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fast-scboot-0.1b6
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مشاهده بیشترتوضیحات
Fast implementation of the stratified cluster bootstrap sampling algorithm.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fast-scboot-0.1b6 |
| نام | fast-scboot |
| نسخه کتابخانه | 0.1b6 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Jay Kim |
| ایمیل نویسنده | mozjay0619@gmail.com |
| آدرس صفحه اصلی | https://github.com/mozjay0619/fast-scboot |
| آدرس اینترنتی | https://pypi.org/project/fast-scboot/ |
| مجوز | DSB 3-clause |
FastScboot
==========
FastScboot is a statistics tool to perform the stratified clustered bootstrap sampling on given data. The algorithm is fast in the sense that the remaining bottleneck to the speed of the algorithm is the speed of memory access during the inplace fancy indexing operation.
Install
-------
::
pip install fast-scboot
Getting started
---------------
First import the package and initialize the ``Sampler`` object.
.. code:: python
from fast_scboot import Sampler
s = Sampler()
Let's create a sample data.
.. code:: python
import numpy as np
import pandas as pd
clusts = np.asarray([0, 1, 1, 2, 0, 1, 1, 0, 2, 2])
strats = np.asarray([0, 0, 0, 0, 1, 1, 1, 2, 2, 2])
data = np.squeeze(np.dstack([strats, clusts])).astype(np.double)
data = pd.DataFrame(data, columns=['strat', 'clust'])
Two preparatory steps are preparing the data, and creating some data cache:
.. code:: python
s.prepare_data(data, 'strat', 'clust')
s.setup_cache()
After that, you can start drawing samples:
.. code:: python
for i in range(100):
sampled = s.sample_data(seed=i)
How does it work?
-----------------
.. image:: https://github.com/mozjay0619/fast-scboot/blob/master/media/image1.png
:width: 600pt
When the ``prepare_data`` method is invoked, once the original data has been sorted by strata and cluster levels, the ``make_index_matrix`` creates three auxiliary arrays: ``idx_mtx``, ``strat_arr``, and ``clust_arr``. The ``idx_mtx`` array stores information on where each cluster begins and how many rows it occupies, as well as the actual cluster value. The ``strat_arr`` is an index array that indexes the strata levels at each of the cluster level. The ``clust_arr`` does the same but for the cluster levels. The reason the values of the ``clust_arr`` are not uniformly increasing like ``strat_arr`` in this example is because internally, the unique indices are created using the Cantor pairing function for speed (and then re-cast into integer using Pandas "cateory" type).
When the ``sample_data`` method is invoked, three additional auxiliary data are created. The ``clust_cnt_arr`` array stores the number of unique cluster values in each strata, in this case, [3, 2, 2]. The total number of unique strata values is stored in the ``num_strats`` variable (3 in this case), and the same for cluster is store in the ``num_clusts`` variable (7 in this case).
.. image:: https://github.com/mozjay0619/fast-scboot/blob/master/media/imageB.png
:width: 270pt
We produce a random array from [0, 1] uniform distribution with size equal to ``num_clusts``. It's important that we invoke random sampling function once because usually it's very expensive to call them repeatedly. Then we use the ``clust_cnt_arr`` and loop through (vectorized using Cython) the uniform random numbers and multiply them by the values in ``clust_cnt_arr``, and then cast them to integer datatype. We are effectively mapping the uniform random values from [0, 1] to appropriate range of integer values, which can be used as randomly bootstrap sampled indices (stored in ``s`` variable) for the ``idx_mtx`` array.
.. image:: https://github.com/mozjay0619/fast-scboot/blob/master/media/image5.png
:width: 365pt
The ``s`` array is used on the ``idx_mtx``, where we are effectively sampling with replacement clusters from each stratum (i.e. from each colored area). Once we have cluster bootstrap sampled ``idx_mtx``, we can use the information stored in that matrix to construct the ``sampled_idxs`` array, which records indices of the sampled data in terms of the indicies of the original data. The final return value is produced by fancy indexing the original data using the ``sampled_idxs``. The native numpy fancy indexing is somewhat costly due to data copy, so we provide our own inplace version of fancy indexing.
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3 | Python |
نحوه نصب
نصب پکیج whl fast-scboot-0.1b6:
pip install fast-scboot-0.1b6.whl
نصب پکیج tar.gz fast-scboot-0.1b6:
pip install fast-scboot-0.1b6.tar.gz