ASReview Makita

[!IMPORTANT] ASReview Makita v1.x and later requires ASReview version 2 (asreview>=2). For ASReview version 1, use ASReview Makita v0.x.

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:

bash pip install asreview-makita

To upgrade to the latest version, use:

bash pip install --upgrade asreview-makita

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

bash 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: bash 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)

bash sh jobs.sh

#### On Windows

cmd 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:

console asreview makita template basic

is equivalent to:

console 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.

console 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

console 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.

[!IMPORTANT]

The default behavior of ASReview regarding prior knowledge sampling has changed. In ASReview version 1, the default values for --n-prior-included and --n-prior-excluded were set to 1. However, in version 2 of ASReview, these values are now set to 0 by default. This means that simulations will begin with top-down screening until sufficient prior knowledge is gathered.

To revert to the previous behavior, use the following options: --n-prior-included 1 --n-prior-excluded 1.

optional arguments:

| Argument | Description | Default Value | | --------------------------------------- | ------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | | -h, --help | Show this help message and exit | | | -p, --project-folder PROJECT_FOLDER | The folder the project will be rendered to | The current working directory | | -d, --data-folder DATA_FOLDER | The dataset source folder | Data folder in working directory | | -j, --job-file JOB_FILE | The name of the file with jobs | jobs.bat for Windows, otherwise jobs.sh | | --prior-seed prior_seed | Seed of the priors | 535 | | --model-seed MODEL_SEED | Seed of the models | 165 | | --template TEMPLATE | Overwrite template with template file path | | | --platform PLATFORM | Platform to run jobs: Windows, Darwin, Linux | The system of rendering templates | | --n-runs N_RUNS | Number of runs | 1 | | --overwrite | Automatically accepts all overwrite requests | | | --ai AI | AI(s) to use. Multiple values can be used to simulate multiple AIs. | Latest Ultra model of ASReview | | --classifier CLASSIFIER | Classifier to use | svm if --ai not set | | --feature-extractor FEATURE_EXTRACTOR | Feature extractor to use | tfidf if --ai not set | | --querier QUERIER | Query strategy to use | max if --ai not set | | --balancer BALANCE_STRATEGY | Balance strategy to use | balanced if --ai not set | | --n-query n_query | Number of instances per query | 1 | | --n-stop n_stop | The number of label actions to simulate | Will stop simulating when all relevant records are found | | --n-prior-included N_PRIOR_INCLUDED | Sample n prior included records. Only for basic and modelmatrix templates. | 0 (just as in ASReview) | | --n-prior-excluded N_PRIOR_EXCLUDED | Sample n prior excluded records. Only for basic and modelmatrix templates. | 0 (just as in ASReview) |

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:

| Argument | Description | Default Value | | --------------------------------------- | ------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | | -h, --help | Show this help message and exit | | | -p, --project-folder PROJECT_FOLDER | The folder the project will be rendered to | The current working directory | | -d, --data-folder DATA_FOLDER | The dataset source folder | Data folder in working directory | | -j, --job-file JOB_FILE | The name of the file with jobs | jobs.bat for Windows, otherwise jobs.sh | | --prior-seed prior_seed | Seed of the priors | 535 | | --model-seed MODEL_SEED | Seed of the models | 165 | | --template TEMPLATE | Overwrite template with template file path | | | --platform PLATFORM | Platform to run jobs: Windows, Darwin, Linux | The system of rendering templates | | --n-priors N_PRIORS | Number of priors | 10 | | --overwrite | Automatically accepts all overwrite requests | | | --ai AI | AI(s) to use. Multiple values can be used to simulate multiple AIs. | Latest Ultra model of ASReview | | --classifier CLASSIFIER | Classifier to use | svm if --ai not set | | --feature-extractor FEATURE_EXTRACTOR | Feature extractor to use | tfidf if --ai not set | | --querier QUERIER | Query strategy to use | max if --ai not set | | --balancer BALANCE_STRATEGY | Balance strategy to use | balanced if --ai not set | | --n-query n_query | Number of instances per query | 1 | | --n-stop n_stop | The number of label actions to simulate | Will stop simulating when all relevant records are found |

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.

[!IMPORTANT]

The default behavior of ASReview regarding prior knowledge sampling has changed. In ASReview version 1, the default values for --n-prior-included and --n-prior-excluded were set to 1. However, in version 2 of ASReview, these values are now set to 0 by default. This means that simulations will begin with top-down screening until sufficient prior knowledge is gathered.

To revert to the previous behavior, use the following options: --n-prior-included 1 --n-prior-excluded 1.

optional arguments:

| Argument | Description | Default Value | | ---------------------------------------- | -------------------------------------------- | -------------------------------------------------------- | | -h, --help | Show this help message and exit | | | -p, --project-folder PROJECT_FOLDER | The folder the project will be rendered to | The current working directory | | -d, --data-folder DATA_FOLDER | The dataset source folder | Data folder in working directory | | -j, --job-file JOB_FILE | The name of the file with jobs | jobs.bat for Windows, otherwise jobs.sh | | --prior-seed prior_seed | Seed of the priors | 535 | | --model-seed MODEL_SEED | Seed of the models | 165 | | --template TEMPLATE | Overwrite template with template file path | | | --platform PLATFORM | Platform to run jobs: Windows, Darwin, Linux | The system of rendering templates | | --n-runs N_RUNS | Number of runs | 1 | | --overwrite | Automatically accepts all overwrite requests | | | --n-query n_query | Number of instances per query | 1 | | --n-stop n_stop | The number of label actions to simulate | Will stop simulating when all relevant records are found | | --n-prior-included N_PRIOR_INCLUDED | Sample n prior included records. Only for basic and modelmatrix templates. | 0 (just as in ASReview) | | --n-prior-excluded N_PRIOR_EXCLUDED | Sample n prior excluded records. Only for basic and modelmatrix templates. | 0 (just as in ASReview) | | --classifiers CLASSIFIERS | Classifiers to use | nb svm | | --feature-extractors FEATURE_EXTRACTOR | Feature extractors to use | tfidf onehot | | --queriers QUERIER | Query strategies to use | max | | --balancers BALANCE_STRATEGY | Balance strategies to use | none balanced | | --impossible-models IMPOSSIBLE_MODELS | Model combinations to exclude | nb,doc2vec nb,sbert |

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:

console 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:

| Argument | Description | Default Value | | --------------------------------------- | ------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | | -h, --help | Show this help message and exit | | | -p, --project-folder PROJECT_FOLDER | The folder the project will be rendered to | The current working directory | | -d, --data-folder DATA_FOLDER | The dataset source folder | Data folder in working directory | | -j, --job-file JOB_FILE | The name of the file with jobs | jobs.bat for Windows, otherwise jobs.sh | | --prior-seed prior_seed | Seed of the priors | 535 | | --model-seed MODEL_SEED | Seed of the models | 165 | | --template TEMPLATE | Overwrite template with template file path | | | --platform PLATFORM | Platform to run jobs: Windows, Darwin, Linux | The system of rendering templates | | --n-runs N_RUNS | Number of runs | 1 | | --overwrite | Automatically accepts all overwrite requests | | | --ai AI | AI(s) to use. Multiple values can be used to simulate multiple AIs. | Latest Ultra model of ASReview | | --classifier CLASSIFIER | Classifier to use | svm if --ai not set | | --feature-extractor FEATURE_EXTRACTOR | Feature extractor to use | tfidf if --ai not set | | --querier QUERIER | Query strategy to use | max if --ai not set | | --balancer BALANCE_STRATEGY | Balance strategy to use | balanced if --ai not set | | --n-query n_query | Number of instances per query | 1 | | --n-stop n_stop | The number of label actions to simulate | Will stop simulating when all relevant records are found |

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.

console 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
• getsettingsfrom_state.py
• merge_descriptives.py
• merge_metrics.py
• merge_tds.py
• splitdatawithmultiplelabels.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 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.