Command Line

ASReview provides a powerful command line interface for running tasks like simulations. For a list of available commands, type asreview --help.

Lab

asreview lab launches the ASReview LAB software (the frontend).

asreview lab [options]
--ip IP

The IP address the server will listen on.

--port PORT

The port the server will listen on.

--clean_project CLEAN_PROJECT

Safe cleanup of temporary files in project.

--clean_all_projects CLEAN_ALL_PROJECTS

Safe cleanup of temporary files in all projects.

--embedding EMBEDDING_FP

File path of embedding matrix. Required for LSTM models.

--seed SEED

Seed for the model (classifiers, balance strategies, feature extraction techniques, and query strategies). Use an integer between 0 and 2^32 - 1.

-h, --help

Show help message and exit.

Simulate

asreview simulate measures the performance of the software on existing systematic reviews. The software shows how many papers you could have potentially skipped during the systematic review. You can use your own labelled dataset

asreview simulate [options] [dataset [dataset ...]]

or one of the benchmark-datasets (see index.csv for dataset IDs).

asreview simulate [options] benchmark: [dataset_id]

Examples:

asreview simulate YOUR_DATA.csv --state_file myreview.h5

asreview simulate benchmark:van_de_Schoot_2017 --state_file myreview.h5
dataset

A dataset to simulate

-m, --model MODEL

The prediction model for Active Learning. Default: nb. (See available options below: Classifiers)

-q, --query_strategy QUERY_STRATEGY

The query strategy for Active Learning. Default: max. (See available options below: Query strategies)

-b, --balance_strategy BALANCE_STRATEGY

Data rebalancing strategy. Helps against imbalanced datasets with few inclusions and many exclusions. Default: double. (See available options below: Balance strategies)

-e, --feature_extraction FEATURE_EXTRACTION

Feature extraction method. Some combinations of feature extraction method and prediction model are not available. Default: tfidf. (See available options below: Feature extraction)

--embedding EMBEDDING_FP

File path of embedding matrix. Required for LSTM models.

--config_file CONFIG_FILE

Configuration file with model settingsand parameter values.

--seed SEED

Seed for the model (classifiers, balance strategies, feature extraction techniques, and query strategies). Use an integer between 0 and 2^32 - 1.

--n_prior_included N_PRIOR_INCLUDED

The number of prior included papers. Only used when prior_idx is not given. Default 1.

--n_prior_excluded N_PRIOR_EXCLUDED

The number of prior excluded papers. Only used when prior_idx is not given. Default 1.

--prior_idx [PRIOR_IDX [PRIOR_IDX ...]]

Prior indices by rownumber (0 is first rownumber).

--prior_record_id [PRIOR_RECORD_ID [PRIOR_RECORD_ID ...]]

Prior indices by record_id.

New in version 0.15.

--included_dataset [INCLUDED_DATASET [INCLUDED_DATASET ...]]

A dataset with papers that should be includedCan be used multiple times.

--excluded_dataset [EXCLUDED_DATASET [EXCLUDED_DATASET ...]]

A dataset with papers that should be excludedCan be used multiple times.

--prior_dataset [PRIOR_DATASET [PRIOR_DATASET ...]]

A dataset with papers from prior studies.

--state_file STATE_FILE, -s STATE_FILE

Location to store the (active learning) state of the simulation. It is possible to output the state to a JSON file (extension .json) or HDF5 file (extension .h5).

--init_seed INIT_SEED

Seed for setting the prior indices if the prior_idx option is not used. If the option prior_idx is used with one or more index, this option is ignored.

--n_instances N_INSTANCES

Number of papers queried each query.Default 1.

--n_queries N_QUERIES

The number of queries. By default, the program stops after all documents are reviewed or is interrupted by the user.

-n N_PAPERS, --n_papers N_PAPERS

The number of papers to be reviewed. By default, the program stops after all documents are reviewed or is interrupted by the user.

--verbose VERBOSE, -v VERBOSE

Verbosity

-h, --help

Show help message and exit.

Note

Some classifiers (models) and feature extraction algorithms require additional dependecies. Use pip install asreview[all] to install all additional dependencies at once.

Simulate-batch

asreview simulate-batch provides the same interface as the asreview simulate, but adds an extra option (--n_runs) to run a batch of simulation runs with the same configuration.

asreview simulate-batch [options] [dataset [dataset ...]]

Warning

The behavior of some arguments of asreview simulate-batch will differ slightly from asreview simulate.

dataset

A dataset to simulate

--n_runs

Number of simulation runs.

Algorithms

New in version 0.14.

asreview algorithms provides an overview of all available active learning model elements (classifiers, query strategies, balance strategies, and feature extraction algorithms) in ASReview.

asreview algorithms

Note

asreview algorithms included models added via extensions. See Extensions for more information on extending ASReview with new models via extensions.