Merge pull request #100 from CamDavidsonPilon/v0.5.0

v0.5.0

.travis.yml

@@ -25,12 +25,12 @@ install:
   - conda info -a
 
   # Should match requirements.txt
-  - conda create -n test-environment python=$TRAVIS_PYTHON_VERSION pip numpy scipy pandas matplotlib
+  - conda create -n test-environment python=$TRAVIS_PYTHON_VERSION pip numpy scipy pandas matplotlib pytest
   - source activate test-environment
   # Build in place so we can run tests
   - python setup.py build_ext --inplace
 # command to run tests
-script: python -m lifelines.tests.test_suite
+script: python -m lifelines.tests -vv
 # Don't want notifications
 notifications:
   email: false

CHANGELOG.md

@@ -1,5 +1,22 @@
 ### Changelogs
 
+#### 0.5.0
+
+- move testing to py.test
+- refactor tests into smaller files
+- make `test_pairwise_logrank_test_with_identical_data_returns_inconclusive` a better test
+- add test for summary()
+- Alternate metrics can be used for `k_fold_cross_validation`.
+
+
+#### 0.4.4
+
+ - Lots of improvements to numerical stability (but something things still need work)
+ - Additions to `summary` in CoxPHFitter.
+ - Make all prediction methods output a DataFrame
+ - Fixes bug in 1-d input not returning in CoxPHFitter
+ - Lots of new tests. 
+
 ####0.4.3
  - refactoring of `qth_survival_times`: it can now accept an iterable (or a scalar still) of probabilities in the q argument, and will return a DataFrame with these as columns. If len(q)==1 and a single survival function is given, will return a scalar, not a DataFrame. Also some good speed improvements.
  - KaplanMeierFitter and NelsonAalenFitter now have a `_label` property that is passed in during the fit.
@@ -45,4 +62,4 @@
  - `CoxFitter` implements Cox Proportional Hazards model in lifelines. 
  - lifelines moves the wheels distributions.
  - tests in the `statistics` module now prints the summary (and still return the regular values)
- - new `BaseFitter` class is inherited from all fitters. 
+ - new `BaseFitter` class is inherited from all fitters. 

README.md

@@ -8,14 +8,14 @@ lifelines
 [What is survival analysis and why should I learn it?](http://lifelines.readthedocs.org/en/latest/Survival%20Analysis%20intro.html)
  Survival analysis was originally developed and applied heavily by the actuarial and medical community. Its purpose was to answer *why do events occur now versus later* under uncertainity (where *events* might refer to deaths, disease remission, etc.). This is great for researchers who are interested in measuring lifetimes: they can answer questions like *what factors might influence deaths?*
 
-But outside of medicine and actuarial science, there are many other interesting and exciting applications of this 
+But outside of medicine and actuarial science, there are many other interesting and exciting applications of this
 lesser-known technique, for example:
 - SaaS providers are interested in measuring customer lifetimes, or time to first behaviours.
 - sociologists are interested in measure political parties lifetimes, or relationships, or marriages
 - Businesses are interested in what variables affect lifetime value
 
-*lifelines* is a pure Python implementation of the best parts of survival analysis. We'd love to hear if you are using *lifelines*, please leave an Issue and let us know your 
-thoughts on the library. 
+*lifelines* is a pure Python implementation of the best parts of survival analysis. We'd love to hear if you are using *lifelines*, please leave an Issue and let us know your
+thoughts on the library.
 
 ### Installation:
 
@@ -26,8 +26,8 @@ The usual Python data stack: Numpy, Scipy, Pandas (a modern version please), Mat
 
 #### Installing
 
-You can install *lifelines* using 
-      
+You can install *lifelines* using
+
        pip install lifelines
 
 
@@ -36,7 +36,13 @@ Or getting the bleeding edge version with:
        pip install --upgrade --no-deps git+https://github.com/CamDavidsonPilon/lifelines.git
 
 
-from the command line. 
+from the command line.
+
+#### Running the tests
+
+You can optionally run the test suite after install with
+
+    python -m lifelines.tests
 
 
 ### *lifelines* Documentation and an Intro to Survival Analysis
@@ -50,7 +56,7 @@ please check out the [Documentation and Tutorials page](http://lifelines.readthe
 
 ## License
 
-The Feedback MIT License (FMIT) 
+The Feedback MIT License (FMIT)
 
 Copyright (c) 2013, Cameron Davidson-Pilon
 

lifelines/datasets/CuZn-LeftCensoredDataset.csv

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