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 |
||
doc2vec |
||
embedding-idf |
||
embedding-lstm |
||
sbert |
Classifiers
Name |
Reference |
Requires |
---|---|---|
nb |
||
svm |
||
logistic |
||
rf |
||
nn-2-layer |
||
lstm-base |
||
lstm-pool |
Query Strategies
Name |
Reference |
Requires |
---|---|---|
max |
||
random |
||
uncertainty |
||
cluster |
||
max_random |
||
max_uncertainty |
Balance Strategies
Name |
Reference |
Requires |
---|---|---|
simple |
||
double |
||
undersample |
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.