asreview.state.SQLiteState

class asreview.state.SQLiteState(read_only=True)[source]

Class for storing the review state.

The results are stored in a sqlite database.

Parameters

read_only (bool) – Open state in read only mode. Default False.

Variables

version (str) – Return the version number of the state.
settings (asreview.settings.ASReviewSettings) – Return an ASReview settings object with model settings and active learning settings.
n_records_labeled (int) – Get the number of labeled records, where each prior is counted individually.
n_priors (int) – Number of priors. If priors have not been selected returns None.
exist_new_labeled_records (bool) – Have there been labeled records added to the state since the last time a model ranking was added to the state?
model_has_trained (bool) – Has the ranking by a model been added to the state?

Attributes

`exist_new_labeled_records`	Return True if there are new labeled records.
`model_has_trained`	Return True if there is data of a trained model in the state.
`n_priors`	Number of records added as prior knowledge.
`n_records`	Number of records in the loop.
`n_records_labeled`	Number labeled records.
`settings`	Settings of the ASReview pipeline.
`version`	Version number of the state.

Methods

`add_labeling_data`(record_ids, labels[, ...])	Add the data corresponding to a labeling action to the state file.
`add_last_probabilities`(probabilities)	Save the probabilities produced by the last classifier.
`add_last_ranking`(ranked_record_ids, ...)	Save the ranking of the last iteration of the model.
`add_note`(note, record_id)	Add a text note to save with a labeled record.
`add_record_table`(record_ids)	Add the record table to the state.
`close`()	Close the files opened by the state.
`delete_record_labeling_data`(record_id)	Delete the labeling data for the given record id.
`get_balance_strategies`([priors, pending])	Get the balance strategies from the state.
`get_classifiers`([priors, pending])	Get the classifiers from the state.
`get_data_by_query_number`(query[, columns])	Get the data of a specific query from the results table.
`get_data_by_record_id`(record_id[, columns])	Get the data of a specific query from the results table.
`get_dataset`([columns, priors, pending])	Get a subset from the results table.
`get_decision_changes`()	Get the record ids for any decision changes.
`get_feature_extraction`([priors, pending])	Get the query strategies from the state.
`get_labeled`()	Get the labeled records in order of labeling.
`get_labeling_times`([time_format, priors, ...])	Get the time of labeling from the state.
`get_labels`([priors, pending])	Get the labels from the state.
`get_last_probabilities`()	Get the probabilities produced by the last classifier.
`get_last_ranking`()	Get the ranking from the state.
`get_order_of_labeling`([priors, pending])	Get full array of record id's in order that they were labeled.
`get_pending`()	Get the record_ids of the records pending a labeling decision.
`get_pool`()	Get the unlabeled, not-pending records in ranking order.
`get_pool_labeled_pending`()	Return the unlabeled pool, labeled and pending records.
`get_priors`([columns])	Get the record ids of the priors.
`get_query_strategies`([priors, pending])	Get the query strategies from the state.
`get_record_table`()	Get the record table of the state.
`get_training_sets`([priors, pending])	Get the training_sets from the state.
`is_empty`()	Check if state has no results.
`query_top_ranked`(n)	Get the top ranked records from the ranking table.
`to_dict`()	Convert state to dictionary.
`update_decision`(record_id, label[, note])	Change the label of an already labeled record.