Update docs and version number (0.4.0)

(cherry picked from commit 5cff7ae)

Conflicts:
	README.md
	docs/conf.py

Author: Irina Nicolae

README.md

@@ -1,4 +1,4 @@
-# Adversarial Robustness Toolbox (ART v0.3.0)
+# Adversarial Robustness Toolbox (ART v0.4.0)
 [![Build Status](https://travis-ci.org/IBM/adversarial-robustness-toolbox.svg?branch=master)](https://travis-ci.org/IBM/adversarial-robustness-toolbox) [![Documentation Status](https://readthedocs.org/projects/adversarial-robustness-toolbox/badge/?version=latest)](http://adversarial-robustness-toolbox.readthedocs.io/en/latest/?badge=latest) [![GitHub version](https://badge.fury.io/gh/IBM%2Fadversarial-robustness-toolbox.svg)](https://badge.fury.io/gh/IBM%2Fadversarial-robustness-toolbox) [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/IBM/adversarial-robustness-toolbox.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/IBM/adversarial-robustness-toolbox/context:python) [![Total alerts](https://img.shields.io/lgtm/alerts/g/IBM/adversarial-robustness-toolbox.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/IBM/adversarial-robustness-toolbox/alerts/)
 
 This is a library dedicated to **adversarial machine learning**. Its purpose is to allow rapid crafting and analysis of attacks and defense methods for machine learning models. The Adversarial Robustness Toolbox provides an implementation for many state-of-the-art methods for attacking and defending classifiers.
@@ -86,15 +86,15 @@ This project uses [DCO](https://developercertificate.org/). Be sure to sign off
 #### Example
 
 ```bash
-git commit -s -m 'Informative commit message'
+git commit -s -m 'Add new feature'
 ```
 
 ## Citing ART
 
 If you use ART for research, please consider citing the following reference paper:
 ```
 @article{art2018,
-    title = {Adversarial Robustness Toolbox v0.3.0},
+    title = {Adversarial Robustness Toolbox v0.4.0},
     author = {Nicolae, Maria-Irina and Sinn, Mathieu and Tran, Minh~Ngoc and Rawat, Ambrish and Wistuba, Martin and Zantedeschi, Valentina and Baracaldo, Nathalie and Chen, Bryant and Ludwig, Heiko and Molloy, Ian and Edwards, Ben},
     journal = {CoRR},
     volume = {1807.01069}

docs/conf.py

@@ -24,9 +24,9 @@
 author = 'Maria-Irina Nicolae'
 
 # The short X.Y version
-version = '0.3'
+version = '0.4'
 # The full version, including alpha/beta/rc tags
-release = '0.3.0'
+release = '0.4.0'
 
 
 # -- General configuration ---------------------------------------------------

docs/index.rst

@@ -62,6 +62,7 @@ The following detector of poisoning attacks is also supported:
 
    modules/attacks
    modules/classifiers
+   modules/data_generators
    modules/defences
    modules/detection
    modules/poison_detection

docs/modules/attacks.rst

@@ -7,6 +7,9 @@ Carlini and Wagner Attack
 .. autoclass:: CarliniL2Method
    :members:
 
+.. autoclass:: CarliniLInfMethod
+   :members:
+
 DeepFool
 --------
 .. autoclass:: DeepFool

docs/modules/classifiers.rst

@@ -7,8 +7,8 @@ Base Class
 .. autoclass:: Classifier
    :members:
 
-Concrete Model Wrappers
------------------------
+Framework-Specific Model Wrappers
+---------------------------------
 .. autoclass:: KerasClassifier
    :members:
 .. autoclass:: MXClassifier
@@ -17,3 +17,8 @@ Concrete Model Wrappers
    :members:
 .. autoclass:: TFClassifier
    :members:
+
+Ensemble Wrapper
+----------------
+.. autoclass:: EnsembleClassifier
+   :members:

docs/modules/data_generators.rst

@@ -0,0 +1,23 @@
+:mod:`art.data_generators`
+==========================
+.. automodule:: art.data_generators
+
+Base Class
+----------
+.. autoclass:: DataGenerator
+   :members:
+
+
+Framework-Specific Data Generators
+----------------------------------
+.. autoclass:: KerasDataGenerator
+   :members:
+
+.. autoclass:: MXDataGenerator
+   :members:
+
+.. autoclass:: PyTorchDataGenerator
+   :members:
+
+.. autoclass:: TFDataGenerator
+   :members:

docs/modules/utils.rst

@@ -14,6 +14,6 @@ Preprocess
 ----------
 .. autofunction:: preprocess
 
-Fixing Random Number Generators Seed
-------------------------------------
+Fixing the Seed for Random Number Generators
+--------------------------------------------
 .. autofunction:: master_seed

setup.py

@@ -21,24 +21,22 @@
 with open("README.md", "r") as fh:
     long_description = fh.read()
 
-install_requires = ['h5py',
-                    'numpy',
+install_requires = ['numpy',
                     'scipy',
-                    'keras',
-                    'tensorflow',
                     'six',
                     'setuptools',
                     'scikit-learn']
 
 tests_require = ['mxnet',
+                 'h5py',
                  'keras',
                  'Pillow',
                  'requests',
                  'tensorflow',
                  'torch']
 
 setup(name='Adversarial Robustness Toolbox',
-      version='0.3.0',
+      version='0.4.0',
       description='IBM Adversarial machine learning toolbox',
       long_description=long_description,
       long_description_content_type='text/markdown',