rename aisdc to sacroml

.github/workflows/lint.yml

@@ -23,5 +23,5 @@ jobs:
 
       - name: pylint
         run: |
-          pylint -v --recursive=True aisdc tests --fail-under 10
+          pylint -v --recursive=True sacroml tests --fail-under 10
 ...

CHANGELOG.md

@@ -1,7 +1,10 @@
 # Changelog
 
+## Version 1.2.1 (TBC)
+
 Changes:
 *   Rename repository from AI-SDC to SACRO-ML ([#298](https://github.com/AI-SDC/SACRO-ML/pull/298))
+*   Rename package from aisdc sacroml ([#298](https://github.com/AI-SDC/SACRO-ML/pull/299))
 
 ## Version 1.2.0 (Jul 11, 2024)
 

CITATION.cff

@@ -1,8 +1,8 @@
 cff-version: 1.2.0
-title: AI-SDC
-version: 1.2.0
-doi: 10.5281/zenodo.12725798
-date-released: 2024-07-11
+title: SACRO-ML
+version: 1.2.1
+doi:
+date-released:
 license: MIT
 repository-code: https://github.com/AI-SDC/SACRO-ML
 languages:
@@ -55,37 +55,37 @@ identifiers:
     description: This DOI represents all versions, and will always resolve to the latest one.
   - type: doi
     value: 10.5281/zenodo.7080280
-    description: This is the archived snapshot of AI-SDC v.1.0.0.
+    description: This is the archived snapshot of SACRO-ML v.1.0.0.
   - type: doi
     value: 10.5281/zenodo.7327390
-    description: This is the archived snapshot of AI-SDC v.1.0.1.
+    description: This is the archived snapshot of SACRO-ML v.1.0.1.
   - type: doi
     value: 10.5281/zenodo.7681783
-    description: This is the archived snapshot of AI-SDC v.1.0.2.
+    description: This is the archived snapshot of SACRO-ML v.1.0.2.
   - type: doi
     value: 10.5281/zenodo.7886945
-    description: This is the archived snapshot of AI-SDC v.1.0.3.
+    description: This is the archived snapshot of SACRO-ML v.1.0.3.
   - type: doi
     value: 10.5281/zenodo.7900410
-    description: This is the archived snapshot of AI-SDC v.1.0.4.
+    description: This is the archived snapshot of SACRO-ML v.1.0.4.
   - type: doi
     value: 10.5281/zenodo.8007939
-    description: This is the archived snapshot of AI-SDC v.1.0.5.
+    description: This is the archived snapshot of SACRO-ML v.1.0.5.
   - type: doi
     value: 10.5281/zenodo.8172371
-    description: This is the archived snapshot of AI-SDC v.1.0.6.
+    description: This is the archived snapshot of SACRO-ML v.1.0.6.
   - type: doi
     value: 10.5281/zenodo.8430143
-    description: This is the archived snapshot of AI-SDC v.1.1.0.
+    description: This is the archived snapshot of SACRO-ML v.1.1.0.
   - type: doi
     value: 10.5281/zenodo.10021954
-    description: This is the archived snapshot of AI-SDC v.1.1.1.
+    description: This is the archived snapshot of SACRO-ML v.1.1.1.
   - type: doi
     value: 10.5281/zenodo.10055182
-    description: This is the archived snapshot of AI-SDC v.1.1.2.
+    description: This is the archived snapshot of SACRO-ML v.1.1.2.
   - type: doi
     value: 10.5281/zenodo.11077535
-    description: This is the archived snapshot of AI-SDC v.1.1.3.
+    description: This is the archived snapshot of SACRO-ML v.1.1.3.
   - type: doi
     value: 10.5281/zenodo.12725798
-    description: This is the archived snapshot of AI-SDC v.1.2.0.
+    description: This is the archived snapshot of SACRO-ML v.1.2.0.

CONTRIBUTING.md

@@ -18,7 +18,7 @@ $ pytest .
 
 ## Directory Structure
 
-* `aisdc` Contains the aisdc source code.
+* `sacroml` Contains the sacroml source code.
     - `attacks` Contains a variety of privacy attacks on machine learning models.
     - `preprocessing` Contains preprocessing modules for test datasets.
     - `safemodel` The safemodel wrappers for common machine learning models.

README.md

@@ -2,32 +2,32 @@
 [![Latest Version](https://img.shields.io/github/v/release/AI-SDC/SACRO-ML?style=flat)](https://github.com/AI-SDC/SACRO-ML/releases)
 [![DOI](https://zenodo.org/badge/518801511.svg)](https://zenodo.org/badge/latestdoi/518801511)
 [![codecov](https://codecov.io/gh/AI-SDC/SACRO-ML/branch/main/graph/badge.svg?token=AXX2XCXUNU)](https://codecov.io/gh/AI-SDC/SACRO-ML)
-[![Python versions](https://img.shields.io/pypi/pyversions/aisdc.svg)](https://pypi.org/project/aisdc)
+[![Python versions](https://img.shields.io/pypi/pyversions/sacroml.svg)](https://pypi.org/project/sacroml)
 
 # SACRO-ML
 
 A collection of tools and resources for managing the [statistical disclosure control](https://en.wikipedia.org/wiki/Statistical_disclosure_control) of trained [machine learning](https://en.wikipedia.org/wiki/Machine_learning) models. For a brief introduction, see [Smith et al. (2022)](https://doi.org/10.48550/arXiv.2212.01233).
 
-The `aisdc` package provides:
+The `sacroml` package provides:
 * A variety of privacy attacks for assessing machine learning models.
 * The safemodel package: a suite of open source wrappers for common machine learning frameworks, including [scikit-learn](https://scikit-learn.org) and [Keras](https://keras.io). It is designed for use by researchers in Trusted Research Environments (TREs) where disclosure control methods must be implemented. Safemodel aims to give researchers greater confidence that their models are more compliant with disclosure control.
 
 ## Installation
 
-[![PyPI package](https://img.shields.io/pypi/v/aisdc.svg)](https://pypi.org/project/aisdc)
+[![PyPI package](https://img.shields.io/pypi/v/sacroml.svg)](https://pypi.org/project/sacroml)
 
-Install `aisdc` and manually copy the [`examples`](examples/).
+Install `sacroml` and manually copy the [`examples`](examples/).
 
 To install only the base package, which includes the attacks used for assessing privacy:
 
 ```
-$ pip install aisdc
+$ pip install sacroml
 ```
 
 To additionally install the safemodel package:
 
 ```
-$ pip install aisdc[safemodel]
+$ pip install sacroml[safemodel]
 ```
 
 Note: macOS users may need to install libomp due to a dependency on XGBoost:

examples/README.md

@@ -37,14 +37,14 @@ $ python -m examples.attack_lira
 For example:
 ```
 $ python -m examples.train_rf_nursery
-$ aisdc gen-attack
-$ aisdc run target_rf_nursery attack.yaml
+$ sacroml gen-attack
+$ sacroml run target_rf_nursery attack.yaml
 ```
 
 If you are unable to use the Python `Target` class to generate the `target_dir/` containing the `target.yaml` you can generate one using the CLI tool:
 
 ```
-$ aisdc gen-target
+$ sacroml gen-target
 ```
 
 ## User Stories

examples/attack_attribute.py

@@ -10,8 +10,8 @@
 
 import logging
 
-from aisdc.attacks import attribute_attack
-from aisdc.attacks.target import Target
+from sacroml.attacks import attribute_attack
+from sacroml.attacks.target import Target
 
 output_dir = "outputs_aia"
 target_dir = "target_rf_nursery"

examples/attack_lira.py

@@ -18,8 +18,8 @@
 
 import logging
 
-from aisdc.attacks.likelihood_attack import LIRAAttack
-from aisdc.attacks.target import Target
+from sacroml.attacks.likelihood_attack import LIRAAttack
+from sacroml.attacks.target import Target
 
 output_dir = "outputs_lira"
 target_dir = "target_rf_breast_cancer"

examples/attack_worstcase.py

@@ -23,8 +23,8 @@
 
 import logging
 
-from aisdc.attacks import worst_case_attack
-from aisdc.attacks.target import Target
+from sacroml.attacks import worst_case_attack
+from sacroml.attacks.target import Target
 
 output_dir = "outputs_worstcase"
 target_dir = "target_rf_breast_cancer"

examples/safemodel.py

@@ -7,8 +7,8 @@
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder, OneHotEncoder
 
-from aisdc.attacks.target import Target
-from aisdc.safemodel.classifiers import SafeDecisionTreeClassifier
+from sacroml.attacks.target import Target
+from sacroml.safemodel.classifiers import SafeDecisionTreeClassifier
 
 output_dir = "outputs_safemodel"
 

examples/train_rf_breast_cancer.py

@@ -6,7 +6,7 @@
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import train_test_split
 
-from aisdc.attacks.target import Target
+from sacroml.attacks.target import Target
 
 output_dir = "target_rf_breast_cancer"
 

examples/train_rf_nursery.py

@@ -8,7 +8,7 @@
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder, OneHotEncoder
 
-from aisdc.attacks.target import Target
+from sacroml.attacks.target import Target
 
 output_dir = "target_rf_nursery"
 

examples/user_stories/user_story_1/user_story_1_researcher_template.py

@@ -22,11 +22,10 @@
 
 import numpy as np
 import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import LabelEncoder, OneHotEncoder
-
 from aisdc.attacks.target import Target
 from aisdc.safemodel.classifiers import SafeDecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
 
 
 def main():

examples/user_stories/user_story_1/user_story_1_tre.py

@@ -13,7 +13,6 @@
 import os
 
 import yaml
-
 from aisdc.attacks.attack_report_formatter import GenerateTextReport
 
 

examples/user_stories/user_story_2/user_story_2_researcher_template.py

@@ -22,10 +22,9 @@
 
 import numpy as np
 import pandas as pd
-from data_processing_researcher import process_dataset
-
 from aisdc.attacks.target import Target
 from aisdc.safemodel.classifiers import SafeDecisionTreeClassifier
+from data_processing_researcher import process_dataset
 
 
 def run_user_story():

examples/user_stories/user_story_2/user_story_2_tre.py

@@ -17,7 +17,6 @@
 import numpy as np
 import pandas as pd
 import yaml
-
 from aisdc.attacks.attack_report_formatter import GenerateTextReport
 from aisdc.attacks.target import Target
 

examples/user_stories/user_story_3/user_story_3_tre.py

@@ -17,7 +17,6 @@
 
 import numpy as np
 import yaml
-
 from aisdc.attacks.attack_report_formatter import GenerateTextReport
 from aisdc.attacks.likelihood_attack import LIRAAttack
 from aisdc.attacks.target import Target

examples/user_stories/user_story_4/user_story_4_tre.py

@@ -15,7 +15,6 @@
 import numpy as np
 import pandas as pd
 import yaml
-
 from aisdc.attacks.attack_report_formatter import GenerateTextReport
 from aisdc.attacks.worst_case_attack import WorstCaseAttack
 

examples/user_stories/user_story_7/user_story_7_researcher_template.py

@@ -26,11 +26,10 @@
 
 import numpy as np
 import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import LabelEncoder, OneHotEncoder
-
 from aisdc.attacks.target import Target
 from aisdc.safemodel.classifiers import SafeDecisionTreeClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
 
 
 def run_user_story():

pyproject.toml

@@ -99,4 +99,4 @@ docstring-code-line-length = 80
 [tool.ruff.lint.extend-per-file-ignores]
 "user_stories/**/*" = ["ANN"]
 "tests/**/*" = ["S101", "PLR2004", "ANN"]
-"aisdc/attacks/structural_attack.py" = ["PLR2004"]
+"sacroml/attacks/structural_attack.py" = ["PLR2004"]

aisdc/attacks/attack.py → sacroml/attacks/attack.py

@@ -11,8 +11,8 @@
 
 from fpdf import FPDF
 
-from aisdc.attacks import report
-from aisdc.attacks.target import Target
+from sacroml.attacks import report
+from sacroml.attacks.target import Target
 
 logger = logging.getLogger(__name__)
 

aisdc/attacks/attribute_attack.py → sacroml/attacks/attribute_attack.py

@@ -12,9 +12,9 @@
 from sklearn.base import BaseEstimator
 from sklearn.preprocessing import OneHotEncoder
 
-from aisdc.attacks import report
-from aisdc.attacks.attack import Attack
-from aisdc.attacks.target import Target
+from sacroml.attacks import report
+from sacroml.attacks.attack import Attack
+from sacroml.attacks.target import Target
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)

aisdc/attacks/factory.py → sacroml/attacks/factory.py

@@ -4,11 +4,11 @@
 
 import yaml
 
-from aisdc.attacks.attribute_attack import AttributeAttack
-from aisdc.attacks.likelihood_attack import LIRAAttack
-from aisdc.attacks.structural_attack import StructuralAttack
-from aisdc.attacks.target import Target
-from aisdc.attacks.worst_case_attack import WorstCaseAttack
+from sacroml.attacks.attribute_attack import AttributeAttack
+from sacroml.attacks.likelihood_attack import LIRAAttack
+from sacroml.attacks.structural_attack import StructuralAttack
+from sacroml.attacks.target import Target
+from sacroml.attacks.worst_case_attack import WorstCaseAttack
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)

aisdc/attacks/likelihood_attack.py → sacroml/attacks/likelihood_attack.py

@@ -10,10 +10,10 @@
 from fpdf import FPDF
 from scipy.stats import norm, shapiro
 
-from aisdc import metrics
-from aisdc.attacks import report
-from aisdc.attacks.attack import Attack
-from aisdc.attacks.target import Target
+from sacroml import metrics
+from sacroml.attacks import report
+from sacroml.attacks.attack import Attack
+from sacroml.attacks.target import Target
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)

aisdc/attacks/report.py → sacroml/attacks/report.py

@@ -9,7 +9,7 @@
 from fpdf import FPDF
 from pypdf import PdfWriter
 
-from aisdc.attacks.attack_report_formatter import GenerateJSONModule
+from sacroml.attacks.attack_report_formatter import GenerateJSONModule
 
 # Adds a border to all pdf cells of set to 1 -- useful for debugging
 BORDER = 0

aisdc/attacks/structural_attack.py → sacroml/attacks/structural_attack.py

@@ -19,8 +19,8 @@
 from sklearn.tree import DecisionTreeClassifier
 from xgboost.sklearn import XGBClassifier
 
-from aisdc.attacks.attack import Attack
-from aisdc.attacks.target import Target
+from sacroml.attacks.attack import Attack
+from sacroml.attacks.target import Target
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)

aisdc/attacks/worst_case_attack.py → sacroml/attacks/worst_case_attack.py

@@ -10,10 +10,10 @@
 from sklearn.metrics import confusion_matrix
 from sklearn.model_selection import train_test_split
 
-from aisdc import metrics
-from aisdc.attacks import report
-from aisdc.attacks.attack import Attack, get_class_by_name
-from aisdc.attacks.target import Target
+from sacroml import metrics
+from sacroml.attacks import report
+from sacroml.attacks.attack import Attack, get_class_by_name
+from sacroml.attacks.target import Target
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)

aisdc/config/attack.py → sacroml/config/attack.py

@@ -6,8 +6,8 @@
 
 import yaml
 
-from aisdc.attacks import factory
-from aisdc.config import utils
+from sacroml.attacks import factory
+from sacroml.config import utils
 
 
 def _get_defaults(name: str) -> dict:

aisdc/config/target.py → sacroml/config/target.py

@@ -6,8 +6,8 @@
 import os
 import sys
 
-from aisdc.attacks.target import Target
-from aisdc.config import utils
+from sacroml.attacks.target import Target
+from sacroml.config import utils
 
 arrays_pro = ["X_train", "y_train", "X_test", "y_test"]
 arrays_raw = ["X", "y", "X_train_orig", "y_train_orig", "X_test_orig", "y_test_orig"]