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cause-effect-0.2.0
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مشاهده بیشتر
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مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
A library for cause-effect relationships.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | cause-effect-0.2.0 |
| نام | cause-effect |
| نسخه کتابخانه | 0.2.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Benjamin Weber |
| ایمیل نویسنده | mail@bwe.im |
| آدرس صفحه اصلی | http://bitbucket.com/hyllos/cause_effect |
| آدرس اینترنتی | https://pypi.org/project/cause-effect/ |
| مجوز | MIT license |
.. image:: http://ci.appveyor.com/api/projects/status/m0f9fw5b670whkw8?svg=true
:target: https://ci.appveyor.com/project/hyllos/cause-effect
Install it
-----------
You can install ``cause_effect`` via:
.. code-block:: bash
$ pip install cause_effect
Alternatively, you can install from the code repository directly:
.. code-block:: bash
$ pip install hg+http://bitbucket.org/hyllos/cause_effect
Core Functions
--------------
``pareto(values)``
Is a pareto distribution present for a list of numbers (``ratio`` <= 1)?
``mccauses(values)``
Which causes have the highest concentration (rank * value)?
``mceffects(values)``
Which effects have the highest concentration?
``separator(values)```
From which value (including) does the highest concentration begin?
``causes(values, effects=0.8)``
Determine causes for specified share of effects.
``effects(values, causes=0.2)``
Determine effects for specified share of causes.
Secondary Functions
-------------------
``ratio(values)``
``entropy`` divided by ``control_limit``.
``entropy(values)``
Calculate entropy for values.
``control_limit(count)``
Calculate control entropy for ``count`` number of elements (length of ``values``).
Tertiary Functions
-------------------
``make_causes(count)``
Return list of causes that is cumulative percent of ``count`` number of elements.
``make_effects(values)``
Return list of effects that is cumulative percent of values.
``make_concentration(values)``
Return list of concentration for list of ``values`` that is rank * value.
``sort_list(values)``
Return sorted list of numbers.
Parameters
-----------
``values`` is a list of numbers.
``effects`` and ``causes`` must be a number between 0 and 1 (including).
``count`` is the length of the list of ``values``.
Use it
------
The function ``pareto`` tells you whether a pareto distribution is present for a list of numbers:
.. code-block:: python
from pareto import pareto, mccauses, mceffects
pareto([789, 621, 109, 65, 45, 30, 27, 15, 12, 9])
True
Here, we have a pareto distribution present.
That is a minority causes a majority of effects.
But which minority causes which majority?
.. code-block:: python
mccauses([789, 621, 109, 65, 45, 30, 27, 15, 12, 9])
0.2
mceffects([789, 621, 109, 65, 45, 30, 27, 15, 12, 9])
0.818815331010453
20% of causes effect 82% of results.
But which values are that 20%?
.. code-block:: python
separator([789, 621, 109, 65, 45, 30, 27, 15, 12, 9])
621
All values greater or equal than 621 are those 20% causing 82% of results.
**That's it.**
Dig Deeper
-----------
How many causes are required for only 90% of effects?
.. code-block:: python
from pareto import causes, effects
causes([789, 621, 109, 65, 45, 30, 27, 15, 12, 9], 0.9)
0.4
40%.
How many effects are behind only 10% of causes?
.. code-block:: python
effects([789, 621, 109, 65, 45, 30, 27, 15, 12, 9], 0.1)
0.458
45.8%.
How does it work?
-----------------
``pareto`` calculates the `entropy`_ for a list of effects:
.. code-block:: python
from pareto import entropy, control_limit, ratio
entropy([789, 621, 109, 65, 45, 30, 27, 15, 12, 9])
1.9593816735406657
It calculates the entropy for a control group of ten elements. That is the length of our list.
.. code-block:: python
control_limit(10)
2.7709505944546686
It then checks ``entropy`` is less or equal than ``control_limit``.
This can be simplified to:
.. code-block:: python
values = [789, 621, 109, 65, 45, 30, 27, 15, 12, 9]
entropy(values) / control_limit(len(values)) <= 1
The left side of the comparison is done by ``ratio``.
So, if you want to find out how nearby or far off you are from a pareto distribution, do:
.. code-block:: python
ratio([109, 65, 45, 30, 27, 15, 12, 9])
1.051
If we remove the first two effects, the ``control_limit`` will be exceeded by the values.
So, we learn here that the pareto distribution disappears with the first two effects.
.. _entropy: http://www.boazronen.org/PDF/The%20Pareto%20managerial%20principle%20-%20when%20does%20it%20apply.pdf
``mccauses`` and ``mceffects`` return the respective share of the causes and effects where concentration (rank * value) is highest.
=======
History
=======
0.2.0 (2016-10-21)
------------------
* Add function separator().
* Streamline tests.
0.1.0 (2016-10-20)
------------------
* First release on PyPI.
نحوه نصب
نصب پکیج whl cause-effect-0.2.0:
pip install cause-effect-0.2.0.whl
نصب پکیج tar.gz cause-effect-0.2.0:
pip install cause-effect-0.2.0.tar.gz