معرفی شرکت ها
boostsa-0.2.9
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 package to compute bootstrap sampling significance test
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | boostsa-0.2.9 |
| نام | boostsa |
| نسخه کتابخانه | 0.2.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Tommaso Fornaciari |
| ایمیل نویسنده | fornaciari@unibocconi.it |
| آدرس صفحه اصلی | https://github.com/fornaciari/bootstrap |
| آدرس اینترنتی | https://pypi.org/project/boostsa/ |
| مجوز | MIT license |
boostsa - BOOtSTrap SAmpling in pyhton
======================================
.. image:: https://img.shields.io/pypi/v/boostsa.svg
:target: https://pypi.python.org/pypi/boostsa
.. image:: https://img.shields.io/github/license/fornaciari/boostsa
:target: https://lbesson.mit-license.org/
:alt: License
.. image:: https://github.com/fornaciari/boostsa/workflows/Python%20Package/badge.svg
:target: https://github.com/fornaciari/boostsa/actions
.. image:: https://readthedocs.org/projects/boostsa/badge/?version=latest
:target: https://boostsa.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. image:: https://colab.research.google.com/assets/colab-badge.svg
:target: https://colab.research.google.com/drive/1pkbjouxjub9ve0PlVZaW_we_r1hz6Hf-#scrollTo=TGj4udXVb6Ji
:alt: Open In Colab
Intro
-----
boostsa - BOOtSTrap SAmpinlg - is a tool to compute bootstrap sampling significance test,
even in the pipeline of a complex experimental design...
- Free software: MIT license
- Documentation: https://boostsa.readthedocs.io.
Google colab
------------
.. |colab1| image:: https://colab.research.google.com/assets/colab-badge.svg
:target: https://colab.research.google.com/drive/1pkbjouxjub9ve0PlVZaW_we_r1hz6Hf-#scrollTo=TGj4udXVb6Ji
:alt: Open In Colab
+----------------------------------------------------------------+--------------------+
| Name | Link |
+================================================================+====================+
| You can try boostsa here: | |colab1| |
+----------------------------------------------------------------+--------------------+
Installation
------------
.. code-block:: bash
pip install -U boostsa
Getting started
---------------
First, import ``boostsa``:
.. code-block:: python
from boostsa import Bootstrap
Then, create a boostrap instance. You will use it to store your experiments' results and to compute the bootstrap sampling significance test:
.. code-block:: python
boot = Bootstrap()
Inputs
^^^^^^
The assumption is that you ran at least two classification task experiments, which you want compare.
One is your *baseline*, or *control*, or *hypothesis 0* (*h0*).
The other one is the *experimental condition* that hopefully beats the baseline, or *treatment*, or *hypothesis 1* (*h1*).
You compare the *h0* and *h1* predictions against the same targets.
Therefore, *h0 predictions*, *h1 predictions* and *targets* will be the your ``Bootstrap`` instance's data inputs.
Outputs
^^^^^^^
By defalut, boostsa produces two output files:
- ``results.tsv``, that contains the experiments' performance and the (possible) significance levels;
- ``outcomes.json``, that contains targets and predictions for all the experimental conditions.
You can define the outputs when you create the instance, using the following parameters:
- ``save_results``, type: ``bool``, default: ``True``. This determines if you want to save the results.
- ``save_outcomes``, type: ``bool``, default: ``True``. This determines if you want to save the experiments' outcomes..
- ``dir_out``, type: ``str``, default: ``''``, that is your working directory. This indicates the directory where to save the results.
For example, if you want to save only the results in a particular folder, you will create an instance like this:
.. code-block:: python
boot = Bootstrap(save_outcomes=False, dir_out='my/favourite/directory/')
Test function
-------------
In the simplest conditions, you will run the bootstrap sampling significance test with the ``test`` function.
It takes the following inputs:
- ``targs``, type: ``list`` or ``str``. They are the targets, or *gold standard*, that you use as benchmark to measure the *h0* and *h1* predictions' performance. They can be a **list of integers**, representing the labels' indexes for each data point, or a string. In such case, the string will be interpreted as the **path** to a text file containing a single integer in each row, having the same meaning as for the list input.
- ``h0_preds``, type: ``list`` or ``str``. The *h0* predictions, in the same formats of ``targs``.
- ``h1_preds``, type: ``list`` or ``str``. The *h1* predictions, in the same formats as above.
- ``h0_name``, type: ``str``, default: ``h0``. Expression to describe the *h0* condition.
- ``h1_name``, type: ``str``, default: ``h1``. Expression to describe the *h1* condition.
- ``n_loops``, type: ``int``, default: ``100``. Number of iterations for computing the bootstrap sampling.
- ``sample_size``, type: ``float``, default: ``.1``. Percentage of data points sampled, with respect to their whole set. The admitted values range between 0.05 (5%) and 0.5 (50%).
- ``verbose``, type: ``bool``, default: ``False``. If true, the experiments' performance is shown.
For example:
.. code-block:: python
boot.test(targs='../test_boot/h0.0/targs.txt', h0_preds='../test_boot/h0.0/preds.txt', h1_preds='../test_boot/h1.0/preds.txt', n_loops=1000, sample_size=.2, verbose=True)
The ouput will be:
.. sourcecode::
total size............... 1000
sample size.............. 200
targs count: ['class 0 freq 465 perc 46.50%', 'class 1 freq 535 perc 53.50%']
h0 preds count: ['class 0 freq 339 perc 33.90%', 'class 1 freq 661 perc 66.10%']
h1 preds count: ['class 0 freq 500 perc 50.00%', 'class 1 freq 500 perc 50.00%']
h0 F-measure............. 67.76 h1 F-measure............. 74.07 diff... 6.31
h0 accuracy.............. 69.0 h1 accuracy.............. 74.1 diff... 5.1
h0 precision............. 69.94 h1 precision............. 74.1 diff... 4.16
h0 recall................ 67.96 h1 recall................ 74.22 diff... 6.26
bootstrap: 100%|███████████████████████████| 1000/1000 [00:07<00:00, 139.84it/s]
count sample diff f1 is twice tot diff f1....... 37 / 1000 p < 0.037 *
count sample diff acc is twice tot diff acc...... 73 / 1000 p < 0.073
count sample diff prec is twice tot diff prec..... 111 / 1000 p < 0.111
count sample diff rec is twice tot diff rec ..... 27 / 1000 p < 0.027 *
Out[3]:
f1 diff_f1 sign_f1 acc diff_acc sign_acc prec diff_prec sign_prec rec diff_rec sign_rec
h0 67.76 69.0 69.94 67.96
h1 74.07 6.31 * 74.1 5.1 74.10 4.16 74.22 6.26 *
That's it!
For more complex experimental designs and technical/ethical considerations, please refer to the documentation page.
نیازمندی
| مقدار | نام |
|---|---|
| ==21.0.1 | pip |
| ==0.37.1 | wheel |
| ==3.3.0 | twine |
| ==1.7.3 | scipy |
| - | tqdm |
| - | pandas |
| - | numpy |
| - | sklearn |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl boostsa-0.2.9:
pip install boostsa-0.2.9.whl
نصب پکیج tar.gz boostsa-0.2.9:
pip install boostsa-0.2.9.tar.gz