oujago 0.1.14
Coding Makes Life Easier


======
Oujago
======

Coding makes life easier. This is a factory contains commonly used
algorithms.


Installation
============

Install ``oujago`` using pip:

.. code-block:: bash

    $> pip install oujago

Install from source code:

.. code-block:: bash

    $> python setup.py clean --all install


Download data from `BaiDuYun <https://pan.baidu.com/s/1i57RVLj>`_:

.. code-block::

    https://pan.baidu.com/s/1i57RVLj



Documentation
=============

Available online documents: `latest <http://oujago.readthedocs.io/en/latest/>`_,
`stable <http://oujago.readthedocs.io/en/stable/>`_,
and `stable <http://oujago.readthedocs.io/en/develop/>`_.


NLP Part
========

Hanzi Converter
---------------

繁简转换器.

.. code-block:: shell

    >>> from oujago.nlp import FJConvert
    >>> FJConvert.to_tradition('繁简转换器')
    '繁簡轉換器'
    >>> FJConvert.to_simplify('繁簡轉換器')
    '繁简转换器'
    >>> FJConvert.same('繁简转换器', '繁簡轉換器')
    >>> True
    >>> FJConvert.same('繁简转换器', '繁簡轉換')
    >>> False


Chinese Segment
---------------

Support ``jieba``, ``LTP``, ``thulac``, ``pynlpir`` etc. public segmentation methods.

.. code-block:: shell

    >>> from oujago.nlp import seg
    >>>
    >>> sentence = "这是一个伸手不见五指的黑夜。我叫孙悟空，我爱北京，我爱Python和C++。"
    >>> seg(sentence, mode='ltp')
    ['这', '是', '一个', '伸手', '不', '见', '五', '指', '的', '黑夜', '。', '我', '叫', '孙悟空',
    '，', '我', '爱', '北京', '，', '我', '爱', 'Python', '和', 'C', '+', '+', '。']
    >>> seg(sentence, mode='jieba')
    ['这是', '一个', '伸手不见五指', '的', '黑夜', '。', '我', '叫', '孙悟空', '，', '我', '爱',
    '北京', '，', '我', '爱', 'Python', '和', 'C++', '。']
    >>> seg(sentence, mode='thulac')
    ['这', '是', '一个', '伸手不见五指', '的', '黑夜', '。', '我', '叫', '孙悟空', '，',
    '我', '爱', '北京', '，', '我', '爱', 'Python', '和', 'C', '+', '+', '。']
    >>> seg(sentence, mode='nlpir')
    ['这', '是', '一个', '伸手', '不见', '五指', '的', '黑夜', '。', '我', '叫', '孙悟空',
    '，', '我', '爱', '北京', '，', '我', '爱', 'Python', '和', 'C++', '。']
    >>>
    >>> seg("这是一个伸手不见五指的黑夜。")
    ['这是', '一个', '伸手不见五指', '的', '黑夜', '。']
    >>> seg("这是一个伸手不见五指的黑夜。", mode='ltp')
    ['这', '是', '一个', '伸手', '不', '见', '五', '指', '的', '黑夜', '。']
    >>> seg('我不喜欢日本和服', mode='jieba')
    ['我', '不', '喜欢', '日本', '和服']
    >>> seg('我不喜欢日本和服', mode='ltp')
    ['我', '不', '喜欢', '日本', '和服']


Part-of-Speech
--------------

.. code-block:: shell

    >>> from oujago.nlp.postag import pos
    >>> pos('我不喜欢日本和服', mode='jieba')
    ['r', 'd', 'v', 'ns', 'nz']
    >>> pos('我不喜欢日本和服', mode='ltp')
    ['r', 'd', 'v', 'ns', 'n']


NN Part
=======

SRU (PyTorch)
-------------

Require packages: ``cupy``, ``pynvrtc``, ``pytorch``.
Comes from `<Training RNNs as Fast as CNNs> <https://arxiv.org/abs/1709.02755>`_ .

The usage of SRU is similar to ``torch.nn.LSTM``.

.. code-block:: python

    import torch
    from torch.autograd import Variable
    from oujago.nn.sru import SRU, SRUCell

    # input has length 20, batch size 32 and dimension 128
    x = Variable(torch.FloatTensor(20, 32, 128).cuda())

    input_size, hidden_size = 128, 128

    rnn = SRU(input_size, hidden_size,
        num_layers = 2,          # number of stacking RNN layers
        dropout = 0.0,           # dropout applied between RNN layers
        rnn_dropout = 0.0,       # variational dropout applied on linear transformation
        use_tanh = 1,            # use tanh?
        use_relu = 0,            # use ReLU?
        bidirectional = False    # bidirectional RNN ?
    )
    rnn.cuda()

    output, hidden = rnn(x)      # forward pass

    # output is (length, batch size, hidden size * number of directions)
    # hidden is (layers, batch size, hidden size * number of directions)


See Language Modeling example: `sru_language_modeling.py <apps/sru_language_modeling>`_


Utils Part
==========

Common Utils
------------


Check weather this ``object`` is an iterable.

.. code-block:: shell

    >>> from oujago.utils.common import is_iterable
    >>> is_iterable([1, 2])
    True
    >>> is_iterable((1, 2))
    True
    >>> is_iterable("123")
    True
    >>> is_iterable(123)
    False


Time Utils
----------

Get current time.

.. code-block:: shell

    >>> from oujago.utils.time import now
    >>> now()
    "2017-04-26-16-44-56"
    >>>
    >>> from oujago.utils.time import today
    >>> today()
    "2017-04-26"

Change the total time into the normal time format.

.. code-block:: shell

    >>> from oujago.utils.time import time_format
    >>> time_format(36)
    "36 s"
    >>> time_format(90)
    "1 min 30 s "
    >>> time_format(5420)
    "1 h 30 min 20 s"
    >>> time_format(20.5)
    "20 s 500 ms"
    >>> time_format(864023)
    '10 d 23 s'





Change Log
==========


0.1.13
------

* PyTorch alexnet, at `2018.03.30`.
* PyTorch densenet, at `2018.03.30`.
* PyTorch inception, at `2018.03.30`.
* PyTorch resnet, at `2018.03.30`.
* PyTorch squeezenet, at `2018.03.30`.
* PyTorch vgg, at `2018.03.30`.
* keras resnet, at `2018.03.30`.


0.1.12
------

* PyTorch SRU Layer  , at `2018.01.21`.
* Format README  , at `2018.01.21`.

0.1.9
-----

* NLP moran NER    , at `2017.07.06`.
* NLP thulac segment     , at `2017.07.06`.
* NLP thulac postag     , at `2017.07.06`.


0.1.8
-----

* NLP moran segment    , at `2017.06.26`.
* NLP moran postag    , at `2017.06.26`.


0.1.7
-----

* NLP jieba segment    , at `2017.06.20`.
* NLP LTP segment     , at `2017.06.20`.
* NLP jieba POSTag    , at `2017.06.20`.
* NLP LTP POSTag    , at `2017.06.20`.
* NLP LTP NER    , at `2017.06.20`.
* NLP LTP Dependecy Parse    , at `2017.06.20`.
* NLP LTP Semantic Role Labeling    , at `2017.06.20`.


0.1.6
-----

* Hanzi Converter    , at `2017.06.19`.
* Chinese Stopwords    , at `2017.06.19`.