release: Initial Open Source Release

Signed-off-by: John Andersen <john.s.andersen@intel.com>

.coveragerc

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+[run]
+source =
+    dffml
+    tests
+branch = True
+
+[report]
+exclude_lines =
+    no cov
+    no qa
+    noqa
+    pragma: no cover
+    if __name__ == .__main__.:

.dockerignore

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+.venv/
+.eggs/
+.mypy_cache/
+*/*.venv/
+*/*.eggs/
+*/*.mypy_cache/

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

.gitignore

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+*.log
+*.pyc
+.cache/
+.coverage
+.idea/
+.vscode/
+*.egg-info/
+build/
+dist/
+docs/build/
+venv/
+wheelhouse/
+*.png
+*.eggs
+*.swp
+.mypy_cache/
+*.egg-info/
+htmlcov/
+.venv/

.pylintrc

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+[MESSAGES CONTROL]
+disable=too-few-public-methods,
+        bad-continuation

.travis.yml

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+language: python
+dist: xenial
+python:
+  - "3.7"
+addons:
+  apt:
+    packages:
+      - git
+      - subversion
+      - cloc
+env:
+  global:
+    - LOGGING=debug
+  matrix:
+    - PLUGIN=.
+    - PLUGIN=model/tensorflow
+    - PLUGIN=feature/git
+before_install:
+  # Update cloc (xenial has bad version)
+  - |
+    curl -o /tmp/cloc -sSL https://github.com/AlDanial/cloc/raw/1.80/cloc
+    sudo cp /tmp/cloc /usr/bin/cloc
+    sudo chmod 755 /usr/bin/cloc
+  - |
+script:
+  - python setup.py install && cd $PLUGIN && python setup.py test

CHANGELOG.md

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+# Changelog
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [Unreleased]
+
+- Nothing yet...
+
+## [0.1.0] - 2019-03-07
+### Added
+- Feature class to collect a feature in a dataset
+- Git features to collect feature data from Git repos
+- Model class to wrap implementations of machine learning models
+- Tensorflow DNN model for generic usage of the DNN estimator
+- CLI interface and framework
+- Source class to manage dataset storage

Dockerfile

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+FROM python:3.7
+
+WORKDIR /usr/src/dffml
+
+COPY . .
+RUN pip install --no-cache-dir . && \
+  cp scripts/docker-entrypoint.sh /usr/bin/ && \
+  chmod 755 /usr/bin/docker-entrypoint.sh
+
+ENTRYPOINT ["/usr/bin/docker-entrypoint.sh"]

LICENSE

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+Copyright (c) 2017-2019 Intel
+
+MIT License
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

MANIFEST.in

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+include LICENSE
+include README.rst

README.rst

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+Data Flow Facilitator for Machine Learning (dffml)
+==================================================
+
+.. image:: https://travis-ci.org/intel/dffml.svg
+    :target: https://travis-ci.org/intel/dffml
+.. image:: https://bestpractices.coreinfrastructure.org/projects/2594/badge
+    :target: https://bestpractices.coreinfrastructure.org/projects/2594
+
+Is DFFML Right For Me?
+----------------------
+
+If you answer yes to any of these questions DFFML can make your life easier.
+
+- Dataset Generation
+
+  - Need to generate a dataset
+  - Need to run asynchronous operations in order to gather dataset (http
+    requests, interaction with command line utilities, etc.)
+
+- Models
+
+  - Want to quickly prototype how machine learning could be used on a dataset
+    without writing a model
+  - Need to write a finely tuned model by interacting with low level APIs of
+    popular machine learning frameworks.
+
+- Storage
+
+  - Need a way to use datasets which could be stored in different locations or
+    formats.
+
+About
+-----
+
+DFFML facilitates data generation, model creation, and use of models via
+services. See `Architecture`_ to learn how it works.
+
+- Facilitates data collection, model creation, and use of models via services.
+- Provides plumbing to facilitate the collection of feature data to create
+  datasets.
+- Allows developers to define their ML models via a standardized API.
+
+  - This let's users try different libraries / models to compare performance.
+
+- Plugin based
+
+  - Features which gather feature data (Number of Git Authors, etc.)
+  - Models which expose ML models via the standard API (Tensorflow, Scikit,
+    etc.)
+  - Sources which load and store feature data (CSV, JSON, MySQL, etc.)
+
+The plumbing DFFML provides enables users to swap out models and features,
+in order to quickly prototype.
+
+Installation
+------------
+
+DFFML currently should work with Python 3.6. However, only Python 3.7 is
+officially supported. This is because there are a lot of nice helper methods
+Python 3.7 implemented that we intend to use instead of re-implementing.
+
+.. code-block:: bash
+
+    python3.7 -m pip install -U dffml
+
+You can also install the Features for Git Version Control, and Models for
+Tensorflow Library all at once.
+
+- `DFFML Features for Git Version Control <feature/git/README.rst>`_
+- `DFFML Models for Tensorflow Library <model/tensorflow/README.rst>`_
+
+If you want a quick how to on the iris dataset head to the
+`DFFML Models for Tensorflow Library <model/tensorflow/README.rst>`_ repo.
+
+.. code-block:: bash
+
+    python3.7 -m pip install -U dffml[git,tensorflow]
+
+Docker Build
+------------
+
+This is a good option if you don't want to deal with installing Python 3.7.
+
+.. code-block:: bash
+
+    docker build -t dffml .
+
+You can then alias dffml to run the docker container.
+
+.. code-block:: bash
+
+    alias dffml="docker run --rm -ti -v $HOME/.local:/home/$USER/.local/ -v $PWD:/workdir -w /workdir -e UID=$(id -u) -e USER=$USER dffml"
+
+This creates an alias that takes your current working directory and mounts it
+into ``/workdir`` as well as your ``$HOME/.local`` to the same in the container.
+
+With the alias, you can run ``dffml`` commands as you would if installed via
+``pip``.
+
+.. code-block:: bash
+
+    dffml list
+
+Keep in mind that if you're working on files they can only be ones in your
+current working directory, and if you want to access network resources and they
+are on your host, you'll have to talk to ``172.17.0.1`` (docker0 inet address)
+instead of ``localhost`` or ``127.0.0.1``.
+
+The purpose of mounting ``$HOME/.local`` is so that if you want to
+``pip install`` anything, you can, and it will persist between invocations due
+to that being on the host.
+
+If you wan to run ``pip`` you can put it after ``dffml``.
+
+.. code-block:: bash
+
+    dffml pip install example
+
+Hacking
+-------
+
+Then install in development mode to the virtualenv and development dependencies.
+
+.. code-block:: bash
+
+    git clone git@github.com:intel/dffml
+    cd dffml
+    pip install --user -e .[git,tensorflow]
+
+Usage
+-----
+
+See `DFFML Models for Tensorflow Library <model/tensorflow/README.rst>`_ repo
+until documentation here is updated with a generic example.
+
+Testing
+-------
+
+.. code-block:: bash
+
+    python3.7 setup.py test
+
+Architecture
+------------
+
+When applying Machine Learning to a new problem developers must first collect
+data for models to train on. DFFML facilitates the collection of feature data
+to create datasets for models to learn on.
+
+.. image:: https://github.com/intel/dffml/raw/master/docs/arch.png
+
+DFFML's architecture can be thought of similarly to a search engine. Each
+**Feature** a developer defines searches for data associated with the unique key
+its provided with. Once the data is found it is added to a **Repo** (repository)
+associated with that unique key. A **Feature**'s search for data is dubbed
+*evaluation*. A **Repo** holds the results of each **Feature**'s evaluation.
+Results are stored under their respective **Feature** names.
+
+To define machine learning a model within DFFML, users create a **Model**.
+Models are responsible for training, assessing accuracy, and making
+predictions. After evaluation a **Repo** can be used by a **Model** for any of
+those tasks. Defining a machine learning model as a **Model** allows users to
+quickly compare accuracy of various models on their gathered dataset.
+
+Once the best most accurate model is known, users can easily integrate use of
+the model into existing applications via the Python API, or a **Service**.
+Services provide applications with ways to access the DFFML API over various
+protocols and deployment scenarios.
+
+Repo
+----
+
+A repo is a repository of information. It is instantiated with a source URL
+which represents or points to where more information on it can be found.
+
+Every repo has (or wants) a classification. Those which already have
+classifications can be used to train Models. The classification of the repo is
+what Education will ask it's models to make predictions on.
+
+Feature
+------
+
+Features are given a repo, containing at the minimum a source URL for it,
+and produce a list of results which represent the evaluation of that feature.
+
+Not all methods are applicable to all repos. As such, all Features implement the
+``applicable`` method.
+
+Feature is the abstract base class for all features. New features must be
+derived from this class and implement the fetch, parse, and calc methods. These
+methods are always called in order by the evaluator. However, they are executed
+in parallel with the same stages of other features.
+
+A feature is provided with a repo
+and is expected to fetch any data it needs to calculate itself when fetch
+is called. All data fetched should be stored in tempdir() if it must reside
+on disk.
+
+Once the appropriate data is fetched the parse method is responsible for
+storing the parts of that data which will be used to calculate in the
+subclass
+
+.. code-block:: python
+
+    from dffml.feature import Feature
+
+    class StringByFT(Feature):
+
+        async def fetch(self):
+            self.__value = '42'
+
+        async def parse(self):
+            self.__value = int(self.__value)
+
+The calc method then uses variables set in parse to calculate the feature.
+
+.. code-block:: python
+
+    async def calc(self):
+        return self.__value * 42
+
+.. code-block:: python
+
+    entry_points={
+        'dffml.feature': [
+            'string_by_42 = mypackage.string_by_42:StringByFT',
+        ],
+    },
+
+Source
+------
+
+Repos come from a source. Sources may contain more information on a repo than
+just it's source URL. Sources are responsible for providing the repos they
+contain and updating those repos upon request.
+
+Model
+-------
+
+Models are feed classified repos from which they learn from during their
+training phase. After training they can be used to make a prediction about the
+classification of a repo.
+
+License
+-------
+
+dffml is distributed under the MIT License, see ``LICENSE``.
+
+Legal
+-----
+
+..
+
+    This software is subject to the U.S. Export Administration Regulations and
+    other U.S. law, and may not be exported or re-exported to certain countries
+    (Cuba, Iran, Crimea Region of Ukraine, North Korea, Sudan, and Syria) or to
+    persons or entities prohibited from receiving U.S. exports (including
+    Denied Parties, Specially Designated Nationals, and entities on the Bureau
+    of Export Administration Entity List or involved with missile technology or
+    nuclear, chemical or biological weapons).