ROOSTER

Contents

ROOSTER#

class star_privateer.ROOSTER(**kwargs)#

ROOSTER object, wrapping a random forest classifiers framework designed to analyse surface rotation in stellar light curves.

__init__(**kwargs)#: Initiate a new ROOSTER instance. A RotClass and a PeriodSel classifiers are both created as attributes of the ROOSTER object. Additional parameters provided when initialising a ROOSTER instance will be passed to sklearn.ensemble.RandomForestClassifier.

analyseSet(features, p_candidates, e_p_err=None, E_p_err=None, feature_names=None)#: Analyse provided targets using ROOSTER.

computePeriodSelTrueAccuracy(target_id, predicted_periods, tolerance=0.1, catalog='santos-19-21')#: Compute PeriodSel true Accuracy for a given sample of target by comparing the reference period value to the value chosen by ROOSTER, with a tolerance interval.

getFeatureNames()#: Get name of feature that ROOSTER requires for classification.

getNumberEltTest()#: Return a tuple of integer, corresponding to the number of elements used to train each ROOSTER classifier.

getNumberEltTrain()#: Return a tuple of integer, corresponding to the number of elements used to train each ROOSTER classifier.

getScore()#: Returns ROOSTER classifying scores. Scores are returned in the following order: RotClassTestScore, PeriodSelTestScore. The ROOSTER instance must have been trained and tested before.

save(filename)#: Save the ROOSTER instance as filename.

test(target_id, p_candidates, features, catalog='santos-19-21', verbose=False, feature_names=None, e_p_err=None, E_p_err=None, tolerance=0.1)#: Test ROOSTER classifiers with the provided test set.

train(target_id, p_candidates, features, feature_names=None, catalog='santos-19-21', verbose=False, tolerance=0.1)#: Train ROOSTER classifiers with the provided training set.

star_privateer.create_rooster_feature_inputs(df, return_err=False)#: Take a DataFrame created by build_catalog_features and return ready-to-use input array for ROOSTER training and classification.