معرفی شرکت ها
fairsearchcore-1.0.4
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 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 Python library with the core algorithms used to do fair search.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fairsearchcore-1.0.4 |
| نام | fairsearchcore |
| نسخه کتابخانه | 1.0.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Ivan Kitanovski |
| ایمیل نویسنده | ivan.kitanovski@gmail.com |
| آدرس صفحه اصلی | https://github.com/fair-search/fairsearchcore-python |
| آدرس اینترنتی | https://pypi.org/project/fairsearchcore/ |
| مجوز | Apache 2.0 |
# Fair search core for Python
[](https://pypi.org/project/fairsearchcore/)
[](https://pypi.org/project/fairsearchcore/)
[](https://pypi.org/project/fairsearchcore/)
[](https://pypi.org/project/fairsearchcore/)
[](https://pypi.org/project/fairsearchcore/)
This is the Python library with the core algorithms used to do [FA*IR](https://arxiv.org/abs/1706.06368) ranking.
## Installation
To install `fairsearchcore`, simply use `pip` (or `pipenv`):
```bash
pip install fairsearcore
```
And, that's it!
## Using it in your code
You need to import the package first:
```{.sourceCode .python}
import fairsearchcore as fsc
```
Creating and analyzing mtables:
```{.sourceCode .python}
k = 20 # number of topK elements returned (value should be between 10 and 400)
p = 0.25 # proportion of protected candidates in the topK elements (value should be between 0.02 and 0.98)
alpha = 0.1 # significance level (value should be between 0.01 and 0.15)
# create the Fair object
fair = fsc.Fair(k, p, alpha)
# create an mtable using alpha unadjusted
mtable = fair.create_unadjusted_mtable()
>> [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3]
# analytically calculate the fail probability
analytical = fair.compute_fail_probability(mtable)
>> 0.11517506930977106
# create an mtable using alpha adjusted
mtable = fair.create_adjusted_mtable()
>> [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2]
# again, analytically calculate the fail probability
analytical = fair.compute_fail_probability(mtable)
>> 0.13421772800000065
```
Generate random rankings and analyze them:
```{.sourceCode .python}
M = 10000 # number of rankings you want to generate (works better with big numbers)
# generate rankings using the simulator (M lists of k objects of class fairsearchcore.models.FairScoreDoc)
rankings = fsc.generate_rankings(M, k, p)
>> [[<FairScoreDoc [Protected]>, <FairScoreDoc [Nonprotected]>, <FairScoreDoc [Protected]>,
<FairScoreDoc [Protected]>, <FairScoreDoc [Nonprotected]>, <FairScoreDoc [Nonprotected]>,
<FairScoreDoc [Nonprotected]>, <FairScoreDoc [Protected]>, <FairScoreDoc [Nonprotected]>,
<FairScoreDoc [Nonprotected]>, <FairScoreDoc [Nonprotected]>, <FairScoreDoc [Nonprotected]>,
<FairScoreDoc [Nonprotected]>, <FairScoreDoc [Protected]>, <FairScoreDoc [Nonprotected]>,
<FairScoreDoc [Nonprotected]>, <FairScoreDoc [Nonprotected]>, <FairScoreDoc [Nonprotected]>,
<FairScoreDoc [Nonprotected]>, <FairScoreDoc [Protected]>],...]
# experimentally calculate the fail probability
experimental = fsc.compute_fail_probability(mtable, rankings)
>> 0.1076
```
Apply a fair re-ranking to a given ranking:
```
# import the FairScoreDoc class
from fairsearchcore.models import FairScoreDoc
# let's manually create an unfair ranking (False -> unprotexted, True -> protected)
unfair_ranking = [FairScoreDoc(20, 20, False), FairScoreDoc(19, 19, False), FairScoreDoc(18, 18, False),
FairScoreDoc(17, 17, False), FairScoreDoc(16, 16, False), FairScoreDoc(15, 15, False),
FairScoreDoc(14, 14, False), FairScoreDoc(13, 13, False), FairScoreDoc(12, 12, False),
FairScoreDoc(11, 11, False), FairScoreDoc(10, 10, False), FairScoreDoc(9, 9, False),
FairScoreDoc(8, 8, False), FairScoreDoc(7, 7, False), FairScoreDoc(6, 6, True),
FairScoreDoc(5, 5, True), FairScoreDoc(4, 4, True), FairScoreDoc(3, 3, True),
FairScoreDoc(2, 2, True), FairScoreDoc(1, 1, True)]
# now re-rank the unfair ranking
fair.re_rank(unfair_ranking)
>> [<FairScoreDoc [Nonprotected]>, <FairScoreDoc [Nonprotected]>, <FairScoreDoc [Nonprotected]>,
<FairScoreDoc [Nonprotected]>, <FairScoreDoc [Nonprotected]>, <FairScoreDoc [Nonprotected]>,
<FairScoreDoc [Nonprotected]>, <FairScoreDoc [Nonprotected]>, <FairScoreDoc [Protected]>,
<FairScoreDoc [Nonprotected]>, <FairScoreDoc [Nonprotected]>, <FairScoreDoc [Nonprotected]>,
<FairScoreDoc [Nonprotected]>, <FairScoreDoc [Nonprotected]>, <FairScoreDoc [Nonprotected]>,
<FairScoreDoc [Protected]>, <FairScoreDoc [Protected]>, <FairScoreDoc [Protected]>,
<FairScoreDoc [Protected]>, <FairScoreDoc [Protected]>]
```
The library contains sufficient code documentation for each of the functions.
## Development
1. Clone this repository `git clone https://github.com/fair-search/fairsearchcore-python.git`
2. Change directory to the directory where you cloned the repository `cd WHERE_ITS_DOWNLOADED/fairsearchcore-python`
3. Use any IDE to work with the code
## Testing
Just run:
```
python setup.py test
```
*Note*: The simulator tests take a *looong* time to execute.
## Credits
The FA*IR algorithm is described on this paper:
* Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, Ricardo Baeza-Yates: "[FA*IR: A Fair Top-k Ranking Algorithm](https://doi.org/10.1145/3132847.3132938)". Proc. of the 2017 ACM on Conference on Information and Knowledge Management (CIKM).
This code was developed by [Ivan Kitanovski](http://ivankitanovski.com/) based on the paper. See the [license](https://github.com/fair-search/fairsearch-core/blob/master/python/LICENSE) file for more information.
## See also
You can also see the [FA*IR plug-in for ElasticSearch](https://github.com/fair-search/fairsearch-elasticsearch-plugin).
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.0 | Python |
نحوه نصب
نصب پکیج whl fairsearchcore-1.0.4:
pip install fairsearchcore-1.0.4.whl
نصب پکیج tar.gz fairsearchcore-1.0.4:
pip install fairsearchcore-1.0.4.tar.gz