semvec/pypi-packages huggingface dataset:
wpxredisorm 1.0.1
a package use orm to operate redis
License: MIT
Keywords: redis python orm

#redis-orm
一个使用redis的key-value的ORM模型的数据操作，使用redis-py作为底层连接。
# 2016-5-14 17:43:04 
目前已经实现的功能：
1.创建记录
2.根据id进行查找
3.根据多个条件进行与关系查找
待实现:
多个条件进行或查找
删除记录
优化查找功能

# 2016年07月03日17:43:39
实现增删改查基本功能
查询方式使用生成器，只用迭代的时候才会返回具体的记录
stacking 0.1.3
A stacking library for ensemble learning
License: MIT
Keywords: stacking,ensemble,machine learning,cross validation,sckit-learn,XGBoost,Keras,Vowpal Wabbit

Library for stacking(Stacked generalization)
============================================

|PyPI version| |license|

About this library(watch test folder for more detailed)
-------------------------------------------------------

1. Set train and test dataset under data/input.

2. Created features from original dataset need to be under
   data/output/features.

3. Models for stacking are defined in scripts under scripts folder.

4. Need to define created features in that scripts.

5. Just run ``sh run.sh`` (``python scripts/XXX.py``)

--------------

Getting started: 30 seconds to stacking
---------------------------------------

--------------

Installation
------------

To install stacking, ``cd`` to the stacking folder and run the install
command:

::

    sudo python setup.py install

You can also install stacking from PyPI:

::

    pip install stacking

--------------

Tree of files
-------------

-  base\_fixed\_fold.py (class of stacking)
-  data/
-  input/

   -  train.csv (train dataset)
   -  test.csv (test dataset)

-  output/

   -  features/
   -  features.csv (features user created)
   -  temp/
   -  temp.csv (files saved in stacking)

-  scripts/
-  script.csv (main script where concrete models defined)

--------------

Details of scripts
------------------

-  base.py:
-  Base models for stacking are defined here (using
   sklearn.base.BaseEstimator).
-  Some models are defined here. e.g., XGBoost, Keras, Vowpal Wabbit.
-  These models are wrapped as scikit-learn like (using
   sklearn.base.ClassifierMixin, sklearn.base.RegressorMixin).
-  That is, model class has some methods, fit(), predict\_proba(), and
   predict().

New user-defined models can be added here.

Scikit-learn models can be used.

Base model have some arguments.

-  's': Stacking. Saving a oof(out-of-fold)
   prediction({model\_name}\_all\_fold.csv) and average of test
   prediction based on train-fold models({model\_name}\_test.csv). These
   files will be used for next level stacking.

-  't': Training with all data and predict
   test({model\_name}\_TestInAllTrainingData.csv). In this training, no
   validation data are used.

-  'st': Stacking and then training with all data and predict test ('s'
   and 't').

-  'cv': Only cross validation without saving the prediction.

Define several models and its parameters used for stacking. Define task
details on the top of script. Train and test feature set are defined
here. Need to define CV-fold index.

Any level stacking can be defined.

--------------

TODO LIST
---------

Need to be more general library.

Please check isuues!!

.. |PyPI version| image:: https://badge.fury.io/py/stacking.svg
   :target: https://badge.fury.io/py/stacking
.. |license| image:: https://img.shields.io/github/license/mashape/apistatus.svg?maxAge=2592000
   :target: https://github.com/ikki407/stacking/LICENSE