SLOSHClassifier (starclass.SLOSHClassifier)

class starclass.SLOSHClassifier(clfile='SLOSH_Classifier_Model.hdf5', mc_iterations=10, *args, **kwargs)

Bases: BaseClassifier

Solar-like Oscillation Shape Hunter (SLOSH) Classifier.

Code author: Marc Hon <mtyh555@uowmail.edu.au>

Code author: James Kuszlewicz <kuszlewicz@mps.mpg.de>

Code author: Rasmus Handberg <rasmush@phys.au.dk>

__init__(clfile='SLOSH_Classifier_Model.hdf5', mc_iterations=10, *args, **kwargs)

Initialization for the class.

Parameters:

saved_models – LIST of saved classifier filenames. Supports multi-classifier predictions.

classify(task)

Classify a star from the lightcurve and other features.

Will run the do_classify() method and check some of the output and calculate various performance metrics.

Parameters:

features (dict) – Dictionary of features, including the lightcurve itself.

Returns:

Dictionary of classifications

Return type:

dict

See also

do_classify(), load_star()

Code author: Rasmus Handberg <rasmush@phys.au.dk>

clear_model_list()

Helper function to clear classifiers in the classifier list. :return: None

close()

Close the classifier.

do_classify(features)

Prediction for a star, producing output determining if it is a solar-like oscillator.

Parameters:

features (dict) – Dictionary of features. Of particular interest should be the lightcurve (lightkurve.TessLightCurve object) and powerspectum which contains the lightcurve and power density spectrum respectively.

Returns:

Dictionary of stellar classifications.

Return type:

dict

feature_importance_complete(tset=None, features=None, probs=None, diagnostics=None)

Function which will be called when feature importance is finishing.

Parameters:

• tset –

• features –

• probs –

• diagnostics –

See also

BaseClassifier.train()

Code author: Rasmus Handberg <rasmush@phys.au.dk>

load(infile)

Loads a classifier model and adds it to the list of classifiers. :param infile: Path to trained model :return: None

load_star(task)

Receive a task from the TaskManager, loads the lightcurve and returns derived features.

Parameters:

task (dict) – Task dictionary as returned by TaskManager.get_task().

Returns:

Dictionary with features.

Return type:

dict

See also

TaskManager.get_task()

Code author: Rasmus Handberg <rasmush@phys.au.dk>

parse_labels(labels)

Convert iterator of labels into full numpy array, with only one label per star.

TODO: How do we handle multiple labels better?

save(outfile)

Saves all loaded classifier models. :param outfile: Base output file name :return: None

save_model(model, model_file)

Saves out trained model : param model: trained model : param model_file: Output file name for model :return: None

test(tset, save=None, feature_importance=False)

Test classifier using training-set, which has been created with a test-fraction.

Parameters:

• tset (TrainingSet) – Training-set to run testing on.

• save (callable, optional) – Function to call for saving test-predictions.

test_complete(tset=None, features=None, probs=None, diagnostics=None)

Function which will be called when training is finishing.

Parameters:

• tset –

• features –

• probs –

• diagnostics –

See also

BaseClassifier.train()

Code author: Rasmus Handberg <rasmush@phys.au.dk>

train(tset)

Trains a fresh classifier using a default NN architecture and parameters as of the Hon et al. (2018) paper.

Parameters:

• train_folder – The folder where training images are kept. These must be separated into subfolders by the image categories. For example: Train_Folder/1/ - Positive Detections; Train_Folder/0/ - Non-Detections

• features (iterator of dicts) – Iterator of features-dictionaries similar to those in do_classify().

• labels (iterator of lists) – For each feature, provides a list of the assigned known StellarClasses identifications.

Returns:

A trained classifier model.

Return type:

model

property classifier_model

property random_seed

Random seed used in derived classifiers.

property random_state

Random state (numpy.random.RandomState) corresponding to random_seed.