Merge pull request #78 from mheilman/rst_readme

convert README to rst for pypi

CHANGELOG.md

@@ -11,6 +11,10 @@ and this project adheres to [Semantic Versioning](http://semver.org/).
 - Added support for the `sample_weight` keyword argument to the `fit`
   method of MLPClassifier and MLPRegressor (#75).
 
+### Changed
+
+- Switched from requiring TensorFlow 1.x to 1.4.x because 1.5.0 was causing
+  Travis CI failures with Python 3.6 (#78).
 
 ## [2.0.0] - 2018-01-17
 

CONTRIBUTING.md

@@ -9,7 +9,7 @@ contributors are expected to adhere to the
 ## Getting Started
 
 1. Fork it ( https://github.com/civisanalytics/muffnn/fork ).
-2. Install it, with the development dependencies.  See `README.md`.
+2. Install it, with the development dependencies.  See `README.rst`.
 3. Make sure you are able to run the test suite locally (`py.test muffnn`).
 4. Create a feature branch (`git checkout -b my-new-feature`).
 5. Make your change. Don't forget tests.

MANIFEST.in

@@ -1,3 +1,3 @@
 include CHANGELOG.md
 include LICENSE.txt
-include README.md
+include README.rst

README.rst

@@ -0,0 +1,70 @@
+muffnn
+======
+
+`scikit-learn <http://scikit-learn.org>`__-compatible neural network
+models in implemented in `TensorFlow <https://www.tensorflow.org/>`__
+
+Installation
+============
+
+Installation with ``pip`` is recommended:
+
+.. code:: bash
+
+    pip install muffnn
+
+You can install the dependencies via:
+
+.. code:: bash
+
+    pip install -r requirements.txt
+
+If you have trouble installing TensorFlow, see `this
+page <https://www.tensorflow.org/install/>`__ for more details.
+
+For development, a few additional dependencies are needed:
+
+.. code:: bash
+
+    pip install -r dev-requirements.txt
+
+Usage
+=====
+
+Each estimator in the code follows the scikit-learn API. Thus usage
+follows the scikit-learn conventions:
+
+.. code:: python
+
+    from muffnn import MLPClassifier
+
+    X, y = load_some_data()
+
+    mlp = MLPClassifier()
+    mlp.fit(X, y)
+
+    X_new = load_some_unlabeled_data()
+    y_pred = mlp.predict(X_new)
+
+Further, serialization of the TensorFlow graph and data is handled
+automatically when the object is pickled:
+
+.. code:: python
+
+    import pickle
+
+    with open('est.pkl', 'wb') as fp:
+        pickle.dump(est, fp)
+
+Contributing
+============
+
+See ``CONTIBUTING.md`` for information about contributing to this
+project.
+
+License
+=======
+
+BSD-3
+
+See ``LICENSE.txt`` for details.

requirements.txt

@@ -2,5 +2,5 @@ numpy~=1.13
 scipy~=0.19
 scikit-learn~=0.19
 six~=1.10
-tensorflow~=1.3
+tensorflow~=1.4.1
 mock==2.0.0 ; python_version < '3.0'

setup.py

@@ -15,7 +15,7 @@
     url='https://github.com/civisanalytics/muffnn',
     description=('Multilayer Feed-Forward Neural Network (MuFFNN) models with '
                  'TensorFlow and scikit-learn'),
-    long_description=open(os.path.join(THIS_DIR, 'README.md')).read(),
+    long_description=open(os.path.join(THIS_DIR, 'README.rst')).read(),
     include_package_data=True,
     license="BSD-3",
     install_requires=['numpy',