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`.