The hcga class

Object-oriented API to hcga.

class hcga.hcga.Hcga

hcga standard object class.

init function.

analyse_features(feature_file=None, results_folder='./results', graph_removal=0.3, interpretability=1, analysis_type='classification', model='XG', compute_shap=True, kfold=True, reduce_set=True, reduced_set_size=100, reduced_set_max_correlation=0.9, plot=True, max_feats_plot=20, max_feats_plot_dendrogram=100, n_repeats=1, n_splits=None, random_state=42, test_size=0.2, trained_model=None, save_model=False)

Analyse features.

combine_features()

Combine features.

extract(n_workers=1, mode='slow', norm=True, stats_level='advanced', runtimes=False, node_feat=True, timeout=30, connected=False, prediction_set=False)

Exctract features.

generate_data(dataset_name='ENZYMES', folder='./datasets')

generate benchmark data.

load_data(dataset='./datasets/ENZYMES.pkl', prediction_graphs=False)

Load dataset.

load_features(feature_file='./results/features.pkl')

Load features.

load_model(model_file='./results/model.pkl')

Load model.

pairwise_classification(feature_file=None, model='XG', graph_removal=0.3, interpretability=1, n_top_features=5, reduce_set=False, reduced_set_size=100, reduced_set_max_correlation=0.5, n_repeats=1, n_splits=None, analysis_type='classification')

Apply pairwise classification.

save_features(feature_file='./results/features.pkl')

Save features.