asreview.review.BaseReview

class asreview.review.BaseReview(as_data, project, model=<asreview.models.classifiers.nb.NaiveBayesClassifier object>, query_model=<asreview.models.query.max.MaxQuery object>, balance_model=<asreview.models.balance.simple.SimpleBalance object>, feature_model=<asreview.models.feature_extraction.tfidf.Tfidf object>, n_papers=None, n_instances=1, stop_if=None, start_idx=[])[source]

Base class for Systematic Review.

Parameters:

  • as_data (asreview.ASReviewData) – The data object which contains the text, labels, etc.

  • project (path-like) – Path to the project file.

  • model (BaseTrainClassifier) – Initialized model to fit the data during active learning. See asreview.models.utils.py for possible models.

  • query_model (BaseQueryStrategy) – 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 (BaseBalance) – Initialized model to redistribute the training data during the active learning process. They might either resample or undersample specific papers.

  • feature_model (BaseFeatureExtraction) – Feature extraction model that converts texts and keywords to feature matrices.

  • 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.

Attributes

settings

Get an ASReview settings object

Methods

review()

Do a full review.

train()

Train a new model on the labeled data.