Phase 5: prepare the 0.1.0 release as garg-aml-smurfing

CITATION.cff

@@ -1,5 +1,5 @@
 cff-version: 1.2.0
-title: garg-aml
+title: garg-aml-smurfing
 message: >-
   If you use this software, please cite both the software and the paper it
   implements.

CONTRIBUTING.md

@@ -72,7 +72,8 @@ registered with:
 | Field | Value |
 |---|---|
 | Owner | `VerbekeLab` |
-| Repository | `garg-aml` |
+| Repository | `garg-aml` (the GitHub repo) |
+| PyPI project | `garg-aml-smurfing` |
 | Workflow | `release.yml` |
 | Environment | `release` |
 
@@ -99,7 +100,7 @@ confirm it works with nothing else present.
 
 ### If you cannot publish to PyPI
 
-The `garg-aml` name has a single owner. If that account is unreachable, the code
+The `garg-aml-smurfing` 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

README.md

@@ -1,4 +1,4 @@
-# garg-aml
+# GARG-AML
 
 Graph-based detection of **smurfing** patterns in transaction networks.
 
@@ -9,16 +9,18 @@ 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.
 
-[![PyPI](https://img.shields.io/pypi/v/garg-aml.svg)](https://pypi.org/project/garg-aml/)
-[![Python](https://img.shields.io/pypi/pyversions/garg-aml.svg)](https://pypi.org/project/garg-aml/)
+[![PyPI](https://img.shields.io/pypi/v/garg-aml-smurfing.svg)](https://pypi.org/project/garg-aml-smurfing/)
+[![Python](https://img.shields.io/pypi/pyversions/garg-aml-smurfing.svg)](https://pypi.org/project/garg-aml-smurfing/)
 [![License: MIT](https://img.shields.io/badge/License-MIT-orange.svg)](LICENSE)
 
 ## Install
 
 ```bash
-pip install garg-aml
+pip install garg-aml-smurfing
 ```
 
+> Installed as **`garg-aml-smurfing`**, imported as **`garg_aml`**. The shorter name was already taken on PyPI by an unrelated project.
+
 ## Use
 
 ```python

docs/decisions/0006-no-torch-dependency.md

@@ -8,7 +8,7 @@ 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
+A practitioner evaluating it should be able to `pip install garg-aml-smurfing` 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.

docs/index.md

@@ -1,12 +1,14 @@
-# garg-aml
+# GARG-AML
 
 Find **smurfing** in a transaction network: one score per account, computed from
 local structure, with nothing to train and no labels required.
 
 ```bash
-pip install garg-aml
+pip install garg-aml-smurfing
 ```
 
+> Installed as **`garg-aml-smurfing`**, imported as **`garg_aml`**. The shorter name was already taken on PyPI by an unrelated project.
+
 ```python
 import garg_aml as ga
 

docs/install.md

@@ -1,7 +1,7 @@
 # Installation
 
 ```bash
-pip install garg-aml
+pip install garg-aml-smurfing
 ```
 
 Python 3.10 or newer. The core depends only on numpy, pandas, networkx and
@@ -11,11 +11,11 @@ scipy — no deep-learning stack, nothing that needs compiling.
 
 | Extra | Install | What it adds |
 |---|---|---|
-| `progress` | `pip install 'garg-aml[progress]'` | `progress=True` progress bars (tqdm) |
-| `parallel` | `pip install 'garg-aml[parallel]'` | `n_jobs` other than 1 (joblib) |
-| `sklearn` | `pip install 'garg-aml[sklearn]'` | the `GargAmlScorer` estimator |
+| `progress` | `pip install 'garg-aml-smurfing[progress]'` | `progress=True` progress bars (tqdm) |
+| `parallel` | `pip install 'garg-aml-smurfing[parallel]'` | `n_jobs` other than 1 (joblib) |
+| `sklearn` | `pip install 'garg-aml-smurfing[sklearn]'` | the `GargAmlScorer` estimator |
 
-Ask for several at once with `pip install 'garg-aml[progress,parallel]'`.
+Ask for several at once with `pip install 'garg-aml-smurfing[progress,parallel]'`.
 
 Each is genuinely optional: the package imports and scores without any of them,
 and reaching for a feature you have not installed raises an error that names the

docs/reproducing.md

@@ -33,7 +33,7 @@ only through a function no experiment called. It is not ported. See
 Pin an exact version when reproducing published numbers:
 
 ```bash
-pip install garg-aml==0.1.0
+pip install garg-aml-smurfing==0.1.0
 ```
 
 Each release is tagged in the repository and archived with a DOI, so a paper can

mkdocs.yml

@@ -1,4 +1,4 @@
-site_name: garg-aml
+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/

pyproject.toml

@@ -3,7 +3,7 @@ requires = ["hatchling>=1.27"]
 build-backend = "hatchling.build"
 
 [project]
-name = "garg-aml"
+name = "garg-aml-smurfing"
 dynamic = ["version"]
 description = "Graph-based detection of smurfing patterns in transaction networks."
 readme = "README.md"

src/garg_aml/__init__.py

@@ -74,7 +74,8 @@ def __getattr__(name: str) -> Any:
             from .estimator import GargAmlScorer
         except ImportError as exc:  # pragma: no cover - depends on the environment
             raise ImportError(
-                "GargAmlScorer needs scikit-learn: pip install 'garg-aml[sklearn]'"
+                "GargAmlScorer needs scikit-learn: "
+                "pip install 'garg-aml-smurfing[sklearn]'"
             ) from exc
         return GargAmlScorer
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

src/garg_aml/api.py

@@ -26,7 +26,7 @@ def _progress(nodes: list, show: bool) -> Iterable:
         from tqdm import tqdm
     except ImportError as exc:  # pragma: no cover - depends on the environment
         raise ImportError(
-            "progress=True needs tqdm: pip install 'garg-aml[progress]'"
+            "progress=True needs tqdm: pip install 'garg-aml-smurfing[progress]'"
         ) from exc
     return tqdm(nodes)
 
@@ -56,7 +56,8 @@ def measure(node: Hashable) -> tuple[float, ...]:
         from joblib import Parallel, delayed
     except ImportError as exc:  # pragma: no cover - depends on the environment
         raise ImportError(
-            "n_jobs other than 1 needs joblib: pip install 'garg-aml[parallel]'"
+            "n_jobs other than 1 needs joblib: "
+            "pip install 'garg-aml-smurfing[parallel]'"
         ) from exc
 
     # Each worker receives its own copy of the graph, so the memory cost scales