Update travis and package requirements

.travis.yml

@@ -29,7 +29,7 @@ addons:
 install:
   - >
       conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION
-      numpy scipy nose pandas matplotlib h5py cvxopt
+      numpy scipy nose pandas matplotlib h5py
   - source activate test-environment
   - pip install pypandoc pylint
   - pip install -r requirements.txt
@@ -45,8 +45,6 @@ env:
 script:
   # download data and models, then run tests
   - mhcflurry-downloads fetch
-  - mhcflurry-downloads fetch models_class1_allele_specific_ensemble
-  - mhcflurry-downloads fetch models_class1_allele_specific_single
   - mhcflurry-downloads info  # just to test this command works
   - nosetests test --with-coverage --cover-package=mhcflurry  && ./lint.sh
 after_success:

README.md

@@ -100,19 +100,6 @@ early stopping. All models use the same [architecture](downloads-generation/mode
 predictions are taken to be the geometric mean of the nM binding affinity
 predictions of the individual models. The training script is [here](downloads-generation/models_class1/GENERATE.sh).
 
-
-## Problems and Solutions
-
-###  undefined symbol
-If you get an error like:
-
-```
-ImportError: _CVXcanon.cpython-35m-x86_64-linux-gnu.so: undefined symbol: _ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEED1Ev
-```
-
-Try installing cvxpy using conda instead of pip.
-
-
 ## Environment variables
 
 The path where MHCflurry looks for model weights and data can be set with the `MHCFLURRY_DOWNLOADS_DIR` environment variable. This directory should contain subdirectories like "models_class1".
\ No newline at end of file

requirements.txt

+six
 numpy>= 1.11
 pandas>=0.13.1
+Keras==2.0.4
 appdirs
 theano
-keras
 scikit-learn
-h5py
 typechecks
-joblib
-cherrypy
-bottle
-six
 mhcnames

setup.py

@@ -49,15 +49,14 @@ with open('mhcflurry/__init__.py', 'r') as f:
 
 if __name__ == '__main__':
     required_packages = [
+        'six',
         'numpy>=1.11',
         'pandas>=0.13.1',
+        'Keras==2.0.4',
         'appdirs',
         'theano',
-        'keras',
         'scikit-learn',
-        'h5py',
         'typechecks',
-        'six',
         'mhcnames',
     ]
     if PY2: