def test_save_and_load_hierarchical_rf(self):
classifier = HierarchicalRandomForest(self.taxonomy_dictionary)
classifier.fit(self.features, self.labels)
predicted_probs = classifier.predict_proba(self.features)
classifier.save_model(self.tmp_dir)
classifier2 = HierarchicalRandomForest(self.taxonomy_dictionary)
classifier2.load_model(self.tmp_dir)
predicted_probs2 = classifier2.predict_proba(self.features)
self.assertTrue((predicted_probs == predicted_probs2).all(axis=None))
def test_larger_dictionary(self):
taxonomy_dictionary = self.taxonomy_dictionary
taxonomy_dictionary[
'Stochastic'] = taxonomy_dictionary['Stochastic'] + ['new class']
classifier = HierarchicalRandomForest(self.taxonomy_dictionary)
classifier.fit(self.features, self.labels)
predicted_probs = classifier.predict_proba(self.features)
self.assertEqual(predicted_probs.shape, (len(self.features), 15))
def test_fit(self):
taxonomy_dictionary = {
'Stochastic': ['LPV', 'QSO', 'YSO', 'CV/Nova', 'Blazar', 'AGN'],
'Periodic': ['RRL', 'EB', 'DSCT', 'Ceph', 'Periodic-Other'],
'Transient': ['SNIa', 'SNII', 'SNIbc']
}
model = HierarchicalRandomForest(taxonomy_dictionary)
model.fit(self.train_features, self.train_labels)
probs = model.predict_proba(self.train_features)
print(probs.head())
predicted_classes = model.predict(self.train_features)
print(predicted_classes.head())
def test_predict_proba(self):
classifier = HierarchicalRandomForest(self.taxonomy_dictionary)
classifier.fit(self.features, self.labels)
predicted_probs = classifier.predict_proba(self.features)
self.is_sum_one(predicted_probs)