- class asreview.review.ReviewSimulate(as_data, *args, n_prior_included=0, n_prior_excluded=0, prior_indices=None, init_seed=None, write_interval=None, **kwargs)
ASReview Simulation mode class.
as_data (asreview.ASReviewData) – The data object which contains the text, labels, etc.
model (BaseModel) – Initialized model to fit the data during active learning. See asreview.models.utils.py for possible models.
query_model (BaseQueryModel) – Initialized model to query new instances for review, such as random sampling or max sampling. See asreview.query_strategies.utils.py for query models.
balance_model (BaseBalanceModel) – Initialized model to redistribute the training data during the active learning process. They might either resample or undersample specific papers.
feature_model (BaseFeatureModel) – Feature extraction model that converts texts and keywords to feature matrices.
n_prior_included (int) – Sample n prior included papers.
n_prior_excluded (int) – Sample n prior excluded papers.
prior_indices (int) – Prior indices by row number.
n_instances (int) – Number of papers to query at each step in the active learning process.
stop_if (int) – Number of steps/queries to perform. Set to None for no limit.
start_idx (numpy.ndarray) – Start the simulation/review with these indices. They are assumed to be already labeled. Failing to do so might result bad behaviour.
init_seed (int) – 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.
state_file (str) – Path to state file.
write_interval (int) – After how many labeled records to write away the simulation data to the state.
Get an ASReview settings object
Do a full review.
Train a new model on the labeled data.