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 webapp).

asreview lab [options]

--ip IP

The IP address the server will listen on.

--port PORT

The port the server will listen on.

--port-retries NUMBER_RETRIES

The number of additional ports to try if the specified port is not available.

--no-browser NO_BROWSER

Do not open ASReview LAB in a browser after startup.

--certfile CERTFILE_FULL_PATH

The full path to an SSL/TLS certificate file.

--keyfile KEYFILE_FULL_PATH

The full path to a private key file for usage with SSL/TLS.

--embedding EMBEDDING_FP

File path of embedding matrix. Required for LSTM models.

--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.

--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 labeled 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.asreview

asreview simulate benchmark:van_de_Schoot_2017 --state_file myreview.asreview

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 settings and 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.

--state_file STATE_FILE, -s STATE_FILE

Location to ASReview project file of simulation.

--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.

--stop_if STOP_IF

The number of label actions to simulate. Default, ‘min’ will stop simulating when all relevant records are found. Use -1 to simulate all labels actions.

-w WRITE_INTERVAL, --write_interval WRITE_INTERVAL

The simulation data will be written away after each set of thismany labeled records. By default only writes away data at the endof the simulation to make it as fast as possible.

--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.

Feature Extraction

Name	Reference	Requires
tfidf	asreview.models.feature_extraction.Tfidf
doc2vec	asreview.models.feature_extraction.Doc2Vec	gensim
embedding-idf	asreview.models.feature_extraction.EmbeddingIdf
embedding-lstm	asreview.models.feature_extraction.EmbeddingLSTM
sbert	asreview.models.feature_extraction.SBERT	sentence_transformers

Classifiers

Name	Reference	Requires
nb	asreview.models.classifiers.NaiveBayesClassifier
svm	asreview.models.classifiers.SVMClassifier
logistic	asreview.models.classifiers.LogisticClassifier
rf	asreview.models.classifiers.RandomForestClassifier
nn-2-layer	asreview.models.classifiers.NN2LayerClassifier	tensorflow
lstm-base	asreview.models.classifiers.LSTMBaseClassifier	tensorflow
lstm-pool	asreview.models.classifiers.LSTMPoolClassifier	tensorflow

Query Strategies

Name	Reference	Requires
max	asreview.models.query.MaxQuery
random	asreview.models.query.RandomQuery
uncertainty	asreview.models.query.UncertaintyQuery
cluster	asreview.models.query.ClusterQuery
max_random	asreview.models.query.MaxRandomQuery
max_uncertainty	asreview.models.query.MaxUncertaintyQuery

Balance Strategies

Name	Reference	Requires
simple	asreview.models.balance.SimpleBalance
double	asreview.models.balance.DoubleBalance
undersample	asreview.models.balance.UndersampleBalance

Algorithms

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 Create extensions for more information on extending ASReview with new models via extensions.