Merge pull request #30 from pymc-learn/dev

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pymc-learn · Nov 26, 2018 · 2b49a97 · 2b49a97
2 parents 26d33dd + 10eb15c
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diff --git a/README.rst b/README.rst
@@ -6,7 +6,7 @@ pymc-learn: Practical Probabilistic Machine Learning in Python
     :alt: Pymc-Learn logo
     :align: center
 
-|Travis| |Coverage| |Docs| |License| |Pypi| |Binder|
+|status| |Travis| |Coverage| |Docs| |License| |Pypi| |Binder|
 
 **Contents:**
 
@@ -80,6 +80,13 @@ parameters and predictions.
 Quick Install
 -----------------
 
+``pymc-learn`` requires a working Python interpreter (2.7 or 3.5+).
+It is recommend installing Python and key numerical libraries using the `Anaconda Distribution <https://www.anaconda.com/download/>`_,
+which has one-click installers available on all major platforms.
+
+Assuming a standard Python environment is installed on your machine
+(including pip), ``pymc-learn`` itself can be installed in one line using pip:
+
 You can install ``pymc-learn`` from PyPi using pip as follows:
 
 .. code-block:: bash
@@ -97,6 +104,14 @@ Or from source as follows:
 .. CAUTION::
    ``pymc-learn`` is under heavy development.
 
+   It is recommended installing ``pymc-learn`` in a Conda environment because it
+   provides `Math Kernel Library <https://anaconda.org/anaconda/mkl-service>`_ (MKL)
+   routines to accelerate math functions. If you are having trouble, try using
+   a distribution of Python that includes these packages like
+   `Anaconda <https://www.anaconda.com/download/>`_.
+
+
+
 Dependencies
 ................
 
@@ -282,7 +297,7 @@ Models project: https://github.com/parsing-science/pymc3_models.
    changelog.rst
    cite.rst
 
-.. |Binder| image:: https://mybinder.org/badge.svg
+.. |Binder| image:: https://img.shields.io/badge/try-online-579ACA.svg?logo=data:image/png;base64,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
    :target: https://mybinder.org/v2/gh/pymc-learn/pymc-learn/master?filepath=%2Fdocs%2Fnotebooks?urlpath=lab
 
 .. |Travis| image:: https://travis-ci.com/pymc-learn/pymc-learn.svg?branch=master
@@ -307,3 +322,5 @@ Models project: https://github.com/parsing-science/pymc3_models.
 
 .. |Pypi| image:: https://badge.fury.io/py/pymc-learn.svg
    :target: https://badge.fury.io/py/pymc-learn
+
+.. |status| image:: https://img.shields.io/badge/Status-Beta-blue.svg
diff --git a/docs/install.rst b/docs/install.rst
@@ -1,8 +1,8 @@
 Install pymc-learn
 ===================
 
-``pymc-learn`` requires a working Python interpreter (2.7 or 3.3+).
-It is recommend installing Python and key numerical libraries using the `Anaconda Distribution <https://www.continuum.io/downloads>`_,
+``pymc-learn`` requires a working Python interpreter (2.7 or 3.5+).
+It is recommend installing Python and key numerical libraries using the `Anaconda Distribution <https://www.anaconda.com/download/>`_,
 which has one-click installers available on all major platforms.
 
 Assuming a standard Python environment is installed on your machine
@@ -25,6 +25,12 @@ Or from source as follows:
 .. CAUTION::
    ``pymc-learn`` is under heavy development.
 
+   It is recommended installing ``pymc-learn`` in a Conda environment because it
+   provides `Math Kernel Library <https://anaconda.org/anaconda/mkl-service>`_ (MKL)
+   routines to accelerate math functions. If you are having trouble, try using
+   a distribution of Python that includes these packages like
+   `Anaconda <https://www.anaconda.com/download/>`_.
+
 
 This also installs required dependencies including Theano.
 For alternative Theano installations (e.g., gpu), please see the

diff --git a/docs/modules/neural_networks.rst b/docs/modules/neural_networks.rst
@@ -7,12 +7,13 @@ Neural network models (supervised)
 .. currentmodule:: pmlearn.neural_network
 
 
-.. warning::
+.. NOTE::
 
-    This implementation is not intended for large-scale applications. In particular,
-    scikit-learn offers no GPU support. For much faster, GPU-based implementations,
-    as well as frameworks offering much more flexibility to build deep learning
-    architectures, see  :ref:`related_projects`.
+    Unlike scikit-learn, this implementation of neural networks in pymc-learn is
+    intended for large-scale applications. Pymc-learn relies on Theano for GPU
+    support.
+
+    scikit-learn offers no GPU support.
 
 .. _multilayer_perceptron:
 

diff --git a/pmlearn/__init__.py b/pmlearn/__init__.py
@@ -10,7 +10,7 @@
 See http://pymc-learn.org for complete documentation.
 """
 
-__version__ = '0.0.1.rc0'
+__version__ = '0.0.1.rc2'
 
 __all__ = ['gaussian_process',
            'linear_model',

diff --git a/requirements-dev.txt b/requirements-dev.txt
@@ -1,4 +1,4 @@
-CommonMark==0.5.4
+CommonMark>=0.5.4
 flake8>=3.5.0
 # gpflowopt>=1.1
 jupyter-sphinx>=0.1.3
@@ -11,6 +11,6 @@ pytest-cov>=2.5.1
 pytest>=3.0.7
 recommonmark>=0.4.0
 sphinx>=1.5.5
-sphinx-autobuild==0.7.1
-sphinx-rtd-theme==0.4.2
+sphinx-autobuild>=0.7.1
+sphinx-rtd-theme>=0.4.2
 pymc_learn_sphinx_theme>=0.1.5
diff --git a/requirements.txt b/requirements.txt
@@ -1,13 +1,13 @@
 future>=0.16.0
-joblib==0.11
-matplotlib==2.1.1
-numpy==1.13.1
-numpydoc==0.7.0
-pandas==0.21.1
-pymc3==3.4.1
-scikit-learn==0.19.1
-scipy==1.0.0
-seaborn==0.8.1
+joblib>=0.11
+matplotlib>=2.1.1
+numpy>=1.13.1
+numpydoc>=0.7.0
+pandas>=0.21.1
+pymc3>=3.4.1
+scikit-learn>=0.19.1
+scipy>=1.0.0
+seaborn>=0.8.1
 six>=1.10.0
 theano>=1.0.0
 tqdm>=4.8.4
diff --git a/setup.cfg b/setup.cfg
@@ -13,4 +13,7 @@ python_files = test_*.py
 
 [pydocstyle]
 add-ignore = D100,D104
-convention = numpy
+convention = numpy
+
+[bdist_wheel]
+universal=1