install_py_modules

Function for installing the necessary python modules

In social and educational settings, the use of Artificial
Intelligence (AI) is a challenging task. Relevant data is often only
available in handwritten forms, or the use of data is restricted by
privacy policies. This often leads to small data sets. Furthermore, in the educational and social sciences,
data is often unbalanced in terms of
frequencies. To support educators as well as educational and social
researchers in using the potentials of AI for their work, this package
provides a unified interface for neural nets in 'keras',
'tensorflow', and 'pytorch' to deal with natural language problems. In addition,
the package ships with a shiny app, providing a graphical user interface.
This allows the usage of AI for people without skills in writing python/R scripts.
The tools integrate existing mathematical and statistical methods for dealing
with small data sets via pseudo-labeling (e.g. Lee (2013)
<https://www.researchgate.net/publication/280581078_Pseudo-Label_The_Simple_and_Efficient_Semi-Supervised_Learning_Method_for_Deep_Neural_Networks>,
Cascante-Bonilla et al. (2020) <doi:10.48550/arXiv.2001.06001>) and
imbalanced data via the creation of synthetic cases (e.g.
Bunkhumpornpat et al. (2012) <doi:10.1007/s10489-011-0287-y>).
Performance evaluation of AI is connected to measures from content
analysis which educational and social researchers are generally more
familiar with (e.g. Berding & Pargmann (2022) <doi:10.30819/5581>,
Gwet (2014) <ISBN:978-0-9708062-8-4>, Krippendorff (2019)
<doi:10.4135/9781071878781>). Estimation of energy consumption and CO2
emissions during model training is done with the 'python' library
'codecarbon'. Finally, all objects created with this package allow to
share trained AI models with other people.

Berding Florian

aifeducation

Artificial Intelligence for Education

Pargmann Julia

Riebenbauer Elisabeth

Rebmann Karin

Slopinski Andreas

install_py_modules function

<dl><dt>envname</dt>
<dd><code>string</code> Name of the environment where the packages should
be installed.</dd>
<dt>install</dt>
<dd><code>character</code> determining which machine learning frameworks
should be installed. <code>install="all"</code> for 'pytorch' and 'tensorflow'.
<code>install="pytorch"</code> for 'pytorch', and <code>install="tensorflow"</code> for 'tensorflow'.</dd>
<dt>tf_version</dt>
<dd><code>string</code> determining the desired version of 'tensorflow'.</dd>
<dt>pytorch_cuda_version</dt>
<dd><code>string</code> determining the desired version of 'cuda' for
'PyTorch'.</dd>
<dt>python_version</dt>
<dd><code>string</code> Python version to use.</dd>
<dt>remove_first</dt>
<dd><code>bool</code> If <code>TRUE</code> removes the environment completely before
recreating the environment and installing the packages. If <code>FALSE</code> the packages
are installed in the existing environment without any prior changes.</dd>
<dt>cpu_only</dt>
<dd><code>bool</code> <code>TRUE</code> installs the cpu only version of the
machine learning frameworks.</dd></dl>

Arguments

Installing necessary python modules to an environment — install_py_modules

<dl>

<dt>envname</dt>
<dd><code>string</code> Name of the environment where the packages should
be installed.</dd>


<dt>install</dt>
<dd><code>character</code> determining which machine learning frameworks
should be installed. <code>install="all"</code> for 'pytorch' and 'tensorflow'.
<code>install="pytorch"</code> for 'pytorch', and <code>install="tensorflow"</code> for 'tensorflow'.</dd>


<dt>tf_version</dt>
<dd><code>string</code> determining the desired version of 'tensorflow'.</dd>


<dt>pytorch_cuda_version</dt>
<dd><code>string</code> determining the desired version of 'cuda' for
'PyTorch'.</dd>


<dt>python_version</dt>
<dd><code>string</code> Python version to use.</dd>


<dt>remove_first</dt>
<dd><code>bool</code> If <code>TRUE</code> removes the environment completely before
recreating the environment and installing the packages. If <code>FALSE</code> the packages
are installed in the existing environment without any prior changes.</dd>


<dt>cpu_only</dt>
<dd><code>bool</code> <code>TRUE</code> installs the cpu only version of the
machine learning frameworks.</dd>

</dl>

install_py_modules: Installing necessary python modules to an environment

Description

Usage

Value

Arguments

See Also