From 7961d6a9f48959c7194e285e9be23b863343c0f8 Mon Sep 17 00:00:00 2001 From: B-Deprez Date: Fri, 18 Sep 2026 13:13:28 +0200 Subject: [PATCH] Phase 1: project skeleton, CI and the frozen-behaviour fixtures --- .github/workflows/ci.yml | 37 ++ .gitignore | 35 ++ .pre-commit-config.yaml | 25 + CHANGELOG.md | 15 + CITATION.cff | 33 ++ CONTRIBUTING.md | 91 ++++ README.md | 45 ++ .../0001-weighted-average-default.md | 17 + docs/decisions/0002-louvain-is-explicit.md | 19 + docs/decisions/0003-frozen-edge-cases.md | 26 + docs/decisions/0004-two-stage-pipeline.md | 17 + .../0005-synthetic-generator-differs.md | 17 + docs/decisions/0006-no-torch-dependency.md | 18 + .../0007-directed-score-is-equation-14.md | 34 ++ docs/decisions/index.md | 19 + docs/index.md | 26 + mkdocs.yml | 35 ++ pyproject.toml | 92 ++++ src/garg_aml/__init__.py | 26 + src/garg_aml/py.typed | 0 tests/__init__.py | 0 tests/conftest.py | 20 + tests/golden/MANIFEST.json | 74 +++ tests/golden/README.md | 86 +++ 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.../golden/toy/features_directed_reduced.csv | 25 + tests/golden/toy/features_undirected_raw.csv | 25 + .../toy/features_undirected_reduced.csv | 25 + tests/golden/toy/measures_directed_raw.csv | 25 + .../golden/toy/measures_directed_reduced.csv | 25 + tests/golden/toy/measures_undirected_raw.csv | 25 + .../toy/measures_undirected_reduced.csv | 25 + tests/golden/toy/reduced_edges_directed.csv | 4 + tests/golden/toy/reduced_edges_undirected.csv | 4 + tests/golden/toy/reduced_nodes_directed.csv | 25 + tests/golden/toy/reduced_nodes_undirected.csv | 25 + tests/golden/toy/scores_directed_raw.csv | 25 + tests/golden/toy/scores_directed_reduced.csv | 25 + tests/golden/toy/scores_undirected_raw.csv | 25 + .../golden/toy/scores_undirected_reduced.csv | 25 + tests/test_golden_fixtures.py | 96 ++++ tests/test_package.py | 18 + tools/README.md | 22 + tools/check_golden.py | 131 +++++ tools/make_golden.py | 327 ++++++++++++ 97 files changed, 12382 insertions(+) create mode 100644 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0000000..531cd33 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,37 @@ +name: CI + +on: + push: + branches: [main] + pull_request: + # Unpinned dependencies: a weekly run surfaces networkx/pandas/numpy drift + # before a user reports it. + schedule: + - cron: "0 6 * * 1" + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.12" + - run: pip install -e ".[dev]" + - run: ruff check . + - run: ruff format --check . + - run: mypy + + test: + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.10", "3.11", "3.12", "3.13"] + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + - run: pip install -e ".[dev]" + - run: pytest diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..3642713 --- /dev/null +++ b/.gitignore @@ -0,0 +1,35 @@ +# --- Private agent instructions -------------------------------------------- +# Steering notes for Claude Code, deliberately not published. Every decision +# recorded there must also exist in docs/decisions/ or CONTRIBUTING.md -- if +# deleting this file would lose information, it was in the wrong file. +CLAUDE.md + +# --- Python ----------------------------------------------------------------- +__pycache__/ +*.py[cod] +*.egg-info/ +.eggs/ +build/ +dist/ + +# --- Virtual environments --------------------------------------------------- +# Keep these OUT of the repo directory: it lives in OneDrive, which would sync +# every file in site-packages. Prefer ~/.venvs/garg-aml. +.venv/ +venv/ +env/ + +# --- Tooling caches --------------------------------------------------------- +.pytest_cache/ +.mypy_cache/ +.ruff_cache/ +.coverage +coverage.xml +htmlcov/ +site/ + +# --- OS / editor ------------------------------------------------------------ +.DS_Store +.idea/ +.vscode/ +*.swp diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..3cc79e2 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,25 @@ +repos: + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.14.2 + hooks: + - id: ruff-check + args: [--fix] + - id: ruff-format + + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.18.2 + hooks: + - id: mypy + additional_dependencies: [numpy, pandas-stubs] + + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 + hooks: + - id: end-of-file-fixer + - id: trailing-whitespace + - id: check-yaml + - id: check-toml + # Golden fixtures are small; this guards against a large file arriving by + # accident, not against them. + - id: check-added-large-files + args: [--maxkb=1024] diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..f584038 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,15 @@ +# Changelog + +All notable changes to this project are documented here, following +[Keep a Changelog](https://keepachangelog.com/en/1.1.0/). This project adheres +to [Semantic Versioning](https://semver.org/spec/v2.0.0.html); while the version +is `0.x` the public API may change with a minor bump, always with an entry here. + +## [Unreleased] + +### Added + +- Project skeleton: packaging, lint/type/test configuration, CI and docs scaffold. +- Frozen-behaviour fixtures under `tests/golden/`, generated from the + pre-extraction implementation, with the tooling that built them in `tools/`. +- Initial architecture decision records under `docs/decisions/`. diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000..5abf591 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,33 @@ +cff-version: 1.2.0 +title: garg-aml +message: >- + If you use this software, please cite both the software and the paper it + implements. +type: software +authors: + - given-names: Bruno + family-names: Deprez + email: bruno.deprez@kuleuven.be + affiliation: KU Leuven +repository-code: "https://github.com/VerbekeLab/garg-aml" +license: MIT +version: 0.1.0.dev0 +preferred-citation: + type: article + title: >- + GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based + Framework for Anti-Money Laundering + authors: + - given-names: Bruno + family-names: Deprez + - given-names: Bart + family-names: Baesens + - given-names: Tim + family-names: Verdonck + - given-names: Wouter + family-names: Verbeke + year: 2025 + journal: arXiv + identifiers: + - type: other + value: "arXiv:2506.04292" diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..b3c0131 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,91 @@ +# Contributing and maintaining + +This file is the maintainer's manual. If you have inherited this package and +read nothing else, read this and `docs/decisions/`. + +## Setup + +Keep the virtual environment **outside** the repository — the working clone sits +in a OneDrive folder, which would otherwise sync all of `site-packages`. + +```bash +python -m venv ~/.venvs/garg-aml +source ~/.venvs/garg-aml/bin/activate +pip install -e ".[dev,docs,progress,parallel,sklearn]" +pre-commit install +``` + +## The rules that matter + +**1. `tests/golden/` is frozen behaviour, not test scaffolding.** Those files +were generated from the implementation that produced the published paper +results. Never edit one to make a test pass — a diff means the code changed. +Regenerating them is a separate commit with its own justification, and it +invalidates comparability with everything published before it. + +**2. Some edge cases look like bugs and are not.** An off-diagonal block with no +free entries has density 1; a node whose block sizes are all zero scores -1; +isolated nodes get zero for every neighbour statistic. Each has an ADR in +`docs/decisions/`. Changing any of them changes every published number. + +**3. The library does no I/O and prints nothing.** Objects in, objects out. No +file paths, no `os.chdir`, no `print`. Use `logging.getLogger("garg_aml")`. + +**4. Core dependencies are numpy, pandas, networkx and scipy.** Everything else +is an optional extra behind a guarded import. In particular the package must +never depend on PyTorch. + +## Checks + +```bash +ruff check . && ruff format --check . +mypy +pytest +``` + +`pre-commit run --all-files` runs the first two. CI runs all three on Python +3.10-3.13, plus a weekly scheduled run against unpinned dependencies so that +upstream drift surfaces before a user reports it. + +Coverage must stay at or above 90 %, and that is a hard failure rather than a +badge. + +## Definition of done + +1. Golden tests pass unchanged. +2. `ruff` and `mypy` are clean. +3. Public API has a NumPy-style docstring with a paper reference and a runnable + doctest. +4. `CHANGELOG.md` updated in the same commit as the change. +5. Any non-obvious decision recorded as an ADR in `docs/decisions/`. + +## Releasing + +Publishing uses **PyPI Trusted Publishing** from GitHub Actions. There is no API +token anywhere, so nothing has to be rotated or handed over. + +1. Update `CHANGELOG.md`: move `[Unreleased]` entries under the new version. +2. Bump `__version__` in `src/garg_aml/__init__.py` and `version:` in + `CITATION.cff`. +3. Commit, then tag: `git tag v0.1.0 && git push origin main --tags`. +4. The `release` workflow builds and publishes; Zenodo mints a DOI from the + GitHub release. + +### If you cannot publish to PyPI + +The `garg-aml` name has a single owner. If that account is unreachable, the code +is still safe — it lives in the `VerbekeLab` organisation — but the name is not +reclaimable quickly. Publish under a new name instead: change `name` in +`pyproject.toml`, register Trusted Publishing for the new project, and release +as above. The Zenodo DOI and git tags remain the citable artefacts either way, +so nothing published in a paper depends on PyPI being writable. + +## Where things live + +| | | +|---|---| +| `src/garg_aml/` | the library; leading-underscore modules are private | +| `tests/golden/` | frozen fixtures — see `tests/golden/README.md` | +| `tools/` | the fixture generator; runs in the research repo, not here | +| `docs/decisions/` | architecture decision records | +| [B-Deprez/GARG-AML](https://github.com/B-Deprez/GARG-AML) | experiments, baselines, paper reproduction | diff --git a/README.md b/README.md new file mode 100644 index 0000000..22be0cc --- /dev/null +++ b/README.md @@ -0,0 +1,45 @@ +# garg-aml + +Graph-based detection of **smurfing** patterns in transaction networks. + +Smurfing moves money from one account to another through intermediate mules, so +source and target never transact directly. In the second-order neighbourhood of +such an account the adjacency matrix splits into blocks whose on-diagonal parts +are empty and whose off-diagonal parts are dense. GARG-AML scores every account +by exactly that contrast — one number in [-1, 1], computed from local structure +alone, with no training and no labels. + +> **Status: pre-release.** The scoring core is being extracted from the +> [research repository](https://github.com/B-Deprez/GARG-AML). The public API +> lands in 0.1.0. + +## Install + +```bash +pip install garg-aml +``` + +## Citation + +If you use this package, please cite the paper: + +```bibtex +@article{deprez2025gargaml, + title = {{GARG-AML} against Smurfing: A Scalable and Interpretable + Graph-Based Framework for Anti-Money Laundering}, + author = {Deprez, Bruno and Baesens, Bart and Verdonck, Tim and + Verbeke, Wouter}, + journal = {arXiv preprint arXiv:2506.04292}, + year = {2025} +} +``` + +## Links + +- Paper: [arXiv:2506.04292](https://arxiv.org/abs/2506.04292) +- Experiments and paper reproduction: [B-Deprez/GARG-AML](https://github.com/B-Deprez/GARG-AML) +- Contributing and release process: [CONTRIBUTING.md](CONTRIBUTING.md) + +## Licence + +MIT — see [LICENSE](LICENSE). diff --git a/docs/decisions/0001-weighted-average-default.md b/docs/decisions/0001-weighted-average-default.md new file mode 100644 index 0000000..b872589 --- /dev/null +++ b/docs/decisions/0001-weighted-average-default.md @@ -0,0 +1,17 @@ +# 0001 — `weighted_average` is the default score type + +**Decision.** `score(...)` defaults to `score_type="weighted_average"`. + +**Context.** Two aggregations exist. `basic` takes an unweighted mean over the +penalty blocks; `weighted_average` weights each block by its number of free +entries — the `l1`, `l3` sizes of Eq. 8 and their directed analogues. The +pre-extraction code defaulted to `basic`, but every experiment in the paper +passed `weighted_average` explicitly. + +**Why.** A default that no published result uses is a trap. Weighting by block +size is also the better estimator: an unweighted mean lets a two-entry block +count as much as a two-hundred-entry one. + +**Consequence.** Anyone porting code that relied on the old default gets +different numbers. This is called out in the changelog and the migration notes; +`score_type="basic"` remains available. diff --git a/docs/decisions/0002-louvain-is-explicit.md b/docs/decisions/0002-louvain-is-explicit.md new file mode 100644 index 0000000..05fb607 --- /dev/null +++ b/docs/decisions/0002-louvain-is-explicit.md @@ -0,0 +1,19 @@ +# 0002 — Louvain reduction is a separate, opt-in step + +**Decision.** `reduce_graph(graph, resolution=10, seed=1997)` is its own +function. `score()` does not reduce unless asked (`score(graph, reduce=True)`). + +**Context.** The published pipeline partitions the graph with Louvain and keeps +only intra-community edges before scoring. On the IBM HI-Small data that removes +the large majority of edges. + +**Why.** The step is **lossy** and it is a modelling choice, not an +implementation detail — it changes which neighbourhoods exist at all, and +reviewers of the paper questioned it directly. Burying it inside the scorer +would hide the single most consequential preprocessing decision from the person +interpreting a score, which is the opposite of what an interpretable method +should do. + +**Consequence.** Users must decide. The documentation explains the trade-off +rather than choosing for them, and `resolution` has to be passed explicitly in +code that reproduces the paper, so a sweep over it is visible in the diff. diff --git a/docs/decisions/0003-frozen-edge-cases.md b/docs/decisions/0003-frozen-edge-cases.md new file mode 100644 index 0000000..14658d9 --- /dev/null +++ b/docs/decisions/0003-frozen-edge-cases.md @@ -0,0 +1,26 @@ +# 0003 — Three degenerate cases return fixed values + +**Decision.** These are preserved exactly as the pre-extraction code computed +them: + +| Case | Value | +|---|---| +| Off-diagonal block with no free entries | density **1** | +| All block sizes zero (undirected) | score **-1** | +| Node with no neighbours | **0** for all four neighbour statistics | + +**Context.** All three arise on tiny or isolated neighbourhoods. The third was +implemented as a bare `except:` around a numpy reduction over an empty list; it +is now an explicit branch with the same result. + +**Why.** They produced the published numbers. Each is also defensible on its own +terms: an empty penalty block should not penalise, a node with no structure is +maximally far from smurfing, and a node with no neighbours has no neighbourhood +statistics to report. But the reason they are *frozen* is reproducibility, not +elegance. + +**Consequence.** Changing any of them changes every published result. They look +like bugs and will attract cleanup; the golden fixtures under `tests/golden/` +fail loudly if one is touched. Louvain reduction leaves many isolated nodes +behind — 18 of the 24 in the toy fixture — so these paths are exercised far more +often than their obscurity suggests. diff --git a/docs/decisions/0004-two-stage-pipeline.md b/docs/decisions/0004-two-stage-pipeline.md new file mode 100644 index 0000000..d5a9016 --- /dev/null +++ b/docs/decisions/0004-two-stage-pipeline.md @@ -0,0 +1,17 @@ +# 0004 — Scoring is two-stage, and there is only one implementation + +**Decision.** The canonical path is `block_measures()` → `score_from_measures()`. +`score()` is a thin wrapper over both, never a parallel implementation. + +**Context.** The pre-extraction code had two paths that computed the same thing: +a serial `GARG_AML(G)` convenience function, and the two-stage route that every +experiment actually used. They had drifted — see +[0007](0007-directed-score-is-equation-14.md). + +**Why.** Two implementations of one formula is how the drift happened. The split +is also genuinely useful: block measures are the expensive part and are worth +persisting, and the cheap aggregation can then be re-run under a different +`score_type` without recomputing anything. + +**Consequence.** A new aggregation or a new score variant extends stage two and +must never introduce a second stage one. diff --git a/docs/decisions/0005-synthetic-generator-differs.md b/docs/decisions/0005-synthetic-generator-differs.md new file mode 100644 index 0000000..7b1f30c --- /dev/null +++ b/docs/decisions/0005-synthetic-generator-differs.md @@ -0,0 +1,17 @@ +# 0005 — The synthetic generator does not reproduce the paper's datasets + +**Decision.** `garg_aml.synthetic` is a fresh networkx implementation. The +igraph-based generator that produced the paper's 66 synthetic datasets stays in +the research repository. + +**Context.** The original generator depends on igraph, and its graphs are +reproducible only under specific igraph versions and RNG seeds. + +**Why.** Requiring igraph for a tutorial is a poor trade, and the package's +generator exists to serve documentation examples and tests — it needs to be +*reproducible*, not *identical to the paper's*. Chasing bit-for-bit parity would +have pinned an extra core dependency for no user-facing benefit. + +**Consequence.** This is the one deliberate non-equivalence in the extraction. +Anything reproducing the paper's synthetic results must use the research +repository's generator. Do not "unify" the two. diff --git a/docs/decisions/0006-no-torch-dependency.md b/docs/decisions/0006-no-torch-dependency.md new file mode 100644 index 0000000..7e339f0 --- /dev/null +++ b/docs/decisions/0006-no-torch-dependency.md @@ -0,0 +1,18 @@ +# 0006 — No PyTorch, ever + +**Decision.** Core dependencies are numpy, pandas, networkx and scipy. PyTorch +is not a dependency of this package under any extra. + +**Context.** The paper compares GARG-AML against a GraphSAGE baseline, which +needs `torch` and `torch-geometric`. That baseline lives in the research +repository. + +**Why.** GARG-AML computes a closed-form structural score; nothing in it learns. +A practitioner evaluating it should be able to `pip install garg-aml` and get a +handful of megabytes. Pulling a deep-learning stack behind an unrelated score +would cost adoption for no functional gain, and would make the package +unusable in the locked-down environments where compliance teams work. + +**Consequence.** Baselines and comparisons belong in the research repository. +scipy is the one non-obvious core dependency, and it is not optional: +`networkx.adjacency_matrix` imports it. diff --git a/docs/decisions/0007-directed-score-is-equation-14.md b/docs/decisions/0007-directed-score-is-equation-14.md new file mode 100644 index 0000000..fadc127 --- /dev/null +++ b/docs/decisions/0007-directed-score-is-equation-14.md @@ -0,0 +1,34 @@ +# 0007 — The directed score is Eq. 14, without the transpose-max + +**Decision.** For a directed graph, `score()` returns Equation (14): + +``` +mean(score01, score12) + - mean(score00, score02, score10, score11, score20, score21, score22) +``` + +The `max(score, score_transposed)` step found in the old +`GARG_AML_node_directed` is **not** ported. + +**Context.** The pre-extraction code computed three directed quantities: +`GARGAML` (Eq. 14 as given), `GARGAML_transposed`, and `GARGAML_max`. The +experiments used the first; the serial convenience function returned the third. +On a 245-node synthetic graph the two differ for 205 nodes, so the choice is +material rather than cosmetic. + +**Why.** Section 3.3 of the paper defines the score as Eq. 14 and contains no +max-with-transpose step. Reverse flow is handled one level up, in the **level +assignment**: Eq. (11) plus "we repeat this selection on the reversed network", +so a node at distance two sits at level 0 when no directed path of length two +reaches it in either direction. That rule is implemented, and is retained. The +transpose-max was an undocumented extra reachable only through a code path no +experiment called. + +**Consequence.** Do not "restore" the max. It is not the paper's reverse-flow +handling, and it was never in the published pipeline. + +**Related.** `GARGAML_max` named two different quantities in the old code — this +transpose-max, and the maximum score among a node's neighbours. In the directed +feature pipeline the second silently overwrote the first, which was harmless +because only the neighbour statistic was ever used as a feature. Here only the +neighbour statistic carries the name. diff --git a/docs/decisions/index.md b/docs/decisions/index.md new file mode 100644 index 0000000..0400d83 --- /dev/null +++ b/docs/decisions/index.md @@ -0,0 +1,19 @@ +# Design decisions + +One short record per non-obvious choice: what was decided, why, and what breaks +if it is reversed. These exist so that a maintainer who did not write the code — +or the paper — can tell a deliberate convention from a bug. + +Several of them freeze behaviour that *looks* wrong. It is not: those exact +values produced the results published in +[arXiv:2506.04292](https://arxiv.org/abs/2506.04292). + +| | | +|---|---| +| [0001](0001-weighted-average-default.md) | `weighted_average` is the default score type | +| [0002](0002-louvain-is-explicit.md) | Louvain reduction is a separate, opt-in step | +| [0003](0003-frozen-edge-cases.md) | Three degenerate cases return fixed values | +| [0004](0004-two-stage-pipeline.md) | Scoring is two-stage, and there is only one implementation | +| [0005](0005-synthetic-generator-differs.md) | The synthetic generator does not reproduce the paper's datasets | +| [0006](0006-no-torch-dependency.md) | No PyTorch, ever | +| [0007](0007-directed-score-is-equation-14.md) | The directed score is Eq. 14, without the transpose-max | diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..0d57617 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,26 @@ +# garg-aml + +Graph-based detection of **smurfing** patterns in transaction networks. + +!!! warning "Pre-release" + The scoring core is being extracted from the + [research repository](https://github.com/B-Deprez/GARG-AML). The public API + and the rest of this documentation land in 0.1.0. + +## What it does + +Smurfing moves money from one account to another through intermediate mules, so +that source and target never transact directly. Order the nodes of an account's +second-order neighbourhood as `[account + 2nd-order neighbours | 1st-order +neighbours]` and the adjacency matrix splits into blocks: for a pure smurfing +pattern the on-diagonal blocks are empty and the off-diagonal blocks are dense. + +GARG-AML scores every account by that contrast. One number in [-1, 1], computed +from local structure alone — no training, no labels, and every score traceable +back to the block densities it came from. + +## Links + +- Paper: [arXiv:2506.04292](https://arxiv.org/abs/2506.04292) +- [Design decisions](decisions/index.md) +- [Experiments and paper reproduction](https://github.com/B-Deprez/GARG-AML) diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 0000000..9a370bc --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,35 @@ +site_name: garg-aml +site_description: Graph-based detection of smurfing patterns in transaction networks +repo_url: https://github.com/VerbekeLab/garg-aml +edit_uri: edit/main/docs/ + +theme: + name: material + palette: + - media: "(prefers-color-scheme: light)" + scheme: default + toggle: { icon: material/weather-night, name: Dark mode } + - media: "(prefers-color-scheme: dark)" + scheme: slate + toggle: { icon: material/weather-sunny, name: Light mode } + features: + - navigation.sections + - content.code.copy + +plugins: + - search + - mkdocstrings: + handlers: + python: + options: + docstring_style: numpy + show_source: true + +markdown_extensions: + - admonition + - pymdownx.superfences + - pymdownx.arithmatex: { generic: true } + +nav: + - Home: index.md + - Decisions: decisions/index.md diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..cb3a249 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,92 @@ +[build-system] +requires = ["hatchling>=1.27"] +build-backend = "hatchling.build" + +[project] +name = "garg-aml" +dynamic = ["version"] +description = "Graph-based detection of smurfing patterns in transaction networks." +readme = "README.md" +requires-python = ">=3.10" +license = "MIT" +license-files = ["LICENSE"] +authors = [{ name = "Bruno Deprez", email = "bruno.deprez@kuleuven.be" }] +keywords = ["anti-money-laundering", "smurfing", "graph", "fraud-detection", "networks"] +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Science/Research", + "Intended Audience :: Financial and Insurance Industry", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Topic :: Scientific/Engineering", + "Typing :: Typed", +] + +# scipy is not optional: networkx builds the per-node adjacency matrix through it. +dependencies = ["numpy", "pandas", "networkx>=3.0", "scipy"] + +[project.optional-dependencies] +progress = ["tqdm"] +parallel = ["joblib"] +sklearn = ["scikit-learn"] +dev = ["ruff", "mypy", "pytest", "pytest-cov", "pre-commit"] +docs = ["mkdocs-material", "mkdocstrings[python]"] + +[project.urls] +Homepage = "https://github.com/VerbekeLab/garg-aml" +Documentation = "https://verbekelab.github.io/garg-aml/" +Source = "https://github.com/VerbekeLab/garg-aml" +Changelog = "https://github.com/VerbekeLab/garg-aml/blob/main/CHANGELOG.md" +Paper = "https://arxiv.org/abs/2506.04292" + +[tool.hatch.version] +path = "src/garg_aml/__init__.py" + +[tool.hatch.build.targets.wheel] +packages = ["src/garg_aml"] + +# --- Lint and format: ruff only, no black/isort/flake8 ---------------------- +[tool.ruff] +line-length = 88 +src = ["src", "tests"] + +# tools/ holds verbatim copies of scripts that run inside the research repo, +# under its conventions (sys.path setup before imports, its own formatting). +# Reformatting them here would make them diverge from the originals they exist +# to document, so they are excluded rather than fixed. +extend-exclude = ["tools"] + +[tool.ruff.lint] +select = ["E", "W", "F", "I", "UP", "B", "SIM", "C90", "D", "NPY", "PD", "RUF"] + +[tool.ruff.lint.pydocstyle] +convention = "numpy" + +[tool.ruff.lint.mccabe] +max-complexity = 10 + +[tool.ruff.lint.per-file-ignores] +"tests/*" = ["D"] # tests document themselves by name +"tools/*" = ["D"] + +# --- Types ------------------------------------------------------------------ +[tool.mypy] +python_version = "3.10" +files = ["src/garg_aml"] +disallow_untyped_defs = true +warn_redundant_casts = true +warn_unused_ignores = true + +[[tool.mypy.overrides]] +module = ["networkx.*", "scipy.*"] +ignore_missing_imports = true + +# --- Tests ------------------------------------------------------------------ +[tool.pytest.ini_options] +testpaths = ["tests"] +addopts = "--strict-markers --cov=garg_aml --cov-report=term-missing --cov-fail-under=90" +markers = [ + "requires_data: needs the IBM AMLworld dataset, which is not redistributable", +] diff --git a/src/garg_aml/__init__.py b/src/garg_aml/__init__.py new file mode 100644 index 0000000..0db6619 --- /dev/null +++ b/src/garg_aml/__init__.py @@ -0,0 +1,26 @@ +""" +GARG-AML: graph-based detection of smurfing patterns in transaction networks. + +Smurfing moves money from one account to another through intermediate mules, so +that source and target never transact directly. In the second-order +neighbourhood of such a node the adjacency matrix splits into blocks whose +on-diagonal parts are empty and whose off-diagonal parts are dense. GARG-AML +scores every node by exactly that contrast, in [-1, 1], with higher meaning more +smurfing-like. + +References +---------- +Deprez, B., Baesens, B., Verdonck, T., & Verbeke, W. (2025). GARG-AML against +Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money +Laundering. arXiv:2506.04292. +""" + +import logging + +__version__ = "0.1.0.dev0" + +# A library attaches no handlers of its own; the application decides. +logging.getLogger(__name__).addHandler(logging.NullHandler()) + +# Populated in Phase 4. Anything absent here is private and unstable. +__all__: list[str] = [] diff --git a/src/garg_aml/py.typed b/src/garg_aml/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..ada34ce --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,20 @@ +import json +from pathlib import Path + +import pytest + +GOLDEN = Path(__file__).parent / "golden" + +CASES = sorted(p.parent.name for p in GOLDEN.glob("*/edges.csv")) +DIRECTIONS = ["undirected", "directed"] +VARIANTS = ["raw", "reduced"] + + +@pytest.fixture(scope="session") +def golden() -> Path: + return GOLDEN + + +@pytest.fixture(scope="session") +def manifest() -> dict: + return json.loads((GOLDEN / "MANIFEST.json").read_text()) diff --git a/tests/golden/MANIFEST.json b/tests/golden/MANIFEST.json new file mode 100644 index 0000000..95fc7b4 --- /dev/null +++ b/tests/golden/MANIFEST.json @@ -0,0 +1,74 @@ +{ + "purpose": "Frozen behaviour of GARG-AML before extraction into the garg-aml package. Never edit these files to make a test pass.", + "generated_by": "scripts/make_golden.py", + "source_repo": "B-Deprez/GARG-AML", + "source_commit": "f627b8a114f5ea3ac7c9030c35901e71d24a758a", + "source_tree_dirty": true, + "settings": { + "seed": 1997, + "louvain_resolution": 10, + "score_types": [ + "basic", + "weighted_average" + ], + "feature_score_type": "weighted_average" + }, + "environment": { + "python": "3.11.0", + "platform": "macOS-27.0-arm64-arm-64bit", + "networkx": "2.8.8", + "numpy": "1.24.4", + "pandas": "1.5.3", + "igraph": "0.11.4" + }, + "cases": { + "toy": { + "undirected": { + "nodes": 24, + "edges": 46, + "reduced_edges": 3 + }, + "directed": { + "nodes": 24, + "edges": 46, + "reduced_edges": 3 + } + }, + "synth_ba": { + "undirected": { + "nodes": 245, + "edges": 503, + "reduced_edges": 228 + }, + "directed": { + "nodes": 245, + "edges": 503, + "reduced_edges": 228 + } + }, + "synth_er": { + "undirected": { + "nodes": 90, + "edges": 112, + "reduced_edges": 68 + }, + "directed": { + "nodes": 90, + "edges": 112, + "reduced_edges": 68 + } + }, + "synth_ws": { + "undirected": { + "nodes": 245, + "edges": 503, + "reduced_edges": 347 + }, + "directed": { + "nodes": 245, + "edges": 503, + "reduced_edges": 348 + } + } + } +} diff --git a/tests/golden/README.md b/tests/golden/README.md new file mode 100644 index 0000000..ebd24c8 --- /dev/null +++ b/tests/golden/README.md @@ -0,0 +1,86 @@ +# Golden fixtures — frozen GARG-AML behaviour + +Generated by `scripts/make_golden.py` from the pre-extraction implementation, as +Phase 0 of the migration to the `garg-aml` package (see `PACKAGING_PLAN.md` §4). + +**These files are the oracle. Never edit one to make a test pass.** A diff means +the code changed, not the fixture. Regenerating them is a separate commit with +its own justification. + +## Layout + +Four cases, each in its own directory: + +| Case | Graph | Nodes | Note | +|---|---|---|---| +| `toy` | the paper's Figure A1–A6 example | 24 | node 23 pays 20/21/22, which all pay 19 — a textbook smurfing pattern | +| `synth_ba` | Barabási-Albert, n=200, m=2, 3 patterns | 245 | dense-ish, hub-structured | +| `synth_er` | Erdős-Rényi, n=200, p=0.001, 3 patterns | 90 | very sparse; most nodes have tiny neighbourhoods | +| `synth_ws` | Watts-Strogatz, n=200, k=2, p=0.01, 3 patterns | 245 | regular ring + rewiring | + +Per case: + +``` +edges.csv the input: source,target +reduced_nodes_.csv Louvain-reduced graph, node list +reduced_edges_.csv Louvain-reduced graph, edge list +measures__.csv stage 1: block measures + block sizes +scores__.csv stage 2a: score, per score_type (column prefix) +features__.csv stage 2b: score + neighbourhood statistics +``` + +`` is `undirected` or `directed`; `` is `raw` (the graph as given) +or `reduced` (after Louvain at `resolution=10`, `seed=1997`). + +## Design decisions + +**The edge list is the input, not the generator.** Graphs are built once with +igraph and written immediately; every downstream number is computed from the +*reloaded* file. Tests therefore need only numpy/pandas/networkx/scipy — no +igraph, no seed, no generator. + +**Reduced graphs are frozen as a node list *and* an edge list.** Louvain keeps +every node and drops only inter-community edges, so it leaves isolated nodes +behind — 18 of the toy graph's 24, for instance. Those are not incidental: an +isolated node is exactly what triggers the frozen D18/D20 edge cases. An edge +list alone cannot represent them. + +**Louvain is frozen, not re-run, for downstream fixtures.** The reduced-variant +measures are computed from the frozen edge list. A networkx upgrade that changed +the partition would break one test (`reduce_graph`) instead of all of them. + +**Node order is sorted out** of every frame, so the fixtures do not encode dict +insertion order. + +**Features use `weighted_average` only** — the documented default and what every +experiment uses. The `basic` path is covered by the score fixtures. + +## Regenerate / verify + +```bash +python scripts/make_golden.py # rewrite the fixtures (deliberate act) +python scripts/check_golden.py # verify the current code still reproduces them +``` + +`check_golden.py` is the harness Phase 2 ports into the package as the L1 test. + +## Verified environments + +64 comparisons, all reproducing, on both ends of the supported range: + +| | python | networkx | pandas | numpy | scipy | +|---|---|---|---|---|---| +| generated on | 3.11.0 | 2.8.8 | 1.5.3 | 1.24.4 | — | +| verified on | 3.11.0 | 3.6.1 | 3.0.6 | 2.4.6 | 1.x | + +The Louvain partition is identical across those networkx versions, so the +fixtures are not environment-locked. + +## Invariants confirmed at generation + +- every score lies in [−1, 1] (0 violations across all 16 score files); +- node 23 of `toy`, the injected smurfing source, scores **exactly 1.0**; +- isolated nodes return 0 for all eight neighbour statistics (**D18**) and a + score of −1 (**D20**); +- the directed score differs from its transpose-max in 205 of 245 nodes on + `synth_ba`, so the max step is not a no-op (see `PACKAGING_PLAN.md` §3, D23). diff --git a/tests/golden/synth_ba/edges.csv b/tests/golden/synth_ba/edges.csv new file mode 100644 index 0000000..703ac40 --- /dev/null +++ b/tests/golden/synth_ba/edges.csv @@ -0,0 +1,504 @@ +source,target +0,1 +0,2 +0,3 +0,4 +0,6 +0,7 +0,8 +0,9 +0,10 +0,12 +0,13 +0,14 +0,17 +0,18 +0,21 +0,23 +0,25 +0,28 +0,33 +0,44 +0,46 +0,54 +0,55 +0,56 +0,63 +0,65 +0,68 +0,96 +0,115 +0,119 +0,124 +0,129 +0,131 +0,136 +0,137 +0,144 +0,151 +0,159 +0,169 +0,175 +0,179 +0,180 +1,2 +1,5 +1,11 +1,12 +1,15 +1,16 +1,20 +1,30 +1,39 +1,41 +1,45 +1,68 +1,106 +1,188 +2,3 +2,4 +2,5 +2,11 +2,22 +2,24 +2,27 +2,28 +2,52 +2,70 +2,75 +2,76 +2,80 +2,93 +2,100 +2,121 +2,123 +3,6 +3,9 +3,17 +3,43 +3,69 +3,74 +3,82 +3,101 +3,102 +3,141 +3,172 +4,32 +4,35 +4,52 +4,54 +4,58 +4,77 +4,83 +4,176 +4,195 +4,196 +5,8 +5,13 +5,25 +5,26 +5,27 +5,61 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index 0000000..5419cd1 --- /dev/null +++ b/tests/golden/toy/reduced_edges_undirected.csv @@ -0,0 +1,4 @@ +source,target +6,8 +17,18 +23,21 diff --git a/tests/golden/toy/reduced_nodes_directed.csv b/tests/golden/toy/reduced_nodes_directed.csv new file mode 100644 index 0000000..6eaf501 --- /dev/null +++ b/tests/golden/toy/reduced_nodes_directed.csv @@ -0,0 +1,25 @@ +node +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 +11 +12 +13 +14 +15 +16 +17 +18 +19 +20 +21 +22 +23 diff --git a/tests/golden/toy/reduced_nodes_undirected.csv b/tests/golden/toy/reduced_nodes_undirected.csv new file mode 100644 index 0000000..6eaf501 --- /dev/null +++ b/tests/golden/toy/reduced_nodes_undirected.csv @@ -0,0 +1,25 @@ +node +0 +1 +2 +3 +4 +5 +6 +7 +8 +9 +10 +11 +12 +13 +14 +15 +16 +17 +18 +19 +20 +21 +22 +23 diff --git a/tests/golden/toy/scores_directed_raw.csv b/tests/golden/toy/scores_directed_raw.csv new file mode 100644 index 0000000..0a205f5 --- /dev/null +++ b/tests/golden/toy/scores_directed_raw.csv @@ -0,0 +1,25 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+12,0.0029761904761904656,0.01838235294117646 +13,0.02083333333333337,0.04779411764705882 +14,-0.04444444444444445,0.1111111111111111 +15,0.08030303030303029,0.059612518628912065 +16,0.15555555555555553,0.1367149758454106 +17,0.023809523809523836,0.25595238095238093 +18,0.023809523809523836,0.25595238095238093 +19,0.23809523809523808,0.21164021164021163 +20,0.6,0.5416666666666666 +21,0.6,0.5416666666666666 +22,0.6,0.5416666666666666 +23,1.0,1.0 diff --git a/tests/golden/toy/scores_undirected_reduced.csv b/tests/golden/toy/scores_undirected_reduced.csv new file mode 100644 index 0000000..a04b34c --- /dev/null +++ b/tests/golden/toy/scores_undirected_reduced.csv @@ -0,0 +1,25 @@ +node,basic__GARGAML,weighted_average__GARGAML +0,1.0,-1 +1,1.0,-1 +2,1.0,-1 +3,1.0,-1 +4,1.0,-1 +5,1.0,-1 +6,1.0,-1 +7,1.0,-1 +8,1.0,-1 +9,1.0,-1 +10,1.0,-1 +11,1.0,-1 +12,1.0,-1 +13,1.0,-1 +14,1.0,-1 +15,1.0,-1 +16,1.0,-1 +17,1.0,-1 +18,1.0,-1 +19,1.0,-1 +20,1.0,-1 +21,1.0,-1 +22,1.0,-1 +23,1.0,-1 diff --git a/tests/test_golden_fixtures.py b/tests/test_golden_fixtures.py new file mode 100644 index 0000000..0d6adb2 --- /dev/null +++ b/tests/test_golden_fixtures.py @@ -0,0 +1,96 @@ +""" +Integrity of the frozen fixtures themselves. + +These check that the oracle is complete and internally consistent. They do not +touch ``garg_aml`` -- comparing the implementation against these numbers is +``test_golden.py``, which arrives with the first extracted module. +""" + +import pandas as pd +import pytest + +from .conftest import CASES, DIRECTIONS, VARIANTS + +MEASURE_COLUMNS = { + "undirected": 3, + "directed": 9, +} + + +def test_cases_are_present(): + assert CASES == ["synth_ba", "synth_er", "synth_ws", "toy"] + + +@pytest.mark.parametrize("case", CASES) +@pytest.mark.parametrize("direction", DIRECTIONS) +def test_reduced_graph_is_frozen_as_nodes_and_edges(golden, case, direction): + # An edge list alone loses the isolated nodes Louvain leaves behind, and + # those are exactly what exercises the frozen degenerate cases. + for part in ("nodes", "edges"): + assert (golden / case / f"reduced_{part}_{direction}.csv").exists() + + +@pytest.mark.parametrize("case", CASES) +@pytest.mark.parametrize("direction", DIRECTIONS) +@pytest.mark.parametrize("variant", VARIANTS) +def test_stages_cover_the_same_nodes(golden, case, direction, variant): + stem = f"{direction}_{variant}" + measures = pd.read_csv(golden / case / f"measures_{stem}.csv") + scores = pd.read_csv(golden / case / f"scores_{stem}.csv", index_col="node") + features = pd.read_csv(golden / case / f"features_{stem}.csv", index_col="node") + + nodes = sorted(measures["node"]) + assert sorted(scores.index) == nodes + assert sorted(features.index) == nodes + + +@pytest.mark.parametrize("case", CASES) +@pytest.mark.parametrize("direction", DIRECTIONS) +@pytest.mark.parametrize("variant", VARIANTS) +def test_measure_and_size_columns_match_the_block_grid( + golden, case, direction, variant +): + measures = pd.read_csv(golden / case / f"measures_{direction}_{variant}.csv") + expected = MEASURE_COLUMNS[direction] + + assert sum(c.startswith("measure_") for c in measures.columns) == expected + assert sum(c.startswith("size_") for c in measures.columns) == expected + + +@pytest.mark.parametrize("case", CASES) +@pytest.mark.parametrize("direction", DIRECTIONS) +@pytest.mark.parametrize("variant", VARIANTS) +def test_every_score_is_in_range(golden, case, direction, variant): + scores = pd.read_csv( + golden / case / f"scores_{direction}_{variant}.csv", index_col="node" + ) + assert scores.min().min() >= -1 + assert scores.max().max() <= 1 + + +def test_toy_smurfing_source_scores_one(golden): + # Node 23 pays 20/21/22, which all pay 19: the textbook pattern, and the + # only score in the fixtures that is exactly at the top of the range. + scores = pd.read_csv(golden / "toy" / "scores_undirected_raw.csv", index_col="node") + assert scores.loc[23, "weighted_average__GARGAML"] == 1.0 + + +def test_isolated_nodes_return_the_frozen_values(golden): + # Louvain at resolution 10 strands most of the toy graph. ADR 0003. + features = pd.read_csv( + golden / "toy" / "features_undirected_reduced.csv", index_col="node" + ) + isolated = features[features["degree"] == 0] + assert len(isolated) == 18 + + statistics = [c for c in features.columns if c not in ("GARGAML", "degree")] + assert (isolated[statistics] == 0).all().all() + assert (isolated["GARGAML"] == -1).all() + + +def test_manifest_records_its_provenance(manifest): + assert manifest["source_commit"] + assert manifest["settings"]["seed"] == 1997 + assert manifest["settings"]["louvain_resolution"] == 10 + for package in ("networkx", "numpy", "pandas"): + assert manifest["environment"][package] diff --git a/tests/test_package.py b/tests/test_package.py new file mode 100644 index 0000000..c83a9e9 --- /dev/null +++ b/tests/test_package.py @@ -0,0 +1,18 @@ +import logging + +import garg_aml + + +def test_version_is_a_string(): + assert isinstance(garg_aml.__version__, str) + assert garg_aml.__version__ + + +def test_library_attaches_no_handlers_of_its_own(): + # A library configures no logging; the application decides. + handlers = logging.getLogger("garg_aml").handlers + assert all(isinstance(h, logging.NullHandler) for h in handlers) + + +def test_public_api_is_explicit(): + assert isinstance(garg_aml.__all__, list) diff --git a/tools/README.md b/tools/README.md new file mode 100644 index 0000000..45f3284 --- /dev/null +++ b/tools/README.md @@ -0,0 +1,22 @@ +# Fixture tooling + +`make_golden.py` and `check_golden.py` generate and verify `tests/golden/`. + +**They run inside the research repository, not here.** Both import the +pre-extraction implementation from `src/methods/`, which is what the fixtures +freeze. Copy them into a checkout of +[B-Deprez/GARG-AML](https://github.com/B-Deprez/GARG-AML) under `scripts/` and +run from its root: + +```bash +python scripts/make_golden.py # rewrite the fixtures (a deliberate act) +python scripts/check_golden.py # verify that implementation still reproduces them +``` + +They live here as the fixtures' provenance: without them there is no record of +how those numbers were produced, or how to regenerate them if the frozen +behaviour is ever deliberately changed. + +From Phase 2 onward the package tests the fixtures directly against +`garg_aml` in `tests/test_golden.py`; `check_golden.py` is that test's ancestor +and is kept for comparison until the extraction is complete. diff --git a/tools/check_golden.py b/tools/check_golden.py new file mode 100644 index 0000000..6bdb14c --- /dev/null +++ b/tools/check_golden.py @@ -0,0 +1,131 @@ +""" +Verify the golden fixtures against the current code. + +This is the harness that Phase 2 ports into the package as the L1 test, so it +deliberately depends on nothing but numpy/pandas/networkx and the frozen CSVs +in ``golden/`` -- no igraph, no seeds, no generator. Run it after any change to +``src/methods/`` or ``src/utils/graph_processing.py``, and on a different +networkx to check the fixtures are portable: + + python scripts/check_golden.py + +Exit code is non-zero if anything drifted. +""" + +import glob +import os +import sys + +DIR = "./" +os.chdir(DIR) +sys.path.append(DIR) + +import networkx as nx +import pandas as pd + +from src.utils.graph_processing import graph_community + +sys.path.insert(0, "scripts") +from make_golden import ( # noqa: E402 -- reuses the generator's own stages + GOLDEN_DIR, + RESOLUTION, + edge_frame, + features_frame, + load_graph, + measures_frame, + node_frame, + scores_frame, +) + +RTOL = 1e-12 + + +def compare(name, produced, expected, results): + """Assert two frames match, and record the outcome rather than raising.""" + try: + pd.testing.assert_frame_equal( + produced.reset_index(drop=True), + expected.reset_index(drop=True), + rtol=RTOL, + check_dtype=False, + ) + except AssertionError as exc: + results.append((name, str(exc).strip().split("\n")[0])) + return False + return True + + +def check_case(case, results): + case_dir = os.path.join(GOLDEN_DIR, case) + checked = 0 + + for directed in [False, True]: + label = "directed" if directed else "undirected" + G = load_graph(os.path.join(case_dir, "edges.csv"), directed) + + # reduce_graph: the version-sensitive one. Louvain's partition is an + # implementation detail of networkx, so this is the fixture expected to + # break first on an upgrade -- deliberately isolated from the rest. + reduced = graph_community(G, resolution=RESOLUTION) + expected_reduced = pd.read_csv( + os.path.join(case_dir, f"reduced_edges_{label}.csv") + ) + compare(f"{case}/reduce_graph.edges[{label}]", edge_frame(reduced), + expected_reduced, results) + compare(f"{case}/reduce_graph.nodes[{label}]", node_frame(reduced), + pd.read_csv(os.path.join(case_dir, f"reduced_nodes_{label}.csv")), + results) + checked += 2 + + # Everything downstream is computed from the *frozen* reduced edge + # list, not from what Louvain just produced, so a partition change + # cannot cascade. + variants = { + "raw": G, + "reduced": load_graph( + os.path.join(case_dir, f"reduced_edges_{label}.csv"), directed, + nodes_path=os.path.join(case_dir, f"reduced_nodes_{label}.csv"), + ), + } + for variant, graph in variants.items(): + measures = measures_frame(graph, directed) + stem = f"{label}_{variant}" + + compare(f"{case}/measures[{stem}]", measures, + pd.read_csv(os.path.join(case_dir, f"measures_{stem}.csv")), + results) + compare(f"{case}/scores[{stem}]", scores_frame(measures, directed), + pd.read_csv(os.path.join(case_dir, f"scores_{stem}.csv"), + index_col="node"), + results) + compare(f"{case}/features[{stem}]", + features_frame(graph, measures, directed), + pd.read_csv(os.path.join(case_dir, f"features_{stem}.csv"), + index_col="node"), + results) + checked += 3 + + return checked + + +if __name__ == "__main__": + cases = sorted( + os.path.basename(os.path.dirname(p)) + for p in glob.glob(os.path.join(GOLDEN_DIR, "*", "edges.csv")) + ) + if not cases: + sys.exit(f"No fixtures in {GOLDEN_DIR}/ -- run scripts/make_golden.py first") + + print(f"networkx {nx.__version__} | pandas {pd.__version__}") + + results, checked = [], 0 + for case in cases: + checked += check_case(case, results) + + print(f"\n{checked} comparisons across {len(cases)} cases") + if results: + print(f"{len(results)} MISMATCH(ES):") + for name, detail in results: + print(f" {name}: {detail}") + sys.exit(1) + print("all fixtures reproduce") diff --git a/tools/make_golden.py b/tools/make_golden.py new file mode 100644 index 0000000..72a674b --- /dev/null +++ b/tools/make_golden.py @@ -0,0 +1,327 @@ +""" +Phase 0 of the packaging migration: freeze the current behaviour as fixtures. + +Runs the *current* GARG-AML implementation over a handful of small, fully +reproducible graphs and writes every intermediate to ``golden/``. Those files +become the oracle the extracted ``garg-aml`` package is tested against: if a +refactor changes any number here, the refactor is wrong. + +Run from the repository root: + + python scripts/make_golden.py + +Design notes +------------ +*The edge list is the fixture input, not the generator.* Graphs are built once +with igraph and immediately written to ``edges.csv``; every downstream number is +then computed from the *reloaded* file. The package's tests never need igraph, +a seed, or this script -- they read the edge list and compare. + +*Louvain is frozen as an edge list, not re-run.* ``reduced_edges_*.csv`` is both +an output (to test ``reduce_graph``) and an input (the reduced-graph measures +are computed from it). A networkx upgrade that changes the Louvain partition +therefore breaks exactly one test instead of all of them. + +*Node order is sorted out.* Every frame is sorted by node before writing, so the +fixtures do not encode dict insertion order. + +*Features use ``weighted_average`` only.* That is the documented default and +what every experiment uses; the ``basic`` path is already covered by the score +fixtures. +""" + +import json +import os +import platform +import random +import subprocess +import sys + +DIR = "./" +os.chdir(DIR) +sys.path.append(DIR) + +import networkx as nx +import numpy as np +import pandas as pd + +from src.methods.GARGAML import ( + GARG_AML_node_directed_measures, + GARG_AML_node_undirected_measures, +) +from src.methods.gargaml_scores import define_gargaml_scores, summarise_gargaml_scores +from src.utils.graph_processing import graph_community + +GOLDEN_DIR = "golden" +SEED = 1997 +RESOLUTION = 10 + +SCORE_TYPES = ["basic", "weighted_average"] +FEATURE_SCORE_TYPE = "weighted_average" + +FEATURE_COLUMNS = [ + "GARGAML", + "GARGAML_min", "GARGAML_mean", "GARGAML_max", "GARGAML_std", + "degree", + "degree_min", "degree_mean", "degree_max", "degree_std", +] + + +# --------------------------------------------------------------------------- +# Case graphs +# --------------------------------------------------------------------------- + +def build_toy(): + """ + The paper's toy example (Figures A1-A6), as built in notebooks/toyexample.ipynb. + + A 20-node Watts-Strogatz ring plus one injected smurfing pattern: node 23 + pays 20, 21 and 22, which all pay 19. Node 23 is the textbook source, 19 the + textbook target, and neither transacts with the other. + """ + import igraph as ig + + random.seed(SEED) + ring = ig.Graph(directed=True).Watts_Strogatz(1, 20, 2, 0.2) + + sources = [e.source for e in ring.es] + targets = [e.target for e in ring.es] + + sources += [23, 23, 23, 20, 21, 22] + targets += [20, 21, 22, 19, 19, 19] + + return list(zip(sources, targets)) + + +def build_synthetic(method, n_nodes, m_edges, p_edges, n_patterns): + """ + One synthetic graph with injected smurfing, using this repo's own generator. + + Mirrors ``create_synthetic_data`` but without its file writes and plots, and + with the RNG seeded so the run is reproducible. + """ + import igraph as ig + + from src.data.synthetic_smurfing import add_smurfing_patterns + + random.seed(SEED) + + if method == "Barabasi-Albert": + rg = ig.Graph().Barabasi(n_nodes, m=m_edges) + elif method == "Erdos-Renyi": + rg = ig.Graph().Erdos_Renyi(n_nodes, p=p_edges / 10) + elif method == "Watts-Strogatz": + rg = ig.Graph().Watts_Strogatz(1, n_nodes, m_edges, p_edges) + else: + raise ValueError("Invalid generation method: " + method) + + list_nodes = rg.vs.indices + random.shuffle(list_nodes) + + rg.vs["new_node"] = False + rg.vs["laundering"] = False + rg.vs["separate"] = False + rg.vs["new_mules"] = False + rg.vs["existing_mules"] = False + rg.es["laundering"] = False + + for pattern in ["separate", "new_mules", "existing_mules"]: + rg, list_nodes = add_smurfing_patterns( + rg, n_patterns, list_nodes, type_pattern=pattern + ) + rg.simplify(combine_edges="max") + + return [(e.source, e.target) for e in rg.es] + + +CASES = { + "toy": build_toy, + "synth_ba": lambda: build_synthetic("Barabasi-Albert", 200, 2, 0, 3), + "synth_er": lambda: build_synthetic("Erdos-Renyi", 200, 0, 0.01, 3), + "synth_ws": lambda: build_synthetic("Watts-Strogatz", 200, 2, 0.01, 3), +} + + +# --------------------------------------------------------------------------- +# Graph loading -- mirrors src/data/graph_construction.py +# --------------------------------------------------------------------------- + +def load_graph(edges_path, directed, nodes_path=None): + """ + Rebuild a graph from a frozen edge list, exactly as the pipeline does. + + ``nodes_path`` restores nodes that carry no edges. Louvain reduction leaves + plenty of those behind -- ``graph_community`` keeps every node and drops + only inter-community edges -- and they are not incidental: an isolated node + is what triggers the frozen D18/D20 edge cases. An edge list alone cannot + represent them, so any graph that has been through reduction is frozen as a + node list *and* an edge list. + """ + G = nx.DiGraph() if directed else nx.Graph() + if nodes_path is not None: + G.add_nodes_from(pd.read_csv(nodes_path)["node"]) + data = pd.read_csv(edges_path) + G.add_edges_from(zip(data["source"], data["target"])) + G.remove_edges_from([(n, n) for n in G.nodes() if G.has_edge(n, n)]) + return G + + +def edge_frame(G): + """Edge list of a graph, sorted, so the fixture is order-independent.""" + edges = sorted((int(u), int(v)) for u, v in G.edges()) + return pd.DataFrame(edges, columns=["source", "target"]) + + +def node_frame(G): + """Node list of a graph, sorted.""" + return pd.DataFrame({"node": sorted(G.nodes)}) + + +# --------------------------------------------------------------------------- +# The three pipeline stages +# --------------------------------------------------------------------------- + +def measures_frame(G, directed): + """Stage 1: per-node block measures and block sizes.""" + nodes = sorted(G.nodes) + + if directed: + G_und = G.to_undirected() + G_rev = G.reverse(copy=True) + columns = ( + [f"measure_{i}{j}" for i in range(3) for j in range(3)] + + [f"size_{i}{j}" for i in range(3) for j in range(3)] + ) + rows = [ + GARG_AML_node_directed_measures(n, G, G_und, G_rev, include_size=True) + for n in nodes + ] + else: + columns = ["measure_1", "measure_2", "measure_3", "size_1", "size_2", "size_3"] + rows = [ + GARG_AML_node_undirected_measures(n, G, include_size=True) for n in nodes + ] + + frame = pd.DataFrame(rows, columns=columns) + frame.insert(0, "node", nodes) + return frame + + +def scores_frame(measures, directed): + """Stage 2a: measures -> score, for every score type, side by side.""" + parts = [] + for score_type in SCORE_TYPES: + scores = define_gargaml_scores(measures, directed, score_type=score_type) + parts.append(scores.add_prefix(score_type + "__")) + return pd.concat(parts, axis=1).sort_index() + + +def features_frame(G, measures, directed): + """Stage 2b: score + neighbourhood summary statistics.""" + scores = define_gargaml_scores(measures, directed, score_type=FEATURE_SCORE_TYPE) + features = summarise_gargaml_scores(G, scores, columns=FEATURE_COLUMNS) + return features.sort_index() + + +# --------------------------------------------------------------------------- +# Driver +# --------------------------------------------------------------------------- + +def write(frame, case, name, index): + path = os.path.join(GOLDEN_DIR, case, name + ".csv") + frame.to_csv(path, index=index, index_label="node" if index else None) + print(f" {path:<58} {len(frame):>5} rows") + + +def process_case(case, build): + print(f"\n=== {case} ===") + case_dir = os.path.join(GOLDEN_DIR, case) + os.makedirs(case_dir, exist_ok=True) + + edges = build() + pd.DataFrame(edges, columns=["source", "target"]).to_csv( + os.path.join(case_dir, "edges.csv"), index=False + ) + + summary = {} + for directed in [False, True]: + label = "directed" if directed else "undirected" + G = load_graph(os.path.join(case_dir, "edges.csv"), directed) + reduced = graph_community(G, resolution=RESOLUTION) + + write(node_frame(reduced), case, f"reduced_nodes_{label}", index=False) + write(edge_frame(reduced), case, f"reduced_edges_{label}", index=False) + + # Reload rather than reuse: the downstream fixtures must be exactly what + # the frozen files reproduce, not what this process happens to hold. + reduced = load_graph( + os.path.join(case_dir, f"reduced_edges_{label}.csv"), directed, + nodes_path=os.path.join(case_dir, f"reduced_nodes_{label}.csv"), + ) + + for variant, graph in [("raw", G), ("reduced", reduced)]: + measures = measures_frame(graph, directed) + write(measures, case, f"measures_{label}_{variant}", index=False) + write(scores_frame(measures, directed), case, + f"scores_{label}_{variant}", index=True) + write(features_frame(graph, measures, directed), case, + f"features_{label}_{variant}", index=True) + + summary[label] = { + "nodes": G.number_of_nodes(), + "edges": G.number_of_edges(), + "reduced_edges": reduced.number_of_edges(), + } + + return summary + + +def manifest(summaries): + """Record exactly what produced these numbers.""" + try: + commit = subprocess.check_output( + ["git", "rev-parse", "HEAD"], text=True + ).strip() + dirty = bool(subprocess.check_output( + ["git", "status", "--porcelain"], text=True + ).strip()) + except (subprocess.CalledProcessError, FileNotFoundError): + commit, dirty = None, None + + return { + "purpose": ( + "Frozen behaviour of GARG-AML before extraction into the garg-aml " + "package. Never edit these files to make a test pass." + ), + "generated_by": "scripts/make_golden.py", + "source_repo": "B-Deprez/GARG-AML", + "source_commit": commit, + "source_tree_dirty": dirty, + "settings": { + "seed": SEED, + "louvain_resolution": RESOLUTION, + "score_types": SCORE_TYPES, + "feature_score_type": FEATURE_SCORE_TYPE, + }, + "environment": { + "python": platform.python_version(), + "platform": platform.platform(), + "networkx": nx.__version__, + "numpy": np.__version__, + "pandas": pd.__version__, + "igraph": __import__("igraph").__version__, + }, + "cases": summaries, + } + + +if __name__ == "__main__": + os.makedirs(GOLDEN_DIR, exist_ok=True) + + summaries = {case: process_case(case, build) for case, build in CASES.items()} + + with open(os.path.join(GOLDEN_DIR, "MANIFEST.json"), "w") as f: + json.dump(manifest(summaries), f, indent=2) + f.write("\n") + + print(f"\nWrote {GOLDEN_DIR}/MANIFEST.json")