ASReview Makita

ASReviews' Makita (MAKe IT
Automatic) is a workflow generator for simulation studies using the command
line interface of ASReview
LAB. Makita can
be used to effortlessly generate the framework and code for your simulation
study.

A simulation involves mimicking the screening process for a systematic review of
a human in interaction with an Active learning model (i.e., a combination of a
feature extractor, classifier, balancing method, and a query strategy). The
simulation reenacts the screening process as if a researcher were using active
learning. The performance of one or multiple model(s) can then be measured by
performance metrics, such as the Work Saved over Sampling, recall at a given
point in the screening process, or the average time to discover a relevant
record.

Using Makita templates, different study structures can be generated to fit the
needs of your very own study. If your study requires a unique template, you can
create a new one and use it instead.

With ASReview LAB, you can
simulate
with the web
interface,
the Python
API, or
the Command Line Interface
(CLI). Makita
makes use of the CLI.

What Makita does:

• Setting up a workflow for running a large-scale simulation study
• Preparing a Github repository
• Automating the many lines of code needed
• Creating a batch script for running the simulation study with just one line of
  code
• Making your research fully reproducible
• Allowing you to add templates to accommodate your own specific research
  question

What Makita does not do:

• Executing jobs or tasks itself
• Being a black-box
• Writing your paper

For a tutorial on using Makita we refer to the Exercise on Using the ASReview
Simulation Mode.

ASReview Makita Helper Tool

For users of ASReview Makita, there's a handy helper tool available. This tool
simplifies the process of formulating your Makita commands, ensuring
compatibility and ease of use. It's designed to work with ASReview Makita
version 1.0 and above.

Makita helper Tool

Installation

ASReview Makita requires Python 3.10 or later. Install the extension using pip:

pip install asreview-makita

To upgrade to the latest version, use:

pip install --upgrade asreview-makita

Once installed, ASReview should automatically detect Makita. To verify the installation, run:

asreview --help

If installed correctly, Makita will appear as an available option.

Getting Started

Render a Study

Follow these steps to set up the framework and code for your simulation study:

1. Install ASReview Makita
   Follow the Installation instructions to set up ASReview Makita.

2. Prepare Your Datasets
   Place all your datasets in a single folder.

3. Choose a Project Folder
   Decide on a path for your project folder. You don't need to create the folder manually; makita will handle it for you.

4. Generate a Simulation Study
   Select a template from the list of templates and create your study using:

   asreview makita template NAME_OF_TEMPLATE -d 'path/to/your/datasets' -p 'path/to/your/project-folder'

   Replace NAME_OF_TEMPLATE with the desired template name (e.g., arfi for the ARFI template).

Run a Study

Follow these steps to execute the simulation study:

1. Install Dependencies
   Ensure all dependencies mentioned in the rendered README.md file are installed.

2. Run the Simulation Study
   Execute the jobs script to run all tasks in the project folder:

   On Unix-based systems (e.g., macOS, Linux)

   sh jobs.sh

   On Windows

   jobs.bat

   The jobs script will process all jobs in the project folder.

Alternative rendering

If you run the makita command in the directory where you want to generate your
project, you don't need to specify the project folder using the -p flag.
Similarly, if your datasets are stored in a folder named data within the same
location, you can omit the -d flag.

For example:

asreview makita template basic

is equivalent to:

asreview makita template basic -p 'current/working/directory' -d 'current/working/directory/data'

Platform support

By default, ASReview Makita renders job files for the platform of rendering. It
is possible to render jobs for other platforms as well. Use the argument
--platform with values "Windows", "Linux", or "Darwin" (MacOS) to change the
output.

asreview makita template basic --platform Windows

By default, the job file depends on the platform. Windows users will see a
jobs.bat file, while other users will see jobs.sh. You can overwrite this
with

asreview makita template basic --job-file my_jobs_file.my_ext

Templates

The following table gives an overview of the available templates. See Getting
started for instructions on usage.

Note: If no seed is set with the template command, the default seed is used.
While this is important for the reproducibility of the results, it may lead to
long-term bias. To avoid seed-related bias across different simulation
studies, a seed should be for the prior records and models.

Basic template

Template name: basic

The basic template prepares a script for conducting a simulation study with one
run using the default ASReview CLI simulate
parameters.

optional arguments:

ARFI template

Template name: arfi

The ARFI template (All relevant, fixed irrelevant) prepares a script for running
a simulation study in such a way that for every relevant record 1 run will be
executed with 10 randomly chosen irrelevant records which are kept constant over
runs. When multiple datasets are available the template orders the tasks in the
job file per dataset.

optional arguments:

Model matrix template

Template name: modelmatrix (formerly multimodel)

The model matrix template prepares a simulation study comparing a matrix of
model components for your dataset(s). All other parameters are the default ASReview CLI simulate
parameters.

optional arguments:

Example usage

If you want to specify certain combinations of classifiers and feature
extractors that should and should not be used, you can use the --classifiers,
--feature-extractors, --queriers, --balancers and
--impossible-models option. For instance, if you want to exclude the
combinations of nb with doc2vec and logistic with tfidf, use the
following command:

asreview makita template modelmatrix --classifiers logistic nb --feature-extractors tfidf doc2vec --queriers max max_random max_uncertainty cluster --impossible-models nb,doc2vec logistic,tfidf

Prior template

Template name: prior

The prior template evaluates how a set of custom prior knowledge might affect
simulation performance. It processes two types of data in the data folder:
labeled dataset(s) to be simulated and labeled dataset(s) to be used as prior
knowledge. The filename(s) of the dataset(s) containing the custom prior
knowledge should use the naming prefix prior_[dataset_name].

The template runs two simulations: the first simulation uses all records from
the prior_ dataset(s) as prior knowledge, and the second uses a 1+1 randomly
chosen set of prior knowledge from the non-prior knowledge dataset as a minimal
training set. Both runs simulate performance on the combined non-prior
dataset(s).

Running this template creates a generated_data folder. This folder contains
two datasets; dataset_with_priors.csv and dataset_without_priors.csv. The
simulations specified in the generated jobs file will use these datasets for
their simulations.

optional arguments:

Example usage

Put at least 2 datasets in the data folder. One starting with the prior_
prefix, and one without this prefix.

note: priors_ will also work.

asreview makita template prior

Advanced usage

Create and use custom templates

It is possible to overwrite the internal templates. This can be useful for
simulation studies with different needs.

1. Select an existing template that looks similar to your needs. For example,
   you want to run ARFI with a different model, then you pick the ARFI
   template.
2. Download the template you selected in step 1 from the Github
   repository.
   Template files have the following structure template_*.txt.template. For
   the ARFI example, the template is
   template_arfi.txt.template.
3. Save the downloaded template somewhere on your computer. The template is a
   so-called "Jinja" template. The template consists of ASReview command line
   commands combined
   with jinja syntax. The Jinja syntax is very intuitive. See this
   Cheatsheet.
4. Edit the Jinja template to your needs.
5. Run the custom template with the command line option --template PATH_TO_MY_TEMPLATE.txt.template. For the ARFI example, this would be asreview makita template arfi --template PATH_TO_MY_TEMPLATE.txt.template. Please keep
   in mind that you follow the usual steps for running a template.
6. A jobs.sh file should be in the your folder.

Please contribute your templates back to the project by making a Pull Request.
Then, we can integrate it in the core of the makita package.

Add and use scripts

Makita can add scripts to your repository. The scripts are mainly pre- and
postprocessing scripts. These scripts are not (yet) available in any existing
ASReview software. Therefore, they can be added manually with asreview makita add-script NAME_OF_SCRIPT.

For example, the results from data describe are merged via the script
merge_descriptives.py (or merge_metrics.py for ASReview insights), using:

1. Collect statistics (with template)
2. Run asreview makita add-script merge_descriptives.py
3. Run python scripts/merge_descriptives.py

Use -s (source) and -o (output) to tweak paths.

Some scripts are added automatically to the folder, as they are part of the
template. For example, the get_plot.py script is added to the generated folder
when using any template, as it is used to generate the plots.

Still, get_plot.py can be used on its own, as it is a standalone script. To
use it, use -s (source) and -o (output) to tweak paths.

Adding a legend to the plot can be done with the -l or --show-legend flag,
with the labels clustered on any of the following: 'filename', 'model', 'classifier', 'querier', 'balancer', 'feature_extraction'

Available scripts

The following scripts are available:

• get_plot.py
• get_settings_from_state.py
• merge_descriptives.py
• merge_metrics.py
• merge_tds.py
• split_data_with_multiple_labels.py [DEPRECATED]

Time to Discovery Tables

The 'merge_tds.py' script creates a table of the time to discovery (TD) values
for each dataset, with each row corresponding to each record ID of the relevant
records in a dataset, and the columns correspond to each simulation run (e.g,
for the modelmatrix template each column corresponds to a simualtion run with
each active learning model). Additionally, the tables includes the
average-record-TD values (the average of the TD values for a record across
multiple simulation runs), and the average-simulation-TD values (the average of
the TD values across all records for a single simulation run).

Run Makita via Docker

To run Makita template with Docker use the following command:

docker run -v $PWD:/app ghcr.io/asreview/asreview makita <YOUR COMMAND>

License

This extension is published under the MIT license.

Contact

This extension is part of the ASReview project
(asreview.ai). It is maintained by the maintainers of
ASReview LAB. See ASReview LAB for
contact information and more resources.