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|
%global _empty_manifest_terminate_build 0
Name: python-catsim
Version: 0.17.2
Release: 1
Summary: Computerized Adaptive Testing Simulator
License: GNU Lesser General Public License v3 (LGPLv3)
URL: https://pypi.org/project/catsim/
Source0: https://mirrors.aliyun.com/pypi/web/packages/9f/7f/575bce3050aa4d3c387a1bdfb0bfcaf3d4b8edbf5d37ef9b18abac9e7e22/catsim-0.17.2.tar.gz
BuildArch: noarch
Requires: python3-scipy
Requires: python3-numexpr
Requires: python3-matplotlib
Requires: python3-scikit-learn
Requires: python3-json-tricks
Requires: python3-tqdm
Requires: python3-numpy
Requires: python3-mypy
Requires: python3-pylama
Requires: python3-yapf
Requires: python3-black
Requires: python3-isort
Requires: python3-Sphinx
Requires: python3-numpydoc
Requires: python3-sphinx-autodoc-annotation
Requires: python3-sphinx-rtd-theme
Requires: python3-m2r2
Requires: python3-bibtex-pygments-lexer
Requires: python3-matplotlib
Requires: python3-nose
Requires: python3-nose-cov
Requires: python3-sklearn
Requires: python3-flake8
Requires: python3-yapf
Requires: python3-twine
Requires: python3-build
%description
[](https://github.com/douglasrizzo/catsim/actions/workflows/test-on-push.yml)
[](https://coveralls.io/github/douglasrizzo/catsim?branch=master)
[](https://badge.fury.io/py/catsim)
[](https://requires.io/github/douglasrizzo/catsim/requirements/?branch=master)
[](http://dx.doi.org/10.5281/zenodo.46420)
**catsim** is a Python package for computerized adaptive testing (CAT)
simulations. It provides multiple methods for:
- [test initialization](https://douglasrizzo.com.br/catsim/initialization.html) (selecting the initial ability of the examinees)
- [item selection](https://douglasrizzo.com.br/catsim/selection.html)
- [ability estimation](https://douglasrizzo.com.br/catsim/estimation.html)
- [test stopping](https://douglasrizzo.com.br/catsim/stopping.html)
These methods can either be used in a standalone fashion
[\[1\]](https://douglasrizzo.com.br/catsim/introduction.html#autonomous-usage)
to power other software or be used with *catsim* to simulate the
application of computerized adaptive tests
[\[2\]](https://douglasrizzo.com.br/catsim/introduction.html#running-simulations),
given a sample of examinees, represented by their ability levels,
and an item bank, represented by their parameters according to some
[logistic Item Response Theory
model](https://douglasrizzo.com.br/catsim/introduction.html#item-response-theory-models).
## What's a CAT
Computerized adaptive tests are educational evaluations, usually taken
by examinees in a computer or some other digital means, in which the
examinee\'s ability is evaluated after the response of each item.
The new ability is then used to select a new item, closer to the
examinee\'s real ability. This method of test application has
several advantages compared to the traditional paper-and-pencil method
or even linear tests applied electronically, since high-ability
examinees are not required to answer all the easy items in a test,
answering only the items that actually give some information regarding
his or hers true knowledge of the subject at matter. A similar, but
inverse effect happens for those examinees of low ability level.
More information is available [in the
docs](https://douglasrizzo.com.br/catsim/introduction.html) and over
at
[Wikipedia](https://en.wikipedia.org/wiki/Computerized_adaptive_testing).
## Installation
Install it using `pip install catsim`.
## Basic Usage
**NEW:** there is now [a Colab Notebook](https://colab.research.google.com/drive/1dBcpXxHuc9YXv9yGllxlahx585hEmdbn?usp=sharing) teaching the basics of catsim!
1. Have an [item matrix](https://douglasrizzo.com.br/catsim/item_matrix.html);
2. Have a sample of examinee proficiencies, or a number of examinees to be generated;
3. Create an [initializer](https://douglasrizzo.com.br/catsim/initialization.html),
an item [selector](https://douglasrizzo.com.br/catsim/selection.html), a
ability [estimator](https://douglasrizzo.com.br/catsim/estimation.html)
and a [stopping criterion](https://douglasrizzo.com.br/catsim/stopping.html);
4. Pass them to a [simulator](https://douglasrizzo.com.br/catsim/simulation.html)
and start the simulation.
5. Access the simulator\'s properties to get specifics of the results;
6. [Plot](https://douglasrizzo.com.br/catsim/plot.html) your results.
```python
from catsim.initialization import RandomInitializer
from catsim.selection import MaxInfoSelector
from catsim.estimation import NumericalSearchEstimator
from catsim.stopping import MaxItemStopper
from catsim.simulation import Simulator
from catsim.cat import generate_item_bank
initializer = RandomInitializer()
selector = MaxInfoSelector()
estimator = NumericalSearchEstimator()
stopper = MaxItemStopper(20)
Simulator(generate_item_bank(100), 10).simulate(initializer, selector, estimator, stopper)
```
## Dependencies
All dependencies are listed on `setup.py` and should be installed
automatically.
To run the tests, you\'ll need to install the testing requirements
`pip install catsim[testing]`.
To generate the documentation, install the necessary dependencies with `pip install catsim[docs]`.
To ensure code is valid and formatted before submission, install the necessary development dependencies with `pip install catsim[dev]`.
## Compatibility
*catsim* is compatible and tested against Python 3.5, 3.6, 3.7, 3.8 and 3.9.
## Important links
- Official source code repo: <https://github.com/douglasrizzo/catsim>
- HTML documentation (stable release):
<https://douglasrizzo.com.br/catsim>
- Issue tracker: <https://github.com/douglasrizzo/catsim/issues>
## Citing catsim
You can cite the package using the following bibtex entry:
```bibtex
@article{catsim,
author = {Meneghetti, Douglas De Rizzo and Aquino Junior, Plinio Thomaz},
title = {Application and simulation of computerized adaptive tests through the package catsim},
year = 2018,
month = jul,
archiveprefix = {arXiv},
eprint = {1707.03012},
eprinttype = {arxiv},
journal = {arXiv:1707.03012 [stat]},
primaryclass = {stat}
}
```
## If you are looking for IRT item parameter estimation...
_catsim_ does not implement item parameter estimation. I have had great joy outsourcing that functionality to the [mirt](https://cran.r-project.org/web//packages/mirt/) R package along the years. However, since many users request packages with item parameter estimation capabilities in the Python ecosystem, here are a few links. While I have not used them personally, specialized packages like these are hard to come by, so I hope these are helpful.
- [eribean/girth](https://github.com/eribean/girth)
- [eribean/girth_mcmc](https://github.com/eribean/girth_mcmc)
- [nd-ball/py-irt](https://github.com/nd-ball/py-irt)
%package -n python3-catsim
Summary: Computerized Adaptive Testing Simulator
Provides: python-catsim
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-catsim
[](https://github.com/douglasrizzo/catsim/actions/workflows/test-on-push.yml)
[](https://coveralls.io/github/douglasrizzo/catsim?branch=master)
[](https://badge.fury.io/py/catsim)
[](https://requires.io/github/douglasrizzo/catsim/requirements/?branch=master)
[](http://dx.doi.org/10.5281/zenodo.46420)
**catsim** is a Python package for computerized adaptive testing (CAT)
simulations. It provides multiple methods for:
- [test initialization](https://douglasrizzo.com.br/catsim/initialization.html) (selecting the initial ability of the examinees)
- [item selection](https://douglasrizzo.com.br/catsim/selection.html)
- [ability estimation](https://douglasrizzo.com.br/catsim/estimation.html)
- [test stopping](https://douglasrizzo.com.br/catsim/stopping.html)
These methods can either be used in a standalone fashion
[\[1\]](https://douglasrizzo.com.br/catsim/introduction.html#autonomous-usage)
to power other software or be used with *catsim* to simulate the
application of computerized adaptive tests
[\[2\]](https://douglasrizzo.com.br/catsim/introduction.html#running-simulations),
given a sample of examinees, represented by their ability levels,
and an item bank, represented by their parameters according to some
[logistic Item Response Theory
model](https://douglasrizzo.com.br/catsim/introduction.html#item-response-theory-models).
## What's a CAT
Computerized adaptive tests are educational evaluations, usually taken
by examinees in a computer or some other digital means, in which the
examinee\'s ability is evaluated after the response of each item.
The new ability is then used to select a new item, closer to the
examinee\'s real ability. This method of test application has
several advantages compared to the traditional paper-and-pencil method
or even linear tests applied electronically, since high-ability
examinees are not required to answer all the easy items in a test,
answering only the items that actually give some information regarding
his or hers true knowledge of the subject at matter. A similar, but
inverse effect happens for those examinees of low ability level.
More information is available [in the
docs](https://douglasrizzo.com.br/catsim/introduction.html) and over
at
[Wikipedia](https://en.wikipedia.org/wiki/Computerized_adaptive_testing).
## Installation
Install it using `pip install catsim`.
## Basic Usage
**NEW:** there is now [a Colab Notebook](https://colab.research.google.com/drive/1dBcpXxHuc9YXv9yGllxlahx585hEmdbn?usp=sharing) teaching the basics of catsim!
1. Have an [item matrix](https://douglasrizzo.com.br/catsim/item_matrix.html);
2. Have a sample of examinee proficiencies, or a number of examinees to be generated;
3. Create an [initializer](https://douglasrizzo.com.br/catsim/initialization.html),
an item [selector](https://douglasrizzo.com.br/catsim/selection.html), a
ability [estimator](https://douglasrizzo.com.br/catsim/estimation.html)
and a [stopping criterion](https://douglasrizzo.com.br/catsim/stopping.html);
4. Pass them to a [simulator](https://douglasrizzo.com.br/catsim/simulation.html)
and start the simulation.
5. Access the simulator\'s properties to get specifics of the results;
6. [Plot](https://douglasrizzo.com.br/catsim/plot.html) your results.
```python
from catsim.initialization import RandomInitializer
from catsim.selection import MaxInfoSelector
from catsim.estimation import NumericalSearchEstimator
from catsim.stopping import MaxItemStopper
from catsim.simulation import Simulator
from catsim.cat import generate_item_bank
initializer = RandomInitializer()
selector = MaxInfoSelector()
estimator = NumericalSearchEstimator()
stopper = MaxItemStopper(20)
Simulator(generate_item_bank(100), 10).simulate(initializer, selector, estimator, stopper)
```
## Dependencies
All dependencies are listed on `setup.py` and should be installed
automatically.
To run the tests, you\'ll need to install the testing requirements
`pip install catsim[testing]`.
To generate the documentation, install the necessary dependencies with `pip install catsim[docs]`.
To ensure code is valid and formatted before submission, install the necessary development dependencies with `pip install catsim[dev]`.
## Compatibility
*catsim* is compatible and tested against Python 3.5, 3.6, 3.7, 3.8 and 3.9.
## Important links
- Official source code repo: <https://github.com/douglasrizzo/catsim>
- HTML documentation (stable release):
<https://douglasrizzo.com.br/catsim>
- Issue tracker: <https://github.com/douglasrizzo/catsim/issues>
## Citing catsim
You can cite the package using the following bibtex entry:
```bibtex
@article{catsim,
author = {Meneghetti, Douglas De Rizzo and Aquino Junior, Plinio Thomaz},
title = {Application and simulation of computerized adaptive tests through the package catsim},
year = 2018,
month = jul,
archiveprefix = {arXiv},
eprint = {1707.03012},
eprinttype = {arxiv},
journal = {arXiv:1707.03012 [stat]},
primaryclass = {stat}
}
```
## If you are looking for IRT item parameter estimation...
_catsim_ does not implement item parameter estimation. I have had great joy outsourcing that functionality to the [mirt](https://cran.r-project.org/web//packages/mirt/) R package along the years. However, since many users request packages with item parameter estimation capabilities in the Python ecosystem, here are a few links. While I have not used them personally, specialized packages like these are hard to come by, so I hope these are helpful.
- [eribean/girth](https://github.com/eribean/girth)
- [eribean/girth_mcmc](https://github.com/eribean/girth_mcmc)
- [nd-ball/py-irt](https://github.com/nd-ball/py-irt)
%package help
Summary: Development documents and examples for catsim
Provides: python3-catsim-doc
%description help
[](https://github.com/douglasrizzo/catsim/actions/workflows/test-on-push.yml)
[](https://coveralls.io/github/douglasrizzo/catsim?branch=master)
[](https://badge.fury.io/py/catsim)
[](https://requires.io/github/douglasrizzo/catsim/requirements/?branch=master)
[](http://dx.doi.org/10.5281/zenodo.46420)
**catsim** is a Python package for computerized adaptive testing (CAT)
simulations. It provides multiple methods for:
- [test initialization](https://douglasrizzo.com.br/catsim/initialization.html) (selecting the initial ability of the examinees)
- [item selection](https://douglasrizzo.com.br/catsim/selection.html)
- [ability estimation](https://douglasrizzo.com.br/catsim/estimation.html)
- [test stopping](https://douglasrizzo.com.br/catsim/stopping.html)
These methods can either be used in a standalone fashion
[\[1\]](https://douglasrizzo.com.br/catsim/introduction.html#autonomous-usage)
to power other software or be used with *catsim* to simulate the
application of computerized adaptive tests
[\[2\]](https://douglasrizzo.com.br/catsim/introduction.html#running-simulations),
given a sample of examinees, represented by their ability levels,
and an item bank, represented by their parameters according to some
[logistic Item Response Theory
model](https://douglasrizzo.com.br/catsim/introduction.html#item-response-theory-models).
## What's a CAT
Computerized adaptive tests are educational evaluations, usually taken
by examinees in a computer or some other digital means, in which the
examinee\'s ability is evaluated after the response of each item.
The new ability is then used to select a new item, closer to the
examinee\'s real ability. This method of test application has
several advantages compared to the traditional paper-and-pencil method
or even linear tests applied electronically, since high-ability
examinees are not required to answer all the easy items in a test,
answering only the items that actually give some information regarding
his or hers true knowledge of the subject at matter. A similar, but
inverse effect happens for those examinees of low ability level.
More information is available [in the
docs](https://douglasrizzo.com.br/catsim/introduction.html) and over
at
[Wikipedia](https://en.wikipedia.org/wiki/Computerized_adaptive_testing).
## Installation
Install it using `pip install catsim`.
## Basic Usage
**NEW:** there is now [a Colab Notebook](https://colab.research.google.com/drive/1dBcpXxHuc9YXv9yGllxlahx585hEmdbn?usp=sharing) teaching the basics of catsim!
1. Have an [item matrix](https://douglasrizzo.com.br/catsim/item_matrix.html);
2. Have a sample of examinee proficiencies, or a number of examinees to be generated;
3. Create an [initializer](https://douglasrizzo.com.br/catsim/initialization.html),
an item [selector](https://douglasrizzo.com.br/catsim/selection.html), a
ability [estimator](https://douglasrizzo.com.br/catsim/estimation.html)
and a [stopping criterion](https://douglasrizzo.com.br/catsim/stopping.html);
4. Pass them to a [simulator](https://douglasrizzo.com.br/catsim/simulation.html)
and start the simulation.
5. Access the simulator\'s properties to get specifics of the results;
6. [Plot](https://douglasrizzo.com.br/catsim/plot.html) your results.
```python
from catsim.initialization import RandomInitializer
from catsim.selection import MaxInfoSelector
from catsim.estimation import NumericalSearchEstimator
from catsim.stopping import MaxItemStopper
from catsim.simulation import Simulator
from catsim.cat import generate_item_bank
initializer = RandomInitializer()
selector = MaxInfoSelector()
estimator = NumericalSearchEstimator()
stopper = MaxItemStopper(20)
Simulator(generate_item_bank(100), 10).simulate(initializer, selector, estimator, stopper)
```
## Dependencies
All dependencies are listed on `setup.py` and should be installed
automatically.
To run the tests, you\'ll need to install the testing requirements
`pip install catsim[testing]`.
To generate the documentation, install the necessary dependencies with `pip install catsim[docs]`.
To ensure code is valid and formatted before submission, install the necessary development dependencies with `pip install catsim[dev]`.
## Compatibility
*catsim* is compatible and tested against Python 3.5, 3.6, 3.7, 3.8 and 3.9.
## Important links
- Official source code repo: <https://github.com/douglasrizzo/catsim>
- HTML documentation (stable release):
<https://douglasrizzo.com.br/catsim>
- Issue tracker: <https://github.com/douglasrizzo/catsim/issues>
## Citing catsim
You can cite the package using the following bibtex entry:
```bibtex
@article{catsim,
author = {Meneghetti, Douglas De Rizzo and Aquino Junior, Plinio Thomaz},
title = {Application and simulation of computerized adaptive tests through the package catsim},
year = 2018,
month = jul,
archiveprefix = {arXiv},
eprint = {1707.03012},
eprinttype = {arxiv},
journal = {arXiv:1707.03012 [stat]},
primaryclass = {stat}
}
```
## If you are looking for IRT item parameter estimation...
_catsim_ does not implement item parameter estimation. I have had great joy outsourcing that functionality to the [mirt](https://cran.r-project.org/web//packages/mirt/) R package along the years. However, since many users request packages with item parameter estimation capabilities in the Python ecosystem, here are a few links. While I have not used them personally, specialized packages like these are hard to come by, so I hope these are helpful.
- [eribean/girth](https://github.com/eribean/girth)
- [eribean/girth_mcmc](https://github.com/eribean/girth_mcmc)
- [nd-ball/py-irt](https://github.com/nd-ball/py-irt)
%prep
%autosetup -n catsim-0.17.2
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-catsim -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.17.2-1
- Package Spec generated
|
