[work in progress] Dropping Python 2 support (#286)

* refactored away externals

* deleted externals

* removed support for old sklearn

* removed 'base.py', importing from sklearn instead

* Revert "flake8 in CI for tests too. verbose output from tests and slowest"

This reverts commit a80848c.

* Revert "deleted externals"

This reverts commit 5325e2a.

* Revert "Revert "deleted externals""

This reverts commit 5d55acf.

* Revert "Revert "flake8 in CI for tests too. verbose output from tests and slowest""

This reverts commit ffb92a7.

* Revert "removed 'base.py', importing from sklearn instead"

This reverts commit 6813f48.

* Revert "Revert "removed 'base.py', importing from sklearn instead""

This reverts commit 46c0187.

* Revert "Revert "Revert "flake8 in CI for tests too. verbose output from tests and slowest"""

This reverts commit f338929.

* Revert "Revert "Revert "deleted externals"""

This reverts commit d53f165.

* Revert "Revert "deleted externals""

This reverts commit 5d55acf.

* Revert "Revert "flake8 in CI for tests too. verbose output from tests and slowest""

This reverts commit ffb92a7.

* Revert "removed 'base.py', importing from sklearn instead"

This reverts commit 6813f48.

* reverted

* deleted 'externals' folder for py2 compatibility

* mentioned python 3.5+ in README and docs

* updated urllib to Python 3 compatibility

* added forgotten import

* trying to use python 3 on circleCI

* Revert "trying to use python 3 on circleCI"

This reverts commit 731f5fd.

* added some lines to circleCI

* using py3 in circleCI

* removed apt get update from circleCI

* activating env in circleCI

* quiet installation with pip

.circleci/config.yml

@@ -15,18 +15,22 @@ jobs:
             - run:
                 name: Setup Python environment
                 command: |
-                    pip install --upgrade pip
-                    pip install sphinx matplotlib coverage Pillow
-                    pip install sphinx-gallery
-                    pip install scipy
-                    pip install scikit-learn
-                    pip install pandas
+                    conda update --yes conda
+                    conda create -n testenv --yes python=3.7
+                    source activate testenv
+                    pip install --upgrade pip -q
+                    pip install sphinx matplotlib coverage Pillow -q
+                    pip install sphinx-gallery -q
+                    pip install scipy scikit-learn pandas -q
                     pip install -e .
-                    pip install sphinx_bootstrap_theme numpydoc
+                    pip install sphinx_bootstrap_theme numpydoc -q
                     pip install -e git+http://github.com/KordingLab/spykes.git#egg=spykes
+                    python --version
+                    pip list
             - run:
                 name: Build the documentation
                 command: |
+                    source activate testenv
                     cd doc && make html
             - store_artifacts:
                 path: doc/_build/html/

.travis.yml

@@ -1,6 +1,5 @@
 language: python
 env:
-    - PYTHON=2.7
     - PYTHON=3.5
     - PYTHON=3.6
     - PYTHON=3.7

README.rst

@@ -34,8 +34,8 @@ specifically,
    provides elastic net and group lasso regularization, but only for
    linear and logistic regression.
 
-**Pyglmnet** is a response to this fragmentation. Here are some
-highlights.
+**Pyglmnet** is a response to this fragmentation. It runs on Python 3.5+,
+and here are some of the highlights.
 
 -  Pyglmnet provides a wide range of noise models (and paired canonical
    link functions): ``'gaussian'``, ``'binomial'``, ``'multinomial'``,
@@ -168,7 +168,7 @@ optimization and pseudo-code.
 
 Here are
 `slides <https://pavanramkumar.github.io/pydata-chicago-2016>`__ from a
-recent talk at `PyData Chicago
+talk at `PyData Chicago
 2016 <http://pydata.org/chicago2016/schedule/presentation/15/>`__,
 corresponding `tutorial
 notebooks <http://github.com/pavanramkumar/pydata-chicago-2016>`__ and a

doc/index.rst

@@ -7,7 +7,7 @@
 Python implementation of regularized generalized linear models
 ==============================================================
 
-Pyglmnet is a Python library implementing generalized linear models (GLMs)
+Pyglmnet is a Python 3.5+ library implementing generalized linear models (GLMs)
 with advanced regularization options. It provides a wide range of noise models
 (with paired canonical link functions) including gaussian, binomial, multinomial,
 poisson, and softplus. It supports a wide range of regularizers: ridge, lasso,

pyglmnet/base.py

@@ -1,8 +1,6 @@
 import warnings
 from distutils.version import LooseVersion
 
-from .externals.six import iteritems
-
 ##############################################################################
 # adapted from scikit-learn
 
@@ -61,10 +59,8 @@ class BaseEstimator(object):
     @classmethod
     def _get_param_names(cls):
         """Get parameter names for the estimator"""
-        try:
-            from inspect import signature
-        except ImportError:
-            from .externals.funcsigs import signature
+        from inspect import signature
+
         # fetch the constructor or the original constructor before
         # deprecation wrapping if any
         init = getattr(cls.__init__, 'deprecated_original', cls.__init__)
@@ -138,7 +134,7 @@ def set_params(self, **params):
             # Simple optimisation to gain speed (inspect is slow)
             return self
         valid_params = self.get_params(deep=True)
-        for key, value in iteritems(params):
+        for key, value in params.items():
             split = key.split('__', 1)
             if len(split) > 1:
                 # nested objects case

pyglmnet/datasets.py

@@ -8,8 +8,7 @@
 import itertools
 import numpy as np
 from scipy.misc import comb
-
-from .externals.six.moves import urllib
+from urllib.request import urlretrieve
 
 pbar = None
 
@@ -50,7 +49,7 @@ def fetch_tikhonov_data(dpath='/tmp/glm-tools'):
     for fname in fnames:
         url = os.path.join(base_url, "tikhonov/%s" % fname)
         fname = os.path.join(dpath, fname)
-        urllib.request.urlretrieve(url, fname, _reporthook)
+        urlretrieve(url, fname, _reporthook)
 
     return dpath
 
@@ -86,7 +85,7 @@ def fetch_community_crime_data(dpath='/tmp/glm-tools'):
     fname = os.path.join(dpath, 'communities.csv')
     base_url = ("http://archive.ics.uci.edu/ml/machine-learning-databases")
     url = os.path.join(base_url, "communities/communities.data")
-    urllib.request.urlretrieve(url, fname, _reporthook)
+    urlretrieve(url, fname, _reporthook)
 
     # Read in the file
     df = pd.read_csv('/tmp/glm-tools/communities.csv', header=None)
@@ -207,8 +206,8 @@ def create_feature_vector_for_sequence(seq,
         pos_file = tempfile.NamedTemporaryFile(bufsize=0)
         neg_file = tempfile.NamedTemporaryFile(bufsize=0)
 
-    urllib.request.urlretrieve(positive_url, pos_file.name, _reporthook)
-    urllib.request.urlretrieve(negative_url, neg_file.name, _reporthook)
+    urlretrieve(positive_url, pos_file.name, _reporthook)
+    urlretrieve(negative_url, neg_file.name, _reporthook)
 
     positive_sequences = [str(line.strip().upper()) for idx, line in
                           enumerate(pos_file.readlines())