Merge pull request #22 from fau-klue/v0.1.7

V0.1.7

.github/workflows/python-build.yml

@@ -1,18 +1,14 @@
 name: build
 
-on:
-  push:
-    branches: [ master ]
-  pull_request:
-    branches: [ master ]
+on: [push, pull_request, workflow_dispatch]
 
 jobs:
   build:
 
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ 3.8, 3.9 ]
+        python-version: [ 3.6, 3.7, 3.8, 3.9 ]
 
     steps:
     - uses: actions/checkout@v2

Pipfile

@@ -13,3 +13,4 @@ twine = "==3.4.2"
 
 [packages]
 pandas = "*"
+scipy = "*"

README.md

@@ -9,15 +9,17 @@ http://www.collocations.de/AM/index.html
 
 # Installation
 
-**Prerequisites:** As of version v0.1.6, this module requires python version 3.8 or above.
+**Prerequisites:**
+- [pandas](https://pandas.pydata.org/)
+- [scipy](https://scipy.org/) (as of version v0.1.7)
 
-**Using pip:**
+**Installation using pip:**
 
     pip install association-measures
 
-**From Source:**
+**Installation from source (requires Cython):**
 
-    # Compile Cython code (requires Cython)
+    # Compile Cython code
     python setup.py build_ext --inplace
 
     # Cython already compiled
@@ -52,7 +54,7 @@ id
 5        arrived  2  15334    5  191998
 ```
 
-Combinations of both are possible, but you should make sure to include four independent values for each rows, and that all of the following equations hold:
+Combinations of both are possible, but you should make sure to include four independent values, and that all of the following equations hold:
 
 - f = O11
 - f1 = O11 + O12
@@ -115,15 +117,21 @@ You can thus `join` the results directly to the input.
 
 ## Association Measures
 
-As of version 0.1.6, the following association measures are supported:
-
-- z-score
-- t-score
-- mutual information
-- dice
-- log-likelihood
-- log-ratio
-- conservative log-ratio
+As of version 0.1.7, the following association measures are supported:
+
+- asymptotic hypothesis tests:
+  - z-score (`z_score`)
+  - t-score (`t_score`)
+  - log-likelihood (`log_likelihood`)
+  - simple-ll (`simple_ll`)
+- point estimates of association strength:
+  - log-ratio (`log_ratio`)
+  - Dice coefficient (`dice`)
+- information theory:
+  - mutual information (`mutual_information`)
+  - local MI (`local_mutual_information`)
+- conservative estimates:
+  - conservative log-ratio (`conservative_log_ratio`)
 
 You can either calculate specific measures:
 
@@ -144,12 +152,13 @@ or calculate all available measures:
 ```python3
 >>> df = am.calculate_measures(df)
 >>> df_am.head()
-               z_score   t_score      dice  log_likelihood  mutual_information  log_ratio  conservative_log_ratio
-appreciated   2.102442  0.840269  0.000130        2.448757            0.796611   3.526202                     0.0
-certain      -0.834636 -0.976603  0.000906       -0.829802           -0.136442  -0.486622                     0.0
-measuring     0.451726  0.361077  0.000130        0.191806            0.194551   0.718847                     0.0
-particularly -0.905150 -1.240035  0.000260       -1.059386           -0.273427  -0.965651                     0.0
-arrived       2.533018  1.131847  0.000261        3.879126            0.699701   2.941240                     0.0
+               z_score   t_score  log_likelihood  simple_ll      dice  log_ratio  mutual_information  local_mutual_information  conservative_log_ratio
+item
+appreciated   2.102442  0.840269        2.448757   1.987992  0.000130   3.526202            0.796611                  0.796611                     0.0
+certain      -0.834636 -0.976603       -0.829802  -0.769331  0.000906  -0.486622           -0.136442                 -0.955094                     0.0
+measuring     0.451726  0.361077        0.191806   0.173788  0.000130   0.718847            0.194551                  0.194551                     0.0
+particularly -0.905150 -1.240035       -1.059386  -0.988997  0.000260  -0.965651           -0.273427                 -0.546853                     0.0
+arrived       2.533018  1.131847        3.879126   3.243141  0.000261   2.941240            0.699701                  1.399402                     0.0
 ```
 
 # Development
@@ -172,13 +181,3 @@ make test
 # Coverage
 make coverage
 ```
-
-## Publish in PyPI
-
-```bash
-make test && make compile
-
-make build
-
-pipenv run twine upload dist/<PATH-TO-NEW-VERSION>.tar.gz
- ```