Make pypi package (#20)

* Make pypi package

* Hack for strange segfault error on linux

.github/workflows/publish_pypi.yml

@@ -0,0 +1,26 @@
+name: Publish Python Package to PyPI
+
+on:
+  release:
+    types: [created]
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v1
+    - name: Set up Python
+      uses: actions/setup-python@v1
+      with:
+        python-version: 3.7
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install setuptools wheel twine
+    - name: Build
+      run: |
+        python setup.py sdist bdist_wheel
+    - name: Publish to PyPI
+      uses: pypa/gh-action-pypi-publish@master
+      with:
+        password: ${{ secrets.PYPI_PASSWORD }}

README.md

@@ -30,7 +30,11 @@ In addition, some useful preprocessing tools are available in the `pycox.preproc
 
 ## Get Started
 
-To get started you first need to [install the package](#installation).
+To get started you first need to install [PyTorch](https://pytorch.org/get-started/locally/).
+You can then install **pycox** with 
+```sh
+pip install pycox
+```
 
 We then recommend to start with [THIS INTRODUCTION NOTEBOOK](https://nbviewer.jupyter.org/github/havakv/pycox/blob/master/examples/01_introduction.ipynb), which explains the general usage of the package in terms of preprocessing, creation of neural networks, model training, and evaluation procedure.
 The notebook use the `LogisticHazard` method for illustration, but most of the principles generalize to the other methods.

tests/test_utils.py

@@ -1,6 +1,5 @@
 import pytest
 import numpy as np
-from lifelines import KaplanMeierFitter
 from pycox import utils
 
 def test_kaplan_meier():
@@ -13,6 +12,7 @@ def test_kaplan_meier():
 @pytest.mark.parametrize('n', [10, 85, 259])
 @pytest.mark.parametrize('p_cens', [0, 0.3, 0.8])
 def test_kaplan_meier_vs_lifelines(n, p_cens):
+    from lifelines import KaplanMeierFitter
     np.random.seed(0)
     durations = np.random.uniform(0, 100, n)
     events = np.random.binomial(1, 1 - p_cens, n).astype('float')