actsnclass.time_domain_loop

actsnclass.time_domain_loop(days: list, output_metrics_file: str, output_queried_file: str, path_to_features_dir: str, strategy: str, batch=1, canonical=False, classifier='RandomForest', features_method='Bazin', path_to_canonical='', path_to_full_lc_features='', queryable=True, screen=True, training='original')

Perform the active learning loop. All results are saved to file.

Parameters:

• days (list) – List of 2 elements. First and last day of observations since the beginning of the survey.
• output_metrics_file (str) – Full path to output file to store metrics for each loop.
• output_queried_file (str) – Full path to output file to store the queried sample.
• path_to_features_dir (str) – Complete path to directory holding features files for all days.
• strategy (str) – Query strategy. Options are ‘UncSampling’ and ‘RandomSampling’.
• batch (int (optional)) – Size of batch to be queried in each loop. Default is 1.
• canonical (bool (optional)) – If True, restrict the search to the canonical sample.
• classifier (str (optional)) – Machine Learning algorithm. Currently only ‘RandomForest’ is implemented.
• features_method (str (optional)) – Feature extraction method. Currently only ‘Bazin’ is implemented.
• path_to_canonical (str (optional)) – Path to canonical sample features files. It is only used if “strategy==canonical”.
• path_to_full_lc_features (str (optional)) – Path to full light curve features file. Only used if training is a number.
• queryable (bool (optional)) – If True, allow queries only on objects flagged as queryable. Default is True.
• screen (bool (optional)) – If True, print on screen number of light curves processed.
• training (str or int (optional)) – Choice of initial training sample. If ‘original’: begin from the train sample flagged in the file If int: choose the required number of samples at random, ensuring that at least half are SN Ia Default is ‘original’.