v0.3.1

.github/workflows/python-build.yml

@@ -8,7 +8,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ 3.8, 3.9, "3.10", "3.11", "3.12" ]
+        python-version: [ 3.9, "3.10", "3.11", "3.12", "3.13" ]
 
     steps:
     - uses: actions/checkout@v2
@@ -19,7 +19,6 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        python -m pip install -q pipenv
         make install
     - name: Build
       run: |

.github/workflows/python-publish.yml

@@ -17,7 +17,6 @@ jobs:
     - name: Install build dependencies
       run: |
         python -m pip install --upgrade pip
-        python -m pip install -q pipenv
         make install
     - name: Build
       run: |

association_measures/comparisons.py

@@ -0,0 +1,17 @@
+"""
+comparison of ranked lists
+
+"""
+
+
+# from rbo import RankingSimilarity
+
+
+# def compare_rbo(df1, df2, p):
+
+#     return RankingSimilarity(df1.index, df2.index).rbo(p=p)
+
+
+# def compare_gwets_ac1(df1, df2):
+
+#     pass

association_measures/resources/measures.tsv

@@ -0,0 +1,18 @@
+association_measures	name	abbreviation	category
+O11	observed frequency	obs.	raw
+E11	expected frequency	exp.	raw
+ipm	observed instances per million	obs. (ipm)	raw
+ipm_expected	expected instances per million	exp. (ipm)	raw
+conservative_log_ratio	Conservative Log Ratio	LRC	conservative estimates
+log_likelihood	Log-Likelihood Ratio	LLR	asymptotic hypothesis tests
+z_score	z-score	z-score	asymptotic hypothesis tests
+t_score	t-score	t-score	asymptotic hypothesis tests
+simple_LL	simple Log-Likelihood Ratio	simple LLR	asymptotic hypothesis tests
+dice	Dice	Dice	point estimates of association strength
+log_ratio	Log Ratio	LR	point estimates of association strength
+min_sensitivity	Minimum Sensitivity	MS	point estimates of association strength
+liddel	Liddell	Liddell	point estimates of association strength
+mutual_information	Mutual Information	MI	information theory
+local_mutual_information	Local Mutual Information	LMI	information theory
+hypergeometric_likelihood	Hypergeometric Likelihood	HL	likelihood measures
+binomial_likelihood	Binomial Likelihood	BL	likelihood measures

association_measures/version.py

@@ -2,5 +2,5 @@
 Association measures are mathematical formulae that interpret cooccurrence frequency data.
 """
 
-VERSION = (0, 3, 0)
+VERSION = (0, 3, 1)
 __version__ = '.'.join(map(str, VERSION))

makefile

@@ -1,18 +1,34 @@
 .PHONY: performance test lint coverage build
 
 install:
-	pipenv install --dev
-performance:
-	pipenv run python3 performance.py
-test:
-	pipenv run pytest -v
+	python3 -m venv venv && \
+	. venv/bin/activate && \
+	pip3 install -U pip setuptools wheel && \
+	pip3 install -r requirements-dev.txt
+
 lint:
-	pipenv run pylint --rcfile=.pylintrc association_measures/*.py
+	. venv/bin/activate && \
+	pylint --rcfile=.pylintrc association_measures/*.py
+
+test:
+	. venv/bin/activate && \
+	pytest -s -v
+
+performance:
+	. venv/bin/activate && \
+	python3 performance.py
+
 coverage:
-	pipenv run pytest --cov-report term-missing -v --cov=association_measures/
+	. venv/bin/activate && \
+	pytest --cov-report term-missing -v --cov=association_measures/
+
 compile:
-	pipenv run python3 setup.py build_ext --inplace
+	. venv/bin/activate && \
+	python3 setup.py build_ext --inplace
+
 build:
-	pipenv run python3 setup.py sdist
+	. venv/bin/activate && \
+	python3 setup.py sdist
+
 clean:
 	rm -rf *.egg-info build/ association_measures/*.so association_measures/*.c dist/

requirements-dev.txt

@@ -0,0 +1,8 @@
+-r requirements.txt
+pytest==7.4.0
+pylint==2.17.5
+pytest-cov==4.1.0
+wheel==0.45.1
+twine==6.1.0
+setuptools==75.8.2
+cython==3.0.12

requirements.txt

@@ -0,0 +1,3 @@
+pandas>=2.2.2,<3.0
+numpy>=2.0,<3.0
+scipy>=1.13.0,<2.0

setup.py

@@ -11,11 +11,13 @@
 EMAIL = '[email protected]'
 AUTHOR = 'Philipp Heinrich & Markus Opolka'
 
-REQUIRES_PYTHON = '>=3.8'
+REQUIRES_PYTHON = '>=3.9'
 REQUIRED = [
     'wheel',
-    'pandas',
-    'scipy'
+    'pandas>=2.2.2,<3.0',
+    'numpy>=2.0,<3.0',
+    'scipy>=1.13.0,<2.0',
+    # 'rbo>=0.1.3,<0.2'
 ]
 
 here = os.path.abspath(os.path.dirname(__file__))
@@ -97,11 +99,11 @@ def run(self):
         'License :: OSI Approved :: MIT License',
         'Programming Language :: Python',
         'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 3.8',
         'Programming Language :: Python :: 3.9',
         'Programming Language :: Python :: 3.10',
         'Programming Language :: Python :: 3.11',
         'Programming Language :: Python :: 3.12',
+        'Programming Language :: Python :: 3.13',
         'Programming Language :: Cython',
     ],
 )

tests/test_nas.py

@@ -0,0 +1,13 @@
+import association_measures.measures as am
+from pandas import DataFrame
+
+
+def test_nas():
+
+    d = DataFrame(data={'O11': 0, 'O12': 10, 'O21': 10, 'O22': 100}, index=['test'])
+    scores = am.score(d)
+    print(scores[['mutual_information', 'local_mutual_information']])
+
+    d = DataFrame(data={'O11': 0, 'O12': 10, 'O21': 0, 'O22': 100}, index=['test'])
+    scores = am.score(d)
+    print(scores[['ipm', 'ipm_expected']])