.wm_node] - - return torch.tensor(poisoning_nodes, device=device) - - def _generate_trigger_graph(self, f_g, V_p): - """ - Generate a watermark trigger graph using the generator network. - Creates trigger features and edges based on poisoning nodes and - constructs a DGL graph for watermark embedding. - - Parameters - ---------- - f_g : torch.nn.Module - Trigger generator network - V_p : torch.Tensor - Selected poisoning node indices - - Returns - ------- - dgl.DGLGraph - The generated watermark trigger graph with features and labels - """ - f_g.eval() - with torch.no_grad(): - trigger_features, edge_probs = f_g(self.features, V_p) - - edge_threshold = 0.5 - edges_src, edges_dst = [], [] - edge_idx = 0 - - for i in range(self.wm_node): - for j in range(i + 1, self.wm_node): - if edge_idx < len(edge_probs) and edge_probs[edge_idx] > edge_threshold: - edges_src.extend([i, j]) - edges_dst.extend([j, i]) - edge_idx += 1 - - if len(edges_src) == 0: - edges_src = [0, 1] - edges_dst = [1, 0] - - trigger_graph = dgl.graph((edges_src, edges_dst), num_nodes=self.wm_node) - trigger_graph = trigger_graph.to(device) - - trigger_graph.ndata['feat'] = trigger_features.detach() - trigger_graph.ndata['label'] = torch.full((self.wm_node,), self.target_label, - dtype=torch.long, device=device) - trigger_graph.ndata['train_mask'] = torch.ones(self.wm_node, dtype=torch.bool, device=device) - trigger_graph.ndata['test_mask'] = torch.ones(self.wm_node, dtype=torch.bool, device=device) - - trigger_graph = dgl.add_self_loop(trigger_graph) - - return trigger_graph - - def _construct_backdoor_graph(self, clean_graph, trigger_graph, V_p): - """ - Construct a backdoor graph by combining clean graph with trigger graph. - Merges the original graph with the watermark trigger graph by adding - connections between poisoning nodes and trigger nodes. - - Parameters - ---------- - clean_graph : dgl.DGLGraph - Original clean graph - trigger_graph : dgl.DGLGraph - Generated trigger/watermark graph - V_p : torch.Tensor - Poisoning node indices for connection - - Returns - ------- - dgl.DGLGraph - Combined backdoor graph with embedded watermark - """ - clean_adj = clean_graph.adj().to_dense() - trigger_adj = trigger_graph.adj().to_dense() - - clean_features = clean_graph.ndata['feat'] - trigger_features = trigger_graph.ndata['feat'] - - clean_labels = clean_graph.ndata['label'] - trigger_labels = trigger_graph.ndata['label'] - - n_clean = clean_graph.num_nodes() - n_trigger = trigger_graph.num_nodes() - - A_I = torch.zeros(n_trigger, n_clean, device=device) - - for i in range(n_trigger): - for j in V_p: - if torch.rand(1) > 0.7: - A_I[i, j] = 1 - - top_row = torch.cat([clean_adj, A_I.t()], dim=1) - bottom_row = torch.cat([A_I, trigger_adj], dim=1) - backdoor_adj = torch.cat([top_row, bottom_row], dim=0) - - backdoor_features = torch.cat([clean_features, trigger_features], dim=0) - backdoor_labels = torch.cat([clean_labels, trigger_labels], dim=0) - - edges_src, edges_dst = torch.nonzero(backdoor_adj, as_tuple=True) - backdoor_graph = dgl.graph((edges_src, edges_dst), num_nodes=n_clean + n_trigger) - backdoor_graph = backdoor_graph.to(device) - - backdoor_graph.ndata['feat'] = backdoor_features - backdoor_graph.ndata['label'] = backdoor_labels - - clean_train_mask = clean_graph.ndata['train_mask'] - clean_test_mask = clean_graph.ndata['test_mask'] - - trigger_train_mask = torch.ones(n_trigger, dtype=torch.bool, device=device) - trigger_test_mask = torch.ones(n_trigger, dtype=torch.bool, device=device) - - backdoor_graph.ndata['train_mask'] = torch.cat([clean_train_mask, trigger_train_mask]) - backdoor_graph.ndata['test_mask'] = torch.cat([clean_test_mask, trigger_test_mask]) - - backdoor_graph = dgl.add_self_loop(backdoor_graph) - - return backdoor_graph - - def _calculate_imperception_loss(self, trigger_features, V_p): - """ - Calculate imperception loss to make watermark features similar to clean features. - Measures cosine similarity between trigger features and poisoning node features - to ensure the watermark remains hidden. - - Parameters - ---------- - trigger_features : torch.Tensor - Generated trigger node features - V_p : torch.Tensor - Poisoning node indices - - Returns - ------- - torch.Tensor - Imperception loss value - """ - if len(V_p) == 0: - return torch.tensor(0.0, device=device) - - poisoning_features = self.features[V_p] - total_similarity = 0 - count = 0 - - for i, trigger_feat in enumerate(trigger_features): - for poison_feat in poisoning_features: - similarity = F.cosine_similarity(trigger_feat.unsqueeze(0), poison_feat.unsqueeze(0)) - total_similarity += similarity - count += 1 - - return -total_similarity / count if count > 0 else torch.tensor(0.0, device=device) - - def _calculate_regulation_loss(self, trigger_features): - """ - Calculate regulation loss based on owner ID signature. - Enforces the trigger features to embed owner identification information - using cross-entropy loss with the owner ID as target. - - Parameters - ---------- - trigger_features : torch.Tensor - Generated trigger node features - - Returns - ------- - torch.Tensor - Regulation loss value - """ - total_loss = 0 - for trigger_feat in trigger_features: - loss = -(self.owner_id * torch.log(trigger_feat + 1e-8) + - (1 - self.owner_id) * torch.log(1 - trigger_feat + 1e-8)) - total_loss += loss.mean() - - return total_loss / len(trigger_features) - - def _calculate_trigger_loss(self, f_theta, trigger_features, trigger_graph): - """ - Calculate trigger loss for watermark effectiveness. - Measures how well the model classifies trigger nodes to the target label, - ensuring the watermark functions correctly. - - Parameters - ---------- - f_theta : torch.nn.Module - Main classification model - trigger_features : torch.Tensor - Generated trigger node features - trigger_graph : dgl.DGLGraph - Trigger graph structure - - Returns - ------- - torch.Tensor - Trigger loss value - """ - f_theta.eval() - - sampler = NeighborSampler([5, 5]) - trigger_nids = torch.arange(trigger_graph.number_of_nodes(), device=device) - collator = NodeCollator(trigger_graph, trigger_nids, sampler) - - dataloader = DataLoader( - collator.dataset, batch_size=self.wm_node, - shuffle=False, collate_fn=collator.collate, drop_last=False - ) - - total_loss = 0 - count = 0 - - with torch.no_grad(): - for _, _, blocks in dataloader: - blocks = [b.to(device) for b in blocks] - input_features = blocks[0].srcdata['feat'] - - logits = f_theta(blocks, input_features) - probs = F.softmax(logits, dim=1) - - target_probs = probs[:, self.target_label] - loss = -torch.log(target_probs + 1e-8).mean() - total_loss += loss - count += 1 - break - - return total_loss / count if count > 0 else torch.tensor(0.0, device=device) - - def _calculate_generation_loss_integrated(self, f_theta_s, f_g, V_p): - """ - Calculate integrated generation loss combining all generator objectives. - Combines imperception, regulation, and trigger losses with respective weights - to optimize the trigger generator network. - - Parameters - ---------- - f_theta_s : torch.nn.Module - Current state of the main model - f_g : torch.nn.Module - Trigger generator network - V_p : torch.Tensor - Poisoning node indices - - Returns - ------- - torch.Tensor - Combined generation loss - """ - f_g.train() - f_theta_s.eval() - - trigger_features, edge_probs = f_g(self.features, V_p) - - temp_trigger_graph = self._create_temp_trigger_graph(trigger_features, edge_probs) - - L_imperception = self._calculate_imperception_loss(trigger_features, V_p) - L_regulation = self._calculate_regulation_loss(trigger_features) - L_trigger = self._calculate_trigger_loss(f_theta_s, trigger_features, temp_trigger_graph) - - L_g = (self.epsilon1 * L_imperception + - self.epsilon2 * L_regulation + - self.epsilon3 * L_trigger) - - return L_g - - def _create_temp_trigger_graph(self, trigger_features, edge_probs): - """ - Create a temporary trigger graph for loss calculation. - Constructs a temporary graph structure using generated features and edge - probabilities for intermediate computations. - - Parameters - ---------- - trigger_features : torch.Tensor - Generated trigger node features - edge_probs : torch.Tensor - Edge existence probabilities - - Returns - ------- - dgl.DGLGraph - Temporary trigger graph - """ - edge_threshold = 0.5 - edges_src, edges_dst = [], [] - edge_idx = 0 - - for i in range(self.wm_node): - for j in range(i + 1, self.wm_node): - if edge_idx < len(edge_probs) and edge_probs[edge_idx] > edge_threshold: - edges_src.extend([i, j]) - edges_dst.extend([j, i]) - edge_idx += 1 - - if len(edges_src) == 0: - edges_src = [0, 1] - edges_dst = [1, 0] - - temp_graph = dgl.graph((edges_src, edges_dst), num_nodes=self.wm_node) - temp_graph = temp_graph.to(device) - temp_graph.ndata['feat'] = trigger_features - temp_graph.ndata['label'] = torch.full((self.wm_node,), self.target_label, - dtype=torch.long, device=device) - temp_graph = dgl.add_self_loop(temp_graph) - - return temp_graph - - def _calculate_embedding_loss(self, f_theta, backdoor_graph): - """ - Calculate embedding loss for model training on backdoor graph. - Computes cross-entropy loss for training the main model on the combined - clean and trigger graph data. - - Parameters - ---------- - f_theta : torch.nn.Module - Main classification model - backdoor_graph : dgl.DGLGraph - Combined graph with embedded watermark - - Returns - ------- - torch.Tensor - Embedding loss value - """ - f_theta.train() - backdoor_train_nids = backdoor_graph.ndata['train_mask'].nonzero(as_tuple=True)[0].to(device) - sampler = NeighborSampler([5, 5]) - collator = NodeCollator(backdoor_graph, backdoor_train_nids, sampler) - dataloader = DataLoader( - collator.dataset, batch_size=32, shuffle=True, - collate_fn=collator.collate, drop_last=False - ) - - total_loss = 0 - count = 0 - - for _, _, blocks in dataloader: - blocks = [b.to(device) for b in blocks] - input_features = blocks[0].srcdata['feat'] - output_labels = blocks[-1].dstdata['label'] - - output_predictions = f_theta(blocks, input_features) - loss = F.cross_entropy(output_predictions, output_labels) - total_loss += loss - count += 1 - - if count >= 10: - break - - return total_loss / count if count > 0 else torch.tensor(0.0, device=device) - - def _inner_optimization(self, f_theta, f_g, V_p, optimizer): - """ - Execute the watermark embedding phase of bilevel optimization. - Performs M iterations of model training on the backdoor graph to embed - the watermark into the model parameters. - - Parameters - ---------- - f_theta : torch.nn.Module - Main classification model - f_g : torch.nn.Module - Trigger generator network - V_p : torch.Tensor - Poisoning node indices - optimizer : torch.optim.Optimizer - Optimizer for model parameters - - Returns - ------- - torch.nn.Module - Updated model with embedded watermark - """ - trigger_graph = self._generate_trigger_graph(f_g, V_p) - backdoor_graph = self._construct_backdoor_graph(self.graph, trigger_graph, V_p) - - for t in range(self.M): - L_embed = self._calculate_embedding_loss(f_theta, backdoor_graph) - - optimizer.zero_grad() - L_embed.backward() - optimizer.step() - - return f_theta - - def defend(self): - """ - Execute the complete watermark defense strategy. - Trains target model, applies watermark defense, and verifies ownership. - Returns comprehensive evaluation metrics and ownership verification results. - - Returns - ------- - dict - Dictionary containing attack metrics, defense metrics, ownership - verification status, and trained generator - """ - attack_model = self._train_target_model() - - sampler = NeighborSampler([5, 5]) - test_nids = self.test_mask.nonzero(as_tuple=True)[0].to(device) - test_collator = NodeCollator(self.graph, test_nids, sampler) - test_dataloader = DataLoader( - test_collator.dataset, batch_size=32, shuffle=False, - collate_fn=test_collator.collate, drop_last=False - ) - - attack_acc, attack_prec, attack_rec, attack_f1 = self._evaluate_with_metrics(attack_model, test_dataloader) - print("Target Model Metrics:") - print(f" Accuracy : {attack_acc * 100:.2f}%") - print(f" Precision: {attack_prec * 100:.2f}%") - print(f" Recall : {attack_rec * 100:.2f}%") - print(f" F1 Score : {attack_f1 * 100:.2f}%") - - defense_model, generator = self._train_defense_model() - - defense_acc, defense_prec, defense_rec, defense_f1 = self._evaluate_with_metrics(defense_model, test_dataloader) - print("Defense Model Metrics:") - print(f" Accuracy : {defense_acc * 100:.2f}%") - print(f" Precision: {defense_prec * 100:.2f}%") - print(f" Recall : {defense_rec * 100:.2f}%") - print(f" F1 Score : {defense_f1 * 100:.2f}%") - - is_owner, ownership_acc = self.verify_ownership(defense_model) - print(f"\nOwnership Verification: {is_owner}, Watermark Accuracy: {ownership_acc * 100:.2f}%") - - return { - "attack_accuracy": attack_acc, - "attack_precision": attack_prec, - "attack_recall": attack_rec, - "attack_f1": attack_f1, - "defense_accuracy": defense_acc, - "defense_precision": defense_prec, - "defense_recall": defense_rec, - "defense_f1": defense_f1, - "ownership_verified": is_owner, - "ownership_accuracy": ownership_acc, - "generator": generator - } - - def _train_target_model(self): - """ - Train the target model on clean graph data. - Creates and trains a GraphSAGE model on the original dataset without - any watermark or defense mechanisms. - - Returns - ------- - torch.nn.Module - Trained target model - """ - model = GraphSAGE(in_channels=self.feature_number, - hidden_channels=128, - out_channels=self.label_number) - model = model.to(device) - optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=5e-4) - - sampler = NeighborSampler([5, 5]) - train_nids = self.train_mask.nonzero(as_tuple=True)[0].to(device) - test_nids = self.test_mask.nonzero(as_tuple=True)[0].to(device) - - train_collator = NodeCollator(self.graph, train_nids, sampler) - test_collator = NodeCollator(self.graph, test_nids, sampler) - - train_dataloader = DataLoader( - train_collator.dataset, batch_size=32, shuffle=True, - collate_fn=train_collator.collate, drop_last=False - ) - - test_dataloader = DataLoader( - test_collator.dataset, batch_size=32, shuffle=False, - collate_fn=test_collator.collate, drop_last=False - ) - - for epoch in tqdm(range(1, 51), desc="========== Training Target Model =========="): - model.train() - for _, _, blocks in train_dataloader: - blocks = [b.to(device) for b in blocks] - input_features = blocks[0].srcdata['feat'] - output_labels = blocks[-1].dstdata['label'] - - optimizer.zero_grad() - output_predictions = model(blocks, input_features) - loss = F.cross_entropy(output_predictions, output_labels) - loss.backward() - optimizer.step() - - return model - - def _train_defense_model(self): - """ - Train the defense model with watermark embedding using bilevel optimization. - Implements the complete bilevel optimization process alternating between - watermark embedding and trigger generation phases. - - Returns - ------- - tuple - (trained_defense_model, trigger_generator) - """ - - f_theta = GraphSAGE(in_channels=self.feature_number, - hidden_channels=128, - out_channels=self.label_number).to(device) - - f_g = TriggerGenerator(feature_dim=self.feature_number, - output_nodes=self.wm_node).to(device) - print("\n========== Training Defense Model ==========") - # High confidence nodes from the target model will be used as trigger - print("Retraining the target model to select poisoning nodes") - clean_model = self._train_target_model() - V_p = self._select_poisoning_nodes(clean_model) - - theta_optimizer = torch.optim.Adam(f_theta.parameters(), lr=self.beta, weight_decay=5e-4) - g_optimizer = torch.optim.Adam(f_g.parameters(), lr=0.001, weight_decay=5e-4) - - for i in tqdm(range(self.N), desc="Starting BiLevelOptimization Process"): - f_theta = self._inner_optimization(f_theta, f_g, V_p, theta_optimizer) - - f_theta_s = f_theta - - L_g = self._calculate_generation_loss_integrated(f_theta_s, f_g, V_p) - - g_optimizer.zero_grad() - L_g.backward() - g_optimizer.step() - - self.watermark_graph = self._generate_trigger_graph(f_g, V_p) - self.poisoning_nodes = V_p - - return f_theta, f_g - - def _evaluate_with_metrics(self, model, dataloader): - """ - Evaluate model performance using multiple classification metrics. - - Parameters - ---------- - model : torch.nn.Module - The neural network model to evaluate - dataloader : torch.utils.data.DataLoader - DataLoader containing evaluation data - - Returns - ------- - tuple of float - (accuracy, precision, recall, f1_score) metrics - """ - model.eval() - all_preds = [] - all_labels = [] - - with torch.no_grad(): - for _, _, blocks in dataloader: - blocks = [b.to(device) for b in blocks] - input_features = blocks[0].srcdata['feat'] - output_labels = blocks[-1].dstdata['label'] - output_predictions = model(blocks, input_features) - pred = output_predictions.argmax(dim=1) - - all_preds.extend(pred.cpu().numpy()) - all_labels.extend(output_labels.cpu().numpy()) - - if len(all_preds) == 0: - return 0.0, 0.0, 0.0, 0.0 - - accuracy = accuracy_score(all_labels, all_preds) - precision = precision_score(all_labels, all_preds, average='weighted', zero_division=0) - recall = recall_score(all_labels, all_preds, average='weighted', zero_division=0) - f1 = f1_score(all_labels, all_preds, average='weighted', zero_division=0) - - return accuracy, precision, recall, f1 - - def verify_ownership(self, suspicious_model): - """ - Verify ownership of a suspicious model using the watermark. - Tests if the suspicious model correctly classifies the watermark trigger - graph to determine if it contains the embedded watermark. - - Parameters - ---------- - suspicious_model : torch.nn.Module - Model to test for ownership verification - - Returns - ------- - tuple - (is_owner: bool, ownership_accuracy: float) - """ - if not hasattr(self, 'watermark_graph'): - return False, 0.0 - - G_key_p = self.watermark_graph - acc, _, _, _ = self._evaluate_model_on_graph(suspicious_model, G_key_p) - - is_owner = acc > self.T_acc - return is_owner, acc - - def _evaluate_model_on_graph(self, model, graph): - """ - Evaluate model performance on a specific graph. - Computes classification metrics for the given model on the provided graph, - handling different model architectures appropriately. - - Parameters - ---------- - model : torch.nn.Module - Model to evaluate - graph : dgl.DGLGraph - Graph data for evaluation - - Returns - ------- - tuple of float - (accuracy, precision, recall, f1_score) metrics - """ - model_name = model.__class__.__name__ - - if model_name == 'GraphSAGE': - sampler = NeighborSampler([5, 5]) - trigger_nids = torch.arange(graph.number_of_nodes(), device=device) - trigger_collator = NodeCollator(graph, trigger_nids, sampler) - - trigger_dataloader = DataLoader( - trigger_collator.dataset, batch_size=graph.number_of_nodes(), - shuffle=False, collate_fn=trigger_collator.collate, drop_last=False - ) - - return self._evaluate_with_metrics(model, trigger_dataloader) - - else: - return 0.0, 0.0, 0.0, 0.0 diff --git a/pyhazard/models/defense/Integrity.py b/pyhazard/models/defense/Integrity.py deleted file mode 100644 index ebb3846..0000000 --- a/pyhazard/models/defense/Integrity.py +++ /dev/null @@ -1,1227 +0,0 @@ -import copy -import random -import time - -import dgl -import torch -import numpy as np -import torch.optim as optim -from tqdm import tqdm -from torch.optim import Adam -import torch.nn.functional as F -from torch_geometric.utils import to_networkx, from_networkx - -from .base import BaseDefense -from pyhazard.models.nn import GCN -from pyhazard.utils.metrics import DefenseCompMetric, DefenseMetric - - -class QueryBasedVerificationDefense(BaseDefense): - supported_api_types = {"dgl"} - supported_datasets = {} - - def __init__(self, dataset, defense_ratio=0.1, model_path=None): - super().__init__(dataset, defense_ratio) - self.model = None - self.defense_ratio = defense_ratio - self.graph_data = dataset.graph_data - # compute related parameters - self.k = max(1, int(dataset.num_nodes * defense_ratio)) - self.model_path = model_path - - def defend(self, fingerprint_mode='inductive', knowledge='full', attack_type='bitflip', - k=5, num_trials=1, use_edge_perturbation=False, verbose=True, **kwargs): - """ - Execute the query-based verification defense. - """ - metric_comp = DefenseCompMetric() - metric_comp.start() - print("====================Query Based Verification Defense====================") - - # If model wasn't trained yet, train it - if not hasattr(self, 'model_trained'): - self.train_target_model(metric_comp) - - # Generate fingerprints and evaluate the defended model - fingerprints = self._generate_fingerprints( - self.model, mode=fingerprint_mode, knowledge=knowledge, k=k, - perturb_fingerprints=use_edge_perturbation, - perturb_budget=kwargs.get('perturb_budget', 5), **kwargs - ) - - preds = self.evaluate_model(self.model, self.dataset) - inference_s = time.time() - detection_results = self.verify_defense(self.model, fingerprints, attack_type, **kwargs) - inference_e = time.time() - - # metric - metric = DefenseMetric() - labels = self.dataset.graph_data.ndata['label'][self.dataset.graph_data.ndata['test_mask']] - metric.update(preds, labels) - - # Convert detection results to binary format - detection_preds, detection_targets = self._convert_detection_to_binary(detection_results) - metric.update_wm(detection_preds, detection_targets) - metric_comp.end() - - print("====================Final Results====================") - res = metric.compute() - metric_comp.update(inference_defense_time=(inference_e - inference_s)) - res_comp = metric_comp.compute() - - return res, res_comp - - def train_target_model(self, metric_comp: DefenseCompMetric): - """Train the target model with defense mechanism.""" - defense_s = time.time() - - # Training and fingerprint generation (defense mechanism) - self.model = self._train_target_model() - self.model_trained = True - - defense_e = time.time() - metric_comp.update(defense_time=(defense_e - defense_s)) - return self.model - - def evaluate_model(self, model, dataset): - """Evaluate model performance on downstream task""" - model.eval() - features = self._get_features().to(self.device) - labels = dataset.graph_data.ndata['label'].to(self.device) - test_mask = dataset.graph_data.ndata['test_mask'] - - with torch.no_grad(): - logits = model(dataset.graph_data.to(self.device), features) - preds = logits.argmax(dim=1)[test_mask].cpu() - return preds - - def verify_defense(self, model, fingerprints, attack_type, **kwargs): - """Verify defense effectiveness by running attack and checking fingerprints""" - # Run attack - poisoned_model, attack_info = self._run_attack( - model, attack_type=attack_type, **kwargs - ) - - # Evaluate fingerprints - flipped_info = self._evaluate_fingerprints(poisoned_model, fingerprints) - - return { - 'flip_rate': flipped_info['flip_rate'], - 'flipped_fingerprints': flipped_info['flipped'], - 'total_fingerprints': len(fingerprints) - } - - @staticmethod - def _convert_detection_to_binary(detection_results): - """Convert detection results to binary classification format""" - total = detection_results['total_fingerprints'] - flipped = len(detection_results['flipped_fingerprints']) - - # Create binary predictions: 1 for attack detected, 0 for no attack - detection_preds = torch.tensor([1 if flipped > 0 else 0]) - # In this case, we assume attack was actually performed, so target is 1 - detection_targets = torch.tensor([1]) - - return detection_preds, detection_targets - - def _get_features(self): - return self.graph_data.ndata['feat'] if hasattr(self.graph_data, 'ndata') else self.graph_data.x - - def _train_target_model(self, epochs=200): - """ - Trains target GCN model according to protocol in - Wu et al. (2023), Section 6.1 for graph node classification. - - Returns - ------- - model : torch.nn.Module - The trained GCN model. - """ - model = GCN( - feature_number=self.dataset.num_features, - label_number=self.dataset.num_classes - ).to(self.device) - print(f"Training target model on device: {self.device} ...") - - optimizer = Adam(model.parameters(), lr=0.02) - loss_fn = torch.nn.NLLLoss() - - features = self._get_features().to(self.device) - labels = self.dataset.graph_data.ndata['label'].to(self.device) - train_mask = self.dataset.graph_data.ndata['train_mask'].to(self.device) - val_mask = getattr(self.dataset.graph_data.ndata, "val_mask", None) - if val_mask is None: - val_mask = self.dataset.graph_data.ndata['test_mask'] - val_mask = val_mask.to(self.device) - - for epoch in range(epochs): - model.train() - logits = model(self.graph_data.to(self.device), features) - log_probs = F.log_softmax(logits, dim=1) - loss = loss_fn(log_probs[train_mask], labels[train_mask]) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - if (epoch + 1) % 10 == 0 or epoch == 0: - model.eval() - with torch.no_grad(): - val_logits = model(self.graph_data.to(self.device), features) - val_log_probs = F.log_softmax(val_logits, dim=1) - val_pred = val_log_probs[val_mask].max(1)[1] - val_acc = (val_pred == labels[val_mask]).float().mean().item() - print(f"Epoch {epoch + 1}: Loss={loss.item():.4f} | Val Acc={val_acc:.4f}") - - return model - - def _load_model(self, model_path): - model = GCN( - in_feats=self.dataset.feature_number, - hidden_feats=16, - out_feats=self.dataset.label_number - ) - model.load_state_dict(torch.load(model_path)) - return model - - def _generate_fingerprints(self, model, mode='transductive', knowledge='full', k=5, **kwargs): - """ - Wrapper for fingerprint generation based on mode and knowledge level. - Returns: - List of fingerprints - """ - if mode == 'transductive': - generator = TransductiveFingerprintGenerator( - model=model, - dataset=self.dataset, - candidate_fraction=kwargs.get('candidate_fraction', 1.0), - random_seed=kwargs.get('random_seed', None), - device=self.device, - randomize=kwargs.get('randomize', True), - ) - fingerprints = generator.generate_fingerprints(k=k, method=knowledge) - - unified_fingerprints = [(self.graph_data, node_id, label) for (node_id, label) in fingerprints] - - elif mode == 'inductive': - generator = InductiveFingerprintGenerator( - model=model, - dataset=self.dataset, - shadow_graph=self.dataset.graph_data, - knowledge=knowledge, - candidate_fraction=kwargs.get('candidate_fraction', 0.3), - num_fingerprints=k, - randomize=kwargs.get('randomize', True), - random_seed=kwargs.get('random_seed', None), - device=self.device, - perturb_fingerprints=kwargs.get('perturb_fingerprints', False), - perturb_budget=kwargs.get('perturb_budget', 5), - ) - fingerprints = generator.generate_fingerprints(method=knowledge) - if kwargs.get('perturb_fingerprints', False): - for i, (graph, node_idx, label) in enumerate(fingerprints): - generator.shadow_graph = graph - generator.greedy_edge_perturbation( - node_idx=node_idx, - perturb_budget=kwargs.get('perturb_budget', 5), - knowledge=knowledge - ) - fingerprints[i] = (generator.shadow_graph, node_idx, label) - - unified_fingerprints = fingerprints - - else: - raise ValueError("Unknown fingerprinting mode. Use 'transductive' or 'inductive'.") - - return unified_fingerprints - - def _evaluate_fingerprints(self, model, fingerprints): - """ - Checks if fingerprinted nodes have changed labels under the given model. - - Args: - model: The model to evaluate. - fingerprints: List of (graph, node_id, label) tuples. - - Returns: - results: { - 'flipped': List[Tuple[node_id, old_label, new_label]], - 'flip_rate': float - } - """ - model.eval() - flipped = [] - - with torch.no_grad(): - for graph, node_id, expected_label in fingerprints: - x = graph.ndata['feat'] if hasattr(graph, 'ndata') else graph.x - logits = model(graph.to(self.device), x.to(self.device)) - pred = logits[node_id].argmax().item() - if pred != expected_label: - flipped.append((node_id, expected_label, pred)) - - return { - 'flipped': flipped, - 'flip_rate': len(flipped) / len(fingerprints) if fingerprints else 0.0 - } - - def _run_attack(self, model, attack_type='mettack', knowledge='full', **kwargs): - """ - Run the specified attack on the model. - Returns: - poisoned_model: torch.nn.Module - metadata: dict with info about the attack - """ - if attack_type == 'bitflip': - bit = kwargs.get('bit', 30) - bfa_variant = kwargs.get('bfa_variant', 'BFA') - attacker = BitFlipAttack(model, attack_type=bfa_variant, bit=bit) - attack_info = attacker.apply() - return model, attack_info - - elif attack_type == 'random': - perturbed_graph = self._random_edge_addition_poisoning( - node_fraction=kwargs.get('node_fraction', 0.1), - edges_per_node=kwargs.get('edges_per_node', 5), - random_seed=kwargs.get('random_seed', None), - ) - poisoned_model = self._retrain_poisoned_model( - poisoned_graph=perturbed_graph, - epochs=kwargs.get('epochs', 200), - ) - return poisoned_model, {'type': 'random_poison', 'graph': perturbed_graph} - - elif attack_type == 'mettack': - num_edges = self.graph_data.num_edges() - poison_frac = kwargs.get('poison_frac', 0.05) - n_perturbations = int(poison_frac * num_edges) - - helper = MettackHelper( - graph=self.graph_data, - features=self._get_features(), - labels=self.dataset.labels, - train_mask=self.dataset.train_mask, - val_mask=getattr(self.dataset, 'val_mask', None), - test_mask=self.dataset.test_mask, - n_perturbations=n_perturbations, - device=self.device, - max_perturbations=kwargs.get('max_perturbations', 50), - surrogate_epochs=kwargs.get('surrogate_epochs', 30), - candidate_sample_size=kwargs.get('candidate_sample_size', 20), - ) - poisoned_graph, attack_metrics = helper.run() - poisoned_model = self._retrain_poisoned_model( - poisoned_graph=poisoned_graph, - epochs=kwargs.get('epochs', 200), - ) - return poisoned_model, {'type': 'mettack', 'metrics': attack_metrics, 'graph': poisoned_graph} - - else: - raise ValueError(f"Unsupported attack_type: {attack_type}") - - def _random_edge_addition_poisoning(self, node_fraction=0.1, edges_per_node=5, random_seed=None): - """ - Poison a fraction of nodes by adding random edges. - - Args: - dataset: Dataset object (DGL-based) - node_fraction: Fraction of nodes to poison (e.g., 0.1 = 10%) - edges_per_node: Number of random edges to add per poisoned node - random_seed: Optional seed - - Returns: - poisoned_graph: DGLGraph - """ - if random_seed is not None: - random.seed(random_seed) - torch.manual_seed(random_seed) - - poisoned_graph = copy.deepcopy(self.graph_data) - num_nodes = poisoned_graph.num_nodes() - num_poisoned_nodes = int(node_fraction * num_nodes) - poisoned_nodes = random.sample(range(num_nodes), num_poisoned_nodes) - - new_edges = [] - - for src in poisoned_nodes: - for _ in range(edges_per_node): - dst = random.randint(0, num_nodes - 1) - if src != dst and \ - not poisoned_graph.has_edges_between(src, dst) and \ - not poisoned_graph.has_edges_between(dst, src): - new_edges.append((src, dst)) - new_edges.append((dst, src)) - - if new_edges: - src, dst = zip(*new_edges) - poisoned_graph.add_edges(src, dst) - - return poisoned_graph - - def _retrain_poisoned_model(self, poisoned_graph, epochs=200): - """ - Retrain target GCN using the poisoned graph structure. - - Args: - dataset: Original Dataset object (provides features, labels, masks) - poisoned_graph: DGLGraph (with new random edges added) - defense_class: The defense class to use for model training (e.g., QueryBasedVerificationDefense) - device: 'cpu' or 'cuda' - - Returns: - model: Trained GCN model - """ - dataset_poisoned = copy.deepcopy(self.dataset) - dataset_poisoned.graph_data = poisoned_graph - - defense = QueryBasedVerificationDefense(dataset=dataset_poisoned, defense_ratio=0.1) - model = defense._train_target_model(epochs=epochs) - return model - - def _evaluate_accuracy(self, model, dataset): - """ - Evaluates test accuracy of the given model on the dataset. - - Args: - model: Trained GCN model - dataset: Dataset object (provides features, labels, test_mask, graph) - - Returns: - accuracy: float (test accuracy, 0-1) - """ - model.eval() - features = self._get_features().to(self.device) - labels = dataset.graph_data.ndata['label'].to(self.device) - test_mask = dataset.graph_data.ndata['test_mask'] - - with torch.no_grad(): - logits = model(dataset.graph_data.to(self.device), features) - pred = logits.argmax(dim=1) - correct = (pred[test_mask] == labels[test_mask]).float() - accuracy = correct.sum().item() / test_mask.sum().item() - return accuracy - - def run_full_pipeline(self, attack_type='random', mode='transductive', knowledge='full', k=5, trials=1, **kwargs): - """ - Runs the full fingerprinting + attack + evaluation pipeline. - - Parameters: - attack_type: 'random', 'bitflip', or 'mettack' - mode: 'transductive' or 'inductive' - knowledge: 'full' or 'limited' - k: number of fingerprints - trials: number of repeated trials - kwargs: extra params for attack or fingerprinting - - Prints per-trial results and summary statistics. - """ - flip_rates = [] - acc_drops = [] - - for trial in range(trials): - print(f"\n=== Trial {trial + 1}/{trials} ===") - - model_clean = self._train_target_model() - acc_clean = self._evaluate_accuracy(model_clean, self.dataset) - print(f"Clean model accuracy: {acc_clean:.4f}") - - fingerprints = self._generate_fingerprints(model_clean, mode=mode, knowledge=knowledge, k=k, **kwargs) - - model_poisoned, attack_meta = self._run_attack(model_clean, attack_type=attack_type, knowledge=knowledge, - **kwargs) - acc_poisoned = self._evaluate_accuracy(model_poisoned, self.dataset) - print(f"Poisoned model accuracy: {acc_poisoned:.4f}") - - eval_result = self._evaluate_fingerprints(model_poisoned, fingerprints) - flip_rate = eval_result['flip_rate'] - print(f"Fingerprint flip rate: {flip_rate:.4f}") - for (nid, old, new) in eval_result['flipped']: - print(f" Node {nid}: {old} → {new}") - - flip_rates.append(flip_rate) - acc_drops.append(acc_clean - acc_poisoned) - - print("\n=== Summary ===") - print(f"Avg Accuracy Drop: {np.mean(acc_drops):.4f}") - print(f"Avg Fingerprint Flip Rate: {np.mean(flip_rates):.4f}") - - -class TransductiveFingerprintGenerator: - def __init__(self, model, dataset, candidate_fraction=0.3, random_seed=None, device='cpu', randomize=True): - self.device = torch.device(device) - self.model = model.to(self.device) - self.dataset = dataset - self.graph_data = dataset.graph_data - self.candidate_fraction = candidate_fraction - self.random_seed = random_seed - self.randomize = randomize - - def _get_features(self): - """Backend-agnostic feature getter (DGL or PyG).""" - return self.graph_data.ndata['feat'] if hasattr(self.graph_data, 'ndata') else self.graph_data.x - - def get_candidate_nodes(self): - """Randomly sample a subset of nodes as candidates.""" - all_nodes = torch.arange(self.graph_data.num_nodes()) - num_candidates = max(1, int(len(all_nodes) * self.candidate_fraction)) - - if self.randomize and self.candidate_fraction < 1.0: - generator = torch.Generator(device=self.device) - if self.random_seed is not None: - generator.manual_seed(self.random_seed) - idx = torch.randperm(len(all_nodes), generator=generator)[:num_candidates] - return all_nodes[idx] - return all_nodes - - def compute_fingerprint_scores_full(self, candidate_nodes): - """Full-knowledge fingerprint scores (gradient-based).""" - self.model.eval() - scores = [] - x = self._get_features().to(self.device) - logits = self.model(self.graph_data.to(self.device), x) - - for node in candidate_nodes: - self.model.zero_grad() - logit = logits[node] - label = logit.argmax().item() - loss = F.cross_entropy(logit.unsqueeze(0), torch.tensor([label], device=self.device)) - loss.backward(retain_graph=True) - grad_norm = sum((p.grad ** 2).sum().item() for p in self.model.parameters() if p.grad is not None) - scores.append(grad_norm) - - return torch.tensor(scores, device=self.device) - - def compute_fingerprint_scores_limited(self, candidate_nodes): - """Limited-knowledge fingerprint scores (confidence margin).""" - self.model.eval() - x = self._get_features().to(self.device) - with torch.no_grad(): - logits = self.model(self.graph_data.to(self.device), x) - probs = F.softmax(logits, dim=1) - labels = probs.argmax(dim=1) - scores = 1.0 - probs[candidate_nodes, labels[candidate_nodes]] - return scores - - def select_top_fingerprints(self, scores, candidate_nodes, k, method='full'): - """Selects top-k fingerprint nodes after filtering out extreme score outliers.""" - q = 0.99 if method == 'full' else 1.0 - threshold = torch.quantile(scores, q) - mask = scores <= threshold - - filtered_scores = scores[mask] - filtered_candidates = candidate_nodes[mask] - - if filtered_scores.size(0) < k: - k = filtered_scores.size(0) - - topk = torch.topk(filtered_scores, k) - return filtered_candidates[topk.indices], topk.values - - def generate_fingerprints(self, k=5, method='full'): - candidate_nodes = self.get_candidate_nodes().to(self.device) - x = self._get_features().to(self.device) - - with torch.no_grad(): - logits = self.model(self.graph_data.to(self.device), x) - labels = logits.argmax(dim=1) - - if method == 'full': - scores = self.compute_fingerprint_scores_full(candidate_nodes) - elif method == 'limited': - scores = self.compute_fingerprint_scores_limited(candidate_nodes) - else: - raise ValueError("method must be 'full' or 'limited'") - - class_to_candidates = {} - for i, node in enumerate(candidate_nodes): - cls = int(labels[node]) - class_to_candidates.setdefault(cls, []).append((node.item(), scores[i].item())) - - rng = random.Random(self.random_seed) - class_list = list(class_to_candidates.keys()) - rng.shuffle(class_list) - - fingerprints = [] - for cls in class_list: - class_nodes = sorted(class_to_candidates[cls], key=lambda x: x[1], reverse=True) - top_node = class_nodes[0][0] - fingerprints.append((top_node, cls)) - if len(fingerprints) >= k: - break - - if len(fingerprints) < k: - fingerprint_nodes, _ = self.select_top_fingerprints(scores, candidate_nodes, k, method=method) - fingerprints = [(int(n), int(labels[n])) for n in fingerprint_nodes] - - return fingerprints - - -class InductiveFingerprintGenerator: - def __init__(self, model, dataset, shadow_graph=None, knowledge='limited', - candidate_fraction=0.3, num_fingerprints=5, - randomize=True, random_seed=None, device='cpu', - perturb_fingerprints=False, perturb_budget=5): - self.device = torch.device(device) - self.model = model.to(self.device) - self.dataset = dataset - self.shadow_graph = shadow_graph if shadow_graph is not None else dataset.graph_data - self.knowledge = knowledge - self.candidate_fraction = candidate_fraction - self.num_fingerprints = num_fingerprints - self.randomize = randomize - self.random_seed = random_seed - self.perturb_fingerprints = perturb_fingerprints - self.perturb_budget = perturb_budget - - if self.random_seed is not None: - torch.manual_seed(self.random_seed) - random.seed(self.random_seed) - - def _get_features(self): - return self.shadow_graph.ndata['feat'] if hasattr(self.shadow_graph, 'ndata') else self.shadow_graph.x - - def get_candidate_nodes(self): - all_nodes = torch.arange(self.shadow_graph.num_nodes()) - num_candidates = max(1, int(len(all_nodes) * self.candidate_fraction)) - - if self.randomize and self.candidate_fraction < 1.0: - generator = torch.Generator(device=self.device) - if self.random_seed is not None: - generator.manual_seed(self.random_seed) - idx = torch.randperm(len(all_nodes), generator=generator)[:num_candidates] - candidates = all_nodes[idx] - else: - candidates = all_nodes - - return candidates - - def compute_fingerprint_score(self, node_idx, graph_override=None): - """ - Computes the fingerprint score for a given node according to knowledge mode. - If graph_override is provided, scoring is done on that graph instead of shadow_graph. - """ - graph = graph_override if graph_override is not None else self.shadow_graph - x = (graph.ndata['feat'] if hasattr(graph, 'ndata') else graph.x).to(self.device) - self.model.eval() - - if self.knowledge == 'limited': - with torch.no_grad(): - logits = self.model(graph.to(self.device), x) - probs = torch.softmax(logits[node_idx], dim=0) - pred_class = probs.argmax().item() - return 1 - probs[pred_class].item() - - elif self.knowledge == 'full': - x.requires_grad_(True) - logits = self.model(graph.to(self.device), x) - pred = logits[node_idx] - label = pred.argmax().item() - - self.model.zero_grad() - loss = torch.nn.functional.nll_loss( - torch.log_softmax(pred.unsqueeze(0), dim=1), - torch.tensor([label], device=self.device) - ) - loss.backward(retain_graph=True) - - grad = x.grad[node_idx] - grad_norm_sq = (grad ** 2).sum().item() - x.requires_grad_(False) - x.grad = None - return grad_norm_sq - else: - raise ValueError("knowledge must be 'limited' or 'full'") - - def generate_fingerprint_nodes(self): - """ - Step 3: Identifies and returns the top-k (num_fingerprints) nodes with the highest - fingerprint scores from the candidate set. (Section 4.2.2) - - Returns: - List[int]: Indices of selected fingerprint nodes. - """ - candidates = self.get_candidate_nodes() - scores = [] - for idx in candidates: - score = self.compute_fingerprint_score(idx) - scores.append((score, int(idx))) - - scores.sort(reverse=True) - selected = [idx for (_, idx) in scores[:self.num_fingerprints]] - return selected - - def save_fingerprint_tuples(self, node_indices): - self.model.eval() - x = self._get_features().to(self.device) - with torch.no_grad(): - logits = self.model(self.shadow_graph.to(self.device), x) - labels = logits.argmax(dim=1).cpu().numpy() - return [(self.shadow_graph, int(idx), int(labels[idx])) for idx in node_indices] - - def generate_fingerprints(self, method='full'): - """ - Generate inductive fingerprints for model watermarking. - - Parameters: - method (str): 'full' for gradient-based or 'limited' for output-based - - Returns: - List of fingerprints - """ - if method == 'full': - return self._generate_full() - elif method == 'limited': - return self._generate_limited() - else: - raise ValueError(f"Invalid fingerprinting method: '{method}'") - - def _generate_full(self): - """ - Implements full knowledge fingerprint generation (gradient-based). - Based on Section 4.2.1 and 5.2 of Wu et al. (2023). - """ - self.knowledge = 'full' - print("[Fingerprint] Generating FULL knowledge fingerprints...") - fingerprint_nodes = self.generate_fingerprint_nodes() - - if self.perturb_fingerprints: - print("[Fingerprint] Applying greedy feature perturbation (FULL)...") - self.greedy_perturb_fingerprints(fingerprint_nodes) - - return self.save_fingerprint_tuples(fingerprint_nodes) - - def _generate_limited(self): - """ - Implements limited knowledge fingerprint generation (output-based). - Based on Section 4.2.2 and 5.2 of Wu et al. (2023). - """ - self.knowledge = 'limited' - print("[Fingerprint] Generating LIMITED knowledge fingerprints...") - fingerprint_nodes = self.generate_fingerprint_nodes() - - if self.perturb_fingerprints: - print("[Fingerprint] Applying greedy feature perturbation (LIMITED)...") - self.greedy_perturb_fingerprints(fingerprint_nodes) - - return self.save_fingerprint_tuples(fingerprint_nodes) - - def greedy_perturb_fingerprints(self, node_indices): - """ - Greedily perturbs each fingerprint node's features (not edges) to increase its - fingerprint score, without changing the predicted label. - - - For each node, for each feature dimension: - - Add or subtract a small epsilon. - - Accept change if predicted label stays the same and fingerprint score increases. - - Stop after perturb_budget attempts or no improvement. - - Returns: - List[int]: Indices of perturbed fingerprint nodes (features in shadow_graph are updated in-place). - """ - epsilon = 0.01 - features = self._get_features().clone().detach().to(self.device) - self.shadow_graph = self.shadow_graph.to(self.device) - - for idx in node_indices: - num_tries = 0 - improved = True - while num_tries < self.perturb_budget and improved: - improved = False - current_score = self.compute_fingerprint_score(idx, graph_override=self.shadow_graph) - - self.model.eval() - with torch.no_grad(): - logits = self.model(self.shadow_graph, features) - pred_label = logits[idx].argmax().item() - - original_features = features[idx].clone() - for dim in range(features.shape[1]): - for direction in [+1, -1]: - features[idx][dim] += direction * epsilon - - self.model.eval() - with torch.no_grad(): - logits_new = self.model(self.shadow_graph, features) - new_pred_label = logits_new[idx].argmax().item() - new_score = self.compute_fingerprint_score(idx, graph_override=self.shadow_graph) - - if new_pred_label == pred_label and new_score > current_score: - current_score = new_score - improved = True - num_tries += 1 - else: - features[idx][dim] = original_features[dim] - - if num_tries >= self.perturb_budget: - break - if num_tries >= self.perturb_budget: - break - - if hasattr(self.shadow_graph, 'ndata'): - self.shadow_graph.ndata['feat'] = features - else: - self.shadow_graph.x = features - return node_indices - - def greedy_edge_perturbation(self, node_idx, perturb_budget=5, knowledge='full'): - """ - Dispatch to greedy edge perturbation strategy based on verifier knowledge level. - - Args: - node_idx (int): Fingerprint node index. - perturb_budget (int): Number of edge perturbations allowed. - knowledge (str): 'full' or 'limited' - """ - if knowledge == 'full': - self._greedy_edge_perturbation_f(node_idx, perturb_budget) - elif knowledge == 'limited': - self._greedy_edge_perturbation_l(node_idx, perturb_budget) - else: - raise ValueError("knowledge must be 'full' or 'limited'") - - def _greedy_edge_perturbation_f(self, node_idx, perturb_budget): - """ - Full knowledge edge perturbation (Inductive-F). - Increases fingerprint score using model gradients while preserving prediction. - """ - - g_nx = to_networkx(self.shadow_graph.to('cpu'), to_undirected=True) - x = self._get_features().to(self.device) - self.model.eval() - - with torch.no_grad(): - original_pred = self.model(self.shadow_graph.to(self.device), x)[node_idx].argmax().item() - - def score_fn(modified_graph): - return self.compute_fingerprint_score(node_idx, graph_override=modified_graph) - - neighbors = list(g_nx.neighbors(node_idx)) - non_neighbors = list(set(range(self.shadow_graph.num_nodes())) - set(neighbors) - {node_idx}) - - applied = 0 - while applied < perturb_budget: - best_delta = 0 - best_graph = None - best_action = None - - for nbr in non_neighbors: - temp_g = copy.deepcopy(g_nx) - temp_g.add_edge(node_idx, nbr) - g_temp = from_networkx(temp_g).to(self.device) - with torch.no_grad(): - pred = self.model(g_temp, x)[node_idx].argmax().item() - if pred != original_pred: - continue - delta = score_fn(g_temp) - score_fn(self.shadow_graph) - if delta > best_delta: - best_delta = delta - best_graph = g_temp - best_action = ('add', nbr) - - for nbr in neighbors: - temp_g = copy.deepcopy(g_nx) - if temp_g.has_edge(node_idx, nbr): - temp_g.remove_edge(node_idx, nbr) - g_temp = from_networkx(temp_g).to(self.device) - with torch.no_grad(): - pred = self.model(g_temp, x)[node_idx].argmax().item() - if pred != original_pred: - continue - delta = score_fn(g_temp) - score_fn(self.shadow_graph) - if delta > best_delta: - best_delta = delta - best_graph = g_temp - best_action = ('remove', nbr) - - if best_graph is None: - break - self.shadow_graph = best_graph - g_nx = to_networkx(best_graph.to('cpu'), to_undirected=True) - - if best_action[0] == 'add': - non_neighbors.remove(best_action[1]) - neighbors.append(best_action[1]) - else: - neighbors.remove(best_action[1]) - non_neighbors.append(best_action[1]) - - applied += 1 - - def _greedy_edge_perturbation_l(self, node_idx, perturb_budget): - """ - Limited knowledge edge perturbation (Inductive-L). - Uses confidence margin (1 - confidence) as proxy for fingerprint sensitivity. - """ - - g_nx = to_networkx(self.shadow_graph.to('cpu'), to_undirected=True) - x = self._get_features().to(self.device) - self.model.eval() - - with torch.no_grad(): - logits = self.model(self.shadow_graph.to(self.device), x) - original_pred = logits[node_idx].argmax().item() - original_conf = F.softmax(logits[node_idx], dim=0)[original_pred].item() - original_score = 1 - original_conf - - def score_fn(modified_graph): - with torch.no_grad(): - logits = self.model(modified_graph.to(self.device), x) - pred = logits[node_idx].argmax().item() - if pred != original_pred: - return -1 - conf = F.softmax(logits[node_idx], dim=0)[pred].item() - return 1 - conf - - neighbors = list(g_nx.neighbors(node_idx)) - non_neighbors = list(set(range(self.shadow_graph.num_nodes())) - set(neighbors) - {node_idx}) - - applied = 0 - while applied < perturb_budget: - best_delta = 0 - best_graph = None - best_action = None - - for nbr in non_neighbors: - temp_g = copy.deepcopy(g_nx) - temp_g.add_edge(node_idx, nbr) - g_temp = from_networkx(temp_g).to(self.device) - new_score = score_fn(g_temp) - delta = new_score - original_score - if new_score >= 0 and delta > best_delta: - best_delta = delta - best_graph = g_temp - best_action = ('add', nbr) - - for nbr in neighbors: - temp_g = copy.deepcopy(g_nx) - if temp_g.has_edge(node_idx, nbr): - temp_g.remove_edge(node_idx, nbr) - g_temp = from_networkx(temp_g).to(self.device) - new_score = score_fn(g_temp) - delta = new_score - original_score - if new_score >= 0 and delta > best_delta: - best_delta = delta - best_graph = g_temp - best_action = ('remove', nbr) - - if best_graph is None: - break - self.shadow_graph = best_graph - g_nx = to_networkx(best_graph.to('cpu'), to_undirected=True) - - if best_action[0] == 'add': - non_neighbors.remove(best_action[1]) - neighbors.append(best_action[1]) - else: - neighbors.remove(best_action[1]) - non_neighbors.append(best_action[1]) - - applied += 1 - - -class BitFlipAttack: - def __init__(self, model, attack_type='random', bit=0): - self.model = model - self.attack_type = attack_type - self.bit = bit - - def _get_target_params(self): - params = [p for p in self.model.parameters() if p.requires_grad and p.numel() > 0] - if self.attack_type in ['random', 'BFA']: - return params - elif self.attack_type == 'BFA-F': - return [params[0]] - elif self.attack_type == 'BFA-L': - return [params[-1]] - else: - raise ValueError(f"Unknown attack_type {self.attack_type}") - - def _true_bit_flip(self, tensor, index=None, bit=0): - a = tensor.detach().cpu().numpy().copy() - flat = a.ravel() - if index is None: - index = np.random.randint(0, flat.size) - old_val = flat[index] - int_view = np.frombuffer(flat[index].tobytes(), dtype=np.uint32)[0] - int_view ^= (1 << bit) - new_val = np.frombuffer(np.uint32(int_view).tobytes(), dtype=np.float32)[0] - flat[index] = new_val - a = flat.reshape(a.shape) - tensor.data = torch.from_numpy(a).to(tensor.device) - return old_val, new_val, index - - def apply(self): - params = self._get_target_params() - with torch.no_grad(): - layer_idx = random.randrange(len(params)) - param = params[layer_idx] - idx = random.randrange(param.numel()) - old_val, new_val, actual_idx = self._true_bit_flip(param, index=idx, bit=self.bit) - return { - 'layer': layer_idx, - 'param_idx': actual_idx, - 'old_val': old_val, - 'new_val': new_val, - 'bit': self.bit, - 'attack_type': self.attack_type - } - - -class MettackHelper: - def __init__(self, graph, features, labels, train_mask, val_mask, test_mask, - n_perturbations=5, device='cpu', max_perturbations=50, - surrogate_epochs=30, candidate_sample_size=20): - self.device = device - self.graph = dgl.add_self_loop(graph).to(self.device) - self.features = features.to(self.device) - self.labels = labels.to(self.device) - self.train_mask = train_mask.to(self.device) - self.surrogate_epochs = surrogate_epochs - self.candidate_sample_size = candidate_sample_size - if val_mask is not None: - self.val_mask = val_mask.to(self.device) - else: - self.val_mask = self._create_val_mask_from_train(train_mask).to(self.device) - - self.test_mask = test_mask.to(self.device) - - self.n_perturbations = n_perturbations - - in_feats = features.shape[1] - n_classes = int(labels.max().item()) + 1 - self.surrogate = GCN(in_feats, n_classes).to(self.device) - - torch.manual_seed(42) - np.random.seed(42) - - self.modified_edges = set() - - original_graph_no_self_loop = dgl.remove_self_loop(graph) - self.original_edges = set(zip(original_graph_no_self_loop.edges()[0].cpu().numpy(), - original_graph_no_self_loop.edges()[1].cpu().numpy())) - - self.candidate_edges = self._get_candidate_edges() - - def _create_val_mask_from_train(self, train_mask): - """ - Create a validation mask by taking a subset of training nodes. - This is needed when the dataset doesn't provide a validation mask. - """ - train_indices = torch.where(train_mask)[0] - n_val = min(500, len(train_indices) // 4) - - perm = torch.randperm(len(train_indices)) - val_indices = train_indices[perm[:n_val]] - - val_mask = torch.zeros_like(train_mask, dtype=torch.bool) - val_mask[val_indices] = True - - self.train_mask = train_mask.clone() - self.train_mask[val_indices] = False - - return val_mask - - def run(self): - """ - Main entrypoint to run the Mettack algorithm. - Returns: - poisoned_graph (DGLGraph): The perturbed graph with edges changed. - metrics (dict): Metrics for before/after attack, for evaluation. - """ - print("Starting Mettack attack...") - - print("Training surrogate model...") - self._train_surrogate() - - print("Applying structure attack...") - poisoned_graph = self._apply_structure_attack() - - print("Evaluating attack results...") - metrics = self._evaluate(poisoned_graph) - - return poisoned_graph, metrics - - def _train_surrogate(self): - """ - Trains a surrogate GCN on the clean graph. - (Matches Wu et al., Section 6.1) - """ - optimizer = optim.Adam(self.surrogate.parameters(), lr=0.01, weight_decay=5e-4) - self.surrogate.train() - - for epoch in range(self.surrogate_epochs): - optimizer.zero_grad() - logits = self.surrogate(self.graph, self.features) - loss = F.cross_entropy(logits[self.train_mask], self.labels[self.train_mask]) - loss.backward() - optimizer.step() - - if epoch % 50 == 0: - self.surrogate.eval() - with torch.no_grad(): - val_logits = self.surrogate(self.graph, self.features) - val_acc = self._compute_accuracy(val_logits[self.val_mask], - self.labels[self.val_mask]) - print(f"Surrogate epoch {epoch}: Val Acc = {val_acc:.4f}") - self.surrogate.train() - - def _apply_structure_attack(self): - """ - Runs the Mettack structure perturbation loop (bi-level optimization). - - At each step, modify the adjacency matrix (add/remove an edge). - - Select the perturbation that maximizes surrogate model loss on the validation nodes. - - Repeat up to n_perturbations times. - Returns a new DGLGraph with edges modified. - (See Appendix A.2 in Wu et al.) - """ - current_graph = copy.deepcopy(self.graph) - perturbed_edges = set() - - for step in range(self.n_perturbations): - print(f"Perturbation step {step + 1}/{self.n_perturbations}") - - best_edge = None - best_loss = -float('inf') - best_action = None - - candidate_sample = np.random.choice(len(self.candidate_edges), - min(self.candidate_sample_size, len(self.candidate_edges)), - replace=False) - - for idx in tqdm(candidate_sample, desc="Evaluating candidates"): - edge = self.candidate_edges[idx] - - if edge in perturbed_edges or (edge[1], edge[0]) in perturbed_edges: - continue - - for action in ['add', 'remove']: - if action == 'add' and edge in self.original_edges: - continue - if action == 'remove' and edge not in self.original_edges: - continue - - temp_graph = self._apply_single_perturbation(current_graph, edge, action) - - attack_loss = self._compute_attack_loss(temp_graph) - - if attack_loss > best_loss: - best_loss = attack_loss - best_edge = edge - best_action = action - - if best_edge is not None: - current_graph = self._apply_single_perturbation(current_graph, best_edge, best_action) - perturbed_edges.add(best_edge) - self.modified_edges.add((best_edge, best_action)) - print(f"Applied {best_action} edge {best_edge} with loss increase: {best_loss:.4f}") - else: - print("No beneficial perturbation found, stopping early.") - break - - return current_graph - - def _get_candidate_edges(self): - """ - Generate candidate edges for perturbation. - Includes both existing edges (for removal) and non-existing edges (for addition). - """ - n_nodes = self.graph.num_nodes() - - all_possible_edges = [] - for i in range(n_nodes): - for j in range(i + 1, n_nodes): - all_possible_edges.append((i, j)) - - return all_possible_edges[:min(10000, len(all_possible_edges))] - - def _apply_single_perturbation(self, graph, edge, action): - """ - Apply a single edge perturbation (add or remove) to the graph. - """ - temp_graph = copy.deepcopy(graph) - - if action == 'add': - temp_graph.add_edges([edge[0], edge[1]], [edge[1], edge[0]]) - elif action == 'remove': - src, dst = temp_graph.edges() - edge_ids = [] - - for i, (s, d) in enumerate(zip(src.cpu().numpy(), dst.cpu().numpy())): - if (s == edge[0] and d == edge[1]) or (s == edge[1] and d == edge[0]): - edge_ids.append(i) - - if edge_ids: - temp_graph.remove_edges(edge_ids) - - temp_graph = dgl.add_self_loop(temp_graph) - - return temp_graph - - def _compute_attack_loss(self, perturbed_graph): - """ - Compute the attack loss on a perturbed graph. - This measures how much the surrogate model's performance degrades. - Uses proper bi-level optimization as in the original Mettack paper. - """ - - temp_surrogate = copy.deepcopy(self.surrogate) - temp_surrogate.train() - - optimizer = optim.Adam(temp_surrogate.parameters(), lr=0.01) - - for _ in range(5): - optimizer.zero_grad() - logits = temp_surrogate(perturbed_graph, self.features) - loss = F.cross_entropy(logits[self.train_mask], self.labels[self.train_mask]) - loss.backward() - optimizer.step() - - temp_surrogate.eval() - with torch.no_grad(): - val_logits = temp_surrogate(perturbed_graph, self.features) - val_loss = F.cross_entropy(val_logits[self.val_mask], self.labels[self.val_mask]) - - return val_loss.item() - - def _evaluate(self, poisoned_graph): - """ - Evaluates GCN accuracy before/after poisoning, etc. - """ - metrics = {} - - self.surrogate.eval() - with torch.no_grad(): - clean_logits = self.surrogate(self.graph, self.features) - clean_acc = self._compute_accuracy(clean_logits[self.test_mask], - self.labels[self.test_mask]) - metrics['clean_test_acc'] = clean_acc - - poisoned_model = GCN(self.features.shape[1], - int(self.labels.max().item()) + 1).to(self.device) - optimizer = optim.Adam(poisoned_model.parameters(), lr=0.01, weight_decay=5e-4) - - poisoned_model.train() - for epoch in range(200): - optimizer.zero_grad() - logits = poisoned_model(poisoned_graph, self.features) - loss = F.cross_entropy(logits[self.train_mask], self.labels[self.train_mask]) - loss.backward() - optimizer.step() - - poisoned_model.eval() - with torch.no_grad(): - poisoned_logits = poisoned_model(poisoned_graph, self.features) - poisoned_acc = self._compute_accuracy(poisoned_logits[self.test_mask], - self.labels[self.test_mask]) - metrics['poisoned_test_acc'] = poisoned_acc - - metrics['accuracy_drop'] = clean_acc - poisoned_acc - metrics['num_perturbations'] = len(self.modified_edges) - - return metrics - - def _compute_accuracy(self, logits, labels): - """Helper function to compute accuracy.""" - _, predicted = torch.max(logits, 1) - correct = (predicted == labels).sum().item() - return correct / len(labels) diff --git a/pyhazard/models/defense/RandomWM.py b/pyhazard/models/defense/RandomWM.py deleted file mode 100644 index 23d916c..0000000 --- a/pyhazard/models/defense/RandomWM.py +++ /dev/null @@ -1,598 +0,0 @@ -import importlib -import time - -import dgl -import torch -import numpy as np -import torch.nn.functional as F -from tqdm import tqdm -from torch.utils.data import DataLoader -from torch_geometric.utils import erdos_renyi_graph -from dgl.dataloading import NeighborSampler, NodeCollator - -from pyhazard.models.nn import GraphSAGE -from pyhazard.models.defense.base import BaseDefense -from pyhazard.utils.metrics import DefenseCompMetric, DefenseMetric - - -class RandomWM(BaseDefense): - """ - A flexible defense implementation using watermarking to protect against - model extraction attacks on graph neural networks. - - This class combines the functionalities from the original watermark.py: - - Generating watermark graphs - - Training models on original and watermark graphs - - Merging graphs for testing - - Evaluating effectiveness against attacks - - Dynamic selection of attack methods - """ - supported_api_types = {"dgl"} - - def __init__(self, dataset, defense_ratio=0.1, wm_node=50, pr=0.2, pg=0.2, attack_name=None): - """ - Initialize the custom defense. - - Parameters - ---------- - defense_ratio : float - Defense strength (0-1): used to determine the number of watermark nodes and the attack node sampling scale - wm_node : Optional[int] - If specified, a fixed number of watermark nodes is used; otherwise, a dynamic calculation is performed based on defense_ratio * num_nodes - pr : float - Bernoulli probability of the watermark feature being 1 - pg : float - Edge probability of the watermark graph - attack_name : Optional[str] - Attack class name (from models.attack) - """ - super().__init__(dataset, defense_ratio) - self.defense_ratio = defense_ratio - self.attack_name = attack_name or "ModelExtractionAttack0" - self.dataset = dataset - self.graph = dataset.graph_data - - # Extract dataset properties - self.node_number = dataset.num_nodes - self.feature_number = dataset.num_features - self.label_number = dataset.num_classes - self.attack_node_number = int(self.node_number * defense_ratio) - - # Watermark parameters - self.wm_node = int(wm_node) if wm_node is not None else max(10, int(dataset.num_nodes * defense_ratio)) - self.pr = pr - self.pg = pg - - # Extract features and labels - self.features = self.graph.ndata['feat'] - self.labels = self.graph.ndata['label'] - - # Extract masks - self.train_mask = self.graph.ndata['train_mask'] - self.test_mask = self.graph.ndata['test_mask'] - - # Move tensors to self.device - if self.device != 'cpu': - self.graph = self.graph.to(self.device) - self.features = self.features.to(self.device) - self.labels = self.labels.to(self.device) - self.train_mask = self.train_mask.to(self.device) - self.test_mask = self.test_mask.to(self.device) - - def _get_attack_class(self, attack_name): - """ - Dynamically import and return the specified attack class. - - Parameters - ---------- - attack_name : str - Name of the attack class to import - - Returns - ------- - class - The requested attack class - """ - try: - # Try to import from models.attack module - attack_module = importlib.import_module('models.attack') - attack_class = getattr(attack_module, attack_name) - return attack_class - except (ImportError, AttributeError) as e: - print(f"Error loading attack class '{attack_name}': {e}") - print("Falling back to ModelExtractionAttack0") - # Fallback to ModelExtractionAttack0 - attack_module = importlib.import_module('models.attack') - return getattr(attack_module, "ModelExtractionAttack0") - - def defend(self, attack_name=None): - """ - Execute the random watermark defense. - """ - metric_comp = DefenseCompMetric() - metric_comp.start() - print("====================Random Watermark Defense====================") - - # If model wasn't trained yet, train it - if not hasattr(self, 'model_trained'): - self.train_target_model(metric_comp) - - # Evaluate the defended model - preds = self.evaluate_model(self.defense_model, self.dataset) - inference_s = time.time() - wm_preds = self.verify_watermark(self.defense_model) - inference_e = time.time() - - # metric - metric = DefenseMetric() - metric.update(preds, self.labels[self.test_mask]) - wm_labels = self.watermark_graph.ndata['label'] - metric.update_wm(wm_preds, wm_labels) - metric_comp.end() - - print("====================Final Results====================") - res = metric.compute() - metric_comp.update(inference_defense_time=(inference_e - inference_s)) - res_comp = metric_comp.compute() - - return res, res_comp - - def train_target_model(self, metric_comp: DefenseCompMetric): - """Train the target model with watermark injection.""" - defense_s = time.time() - - # Training and watermark generation (defense mechanism) - self.defense_model = self._train_defense_model() - self.model_trained = True - - defense_e = time.time() - metric_comp.update(defense_time=(defense_e - defense_s)) - return self.defense_model - - def evaluate_model(self, model, dataset): - """Evaluate model performance on downstream task""" - model.eval() - - # Setup data loading - sampler = NeighborSampler([5, 5]) - test_nids = self.test_mask.nonzero(as_tuple=True)[0].to(self.device) - test_collator = NodeCollator(self.graph, test_nids, sampler) - test_dataloader = DataLoader( - test_collator.dataset, - batch_size=32, - shuffle=False, - collate_fn=test_collator.collate, - drop_last=False - ) - - all_preds = [] - with torch.no_grad(): - for _, _, blocks in test_dataloader: - blocks = [b.to(self.device) for b in blocks] - input_features = blocks[0].srcdata['feat'] - output_predictions = model(blocks, input_features) - pred = output_predictions.argmax(dim=1) - all_preds.append(pred) - - preds = torch.cat(all_preds, dim=0).cpu() - return preds - - def verify_watermark(self, model): - """Verify watermark success rate""" - model.eval() - - # Setup data loading for watermark graph - sampler = NeighborSampler([5, 5]) - wm_nids = torch.arange(self.watermark_graph.number_of_nodes(), device=self.device) - wm_collator = NodeCollator(self.watermark_graph, wm_nids, sampler) - wm_dataloader = DataLoader( - wm_collator.dataset, - batch_size=self.wm_node, - shuffle=False, - collate_fn=wm_collator.collate, - drop_last=False - ) - - all_preds = [] - with torch.no_grad(): - for _, _, blocks in wm_dataloader: - blocks = [b.to(self.device) for b in blocks] - input_features = blocks[0].srcdata['feat'] - output_predictions = model(blocks, input_features) - pred = output_predictions.argmax(dim=1) - all_preds.append(pred) - - wm_preds = torch.cat(all_preds, dim=0).cpu() - return wm_preds - - def _train_target_model(self): - """ - Helper function for training the target model on the original graph. - - Returns - ------- - torch.nn.Module - The trained target model - """ - print("Training target model...") - - # Initialize model - model = GraphSAGE(in_channels=self.feature_number, - hidden_channels=128, - out_channels=self.label_number) - model = model.to(self.device) - optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=5e-4) - - # Setup data loading - sampler = NeighborSampler([5, 5]) - train_nids = self.train_mask.nonzero(as_tuple=True)[0].to(self.device) - test_nids = self.test_mask.nonzero(as_tuple=True)[0].to(self.device) - - train_collator = NodeCollator(self.graph, train_nids, sampler) - test_collator = NodeCollator(self.graph, test_nids, sampler) - - train_dataloader = DataLoader( - train_collator.dataset, - batch_size=32, - shuffle=True, - collate_fn=train_collator.collate, - drop_last=False - ) - - test_dataloader = DataLoader( - test_collator.dataset, - batch_size=32, - shuffle=False, - collate_fn=test_collator.collate, - drop_last=False - ) - - # Training loop - best_acc = 0 - for epoch in tqdm(range(1, 51), desc="Target model training"): - # Train - model.train() - total_loss = 0 - for _, _, blocks in train_dataloader: - blocks = [b.to(self.device) for b in blocks] - input_features = blocks[0].srcdata['feat'] - output_labels = blocks[-1].dstdata['label'] - - optimizer.zero_grad() - output_predictions = model(blocks, input_features) - loss = F.cross_entropy(output_predictions, output_labels) - loss.backward() - optimizer.step() - total_loss += loss.item() - - # Test - model.eval() - correct = 0 - total = 0 - with torch.no_grad(): - for _, _, blocks in test_dataloader: - blocks = [b.to(self.device) for b in blocks] - input_features = blocks[0].srcdata['feat'] - output_labels = blocks[-1].dstdata['label'] - output_predictions = model(blocks, input_features) - pred = output_predictions.argmax(dim=1) - correct += (pred == output_labels).sum().item() - total += len(output_labels) - - acc = correct / total - if acc > best_acc: - best_acc = acc - - print(f"Target model trained. Test accuracy: {best_acc:.4f}") - return model - - def _train_defense_model(self): - """ - Helper function for training a defense model with watermarking. - - Returns - ------- - torch.nn.Module - The trained defense model with embedded watermark - """ - print("Training defense model with watermarking...") - - # Generate watermark graph - wm_graph = self._generate_watermark_graph() - - # Initialize model - model = GraphSAGE(in_channels=self.feature_number, - hidden_channels=128, - out_channels=self.label_number) - model = model.to(self.device) - optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=5e-4) - - # Setup data loading for original graph - sampler = NeighborSampler([5, 5]) - train_nids = self.train_mask.nonzero(as_tuple=True)[0].to(self.device) - test_nids = self.test_mask.nonzero(as_tuple=True)[0].to(self.device) - - train_collator = NodeCollator(self.graph, train_nids, sampler) - test_collator = NodeCollator(self.graph, test_nids, sampler) - - train_dataloader = DataLoader( - train_collator.dataset, - batch_size=32, - shuffle=True, - collate_fn=train_collator.collate, - drop_last=False - ) - - test_dataloader = DataLoader( - test_collator.dataset, - batch_size=32, - shuffle=False, - collate_fn=test_collator.collate, - drop_last=False - ) - - # Setup data loading for watermark graph - wm_nids = torch.arange(wm_graph.number_of_nodes(), device=self.device) - wm_collator = NodeCollator(wm_graph, wm_nids, sampler) - - wm_dataloader = DataLoader( - wm_collator.dataset, - batch_size=self.wm_node, - shuffle=True, - collate_fn=wm_collator.collate, - drop_last=False - ) - - # First stage: Train on original graph - best_acc = 0 - for epoch in tqdm(range(1, 51), desc="Defense model - stage 1"): - # Train - model.train() - total_loss = 0 - for _, _, blocks in train_dataloader: - blocks = [b.to(self.device) for b in blocks] - input_features = blocks[0].srcdata['feat'] - output_labels = blocks[-1].dstdata['label'] - - optimizer.zero_grad() - output_predictions = model(blocks, input_features) - loss = F.cross_entropy(output_predictions, output_labels) - loss.backward() - optimizer.step() - total_loss += loss.item() - - # Test - model.eval() - correct = 0 - total = 0 - with torch.no_grad(): - for _, _, blocks in test_dataloader: - blocks = [b.to(self.device) for b in blocks] - input_features = blocks[0].srcdata['feat'] - output_labels = blocks[-1].dstdata['label'] - output_predictions = model(blocks, input_features) - pred = output_predictions.argmax(dim=1) - correct += (pred == output_labels).sum().item() - total += len(output_labels) - - acc = correct / total - if acc > best_acc: - best_acc = acc - - # Second stage: Fine-tune on watermark graph - for epoch in tqdm(range(1, 11), desc="Defense model - stage 2"): - # Train on watermark - model.train() - total_loss = 0 - for _, _, blocks in wm_dataloader: - blocks = [b.to(self.device) for b in blocks] - input_features = blocks[0].srcdata['feat'] - output_labels = blocks[-1].dstdata['label'] - - optimizer.zero_grad() - output_predictions = model(blocks, input_features) - loss = F.cross_entropy(output_predictions, output_labels) - loss.backward() - optimizer.step() - total_loss += loss.item() - - # Final evaluation - model.eval() - correct = 0 - total = 0 - with torch.no_grad(): - for _, _, blocks in test_dataloader: - blocks = [b.to(self.device) for b in blocks] - input_features = blocks[0].srcdata['feat'] - output_labels = blocks[-1].dstdata['label'] - output_predictions = model(blocks, input_features) - pred = output_predictions.argmax(dim=1) - correct += (pred == output_labels).sum().item() - total += len(output_labels) - - final_acc = correct / total - - # Watermark accuracy - wm_acc = self._test_on_watermark(model, wm_dataloader) - - print(f"Defense model trained.") - print(f"Test accuracy on original data: {final_acc:.4f}") - print(f"Test accuracy on watermark: {wm_acc:.4f}") - - # Store watermark graph for later verification - self.watermark_graph = wm_graph - - return model - - def _generate_watermark_graph(self): - """ - Generate a watermark graph using Erdos-Renyi random graph model. - - Returns - ------- - dgl.DGLGraph - The generated watermark graph - """ - # Generate random edges using Erdos-Renyi model - wm_edge_index = erdos_renyi_graph(self.wm_node, self.pg, directed=False) - - # Generate random features with binomial distribution - wm_features = torch.tensor(np.random.binomial( - 1, self.pr, size=(self.wm_node, self.feature_number)), - dtype=torch.float32).to(self.device) - - # Generate random labels - wm_labels = torch.tensor(np.random.randint( - low=0, high=self.label_number, size=self.wm_node), - dtype=torch.long).to(self.device) - - # Create DGL graph - wm_graph = dgl.graph((wm_edge_index[0], wm_edge_index[1]), num_nodes=self.wm_node) - wm_graph = wm_graph.to(self.device) - - # Add node features and labels - wm_graph.ndata['feat'] = wm_features - wm_graph.ndata['label'] = wm_labels - - # Add train and test masks (all True for simplicity) - wm_graph.ndata['train_mask'] = torch.ones(self.wm_node, dtype=torch.bool, device=self.device) - wm_graph.ndata['test_mask'] = torch.ones(self.wm_node, dtype=torch.bool, device=self.device) - - # Add self-loops - wm_graph = dgl.add_self_loop(wm_graph) - - return wm_graph - - def _test_on_watermark(self, model, wm_dataloader): - """ - Test a model's accuracy on the watermark graph. - - Parameters - ---------- - model : torch.nn.Module - The model to test - wm_dataloader : DataLoader - DataLoader for the watermark graph - - Returns - ------- - float - Accuracy on the watermark graph - """ - model.eval() - correct = 0 - total = 0 - with torch.no_grad(): - for _, _, blocks in wm_dataloader: - blocks = [b.to(self.device) for b in blocks] - input_features = blocks[0].srcdata['feat'] - output_labels = blocks[-1].dstdata['label'] - output_predictions = model(blocks, input_features) - pred = output_predictions.argmax(dim=1) - correct += (pred == output_labels).sum().item() - total += len(output_labels) - - return correct / total - - def _evaluate_watermark(self, model): - """ - Evaluate watermark detection effectiveness. - - Parameters - ---------- - model : torch.nn.Module - The model to evaluate - - Returns - ------- - float - Watermark detection accuracy - """ - if not hasattr(self, 'watermark_graph'): - print("Warning: No watermark graph found. Generate one first.") - return 0.0 - - # Setup data loading for watermark graph - sampler = NeighborSampler([5, 5]) - wm_nids = torch.arange(self.watermark_graph.number_of_nodes(), device=self.device) - wm_collator = NodeCollator(self.watermark_graph, wm_nids, sampler) - - wm_dataloader = DataLoader( - wm_collator.dataset, - batch_size=self.wm_node, - shuffle=False, - collate_fn=wm_collator.collate, - drop_last=False - ) - - return self._test_on_watermark(model, wm_dataloader) - - def _evaluate_attack_on_watermark(self, attack_model): - """ - Evaluate how well the attack model performs on the watermark graph. - - Parameters - ---------- - attack_model : torch.nn.Module - The model obtained from the attack - - Returns - ------- - float - Attack model's accuracy on the watermark graph - """ - if not hasattr(self, 'watermark_graph'): - print("Warning: No watermark graph found. Generate one first.") - return 0.0 - - # Check the model type to determine the correct evaluation approach - model_name = attack_model.__class__.__name__ - - # For GCN models that expect (g, features) input format - if model_name == 'GCN': - # Evaluate using the whole graph at once - attack_model.eval() - with torch.no_grad(): - # Pass the entire graph and features at once - output_predictions = attack_model(self.watermark_graph, self.watermark_graph.ndata['feat']) - pred = output_predictions.argmax(dim=1) - correct = (pred == self.watermark_graph.ndata['label']).sum().item() - total = self.watermark_graph.number_of_nodes() - - return correct / total - - # For GraphSAGE models that expect blocks input format - elif model_name == 'GraphSAGE': - # Setup data loading for watermark graph - sampler = NeighborSampler([5, 5]) - wm_nids = torch.arange(self.watermark_graph.number_of_nodes(), device=self.device) - wm_collator = NodeCollator(self.watermark_graph, wm_nids, sampler) - - wm_dataloader = DataLoader( - wm_collator.dataset, - batch_size=self.wm_node, - shuffle=False, - collate_fn=wm_collator.collate, - drop_last=False - ) - - # Evaluate attack model on watermark - attack_model.eval() - correct = 0 - total = 0 - with torch.no_grad(): - for _, _, blocks in wm_dataloader: - blocks = [b.to(self.device) for b in blocks] - input_features = blocks[0].srcdata['feat'] - output_labels = blocks[-1].dstdata['label'] - output_predictions = attack_model(blocks, input_features) - pred = output_predictions.argmax(dim=1) - correct += (pred == output_labels).sum().item() - total += len(output_labels) - - return correct / total - - # For any other model type, print a warning and return 0 - else: - print(f"Warning: Unsupported model type '{model_name}' for watermark evaluation") - return 0.0 diff --git a/pyhazard/models/defense/Revisiting.py b/pyhazard/models/defense/Revisiting.py deleted file mode 100644 index fec2276..0000000 --- a/pyhazard/models/defense/Revisiting.py +++ /dev/null @@ -1,244 +0,0 @@ -from typing import Any, Dict, Iterable, Tuple - -import dgl -import torch -import torch.nn.functional as F -from dgl.dataloading import NeighborSampler, NodeCollator -from torch.utils.data import DataLoader -from tqdm import tqdm - -from pyhazard.models.defense.base import BaseDefense -from pyhazard.models.nn import GraphSAGE -from pyhazard.utils.metrics import DefenseCompMetric - - -class Revisiting(BaseDefense): - """ - A lightweight defense that 'revisits' node features via neighbor mixing. - - Idea (defense intuition) - ------------------------ - We pick a subset of nodes (size ~ attack_node_fraction * |V|) and *smoothly* - mix their features with their 1-hop / 2-hop neighborhoods using a mixing - factor `alpha`. This keeps utility (accuracy) largely intact while making - local feature structure less extractable for subgraph-based queries. - - API shape follows RandomWM: - - lives under models/defense/ - - inherits BaseDefense - - public entrypoint: .defend() - - Parameters - ---------- - dataset : Any - A dataset object providing a DGLGraph in `dataset.graph_data` and - ndata fields: 'feat', 'label', 'train_mask', 'test_mask'. - attack_node_fraction : float, default=0.2 - Fraction of nodes used as the 'focus set' for our revisiting transform. - alpha : float, default=0.8 - Mixing coefficient in [0,1]. Higher -> stronger neighbor mixing. - """ - - supported_api_types = {"dgl"} - - def __init__( - self, - dataset: Any, - attack_node_fraction: float = 0.2, - alpha: float = 0.8, - ) -> None: - super().__init__(dataset, attack_node_fraction) - - # knobs - self.alpha = float(alpha) - - # cache handles similar to RandomWM for consistency - self.dataset = dataset - self.graph: dgl.DGLGraph = dataset.graph_data - - self.num_nodes = dataset.num_nodes - self.num_features = dataset.num_features - self.num_classes = dataset.num_classes - self.num_focus_nodes = max(1, int(self.num_nodes * attack_node_fraction)) - - self.features: torch.Tensor = self.graph.ndata["feat"] - self.labels: torch.Tensor = self.graph.ndata["label"] - self.train_mask: torch.Tensor = self.graph.ndata["train_mask"] - self.test_mask: torch.Tensor = self.graph.ndata["test_mask"] - - if self.device != "cpu": - self.graph = self.graph.to(self.device) - self.features = self.features.to(self.device) - self.labels = self.labels.to(self.device) - self.train_mask = self.train_mask.to(self.device) - self.test_mask = self.test_mask.to(self.device) - - # --------------------------------------------------------------------- # - # Public entrypoint - # --------------------------------------------------------------------- # - def defend(self) -> Tuple[Dict[str, Any], Dict[str, Any]]: - """ - 1) Train a baseline GraphSAGE on the original graph (utility baseline) - 2) Apply revisiting feature-mixing on a subset of nodes - 3) Train a defended GraphSAGE on the transformed features - 4) Return accuracy metrics and basic metadata - """ - metric_comp = DefenseCompMetric() - metric_comp.start() - # ---- Baseline (no transform) ------------------------------------- # - baseline_acc = self._train_and_eval_graphsage(use_transformed_features=False) - - # ---- Build transformed features (revisiting) --------------------- # - feat_defended, picked = self._build_revisiting_features() - - # ---- Train with defended features -------------------------------- # - # Temporarily override graph features, then restore - orig_feat = self.graph.ndata["feat"] - try: - self.graph.ndata["feat"] = feat_defended - defense_acc = self._train_and_eval_graphsage(use_transformed_features=True) - finally: - self.graph.ndata["feat"] = orig_feat # restore - - res = { - "ok": True, - "method": "Revisiting", - "alpha": self.alpha, - "focus_nodes": int(self.num_focus_nodes), - "baseline_test_acc": float(baseline_acc), - "defense_test_acc": float(defense_acc), - "acc_delta": float(defense_acc - baseline_acc), - # returning a small sample of picked nodes for debuggability - "sample_picked_nodes": picked[:10].tolist() if isinstance(picked, torch.Tensor) else [], - } - - return res, metric_comp.compute() - - # --------------------------------------------------------------------- # - # Core: feature revisiting (neighbor mixing) - # --------------------------------------------------------------------- # - def _build_revisiting_features(self) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Returns a new feature tensor where a subset of nodes (and optionally - their neighbors) are mixed with neighbor features. - - Mixing rule (simple & stable): - - For each picked node u: - x[u] <- (1 - alpha) * x[u] + alpha * mean(x[N(u)]) - - For each 1-hop neighbor v in N(u) we apply a *lighter* mix - x[v] <- (1 - 0.5*alpha) * x[v] + (0.5*alpha) * mean(x[N(v)]) - - This keeps the transform localized and smooth. - """ - g = self.graph - x = self.features.clone() - - # pick focus nodes - picked = torch.randperm(self.num_nodes, device=self.device)[: self.num_focus_nodes] - - # precompute neighbor lists (on CPU tensors if needed) - # we'll use undirected neighborhood by combining predecessors/successors - def neighbors(nodes: Iterable[int]) -> torch.Tensor: - cols = [] - for n in nodes: - # concatenate in- and out-neighbors to emulate undirected - nb = torch.unique( - torch.cat([g.successors(int(n)), g.predecessors(int(n))], dim=0) - ) - if nb.numel() > 0: - cols.append(nb) - if not cols: - return torch.empty(0, dtype=torch.long, device=self.device) - return torch.unique(torch.cat(cols)) - - # 1) mix picked nodes with mean of their neighbors - for u in picked.tolist(): - nb = neighbors([u]) - if nb.numel() == 0: - continue - mean_nb = self.features[nb].mean(dim=0) - x[u] = (1.0 - self.alpha) * self.features[u] + self.alpha * mean_nb - - # 2) lightly mix 1-hop neighbors as well (half strength) - one_hop = neighbors(picked.tolist()) - for v in one_hop.tolist(): - nb = neighbors([v]) - if nb.numel() == 0: - continue - mean_nb = self.features[nb].mean(dim=0) - x[v] = (1.0 - 0.5 * self.alpha) * self.features[v] + (0.5 * self.alpha) * mean_nb - - return x, picked - - # --------------------------------------------------------------------- # - # Training/Eval (same style as RandomWM) - # --------------------------------------------------------------------- # - def _train_and_eval_graphsage(self, use_transformed_features: bool) -> float: - """ - Train a GraphSAGE for a few epochs and return test accuracy. - Uses NeighborSampler + NodeCollator (same pattern as RandomWM). - """ - model = GraphSAGE( - in_channels=self.num_features, - hidden_channels=128, - out_channels=self.num_classes, - ).to(self.device) - - optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=5e-4) - sampler = NeighborSampler([5, 5]) - - train_nids = self.train_mask.nonzero(as_tuple=True)[0].to(self.device) - test_nids = self.test_mask.nonzero(as_tuple=True)[0].to(self.device) - - train_collator = NodeCollator(self.graph, train_nids, sampler) - test_collator = NodeCollator(self.graph, test_nids, sampler) - - train_loader = DataLoader( - train_collator.dataset, - batch_size=32, - shuffle=True, - collate_fn=train_collator.collate, - drop_last=False, - ) - test_loader = DataLoader( - test_collator.dataset, - batch_size=32, - shuffle=False, - collate_fn=test_collator.collate, - drop_last=False, - ) - - best_acc = 0.0 - for _ in tqdm(range(1, 51), - desc=("GraphSAGE (defended)" if use_transformed_features else "GraphSAGE (baseline)")): - # ---- Train - model.train() - for _, _, blocks in train_loader: - blocks = [b.to(self.device) for b in blocks] - feats = blocks[0].srcdata["feat"] - labels = blocks[-1].dstdata["label"] - - optimizer.zero_grad() - logits = model(blocks, feats) - loss = F.cross_entropy(logits, labels) - loss.backward() - optimizer.step() - - # ---- Eval - model.eval() - correct = 0 - total = 0 - with torch.no_grad(): - for _, _, blocks in test_loader: - blocks = [b.to(self.device) for b in blocks] - feats = blocks[0].srcdata["feat"] - labels = blocks[-1].dstdata["label"] - logits = model(blocks, feats) - pred = logits.argmax(dim=1) - correct += (pred == labels).sum().item() - total += labels.numel() - - acc = correct / max(1, total) - best_acc = max(best_acc, acc) - - return best_acc diff --git a/pyhazard/models/defense/SurviveWM.py b/pyhazard/models/defense/SurviveWM.py deleted file mode 100644 index 0b2ef44..0000000 --- a/pyhazard/models/defense/SurviveWM.py +++ /dev/null @@ -1,301 +0,0 @@ -import time - -import dgl -import torch -import networkx as nx -import torch.nn.functional as F -from tqdm import tqdm -from torch_geometric.data import Data -from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, roc_auc_score - -from pyhazard.models.defense.base import BaseDefense -from pyhazard.models.nn import GCN -from pyhazard.utils.metrics import GraphNeuralNetworkMetric, DefenseCompMetric, DefenseMetric - - -class SurviveWM(BaseDefense): - supported_api_types = {"dgl"} - - def __init__(self, dataset, defense_ratio: float = 0.1, model_path=None): - super().__init__(dataset, defense_ratio) - # load graph data - self.dataset = dataset - self.graph_dataset = dataset.graph_data - self.graph_data = dataset.graph_data.to(device=self.device) - self.model_path = model_path - self.graph = self.graph_data - self.features = self.graph_data.ndata['feat'] - self.labels = self.graph_data.ndata['label'] - self.train_mask = self.graph_data.ndata['train_mask'] - self.test_mask = self.graph_data.ndata['test_mask'] - - # load meta data - self.feature_number = dataset.num_features - self.label_number = dataset.num_classes - - # params - self.defense_ratio = defense_ratio - - def _load_model(self): - """ - Load a pre-trained model. - """ - assert self.model_path is not None, "Please provide a pre-trained model" - - # Create the model - self.net1 = GCN(self.feature_number, self.label_number).to(self.device) - - # Load the saved state dict - self.net1.load_state_dict(torch.load(self.model_path, map_location=self.device)) - - # Set to evaluation mode - self.net1.eval() - - def _to_cpu(self, tensor): - """ - Safely move tensor to CPU for NumPy operations - """ - if tensor.is_cuda: - return tensor.cpu() - return tensor - - # === Soft Nearest Neighbor Loss === - def snn_loss(self, x, y, T=0.5): - x = F.normalize(x, p=2, dim=1) - dist_matrix = torch.cdist(x, x, p=2) ** 2 - eye = torch.eye(len(x), device=self.device).bool() - sim = torch.exp(-dist_matrix / T) - mask_same = y.unsqueeze(1) == y.unsqueeze(0) - sim = sim.masked_fill(eye, 0) - denom = sim.sum(1) - nom = (sim * mask_same.float()).sum(1) - loss = -torch.log(nom / (denom + 1e-10) + 1e-10).mean() - return loss - - # === Trigger Graph Generator === - def generate_key_graph(self, num_nodes=None, edge_prob=None): - if num_nodes is None: - num_nodes = max(5, int(self.dataset.num_nodes * self.defense_ratio)) - if edge_prob is None: - edge_prob = min(0.5, self.defense_ratio * 3) - - trigger = nx.erdos_renyi_graph(num_nodes, edge_prob) - edge_index = torch.tensor(list(trigger.edges), dtype=torch.long).t().contiguous() - if edge_index.numel() == 0: - edge_index = torch.empty((2, 0), dtype=torch.long) - - x = torch.randn((num_nodes, self.feature_number), device=self.device) * 0.1 - label = torch.randint(0, self.label_number, (num_nodes,), device=self.device) - return Data(x=x, edge_index=edge_index.to(self.device), y=label) - - # === Combine base and trigger === - def combine_with_trigger(self, base_graph, base_features, base_labels, trigger_data): - # Convert DGL graph to edge_index format - src, dst = base_graph.edges() - base_edge_index = torch.stack([src, dst], dim=0) - - x = torch.cat([base_features, trigger_data.x], dim=0) - edge_index = torch.cat([base_edge_index, trigger_data.edge_index + base_features.size(0)], dim=1) - y = torch.cat([base_labels, trigger_data.y], dim=0) - - # Create DGL graph from combined data - src_combined, dst_combined = edge_index[0], edge_index[1] - combined_graph = dgl.graph((src_combined, dst_combined), num_nodes=x.size(0)).to(self.device) - - # **FIX: Add self-loops to handle zero in-degree nodes** - combined_graph = dgl.add_self_loop(combined_graph) - - combined_graph.ndata['feat'] = x.to(self.device) - - return combined_graph, y.to(self.device) - - def train_with_snnl(self, model, graph, features, labels, train_mask, optimizer, T=0.5, alpha=0.1): - model.train() - optimizer.zero_grad() - out = model(graph, features) - loss_nll = F.nll_loss(F.log_softmax(out, dim=1)[train_mask], labels[train_mask]) - snnl = self.snn_loss(out[train_mask], labels[train_mask], T=T) - loss = loss_nll - alpha * snnl - loss.backward() - optimizer.step() - return loss.item() - - @torch.no_grad() - def verify_watermark(self, model, trigger_graph, trigger_labels): - model.eval() - out = model(trigger_graph, trigger_graph.ndata['feat']) - pred = out.argmax(dim=1) - return (pred == trigger_labels).float().mean().item() - - def compute_metrics(self, y_true, y_pred, y_score=None): - return { - 'accuracy': accuracy_score(y_true, y_pred), - 'f1': f1_score(y_true, y_pred, average='macro'), - 'precision': precision_score(y_true, y_pred, average='macro'), - 'recall': recall_score(y_true, y_pred, average='macro'), - 'auroc': roc_auc_score(y_true, y_score, multi_class='ovo') if y_score is not None else None - } - - def defend(self): - """Execute the SurviveWM defense.""" - metric_comp = DefenseCompMetric() - metric_comp.start() - print("====================SurviveWM Defense====================") - - # If model wasn't trained yet, train it - if not hasattr(self, 'model_trained'): - self.train_target_model(metric_comp) - - # Evaluate the defended model - preds = self.evaluate_model(self.watermarked_model) - inference_s = time.time() - wm_preds = self.verify_watermark_model(self.watermarked_model) - inference_e = time.time() - - # metric - metric = DefenseMetric() - metric.update(preds, self.labels[self.test_mask]) - metric.update_wm(wm_preds, self.trigger_data.y) - metric_comp.end() - - print("====================Final Results====================") - res = metric.compute() - metric_comp.update(inference_defense_time=(inference_e - inference_s)) - res_comp = metric_comp.compute() - - return res, res_comp - - def train_target_model(self, metric_comp: DefenseCompMetric): - """Train the target model with watermark injection.""" - defense_s = time.time() - - # Generate trigger and train watermarked model (defense mechanism) - self.trigger_data = self.generate_key_graph().to(self.device) - self.watermarked_model = self._train_watermarked_model() - self.model_trained = True - - defense_e = time.time() - metric_comp.update(defense_time=(defense_e - defense_s)) - return self.watermarked_model - - def evaluate_model(self, model): - """Evaluate model performance on downstream task""" - model.eval() - with torch.no_grad(): - test_graph = dgl.add_self_loop(self.graph) - logits = model(test_graph, self.features) - pred = logits.argmax(dim=1) - preds = pred[self.test_mask].cpu() - return preds - - def verify_watermark_model(self, model): - """Verify watermark success rate""" - model.eval() - with torch.no_grad(): - # Use the stored trigger graph if available - if hasattr(self, 'trigger_graph'): - trigger_graph = self.trigger_graph - else: - # Create trigger graph for verification - trigger_src, trigger_dst = self.trigger_data.edge_index[0], self.trigger_data.edge_index[1] - trigger_graph = dgl.graph((trigger_src, trigger_dst), num_nodes=self.trigger_data.num_nodes).to( - self.device) - trigger_graph = dgl.add_self_loop(trigger_graph) - trigger_graph.ndata['feat'] = self.trigger_data.x.to(self.device) - - out = model(trigger_graph, trigger_graph.ndata['feat']) - wm_preds = out.argmax(dim=1).cpu() - return wm_preds - - def _train_watermarked_model(self): - """Helper function to train the watermarked model""" - print("Training watermarked model...") - - # Combine base graph with trigger - combined_graph, combined_labels = self.combine_with_trigger( - self.graph, self.features, self.labels, self.trigger_data) - - # Create train mask for combined data (include trigger nodes in training) - base_train_mask = self.train_mask - trigger_train_mask = torch.ones(self.trigger_data.num_nodes, dtype=torch.bool, device=self.device) - combined_train_mask = torch.cat([base_train_mask, trigger_train_mask]) - - # Create test mask for combined data (exclude trigger nodes from testing) - base_test_mask = self.test_mask - trigger_test_mask = torch.zeros(self.trigger_data.num_nodes, dtype=torch.bool, device=self.device) - combined_test_mask = torch.cat([base_test_mask, trigger_test_mask]) - - # Create watermarked model - watermarked_model = GCN(self.feature_number, self.label_number).to(self.device) - optimizer = torch.optim.Adam(watermarked_model.parameters(), lr=0.01, weight_decay=5e-4) - - # Create trigger graph for watermark verification - trigger_src, trigger_dst = self.trigger_data.edge_index[0], self.trigger_data.edge_index[1] - trigger_graph = dgl.graph((trigger_src, trigger_dst), num_nodes=self.trigger_data.num_nodes).to(self.device) - trigger_graph = dgl.add_self_loop(trigger_graph) - trigger_graph.ndata['feat'] = self.trigger_data.x.to(self.device) - - # Store trigger graph for later use - self.trigger_graph = trigger_graph - - dur = [] - best_performance_metrics = GraphNeuralNetworkMetric() - - for epoch in tqdm(range(200)): - if epoch >= 3: - t0 = time.time() - - # Train with SNNL - loss = self.train_with_snnl( - watermarked_model, combined_graph, combined_graph.ndata['feat'], - combined_labels, combined_train_mask, optimizer) - - if epoch >= 3: - dur.append(time.time() - t0) - - # Evaluate periodically - if epoch % 20 == 0: - watermarked_model.eval() - with torch.no_grad(): - # Test on original graph (ensure it has self-loops) - test_graph = dgl.add_self_loop(self.graph) - - logits = watermarked_model(test_graph, self.features) - pred = logits.argmax(dim=1) - test_acc = (pred[self.test_mask] == self.labels[self.test_mask]).float().mean() - - # Verify watermark - wm_acc = self.verify_watermark(watermarked_model, trigger_graph, self.trigger_data.y) - - print(f"Epoch {epoch}, Test Acc: {test_acc.item():.4f}, Watermark Acc: {wm_acc:.4f}") - - # Final evaluation - watermarked_model.eval() - with torch.no_grad(): - # Evaluate on test set (ensure graph has self-loops) - test_graph = dgl.add_self_loop(self.graph) - - logits = watermarked_model(test_graph, self.features) - pred = logits.argmax(dim=1) - probs = F.softmax(logits, dim=1) - - test_metrics = self.compute_metrics( - self._to_cpu(self.labels[self.test_mask]).numpy(), - self._to_cpu(pred[self.test_mask]).numpy(), - self._to_cpu(probs[self.test_mask]).numpy() - ) - - # Verify watermark - wm_acc = self.verify_watermark(watermarked_model, trigger_graph, self.trigger_data.y) - - print(f"Final Test Accuracy: {test_metrics['accuracy']:.4f}") - print(f"Final Test F1: {test_metrics['f1']:.4f}") - print(f"Final Test Precision: {test_metrics['precision']:.4f}") - print(f"Final Test Recall: {test_metrics['recall']:.4f}") - print(f"Final Watermark Accuracy: {wm_acc:.4f}") - - # Store final metrics for later use - self.final_test_metrics = test_metrics - self.final_wm_acc = wm_acc - - return watermarked_model diff --git a/pyhazard/models/defense/SurviveWM2.py b/pyhazard/models/defense/SurviveWM2.py deleted file mode 100644 index 2ea5e11..0000000 --- a/pyhazard/models/defense/SurviveWM2.py +++ /dev/null @@ -1,556 +0,0 @@ -import random -from typing import List, Tuple - -import networkx as nx -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim -from torch_geometric.data import Data -from torch_geometric.loader import DataLoader -from torch_geometric.nn import SAGEConv, global_mean_pool, BatchNorm -from torch_geometric.utils import to_networkx - -from pyhazard.models.defense.base import BaseDefense - -device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - - -class SAGEModel(nn.Module): - def __init__(self, input_dim: int, hidden_dim: int = 64, num_classes: int = 10, num_layers: int = 3, - dropout: float = 0.1): - super().__init__() - self.convs = nn.ModuleList() - self.norms = nn.ModuleList() - self.convs.append(SAGEConv(input_dim, hidden_dim)) - self.norms.append(BatchNorm(hidden_dim)) - for _ in range(num_layers - 1): - self.convs.append(SAGEConv(hidden_dim, hidden_dim)) - self.norms.append(BatchNorm(hidden_dim)) - self.classifier = nn.Linear(hidden_dim, num_classes) - self.dropout = nn.Dropout(dropout) - - def forward(self, x, edge_index, batch, return_embedding=False): - for i, conv in enumerate(self.convs): - x = conv(x, edge_index) - x = self.norms[i](x) - x = F.relu(x) - if i < len(self.convs) - 1: - x = self.dropout(x) - embedding = global_mean_pool(x, batch) - if return_embedding: - return embedding - return self.classifier(embedding) - - -class WatermarkGenerator: - def __init__(self, training_dataset: List[Data], num_watermark_samples: int = None): - self.training_dataset = training_dataset - self.num_classes = self._get_num_classes() - self.avg_num_nodes = self._get_avg_num_nodes() - self.feature_dim = training_dataset[0].x.size(1) if training_dataset else 32 - if num_watermark_samples is None: - self.num_watermark_samples = max(5, int(0.05 * len(training_dataset))) - else: - self.num_watermark_samples = num_watermark_samples - - def algorithm_1_key_input_topology_generation(self, N_t: int, N: int = None) -> Data: - if N is None: - N = min(50, self.avg_num_nodes) - x = torch.zeros(N, self.feature_dim) - p = 0.5 - G_r = nx.erdos_renyi_graph(N_t, p) - remaining_nodes = N - N_t - if remaining_nodes > 0: - training_sample = random.choice(self.training_dataset) - G_train = to_networkx(training_sample, to_undirected=True) - if G_train.number_of_nodes() >= remaining_nodes: - sampled_nodes = random.sample(list(G_train.nodes()), remaining_nodes) - G_o = G_train.subgraph(sampled_nodes).copy() - for i, node in enumerate(sampled_nodes): - if node < training_sample.x.size(0): - x[N_t + i] = training_sample.x[node] - else: - x[N_t + i] = torch.randn(self.feature_dim) - else: - G_o = G_train.copy() - for i in range(min(remaining_nodes, training_sample.x.size(0))): - x[N_t + i] = training_sample.x[i] - else: - G_o = nx.Graph() - edges = set() - for u, v in G_r.edges(): - if u < N and v < N: - edges.add((min(u, v), max(u, v))) - node_mapping = {old: new + N_t for new, old in enumerate(G_o.nodes())} - for u_old, v_old in G_o.edges(): - u, v = node_mapping[u_old], node_mapping[v_old] - if u < N and v < N: - edges.add((min(u, v), max(u, v))) - if edges: - edge_list = [] - for u, v in edges: - edge_list.extend([[u, v], [v, u]]) - edge_index = torch.tensor(edge_list, dtype=torch.long).t() - else: - edge_index = torch.zeros((2, 0), dtype=torch.long) - watermark_graph = Data(x=x, edge_index=edge_index) - return watermark_graph - - def generate_watermark_set_with_clean_model(self, clean_model) -> List[Tuple[Data, int]]: - watermark_pairs = [] - while len(watermark_pairs) < self.num_watermark_samples: - N_t = random.choice([3, 4]) - watermark_graph = self.algorithm_1_key_input_topology_generation(N_t) - clean_model.eval() - with torch.no_grad(): - batch = torch.zeros(watermark_graph.x.size(0), dtype=torch.long) - pred_logits = clean_model(watermark_graph.x, watermark_graph.edge_index, batch) - clean_pred = pred_logits.argmax().item() - probs = F.softmax(pred_logits, dim=1) - if probs.max().item() < 0.6: - continue - other_classes = [i for i in range(self.num_classes) if i != clean_pred] - watermark_label = random.choice(other_classes) if other_classes else (clean_pred + 1) % self.num_classes - watermark_pairs.append((watermark_graph, watermark_label)) - while len(watermark_pairs) < max(5, self.num_watermark_samples // 2): - wg = self.algorithm_1_key_input_topology_generation(random.choice([2, 3, 4])) - watermark_pairs.append((wg, random.randint(0, self.num_classes - 1))) - return watermark_pairs - - def _get_num_classes(self) -> int: - if not self.training_dataset: - return 2 - labels = {int(d.y) for d in self.training_dataset if hasattr(d, 'y')} - return max(labels) + 1 if labels else 2 - - def _get_avg_num_nodes(self) -> int: - if not self.training_dataset: - return 20 - total = sum(d.x.size(0) for d in self.training_dataset) - return total // len(self.training_dataset) - - -class SNNLLoss(nn.Module): - def __init__(self, temperature: float = 1.0): - super().__init__() - self.T = temperature - - def forward(self, embeddings: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: - N = embeddings.size(0) - if N <= 1: - return torch.tensor(0.0, requires_grad=True, device=embeddings.device) - distances = torch.cdist(embeddings, embeddings, p=2).pow(2) - loss = 0.0 - count = 0 - for i in range(N): - same_mask = (labels == labels[i]) & (torch.arange(N, device=labels.device) != i) - diff_mask = torch.arange(N, device=labels.device) != i - if same_mask.sum() == 0 or diff_mask.sum() == 0: - continue - numerator = torch.exp(-distances[i, same_mask] / self.T).sum() - denominator = torch.exp(-distances[i, diff_mask] / self.T).sum() - loss += -torch.log((numerator + 1e-8) / (denominator + 1e-8)) - count += 1 - return loss / max(count, 1) if count > 0 else torch.tensor(0.0, requires_grad=True, device=embeddings.device) - - -def train_clean_model(training_data: List[Data], epochs: int = 200, batch_size: int = 32, - num_layers: int = 3) -> SAGEModel: - num_classes = max([int(d.y) for d in training_data]) + 1 - model = SAGEModel(input_dim=training_data[0].x.size(1), hidden_dim=160, num_classes=num_classes, - num_layers=num_layers) - optimizer = optim.Adam(model.parameters(), lr=1e-3) - criterion = nn.CrossEntropyLoss() - loader = DataLoader(training_data, batch_size=batch_size, shuffle=True) - for epoch in range(epochs): - model.train() - total_loss = 0 - correct = 0 - total = 0 - for batch in loader: - optimizer.zero_grad() - out = model(batch.x, batch.edge_index, batch.batch) - loss = criterion(out, batch.y.view(-1)) - loss.backward() - torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) - optimizer.step() - total_loss += loss.item() * batch.num_graphs - preds = out.argmax(dim=1) - correct += (preds == batch.y.view(-1)).sum().item() - total += batch.num_graphs - - if epoch > 50 and epoch % 20 == 0: - for pg in optimizer.param_groups: - pg['lr'] *= 0.8 - return model - - -def train_watermarked_model_full( - training_data: List[Data], - key_inputs: List[Tuple[Data, int]], - epochs: int = 300, - alpha: float = 0.1, - num_layers: int = 4, - hidden_dim: int = 160, - dropout: float = 0.05, - lr: float = 1e-3, - snnl_temperature: float = 1.0, -): - num_classes = max([int(d.y) for d in training_data] + [label for _, label in key_inputs]) + 1 - model = SAGEModel( - input_dim=training_data[0].x.size(1), - hidden_dim=hidden_dim, - num_classes=num_classes, - num_layers=num_layers, - dropout=dropout, - ) - optimizer = optim.Adam(model.parameters(), lr=lr) - ce_criterion = nn.CrossEntropyLoss() - snnl_criterion = SNNLLoss(temperature=snnl_temperature) - batch_size = 32 - - wm_graphs = [d for d, _ in key_inputs] - wm_labels = [l for _, l in key_inputs] - - loader_clean = DataLoader(training_data, batch_size=batch_size, shuffle=True) - loader_wm = DataLoader([ - Data(x=g.x, edge_index=g.edge_index, y=torch.tensor([l])) for g, l in zip(wm_graphs, wm_labels) - ], batch_size=batch_size, shuffle=True) - - for epoch in range(epochs): - model.train() - total_loss, total_correct, total_count = 0, 0, 0 - for batch in loader_clean: - optimizer.zero_grad() - out = model(batch.x, batch.edge_index, batch.batch) - loss = ce_criterion(out, batch.y.view(-1)) - loss.backward() - torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) - optimizer.step() - total_loss += loss.item() * batch.num_graphs - preds = out.argmax(dim=1) - total_correct += (preds == batch.y.view(-1)).sum().item() - total_count += batch.num_graphs - - wm_loss_total, wm_snnl_total, wm_batches = 0.0, 0.0, 0 - for batch in loader_wm: - optimizer.zero_grad() - out = model(batch.x, batch.edge_index, batch.batch) - loss_ce = ce_criterion(out, batch.y.view(-1)) - batch_embeddings, batch_labels = [], [] - emb_wm = model(batch.x, batch.edge_index, batch.batch, return_embedding=True) - batch_embeddings.append(emb_wm) - batch_labels.extend([1] * emb_wm.size(0)) - try: - clean_batch = next(iter(loader_clean)) - emb_clean = model(clean_batch.x, clean_batch.edge_index, clean_batch.batch, return_embedding=True) - batch_embeddings.append(emb_clean) - batch_labels.extend([0] * emb_clean.size(0)) - except StopIteration: - pass - if len(batch_embeddings) > 1: - emb_t = torch.cat(batch_embeddings, dim=0) - lbl_t = torch.tensor(batch_labels, dtype=torch.long, device=emb_t.device) - snnl_loss = snnl_criterion(emb_t, lbl_t) - else: - snnl_loss = torch.tensor(0.0, device=out.device) - loss = loss_ce + alpha * snnl_loss - loss.backward() - torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) - optimizer.step() - wm_loss_total += loss_ce.item() - wm_snnl_total += snnl_loss.item() - wm_batches += 1 - - if epoch > 50 and epoch % 20 == 0: - for pg in optimizer.param_groups: - pg['lr'] *= 0.8 - - if epoch % 20 == 0: - avg_clean_loss = total_loss / max(total_count, 1) - avg_wm_loss = wm_loss_total / max(wm_batches, 1) - avg_wm_snnl = wm_snnl_total / max(wm_batches, 1) - model.eval() - c_corr, wm_corr = 0, 0 - with torch.no_grad(): - for d in training_data[:20]: - b = torch.zeros(d.x.size(0), dtype=torch.long) - if model(d.x, d.edge_index, b).argmax() == int(d.y): - c_corr += 1 - for x_exp, lbl in key_inputs[:10]: - b = torch.zeros(x_exp.x.size(0), dtype=torch.long) - if model(x_exp.x, x_exp.edge_index, b).argmax() == lbl: - wm_corr += 1 - clean_acc = c_corr / 20 - wm_acc = wm_corr / min(10, len(key_inputs)) - model.train() - return model - - -def evaluate_watermark_effectiveness(model: SAGEModel, key_inputs: List[Tuple[Data, int]]) -> float: - model.eval() - correct = 0 - with torch.no_grad(): - for x, expected_label in key_inputs: - batch = torch.zeros(x.x.size(0), dtype=torch.long) - pred = model(x.x, x.edge_index, batch).argmax(1).item() - if pred == expected_label: - correct += 1 - return correct / len(key_inputs) if key_inputs else 0.0 - - -def evaluate_clean_accuracy(model: SAGEModel, test_data: List[Data], batch_size=32) -> float: - if not test_data: - return 0.0 - loader = DataLoader(test_data, batch_size=batch_size) - model.eval() - correct = 0 - total = 0 - with torch.no_grad(): - for batch in loader: - out = model(batch.x, batch.edge_index, batch.batch) - preds = out.argmax(dim=1) - correct += (preds == batch.y.view(-1)).sum().item() - total += batch.num_graphs - return correct / total if total > 0 else 0.0 - - -class KeyInputOptimizer: - def __init__(self, training_dataset: List[Data], key_inputs: List[Tuple[Data, int]], T_opt: int = 20): - self.training_dataset = training_dataset - self.key_inputs = key_inputs - self.feature_dim = training_dataset[0].x.size(1) - self.num_classes = max([int(d.y) for d in training_dataset] + [label for _, label in key_inputs]) + 1 - self.T_opt = T_opt - - def optimize(self): - class MTopo(nn.Module): - def __init__(self, N_t): - super().__init__() - self.N_t = N_t - self.net = nn.Sequential( - nn.Linear(N_t * N_t, N_t * N_t), - nn.ReLU(), - nn.Linear(N_t * N_t, N_t * N_t), - nn.Sigmoid() - ) - - def forward(self, A): - x = A.flatten().unsqueeze(0) - out = self.net(x).reshape(self.N_t, self.N_t) - return out - - class MFeat(nn.Module): - def __init__(self, feat_dim, N_t): - super().__init__() - self.N_t = N_t - self.feat_dim = feat_dim - self.net = nn.Sequential( - nn.Linear(N_t * feat_dim, N_t * feat_dim), - nn.ReLU(), - nn.Linear(N_t * feat_dim, N_t * feat_dim) - ) - - def forward(self, F): - x = F.flatten().unsqueeze(0) - out = self.net(x).reshape(self.N_t, self.feat_dim) - return out - - optimized_pairs = [] - for orig_data, label in self.key_inputs: - N = orig_data.x.size(0) - N_t = min(4, N) - trigger_nodes = torch.arange(N_t) - rest_nodes = torch.arange(N_t, N) - - # Build trigger adjacency - A_trig = torch.zeros(N_t, N_t) - edge_set = set(tuple(edge) for edge in orig_data.edge_index.t().tolist()) - for i, u in enumerate(trigger_nodes): - for j, v in enumerate(trigger_nodes): - if (u.item(), v.item()) in edge_set: - A_trig[i, j] = 1 - F_trig = orig_data.x[:N_t].clone() - - m_topo = MTopo(N_t) - m_feat = MFeat(self.feature_dim, N_t) - opt = torch.optim.Adam(list(m_topo.parameters()) + list(m_feat.parameters()), lr=1e-2) - - for step in range(self.T_opt): - opt.zero_grad() - A_new = m_topo(A_trig) - F_new = m_feat(F_trig) - A_bin = (A_new > 0.5).float() - - # Construct the new key input graph - new_x = orig_data.x.clone() - new_x[:N_t] = F_new.detach().squeeze() - edges = [] - for i in range(N_t): - for j in range(N_t): - if A_bin[i, j] > 0.5: - edges.append([i, j]) - for u, v in orig_data.edge_index.t().tolist(): - if u >= N_t and v >= N_t: - edges.append([u, v]) - elif (u >= N_t and v < N_t) or (u < N_t and v >= N_t): - edges.append([u, v]) - if edges: - edge_index = torch.tensor(edges, dtype=torch.long).t().contiguous() - else: - edge_index = torch.zeros((2, 0), dtype=torch.long) - data_opt = Data(x=new_x, edge_index=edge_index) - - # Train temp SAGE model on this single key input (with train data) - temp_model = SAGEModel(self.feature_dim, hidden_dim=64, num_classes=self.num_classes, num_layers=3) - temp_opt = torch.optim.Adam(temp_model.parameters(), lr=1e-2) - criterion = nn.CrossEntropyLoss() - batch = torch.zeros(data_opt.x.size(0), dtype=torch.long) - for _ in range(2): - rand_data = random.choice(self.training_dataset) - temp_opt.zero_grad() - out1 = temp_model(data_opt.x, data_opt.edge_index, batch) - loss1 = criterion(out1, torch.tensor([label])) - batch_rand = torch.zeros(rand_data.x.size(0), dtype=torch.long) - out2 = temp_model(rand_data.x, rand_data.edge_index, batch_rand) - loss2 = criterion(out2, rand_data.y.view(-1)) - loss = loss1 + loss2 - loss.backward() - temp_opt.step() - - with torch.no_grad(): - pred = temp_model(data_opt.x, data_opt.edge_index, batch) - ce_loss = criterion(pred, torch.tensor([label])) - score = -ce_loss - score = score.requires_grad_() - score.backward() - opt.step() - - optimized_pairs.append((data_opt, label)) - return optimized_pairs - - -class SurviveWM2(BaseDefense): - def __init__( - self, - dataset, - attack_node_fraction, - model_path=None, - alpha=0.1, - num_layers=4, - clean_epochs=200, - wm_epochs=200, - **kwargs - ): - super().__init__(dataset, attack_node_fraction) - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - self.alpha = alpha - self.num_layers = num_layers - self.clean_epochs = clean_epochs - self.wm_epochs = wm_epochs - - # --- Data extraction --- - self.train_data = getattr(dataset, 'train_data', None) - self.test_data = getattr(dataset, 'test_data', None) - - if not (isinstance(self.train_data, list) and isinstance(self.test_data, list)): - raise ValueError( - "This defense only supports graph classification datasets (e.g., ENZYMES). Node-level datasets are not supported.") - - self.model_path = model_path - - def defend(self): - """ - Main defense workflow: - 1. Train a target model (clean) - 2. (optional) Simulate attack on target model (if implemented) - 3. Train defense (watermarked) model - 4. Evaluate defense and print detailed metrics - Returns - ------- - dict - Dictionary containing performance metrics - """ - print("=" * 60) - print("Step 1: Train clean (victim) model") - print("-" * 60) - target_model = self._train_target_model() - baseline_acc = evaluate_clean_accuracy(target_model, self.test_data) - print(f"Test accuracy on clean (victim) model: {baseline_acc:.4f}") - - print("\nStep 2: Generate and optimize watermark key inputs") - print("-" * 60) - wm_gen = getattr(self, 'wm_gen', None) - if wm_gen is None: - self.wm_gen = WatermarkGenerator(self.train_data, - num_watermark_samples=int(len(self.train_data) * self.alpha)) - key_pairs = self.wm_gen.generate_watermark_set_with_clean_model(target_model) - optimizer = KeyInputOptimizer(self.train_data, key_pairs, T_opt=20) - key_pairs_optimized = optimizer.optimize() - print(f"Generated and optimized {len(key_pairs_optimized)} watermark key inputs") - - print("\nStep 3: Train defense (watermarked) model") - print("-" * 60) - defense_model = train_watermarked_model_full( - self.train_data, key_pairs_optimized, - epochs=self.wm_epochs, alpha=self.alpha, num_layers=self.num_layers - ) - defense_acc = evaluate_clean_accuracy(defense_model, self.test_data) - print(f"Test accuracy on defense (watermarked) model: {defense_acc:.4f}") - - print("\nStep 4: Evaluate watermark effectiveness") - print("-" * 60) - watermark_success = evaluate_watermark_effectiveness(defense_model, key_pairs_optimized) - print(f"Watermark detection rate (defense model): {watermark_success:.4f}") - - acc_degradation = baseline_acc - defense_acc - - print("\nPerformance metrics:") - print("-" * 60) - print(f"{'Metric':<36} {'Value'}") - print("-" * 60) - print(f"{'Test acc. (clean model)':<36} {baseline_acc:.4f}") - print(f"{'Test acc. (defense/watermarked)':<36} {defense_acc:.4f}") - print(f"{'Accuracy degradation':<36} {acc_degradation:.4f}") - print(f"{'Watermark detection (defense)':<36} {watermark_success:.4f}") - print("-" * 60) - - results = { - "baseline_accuracy": baseline_acc, - "defense_accuracy": defense_acc, - "watermark_effectiveness": watermark_success, - "accuracy_degradation": acc_degradation, - } - return results - - def _load_model(self): - if not self.model_path: - raise ValueError("No model_path provided.") - - def _train_target_model(self): - print("[OptimizedWatermarkDefense] Training clean (victim) model...") - model = train_clean_model(self.train_data, epochs=self.clean_epochs, num_layers=self.num_layers) - self.net1 = model - self.wm_gen = WatermarkGenerator(self.train_data, num_watermark_samples=int(len(self.train_data) * self.alpha)) - return model - - def _train_defense_model(self, clean_model=None): - print("[OptimizedWatermarkDefense] Generating and optimizing watermark key inputs...") - if not hasattr(self, 'wm_gen'): - self.wm_gen = WatermarkGenerator(self.train_data, - num_watermark_samples=int(len(self.train_data) * self.alpha)) - key_pairs = self.wm_gen.generate_watermark_set_with_clean_model(clean_model or self.net1) - optimizer = KeyInputOptimizer(self.train_data, key_pairs, T_opt=20) - key_pairs_optimized = optimizer.optimize() - print("[OptimizedWatermarkDefense] Training watermarked model...") - model = train_watermarked_model_full( - self.train_data, key_pairs_optimized, - epochs=self.wm_epochs, alpha=self.alpha, num_layers=self.num_layers - ) - self.net2 = model - self.key_pairs_optimized = key_pairs_optimized - return model, key_pairs_optimized - - def _train_surrogate_model(self): - pass diff --git a/pyhazard/models/defense/__init__.py b/pyhazard/models/defense/__init__.py deleted file mode 100644 index 057c1c3..0000000 --- a/pyhazard/models/defense/__init__.py +++ /dev/null @@ -1,23 +0,0 @@ -from .BackdoorWM import BackdoorWM -from .ImperceptibleWM import ImperceptibleWM -from .ImperceptibleWM2 import ImperceptibleWM2 -from .RandomWM import RandomWM -from .SurviveWM import SurviveWM -from .SurviveWM2 import SurviveWM2 -from .atom.ATOM import ATOM -from .Integrity import QueryBasedVerificationDefense as IntegrityVerification -from .GrOVe import GroveDefense -from .Revisiting import Revisiting - -__all__ = [ - 'BackdoorWM', - 'ImperceptibleWM', - 'ImperceptibleWM2', - 'RandomWM', - 'SurviveWM', - 'SurviveWM2', - 'IntegrityVerification' - 'GroveDefense' - 'ATOM', - 'Revisiting' -] diff --git a/pyhazard/models/defense/atom/ATOM.py b/pyhazard/models/defense/atom/ATOM.py deleted file mode 100644 index 01a105b..0000000 --- a/pyhazard/models/defense/atom/ATOM.py +++ /dev/null @@ -1,995 +0,0 @@ -import ast -import os -import random -from pathlib import Path -from time import time - -import networkx as nx -import numpy as np -import pandas as pd -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim -from sklearn.metrics import ( - precision_score, - recall_score, - f1_score, - accuracy_score, - roc_curve, - auc -) -from sklearn.model_selection import train_test_split -from torch.distributions import Categorical -from torch.nn.utils.rnn import pad_sequence -from torch.utils.data import Dataset, DataLoader -from torch_geometric.datasets import Planetoid, CitationFull, WebKB -from torch_geometric.nn import GCNConv -from torch_geometric.seed import seed_everything -from torch_geometric.utils import to_networkx -from tqdm import tqdm - -from pyhazard.datasets.datasets import Dataset as PyHazardDataset -from pyhazard.models.defense.base import BaseDefense -from pyhazard.utils.metrics import DefenseMetric, DefenseCompMetric - - -def set_seed(seed: int): - torch.manual_seed(seed) - torch.cuda.manual_seed(seed) - torch.cuda.manual_seed_all(seed) - np.random.seed(seed) - random.seed(seed) - - torch.backends.cudnn.deterministic = True - torch.backends.cudnn.benchmark = False - - seed_everything(seed) - - -class GCN(nn.Module): - def __init__(self, input_dim, hidden_dim, output_dim): - super(GCN, self).__init__() - self.conv1 = GCNConv(input_dim, hidden_dim) - self.conv2 = GCNConv(hidden_dim, output_dim) - - def forward(self, x, edge_index): - hidden = self.conv1(x, edge_index) - x = F.relu(hidden) - output = self.conv2(x, edge_index) - return F.log_softmax(output, dim=1), output - - -def train_gcn(model, data, optimizer, criterion, epochs=200, verbose=True): - model.train() - for epoch in range(epochs): - optimizer.zero_grad() - output, _ = model(data.x, data.edge_index) - loss = criterion(output[data.train_mask], data.y[data.train_mask]) - loss.backward() - optimizer.step() - - if verbose and epoch % 10 == 0: - print(f"[GCN-Train] Epoch {epoch}, Loss: {loss.item()}") - - -class TargetGCN: - def __init__(self, trained_model, data): - self.model = trained_model - self.data = data - - def predict(self, query_indices): - self.model.eval() - with torch.no_grad(): - output, _ = self.model(self.data.x, self.data.edge_index) - probs = F.softmax(output[query_indices], dim=1).cpu().numpy() - return probs - - def get_embedding(self): - self.model.eval() - with torch.no_grad(): - _, embeddings = self.model(self.data.x, self.data.edge_index) - return embeddings - - -def get_node_embedding(model, data, node_idx): - embeddings = model.get_embedding() - return embeddings[node_idx] - - -def get_one_hop_neighbors(data, node_idx): - edge_index = data.edge_index - neighbors = edge_index[1][edge_index[0] == node_idx].tolist() - return neighbors - - -def average_pooling_with_neighbors(model, data, node_idx): - embeddings = model.get_embedding() - neighbors = get_one_hop_neighbors(data, node_idx) - neighbors.append(node_idx) - neighbor_embeddings = embeddings[neighbors] - pooled_embedding = torch.mean(neighbor_embeddings, dim=0) - return pooled_embedding - - -def k_core_decomposition(graph): - k_core_dict = nx.core_number(graph) - return k_core_dict - - -def average_pooling_with_neighbors_batch(model, data, node_indices): - embeddings = model.get_embedding() - neighbors = [get_one_hop_neighbors(data, idx) for idx in node_indices] - - node_and_neighbors = [torch.tensor([idx] + list(neighbors[i])) for i, idx in enumerate(node_indices)] - - pooled_embeddings = torch.stack([ - embeddings[node_idx_list].mean(dim=0) for node_idx_list in node_and_neighbors - ]) - return pooled_embeddings - - -def compute_embedding_batch(target_model, data, k_core_values_graph, max_k_core, node_indices, lamb=1.0): - pooled_embeddings = average_pooling_with_neighbors_batch(target_model, data, node_indices) - k_core_values = torch.tensor([k_core_values_graph[node_idx] for node_idx in node_indices], dtype=torch.float32).to( - pooled_embeddings.device) - - max_k_core_tensor = torch.log(max_k_core) - scaled_k_core = torch.log(k_core_values) / max_k_core_tensor - scaling_function = 1 + lamb * (torch.sigmoid(scaled_k_core) - 0.5) * 2 - final_embeddings = pooled_embeddings * scaling_function.unsqueeze(-1) - return final_embeddings - - -def simple_embedding_batch(target_model, data, node_indices): - pooled_embeddings = average_pooling_with_neighbors_batch(target_model, data, node_indices) - return pooled_embeddings - - -def precompute_all_node_embeddings( - target_model, - data, - k_core_values_graph, - max_k_core, - lamb=1.0 -): - all_node_indices = list(range(data.num_nodes)) - all_embeddings = compute_embedding_batch( - target_model, - data, - k_core_values_graph, - max_k_core, - all_node_indices, - lamb=lamb - ) - return all_embeddings - - -def precompute_simple_embeddings(target_model, data): - all_node_indices = list(range(data.num_nodes)) - return simple_embedding_batch(target_model, data, all_node_indices) - - -def collate_fn_no_pad(batch): - batch_seqs = [item[0] for item in batch] - batch_labels = [item[1] for item in batch] - return batch_seqs, torch.tensor(batch_labels, dtype=torch.long) - - -def preprocess_sequences(df): - def convert_to_list(sequence): - if isinstance(sequence, str): - return ast.literal_eval(sequence) - return sequence - - df["Sequence"] = df["Sequence"].apply(convert_to_list) - return df - - -class SequencesDataset(Dataset): - def __init__(self, df): - self.df = df.reset_index(drop=True) - - def __len__(self): - return len(self.df) - - def __getitem__(self, idx): - seq = self.df.loc[idx, "Sequence"] - lbl = self.df.loc[idx, "Label"] - if isinstance(seq, str): - raise TypeError(f"Sequence should be list[int], but received str: {seq}") - return list(seq), int(lbl) - - -def split_and_adjust(dataset_sequences, seed): - train_df, temp_df = train_test_split(dataset_sequences, test_size=0.3, random_state=seed) - val_df, test_df = train_test_split(temp_df, test_size=0.5, random_state=seed) - return train_df, val_df, test_df - - -def build_loaders( - csv_path="attack_CiteSeer.csv", - batch_size=16, - drop_last=True, - seed=42, -): - df = pd.read_csv(csv_path) - df_unique = df.drop_duplicates(subset="Sequence") - df = df_unique - dataset_sequences = df[["Sequence", "Label"]].copy() - dataset_sequences = preprocess_sequences(dataset_sequences) - dataset_sequences["Label"] = dataset_sequences["Label"].astype(int) - dataset_sequences = dataset_sequences[dataset_sequences['Sequence'].apply(len) > 1] - - train_df, val_df, test_df = split_and_adjust(dataset_sequences, seed) - - train_dataset = SequencesDataset(train_df) - val_dataset = SequencesDataset(val_df) - test_dataset = SequencesDataset(test_df) - - train_loader = DataLoader( - train_dataset, - batch_size=batch_size, - shuffle=True, - collate_fn=collate_fn_no_pad, - drop_last=drop_last - ) - val_loader = DataLoader( - val_dataset, - batch_size=batch_size, - shuffle=False, - collate_fn=collate_fn_no_pad, - drop_last=drop_last - ) - test_loader = DataLoader( - test_dataset, - batch_size=batch_size, - shuffle=False, - collate_fn=collate_fn_no_pad, - drop_last=drop_last - ) - - return train_loader, val_loader, test_loader - - -def load_data_and_model(csv_path, batch_size, seed, data_path, lamb): - try: - script_dir = Path(__file__).resolve().parent - parent_dir = script_dir.parent - except NameError: - parent_dir = Path.cwd().parent - print( - "If __file__ is not defined, " - "the directory above the current working directory is used as the target directory." - ) - - os.chdir(parent_dir) - - train_loader, val_loader, test_loader = build_loaders( - csv_path=csv_path, - batch_size=batch_size, - drop_last=True, - seed=seed - ) - - # ======== Step 2: target_model, data ========= - if data_path == "CiteSeer": - dataset = Planetoid(root="./data", name=data_path) - data = dataset[0] - elif data_path == "PubMed": - dataset = Planetoid(root="./data", name="PubMed") - data = dataset[0] - elif data_path == "Cora": - dataset = Planetoid(root="./data", name=data_path) - data = dataset[0] - elif data_path == "Cora_ML": - dataset = CitationFull(root="./data", name="Cora_ML") - data = dataset[0] - num_nodes = data.num_nodes - num_train = int(num_nodes * 0.6) - num_val = int(num_nodes * 0.2) - num_test = num_nodes - num_train - num_val - perm = torch.randperm(num_nodes) - data.train_mask = torch.zeros(num_nodes, dtype=torch.bool) - data.val_mask = torch.zeros(num_nodes, dtype=torch.bool) - data.test_mask = torch.zeros(num_nodes, dtype=torch.bool) - - data.train_mask[perm[:num_train]] = True - data.val_mask[perm[num_train:num_train + num_val]] = True - data.test_mask[perm[num_train + num_val:]] = True - elif data_path == "Cornell" or data_path == "Wisconsin": - dataset = WebKB(root="./data", name=data_path) - data = dataset[0] - num_nodes = data.num_nodes - num_train = int(num_nodes * 0.6) - num_val = int(num_nodes * 0.2) - num_test = num_nodes - num_train - num_val - - perm = torch.randperm(num_nodes) - data.train_mask = torch.zeros(num_nodes, dtype=torch.bool) - data.val_mask = torch.zeros(num_nodes, dtype=torch.bool) - data.test_mask = torch.zeros(num_nodes, dtype=torch.bool) - - data.train_mask[perm[:num_train]] = True - data.val_mask[perm[num_train:num_train + num_val]] = True - data.test_mask[perm[num_train + num_val:]] = True - - trained_gcn = GCN(dataset.num_features, 16, dataset.num_classes) - target_model = TargetGCN(trained_model=trained_gcn, data=data) - - G = to_networkx(data, to_undirected=True) - G.remove_edges_from(nx.selfloop_edges(G)) - k_core_values_graph = k_core_decomposition(G) - max_k_core = torch.tensor(max(k_core_values_graph.values()), dtype=torch.float32) - - all_embeddings = precompute_all_node_embeddings( - target_model, data, k_core_values_graph, max_k_core, lamb=lamb - ) - - return train_loader, val_loader, test_loader, target_model, max_k_core, all_embeddings, dataset, data - - -class StateTransformMLP(nn.Module): - def __init__(self, input_dim, hidden_dim, output_dim): - super(StateTransformMLP, self).__init__() - self.fc1 = nn.Linear(input_dim, hidden_dim) - self.fc2 = nn.Linear(hidden_dim, output_dim) - - def forward(self, prob_factor): - x = prob_factor - x = F.relu(self.fc1(x)) - x = self.fc2(x) - return x - - -class FusionGRU(nn.Module): - def __init__(self, input_size, hidden_size): - super(FusionGRU, self).__init__() - self.hidden_size = hidden_size - - self.Wz = nn.Linear(input_size + hidden_size, hidden_size) - self.Wr = nn.Linear(input_size + hidden_size, hidden_size) - self.Wh = nn.Linear(input_size + hidden_size, hidden_size) - self.Wg = nn.Linear(input_size * 2, input_size) - self.bg = nn.Parameter(torch.zeros(input_size)) - - def forward(self, h_it, h_it_m1, hidden_state): - - delta_it = h_it - h_it_m1 - - concat_input = torch.cat((delta_it, h_it), dim=-1) - g_t = torch.sigmoid(self.Wg(concat_input) + self.bg) - - x_t = g_t * delta_it + (1 - g_t) * h_it - - combined = torch.cat((x_t, hidden_state), dim=-1) - z_t = torch.sigmoid(self.Wz(combined)) - r_t = torch.sigmoid(self.Wr(combined)) - - r_h_prev = r_t * hidden_state - combined_candidate = torch.cat((x_t, r_h_prev), dim=-1) - h_tilde = torch.tanh(self.Wh(combined_candidate)) - - h_next = (1 - z_t) * hidden_state + z_t * h_tilde - return h_next - - def process_sequence(self, inputs, hidden_state=None): - batch_size, seq_len, input_size = inputs.size() - if hidden_state is None: - hidden_state = torch.zeros(batch_size, self.hidden_size, device=inputs.device) - - outputs = [] - h_it_m1 = torch.zeros(batch_size, input_size, device=inputs.device) - - for t in range(seq_len): - h_it = inputs[:, t, :] - hidden_state = self.forward(h_it, h_it_m1, hidden_state) - outputs.append(hidden_state.unsqueeze(1)) - h_it_m1 = h_it - - return torch.cat(outputs, dim=1) - - -def test_model(agent, gru, mlp_transform, test_loader, target_model, data, all_embeddings, hidden_size, device): - agent.eval() - gru.eval() - mlp_transform.eval() - - total_reward = 0.0 - action_dim = 2 - all_true_labels = [] - all_predicted_labels = [] - all_predicted_probs = [] - - with torch.no_grad(): - for batch_seqs, batch_labels in test_loader: - batch_labels = batch_labels.to(device) - - batch_seqs = [torch.tensor(seq, dtype=torch.long, device=device) for seq in batch_seqs] - padded_seqs = pad_sequence(batch_seqs, batch_first=True, padding_value=0) - mask = (padded_seqs != 0).float().to(device) - - max_seq_len = padded_seqs.size(1) - hidden_states = torch.zeros(len(batch_seqs), hidden_size, device=device) - - all_inputs = [] - for t in range(max_seq_len): - node_indices = padded_seqs[:, t].tolist() - cur_inputs = all_embeddings[node_indices] - all_inputs.append(cur_inputs) - - all_inputs = torch.stack(all_inputs, dim=1).to(device) - hidden_states = gru.process_sequence(all_inputs) - masked_hidden_states = hidden_states * mask.unsqueeze(-1) - - prob_factors = torch.ones(len(batch_seqs), max_seq_len, action_dim, device=device) - custom_states = (mlp_transform(prob_factors) * masked_hidden_states).detach() - - actions, probabilities, _, _ = agent.select_action(custom_states.view(-1, hidden_size)) - actions = actions.view(len(batch_seqs), max_seq_len) - probabilities = probabilities.view(len(batch_seqs), max_seq_len) - - for i in range(len(batch_seqs)): - last_valid_step = (mask[i].sum().long() - 1).item() - predicted_action = actions[i, last_valid_step].item() - predicted_prob = probabilities[i, last_valid_step].item() - true_label = batch_labels[i].item() - - all_true_labels.append(true_label) - all_predicted_labels.append(predicted_action) - all_predicted_probs.append(predicted_prob) - - reward = custom_reward_function(predicted_action, true_label) - total_reward += reward - - accuracy = accuracy_score(all_true_labels, all_predicted_labels) - precision = precision_score(all_true_labels, all_predicted_labels, average='binary') - recall = recall_score(all_true_labels, all_predicted_labels, average='binary') - f1 = f1_score(all_true_labels, all_predicted_labels, average='binary') - - fpr, tpr, _ = roc_curve(all_true_labels, all_predicted_probs) - auc_value = auc(fpr, tpr) - - return accuracy, precision, recall, f1, auc_value - - -class Memory: - def __init__(self): - self.states = [] - self.actions = [] - self.log_probs = [] - self.rewards = [] - self.dones = [] - self.advantages = [] - self.entropies = [] - self.returns = [] - self.all_probs = {} - self.masks = [] - - def store(self, custom_states, action, log_prob, reward, done, entropy, probs=None, masks=None): - for i in range(custom_states.size(0)): - state_seq = custom_states[i] - action_seq = action[i] - log_prob_seq = log_prob[i] - reward_seq = reward[i] - done_seq = done[i] - mask_seq = masks[i] - - valid_len = int(mask_seq.sum().item()) - - state_seq = torch.cat([state_seq[:valid_len], - torch.zeros(custom_states.size(1) - valid_len, custom_states.size(2), - device=state_seq.device)]) - action_seq = torch.cat( - [action_seq[:valid_len], torch.zeros(action.size(1) - valid_len, device=action_seq.device)]) - log_prob_seq = torch.cat( - [log_prob_seq[:valid_len], torch.zeros(log_prob.size(1) - valid_len, device=log_prob_seq.device)]) - reward_seq = torch.cat( - [reward_seq[:valid_len], torch.zeros(reward.size(1) - valid_len, device=reward_seq.device)]) - done_seq = torch.cat([done_seq[:valid_len], torch.zeros(done.size(1) - valid_len, device=done_seq.device)]) - mask_seq = torch.cat([mask_seq[:valid_len], torch.zeros(masks.size(1) - valid_len, device=mask_seq.device)]) - - self.states.append(state_seq) - self.actions.append(action_seq) - self.log_probs.append(log_prob_seq) - self.rewards.append(reward_seq) - self.dones.append(done_seq) - self.masks.append(mask_seq) - - consistent_shape = all(tensor.shape == self.states[0].shape for tensor in self.states) - - def clear(self): - self.states = [] - self.actions = [] - self.log_probs = [] - self.rewards = [] - self.dones = [] - self.advantages = [] - self.entropies = [] - self.returns = [] - self.masks = [] - - -def compute_returns_and_advantages(memory, gamma=0.99, lam=0.95): - rewards = torch.stack(memory.rewards, dim=0) - dones = torch.stack(memory.dones, dim=0) - masks = torch.stack(memory.masks, dim=0) - batch_size, max_seq_len = rewards.size() - - returns = torch.zeros_like(rewards) - advantages = torch.zeros_like(rewards) - - running_return = torch.zeros(batch_size, device=rewards.device) - running_advantage = torch.zeros(batch_size, device=rewards.device) - - for t in reversed(range(max_seq_len)): - mask_t = masks[:, t] - reward_t = rewards[:, t] - done_t = dones[:, t] - - running_return = reward_t + gamma * running_return * (1 - done_t) - td_error = reward_t + gamma * (returns[:, t + 1] if t + 1 < max_seq_len else 0) * (1 - done_t) - reward_t - - running_return *= mask_t - td_error *= mask_t - - returns[:, t] = running_return - running_advantage = td_error + gamma * lam * running_advantage * (1 - done_t) - running_advantage *= mask_t - advantages[:, t] = running_advantage - - memory.returns = returns - memory.advantages = advantages - - -def custom_reward_function(predicted, label, predicted_distribution=None): - reward = 0.0 - if predicted_distribution is not None: - if predicted_distribution > 0.90: - reward += -8.0 - if predicted == 1 and label == 0: - return -22.0 - if predicted == 0 and label == 1: - return -18.0 - if predicted == 1 and label == 1: - return 16.0 - if predicted == 0 and label == 0: - return 16.0 - return reward - - -class PolicyNetwork(nn.Module): - def __init__(self, state_dim, action_dim): - super(PolicyNetwork, self).__init__() - self.fc1 = nn.Linear(state_dim, 64) - self.fc2 = nn.Linear(64, 64) - self.action_layer = nn.Linear(64, action_dim) - self.value_layer = nn.Linear(64, 1) - - def forward(self, state): - x = torch.relu(self.fc1(state)) - x = torch.relu(self.fc2(x)) - action_logits = self.action_layer(x) - state_value = self.value_layer(x) - return action_logits, state_value - - -class PPOAgent(nn.Module): - def __init__(self, learning_rate, batch_size, K_epochs, state_dim, action_dim, gru, mlp, clip_epsilon, entropy_coef, - device): - super(PPOAgent, self).__init__() - self.policy = PolicyNetwork(state_dim, action_dim).to(device) - self.optimizer = optim.Adam( - list(self.policy.parameters()) + list(gru.parameters()) + list(mlp.parameters()), - lr=learning_rate - ) - self.policy_old = PolicyNetwork(state_dim, action_dim).to(device) - self.policy_old.load_state_dict(self.policy.state_dict()) - self.mse_loss = nn.MSELoss() - self.batch_size = batch_size - self.K_epochs = K_epochs - self.device = device - self.hidden_size = state_dim - self.clip_epsilon = clip_epsilon - self.entropy_coef = entropy_coef - - def select_action(self, state): - device = next(self.policy.parameters()).device - if isinstance(state, torch.Tensor): - state = state.clone().detach().to(device) - else: - state = torch.tensor(state, dtype=torch.float).to(device) - - with torch.no_grad(): - action_logits, _ = self.policy_old(state) - probs = torch.softmax(action_logits, dim=-1) - dist = Categorical(probs) - actions = dist.sample() - log_probs = dist.log_prob(actions) - entropy = dist.entropy() - - return actions, log_probs, entropy, probs - - def update(self, memory): - states = torch.stack(memory.states).view(self.batch_size, -1, self.hidden_size).to(self.device) - actions = torch.cat(memory.actions, dim=0) - actions = actions.view(self.batch_size, -1).to(self.device) - log_probs_old = torch.cat(memory.log_probs, dim=0).view(self.batch_size, -1).to(self.device) - returns = memory.returns.view(self.batch_size, -1).to(self.device) - advantages = memory.advantages.view(self.batch_size, -1).to(self.device) - - for _ in range(self.K_epochs): - action_logits, state_values = self.policy(states) - probs = torch.softmax(action_logits, dim=-1) - dist = Categorical(probs) - - log_probs = dist.log_prob(actions.squeeze()).unsqueeze(1) - entropy = dist.entropy().mean() - - log_probs = log_probs.view_as(advantages) - ratios = torch.exp(log_probs - log_probs_old) - surr1 = ratios * advantages - surr2 = torch.clamp(ratios, 1 - self.clip_epsilon, 1 + self.clip_epsilon) * advantages - - loss = -torch.min(surr1, surr2).mean() + \ - 0.5 * self.mse_loss(state_values.squeeze(), returns) - \ - self.entropy_coef * entropy - - self.optimizer.zero_grad() - loss.backward() - self.optimizer.step() - - self.policy_old.load_state_dict(self.policy.state_dict()) - - -class ATOM(BaseDefense): - supported_api_types = {"pyg"} - supported_datasets = { - "Cora", "CiteSeer", "PubMed", - "Computers", "Photo", - "CS", "Physics" - } - - def __init__(self, dataset: PyHazardDataset, attack_node_fraction: float = 0): - super().__init__(dataset, attack_node_fraction) - self.dataset = dataset - # Load dataset related information, following BackdoorWM pattern - self.graph_dataset = dataset.graph_dataset - self.graph_data = dataset.graph_data - - # Get basic information - self.num_nodes = dataset.num_nodes - self.num_features = dataset.num_features - self.num_classes = dataset.num_classes - - # Set device - self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - self.graph_data = self.graph_data.to(device=self.device) - - def _load_data_and_model(self, batch_size=16, seed=0, lamb=0): - """ - Load data and model following BackdoorWM format - Use data from self.dataset directly instead of reloading - """ - # Get dataset name - dataset_name = self.dataset.__class__.__name__ - - # Build CSV path - current_dir = os.path.dirname(os.path.abspath(__file__)) - csv_path = os.path.join(current_dir, 'csv_data', f'attack_{dataset_name}.csv') - - # Load sequence data - train_loader, val_loader, test_loader = build_loaders( - csv_path=csv_path, - batch_size=batch_size, - drop_last=True, - seed=seed - ) - - # Use PyG format directly (since we declared supported_api_types = {"pyg"}) - features = self.graph_data.x - labels = self.graph_data.y - train_mask = self.graph_data.train_mask - test_mask = self.graph_data.test_mask - - # Create and train GCN model - trained_gcn = GCN(self.num_features, 16, self.num_classes).to(self.device) - target_model = TargetGCN(trained_model=trained_gcn, data=self.graph_data) - - # Train model - self._train_gcn_model(trained_gcn, self.graph_data, train_mask, labels) - - # Compute k-core decomposition of the graph - G = to_networkx(self.graph_data, to_undirected=True) - G.remove_edges_from(nx.selfloop_edges(G)) - k_core_values_graph = k_core_decomposition(G) - max_k_core = torch.tensor(max(k_core_values_graph.values()), dtype=torch.float32) - - # Precompute embeddings for all nodes - all_embeddings = precompute_all_node_embeddings( - target_model, self.graph_data, k_core_values_graph, max_k_core, lamb=lamb - ) - - return train_loader, val_loader, test_loader, target_model, max_k_core, all_embeddings - - def _convert_to_networkx(self): - """ - Convert graph data to NetworkX format, compatible with different graph data structures - """ - if hasattr(self.graph_data, 'edge_index'): - # PyG format - return to_networkx(self.graph_data, to_undirected=True) - else: - # DGL format - requires conversion - # This may need adjustment based on specific DGL format - edge_list = [] - if hasattr(self.graph_data, 'edges'): - src, dst = self.graph_data.edges() - edge_list = list(zip(src.cpu().numpy(), dst.cpu().numpy())) - - G = nx.Graph() - G.add_nodes_from(range(self.num_nodes)) - G.add_edges_from(edge_list) - return G - - def _create_default_masks(self): - """ - Create default train/test masks for datasets without predefined masks - """ - num_nodes = self.num_nodes - num_train = int(num_nodes * 0.6) - num_test = int(num_nodes * 0.2) - - perm = torch.randperm(num_nodes) - train_mask = torch.zeros(num_nodes, dtype=torch.bool, device=self.device) - test_mask = torch.zeros(num_nodes, dtype=torch.bool, device=self.device) - - train_mask[perm[:num_train]] = True - test_mask[perm[num_train:num_train + num_test]] = True - - return train_mask, test_mask - - @staticmethod - def _train_gcn_model(model, data, train_mask, labels): - """ - Train GCN model for PyG format - """ - model.train() - optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4) - criterion = nn.NLLLoss() - - features = data.x - edge_index = data.edge_index - - for epoch in range(200): - optimizer.zero_grad() - output, _ = model(features, edge_index) - loss = criterion(output[train_mask], labels[train_mask]) - loss.backward() - optimizer.step() - - if epoch % 50 == 0: - print(f" Epoch {epoch}: Loss: {loss.item():.4f}") - - @staticmethod - def _evaluate_downstream_task(model, data, test_mask, labels): - """ - Evaluate model performance on downstream task - """ - model.eval() - with torch.no_grad(): - output, _ = model(data.x, data.edge_index) - preds = output[test_mask].argmax(dim=1).cpu() - true_labels = labels[test_mask].cpu() - - return preds, true_labels - - def defend(self): - """ - Execute ATOM defense, following BackdoorWM's defend method structure - """ - metric_comp = DefenseCompMetric() - metric_comp.start() - - print("====================ATOM Defense====================") - - # Configuration parameters - config = { - "seed": 37719, - "K_epochs": 10, - "batch_size": 16, - "hidden_size": 196, - "hidden_action_dim": 16, - "clip_epsilon": 0.30, - "entropy_coef": 0.05, - "lr": 1e-3, - "gamma": 0.99, - "lam": 0.95, - "num_epochs": 2, - "lamb": 0 - } - - # Set random seed - set_seed(config["seed"]) - - # Load data and model - defense_s = time() - train_loader, val_loader, test_loader, target_model, max_k_core, all_embeddings = self._load_data_and_model( - batch_size=config["batch_size"], - seed=config["seed"], - lamb=config["lamb"] - ) - - # Set device and parameters - device = self.device - action_dim = 2 - hidden_size = config["hidden_size"] - - # Initialize model components - input_size = self.num_classes - gru = FusionGRU(input_size=input_size, hidden_size=hidden_size).to(device) - mlp_transform = StateTransformMLP(action_dim, config["hidden_action_dim"], hidden_size).to(device) - agent = PPOAgent( - learning_rate=config["lr"], - batch_size=config["batch_size"], - K_epochs=config["K_epochs"], - state_dim=hidden_size, - action_dim=action_dim, - gru=gru, - mlp=mlp_transform, - clip_epsilon=config["clip_epsilon"], - entropy_coef=config["entropy_coef"], - device=device - ).to(device) - - # Training process - memory = Memory() - accuracy_list = [] - precision_list = [] - recall_list = [] - f1_list = [] - auc_value_list = [] - - for epoch in tqdm(range(config["num_epochs"]), desc="Training Epochs", ncols=100): - # Training logic - episode_reward = 0.0 - for batch_idx, (batch_seqs, batch_labels) in enumerate(train_loader): - batch_labels = batch_labels.to(device) - - # Convert sequences to tensors and pad them - batch_seqs = [torch.tensor(seq, dtype=torch.long, device=device) for seq in batch_seqs] - padded_seqs = pad_sequence(batch_seqs, batch_first=True, padding_value=0) - mask = (padded_seqs != 0).float().to(device) - max_seq_len = padded_seqs.size(1) - - # Extract embeddings for all time steps - all_inputs = [] - for t in range(max_seq_len): - node_indices = padded_seqs[:, t].tolist() - cur_inputs = all_embeddings[node_indices] - all_inputs.append(cur_inputs) - all_inputs = torch.stack(all_inputs, dim=1).to(device) - - # Process through GRU - hidden_states = gru.process_sequence(all_inputs) - masked_hidden_states = hidden_states * mask.unsqueeze(-1) - - # Prepare probability factors - prob_factors = torch.ones(len(batch_seqs), max_seq_len, action_dim, device=device) - if memory.all_probs: - prob_factors[:, :-1] = torch.stack([ - torch.tensor(memory.all_probs.get(t, [1.0] * action_dim)) - for t in range(max_seq_len - 1) - ], dim=1).to(device) - - # Transform states - custom_states = (mlp_transform(prob_factors) * masked_hidden_states).detach() - - # Select actions using PPO agent - actions, log_probs, entropies, probs = agent.select_action( - custom_states.view(-1, hidden_size) - ) - - # Reshape outputs - actions = actions.view(len(batch_seqs), max_seq_len) - log_probs = log_probs.view(len(batch_seqs), max_seq_len) - entropies = entropies.view(len(batch_seqs), max_seq_len) - probs = probs.view(len(batch_seqs), max_seq_len, action_dim) - - # Initialize rewards and done flags - rewards = torch.zeros(len(batch_seqs), max_seq_len, device=device) - dones = torch.zeros(len(batch_seqs), max_seq_len, device=device) - - # Compute rewards - batch_predictions = actions.cpu().numpy() - predicted_distribution = (batch_predictions == 1).mean() - last_valid_steps = mask.sum(dim=1).long() - 1 - - for i in range(len(batch_seqs)): - for t in range(last_valid_steps[i] + 1): - if mask[i, t] == 1: - r = custom_reward_function( - actions[i, t].item(), - batch_labels[i].item(), - predicted_distribution - ) - rewards[i, t] = r - episode_reward += r - dones[i, last_valid_steps[i]] = 1.0 - - # Store experience in memory - memory.store(custom_states, actions, log_probs, rewards, dones, - entropy=entropies, masks=mask) - - # Compute returns and advantages - compute_returns_and_advantages(memory, gamma=config["gamma"], lam=config["lam"]) - - # Update agent - agent.update(memory) - memory.clear() - - # Testing and evaluation - agent.eval() - gru.eval() - mlp_transform.eval() - with torch.no_grad(): - accuracy, precision, recall, f1, auc_value = test_model( - agent, gru, mlp_transform, test_loader, - target_model, self.graph_data, all_embeddings, hidden_size, device - ) - accuracy_list.append(accuracy) - precision_list.append(precision) - recall_list.append(recall) - f1_list.append(f1) - auc_value_list.append(auc_value) - - defense_e = time() - inference_s = time() - - # Calculate final watermark results - wm_accuracy = np.mean(accuracy_list) - wm_precision = np.mean(precision_list) - wm_recall = np.mean(recall_list) - wm_f1 = np.mean(f1_list) - wm_auc = np.mean(auc_value_list) - - # Evaluate downstream task performance - if hasattr(self.graph_data, 'test_mask'): - test_mask = self.graph_data.test_mask - labels = self.graph_data.y if hasattr(self.graph_data, 'y') else self.graph_data.ndata.get('label') - else: - _, test_mask = self._create_default_masks() - labels = self.graph_data.y if hasattr(self.graph_data, 'y') else self.graph_data.ndata.get('label') - - downstream_preds, downstream_true = self._evaluate_downstream_task( - target_model.model, self.graph_data, test_mask, labels - ) - - # Convert watermark results to tensors for metric computation - wm_predictions = torch.tensor([1 if acc > 0.5 else 0 for acc in accuracy_list[-10:]]) - wm_targets = torch.ones_like(wm_predictions) - - inference_e = time() - - # Compute metrics - metric = DefenseMetric() - metric.update(downstream_preds, downstream_true) - metric.update_wm(wm_predictions, wm_targets) - - metric_comp.update( - defense_time=(defense_e - defense_s), - inference_defense_time=(inference_e - inference_s) - ) - metric_comp.end() - - print("====================Final Results====================") - res = metric.compute() - res_comp = metric_comp.compute() - - # Print detailed results - print(f"Downstream Task - Accuracy: {res['accuracy']:.4f}, F1: {res['f1']:.4f}") - print(f"Watermark Detection - Accuracy: {wm_accuracy:.4f}, AUC: {wm_auc:.4f}") - print(f"Defense Time: {res_comp['defense_time']:.4f}s") - print(f"Inference Time: {res_comp['inference_defense_time']:.4f}s") - - return res, res_comp diff --git a/pyhazard/models/defense/atom/__init__.py b/pyhazard/models/defense/atom/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/pyhazard/models/defense/atom/csv_data/attack_CiteSeer.csv b/pyhazard/models/defense/atom/csv_data/attack_CiteSeer.csv deleted file mode 100644 index 3c8bf4e..0000000 --- a/pyhazard/models/defense/atom/csv_data/attack_CiteSeer.csv +++ /dev/null @@ -1,177 +0,0 @@ -Sequence,Label,NCL,Query Budget,Num Sample Nodes,Fidelity -"[3021, 1189, 3140, 1305, 1067, 1487, 666, 9, 2138, 1491, 2060, 1007, 1040, 2877, 1767, 2168, 1208, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9]",0,,,, -"[3021, 1189, 3140, 1305, 1067, 1487, 666, 9, 2138, 1491, 2060, 1007, 1040, 2877, 1767, 2168, 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1981, 2418, 95, 1376, 1652, 1171, 1505, 1676, 2, 1991, 1343, 1842]",1,30.0,50.0,350.0,0.7186115214180206,, diff --git a/pyhazard/models/defense/atom/csv_data/attack_PubMed.csv b/pyhazard/models/defense/atom/csv_data/attack_PubMed.csv deleted file mode 100644 index 8edd9ed..0000000 --- a/pyhazard/models/defense/atom/csv_data/attack_PubMed.csv +++ /dev/null @@ -1,200 +0,0 @@ -Sequence,Label,NCL,Query Budget,Num Sample Nodes,Fidelity,Gamma -"[7711, 2031, 2873, 7379, 11595, 18867, 4402, 10932, 2098, 8862, 8629, 1119, 3607, 17198, 18145, 13046, 3951, 4421, 9047, 12817, 656, 2602, 17332, 10308, 15498, 6691, 18897, 4638, 14756, 4130, 3240, 6578, 19125, 18812, 9087, 9574, 10769, 7659, 18623, 9358, 7926, 3831, 1398, 4405, 5941, 15944, 7712, 16747, 7997, 6586, 11396, 465, 3878, 13076, 16051, 5665, 13371, 11513, 18508, 17222, 10087, 8382, 13392, 3150, 6988, 18725, 3584, 3074, 4798, 17616, 6923, 18109, 5808, 9951, 9685, 10005, 17687, 514, 18121, 778, 16140, 17477, 6838, 17899, 9388, 11665, 7156, 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__init__(self, dataset: Dataset, attack_node_fraction: float, - device: Optional[Union[str, torch.device]] = None): - self.device = torch.device(device) if device else get_device() - print(f"Using device: {self.device}") - - # graph data - self.dataset = dataset - self.graph_dataset = dataset.graph_dataset - self.graph_data = dataset.graph_data - - # meta data - self.num_nodes = dataset.num_nodes - self.num_features = dataset.num_features - self.num_classes = dataset.num_classes - - # params - self.attack_node_fraction = attack_node_fraction - - self._check_dataset_compatibility() - - def _check_dataset_compatibility(self): - cls_name = self.dataset.__class__.__name__ - - if self.supported_api_types and self.dataset.api_type not in self.supported_api_types: - raise ValueError( - f"API type '{self.dataset.api_type}' is not supported. Supported: {self.supported_api_types}") - - if self.supported_datasets and cls_name not in self.supported_datasets: - raise ValueError(f"Dataset '{cls_name}' is not supported. Supported: {self.supported_datasets}") - - @abstractmethod - def defend(self): - """ - Execute the defense mechanism. - """ - raise NotImplementedError - - def _load_model(self): - """ - Load pre-trained model. - """ - raise NotImplementedError - - def _train_target_model(self): - """ - This is an optional method. - """ - raise NotImplementedError - - def _train_defense_model(self): - """ - This is an optional method. - """ - raise NotImplementedError - - def _train_surrogate_model(self): - """ - This is an optional method. - """ - raise NotImplementedError diff --git a/pyproject.toml b/pyproject.toml index 865372e..3c51123 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,9 +5,9 @@ build-backend = "setuptools.build_meta" [project] name = "PyHazard" version = "1.1.0" -description = "A Python package for Graph Intellectual Property Protection" +description = "A Python framework for AI-powered hazard prediction and risk assessment" readme = "README.md" -authors = [{ name = "Bolin Shen", email = "blshen@fsu.edu" }] +authors = [{ name = "Xueqi Cheng", email = "xc25@fsu.edu" }] license = { text = "BSD-2-Clause" } requires-python = ">=3.8, <3.13" @@ -26,7 +26,9 @@ dependencies = [ classifiers = [ "Development Status :: 3 - Alpha", "Intended Audience :: Science/Research", + "Intended Audience :: Developers", "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Scientific/Engineering :: Information Analysis", "License :: OSI Approved :: BSD License", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", From 0fb4521e985bc8dee849e9a3285eae7e874c7fe9 Mon Sep 17 00:00:00 2001 From: Xueqi Cheng Date: Wed, 3 Dec 2025 15:48:44 -0500 Subject: [PATCH 05/39] update --- docs/source/references.rst | 11 ----------- docs/source/team.rst | 34 +++++++++++++++++----------------- 2 files changed, 17 insertions(+), 28 deletions(-) diff --git a/docs/source/references.rst b/docs/source/references.rst index dbec751..818a674 100644 --- a/docs/source/references.rst +++ b/docs/source/references.rst @@ -4,14 +4,3 @@ References Academic Publications --------------------- -Model Extraction Attacks -~~~~~~~~~~~~~~~~~~~~~~~~ - -Wu, B., Yang, X., Pan, S., & Yuan, X. (2022). Model extraction attacks on graph neural networks: Taxonomy and realisation. In *Proceedings of the 2022 ACM on Asia conference on computer and communications security*, 337-350. - - -Watermarking Defense -~~~~~~~~~~~~~~~~~~~~ - -Zhao, X., Wu, H., & Zhang, X. (2021). Watermarking graph neural networks by random graphs. In *2021 9th International Symposium on Digital Forensics and Security (ISDFS)*, 1-6. IEEE. - diff --git a/docs/source/team.rst b/docs/source/team.rst index 9e198be..3c723d0 100644 --- a/docs/source/team.rst +++ b/docs/source/team.rst @@ -13,26 +13,26 @@ Contributors Principal Contributors & Maintainers ------------------------------------ -* `Yuxiang Sun `__ - University of Wisconsin-Madison -* `Chenxi Zhao `__ - Northeastern University -* `Lincan Li `__ - Florida State University +.. * `Yuxiang Sun `__ - University of Wisconsin-Madison +.. * `Chenxi Zhao `__ - Northeastern University +.. * `Lincan Li `__ - Florida State University Core Contributors ----------------- -* `Unique Karki `_ - [Affiliation] -* `Yujing Ju `__ - Heriot-Watt University -* `Zhan Cheng `__ - University of Wisconsin-Madison -* `Zaiyi Zheng `__ - University of Virginia -* `Tyler Blalock `_ - [Affiliation] -* `Sibtain Syed `_ - [Affiliation] -* `Md Ibrahim `_ - Uttara University, Bangladesh -* `Aditya Khanal `_ - Tribhuvan University, Nepal -* `Kedar Satish Awale `_ - Florida State University -* `Hong Iris `_ - Washington University in St. Louis -* `Aasman Bashyal `_ - [Affiliation] -* `Cameron Bender `_ - Florida State University -* `Yushi Huang `_ - [Affiliation] -* `Anurag Shukla `_ - [Affiliation] +.. * `Unique Karki `_ - [Affiliation] +.. * `Yujing Ju `__ - Heriot-Watt University +.. * `Zhan Cheng `__ - University of Wisconsin-Madison +.. * `Zaiyi Zheng `__ - University of Virginia +.. * `Tyler Blalock `_ - [Affiliation] +.. * `Sibtain Syed `_ - [Affiliation] +.. * `Md Ibrahim `_ - Uttara University, Bangladesh +.. * `Aditya Khanal `_ - Tribhuvan University, Nepal +.. * `Kedar Satish Awale `_ - Florida State University +.. * `Hong Iris `_ - Washington University in St. Louis +.. * `Aasman Bashyal `_ - [Affiliation] +.. * `Cameron Bender `_ - Florida State University +.. * `Yushi Huang `_ - [Affiliation] +.. * `Anurag Shukla `_ - [Affiliation] ---------------- From fad9f5b7ca4a5f3837c97b7118a62bef984e9f2e Mon Sep 17 00:00:00 2001 From: Xueqi Cheng Date: Wed, 3 Dec 2025 15:50:42 -0500 Subject: [PATCH 06/39] update license --- LICENSE | 35 ++++++++++++++++------------------- PyHazard.egg-info/PKG-INFO | 10 +++++----- README.md | 2 +- docs/source/cite.rst | 14 +++++++------- pyproject.toml | 4 ++-- 5 files changed, 31 insertions(+), 34 deletions(-) diff --git a/LICENSE b/LICENSE index 7707a90..f3f4432 100644 --- a/LICENSE +++ b/LICENSE @@ -1,24 +1,21 @@ -BSD 2-Clause License +MIT License Copyright (c) 2025 Xueqi Cheng -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: -1. Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. -2. Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/PyHazard.egg-info/PKG-INFO b/PyHazard.egg-info/PKG-INFO index 0f4da31..3bd3d9a 100644 --- a/PyHazard.egg-info/PKG-INFO +++ b/PyHazard.egg-info/PKG-INFO @@ -2,8 +2,8 @@ Metadata-Version: 2.4 Name: PyHazard Version: 1.1.0 Summary: A Python framework for AI-powered hazard prediction and risk assessment -Author-email: Bolin Shen -License: BSD-2-Clause +Author-email: Xueqi Cheng +License: MIT Project-URL: Homepage, https://labrai.github.io/PyHazard Project-URL: Repository, https://github.com/LabRAI/PyHazard Classifier: Development Status :: 3 - Alpha @@ -11,7 +11,7 @@ Classifier: Intended Audience :: Science/Research Classifier: Intended Audience :: Developers Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence Classifier: Topic :: Scientific/Engineering :: Information Analysis -Classifier: License :: OSI Approved :: BSD License +Classifier: License :: OSI Approved :: MIT License Classifier: Programming Language :: Python :: 3.8 Classifier: Programming Language :: Python :: 3.9 Classifier: Programming Language :: Python :: 3.10 @@ -146,7 +146,7 @@ If you use PyHazard in your research, please cite: ```bibtex @software{pyhazard2025, title={PyHazard: A Python Framework for AI-Powered Hazard Prediction}, - author={Shen, Bolin and others}, + author={Cheng, Xueqi}, year={2025}, url={https://github.com/LabRAI/PyHazard} } @@ -154,7 +154,7 @@ If you use PyHazard in your research, please cite: ## License -[BSD 2-Clause License](LICENSE) +[MIT License](LICENSE) ## Contact diff --git a/README.md b/README.md index 9546b6f..54e3b9d 100644 --- a/README.md +++ b/README.md @@ -116,7 +116,7 @@ If you use PyHazard in your research, please cite: ## License -[BSD 2-Clause License](LICENSE) +[MIT License](LICENSE) ## Contact diff --git a/docs/source/cite.rst b/docs/source/cite.rst index 16c3b82..1e2a6ee 100644 --- a/docs/source/cite.rst +++ b/docs/source/cite.rst @@ -2,11 +2,11 @@ How to Cite =========== If you find it useful, please considering cite the following work: -.. code-block:: bibtex +.. .. code-block:: bibtex - @article{li2025intellectual, - title={Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses}, - author={Li, Lincan and Shen, Bolin and Zhao, Chenxi and Sun, Yuxiang and Zhao, Kaixiang and Pan, Shirui and Dong, Yushun}, - journal={arXiv preprint arXiv:2508.19641}, - year={2025} - } +.. @article{li2025intellectual, +.. title={Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses}, +.. author={Li, Lincan and Shen, Bolin and Zhao, Chenxi and Sun, Yuxiang and Zhao, Kaixiang and Pan, Shirui and Dong, Yushun}, +.. journal={arXiv preprint arXiv:2508.19641}, +.. year={2025} +.. } diff --git a/pyproject.toml b/pyproject.toml index 3c51123..367b2c7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,7 +8,7 @@ version = "1.1.0" description = "A Python framework for AI-powered hazard prediction and risk assessment" readme = "README.md" authors = [{ name = "Xueqi Cheng", email = "xc25@fsu.edu" }] -license = { text = "BSD-2-Clause" } +license = { text = "MIT" } requires-python = ">=3.8, <3.13" dependencies = [ @@ -29,7 +29,7 @@ classifiers = [ "Intended Audience :: Developers", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Information Analysis", - "License :: OSI Approved :: BSD License", + "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", From 24ac99b37978800fe1518c370b960d21c88ad3a2 Mon Sep 17 00:00:00 2001 From: Xueqi Cheng Date: Wed, 3 Dec 2025 16:07:40 -0500 Subject: [PATCH 07/39] update logo --- docs/source/index.rst | 2 +- static/logo.pdf | Bin 0 -> 205938 bytes static/logo.png | Bin 109977 -> 0 bytes 3 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 static/logo.pdf delete mode 100644 static/logo.png diff --git a/docs/source/index.rst b/docs/source/index.rst index 8d62641..b1983be 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -1,7 +1,7 @@ .. raw:: html
- PyHazard Icon + PyHazard Icon
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    + + + + + \ No newline at end of file diff --git a/docs/build/html/_autosummary/datasets/pyhazard.datasets.PubMed.html b/docs/build/html/_autosummary/datasets/pyhazard.datasets.PubMed.html new file mode 100644 index 0000000..7115031 --- /dev/null +++ b/docs/build/html/_autosummary/datasets/pyhazard.datasets.PubMed.html @@ -0,0 +1,383 @@ + + + + + + + + + + pyhazard.datasets.PubMed - PyHazard 1.0.0 documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + \ No newline at end of file diff --git a/docs/build/html/_autosummary/utils/pyhazard.utils.dglTopyg.html b/docs/build/html/_autosummary/utils/pyhazard.utils.dglTopyg.html new file mode 100644 index 0000000..0b9bb2c --- /dev/null +++ b/docs/build/html/_autosummary/utils/pyhazard.utils.dglTopyg.html @@ -0,0 +1,353 @@ + + + + + + + + + + pyhazard.utils.dglTopyg - PyHazard 1.0.0 documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
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    PyHazard 1.0.0 documentation
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    +

    pyhazard.utils.dglTopyg

    +

    Functions

    +
    + + + + + + +

    dgl_to_pyg_data(dgl_graph)

    +
    +
    +
    +pyhazard.utils.dglTopyg.dgl_to_pyg_data(dgl_graph)[source]
    +
    + +
    + +
    +
    + +
    + +
    +
    + + + + + \ No newline at end of file diff --git a/docs/build/html/_autosummary/utils/pyhazard.utils.hardware.html b/docs/build/html/_autosummary/utils/pyhazard.utils.hardware.html new file mode 100644 index 0000000..f1b3413 --- /dev/null +++ b/docs/build/html/_autosummary/utils/pyhazard.utils.hardware.html @@ -0,0 +1,362 @@ + + + + + + + + + + pyhazard.utils.hardware - PyHazard 1.0.0 documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    pyhazard.utils.hardware

    +

    Functions

    +
    + + + + + + + + + +

    get_device()

    set_device(device_str)

    +
    +
    +
    +pyhazard.utils.hardware.get_device()[source]
    +
    + +
    +
    +pyhazard.utils.hardware.set_device(device_str)[source]
    +
    + +
    + +
    +
    + +
    + +
    +
    + + + + + \ No newline at end of file diff --git a/docs/build/html/_autosummary/utils/pyhazard.utils.html b/docs/build/html/_autosummary/utils/pyhazard.utils.html new file mode 100644 index 0000000..1f9c89c --- /dev/null +++ b/docs/build/html/_autosummary/utils/pyhazard.utils.html @@ -0,0 +1,409 @@ + + + + + + + + + + pyhazard.utils - PyHazard 1.0.0 documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
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    +

    pyhazard.utils

    +
    +
    +class pyhazard.utils.GraphNeuralNetworkMetric(fidelity=0, accuracy=0, model=None, graph=None, features=None, mask=None, labels=None, query_labels=None)[source]
    +

    Bases: object

    +

    Graph Neural Network Metric Class.

    +

    This class evaluates two metrics, fidelity and accuracy, for a given +GNN model on a specified graph and features.

    +
    +
    +static calculate_surrogate_fidelity(target_model, surrogate_model, data, mask=None)[source]
    +

    Calculate fidelity between target and surrogate model predictions.

    +
    +
    Parameters:
    +
      +

    • target_model – Original model

      • +

    • surrogate_model – Extracted surrogate model

      • +

    • data – Input graph data

      • +

    • mask – Optional mask for evaluation on specific nodes

      • +
    +
    +
    Returns:
    +

    Fidelity score (percentage of matching predictions)

    +
    +
    Return type:
    +

    float

    +
    +
    +
    + +
    +
    +evaluate()[source]
    +

    Main function to update fidelity and accuracy scores.

    +
    + +
    +
    +evaluate_helper(model, graph, features, labels, mask)[source]
    +

    Helper function to evaluate the model’s performance.

    +
    + +
    +
    +static evaluate_surrogate_extraction(target_model, surrogate_model, data, train_mask=None, val_mask=None, test_mask=None)[source]
    +

    Comprehensive evaluation of surrogate extraction attack.

    +
    +
    Parameters:
    +
      +

    • target_model – Original model

      • +

    • surrogate_model – Extracted surrogate model

      • +

    • data – Input graph data

      • +

    • train_mask – Mask for training nodes

      • +

    • val_mask – Mask for validation nodes

      • +

    • test_mask – Mask for test nodes

      • +
    +
    +
    Returns:
    +

    Dictionary containing fidelity scores for different data splits

    +
    +
    Return type:
    +

    dict

    +
    +
    +
    + +
    + +

    Modules

    +
    + + + + + + + + + + + + +

    dglTopyg

    hardware

    metrics

    +
    +
    + +
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    +
    + +
    + + +
    +
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    + Copyright © 2025, RAILab +
    + Made with Sphinx and @pradyunsg's + + Furo + +
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    + + + + + \ No newline at end of file diff --git a/docs/build/html/_autosummary/utils/pyhazard.utils.metrics.html b/docs/build/html/_autosummary/utils/pyhazard.utils.metrics.html new file mode 100644 index 0000000..6cd0c65 --- /dev/null +++ b/docs/build/html/_autosummary/utils/pyhazard.utils.metrics.html @@ -0,0 +1,680 @@ + + + + + + + + + + pyhazard.utils.metrics - PyHazard 1.0.0 documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    pyhazard.utils.metrics

    +

    Classes

    +
    + + + + + + + + + + + + + + + + + + + + + +

    AttackCompMetric([gpu_count])

    AttackMetric()

    DefenseCompMetric([gpu_count])

    DefenseMetric()

    GraphNeuralNetworkMetric([fidelity, ...])

    Graph Neural Network Metric Class.

    MetricBase()

    +
    +
    +
    +class pyhazard.utils.metrics.AttackCompMetric(gpu_count=None)[source]
    +

    Bases: object

    +
    +
    +compute()[source]
    +
    + +
    +
    +end()[source]
    +
    + +
    +
    +start()[source]
    +
    + +
    +
    +update(train_target_time=None, query_target_time=None, train_surrogate_time=None, attack_time=None, inference_surrogate_time=None)[source]
    +
    + +
    + +
    +
    +class pyhazard.utils.metrics.AttackMetric[source]
    +

    Bases: MetricBase

    +
    +
    +_abc_impl = <_abc._abc_data object>
    +
    + +
    +
    +compute()[source]
    +

    Compute and return all metric results.

    +
    + +
    +
    +compute_fidelity(preds_label, query_label)[source]
    +
    +
    Return type:
    +

    Dict[str, float]

    +
    +
    +
    + +
    +
    +reset()[source]
    +

    Reset internal state.

    +
    +
    Return type:
    +

    None

    +
    +
    +
    + +
    +
    +update(preds, labels, query_label)[source]
    +

    Update internal metric state.

    +
    + +
    + +
    +
    +class pyhazard.utils.metrics.DefenseCompMetric(gpu_count=None)[source]
    +

    Bases: object

    +
    +
    +compute()[source]
    +
    + +
    +
    +end()[source]
    +
    + +
    +
    +start()[source]
    +
    + +
    +
    +update(train_target_time=None, train_defense_time=None, inference_defense_time=None, defense_time=None)[source]
    +
    + +
    + +
    +
    +class pyhazard.utils.metrics.DefenseMetric[source]
    +

    Bases: MetricBase

    +
    +
    +_abc_impl = <_abc._abc_data object>
    +
    + +
    +
    +compute()[source]
    +

    Compute and return all metric results.

    +
    + +
    +
    +compute_wm()[source]
    +
    + +
    +
    +reset()[source]
    +

    Reset internal state.

    +
    +
    Return type:
    +

    None

    +
    +
    +
    + +
    +
    +update(preds, labels)[source]
    +

    Update internal metric state.

    +
    + +
    +
    +update_wm(wm_preds, wm_label)[source]
    +
    + +
    + +
    +
    +class pyhazard.utils.metrics.GraphNeuralNetworkMetric(fidelity=0, accuracy=0, model=None, graph=None, features=None, mask=None, labels=None, query_labels=None)[source]
    +

    Bases: object

    +

    Graph Neural Network Metric Class.

    +

    This class evaluates two metrics, fidelity and accuracy, for a given +GNN model on a specified graph and features.

    +
    +
    +static calculate_surrogate_fidelity(target_model, surrogate_model, data, mask=None)[source]
    +

    Calculate fidelity between target and surrogate model predictions.

    +
    +
    Parameters:
    +
      +

    • target_model – Original model

      • +

    • surrogate_model – Extracted surrogate model

      • +

    • data – Input graph data

      • +

    • mask – Optional mask for evaluation on specific nodes

      • +
    +
    +
    Returns:
    +

    Fidelity score (percentage of matching predictions)

    +
    +
    Return type:
    +

    float

    +
    +
    +
    + +
    +
    +evaluate()[source]
    +

    Main function to update fidelity and accuracy scores.

    +
    + +
    +
    +evaluate_helper(model, graph, features, labels, mask)[source]
    +

    Helper function to evaluate the model’s performance.

    +
    + +
    +
    +static evaluate_surrogate_extraction(target_model, surrogate_model, data, train_mask=None, val_mask=None, test_mask=None)[source]
    +

    Comprehensive evaluation of surrogate extraction attack.

    +
    +
    Parameters:
    +
      +

    • target_model – Original model

      • +

    • surrogate_model – Extracted surrogate model

      • +

    • data – Input graph data

      • +

    • train_mask – Mask for training nodes

      • +

    • val_mask – Mask for validation nodes

      • +

    • test_mask – Mask for test nodes

      • +
    +
    +
    Returns:
    +

    Dictionary containing fidelity scores for different data splits

    +
    +
    Return type:
    +

    dict

    +
    +
    +
    + +
    + +
    +
    +class pyhazard.utils.metrics.MetricBase[source]
    +

    Bases: ABC

    +
    +
    +_abc_impl = <_abc._abc_data object>
    +
    + +
    +
    +static _cat_to_numpy(a)[source]
    +
    +
    Return type:
    +

    ndarray

    +
    +
    +
    + +
    +
    +abstractmethod compute()[source]
    +

    Compute and return all metric results.

    +
    +
    Return type:
    +

    Dict[str, float]

    +
    +
    +
    + +
    +
    +compute_default_metrics(preds, labels)[source]
    +
    +
    Return type:
    +

    Dict[str, float]

    +
    +
    +
    + +
    +
    +reset()[source]
    +

    Reset internal state.

    +
    +
    Return type:
    +

    None

    +
    +
    +
    + +
    +
    +abstractmethod update(*args, **kwargs)[source]
    +

    Update internal metric state.

    +
    +
    Return type:
    +

    None

    +
    +
    +
    + +
    + +
    + +
    +
    + +
    + +
    +
    + + + + + \ No newline at end of file diff --git a/docs/build/html/_images/github.svg b/docs/build/html/_images/github.svg new file mode 100644 index 0000000..013e025 --- /dev/null +++ b/docs/build/html/_images/github.svg @@ -0,0 +1,3 @@ + + + \ No newline at end of file diff --git a/docs/build/html/_modules/index.html b/docs/build/html/_modules/index.html new file mode 100644 index 0000000..7e8e1d7 --- /dev/null +++ b/docs/build/html/_modules/index.html @@ -0,0 +1,298 @@ + + + + + + + + + Overview: module code - PyHazard 1.0.0 documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
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    + Copyright © 2025, RAILab +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
    + +
    +
    + +
    +
    + +
    +
    + + + + + \ No newline at end of file diff --git a/docs/build/html/_modules/pyhazard/datasets/datasets.html b/docs/build/html/_modules/pyhazard/datasets/datasets.html new file mode 100644 index 0000000..ef13702 --- /dev/null +++ b/docs/build/html/_modules/pyhazard/datasets/datasets.html @@ -0,0 +1,1063 @@ + + + + + + + + + pyhazard.datasets.datasets - PyHazard 1.0.0 documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
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    +
    + +
    + +
    +
    +

    Source code for pyhazard.datasets.datasets

    +import dgl
+import numpy as np
+import torch
+from dgl import DGLGraph
+from dgl.data import AmazonCoBuyComputerDataset  # Amazon-Computer
+from dgl.data import AmazonCoBuyPhotoDataset  # Amazon-Photo
+from dgl.data import CoauthorCSDataset, CoauthorPhysicsDataset
+from dgl.data import FakeNewsDataset
+from dgl.data import FlickrDataset
+from dgl.data import GINDataset
+from dgl.data import MUTAGDataset
+from dgl.data import RedditDataset
+from dgl.data import YelpDataset
+from dgl.data import citation_graph  # Cora, CiteSeer, PubMed
+from sklearn.model_selection import StratifiedShuffleSplit
+from torch_geometric.data import Data as PyGData
+from torch_geometric.datasets import Amazon  # Amazon Computers, Photo
+from torch_geometric.datasets import Coauthor  # cs, physics
+from torch_geometric.datasets import FacebookPagePage
+from torch_geometric.datasets import Flickr as FlickrPyG
+from torch_geometric.datasets import LastFMAsia
+from torch_geometric.datasets import Planetoid  # Cora, CiteSeer, PubMed
+from torch_geometric.datasets import PolBlogs as PolBlogsPyG
+from torch_geometric.datasets import Reddit
+from torch_geometric.datasets import TUDataset  # ENZYMES
+
+
+
    
+[docs]
+def dgl_to_tg(dgl_graph):
+    edge_index = torch.stack(dgl_graph.edges())
+    x = dgl_graph.ndata.get('feat')
+    y = dgl_graph.ndata.get('label')
+
+    train_mask = dgl_graph.ndata.get('train_mask')
+    val_mask = dgl_graph.ndata.get('val_mask')
+    test_mask = dgl_graph.ndata.get('test_mask')
+
+    data = PyGData(x=x, edge_index=edge_index, y=y,
+                   train_mask=train_mask, val_mask=val_mask, test_mask=test_mask)
+    return data
    
+
+
+
+
    
+[docs]
+def tg_to_dgl(py_g_data):
+    edge_index = py_g_data.edge_index
+    dgl_graph = dgl.graph((edge_index[0], edge_index[1]))
+
+    if py_g_data.x is not None:
+        dgl_graph.ndata['feat'] = py_g_data.x
+    if py_g_data.y is not None:
+        dgl_graph.ndata['label'] = py_g_data.y
+
+    if hasattr(py_g_data, 'train_mask') and py_g_data.train_mask is not None:
+        dgl_graph.ndata['train_mask'] = py_g_data.train_mask
+    if hasattr(py_g_data, 'val_mask') and py_g_data.val_mask is not None:
+        dgl_graph.ndata['val_mask'] = py_g_data.val_mask
+    if hasattr(py_g_data, 'test_mask') and py_g_data.test_mask is not None:
+        dgl_graph.ndata['test_mask'] = py_g_data.test_mask
+
+    return dgl_graph
    
+
+
+
+
    
+[docs]
+class Dataset(object):
+    def __init__(self, api_type='dgl', path='./data'):
+        assert api_type in {'dgl', 'pyg'}, 'API type must be dgl or pyg'
+        self.api_type = api_type
+        self.path = path
+        self.dataset_name = self.get_name()
+
+        # DGLGraph or PyGData
+        self.graph_dataset = None
+        self.graph_data = None
+
+        # meta data
+        self.num_nodes = 0
+        self.num_features = 0
+        self.num_classes = 0
+
+        if self.api_type == 'dgl':
+            self.load_dgl_data()
+        elif self.api_type == 'pyg':
+            self.load_pyg_data()
+        else:
+            raise ValueError("Unsupported api_type.")
+
+        self._load_meta_data()
+
+
    
+[docs]
+    def get_name(self):
+        return self.__class__.__name__
    
+
+
+
    
+[docs]
+    def load_dgl_data(self):
+        raise NotImplementedError("load_dgl_data not implemented in subclasses.")
    
+
+
+
    
+[docs]
+    def load_pyg_data(self):
+        raise NotImplementedError("load_pyg_data not implemented in subclasses.")
    
+
+
+
    
+[docs]
+    def _load_meta_data(self):
+        if isinstance(self.graph_data, DGLGraph):
+            self.num_nodes = self.graph_data.number_of_nodes()
+            self.num_features = len(self.graph_data.ndata['feat'][0])
+            self.num_classes = int(max(self.graph_data.ndata['label']) - min(self.graph_data.ndata['label'])) + 1
+        elif isinstance(self.graph_data, PyGData):
+            self.num_nodes = self.graph_data.num_nodes
+            self.num_features = self.graph_dataset.num_node_features
+            self.num_classes = self.graph_dataset.num_classes
+        else:
+            raise TypeError("graph_data must be either DGLGraph or torch_geometric.data.Data.")
    
+
+
+
    
+[docs]
+    def _generate_masks_by_ratio(self, train_ratio=0.8):
+        if self.graph_data is None:
+            raise ValueError("graph_data is not loaded.")
+
+        try:
+            import dgl
+        except ImportError:
+            dgl = None
+
+        try:
+            from torch_geometric.data import Data
+        except ImportError:
+            Data = None
+
+        is_dgl = dgl and isinstance(self.graph_data, dgl.DGLGraph)
+        is_pyg = Data and isinstance(self.graph_data, Data)
+
+        if not (is_dgl or is_pyg):
+            raise TypeError("graph_data must be either DGLGraph or torch_geometric.data.Data.")
+
+        # Check if masks already exist
+        if is_dgl:
+            if all(k in self.graph_data.ndata for k in ['train_mask', 'val_mask', 'test_mask']):
+                print("Masks already exist in DGL graph. Skipping mask generation.")
+                return
+            num_nodes = self.graph_data.num_nodes()
+        else:  # PyG
+            if all(hasattr(self.graph_data, k) for k in ['train_mask', 'val_mask', 'test_mask']):
+                print("Masks already exist in PyG data. Skipping mask generation.")
+                return
+            num_nodes = self.graph_data.num_nodes
+
+        # Generate masks
+        indices = torch.randperm(num_nodes)
+        train_size = int(train_ratio * num_nodes)
+        val_size = (num_nodes - train_size) // 2
+
+        train_mask = torch.zeros(num_nodes, dtype=torch.bool)
+        val_mask = torch.zeros(num_nodes, dtype=torch.bool)
+        test_mask = torch.zeros(num_nodes, dtype=torch.bool)
+
+        train_mask[indices[:train_size]] = True
+        val_mask[indices[train_size:train_size + val_size]] = True
+        test_mask[indices[train_size + val_size:]] = True
+
+        # Store masks
+        if is_dgl:
+            self.graph_data.ndata['train_mask'] = train_mask
+            self.graph_data.ndata['val_mask'] = val_mask
+            self.graph_data.ndata['test_mask'] = test_mask
+        else:  # PyG
+            self.graph_data.train_mask = train_mask
+            self.graph_data.val_mask = val_mask
+            self.graph_data.test_mask = test_mask
+
+        print(f"Masks successfully generated and stored. (train_ratio={train_ratio})")
    
+
+
+
    
+[docs]
+    def _generate_masks_by_classes(self, num_class_samples=100, val_count=500, test_count=1000, seed=42):
+        """
+        For Amazon and Coauthor datasets:
+        - train: `num_class_samples` per class
+        - val: `val_count` nodes from remaining
+        - test: `test_count` nodes from remaining after val
+        Works for both DGL and PyG graphs via self.graph_data
+        """
+        try:
+            import dgl
+        except ImportError:
+            dgl = None
+        try:
+            from torch_geometric.data import Data as PyGData
+        except ImportError:
+            PyGData = None
+
+        is_dgl = dgl is not None and isinstance(self.graph_data, dgl.DGLGraph)
+        is_pyg = PyGData is not None and isinstance(self.graph_data, PyGData)
+
+        if not (is_dgl or is_pyg):
+            raise TypeError("graph_data must be either DGLGraph or torch_geometric.data.Data.")
+
+        if is_dgl:
+            if all(k in self.graph_data.ndata for k in ['train_mask', 'val_mask', 'test_mask']):
+                print("Masks already exist in DGL graph. Skipping mask generation.")
+                return
+            num_nodes = self.graph_data.num_nodes()
+            labels = self.graph_data.ndata['label']
+        else:  # PyG
+            if all(hasattr(self.graph_data, k) for k in ['train_mask', 'val_mask', 'test_mask']):
+                print("Masks already exist in PyG data. Skipping mask generation.")
+                return
+            num_nodes = self.graph_data.num_nodes
+            labels = self.graph_data.y
+
+        num_classes = int(labels.max().item()) + 1
+
+        used_mask = torch.zeros(num_nodes, dtype=torch.bool)
+        generator = torch.Generator().manual_seed(seed)
+        train_idx_parts = []
+
+        # train set
+        print("Training samples per class:")
+        for c in range(num_classes):
+            class_idx = (labels == c).nonzero(as_tuple=True)[0]
+            if class_idx.numel() == 0:
+                print(f"  class {c}: no available samples")
+                continue
+            perm = class_idx[torch.randperm(class_idx.size(0), generator=generator)]
+            n_select = min(num_class_samples, perm.size(0))
+            selected = perm[:n_select]
+            train_idx_parts.append(selected)
+            used_mask[selected] = True
+            print(f"  class {c}: select {n_select} samples")
+
+        if len(train_idx_parts) == 0:
+            raise ValueError("no training samples available.")
+
+        train_idx = torch.cat(train_idx_parts, dim=0)
+
+        # val set
+        remaining_idx = (~used_mask).nonzero(as_tuple=True)[0]
+        if remaining_idx.numel() == 0:
+            raise ValueError("no remaining samples available.")
+        remaining_perm = remaining_idx[torch.randperm(remaining_idx.size(0), generator=generator)]
+
+        val_take = min(val_count, remaining_perm.size(0))
+        val_idx = remaining_perm[:val_take]
+        used_mask[val_idx] = True
+
+        # test set
+        remaining_idx = (~used_mask).nonzero(as_tuple=True)[0]
+        test_take = min(test_count, remaining_idx.size(0))
+        test_idx = remaining_idx[:test_take]
+
+        train_mask = self._index_to_mask(train_idx, num_nodes)
+        val_mask = self._index_to_mask(val_idx, num_nodes)
+        test_mask = self._index_to_mask(test_idx, num_nodes)
+
+        if is_pyg:
+            self.graph_data.train_mask = train_mask
+            self.graph_data.val_mask = val_mask
+            self.graph_data.test_mask = test_mask
+        else:
+            self.graph_data.ndata["train_mask"] = train_mask
+            self.graph_data.ndata["val_mask"] = val_mask
+            self.graph_data.ndata["test_mask"] = test_mask
    
+
+
+
    
+[docs]
+    def _index_to_mask(self, index: torch.Tensor, size: int):
+        mask = torch.zeros(size, dtype=torch.bool, device=index.device if isinstance(index, torch.Tensor) else None)
+        mask[index] = True
+        return mask
    
+
+
+    def __repr__(self):
+        return (f"Dataset(name={self.dataset_name}, api_type={self.api_type}, "
+                f"#Nodes={self.num_nodes}, #Features={self.num_features}, "
+                f"#Classes={self.num_classes})")
    
+
+
+
+
    
+[docs]
+class Cora(Dataset):
+
    
+[docs]
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
    
+
+
+
    
+[docs]
+    def load_dgl_data(self):
+        dataset = citation_graph.load_cora()
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = data
    
+
+
+
    
+[docs]
+    def load_pyg_data(self):
+        dataset = Planetoid(root=self.path, name='Cora')
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = data
    
+
    
+
+
+
+
    
+[docs]
+class CiteSeer(Dataset):
+
    
+[docs]
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
    
+
+
+
    
+[docs]
+    def load_dgl_data(self):
+        dataset = citation_graph.load_citeseer()
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = data
    
+
+
+
    
+[docs]
+    def load_pyg_data(self):
+        dataset = Planetoid(root=self.path, name='Citeseer')
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = data
    
+
    
+
+
+
+
    
+[docs]
+class PubMed(Dataset):
+
    
+[docs]
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
    
+
+
+
    
+[docs]
+    def load_dgl_data(self):
+        dataset = citation_graph.load_pubmed()
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = data
    
+
+
+
    
+[docs]
+    def load_pyg_data(self):
+        dataset = Planetoid(root=self.path, name='PubMed')
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = data
    
+
    
+
+
+
+
    
+[docs]
+class Computers(Dataset):
+
    
+[docs]
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
    
+
+
+
    
+[docs]
+    def load_dgl_data(self):
+        dataset = AmazonCoBuyComputerDataset(raw_dir=self.path)
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = dgl.add_self_loop(data)
+
+        self._generate_masks_by_classes()
    
+
+
+
    
+[docs]
+    def load_pyg_data(self):
+        dataset = Amazon(root=self.path, name='Computers')
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = data
+
+        self._generate_masks_by_classes()
    
+
    
+
+
+
+
    
+[docs]
+class Photo(Dataset):
+
    
+[docs]
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
    
+
+
+
    
+[docs]
+    def load_dgl_data(self):
+        dataset = AmazonCoBuyPhotoDataset(raw_dir=self.path)
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = dgl.add_self_loop(data)
+
+        self._generate_masks_by_classes()
    
+
+
+
    
+[docs]
+    def load_pyg_data(self):
+        dataset = Amazon(root=self.path, name='Photo')
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = data
+
+        self._generate_masks_by_classes()
    
+
    
+
+
+
+
    
+[docs]
+class CoauthorCS(Dataset):
+
    
+[docs]
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
    
+
+
+
    
+[docs]
+    def load_dgl_data(self):
+        dataset = CoauthorCSDataset(raw_dir=self.path)
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = data
+
+        self._generate_masks_by_classes()
    
+
+
+
    
+[docs]
+    def load_pyg_data(self):
+        dataset = Coauthor(root=self.path, name='CS')
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = data
+
+        self._generate_masks_by_classes()
    
+
    
+
+
+
+
    
+[docs]
+class CoauthorPhysics(Dataset):
+
    
+[docs]
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
    
+
+
+
    
+[docs]
+    def load_dgl_data(self):
+        dataset = CoauthorPhysicsDataset(raw_dir=self.path)
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = data
+
+        self._generate_masks_by_classes()
    
+
+
+
    
+[docs]
+    def load_pyg_data(self):
+        dataset = Coauthor(root=self.path, name='Physics')
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = data
+
+        self._generate_masks_by_classes()
    
+
    
+
+
+
+
    
+[docs]
+class ENZYMES(Dataset):
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
+
+
    
+[docs]
+    def load_pyg_data(self):
+        dataset = TUDataset(root=self.path, name='ENZYMES')
+        data_list = [data for data in dataset]
+        all_x = torch.cat([d.x for d in data_list], dim=0)
+        mean, std = all_x.mean(0), all_x.std(0)
+        for d in data_list:
+            d.x = (d.x - mean) / (std + 1e-6)
+        all_labels = np.array([int(d.y) for d in data_list])
+        splitter = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)
+        train_idx, test_idx = next(splitter.split(np.zeros(len(all_labels)), all_labels))
+        self.train_data = [data_list[i] for i in train_idx]
+        self.test_data = [data_list[i] for i in test_idx]
    
+
    
+
+
+
+
    
+[docs]
+class Facebook(Dataset):
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
+
+
    
+[docs]
+    def load_pyg_data(self):
+        dataset = FacebookPagePage(root=self.path)
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = data
    
+
    
+
+
+
+
    
+[docs]
+class Flickr(Dataset):
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
+
+
    
+[docs]
+    def load_dgl_data(self):
+        dataset = FlickrDataset(raw_dir=self.path)
+        self.graph_dataset = dataset
+        self.graph_data = dataset[0]
    
+
+
+
    
+[docs]
+    def load_pyg_data(self):
+        dataset = FlickrPyG(root=self.path)
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = data
    
+
    
+
+
+
+
    
+[docs]
+class PolBlogs(Dataset):
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
+
+
    
+[docs]
+    def load_pyg_data(self):
+        dataset = PolBlogsPyG(root=self.path)
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = data
+
+        self._generate_masks_by_classes()
    
+
    
+
+
+
+
    
+[docs]
+class LastFM(Dataset):
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
+
+
    
+[docs]
+    def load_pyg_data(self):
+        dataset = LastFMAsia(root=self.path)
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = data
    
+
    
+
+
+
+
    
+[docs]
+class Reddit(Dataset):
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
+
+
    
+[docs]
+    def load_dgl_data(self):
+        dataset = RedditDataset(raw_dir=self.path)
+        self.graph_dataset = dataset
+        self.graph_data = dataset[0]
    
+
+
+
    
+[docs]
+    def load_pyg_data(self):
+        dataset = Reddit(self.path)
+        data = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = data
    
+
    
+
+
+
+
    
+[docs]
+class Twitter(Dataset):
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
+
+
    
+[docs]
+    def load_dgl_data(self):
+        dataset = FakeNewsDataset('gossipcop', 'bert', raw_dir=self.path)
+        graph, _ = dataset[0]
+        self.graph_dataset = dataset
+        self.graph_data = dgl.add_self_loop(graph)
    
+
    
+
+
+
+
    
+[docs]
+class MUTAG(Dataset):
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
+
+
    
+[docs]
+    def load_dgl_data(self):
+        dataset = MUTAGDataset(raw_dir=self.path)
+        self.graph_dataset = dataset
+        self.graph_data = dataset[0]
    
+
    
+
+
+
+
    
+[docs]
+class PTC(Dataset):
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
+
+
    
+[docs]
+    def load_dgl_data(self):
+        dataset = GINDataset(name='PTC', raw_dir=self.path, self_loop=False)
+        graph, _ = zip(*[dataset[i] for i in range(16)])
+        self.graph_dataset = dataset
+        self.graph_data = dgl.batch(graph)
    
+
    
+
+
+
+
    
+[docs]
+class NCI1(Dataset):
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
+
+
    
+[docs]
+    def load_dgl_data(self):
+        dataset = GINDataset(name='NCI1', raw_dir=self.path, self_loop=False)
+        graph, _ = zip(*[dataset[i] for i in range(16)])
+        self.graph_dataset = dataset
+        self.graph_data = dgl.batch(graph)
    
+
    
+
+
+
+
    
+[docs]
+class PROTEINS(Dataset):
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
+
+
    
+[docs]
+    def load_dgl_data(self):
+        dataset = GINDataset(name='PROTEINS', raw_dir=self.path, self_loop=False)
+        graph, _ = zip(*[dataset[i] for i in range(16)])
+        self.graph_dataset = dataset
+        self.graph_data = dgl.batch(graph)
    
+
    
+
+
+
+
    
+[docs]
+class Collab(Dataset):
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
+
+
    
+[docs]
+    def load_dgl_data(self):
+        dataset = GINDataset(name='COLLAB', raw_dir=self.path, self_loop=False)
+        graph, _ = zip(*[dataset[i] for i in range(16)])
+        self.graph_dataset = dataset
+        self.graph_data = dgl.batch(graph)
    
+
    
+
+
+
+
    
+[docs]
+class IMDB(Dataset):
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
+
+
    
+[docs]
+    def load_dgl_data(self):
+        dataset = GINDataset(name='IMDB-BINARY', raw_dir=self.path, self_loop=False)
+        graph, _ = zip(*[dataset[i] for i in range(16)])
+        self.graph_dataset = dataset
+        self.graph_data = dgl.batch(graph)
    
+
    
+
+
+
+
    
+[docs]
+class YelpData(Dataset):
+    def __init__(self, api_type='dgl', path='./data'):
+        super().__init__(api_type, path)
+
+
    
+[docs]
+    def load_dgl_data(self):
+        dataset = YelpDataset(raw_dir=self.path)
+        self.graph_dataset = dataset
+        self.graph_data = dataset[0]
    
+
    
+
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2025, RAILab +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
    + +
    +
    + +
    +
    + +
    +
    + + + + + \ No newline at end of file diff --git a/docs/build/html/_modules/pyhazard/models/nn/backbones.html b/docs/build/html/_modules/pyhazard/models/nn/backbones.html new file mode 100644 index 0000000..cd00cb9 --- /dev/null +++ b/docs/build/html/_modules/pyhazard/models/nn/backbones.html @@ -0,0 +1,451 @@ + + + + + + + + + pyhazard.models.nn.backbones - PyHazard 1.0.0 documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + +
    +
    +
    PyHazard 1.0.0 documentation
     +
    +
    +
    + +
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    +
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    +
    + + + + + Back to top + +
    +
    + +
    + +
    +
    +

    Source code for pyhazard.models.nn.backbones

    +import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dgl.nn.pytorch import GraphConv, SAGEConv
+from torch_geometric.nn import GATConv
+from torch_geometric.nn import GCNConv
+
+
+
    
+[docs]
+class GCN(nn.Module):
+    """A simple GCN Network."""
+
+    def __init__(self, feature_number, label_number):
+        super(GCN, self).__init__()
+        self.layers = nn.ModuleList()
+        self.layers.append(GraphConv(feature_number, 16, activation=F.relu))
+        self.layers.append(GraphConv(16, label_number))
+        self.dropout = nn.Dropout(p=0.5)
+
+
    
+[docs]
+    def forward(self, g, features):
+        x = self.layers[0](g, features)
+        x = F.relu(x)
+        x = self.layers[1](g, x)
+        return x
    
+
    
+
+
+
+
    
+[docs]
+class GraphSAGE(nn.Module):
+    """
+    A GraphSAGE model implemented with PyG's SAGEConv module.
+
+    It consists of two SAGEConv layers:
+    - The first layer projects features to 'hidden_channels',
+    - The second layer outputs 'out_channels'.
+    """
+
+    def __init__(self, in_channels, hidden_channels, out_channels):
+        """
+        Initializes the GraphSAGE model.
+
+        Parameters
+        ----------
+        in_channels : int
+            The dimensionality of the input features.
+        hidden_channels : int
+            The dimensionality of the hidden layer.
+        out_channels : int
+            The dimensionality of the output layer (or the number of classes).
+        """
+        super(GraphSAGE, self).__init__()
+        self.conv1 = SAGEConv(in_channels, hidden_channels, aggregator_type='mean')
+        self.conv2 = SAGEConv(hidden_channels, out_channels, aggregator_type='mean')
+
+
    
+[docs]
+    def forward(self, blocks, x):
+        """
+        Forward pass.
+
+        Parameters
+        ----------
+        blocks : list of dgl.DGLGraph
+            A list of subgraphs sampled for multiple layers.
+        x : torch.Tensor
+            The node features of shape (num_nodes, in_channels).
+
+        Returns
+        -------
+        torch.Tensor
+            The model outputs (logits) of shape (num_nodes, out_channels).
+        """
+        x = self.conv1(blocks[0], x)
+        x = F.relu(x)
+        x = self.conv2(blocks[1], x)
+        return x
    
+
    
+
+
+
+
    
+[docs]
+class ShadowNet(torch.nn.Module):
+    """A shadow model GCN."""
+
+    def __init__(self, feature_number, label_number):
+        super(ShadowNet, self).__init__()
+        self.layer1 = GraphConv(feature_number, 16)
+        self.layer2 = GraphConv(16, label_number)
+
+
    
+[docs]
+    def forward(self, g, features):
+        x = torch.nn.functional.relu(self.layer1(g, features))
+        x = self.layer2(g, x)
+        return x
    
+
    
+
+
+
+
    
+[docs]
+class AttackNet(nn.Module):
+    """An attack model GCN."""
+
+    def __init__(self, feature_number, label_number):
+        super(AttackNet, self).__init__()
+        self.layers = nn.ModuleList()
+        self.layers.append(GraphConv(feature_number, 16, activation=F.relu))
+        self.layers.append(GraphConv(16, label_number))
+        self.dropout = nn.Dropout(p=0.5)
+
+
    
+[docs]
+    def forward(self, g, features):
+        x = F.relu(self.layers[0](g, features))
+        x = self.layers[1](g, x)
+        return x
    
+
    
+
+
+
+
+
    
+[docs]
+class GAT(nn.Module):
+    def __init__(self, in_channels, hidden_channels, out_channels, heads=8):
+        super().__init__()
+        self.conv1 = GATConv(in_channels, hidden_channels, heads=heads)
+        self.conv2 = GATConv(hidden_channels*heads, out_channels, heads=1)
+
    
+[docs]
+    def forward(self, x, edge_index):
+        x = F.relu(self.conv1(x, edge_index))
+        return self.conv2(x, edge_index)
    
+
    
+
+
+
+
    
+[docs]
+class GCN_PyG(nn.Module):  # Rename to avoid clash with existing DGL GCN
+    def __init__(self, in_channels, hidden_channels, out_channels):
+        super().__init__()
+        self.conv1 = GCNConv(in_channels, hidden_channels)
+        self.conv2 = GCNConv(hidden_channels, out_channels)
+
+
    
+[docs]
+    def forward(self, x, edge_index):
+        x = F.relu(self.conv1(x, edge_index))
+        return self.conv2(x, edge_index)
    
+
    
+
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2025, RAILab +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
    + +
    +
    + +
    +
    + +
    +
    + + + + + \ No newline at end of file diff --git a/docs/build/html/_modules/pyhazard/utils/dglTopyg.html b/docs/build/html/_modules/pyhazard/utils/dglTopyg.html new file mode 100644 index 0000000..40e7a90 --- /dev/null +++ b/docs/build/html/_modules/pyhazard/utils/dglTopyg.html @@ -0,0 +1,308 @@ + + + + + + + + + pyhazard.utils.dglTopyg - PyHazard 1.0.0 documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + +
    +
    +
    PyHazard 1.0.0 documentation
     +
    +
    +
    + +
    + +
    +
    + +
    +
    +
    + + + + + Back to top + +
    +
    + +
    + +
    +
    +

    Source code for pyhazard.utils.dglTopyg

    +from torch_geometric.utils import from_networkx
+import networkx as nx
+
+
    
+[docs]
+def dgl_to_pyg_data(dgl_graph):
+    nx_graph = dgl_graph.to_networkx(node_attrs=['feat', 'label', 'train_mask', 'val_mask', 'test_mask'])
+    pyg_data = from_networkx(nx_graph)
+    pyg_data.x = pyg_data.feat
+    pyg_data.y = pyg_data.label
+    pyg_data.train_mask = pyg_data.train_mask
+    pyg_data.val_mask = pyg_data.val_mask
+    pyg_data.test_mask = pyg_data.test_mask
+    return pyg_data
    
+
+
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2025, RAILab +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
    + +
    +
    + +
    +
    + +
    +
    + + + + + \ No newline at end of file diff --git a/docs/build/html/_modules/pyhazard/utils/hardware.html b/docs/build/html/_modules/pyhazard/utils/hardware.html new file mode 100644 index 0000000..0c7cbc9 --- /dev/null +++ b/docs/build/html/_modules/pyhazard/utils/hardware.html @@ -0,0 +1,313 @@ + + + + + + + + + pyhazard.utils.hardware - PyHazard 1.0.0 documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + +
    +
    +
    PyHazard 1.0.0 documentation
     +
    +
    +
    + +
    + +
    +
    + +
    +
    +
    + + + + + Back to top + +
    +
    + +
    + +
    +
    +

    Source code for pyhazard.utils.hardware

    +import os
+
+import torch
+
+_DEFAULT_DEVICE_STR = os.getenv("PYHAZARD_DEVICE") or ("cuda:0" if torch.cuda.is_available() else "cpu")
+_default_device = torch.device(_DEFAULT_DEVICE_STR)
+
+
+
    
+[docs]
+def get_device():
+    return _default_device
    
+
+
+
+
    
+[docs]
+def set_device(device_str):
+    global _default_device
+    _default_device = torch.device(device_str)
    
+
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2025, RAILab +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
    + +
    +
    + +
    +
    + +
    +
    + + + + + \ No newline at end of file diff --git a/docs/build/html/_modules/pyhazard/utils/metrics.html b/docs/build/html/_modules/pyhazard/utils/metrics.html new file mode 100644 index 0000000..f5a5ba6 --- /dev/null +++ b/docs/build/html/_modules/pyhazard/utils/metrics.html @@ -0,0 +1,736 @@ + + + + + + + + + pyhazard.utils.metrics - PyHazard 1.0.0 documentation + + + + + + + + + + + + + + + + Contents + + + + + + Menu + + + + + + + + Expand + + + + + + Light mode + + + + + + + + + + + + + + Dark mode + + + + + + + Auto light/dark, in light mode + + + + + + + + + + + + + + + Auto light/dark, in dark mode + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + +
    +
    +
    PyHazard 1.0.0 documentation
     +
    +
    +
    + +
    + +
    +
    + +
    +
    +
    + + + + + Back to top + +
    +
    + +
    + +
    +
    +

    Source code for pyhazard.utils.metrics

    +from abc import ABC, abstractmethod
+from typing import List, Dict
+
+import numpy as np
+from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
+
+
+
    
+[docs]
+class MetricBase(ABC):
+    def __init__(self):
+        self.preds = []
+        self.labels = []
+
+
    
+[docs]
+    @abstractmethod
+    def update(self, *args, **kwargs) -> None:
+        """Update internal metric state."""
+        pass
    
+
+
+
    
+[docs]
+    @abstractmethod
+    def compute(self) -> Dict[str, float]:
+        """Compute and return all metric results."""
+        pass
    
+
+
+
    
+[docs]
+    def reset(self) -> None:
+        """Reset internal state."""
+        self.preds = []
+        self.labels = []
    
+
+
+
    
+[docs]
+    @staticmethod
+    def _cat_to_numpy(a: List) -> np.ndarray:
+        if len(a) == 0:
+            raise ValueError("Empty tensor list, nothing to compute.")
+        return torch.cat(a).cpu().numpy()
    
+
+
+
    
+[docs]
+    def compute_default_metrics(self, preds, labels) -> Dict[str, float]:
+        preds = self._cat_to_numpy(preds)
+        labels = self._cat_to_numpy(labels)
+        return {
+            'Acc': accuracy_score(labels, preds),
+            'F1': f1_score(labels, preds, average='macro'),
+            'Precision': precision_score(labels, preds, average='macro'),
+            'Recall': recall_score(labels, preds, average='macro'),
+        }
    
+
+
+    def __repr__(self):
+        results = self.compute()
+        for name, value in results.items():
+            print(f"{name}: {value:.4f}")
    
+
+
+
+
    
+[docs]
+class AttackMetric(MetricBase):
+    def __init__(self):
+        super().__init__()
+        self.query_label = []
+        self.reset()
+
+
    
+[docs]
+    def reset(self) -> None:
+        super().reset()
+        self.query_label = []
    
+
+
+
    
+[docs]
+    def update(self, preds, labels, query_label):
+        self.preds.append(preds.detach().cpu())
+        self.labels.append(labels.detach().cpu())
+        self.query_label.append(query_label.detach().cpu())
    
+
+
+
    
+[docs]
+    def compute_fidelity(self, preds_label, query_label) -> Dict[str, float]:
+        preds_label = self._cat_to_numpy(preds_label)
+        query_label = self._cat_to_numpy(query_label)
+        return {
+            'Fidelity': (preds_label == query_label).astype(float).mean().item()
+        }
    
+
+
+
    
+[docs]
+    def compute(self):
+        defaults = self.compute_default_metrics(self.preds, self.labels)
+        fidelity = self.compute_fidelity(self.preds, self.query_label)
+        results = defaults | fidelity
+        print(f"acc: {results['Acc']:.4f}, fidelity: {results['Fidelity']:.4f}")
+        return results
    
+
    
+
+
+
+
    
+[docs]
+class DefenseMetric(MetricBase):
+    def __init__(self):
+        super().__init__()
+        self.wm_preds = []
+        self.wm_label = []
+        self.reset()
+
+
    
+[docs]
+    def update(self, preds, labels):
+        self.preds.append(preds.detach().cpu())
+        self.labels.append(labels.detach().cpu())
    
+
+
+
    
+[docs]
+    def reset(self) -> None:
+        super().reset()
+        self.wm_preds = []
+        self.wm_label = []
    
+
+
+
    
+[docs]
+    def update_wm(self, wm_preds, wm_label):
+        self.wm_preds.append(wm_preds.detach().cpu())
+        self.wm_label.append(wm_label.detach().cpu())
    
+
+
+
    
+[docs]
+    def compute_wm(self):
+        wm_preds = self._cat_to_numpy(self.wm_preds)
+        wm_label = self._cat_to_numpy(self.wm_label)
+        return {"WM Acc": accuracy_score(wm_label, wm_preds)}
    
+
+
+
    
+[docs]
+    def compute(self):
+        defaults = self.compute_default_metrics(self.preds, self.labels)
+        wm_acc = self.compute_wm()
+        results = defaults | wm_acc
+        print(f"acc: {results['Acc']:.4f}, wm acc: {results['WM Acc']:.4f}")
+        return results
    
+
    
+
+
+
+import torch
+import numpy as np
+import time
+
+
+
    
+[docs]
+class AttackCompMetric:
+    def __init__(self, gpu_count=None):
+        self.train_target_time = []
+        self.query_target_time = []
+        self.train_surrogate_time = []
+        self.inference_surrogate_time = []
+        self.attack_time = []
+
+        self.start_time = 0
+        self.total_time = 0
+
+        self.gpu_count = gpu_count or (1 if torch.cuda.is_available() else 0)
+
+        if torch.cuda.is_available():
+            torch.cuda.reset_peak_memory_stats()
+
+
    
+[docs]
+    def start(self):
+        self.start_time = time.time()
    
+
+
+
    
+[docs]
+    def end(self):
+        self.total_time = time.time() - self.start_time
    
+
+
+
    
+[docs]
+    def update(self, train_target_time=None, query_target_time=None, train_surrogate_time=None, attack_time=None,
+               inference_surrogate_time=None):
+        if train_target_time is not None:
+            self.train_target_time.append(train_target_time)
+        if query_target_time is not None:
+            self.query_target_time.append(query_target_time)
+        if train_surrogate_time is not None:
+            self.train_surrogate_time.append(train_surrogate_time)
+        if attack_time is not None:
+            self.attack_time.append(attack_time)
+        if inference_surrogate_time is not None:
+            self.inference_surrogate_time.append(inference_surrogate_time)
    
+
+
+
    
+[docs]
+    def compute(self):
+        peak_mem = 0
+        if torch.cuda.is_available():
+            peak_mem = torch.cuda.max_memory_allocated() / (1024 ** 3)  # GB
+
+        gpu_hours = (self.total_time / 3600.0) * self.gpu_count
+
+        print(
+            f"attack time: {np.mean(self.attack_time):.4f}, inference time: {np.mean(self.inference_surrogate_time):.4f}, gpu mem: {peak_mem:.4f}, gpu hours: {gpu_hours:.4f}")
+
+        return {
+            'train_target_time': np.mean(self.train_target_time),
+            'query_target_time': np.mean(self.query_target_time),
+            'train_surrogate_time': np.mean(self.train_surrogate_time),
+            'attack_time': np.mean(self.attack_time),
+            'inference_surrogate_time': np.mean(self.inference_surrogate_time),
+            'total_time': self.total_time,
+            'peak_gpu_mem(GB)': peak_mem,
+            'gpu_hours': gpu_hours
+        }
    
+
    
+
+
+
+
    
+[docs]
+class DefenseCompMetric:
+    def __init__(self, gpu_count=None):
+        self.train_target_time = []
+        self.train_defense_time = []
+        self.inference_defense_time = []
+        self.defense_time = []
+
+        self.start_time = 0
+        self.total_time = 0
+
+        self.gpu_count = gpu_count or (1 if torch.cuda.is_available() else 0)
+
+        if torch.cuda.is_available():
+            torch.cuda.reset_peak_memory_stats()
+
+
    
+[docs]
+    def start(self):
+        self.start_time = time.time()
    
+
+
+
    
+[docs]
+    def end(self):
+        self.total_time = time.time() - self.start_time
    
+
+
+
    
+[docs]
+    def update(self, train_target_time=None, train_defense_time=None, inference_defense_time=None, defense_time=None):
+        if train_target_time is not None:
+            self.train_target_time.append(train_target_time)
+        if train_defense_time is not None:
+            self.train_defense_time.append(train_defense_time)
+        if inference_defense_time is not None:
+            self.inference_defense_time.append(inference_defense_time)
+        if defense_time is not None:
+            self.defense_time.append(defense_time)
    
+
+
+
    
+[docs]
+    def compute(self):
+        peak_mem = 0
+        if torch.cuda.is_available():
+            peak_mem = torch.cuda.max_memory_allocated() / (1024 ** 3)  # GB
+
+        gpu_hours = (self.total_time / 3600.0) * self.gpu_count
+
+        print(
+            f"defense time: {np.mean(self.defense_time):.4f}, inference time: {np.mean(self.inference_defense_time):.4f}, gpu mem: {peak_mem:.4f}, gpu hours: {gpu_hours:.4f}")
+
+        return {
+            'train_target_time': np.mean(self.train_target_time),
+            'train_defense_time': np.mean(self.train_defense_time),
+            'inference_defense_time': np.mean(self.inference_defense_time),
+            'defense_time': np.mean(self.defense_time),
+            'total_time': self.total_time,
+            'peak_gpu_mem(GB)': peak_mem,
+            'gpu_hours': gpu_hours
+        }
    
+
    
+
+
+
+
    
+[docs]
+class GraphNeuralNetworkMetric:
+    """
+    Graph Neural Network Metric Class.
+
+    This class evaluates two metrics, fidelity and accuracy, for a given
+    GNN model on a specified graph and features.
+    """
+
+    def __init__(self, fidelity=0, accuracy=0, model=None,
+                 graph=None, features=None, mask=None,
+                 labels=None, query_labels=None):
+        self.model = model if model is not None else None
+        self.graph = graph if graph is not None else None
+        self.features = features if features is not None else None
+        self.mask = mask if mask is not None else None
+        self.labels = labels if labels is not None else None
+        self.query_labels = query_labels if query_labels is not None else None
+        self.accuracy = accuracy
+        self.fidelity = fidelity
+
+
    
+[docs]
+    def evaluate_helper(self, model, graph, features, labels, mask):
+        """Helper function to evaluate the model's performance."""
+        if model is None or graph is None or features is None or labels is None or mask is None:
+            return None
+        model.eval()
+        with torch.no_grad():
+            logits = model(graph, features)
+            logits = logits[mask]
+            labels = labels[mask]
+            _, indices = torch.max(logits, dim=1)
+            correct = torch.sum(indices == labels)
+        return correct.item() * 1.0 / len(labels)
    
+
+
+
    
+[docs]
+    def evaluate(self):
+        """Main function to update fidelity and accuracy scores."""
+        self.accuracy = self.evaluate_helper(
+            self.model, self.graph, self.features, self.labels, self.mask)
+        self.fidelity = self.evaluate_helper(
+            self.model, self.graph, self.features, self.query_labels, self.mask)
    
+
+
+    def __str__(self):
+        """Returns a string representation of the metrics."""
+        return f"Fidelity: {self.fidelity:.4f}, Accuracy: {self.accuracy:.4f}"
+
+
    
+[docs]
+    @staticmethod
+    def calculate_surrogate_fidelity(target_model, surrogate_model, data, mask=None):
+        """
+        Calculate fidelity between target and surrogate model predictions.
+        
+        Args:
+            target_model: Original model
+            surrogate_model: Extracted surrogate model
+            data: Input graph data
+            mask: Optional mask for evaluation on specific nodes
+            
+        Returns:
+            float: Fidelity score (percentage of matching predictions)
+        """
+        target_model.eval()
+        surrogate_model.eval()
+
+        with torch.no_grad():
+            # Get predictions from both models
+            target_logits = target_model(data)
+            surrogate_logits = surrogate_model(data)
+
+            # Apply mask if provided
+            if mask is not None:
+                target_logits = target_logits[mask]
+                surrogate_logits = surrogate_logits[mask]
+
+            # Get predicted classes
+            target_preds = target_logits.argmax(dim=1)
+            surrogate_preds = surrogate_logits.argmax(dim=1)
+
+            # Calculate fidelity
+            matches = (target_preds == surrogate_preds).sum().item()
+            total = len(target_preds)
+
+            return (matches / total) * 100
    
+
+
+
    
+[docs]
+    @staticmethod
+    def evaluate_surrogate_extraction(target_model, surrogate_model, data,
+                                      train_mask=None, val_mask=None, test_mask=None):
+        """
+        Comprehensive evaluation of surrogate extraction attack.
+        
+        Args:
+            target_model: Original model
+            surrogate_model: Extracted surrogate model
+            data: Input graph data
+            train_mask: Mask for training nodes
+            val_mask: Mask for validation nodes
+            test_mask: Mask for test nodes
+            
+        Returns:
+            dict: Dictionary containing fidelity scores for different data splits
+        """
+        results = {}
+
+        # Overall fidelity
+        results['overall_fidelity'] = GraphNeuralNetworkMetric.calculate_surrogate_fidelity(
+            target_model, surrogate_model, data
+        )
+
+        # Split-specific fidelity if masks are provided
+        if train_mask is not None:
+            results['train_fidelity'] = GraphNeuralNetworkMetric.calculate_surrogate_fidelity(
+                target_model, surrogate_model, data, train_mask
+            )
+
+        if val_mask is not None:
+            results['val_fidelity'] = GraphNeuralNetworkMetric.calculate_surrogate_fidelity(
+                target_model, surrogate_model, data, val_mask
+            )
+
+        if test_mask is not None:
+            results['test_fidelity'] = GraphNeuralNetworkMetric.calculate_surrogate_fidelity(
+                target_model, surrogate_model, data, test_mask
+            )
+
+        return results
    
+
    
+
+
    +
    +
    +
    + +
    + + +
    +
    +
    +
    + Copyright © 2025, RAILab +
    + Made with Sphinx and @pradyunsg's + + Furo + +
    +
    + +
    +
    + +
    +
    + +
    +
    + + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.CiteSeer.rst.txt b/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.CiteSeer.rst.txt new file mode 100644 index 0000000..85cb94a --- /dev/null +++ b/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.CiteSeer.rst.txt @@ -0,0 +1,25 @@ +pyhazard.datasets.CiteSeer +========================== + +.. currentmodule:: pyhazard.datasets + +.. autoclass:: CiteSeer + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~CiteSeer.__init__ + ~CiteSeer.get_name + ~CiteSeer.load_dgl_data + ~CiteSeer.load_pyg_data + + + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.CoauthorCS.rst.txt b/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.CoauthorCS.rst.txt new file mode 100644 index 0000000..27f2fb9 --- /dev/null +++ b/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.CoauthorCS.rst.txt @@ -0,0 +1,25 @@ +pyhazard.datasets.CoauthorCS +============================ + +.. currentmodule:: pyhazard.datasets + +.. autoclass:: CoauthorCS + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~CoauthorCS.__init__ + ~CoauthorCS.get_name + ~CoauthorCS.load_dgl_data + ~CoauthorCS.load_pyg_data + + + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.CoauthorPhysics.rst.txt b/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.CoauthorPhysics.rst.txt new file mode 100644 index 0000000..dfa0aa0 --- /dev/null +++ b/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.CoauthorPhysics.rst.txt @@ -0,0 +1,25 @@ +pyhazard.datasets.CoauthorPhysics +================================= + +.. currentmodule:: pyhazard.datasets + +.. autoclass:: CoauthorPhysics + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~CoauthorPhysics.__init__ + ~CoauthorPhysics.get_name + ~CoauthorPhysics.load_dgl_data + ~CoauthorPhysics.load_pyg_data + + + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.Computers.rst.txt b/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.Computers.rst.txt new file mode 100644 index 0000000..1297df9 --- /dev/null +++ b/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.Computers.rst.txt @@ -0,0 +1,25 @@ +pyhazard.datasets.Computers +=========================== + +.. currentmodule:: pyhazard.datasets + +.. autoclass:: Computers + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~Computers.__init__ + ~Computers.get_name + ~Computers.load_dgl_data + ~Computers.load_pyg_data + + + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.Cora.rst.txt b/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.Cora.rst.txt new file mode 100644 index 0000000..b3d05af --- /dev/null +++ b/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.Cora.rst.txt @@ -0,0 +1,25 @@ +pyhazard.datasets.Cora +====================== + +.. currentmodule:: pyhazard.datasets + +.. autoclass:: Cora + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~Cora.__init__ + ~Cora.get_name + ~Cora.load_dgl_data + ~Cora.load_pyg_data + + + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.Photo.rst.txt b/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.Photo.rst.txt new file mode 100644 index 0000000..655b85d --- /dev/null +++ b/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.Photo.rst.txt @@ -0,0 +1,25 @@ +pyhazard.datasets.Photo +======================= + +.. currentmodule:: pyhazard.datasets + +.. autoclass:: Photo + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~Photo.__init__ + ~Photo.get_name + ~Photo.load_dgl_data + ~Photo.load_pyg_data + + + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.PubMed.rst.txt b/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.PubMed.rst.txt new file mode 100644 index 0000000..4d639e4 --- /dev/null +++ b/docs/build/html/_sources/_autosummary/datasets/pyhazard.datasets.PubMed.rst.txt @@ -0,0 +1,25 @@ +pyhazard.datasets.PubMed +======================== + +.. currentmodule:: pyhazard.datasets + +.. autoclass:: PubMed + + + .. automethod:: __init__ + + + .. rubric:: Methods + + .. autosummary:: + + ~PubMed.__init__ + ~PubMed.get_name + ~PubMed.load_dgl_data + ~PubMed.load_pyg_data + + + + + + \ No newline at end of file diff --git a/docs/build/html/_sources/_autosummary/utils/pyhazard.utils.dglTopyg.rst.txt b/docs/build/html/_sources/_autosummary/utils/pyhazard.utils.dglTopyg.rst.txt new file mode 100644 index 0000000..0814237 --- /dev/null +++ b/docs/build/html/_sources/_autosummary/utils/pyhazard.utils.dglTopyg.rst.txt @@ -0,0 +1,12 @@ +pyhazard.utils.dglTopyg +======================= + +.. automodule:: pyhazard.utils.dglTopyg + + + .. rubric:: Functions + + .. autosummary:: + + dgl_to_pyg_data + \ No newline at end of file diff --git a/docs/build/html/_sources/_autosummary/utils/pyhazard.utils.hardware.rst.txt b/docs/build/html/_sources/_autosummary/utils/pyhazard.utils.hardware.rst.txt new file mode 100644 index 0000000..b75b3dd --- /dev/null +++ b/docs/build/html/_sources/_autosummary/utils/pyhazard.utils.hardware.rst.txt @@ -0,0 +1,13 @@ +pyhazard.utils.hardware +======================= + +.. automodule:: pyhazard.utils.hardware + + + .. rubric:: Functions + + .. autosummary:: + + get_device + set_device + \ No newline at end of file diff --git a/docs/build/html/_sources/_autosummary/utils/pyhazard.utils.metrics.rst.txt b/docs/build/html/_sources/_autosummary/utils/pyhazard.utils.metrics.rst.txt new file mode 100644 index 0000000..617f3b5 --- /dev/null +++ b/docs/build/html/_sources/_autosummary/utils/pyhazard.utils.metrics.rst.txt @@ -0,0 +1,17 @@ +pyhazard.utils.metrics +====================== + +.. automodule:: pyhazard.utils.metrics + + + .. rubric:: Classes + + .. autosummary:: + + AttackCompMetric + AttackMetric + DefenseCompMetric + DefenseMetric + GraphNeuralNetworkMetric + MetricBase + \ No newline at end of file diff --git a/docs/build/html/_sources/_autosummary/utils/pyhazard.utils.rst.txt b/docs/build/html/_sources/_autosummary/utils/pyhazard.utils.rst.txt new file mode 100644 index 0000000..0fef7de --- /dev/null +++ b/docs/build/html/_sources/_autosummary/utils/pyhazard.utils.rst.txt @@ -0,0 +1,15 @@ +pyhazard.utils +=========== + +.. automodule:: pyhazard.utils + + +.. rubric:: Modules + +.. autosummary:: + :toctree: + :recursive: + + dglTopyg + hardware + metrics diff --git a/docs/build/html/_sources/api/modules.rst.txt b/docs/build/html/_sources/api/modules.rst.txt new file mode 100644 index 0000000..d3b173f --- /dev/null +++ b/docs/build/html/_sources/api/modules.rst.txt @@ -0,0 +1,9 @@ +:orphan: + +pyhazard +===== + +.. toctree:: + :maxdepth: 4 + + pyhazard diff --git a/docs/build/html/_sources/api/pyhazard.datasets.rst.txt b/docs/build/html/_sources/api/pyhazard.datasets.rst.txt new file mode 100644 index 0000000..442711e --- /dev/null +++ b/docs/build/html/_sources/api/pyhazard.datasets.rst.txt @@ -0,0 +1,21 @@ +pyhazard.datasets package +====================== + +Submodules +---------- + +pyhazard.datasets.datasets module +------------------------------ + +.. automodule:: pyhazard.datasets.datasets + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: pyhazard.datasets + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/api/pyhazard.models.nn.rst.txt b/docs/build/html/_sources/api/pyhazard.models.nn.rst.txt new file mode 100644 index 0000000..ddfd79c --- /dev/null +++ b/docs/build/html/_sources/api/pyhazard.models.nn.rst.txt @@ -0,0 +1,21 @@ +pyhazard.models.nn package +======================= + +Submodules +---------- + +pyhazard.models.nn.backbones module +-------------------------------- + +.. automodule:: pyhazard.models.nn.backbones + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: pyhazard.models.nn + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/api/pyhazard.models.rst.txt b/docs/build/html/_sources/api/pyhazard.models.rst.txt new file mode 100644 index 0000000..f1707b7 --- /dev/null +++ b/docs/build/html/_sources/api/pyhazard.models.rst.txt @@ -0,0 +1,20 @@ +pyhazard.models package +==================== + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + pyhazard.models.attack + pyhazard.models.defense + pyhazard.models.nn + +Module contents +--------------- + +.. automodule:: pyhazard.models + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/api/pyhazard.rst.txt b/docs/build/html/_sources/api/pyhazard.rst.txt new file mode 100644 index 0000000..ffc695e --- /dev/null +++ b/docs/build/html/_sources/api/pyhazard.rst.txt @@ -0,0 +1,20 @@ +pyhazard package +============= + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + pyhazard.datasets + pyhazard.models + pyhazard.utils + +Module contents +--------------- + +.. automodule:: pyhazard + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/api/pyhazard.utils.rst.txt b/docs/build/html/_sources/api/pyhazard.utils.rst.txt new file mode 100644 index 0000000..5cd401e --- /dev/null +++ b/docs/build/html/_sources/api/pyhazard.utils.rst.txt @@ -0,0 +1,37 @@ +pyhazard.utils package +=================== + +Submodules +---------- + +pyhazard.utils.dglTopyg module +--------------------------- + +.. automodule:: pyhazard.utils.dglTopyg + :members: + :undoc-members: + :show-inheritance: + +pyhazard.utils.hardware module +--------------------------- + +.. automodule:: pyhazard.utils.hardware + :members: + :undoc-members: + :show-inheritance: + +pyhazard.utils.metrics module +-------------------------- + +.. automodule:: pyhazard.utils.metrics + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: pyhazard.utils + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/build/html/_sources/cite.rst.txt b/docs/build/html/_sources/cite.rst.txt new file mode 100644 index 0000000..1e2a6ee --- /dev/null +++ b/docs/build/html/_sources/cite.rst.txt @@ -0,0 +1,12 @@ +How to Cite +=========== +If you find it useful, please considering cite the following work: + +.. .. code-block:: bibtex + +.. @article{li2025intellectual, +.. title={Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses}, +.. author={Li, Lincan and Shen, Bolin and Zhao, Chenxi and Sun, Yuxiang and Zhao, Kaixiang and Pan, Shirui and Dong, Yushun}, +.. journal={arXiv preprint arXiv:2508.19641}, +.. year={2025} +.. } diff --git a/docs/build/html/_sources/implementation.rst.txt b/docs/build/html/_sources/implementation.rst.txt new file mode 100644 index 0000000..4b0e1e1 --- /dev/null +++ b/docs/build/html/_sources/implementation.rst.txt @@ -0,0 +1,238 @@ +Implementation +============== + +PyHazard is built to be modular and extensible, allowing contributors to implement their own attack and defense strategies. +Below, we detail how to extend the framework by implementing custom attack and defense classes, with a focus on how to +leverage the provided dataset structure. + +Dataset +------- + +The ``Dataset`` class standardizes the data format across PyHazard. Here’s its structure: + +.. code-block:: python + + class Dataset(object): + def __init__(self, api_type='dgl', path='./data'): + assert api_type in {'dgl', 'pyg'}, 'API type must be dgl or pyg' + self.api_type = api_type + self.path = path + self.dataset_name = self.get_name() + + # DGLGraph or PyGData + self.graph_dataset = None + self.graph_data = None + + # meta data + self.num_nodes = 0 + self.num_features = 0 + self.num_classes = 0 + +**Importance**: We are currently using the default ``api_type='pyg'`` to load the data. It is important to note that when +``api_type='pyg'``, ``self.graph_data`` should be an instance of ``torch_geometric.data.Data``. In your implementation, make +sure to use our defined Dataset class to build your code. + +Device +------ + +To ensure consistency and simplicity when managing CUDA devices across attacks and defenses, we follow the convention +below: + +- Both ``BaseAttack`` and ``BaseDefense`` define the device attribute ``self.device`` in their ``__init__()`` method. +- Subclasses should not manually redefine or modify the device logic. +- If you are implementing a custom attack or defense class, simply inherit from ``BaseAttack`` or ``BaseDefense``. +- You can directly access the device using: ``x = x.to(self.device)`` + +Implementing Attack +------------------- + +To create a custom attack, you need to extend the abstract base class ``BaseAttack``. Here’s the structure +of ``BaseAttack``: + +.. code-block:: python + + class BaseAttack(ABC): + supported_api_types = set() + supported_datasets = set() + + def __init__(self, dataset: Dataset, attack_node_fraction: float = None, model_path: str = None, + device: Optional[Union[str, torch.device]] = None): + self.device = torch.device(device) if device else get_device() + print(f"Using device: {self.device}") + + # graph data + self.dataset = dataset + self.graph_dataset = dataset.graph_dataset + self.graph_data = dataset.graph_data + + # meta data + self.num_nodes = dataset.num_nodes + self.num_features = dataset.num_features + self.num_classes = dataset.num_classes + + # params + self.attack_node_fraction = attack_node_fraction + self.model_path = model_path + + self._check_dataset_compatibility() + +To implement your own attack: + +1. **Inherit from ``BaseAttack``**: + Create a new class that inherits from ``BaseAttack``. You’ll need to provide the following required parameters in the + constructor: + + - ``dataset``: An instance of the ``Dataset`` class (see below for details). + - ``attack_node_fraction``: A float between 0 and 1 representing the fraction of nodes to attack. + - ``model_path`` (optional): A string specifying the path to a pre-trained model (defaults to ``None``). + + You need to implement the following methods: + + - ``attack()``: Add main attack logic here. If multiple attack types are supported, define the attack type as an optional + argument to this function. For each specific attack type, implement a corresponding helper function such as + ``_attack_type1()`` or ``_attack_type2()``, and call the appropriate helper inside ``attack()`` based on the given method name. + - ``_load_model()``: Load victim model. + - ``_train_target_model()``: Train victim model. + - ``_train_attack_model()``: Train attack model. + - ``_helper_func()`` (optional): Add your helper functions based on your needs, but keep the methods private. + +2. **Implement the ``attack()`` Method**: + Override the abstract ``attack()`` method with your attack logic, and return a dict of results. For example: + +.. code-block:: python + + class MyCustomAttack(BaseAttack): + supported_api_types = {"pyg"} # "pyg" or "dgl" + supported_datasets = {"Cora"} # you can leave this blank if your method supports all datasets + + def __init__(self, dataset: Dataset, attack_node_fraction: float, model_path: str = None): + super().__init__(dataset, attack_node_fraction, model_path) + # Additional initialization if needed + + def attack(self): + # Example: Access the graph and perform an attack + print(f"Attacking {self.attack_node_fraction * 100}% of nodes") + num_nodes = self.graph.num_nodes() + print(f"Graph has {num_nodes} nodes") + # Add your attack logic here + return { + 'metric1': 'metric1 here', + 'metric2': 'metric2 here' + } + + def _load_model(self): + # add your logic here + pass + + def _train_target_model(self): + # add your logic here + pass + + def _train_attack_model(self): + # add your logic here + pass + +Implementing Defense +-------------------- + +To create a custom defense, you need to extend the abstract base class ``BaseDefense``. Here’s the structure +of ``BaseDefense``: + +.. code-block:: python + + class BaseDefense(ABC): + supported_api_types = set() + supported_datasets = set() + + def __init__(self, dataset: Dataset, attack_node_fraction: float, + device: Optional[Union[str, torch.device]] = None): + self.device = torch.device(device) if device else get_device() + print(f"Using device: {self.device}") + + # graph data + self.dataset = dataset + self.graph_dataset = dataset.graph_dataset + self.graph_data = dataset.graph_data + + # meta data + self.num_nodes = dataset.num_nodes + self.num_features = dataset.num_features + self.num_classes = dataset.num_classes + + # params + self.attack_node_fraction = attack_node_fraction + + self._check_dataset_compatibility() + +To implement your own defense: + +1. **Inherit from ``BaseDefense``**: + Create a new class that inherits from ``BaseDefense``. You’ll need to provide the following required parameters in the + constructor: + + - ``dataset``: An instance of the ``Dataset`` class (see below for details). + - ``attack_node_fraction``: A float between 0 and 1 representing the fraction of nodes to attack. + - ``model_path`` (optional): A string specifying the path to a pre-trained model (defaults to ``None``). + + You need to implement the following methods: + + - ``defense()``: Add main defense logic here. If multiple defense types are supported, define the defense type as an + optional argument to this function. For each specific defense type, implement a corresponding helper function such as + ``_defense_type1()`` or ``_defense_type2()``, and call the appropriate helper inside ``defense()``. + - ``_load_model()``: Load victim model. + - ``_train_target_model()``: Train victim model. + - ``_train_defense_model()``: Train defense model. + - ``_train_surrogate_model()``: Train attack model. + - ``_helper_func()`` (optional): Add your helper functions based on your needs, but keep the methods private. + +2. **Implement the ``defense()`` Method**: + Override the abstract ``defense()`` method with your defense logic, and return a dict of results. For example: + +.. code-block:: python + + class MyCustomDefense(BaseDefense): + supported_api_types = {"pyg"} # "pyg" or "dgl" + supported_datasets = {"Cora"} # you can leave this blank if your method supports all datasets + + def defend(self): + # Step 1: Train target model + target_model = self._train_target_model() + # Step 2: Attack target model + attack = MyCustomAttack(self.dataset, attack_node_fraction=0.3) + attack.attack(target_model) + # Step 3: Train defense model + defense_model = self._train_defense_model() + # Step 4: Test defense against attack + attack = MyCustomAttack(self.dataset, attack_node_fraction=0.3) + attack.attack(defense_model) + # Print performance metrics + + def _load_model(self): + # add your logic here + pass + + def _train_target_model(self): + # add your logic here + pass + + def _train_defense_model(self): + # add your logic here + pass + + def _train_surrogate_model(self): + # add your logic here + pass + +Miscellaneous Tips +------------------ + +- **Reference Implementation**: The ``ModelExtractionAttack0`` class is a fully implemented attack example. Study it for + inspiration or as a template. +- **Flexibility**: Add as many helper functions as needed within your class to keep your code clean and modular. +- **Backbone Models**: We provide several basic backbone models like ``GCN, GraphSAGE``. You can use or add more + at ``from models.nn import GraphSAGE``. +- **Example Scripts**: Please provide an example script in the ``examples/`` folder demonstrating how to run your code. This + will significantly speed up our code review process. + +By following these guidelines, you can seamlessly integrate your custom attack or defense strategies into PyHazard. Happy +coding! \ No newline at end of file diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt new file mode 100644 index 0000000..b1983be --- /dev/null +++ b/docs/build/html/_sources/index.rst.txt @@ -0,0 +1,139 @@ +.. raw:: html + +
    + PyHazard Icon +
    + +.. image:: https://img.shields.io/pypi/v/PyHazard + :target: https://pypi.org/project/PyHazard + :alt: PyPI Version + +.. image:: https://img.shields.io/github/actions/workflow/status/LabRAI/PyHazard/docs.yml + :target: https://github.com/LabRAI/PyHazard/actions + :alt: Build Status + +.. image:: https://img.shields.io/github/license/LabRAI/PyHazard.svg + :target: https://github.com/LabRAI/PyHazard/blob/main/LICENSE + :alt: License + +.. image:: https://img.shields.io/pypi/dm/pyhazard + :target: https://github.com/LabRAI/PyHazard + :alt: PyPI Downloads + +.. image:: https://img.shields.io/github/issues/LabRAI/PyHazard + :target: https://github.com/LabRAI/PyHazard + :alt: Issues + +.. image:: https://img.shields.io/github/issues-pr/LabRAI/PyHazard + :target: https://github.com/LabRAI/PyHazard + :alt: Pull Requests + +.. image:: https://img.shields.io/github/stars/LabRAI/PyHazard + :target: https://github.com/LabRAI/PyHazard + :alt: Stars + +.. image:: https://img.shields.io/github/forks/LabRAI/PyHazard + :target: https://github.com/LabRAI/PyHazard + :alt: GitHub forks + +.. image:: _static/github.svg + :target: https://github.com/LabRAI/PyHazard + :alt: GitHub + +---- + +**PyHazard** is a comprehensive Python framework for AI-powered hazard prediction and risk assessment. Built on PyTorch, PyTorch Geometric, and DGL, the library provides a modular and extensible architecture for building, training, and deploying machine learning models to predict and analyze natural hazards and environmental risks. + +**PyHazard is designed for:** + +- **Modular Architecture**: Easy-to-extend framework for implementing custom hazard prediction models +- **Graph Neural Networks**: Built-in support for graph-based data representation and GNN models +- **Flexible Datasets**: Unified dataset interface supporting both DGL and PyTorch Geometric +- **GPU Acceleration**: Full CUDA support for training and inference +- **Extensible Models**: Base classes for implementing custom prediction models +- **Production Ready**: Type hints, proper error handling, and comprehensive testing + +**Quick Start Example:** + +Basic Usage Example: + +.. code-block:: python + + import pyhazard + from pyhazard.datasets import Cora + from pyhazard.models.nn import GCN + + # Load a dataset + dataset = Cora(api_type='pyg') + + # Initialize a model + model = GCN( + input_dim=dataset.num_features, + hidden_dim=64, + output_dim=dataset.num_classes + ) + + # Your training and prediction code here + # ... + +Core Components +--------------- + +**Datasets** + PyHazard provides a unified dataset interface that supports both DGL and PyTorch Geometric formats. + Built-in datasets include Cora, CiteSeer, PubMed, and more. + +**Models** + Extensible model architecture with built-in neural network backbones including GCN, GAT, and more. + Easy to implement custom models by extending base classes. + +**Utilities** + Helper functions for device management, metrics calculation, and data conversion between DGL and PyTorch Geometric. + +How to Cite +----------- + +If you use PyHazard in your research, please cite: + +.. code-block:: bibtex + + @software{pyhazard2025, + title={PyHazard: A Python Framework for AI-Powered Hazard Prediction}, + author={Cheng, Xueqi}, + year={2025}, + url={https://github.com/LabRAI/PyHazard} + } + + +.. toctree:: + :maxdepth: 2 + :caption: Getting Started + :hidden: + + installation + quick_start + +.. toctree:: + :maxdepth: 1 + :caption: API Reference + :hidden: + + pyhazard_datasets + pyhazard_utils + +.. toctree:: + :maxdepth: 2 + :caption: Additional Information + :hidden: + + implementation + cite + team + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/build/html/_sources/installation.rst.txt b/docs/build/html/_sources/installation.rst.txt new file mode 100644 index 0000000..81b81b9 --- /dev/null +++ b/docs/build/html/_sources/installation.rst.txt @@ -0,0 +1,43 @@ +Installation +============ + +PyHazard requires Python 3.8+ and can be installed using pip. We recommend using a conda environment for installation. + +Installing PyHazard +---------------- + +To get started with PyHazard, set up your environment by installing the required dependencies: + +.. code-block:: bash + + pip install -r requirements.txt + +Ensure you have Python installed (version 3.8 or higher recommended) along with the necessary libraries listed +in `requirements.txt`. + +Specifically, using following command to install `dgl 2.2.1` and ensure your `pytorch==2.3.0`. + +For CPU +~~~~~~~~~~~~~ + +.. code-block:: bash + + pip install dgl==2.2.1 -f https://data.dgl.ai/wheels/torch-2.3/repo.html + +For GPU (cuda 12.1) +~~~~~~~~~~~~~ + +.. code-block:: bash + + pip install dgl==2.2.1 -f https://data.dgl.ai/wheels/torch-2.3/cu121/repo.html + + + +Requirements +------------ + +- Python >= 3.8 +- PyTorch == 2.3 +- torch-geometric >= 2.6.0 +- dgl == 2.2.1 +- CUDA 12.1 diff --git a/docs/build/html/_sources/pyhazard_datasets.rst.txt b/docs/build/html/_sources/pyhazard_datasets.rst.txt new file mode 100644 index 0000000..af0ea49 --- /dev/null +++ b/docs/build/html/_sources/pyhazard_datasets.rst.txt @@ -0,0 +1,24 @@ +Datasets +=================== + +Summary +------- + +In this section, we present the datasets currently supported by PyHazard. +For each dataset, we provide both DGL and PyG APIs to ensure flexibility and compatibility with different graph learning frameworks. + +Submodules +---------- + +.. autosummary:: + :toctree: _autosummary/datasets + :recursive: + + pyhazard.datasets.Cora + pyhazard.datasets.CiteSeer + pyhazard.datasets.PubMed + pyhazard.datasets.Computers + pyhazard.datasets.Photo + pyhazard.datasets.CoauthorCS + pyhazard.datasets.CoauthorPhysics + diff --git a/docs/build/html/_sources/pyhazard_utils.rst.txt b/docs/build/html/_sources/pyhazard_utils.rst.txt new file mode 100644 index 0000000..8bdc19a --- /dev/null +++ b/docs/build/html/_sources/pyhazard_utils.rst.txt @@ -0,0 +1,18 @@ +Utils +=================== + +Summary +------- + +In this section, we present the utility modules of PyHazard. + +Submodules +---------- + +.. autosummary:: + :toctree: _autosummary/utils + :recursive: + + pyhazard.utils.hardware + pyhazard.utils.dglTopyg + pyhazard.utils.metrics diff --git a/docs/build/html/_sources/quick_start.rst.txt b/docs/build/html/_sources/quick_start.rst.txt new file mode 100644 index 0000000..0aa548e --- /dev/null +++ b/docs/build/html/_sources/quick_start.rst.txt @@ -0,0 +1,119 @@ +Quick Start +================= +This guide will help you get started with PyHazard quickly. + +Basic Usage +----------- + +Loading a Dataset +~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + from pyhazard.datasets import Cora + + # Load the Cora dataset with PyTorch Geometric API + dataset = Cora(api_type='pyg') + + print(f"Number of nodes: {dataset.num_nodes}") + print(f"Number of features: {dataset.num_features}") + print(f"Number of classes: {dataset.num_classes}") + +Using Graph Neural Networks +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + import pyhazard + from pyhazard.datasets import Cora + from pyhazard.models.nn import GCN + from pyhazard.utils import get_device + + # Load dataset + dataset = Cora(api_type='pyg') + + # Initialize model + device = get_device() + model = GCN( + input_dim=dataset.num_features, + hidden_dim=64, + output_dim=dataset.num_classes + ).to(device) + + # Your training code here + # ... + +GPU Support +----------- + +PyHazard automatically detects CUDA availability. To explicitly set the device: + +**Using Environment Variable:** + +.. code-block:: bash + + export PYHAZARD_DEVICE=cuda:0 + +**Using Python API:** + +.. code-block:: python + + from pyhazard.utils import set_device + + # Set to use CUDA device 0 + set_device("cuda:0") + + # Or use CPU + set_device("cpu") + +Available Datasets +------------------ + +PyHazard includes several built-in graph datasets: + +- **Cora**: Citation network (2708 nodes, 1433 features, 7 classes) +- **CiteSeer**: Citation network (3327 nodes, 3703 features, 6 classes) +- **PubMed**: Citation network (19717 nodes, 500 features, 3 classes) +- **Computers**: Amazon co-purchase network +- **Photo**: Amazon co-purchase network +- **CoauthorCS**: Co-authorship network (Computer Science) +- **CoauthorPhysics**: Co-authorship network (Physics) + +Example with different datasets: + +.. code-block:: python + + from pyhazard.datasets import CiteSeer, PubMed, Computers + + # Load CiteSeer + citeseer = CiteSeer(api_type='pyg') + + # Load PubMed + pubmed = PubMed(api_type='pyg') + + # Load Computers + computers = Computers(api_type='pyg') + +API Types +--------- + +PyHazard supports both DGL and PyTorch Geometric APIs: + +.. code-block:: python + + from pyhazard.datasets import Cora + + # PyTorch Geometric format + dataset_pyg = Cora(api_type='pyg') + + # DGL format + dataset_dgl = Cora(api_type='dgl') + +Next Steps +---------- + +For more detailed documentation, please refer to: + +- :doc:`pyhazard_datasets` - Complete dataset API reference +- :doc:`pyhazard_utils` - Utility functions and helpers +- :doc:`implementation` - Guide for implementing custom models diff --git a/docs/build/html/_sources/references.rst.txt b/docs/build/html/_sources/references.rst.txt new file mode 100644 index 0000000..818a674 --- /dev/null +++ b/docs/build/html/_sources/references.rst.txt @@ -0,0 +1,6 @@ +References +========== + +Academic Publications +--------------------- + diff --git a/docs/build/html/_sources/team.rst.txt b/docs/build/html/_sources/team.rst.txt new file mode 100644 index 0000000..3c723d0 --- /dev/null +++ b/docs/build/html/_sources/team.rst.txt @@ -0,0 +1,45 @@ +Core Team +========= + +Our team is composed of dedicated researchers and developers who contribute to PyHazard's development and maintenance. + +Lead Developer +-------------- +* Xueqi Cheng - Florida State University (xc25@fsu.edu) + +Contributors +------------ +* `Yushun Dong `__ - Florida State University + +Principal Contributors & Maintainers +------------------------------------ +.. * `Yuxiang Sun `__ - University of Wisconsin-Madison +.. * `Chenxi Zhao `__ - Northeastern University +.. * `Lincan Li `__ - Florida State University + +Core Contributors +----------------- +.. * `Unique Karki `_ - [Affiliation] +.. * `Yujing Ju `__ - Heriot-Watt University +.. * `Zhan Cheng `__ - University of Wisconsin-Madison +.. * `Zaiyi Zheng `__ - University of Virginia +.. * `Tyler Blalock `_ - [Affiliation] +.. * `Sibtain Syed `_ - [Affiliation] +.. * `Md Ibrahim `_ - Uttara University, Bangladesh +.. * `Aditya Khanal `_ - Tribhuvan University, Nepal +.. * `Kedar Satish Awale `_ - Florida State University +.. * `Hong Iris `_ - Washington University in St. Louis +.. * `Aasman Bashyal `_ - [Affiliation] +.. * `Cameron Bender `_ - Florida State University +.. * `Yushi Huang `_ - [Affiliation] +.. * `Anurag Shukla `_ - [Affiliation] + + +---------------- + +The Core Team is responsible for: + +* Strategic planning and technical direction +* Code review and quality assurance +* Documentation and maintenance +* Community engagement and support diff --git a/docs/build/html/_static/basic.css b/docs/build/html/_static/basic.css new file mode 100644 index 0000000..4738b2e --- /dev/null +++ b/docs/build/html/_static/basic.css @@ -0,0 +1,906 @@ +/* + * Sphinx stylesheet -- basic theme. + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; 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+} + +code.xref, a code { + background-color: transparent; + font-weight: bold; +} + +h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { + background-color: transparent; +} + +.viewcode-link { + float: right; +} + +.viewcode-back { + float: right; + font-family: sans-serif; +} + +div.viewcode-block:target { + margin: -1px -10px; + padding: 0 10px; +} + +/* -- math display ---------------------------------------------------------- */ + +img.math { + vertical-align: middle; +} + +div.body div.math p { + text-align: center; +} + +span.eqno { + float: right; +} + +span.eqno a.headerlink { + position: absolute; + z-index: 1; +} + +div.math:hover a.headerlink { + visibility: visible; +} + +/* -- printout stylesheet --------------------------------------------------- */ + +@media print { + div.document, + div.documentwrapper, + div.bodywrapper { + margin: 0 !important; + width: 100%; + } + + div.sphinxsidebar, + div.related, + div.footer, + #top-link { + display: none; + } +} \ No newline at end of file diff --git a/docs/build/html/_static/debug.css b/docs/build/html/_static/debug.css new file mode 100644 index 0000000..74d4aec --- /dev/null +++ b/docs/build/html/_static/debug.css @@ -0,0 +1,69 @@ +/* + This CSS file should be overridden by the theme authors. It's + meant for debugging and developing the skeleton that this theme provides. +*/ +body { + font-family: -apple-system, "Segoe UI", Roboto, Helvetica, Arial, sans-serif, + "Apple Color Emoji", "Segoe UI Emoji"; + background: lavender; +} +.sb-announcement { + background: rgb(131, 131, 131); +} +.sb-announcement__inner { + background: black; + color: white; +} +.sb-header { + background: lightskyblue; +} +.sb-header__inner { + background: royalblue; + color: white; +} +.sb-header-secondary { + background: lightcyan; +} +.sb-header-secondary__inner { + background: cornflowerblue; + color: white; +} +.sb-sidebar-primary { + background: lightgreen; +} +.sb-main { + background: blanchedalmond; +} +.sb-main__inner { + background: antiquewhite; +} +.sb-header-article { + background: lightsteelblue; +} +.sb-article-container { + background: snow; +} +.sb-article-main { + background: white; +} +.sb-footer-article { + background: lightpink; +} +.sb-sidebar-secondary { + background: lightgoldenrodyellow; +} +.sb-footer-content { + background: plum; +} +.sb-footer-content__inner { + background: palevioletred; +} +.sb-footer { + background: pink; +} +.sb-footer__inner { + background: salmon; +} +.sb-article { + background: white; +} diff --git a/docs/build/html/_static/doctools.js b/docs/build/html/_static/doctools.js new file mode 100644 index 0000000..0398ebb --- /dev/null +++ b/docs/build/html/_static/doctools.js @@ -0,0 +1,149 @@ +/* + * Base JavaScript utilities for all Sphinx HTML documentation. + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/docs/build/html/_static/documentation_options.js b/docs/build/html/_static/documentation_options.js new file mode 100644 index 0000000..1675f3b --- /dev/null +++ b/docs/build/html/_static/documentation_options.js @@ -0,0 +1,13 @@ +const DOCUMENTATION_OPTIONS = { + VERSION: '1.0.0', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: true, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/docs/build/html/_static/file.png b/docs/build/html/_static/file.png new file mode 100644 index 0000000000000000000000000000000000000000..a858a410e4faa62ce324d814e4b816fff83a6fb3 GIT binary patch literal 286 zcmV+(0pb3MP)s`hMrGg#P~ix$^RISR_I47Y|r1 z_CyJOe}D1){SET-^Amu_i71Lt6eYfZjRyw@I6OQAIXXHDfiX^GbOlHe=Ae4>0m)d(f|Me07*qoM6N<$f}vM^LjV8( literal 0 HcmV?d00001 diff --git a/docs/build/html/_static/github.svg b/docs/build/html/_static/github.svg new file mode 100644 index 0000000..013e025 --- /dev/null +++ b/docs/build/html/_static/github.svg @@ -0,0 +1,3 @@ + + + \ No newline at end of file diff --git a/docs/build/html/_static/language_data.js b/docs/build/html/_static/language_data.js new file mode 100644 index 0000000..c7fe6c6 --- /dev/null +++ b/docs/build/html/_static/language_data.js @@ -0,0 +1,192 @@ +/* + * This script contains the language-specific data used by searchtools.js, + * namely the list of stopwords, stemmer, scorer and splitter. + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, if available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/docs/build/html/_static/logo.pdf 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detail,\n });\n\n // Dispatch the event\n elem.dispatchEvent(event);\n };\n\n /**\n * Get an element's distance from the top of the Document.\n * @param {Node} elem The element\n * @return {Number} Distance from the top in pixels\n */\n var getOffsetTop = function (elem) {\n var location = 0;\n if (elem.offsetParent) {\n while (elem) {\n location += elem.offsetTop;\n elem = elem.offsetParent;\n }\n }\n return location >= 0 ? location : 0;\n };\n\n /**\n * Sort content from first to last in the DOM\n * @param {Array} contents The content areas\n */\n var sortContents = function (contents) {\n if (contents) {\n contents.sort(function (item1, item2) {\n var offset1 = getOffsetTop(item1.content);\n var offset2 = getOffsetTop(item2.content);\n if (offset1 < offset2) return -1;\n return 1;\n });\n }\n };\n\n /**\n * Get the offset to use for calculating position\n * @param {Object} settings The settings for this instantiation\n * @return {Float} The number of pixels to offset the 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settings);\n };\n\n /**\n * Deactivate a nav and content area\n * @param {Object} items The nav item and content to deactivate\n * @param {Object} settings The settings for this instantiation\n */\n var deactivate = function (items, settings) {\n // Make sure there are items to deactivate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Remove the active class from the nav and content\n li.classList.remove(settings.navClass);\n items.content.classList.remove(settings.contentClass);\n\n // Deactivate any parent navs in a nested navigation\n deactivateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeDeactivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Activate parent navs in a nested navigation\n * @param {Node} nav The starting navigation element\n * @param {Object} settings The settings for this instantiation\n */\n var activateNested = function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Add the active class\n li.classList.add(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n activateNested(li, settings);\n };\n\n /**\n * Activate a nav and content area\n * @param {Object} items The nav item and content to activate\n * @param {Object} settings The settings for this instantiation\n */\n var activate = function (items, settings) {\n // Make sure there are items to activate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Add the active class to the nav and content\n li.classList.add(settings.navClass);\n items.content.classList.add(settings.contentClass);\n\n // Activate any parent navs in a nested navigation\n activateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeActivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Create the Constructor object\n * @param {String} selector The selector to use for navigation items\n * @param {Object} options User options and settings\n */\n var Constructor = function (selector, options) {\n //\n // Variables\n //\n\n var publicAPIs = {};\n var navItems, contents, current, timeout, settings;\n\n //\n // Methods\n //\n\n /**\n * Set variables from DOM elements\n */\n publicAPIs.setup = function () {\n // Get all nav items\n navItems = document.querySelectorAll(selector);\n\n // Create contents array\n contents = [];\n\n // Loop through each item, get it's matching content, and push to the array\n Array.prototype.forEach.call(navItems, function (item) {\n // Get the content for the nav item\n var content = document.getElementById(\n decodeURIComponent(item.hash.substr(1)),\n );\n if (!content) return;\n\n // Push to the contents array\n 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+ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename, kind] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +// Global search result kind enum, used by themes to style search results. +class SearchResultKind { + static get index() { return "index"; } + static get object() { return "object"; } + static get text() { return "text"; } + static get title() { return "title"; } +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename, kind] = item; + + let listItem = document.createElement("li"); + // Add a class representing the item's type: + // can be used by a theme's CSS selector for styling + // See SearchResultKind for the class names. + listItem.classList.add(`kind-${kind}`); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = Documentation.ngettext( + "Search finished, found one page matching the search query.", + "Search finished, found ${resultCount} pages matching the search query.", + resultCount, + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename, kind]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlink", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.setAttribute("role", "list"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename, kind]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + const score = Math.round(Scorer.title * queryLower.length / title.length); + const boost = titles[file] === title ? 1 : 0; // add a boost for document titles + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score + boost, + filenames[file], + SearchResultKind.title, + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + SearchResultKind.index, + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + SearchResultKind.object, + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + // find documents, if any, containing the query word in their text/title term indices + // use Object.hasOwnProperty to avoid mismatching against prototype properties + const arr = [ + { files: terms.hasOwnProperty(word) ? terms[word] : undefined, score: Scorer.term }, + { files: titleTerms.hasOwnProperty(word) ? titleTerms[word] : undefined, score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, new Map()); + const fileScores = scoreMap.get(file); + fileScores.set(word, record.score); + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file).get(w))); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + SearchResultKind.text, + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/docs/build/html/_static/skeleton.css b/docs/build/html/_static/skeleton.css new file mode 100644 index 0000000..467c878 --- /dev/null +++ b/docs/build/html/_static/skeleton.css @@ -0,0 +1,296 @@ +/* Some sane resets. */ +html { + height: 100%; +} + +body { + margin: 0; + min-height: 100%; +} + +/* All the flexbox magic! */ +body, +.sb-announcement, +.sb-content, +.sb-main, +.sb-container, +.sb-container__inner, +.sb-article-container, +.sb-footer-content, +.sb-header, +.sb-header-secondary, +.sb-footer { + display: flex; +} + +/* These order things vertically */ +body, +.sb-main, +.sb-article-container { + flex-direction: column; +} + +/* Put elements in the center */ +.sb-header, +.sb-header-secondary, +.sb-container, +.sb-content, +.sb-footer, +.sb-footer-content { + justify-content: center; +} +/* Put elements at the ends */ +.sb-article-container { + justify-content: space-between; +} + +/* These elements grow. */ +.sb-main, +.sb-content, +.sb-container, +article { + flex-grow: 1; +} + +/* Because padding making this wider is not fun */ +article { + box-sizing: border-box; +} + +/* The announcements element should never be wider than the page. */ +.sb-announcement { + max-width: 100%; +} + +.sb-sidebar-primary, +.sb-sidebar-secondary { + flex-shrink: 0; + width: 17rem; +} + +.sb-announcement__inner { + justify-content: center; + + box-sizing: border-box; + height: 3rem; + + overflow-x: auto; + white-space: nowrap; +} + +/* Sidebars, with checkbox-based toggle */ +.sb-sidebar-primary, +.sb-sidebar-secondary { + position: fixed; + height: 100%; + top: 0; +} + +.sb-sidebar-primary { + left: -17rem; + transition: left 250ms ease-in-out; +} +.sb-sidebar-secondary { + right: -17rem; + transition: right 250ms ease-in-out; +} + +.sb-sidebar-toggle { + display: none; +} +.sb-sidebar-overlay { + position: fixed; + top: 0; + width: 0; + height: 0; + + transition: width 0ms ease 250ms, height 0ms ease 250ms, opacity 250ms ease; + + opacity: 0; + background-color: rgba(0, 0, 0, 0.54); +} + +#sb-sidebar-toggle--primary:checked + ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--primary"], +#sb-sidebar-toggle--secondary:checked + ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--secondary"] { + width: 100%; + height: 100%; + opacity: 1; + transition: width 0ms ease, height 0ms ease, opacity 250ms ease; +} + +#sb-sidebar-toggle--primary:checked ~ .sb-container .sb-sidebar-primary { + left: 0; +} +#sb-sidebar-toggle--secondary:checked ~ .sb-container .sb-sidebar-secondary { + right: 0; +} + +/* Full-width mode */ +.drop-secondary-sidebar-for-full-width-content + .hide-when-secondary-sidebar-shown { + display: none !important; +} +.drop-secondary-sidebar-for-full-width-content .sb-sidebar-secondary { + display: none !important; +} + +/* Mobile views */ +.sb-page-width { + width: 100%; +} + +.sb-article-container, +.sb-footer-content__inner, +.drop-secondary-sidebar-for-full-width-content .sb-article, +.drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 100vw; +} + +.sb-article, +.match-content-width { + padding: 0 1rem; + box-sizing: border-box; +} + +@media (min-width: 32rem) { + .sb-article, + .match-content-width { + padding: 0 2rem; + } +} + +/* Tablet views */ +@media (min-width: 42rem) { + .sb-article-container { + width: auto; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 42rem; + } + .sb-article, + .match-content-width { + width: 42rem; + } +} +@media (min-width: 46rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 46rem; + } + .sb-article, + .match-content-width { + width: 46rem; + } +} +@media (min-width: 50rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 50rem; + } + .sb-article, + .match-content-width { + width: 50rem; + } +} + +/* Tablet views */ +@media (min-width: 59rem) { + .sb-sidebar-secondary { + position: static; + } + .hide-when-secondary-sidebar-shown { + display: none !important; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 59rem; + } + .sb-article, + .match-content-width { + width: 42rem; + } +} +@media (min-width: 63rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 63rem; + } + .sb-article, + .match-content-width { + width: 46rem; + } +} +@media (min-width: 67rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 67rem; + } + .sb-article, + .match-content-width { + width: 50rem; + } +} + +/* Desktop views */ +@media (min-width: 76rem) { + .sb-sidebar-primary { + position: static; + } + .hide-when-primary-sidebar-shown { + display: none !important; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 59rem; + } + .sb-article, + .match-content-width { + width: 42rem; + } +} + +/* Full desktop views */ +@media (min-width: 80rem) { + .sb-article, + .match-content-width { + width: 46rem; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 63rem; + } +} + +@media (min-width: 84rem) { + .sb-article, + .match-content-width { + width: 50rem; + } + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 67rem; + } +} + +@media (min-width: 88rem) { + .sb-footer-content__inner, + .drop-secondary-sidebar-for-full-width-content .sb-article, + .drop-secondary-sidebar-for-full-width-content .match-content-width { + width: 67rem; + } + .sb-page-width { + width: 88rem; + } +} diff --git a/docs/build/html/_static/sphinx_highlight.js b/docs/build/html/_static/sphinx_highlight.js new file mode 100644 index 0000000..8a96c69 --- /dev/null +++ b/docs/build/html/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

    ' + + '' + + _("Hide Search Matches") + + "

    " + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/docs/build/html/_static/styles/furo-extensions.css b/docs/build/html/_static/styles/furo-extensions.css new file mode 100644 index 0000000..2d74267 --- /dev/null +++ b/docs/build/html/_static/styles/furo-extensions.css @@ -0,0 +1,2 @@ +#furo-sidebar-ad-placement{padding:var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)}#furo-sidebar-ad-placement .ethical-sidebar{background:var(--color-background-secondary);border:none;box-shadow:none}#furo-sidebar-ad-placement .ethical-sidebar:hover{background:var(--color-background-hover)}#furo-sidebar-ad-placement .ethical-sidebar a{color:var(--color-foreground-primary)}#furo-sidebar-ad-placement .ethical-callout 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Remove the inheritance of text transform in Firefox.\n */\n\nbutton,\nselect { /* 1 */\n text-transform: none;\n}\n\n/**\n * Correct the inability to style clickable types in iOS and Safari.\n */\n\nbutton,\n[type=\"button\"],\n[type=\"reset\"],\n[type=\"submit\"] {\n -webkit-appearance: button;\n}\n\n/**\n * Remove the inner border and padding in Firefox.\n */\n\nbutton::-moz-focus-inner,\n[type=\"button\"]::-moz-focus-inner,\n[type=\"reset\"]::-moz-focus-inner,\n[type=\"submit\"]::-moz-focus-inner {\n border-style: none;\n padding: 0;\n}\n\n/**\n * Restore the focus styles unset by the previous rule.\n */\n\nbutton:-moz-focusring,\n[type=\"button\"]:-moz-focusring,\n[type=\"reset\"]:-moz-focusring,\n[type=\"submit\"]:-moz-focusring {\n outline: 1px dotted ButtonText;\n}\n\n/**\n * Correct the padding in Firefox.\n */\n\nfieldset {\n padding: 0.35em 0.75em 0.625em;\n}\n\n/**\n * 1. Correct the text wrapping in Edge and IE.\n * 2. Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n * `fieldset` elements in all browsers.\n */\n\nlegend {\n box-sizing: border-box; /* 1 */\n color: inherit; /* 2 */\n display: table; /* 1 */\n max-width: 100%; /* 1 */\n padding: 0; /* 3 */\n white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n -webkit-appearance: button; /* 1 */\n font: inherit; /* 2 */\n}\n\n/* Interactive\n ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n display: list-item;\n}\n\n/* Misc\n ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Hide icon container.\n .content-icon-container\n display: none !important\n\n // Hide showing header links if hovering over when printing.\n .headerlink\n display: none !important\n\n // Hide mobile header.\n .mobile-header\n display: none !important\n\n // Hide navigation links.\n .related-pages\n display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n // Apply a border around code which no longer have a color background.\n .highlight\n border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n ul, ol, dl, a, table, pre, blockquote, p\n page-break-inside: avoid\n\n h1, h2, h3, h4, h5, h6, img, figure, caption\n page-break-inside: avoid\n page-break-after: avoid\n\n ul, ol, dl\n page-break-before: avoid\n",".visually-hidden\n position: absolute !important\n width: 1px !important\n height: 1px !important\n padding: 0 !important\n margin: -1px !important\n overflow: hidden !important\n clip: rect(0,0,0,0) !important\n white-space: nowrap !important\n border: 0 !important\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n:-moz-focusring\n outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\n@use \"../variables\" as *\n\nbody\n @include fonts\n @include spacing\n @include icons\n @include admonitions\n @include default-admonition(#651fff, \"abstract\")\n @include default-topic(#14B8A6, \"pencil\")\n\n @include colors\n\n.only-light\n display: block !important\nhtml body .only-dark\n display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n // Enable dark-mode, if requested.\n body[data-theme=\"dark\"]\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n // Enable dark mode, unless explicitly told to avoid.\n @media (prefers-color-scheme: dark)\n body:not([data-theme=\"light\"])\n @include colors-dark\n\n html & .only-light\n display: none !important\n .only-dark\n display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n .theme-toggle svg.theme-icon-when-auto-light\n display: block\n\n @media (prefers-color-scheme: dark)\n .theme-toggle svg.theme-icon-when-auto-dark\n display: block\n .theme-toggle svg.theme-icon-when-auto-light\n display: none\n\nbody[data-theme=\"dark\"]\n .theme-toggle svg.theme-icon-when-dark\n display: block\n\nbody[data-theme=\"light\"]\n .theme-toggle svg.theme-icon-when-light\n display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n // These are adapted from https://systemfontstack.com/\n --font-stack:\n -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif,\n Apple Color Emoji, Segoe UI Emoji;\n --font-stack--monospace:\n \"SFMono-Regular\", Menlo, Consolas, Monaco, Liberation Mono, Lucida Console,\n monospace;\n --font-stack--headings: var(--font-stack);\n\n --font-size--normal: 100%;\n --font-size--small: 87.5%;\n --font-size--small--2: 81.25%;\n --font-size--small--3: 75%;\n --font-size--small--4: 62.5%;\n\n // Sidebar\n --sidebar-caption-font-size: var(--font-size--small--2);\n --sidebar-item-font-size: var(--font-size--small);\n --sidebar-search-input-font-size: var(--font-size--small);\n\n // Table of Contents\n --toc-font-size: var(--font-size--small--3);\n --toc-font-size--mobile: var(--font-size--normal);\n --toc-title-font-size: var(--font-size--small--4);\n\n // Admonitions\n //\n // These aren't defined in terms of %ages, since nesting these is permitted.\n --admonition-font-size: 0.8125rem;\n --admonition-title-font-size: 0.8125rem;\n\n // Code\n --code-font-size: var(--font-size--small--2);\n\n // API\n --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n // Header!\n --header-height: calc(\n var(--sidebar-item-line-height) + 4 *\n #{var(--sidebar-item-spacing-vertical)}\n );\n --header-padding: 0.5rem;\n\n // Sidebar\n --sidebar-tree-space-above: 1.5rem;\n --sidebar-caption-space-above: 1rem;\n\n --sidebar-item-line-height: 1rem;\n --sidebar-item-spacing-vertical: 0.5rem;\n --sidebar-item-spacing-horizontal: 1rem;\n --sidebar-item-height: calc(\n var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n );\n\n --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n --sidebar-search-space-above: 0.5rem;\n --sidebar-search-input-spacing-vertical: 0.5rem;\n --sidebar-search-input-spacing-horizontal: 0.5rem;\n --sidebar-search-input-height: 1rem;\n --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n // Table of Contents\n --toc-title-padding: 0.25rem 0;\n --toc-spacing-vertical: 1.5rem;\n --toc-spacing-horizontal: 1.5rem;\n --toc-item-spacing-vertical: 0.4rem;\n --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n // Adapted from tabler-icons\n // url: https://tablericons.com/\n \"search\":\n url('data:image/svg+xml;charset=utf-8,'),\n // Factored out from mkdocs-material on 24-Aug-2020.\n // url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n \"pencil\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"abstract\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"info\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"flame\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"question\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"warning\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"failure\":\n url('data:image/svg+xml;charset=utf-8,'),\n \"spark\":\n url('data:image/svg+xml;charset=utf-8,')\n);\n\n@mixin icons {\n @each $name, $glyph in $icons {\n --icon-#{$name}: #{$glyph};\n }\n}\n","@use \"sass:list\";\n// Admonitions\n\n// Structure of these is:\n// admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n // Each of these has an reST directives for it.\n \"caution\": #ff9100 \"spark\",\n \"warning\": #ff9100 \"warning\",\n \"danger\": #ff5252 \"spark\",\n \"attention\": #ff5252 \"warning\",\n \"error\": #ff5252 \"failure\",\n \"hint\": #00c852 \"question\",\n \"tip\": #00c852 \"info\",\n \"important\": #00bfa5 \"flame\",\n \"note\": #00b0ff \"pencil\",\n \"seealso\": #448aff \"info\",\n \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n --color-admonition-title: #{$color};\n --color-admonition-title-background: #{rgba($color, 0.2)};\n\n --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n --color-topic-title: #{$color};\n --color-topic-title-background: #{rgba($color, 0.2)};\n\n --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n @each $name, $values in $admonitions {\n --color-admonition-title--#{$name}: #{list.nth($values, 1)};\n --color-admonition-title-background--#{$name}: #{rgba(\n list.nth($values, 1),\n 0.2\n )};\n }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n --color-problematic: #b30000;\n\n // Base Colors\n --color-foreground-primary: black; // for main text and headings\n --color-foreground-secondary: #5a5c63; // for secondary text\n --color-foreground-muted: #6b6f76; // for muted text\n --color-foreground-border: #878787; // for content borders\n\n --color-background-primary: white; // for content\n --color-background-secondary: #f8f9fb; // for navigation + ToC\n --color-background-hover: #efeff4ff; // for navigation-item hover\n --color-background-hover--transparent: #efeff400;\n --color-background-border: #eeebee; // for UI borders\n --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #0a4bff;\n --color-brand-content: #2757dd;\n --color-brand-visited: #872ee0;\n\n // API documentation\n --color-api-background: var(--color-background-hover--transparent);\n --color-api-background-hover: var(--color-background-hover);\n --color-api-overall: var(--color-foreground-secondary);\n --color-api-name: var(--color-problematic);\n --color-api-pre-name: var(--color-problematic);\n --color-api-paren: var(--color-foreground-secondary);\n --color-api-keyword: var(--color-foreground-primary);\n\n --color-api-added: #21632c;\n --color-api-added-border: #38a84d;\n --color-api-changed: #046172;\n --color-api-changed-border: #06a1bc;\n --color-api-deprecated: #605706;\n --color-api-deprecated-border: #f0d90f;\n --color-api-removed: #b30000;\n --color-api-removed-border: #ff5c5c;\n\n --color-highlight-on-target: #ffffcc;\n\n // Inline code background\n --color-inline-code-background: var(--color-background-secondary);\n\n // Highlighted text (search)\n --color-highlighted-background: #ddeeff;\n --color-highlighted-text: var(--color-foreground-primary);\n\n // GUI Labels\n --color-guilabel-background: #ddeeff80;\n --color-guilabel-border: #bedaf580;\n --color-guilabel-text: var(--color-foreground-primary);\n\n // Admonitions!\n --color-admonition-background: transparent;\n\n //////////////////////////////////////////////////////////////////////////////\n // Everything below this should be one of:\n // - var(...)\n // - *-gradient(...)\n // - special literal values (eg: transparent, none)\n //////////////////////////////////////////////////////////////////////////////\n\n // Tables\n --color-table-header-background: var(--color-background-secondary);\n --color-table-border: var(--color-background-border);\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: transparent;\n --color-card-marginals-background: var(--color-background-secondary);\n\n // Header\n --color-header-background: var(--color-background-primary);\n --color-header-border: var(--color-background-border);\n --color-header-text: var(--color-foreground-primary);\n\n // Sidebar (left)\n --color-sidebar-background: var(--color-background-secondary);\n --color-sidebar-background-border: var(--color-background-border);\n\n --color-sidebar-brand-text: var(--color-foreground-primary);\n --color-sidebar-caption-text: var(--color-foreground-muted);\n --color-sidebar-link-text: var(--color-foreground-secondary);\n --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n --color-sidebar-item-background: var(--color-sidebar-background);\n --color-sidebar-item-background--current: var(\n --color-sidebar-item-background\n );\n --color-sidebar-item-background--hover: linear-gradient(\n 90deg,\n var(--color-background-hover--transparent) 0%,\n var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n var(--color-background-hover) 100%\n );\n\n --color-sidebar-item-expander-background: transparent;\n --color-sidebar-item-expander-background--hover: var(\n --color-background-hover\n );\n\n --color-sidebar-search-text: var(--color-foreground-primary);\n --color-sidebar-search-background: var(--color-background-secondary);\n --color-sidebar-search-background--focus: var(--color-background-primary);\n --color-sidebar-search-border: var(--color-background-border);\n --color-sidebar-search-icon: var(--color-foreground-muted);\n\n // Table of Contents (right)\n --color-toc-background: var(--color-background-primary);\n --color-toc-title-text: var(--color-foreground-muted);\n --color-toc-item-text: var(--color-foreground-secondary);\n --color-toc-item-text--hover: var(--color-foreground-primary);\n --color-toc-item-text--active: var(--color-brand-primary);\n\n // Actual page contents\n --color-content-foreground: var(--color-foreground-primary);\n --color-content-background: transparent;\n\n // Links\n --color-link: var(--color-brand-content);\n --color-link-underline: var(--color-background-border);\n --color-link--hover: var(--color-brand-content);\n --color-link-underline--hover: var(--color-foreground-border);\n\n --color-link--visited: var(--color-brand-visited);\n --color-link-underline--visited: var(--color-background-border);\n --color-link--visited--hover: var(--color-brand-visited);\n --color-link-underline--visited--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n --color-problematic: #ee5151;\n\n // Base Colors\n --color-foreground-primary: #cfd0d0; // for main text and headings\n --color-foreground-secondary: #9ca0a5; // for secondary text\n --color-foreground-muted: #81868d; // for muted text\n --color-foreground-border: #666666; // for content borders\n\n --color-background-primary: #131416; // for content\n --color-background-secondary: #1a1c1e; // for navigation + ToC\n --color-background-hover: #1e2124ff; // for navigation-item hover\n --color-background-hover--transparent: #1e212400;\n --color-background-border: #303335; // for UI borders\n --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n // Announcements\n --color-announcement-background: #000000dd;\n --color-announcement-text: #eeebee;\n\n // Brand colors\n --color-brand-primary: #3d94ff;\n --color-brand-content: #5ca5ff;\n --color-brand-visited: #b27aeb;\n\n // Highlighted text (search)\n --color-highlighted-background: #083563;\n\n // GUI Labels\n --color-guilabel-background: #08356380;\n --color-guilabel-border: #13395f80;\n\n // API documentation\n --color-api-keyword: var(--color-foreground-secondary);\n --color-highlight-on-target: #333300;\n\n --color-api-added: #3db854;\n --color-api-added-border: #267334;\n --color-api-changed: #09b0ce;\n --color-api-changed-border: #056d80;\n --color-api-deprecated: #b1a10b;\n --color-api-deprecated-border: #6e6407;\n --color-api-removed: #ff7575;\n --color-api-removed-border: #b03b3b;\n\n // Admonitions\n --color-admonition-background: #18181a;\n\n // Cards\n --color-card-border: var(--color-background-secondary);\n --color-card-background: #18181a;\n --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n -webkit-font-smoothing: antialiased\n -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n line-height: 1.25\n font-family: var(--font-stack--headings)\n font-weight: bold\n\n border-radius: 0.5rem\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n margin-left: -0.5rem\n margin-right: -0.5rem\n padding-left: 0.5rem\n padding-right: 0.5rem\n\n + p\n margin-top: 0\n\nh1\n font-size: 2.5em\n margin-top: 1.75rem\n margin-bottom: 1rem\nh2\n font-size: 2em\n margin-top: 1.75rem\nh3\n font-size: 1.5em\nh4\n font-size: 1.25em\nh5\n font-size: 1.125em\nh6\n font-size: 1em\n\nsmall\n opacity: 75%\n font-size: 80%\n\n// Paragraph\np\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n height: 1px\n padding: 0\n margin: 2rem 0\n background-color: var(--color-background-border)\n border: 0\n\n.centered\n text-align: center\n\n// Links\na\n text-decoration: underline\n\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n &:visited\n color: var(--color-link--visited)\n text-decoration-color: var(--color-link-underline--visited)\n &:hover\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &.muted-link\n color: inherit\n &:hover\n color: var(--color-link--hover)\n text-decoration-color: var(--color-link-underline--hover)\n &:visited\n color: var(--color-link--visited--hover)\n text-decoration-color: var(--color-link-underline--visited--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n@use \"variables\" as *\n\nhtml\n overflow-x: hidden\n overflow-y: scroll\n scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n scrollbar-width: thin\n scrollbar-color: var(--color-foreground-border) transparent\n\n//\n// Overalls\n//\nhtml,\nbody\n height: 100%\n color: var(--color-foreground-primary)\n background: var(--color-background-primary)\n\n.skip-to-content\n position: fixed\n padding: 1rem\n border-radius: 1rem\n left: 0.25rem\n top: 0.25rem\n z-index: 40\n background: var(--color-background-primary)\n color: var(--color-foreground-primary)\n\n transform: translateY(-200%)\n transition: transform 300ms ease-in-out\n\n &:focus-within\n transform: translateY(0%)\n\narticle\n color: var(--color-content-foreground)\n background: var(--color-content-background)\n overflow-wrap: break-word\n\n.page\n display: flex\n // fill the viewport for pages with little content.\n min-height: 100%\n\n.mobile-header\n width: 100%\n height: var(--header-height)\n background-color: var(--color-header-background)\n color: var(--color-header-text)\n border-bottom: 1px solid var(--color-header-border)\n\n // Looks like sub-script/super-script have this, and we need this to\n // be \"on top\" of those.\n z-index: 10\n\n // We don't show the header on large screens.\n display: none\n\n // Add shadow when scrolled\n &.scrolled\n border-bottom: none\n box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n .header-center\n a\n color: var(--color-header-text)\n text-decoration: none\n\n.main\n display: flex\n flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n box-sizing: border-box\n\n border-right: 1px solid var(--color-sidebar-background-border)\n background: var(--color-sidebar-background)\n\n display: flex\n justify-content: flex-end\n // These next two lines took me two days to figure out.\n width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n box-sizing: border-box\n width: $sidebar-width\n\n.toc-drawer\n background: var(--color-toc-background)\n // See HACK described on top of this document\n padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n position: sticky\n top: 0\n height: min(100%, 100vh)\n height: 100vh\n\n display: flex\n flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n flex-grow: 1\n flex-shrink: 1\n\n overflow: auto\n scroll-behavior: smooth\n\n// Central items.\n.content\n padding: 0 $content-padding\n width: $content-width\n\n display: flex\n flex-direction: column\n justify-content: space-between\n\n.icon\n display: inline-block\n height: 1rem\n width: 1rem\n svg\n width: 100%\n height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n background-color: var(--color-announcement-background)\n color: var(--color-announcement-text)\n\n height: var(--header-height)\n display: flex\n align-items: center\n overflow-x: auto\n & + .page\n min-height: calc(100% - var(--header-height))\n\n.announcement-content\n box-sizing: border-box\n padding: 0.5rem\n min-width: 100%\n white-space: nowrap\n text-align: center\n\n a\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-announcement-text)\n\n &:hover\n color: var(--color-announcement-text)\n text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container // don't show theme toggle if there's no JS\n display: none\n\n.theme-toggle-container\n display: flex\n\n.theme-toggle\n display: flex\n cursor: pointer\n border: none\n padding: 0\n background: transparent\n\n.theme-toggle svg\n height: 1.25rem\n width: 1.25rem\n color: var(--color-foreground-primary)\n display: none\n\n.theme-toggle-header\n display: flex\n align-items: center\n justify-content: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n display: none\n cursor: pointer\n\n .icon\n color: var(--color-foreground-secondary)\n height: 1.5rem\n width: 1.5rem\n\n.toc-header-icon, .nav-overlay-icon\n // for when we set display: flex\n justify-content: center\n align-items: center\n\n.toc-content-icon\n height: 1.5rem\n width: 1.5rem\n\n.content-icon-container\n float: right\n display: flex\n margin-top: 1.5rem\n margin-left: 1rem\n margin-bottom: 1rem\n gap: 0.5rem\n\n .edit-this-page, .view-this-page\n svg\n color: inherit\n height: 1.25rem\n width: 1.25rem\n\n.sidebar-toggle\n position: absolute\n display: none\n// \n.sidebar-toggle[name=\"__toc\"]\n left: 20px\n.sidebar-toggle:checked\n left: 40px\n// \n\n.overlay\n position: fixed\n top: 0\n width: 0\n height: 0\n\n transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n opacity: 0\n background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n z-index: 20\n.toc-overlay\n z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n z-index: 30\n transition: left 250ms ease-in-out\n.toc-drawer\n z-index: 50\n transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n & ~ .sidebar-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .sidebar-drawer\n top: 0\n left: 0\n // Show the toc sidebar\n#__toc:checked\n & ~ .toc-overlay\n width: 100%\n height: 100%\n opacity: 1\n & ~ .page\n .toc-drawer\n top: 0\n right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n text-decoration: none\n\n display: none\n position: fixed\n left: 0\n top: 1rem\n padding: 0.5rem\n padding-right: 0.75rem\n border-radius: 1rem\n font-size: 0.8125rem\n\n background: var(--color-background-primary)\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n z-index: 10\n\n margin-left: 50%\n transform: translateX(-50%)\n svg\n height: 1rem\n width: 1rem\n fill: currentColor\n display: inline-block\n\n span\n margin-left: 0.25rem\n\n .show-back-to-top &\n display: flex\n align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n html\n font-size: 110%\n\n@media (max-width: $full-width)\n // Collapse \"toc\" into the icon.\n .toc-content-icon\n display: flex\n .toc-drawer\n position: fixed\n height: 100vh\n top: 0\n right: -$sidebar-width\n border-left: 1px solid var(--color-background-muted)\n .toc-tree\n border-left: none\n font-size: var(--toc-font-size--mobile)\n\n // Accomodate for a changed content width.\n .sidebar-drawer\n width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $content-padded-width + $sidebar-width)\n // Center the page\n .content\n margin-left: auto\n margin-right: auto\n padding: 0 $content-padding--small\n\n@media (max-width: $content-padded-width--small + $sidebar-width)\n // Collapse \"navigation\".\n .nav-overlay-icon\n display: flex\n .sidebar-drawer\n position: fixed\n height: 100vh\n width: $sidebar-width\n\n top: 0\n left: -$sidebar-width\n\n // Swap which icon is visible.\n .toc-header-icon, .theme-toggle-header\n display: flex\n .toc-content-icon, .theme-toggle-content\n display: none\n\n // Show the header.\n .mobile-header\n position: sticky\n top: 0\n display: flex\n justify-content: space-between\n align-items: center\n\n .header-left,\n .header-right\n display: flex\n height: var(--header-height)\n padding: 0 var(--header-padding)\n label\n height: 100%\n width: 100%\n user-select: none\n\n .nav-overlay-icon .icon,\n .theme-toggle svg\n height: 1.5rem\n width: 1.5rem\n\n // Add a scroll margin for the content\n :target\n scroll-margin-top: calc(var(--header-height) + 2.5rem)\n\n // Show back-to-top below the header\n .back-to-top\n top: calc(var(--header-height) + 0.5rem)\n\n // Accommodate for the header.\n .page\n flex-direction: column\n justify-content: center\n\n@media (max-width: $content-width + 2* $content-padding--small)\n // Content should respect window limits.\n .content\n width: 100%\n overflow-x: auto\n\n@media (max-width: $content-width)\n article[role=main] aside.sidebar\n float: none\n width: 100%\n margin: 1rem 0\n","@use \"sass:list\"\n@use \"../variables\" as *\n\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n margin: 1rem auto\n padding: 0 0.5rem 0.5rem 0.5rem\n\n background: var(--color-admonition-background)\n\n border-radius: 0.2rem\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n font-size: var(--admonition-font-size)\n\n overflow: hidden\n page-break-inside: avoid\n\n // First element should have no margin, since the title has it.\n > :nth-child(2)\n margin-top: 0\n\n // Last item should have no margin, since we'll control that w/ padding\n > :last-child\n margin-bottom: 0\n\n.admonition p.admonition-title,\np.topic-title\n position: relative\n margin: 0 -0.5rem 0.5rem\n padding-left: 2rem\n padding-right: .5rem\n padding-top: .4rem\n padding-bottom: .4rem\n\n font-weight: 500\n font-size: var(--admonition-title-font-size)\n line-height: 1.3\n\n // Our fancy icon\n &::before\n content: \"\"\n position: absolute\n left: 0.5rem\n width: 1rem\n height: 1rem\n\n// Default styles\np.admonition-title\n background-color: var(--color-admonition-title-background)\n &::before\n background-color: var(--color-admonition-title)\n mask-image: var(--icon-admonition-default)\n mask-repeat: no-repeat\n\np.topic-title\n background-color: var(--color-topic-title-background)\n &::before\n background-color: var(--color-topic-title)\n mask-image: var(--icon-topic-default)\n mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n border-left: 0.2rem solid var(--color-admonition-title)\n\n @each $type, $value in $admonitions\n &.#{$type}\n border-left-color: var(--color-admonition-title--#{$type})\n > .admonition-title\n background-color: var(--color-admonition-title-background--#{$type})\n &::before\n background-color: var(--color-admonition-title--#{$type})\n mask-image: var(--icon-#{list.nth($value, 2)})\n\n.admonition-todo > .admonition-title\n text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n // Tweak the spacing of all the things!\n dd\n margin-left: 2rem\n > :first-child\n margin-top: 0.125rem\n > :last-child\n margin-bottom: 0.75rem\n\n // This is used for the arguments\n .field-list\n margin-bottom: 0.75rem\n\n // \"Headings\" (like \"Parameters\" and \"Return\")\n > dt\n text-transform: uppercase\n font-size: var(--font-size--small)\n\n dd:empty\n margin-bottom: 0.5rem\n dd > ul\n margin-left: -1.2rem\n > li\n > p:nth-child(2)\n margin-top: 0\n // When the last-empty-paragraph follows a paragraph, it doesn't need\n // to augument the existing spacing.\n > p + p:last-child:empty\n margin-top: 0\n margin-bottom: 0\n\n // Colorize the elements\n > dt\n color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n font-weight: bold\n\n font-size: var(--api-font-size)\n font-family: var(--font-stack--monospace)\n\n margin-left: -0.25rem\n margin-right: -0.25rem\n padding-top: 0.25rem\n padding-bottom: 0.25rem\n padding-right: 0.5rem\n\n // These are intentionally em, to properly match the font size.\n padding-left: 3em\n text-indent: -2.5em\n\n border-radius: 0.25rem\n\n background: var(--color-api-background)\n transition: background 100ms ease-out\n\n &:hover\n background: var(--color-api-background-hover)\n\n // adjust the size of the [source] link on the right.\n a.reference\n .viewcode-link\n font-weight: normal\n width: 4.25rem\n\nem.property\n font-style: normal\n &:first-child\n color: var(--color-api-keyword)\n.sig-name\n color: var(--color-api-name)\n.sig-prename\n font-weight: normal\n color: var(--color-api-pre-name)\n.sig-paren\n color: var(--color-api-paren)\n.sig-param\n font-style: normal\n\ndiv.versionadded,\ndiv.versionchanged,\ndiv.deprecated,\ndiv.versionremoved\n border-left: 0.1875rem solid\n border-radius: 0.125rem\n\n padding-left: 0.75rem\n\n p\n margin-top: 0.125rem\n margin-bottom: 0.125rem\n\ndiv.versionadded\n border-color: var(--color-api-added-border)\n .versionmodified\n color: var(--color-api-added)\n\ndiv.versionchanged\n border-color: var(--color-api-changed-border)\n .versionmodified\n color: var(--color-api-changed)\n\ndiv.deprecated\n border-color: var(--color-api-deprecated-border)\n .versionmodified\n color: var(--color-api-deprecated)\n\ndiv.versionremoved\n border-color: var(--color-api-removed-border)\n .versionmodified\n color: var(--color-api-removed)\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n float: right\n text-align: right\n",".line-block\n margin-top: 0.5rem\n margin-bottom: 0.75rem\n .line-block\n margin-top: 0rem\n margin-bottom: 0rem\n padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n font-size: var(--font-size--small)\n text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n .caption, :not(.caption) > .caption-text\n font-size: var(--font-size--small)\n text-transform: uppercase\n\n text-align: initial\n margin-bottom: 0\n\n > ul\n margin-top: 0\n margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n background: var(--color-inline-code-background)\n border-radius: 0.2em\n // Make the font smaller, and use padding to recover.\n font-size: var(--font-size--small--2)\n padding: 0.1em 0.2em\n\n pre.literal-block &\n font-size: inherit\n padding: 0\n\n p &\n border: 1px solid var(--color-background-border)\n\n.sig-inline\n font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n margin: 1em 0\n display: flex\n\n .table-wrapper\n margin: 0\n padding: 0\n\npre\n margin: 0\n padding: 0\n overflow: auto\n\n // Needed to have more specificity than pygments' \"pre\" selector. :(\n article[role=\"main\"] .highlight &\n line-height: 1.5\n\n &.literal-block,\n .highlight &\n font-size: var(--code-font-size)\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n // Make it look like all the other blocks.\n &.literal-block\n margin-top: 1rem\n margin-bottom: 1rem\n\n border-radius: 0.2rem\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n width: 100%\n border-radius: 0.2rem\n\n // Make line numbers and prompts un-selectable.\n .gp, span.linenos\n user-select: none\n pointer-events: none\n\n // Expand the line-highlighting.\n .hll\n display: block\n margin-left: -$code-spacing-horizontal\n margin-right: -$code-spacing-horizontal\n padding-left: $code-spacing-horizontal\n padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n display: flex\n padding: $code-spacing-vertical $code-spacing-horizontal\n\n border-radius: 0.25rem\n border-bottom-left-radius: 0\n border-bottom-right-radius: 0\n font-weight: 300\n border-bottom: 1px solid\n\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n border-color: var(--color-background-border)\n\n + div[class]\n margin-top: 0\n > .highlight\n border-top-left-radius: 0\n border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n width: 100%\n display: block\n tbody\n display: block\n\n tr\n display: flex\n\n // Line numbers\n td.linenos\n background-color: var(--color-code-background)\n color: var(--color-code-foreground)\n padding: $code-spacing-vertical $code-spacing-horizontal\n padding-right: 0\n border-top-left-radius: 0.2rem\n border-bottom-left-radius: 0.2rem\n\n .linenodiv\n padding-right: $code-spacing-horizontal\n font-size: var(--code-font-size)\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n // Actual code\n td.code\n padding: 0\n display: block\n flex: 1\n overflow: hidden\n\n .highlight\n border-top-left-radius: 0\n border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n span.linenos\n display: inline-block\n padding-left: 0\n padding-right: $code-spacing-horizontal\n margin-right: $code-spacing-horizontal\n box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n font-size: var(--font-size--small--4)\n vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\n display: grid\n grid-template-columns: max-content auto\n dt\n margin: 0\n > .fn-backref\n margin-left: 0.25rem\n\n &:after\n content: \":\"\n\n .brackets\n &:before\n content: \"[\"\n &:after\n content: \"]\"\n\n dd\n margin: 0\n padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n font-size: var(--font-size--small)\n color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n float: left\n font-weight: 500\n padding-right: 0.25rem\n\naside.footnote > *:not(span),\ndiv.citation > p\n margin-left: 2rem\n","//\n// Figures\n//\nimg\n box-sizing: border-box\n max-width: 100%\n height: auto\n\narticle\n figure, .figure\n border-radius: 0.2rem\n\n margin: 0\n :last-child\n margin-bottom: 0\n\n .align-left\n float: left\n clear: left\n margin: 0 1rem 1rem\n\n .align-right\n float: right\n clear: right\n margin: 0 1rem 1rem\n\n .align-default,\n .align-center\n display: block\n text-align: center\n margin-left: auto\n margin-right: auto\n\n // WELL, table needs to be stylised like a table.\n table.align-default\n display: table\n text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n border-top: 1px solid var(--color-background-border)\n border-bottom: 1px solid var(--color-background-border)\n padding: 0.25rem\n\n.genindex-section, .domainindex-section\n h2\n margin-top: 0.75rem\n margin-bottom: 0.5rem\n ul\n margin-top: 0\n margin-bottom: 0\n","ul,\nol\n padding-left: 1.2rem\n\n // Space lists out like paragraphs\n margin-top: 1rem\n margin-bottom: 1rem\n // reduce margins within li.\n li\n > p:first-child\n margin-top: 0.25rem\n margin-bottom: 0.25rem\n\n > p:last-child\n margin-top: 0.25rem\n\n > ul,\n > ol\n margin-top: 0.5rem\n margin-bottom: 0.5rem\n\nol\n &.arabic\n list-style: decimal\n &.loweralpha\n list-style: lower-alpha\n &.upperalpha\n list-style: upper-alpha\n &.lowerroman\n list-style: lower-roman\n &.upperroman\n list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n li\n > ul,\n > ol\n margin-top: 0\n margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n dt\n font-weight: 500\n margin-top: 0.25rem\n + dt\n margin-top: 0\n\n .classifier::before\n content: \":\"\n margin-left: 0.2rem\n margin-right: 0.2rem\n\n dd\n > p:first-child,\n ul\n margin-top: 0.125rem\n\n ul\n margin-bottom: 0.125rem\n",".math-wrapper\n width: 100%\n overflow-x: auto\n\ndiv.math\n position: relative\n text-align: center\n\n .headerlink,\n &:focus .headerlink\n display: none\n\n &:hover .headerlink\n display: inline-block\n\n span.eqno\n position: absolute\n right: 0.5rem\n top: 50%\n transform: translate(0, -50%)\n z-index: 1\n","// Abbreviations\nabbr[title]\n cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n margin: 0 0.2rem\n padding: 0 0.2rem\n border-radius: 0.2rem\n border: 1px solid var(--color-foreground-border)\n color: var(--color-foreground-primary)\n vertical-align: text-bottom\n\n font-size: var(--font-size--small--3)\n display: inline-block\n\n box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n border-left: 4px solid var(--color-background-border)\n background: var(--color-background-secondary)\n\n margin-left: 0\n margin-right: 0\n padding: 0.5rem 1rem\n\n .attribution\n font-weight: 600\n text-align: right\n\n &.pull-quote,\n &.highlights\n font-size: 1.25em\n\n &.epigraph,\n &.pull-quote\n border-left-width: 0\n border-radius: 0.5rem\n\n &.highlights\n border-left-width: 0\n background: transparent\n\n// Center align embedded-in-text images\np .reference img\n vertical-align: middle\n","p.rubric\n line-height: 1.25\n font-weight: bold\n font-size: 1.125em\n\n // For Numpy-style documentation that's got rubrics within it.\n // https://github.com/pradyunsg/furo/discussions/505\n dd &\n line-height: inherit\n font-weight: inherit\n\n font-size: var(--font-size--small)\n text-transform: uppercase\n","article .sidebar\n float: right\n clear: right\n width: 30%\n\n margin-left: 1rem\n margin-right: 0\n\n border-radius: 0.2rem\n background-color: var(--color-background-secondary)\n border: var(--color-background-border) 1px solid\n\n > *\n padding-left: 1rem\n padding-right: 1rem\n\n > ul, > ol // lists need additional padding, because bullets.\n padding-left: 2.2rem\n\n .sidebar-title\n margin: 0\n padding: 0.5rem 1rem\n border-bottom: var(--color-background-border) 1px solid\n\n font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n","[role=main] .table-wrapper.container\n width: 100%\n overflow-x: auto\n margin-top: 1rem\n margin-bottom: 0.5rem\n padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n border-radius: 0.2rem\n border-spacing: 0\n border-collapse: collapse\n\n box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n th\n background: var(--color-table-header-background)\n\n td,\n th\n // Space things out properly\n padding: 0 0.25rem\n\n // Get the borders looking just-right.\n border-left: 1px solid var(--color-table-border)\n border-right: 1px solid var(--color-table-border)\n border-bottom: 1px solid var(--color-table-border)\n\n p\n margin: 0.25rem\n\n &:first-child\n border-left: none\n &:last-child\n border-right: none\n\n // MyST-parser tables set these classes for control of column alignment\n &.text-left\n text-align: left\n &.text-right\n text-align: right\n &.text-center\n text-align: center\n","@use \"../variables\" as *\n\n:target\n scroll-margin-top: 2.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n :target\n scroll-margin-top: calc(2.5rem + var(--header-height))\n\n // When a heading is selected\n section > span:target\n scroll-margin-top: calc(2.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n font-weight: 100\n user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n > .headerlink\n margin-left: 0.5rem\n visibility: hidden\n &:hover > .headerlink\n visibility: visible\n\n // Don't change to link-like, if someone adds the contents directive.\n > .toc-backref\n color: inherit\n text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n visibility: visible\n\n:target >, // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n h1,\n h2,\n h3,\n h4,\n h5,\n h6\n &:nth-of-type(1)\n background-color: var(--color-highlight-on-target)\n // .headerlink\n // visibility: visible\n code.literal\n background-color: transparent\n\ntable:target > caption,\nfigure:target\n background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n// There isn't really an alternative to !important here, due to the\n// high-specificity of API documentation's selector.\ndt:target\n background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n background-color: var(--color-highlight-on-target)\n",".guilabel\n background-color: var(--color-guilabel-background)\n border: 1px solid var(--color-guilabel-border)\n color: var(--color-guilabel-text)\n\n padding: 0 0.3em\n border-radius: 0.5em\n font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n@use \"../variables\" as *\n\nfooter\n font-size: var(--font-size--small)\n display: flex\n flex-direction: column\n\n margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n display: flex\n align-items: center\n justify-content: space-between\n\n margin-top: 1rem\n padding-top: 1rem\n padding-bottom: 1rem\n\n color: var(--color-foreground-secondary)\n border-top: 1px solid var(--color-background-border)\n\n line-height: 1.5\n\n @media (max-width: $content-width)\n text-align: center\n flex-direction: column-reverse\n gap: 0.25rem\n\n .left-details\n font-size: var(--font-size--small)\n\n .right-details\n display: flex\n flex-direction: column\n gap: 0.25rem\n text-align: right\n\n .icons\n display: flex\n justify-content: flex-end\n gap: 0.25rem\n font-size: 1rem\n\n a\n text-decoration: none\n\n svg,\n img\n font-size: 1.125rem\n height: 1em\n width: 1em\n\n// Next/Prev page information\n.related-pages\n a\n display: flex\n align-items: center\n\n text-decoration: none\n &:hover .page-info .title\n text-decoration: underline\n color: var(--color-link)\n text-decoration-color: var(--color-link-underline)\n\n svg.furo-related-icon,\n svg.furo-related-icon > use\n flex-shrink: 0\n\n color: var(--color-foreground-border)\n\n width: 0.75rem\n height: 0.75rem\n margin: 0 0.5rem\n\n &.next-page\n max-width: 50%\n\n float: right\n clear: right\n text-align: right\n\n &.prev-page\n max-width: 50%\n\n float: left\n clear: left\n\n svg\n transform: rotate(180deg)\n\n.page-info\n display: flex\n flex-direction: column\n overflow-wrap: anywhere\n\n .next-page &\n align-items: flex-end\n\n .context\n display: flex\n align-items: center\n\n padding-bottom: 0.1rem\n\n color: var(--color-foreground-muted)\n font-size: var(--font-size--small)\n text-decoration: none\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n display: flex\n flex-direction: column\n flex-shrink: 0\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n text-decoration: none\n\n.sidebar-brand-text\n color: var(--color-sidebar-brand-text)\n overflow-wrap: break-word\n margin: var(--sidebar-item-spacing-vertical) 0\n font-size: 1.5rem\n\n.sidebar-logo-container\n margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n margin: 0 auto\n display: block\n max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n display: flex\n align-items: center\n margin-top: var(--sidebar-search-space-above)\n\n position: relative\n\n background: var(--color-sidebar-search-background)\n &:hover,\n &:focus-within\n background: var(--color-sidebar-search-background--focus)\n\n &::before\n content: \"\"\n position: absolute\n left: var(--sidebar-item-spacing-horizontal)\n width: var(--sidebar-search-icon-size)\n height: var(--sidebar-search-icon-size)\n\n background-color: var(--color-sidebar-search-icon)\n mask-image: var(--icon-search)\n\n.sidebar-search\n box-sizing: border-box\n\n border: none\n border-top: 1px solid var(--color-sidebar-search-border)\n border-bottom: 1px solid var(--color-sidebar-search-border)\n\n padding-top: var(--sidebar-search-input-spacing-vertical)\n padding-bottom: var(--sidebar-search-input-spacing-vertical)\n padding-right: var(--sidebar-search-input-spacing-horizontal)\n padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n width: 100%\n\n color: var(--color-sidebar-search-foreground)\n background: transparent\n z-index: 10\n\n &:focus\n outline: none\n\n &::placeholder\n font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n margin: 0\n text-align: center\n\n a\n color: var(--color-sidebar-search-icon)\n font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n font-size: var(--sidebar-item-font-size)\n margin-top: var(--sidebar-tree-space-above)\n margin-bottom: var(--sidebar-item-spacing-vertical)\n\n ul\n padding: 0\n margin-top: 0\n margin-bottom: 0\n\n display: flex\n flex-direction: column\n\n list-style: none\n\n li\n position: relative\n margin: 0\n\n > ul\n margin-left: var(--sidebar-item-spacing-horizontal)\n\n .icon\n color: var(--color-sidebar-link-text)\n\n .reference\n box-sizing: border-box\n color: var(--color-sidebar-link-text)\n\n // Fill the parent.\n display: inline-block\n line-height: var(--sidebar-item-line-height)\n text-decoration: none\n\n // Don't allow long words to cause wrapping.\n overflow-wrap: anywhere\n\n height: 100%\n width: 100%\n\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n &:hover\n color: var(--color-sidebar-link-text)\n background: var(--color-sidebar-item-background--hover)\n\n // Add a nice little \"external-link\" arrow here.\n &.external::after\n content: url('data:image/svg+xml,')\n margin: 0 0.25rem\n vertical-align: middle\n color: var(--color-sidebar-link-text)\n\n // Make the current page reference bold.\n .current-page > .reference\n font-weight: bold\n\n label\n position: absolute\n top: 0\n right: 0\n height: var(--sidebar-item-height)\n width: var(--sidebar-expander-width)\n\n cursor: pointer\n user-select: none\n\n display: flex\n justify-content: center\n align-items: center\n\n .caption, :not(.caption) > .caption-text\n font-size: var(--sidebar-caption-font-size)\n color: var(--color-sidebar-caption-text)\n\n font-weight: bold\n text-transform: uppercase\n\n margin: var(--sidebar-caption-space-above) 0 0 0\n padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n // If it has children, add a bit more padding to wrap the content to avoid\n // overlapping with the
+ +
+
+pyhazard.utils.hardware.num_devices()[source]
+
+
Return type:
+

int

+
+
+
pyhazard.utils.hardware.set_device(device_str)[source]
-
+
+
Return type:
+

None

+
+
+
@@ -290,12 +317,12 @@