def compute_ambiguity(self, dataset):
"""
Finds occurrences of spans from 'dataset' that intersect with a span from this annotation but do not have this spans label.
label. If 'dataset' comprises a models predictions, this method provides a strong indicators
of a model's in-ability to dis-ambiguate between entities. For a full analysis, compute a confusion matrix.
:param dataset: a Dataset object containing a predicted version of this dataset.
:param leniency: a floating point value between [0,1] defining the leniency of the character spans to count as different. A value of zero considers only exact character matches while a positive value considers entities that differ by up to :code:`ceil(leniency * len(span)/2)` on either side.
:return: a dictionary containing the ambiguity computations on each gold, predicted file pair
"""
if not isinstance(dataset, Dataset):
raise ValueError("dataset must be instance of Dataset")
# verify files are consistent
diff = set([file.ann_path.split(os.sep)[-1] for file in self]).difference(set([file.ann_path.split(os.sep)[-1] for file in dataset]))
if diff:
raise ValueError("Dataset of predictions is missing the files: " + str(list(diff)))
#Dictionary storing ambiguity over dataset
ambiguity_dict = {}
for gold_data_file in self:
prediction_iter = iter(dataset)
prediction_data_file = next(prediction_iter)
while str(gold_data_file) != str(prediction_data_file):
prediction_data_file = next(prediction_iter)
gold_annotation = Annotations(gold_data_file.ann_path)
pred_annotation = Annotations(prediction_data_file.ann_path)
# compute matrix on the Annotation file level
ambiguity_dict[str(gold_data_file)] = gold_annotation.compute_ambiguity(pred_annotation)
return ambiguity_dict
def test_compute_ambiguity(self):
annotations1 = Annotations(join(self.dataset.get_data_directory(),
self.ann_files[0]),
annotation_type='ann')
annotations2 = Annotations(join(self.dataset.get_data_directory(),
self.ann_files[0]),
annotation_type='ann')
label, start, end, text = annotations2.get_entity_annotations()[0]
annotations2.add_entity('incorrect_label', start, end, text)
self.assertEqual(len(annotations1.compute_ambiguity(annotations2)), 1)
