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ann_converter.py
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import xml.dom.minidom
import xml.etree.ElementTree as ET
import datetime
from os import listdir
from os.path import isfile, join
import utils
import csv
class AnnConverter(object):
@staticmethod
def get_semehr_ann_label(ann):
str_context = ''
if ann.negation != 'Affirmed':
str_context += ann.negation + '_'
if ann.temporality != 'Recent':
str_context += ann.temporality + '_'
if ann.experiencer != 'Patient':
str_context += ann.experiencer + '_'
minor_type = ann.minor_type if hasattr(ann, 'minor_type') else ann.sty
return '%s%s' % (str_context, minor_type)
@staticmethod
def to_eHOST(doc_key, anns, file_pattern='%s.txt', id_pattern='smehr-%s-%s'):
elem_annotations = ET.Element("annotations")
elem_annotations.set('textSource', file_pattern % doc_key)
idx = 0
for d in anns: #sorted(anns, key=lambda x: x['ann'].start): # was just anns
ann = d['ann']
idx += 1
mention_id = id_pattern % (doc_key, idx)
AnnConverter.create_elem_ann(elem_annotations, mention_id, ann.start, ann.end, ann.str,
AnnConverter.get_semehr_ann_label(ann))
tree = ET.ElementTree(elem_annotations)
#return ET.tostring(elem_annotations, encoding='utf8', method='xml')
return xml.dom.minidom.parseString(ET.tostring(elem_annotations)).toprettyxml(indent=" ")
@staticmethod
def create_elem_ann(elem_annotations, mention_id, start, end, str, class_label):
elem_ann = ET.SubElement(elem_annotations, "annotation")
elem_mention = ET.SubElement(elem_ann, "mention")
elem_mention.set('id', mention_id)
elem_annotator = ET.SubElement(elem_ann, "annotator")
elem_annotator.set('id', 'semehr')
elem_annotator.text = 'semehr'
elem_span = ET.SubElement(elem_ann, "span")
elem_span.set('start', '%s' % start)
elem_span.set('end', '%s' % end)
elem_spanText = ET.SubElement(elem_ann, "spannedText")
elem_spanText.text = str
elem_date = ET.SubElement(elem_ann, "creationDate")
elem_date.text = datetime.datetime.now().strftime("%a %B %d %X %Z %Y")
#
elem_class = ET.SubElement(elem_annotations, "classMention")
elem_class.set('id', mention_id)
elem_mention_class = ET.SubElement(elem_class, "mentionClass")
elem_mention_class.set('id', class_label)
elem_mention_class.text = str
return elem_ann
@staticmethod
def load_ann_file(f, do_multi=True):
tree = ET.parse(f)
doc = tree.getroot()
ann2label = {}
ann2freq = {}
for ann in doc.findall("annotation"):
m_id = ann.find("mention").attrib["id"]
cm = doc.find('.//classMention[@id="' + m_id + '"]')
cls =cm.find('mentionClass').attrib["id"]
m_span = ann.find("span").attrib
annid = 'm-%s-%s' % (m_span['start'], m_span['end'])
m_text = ann.find("spannedText").text
freq = 0
if annid not in ann2freq:
ann2freq[annid] = 1
else:
if do_multi:
ann2freq[annid] += 1
annid_freq = '%s:%s' % (annid, ann2freq[annid])
ann2label[annid_freq] = {"text": m_text, "class": cls}
return ann2label
@staticmethod
def convert_csv_annotations(csv_file, text_folder, ann_folder, mapping_file, annotated_anns_file,
id_pattern='%s-%s', ann_file_pattern='%s.txt.knowtator.xml'):
with open(csv_file, newline='') as cf:
reader = csv.DictReader(cf)
label2concepts = {}
d2annotated_anns = {}
for r in reader:
d2annotated_anns[r['doc_id'] + ".txt"] = [{'s': r['start'], 'e': r['end']}]
if r['Skip Document'] != 'Yes':
utils.save_string(r['text'], join(text_folder, r['doc_id'] + ".txt"))
elem_annotations = ET.Element("annotations")
elem_annotations.set('textSource', r['doc_id'])
mention_id = id_pattern % (r['doc_id'], 0)
if r['Correct'] == 'Yes' and r['Negation'] == 'NOT Negated':
AnnConverter.create_elem_ann(elem_annotations, mention_id,
r['start'], r['end'], r['string_orig'], r['icd10-ch'])
xml = ET.tostring(elem_annotations, encoding='unicode', method='xml')
utils.save_string(xml, join(ann_folder, ann_file_pattern % r['doc_id']))
if r['icd10-ch'] not in label2concepts:
label2concepts[r['icd10-ch']] = []
if r['cui'] not in label2concepts[r['icd10-ch']]:
label2concepts[r['icd10-ch']].append(r['cui'])
utils.save_json_array(label2concepts, mapping_file)
utils.save_json_array(d2annotated_anns, annotated_anns_file)
@staticmethod
def populate_inter_annotator_results(ann_folder_1, ann_folder_2, output_file, missing_file,
correct_labels = ["VERIFIED_CORRECT"]):
ann_files = [f for f in listdir(ann_folder_1) if isfile(join(ann_folder_1, f))]
all_mentions = 0
missed = []
mismatched = []
for f in ann_files:
ann1 = AnnConverter.load_ann_file(join(ann_folder_1, f))
ann2 = AnnConverter.load_ann_file(join(ann_folder_2, f))
all_mentions += len(ann1)
for ann in ann1:
if ann not in ann2:
missed.append('%s\t%s\t%s' % (ann, ann1[ann]['text'], ann1[ann]['class']))
elif ann2[ann]['class'] != ann1[ann]['class'] and ann1[ann]['class'] not in correct_labels:
mismatched.append('%s\t%s\t%s\t%s\t%s' % (f, ann, ann1[ann]['text'], ann1[ann]['class'], ann2[ann]['class']))
print('\n'.join(mismatched))
print(len(missed), all_mentions)
utils.save_string('\n'.join(mismatched), output_file)
utils.save_string('\n'.join(missed), missing_file)
@staticmethod
def calculate_IAA(ann_folder_1, ann_folder_2, output_file):
from sklearn.metrics import cohen_kappa_score
ann_files = [f for f in listdir(ann_folder_1) if isfile(join(ann_folder_1, f))]
ann1_annotations = {}
ann2_annotations = {}
for f in ann_files:
ann1 = AnnConverter.load_ann_file(join(ann_folder_1, f), do_multi=False)
ann2 = AnnConverter.load_ann_file(join(ann_folder_2, f), do_multi=False)
for ann in ann1:
ann1_annotations['%s_%s' % (f, ann)] = ann1[ann]['class']
for ann in ann2:
ann2_annotations['%s_%s' % (f, ann)] = ann2[ann]['class']
merged_anns = list(set(list(ann1_annotations.keys()) + list(ann2_annotations.keys())))
ann1_merged = []
ann2_merged = []
label_missed = 'missed'
cat2pares = {'subject': {'ann1': [], 'ann2': []},
'irrelevant': {'ann1': [], 'ann2': []},
'trajectory': {'ann1': [], 'ann2': []},
}
output = []
for ann in merged_anns:
ann1_label = label_missed if ann not in ann1_annotations else ann1_annotations[ann]
ann2_label = label_missed if ann not in ann2_annotations else ann2_annotations[ann]
ann1_merged.append(ann1_label)
ann2_merged.append(ann2_label)
if ann1_label == 'Irrelevant_label' or ann2_label == 'Irrelevant_label':
cat2pares['irrelevant']['ann1'].append(ann1_label)
cat2pares['irrelevant']['ann2'].append(ann2_label)
elif ann1_label in ['Trajectory_Subject', 'General_Trajectory'] or ann2_label in ['Trajectory_Subject', 'General_Trajectory']:
cat2pares['subject']['ann1'].append(ann1_label)
cat2pares['subject']['ann2'].append(ann2_label)
elif ann1_label in ['better(Trajetory)', 'worse(Trajectory)'] or ann2_label in ['better(Trajetory)', 'worse(Trajectory)']:
cat2pares['trajectory']['ann1'].append(ann1_label)
cat2pares['trajectory']['ann2'].append(ann2_label)
output.append('%s\t%s\t%s' % (ann, ann1_label, ann2_label))
print('kappa score: [%s]', cohen_kappa_score(ann1_merged, ann2_merged))
for cat in cat2pares:
print('%s kappa score: [%s]' % (cat, cohen_kappa_score(cat2pares[cat]['ann1'], cat2pares[cat]['ann2'])))
utils.save_string('\n'.join(output), output_file)
if __name__ == "__main__":
# AnnConverter.load_ann_file('S:/NLP/annotation_Steven/stroke_nlp/saved/Stroke_id_105.txt.knowtator.xml')
# AnnConverter.populate_inter_annotator_results('S:/NLP/annotation_Kristiina/stroke_nlp/saved',
# 'S:/NLP/annotation_Steven/stroke_nlp/saved', 'mismatched.tsv')
# AnnConverter.populate_inter_annotator_results('S:/NLP/annotation_Steven/stroke_nlp/saved',
# 'P:/wuh/SemEHR-working/outputs/nlp2phenome',
# 'kristiina_corrections.tsv', 'steven_added.tsv')
ann_folder = '/data/annotated_data/'
ann_files = [f for f in listdir(ann_folder) if isfile(join(ann_folder, f))]
for f in ann_files:
print('processing %s...' % f)
AnnConverter.convert_csv_annotations(join(ann_folder, f), join(ann_folder, 'corpus'), join(ann_folder, 'gold'), join(ann_folder, 'concept_mapping.json'), join(ann_folder, 'annotated_anns.json'))