semvec/pypi-packages huggingface dataset:
FastIV 0.1
A package to compute weight of evidence (WOE) and Information Value (IV) easily, with cross-features supported

## 使用指南

### 安装
可以选择通过`setup.py`安装或者通过`pip`安装
```
cd fastiv
python setup.py install
```
或者
```
pip install fastiv
```

### 使用

`FastIV`支持便捷的`Information Value (IV)`计算，同时也支持交叉特征的计算。IV计算是通过使用决策树的方式，确定最终分箱的方法，从而计算出相应的IV值。
通过使用交叉的方式，可以确定组合特征的IV值，从中可以选出更有效的特征，加入到模型中。这一方法的思想，借鉴于第四范式的“AutoCross”。其中特征之间的交叉过程，
是通过决策树的分裂来实现的。
同时，我们提供了将决策树按照一定格式输出的接口，可以方便的将分裂节点打印出来。使用该包的调用代码示例：
```
from fastiv import FastIV

fiv = FastIV(criterion="entropy",
             min_samples_leaf=50,
             max_leaf_nodes=8,
             others_threshold=200)

# 选择要交叉的特征
features = ['feature1', 'feature2']

# 计算iv和iv_dict
iv, iv_dict = fiv.fast_iv(df[features], y)

# 以DataFrame格式输出分箱情况
df_export = fiv.export(mode="df")

# 输入特征，返回所属箱对应的节点索引
bins = fiv.transform(df[features].values)
```
完整的代码可以参考`example.py`
sleepgraph 5.6
Suspend/Resume timing and analysis tool

This tool is designed to assist kernel and OS developers in optimizing
their linux stack's suspend/resume time. Using a kernel image built
with a few extra options enabled, the tool will execute a suspend and
capture dmesg and ftrace data until resume is complete. This data is
transformed into a device timeline and an optional callgraph to give
a detailed view of which devices/subsystems are taking the most
time in suspend/resume.
django-matplotlib 0.1
Matplotlib field for Django
Requires Python: >=3.5
Keywords: django,matplotlib,python

=================
Django Matplotlib
=================

.. image:: https://travis-ci.com/scidam/django_matplotlib.svg?branch=master
    :target: https://travis-ci.com/scidam/django_matplotlib

Django_matplotlib is a reusable
Django app for embedding matplotlib figures
into Django driven websites. It can be easily integrated with Django
admin app and used in templates via forms.

Documentation: http://django-matplotlib.readthedocs.org/

Requirements
------------

Django 1.11+, <3.0; Python 3.5+, <3.8.


Quick start
-----------

1. Clone git repository to place where Django can find it:

.. code-block::

   pip install django-matplotlib

2. Add "django_matplotlib" to your INSTALLED_APPS setting like this:

.. code-block:: python

    INSTALLED_APPS = [
        ...
        'django_matplotlib',
        ...
    ]

3. Use MatplotlibField in your `models.py`, e.g.:

.. code-block:: python

    from django.db import models
    from django_matplotlib.fields import MatplotlibFigureField

    class MyModelWithFigure(models.Model):
        # ... other fields 
        # figures.py should be in the same directory where models.py is placed.
        # see  ./django_matplotlib/figures.py for example.
        fig = MatplotlibFigureField(figure='test_figure', verbose_name='figure',
                                    silent=True)
        # ... other fields 


4. Generate and apply migrations.


.. note::

    It is assumed that you have Django installed already. Additionally,
    you will need to install `matplotlib` to use `MatplotlibFigureField`
    in your models.


Author
------

Dmitry E. Kislov

E-mail: kislov@easydan.com