{ "info": { "author": "boredbird", "author_email": "1002937942@qq.com", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: Implementation", "Topic :: Software Development :: Libraries" ], "description": "woe\n===\n\n.. image:: https://travis-ci.org/justdoit0823/pywxclient.svg?branch=master\n :target: https://travis-ci.org/justdoit0823/pywxclient\n\nversion: 0.1.4\n\nTools for WoE Transformation mostly used in ScoreCard Model for credit rating\n\nInstallation\n--------------------------------\n\nWe can simply use pip to install, as the following:\n\n.. code-block:: bash\n\n $ pip install woe\n\nor installing from git\n\n.. code-block:: bash\n\n $ pip install git+https://github.com/boredbird/woe\n\n\nFeatures\n========\n\n * Split tree with IV criterion\n\n * Rich and plentiful model eval methods\n\n * Unified format and easy for output\n\n * Storage of IV tree for follow-up use\n\n\n\n**woe** module function tree\n============================\n\n:: \n\n\t|- __init__\n\t|- config.py \n\t| |-- config\n\t| \t|-- __init__\n\t|\t\t|-- change_config_var_dtype()\n\t|\t\t|-- load_file()\n\t|- eval.py \n\t| |-- compute_ks()\n\t| |-- eval_data_summary()\n\t| |-- eval_feature_detail()\n\t| |-- eval_feature_stability()\n\t| |-- eval_feature_summary()\n\t| |-- eval_model_stability()\n\t| |-- eval_model_summary()\n\t| |-- eval_segment_metrics()\n\t| |-- plot_ks()\n\t| |-- proc_cor_eval()\n\t| |-- proc_validation()\n\t| |-- wald_test()\n\t|- feature_process.py \n\t| |-- binning_data_split()\n\t| |-- calculate_iv_split()\n\t| |-- calulate_iv()\n\t| |-- change_feature_dtype()\n\t| |-- check_point()\n\t| |-- fillna()\n\t| |-- format_iv_split()\n\t| |-- proc_woe_continuous()\n\t| |-- proc_woe_discrete()\n\t| |-- process_train_woe()\n\t| |-- process_woe_trans()\n\t| |-- search()\n\t| |-- woe_trans()\n\t|- ftrl.py \n\t| |-- FTRL()\n\t| |-- LR()\n\t|- GridSearch.py \n\t| |-- fit_single_lr()\n\t| |-- grid_search_lr_c()\n\t| |-- grid_search_lr_c_main()\n\t| |-- grid_search_lr_validation()\n\n\nExamples\n========\n\nIn the examples directory, there is a simple woe transformation program as tutorials.\n\nOr you can write a more complex program with this `woe` package.\n\nVersion Records\n================\nwoe 0.1.4 2018-03-01\n\t* support py3\n\nwoe 0.1.3 2018-02-09\n\n\t* woe.feature_process.proc_woe_discrete(): fix bug when deal with discrete varibales\n\t* woe.eval.eval_feature_detail(): fix bug : utf-8 output file format\n\t* woe.GridSearch.grid_search_lr_c_main(): add function warper for convenience and high efficiency\n\t* woe.GridSearch.grid_search_lr_c_validation(): monitor the ks performance of training sets and test sets on different 'c'\n\t* supplement examples test scripts\n\n\nwoe 0.1.2 2017-12-05\n\n\t* woe.ftrl.FTRL(): add online learning module\n\nwoe 0.1.1 2017-11-28\n\n\t* woe.config.load_file(): change param data_path to be optional\n\t* woe.eval.eval_feature_stability(): fix bug : psi_dict['stability_index'] computation error\n\t* woe.feature_process.change_feature_dtype(): add friendly tips when encounter a error\n\t* woe.feature_process.calulate_iv(): refactor the code\n\t* woe.feature_process.calculate_iv_split(): refactor the code\n\t* woe.feature_process.binning_data_split(): reduce the number of len() function calls with __len__() and shape attributes;replace namedtuple with dict\n\t* woe.feature_process.fillna(): new added function to fill null value\n\t* woe.GridSearch.grid_search_lr_c(): list of regularization parameter c specified inside the function is changed to the user specified\n\t\nwoe 0.0.9 2017-11-21\n\n\t* Add module : GridSearch for the search of optimal hyper parametric C in 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