Sklearn plus is an end-to-end neural network API, written in Python. Currently we focus on Natural Language Processing(NLP) tasks. It is based on Tensoflow and scikit-learn. It assembles many preprocessing function, utils and deep learning models about NLP tasks.

It was developed with a focus on expanding sklearn and making using deep learning model to handle NLP tasks easier.

End-To-End. Sklearn plus is based on Tensorflow and implements many classical models. Also it offers easy and consistent API with sklearn style for specific NLP task.

Easy extensibility. Sklearn plus inherits sklearn base classes and follows sklearn API design principles. It is easy to assemble new models, preprocessing functions and utils in it.

• TODO

There are two ways to install Sklearn plus:

1. Install sklearn plus from PyPI(recommended):

sudo pip install sklearn-plus

If you are using a virtualenv, you may want to avoid using sudo:

pip install sklearn-plus

2. Install sklearn plus from the GitHub source:

git clone https://github.com/ybbaigo/sklearn-plus.git
cd sklearn-plus
sudo python setup.py install

Here's a quick guide to submitting your improvements:

1. Write the code. There are three base modules in sklearn plus: preprocess, utils and nerual_network. Write your code in the three modules and reference to the samples in them. We use PEP8 syntax conventions.
2. Write the docstrings. Make sure any code you touch still has up-to-date docstrings and documentation. Please follow the numpydoc_style.
3. Write tests. Your code should have full unit test coverage. If you want to see your PR merged promptly, this is crucial.
4. Make sure all tests are passing. Make sure your code tests can pass on Python 2.7 and Python 3.6 with Tensorflow 1.1.0.
5. Make sure that your PR does not add PEP8 violations. Make sure that your PR does not add PEP8 violations you can check it by flake8:

• install flake8: `pip install flake8`
• check: `flake8 path/to/code/`

6. Commit, use appropriate, descriptive commit messages.
7. Submit your PR. If you have complete (and passing) unit tests as well as proper docstrings/documentation, your PR is likely to be merged promptly.

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.