XGBClassifier (starclass.XGBClassifier)

class starclass.XGBClassifier(clfile='xgb_classifier.json', *args, **kwargs)

Bases: BaseClassifier

General XGB Classification

Code author: Refilwe Kgoadi <refilwe.kgoadi1@my.jcu.edu.au>

__init__(clfile='xgb_classifier.json', *args, **kwargs)

Initialize the classifier object with optimised parameters.

Parameters:

• clfile (str) – saved classifier file.

• n_estimators (int) – number of boosted trees in the ensemble.

• max_depth (int) – maximum depth of each tree in the ensemble.

• learning_rate – boosting learning rate.

• reg_alpha – L1 regularization on the features.

• objective – learning objective of the algorithm.

• booster – booster used in the tree.

• eval_metric – Evaluation metric.

Code author: Refilwe Kgoadi <refilwe.kgoadi1@my.jcu.edu.au>

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>

close()

Close the classifier.

do_classify(features)

My classification that will be run on each lightcurve

Parameters:

features (dict) – Dictionary of other features.

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)

Load the xgb clasifier.

Parameters:

infile (str) – Path to file from which to load the trained XGB classifier model.

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)

Save xgb classifier object.

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, savecl=True, recalc=False, overwrite=False)

Training classifier using the …

property classifier_model

property random_seed

Random seed used in derived classifiers.

property random_state

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