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test_labels.py
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from typing import List
from flair.data import Label, Relation, Sentence, Span
def test_token_tags():
# example sentence
sentence = Sentence("I love Berlin")
# set 4 labels for 2 tokens ('love' is tagged twice)
sentence[1].add_label("pos", "verb")
sentence[1].add_label("sentiment", "positive")
sentence[2].add_label("pos", "proper noun")
sentence[0].add_label("pos", "pronoun")
# check if there are three POS labels with correct text and values
labels: List[Label] = sentence.get_labels("pos")
assert len(labels) == 3
assert labels[0].data_point.text == "I"
assert labels[0].value == "pronoun"
assert labels[1].data_point.text == "love"
assert labels[1].value == "verb"
assert labels[2].data_point.text == "Berlin"
assert labels[2].value == "proper noun"
# check if there are is one SENTIMENT label with correct text and values
labels: List[Label] = sentence.get_labels("sentiment")
assert len(labels) == 1
assert labels[0].data_point.text == "love"
assert labels[0].value == "positive"
# check if all tokens are correctly labeled
assert len(sentence) == 3
assert sentence[0].text == "I"
assert sentence[1].text == "love"
assert sentence[2].text == "Berlin"
assert len(sentence[0].get_labels("pos")) == 1
assert len(sentence[1].get_labels("pos")) == 1
assert len(sentence[1].labels) == 2
assert len(sentence[2].get_labels("pos")) == 1
assert sentence[1].get_label("pos").value == "verb"
assert sentence[1].get_label("sentiment").value == "positive"
# remove the pos label from the last word
sentence[2].remove_labels("pos")
# there should be 2 POS labels left
labels: List[Label] = sentence.get_labels("pos")
assert len(labels) == 2
assert len(sentence[0].get_labels("pos")) == 1
assert len(sentence[1].get_labels("pos")) == 1
assert len(sentence[1].labels) == 2
assert len(sentence[2].get_labels("pos")) == 0
# now remove all pos tags
sentence.remove_labels("pos")
print(sentence[0].get_labels("pos"))
assert len(sentence.get_labels("pos")) == 0
assert len(sentence.get_labels("sentiment")) == 1
assert len(sentence.labels) == 1
assert len(sentence[0].get_labels("pos")) == 0
assert len(sentence[1].get_labels("pos")) == 0
assert len(sentence[2].get_labels("pos")) == 0
def test_span_tags():
# set 3 labels for 2 spans (HU is tagged twice)
sentence = Sentence("Humboldt Universität zu Berlin is located in Berlin .")
sentence[0:4].add_label("ner", "Organization")
sentence[0:4].add_label("ner", "University")
sentence[7:8].add_label("ner", "City")
# check if there are three labels with correct text and values
labels: List[Label] = sentence.get_labels("ner")
assert len(labels) == 3
assert labels[0].data_point.text == "Humboldt Universität zu Berlin"
assert labels[0].value == "Organization"
assert labels[1].data_point.text == "Humboldt Universität zu Berlin"
assert labels[1].value == "University"
assert labels[2].data_point.text == "Berlin"
assert labels[2].value == "City"
# check if there are two spans with correct text and values
spans: List[Span] = sentence.get_spans("ner")
assert len(spans) == 2
assert spans[0].text == "Humboldt Universität zu Berlin"
assert len(spans[0].get_labels("ner")) == 2
assert spans[1].text == "Berlin"
assert spans[1].get_label("ner").value == "City"
# now delete the NER tags of "Humboldt-Universität zu Berlin"
sentence[0:4].remove_labels("ner")
# should be only one NER label left
labels: List[Label] = sentence.get_labels("ner")
assert len(labels) == 1
assert labels[0].data_point.text == "Berlin"
assert labels[0].value == "City"
# and only one NER span
spans: List[Span] = sentence.get_spans("ner")
assert len(spans) == 1
assert spans[0].text == "Berlin"
assert spans[0].get_label("ner").value == "City"
def test_different_span_tags():
# set 3 labels for 2 spans (HU is tagged twice with different tags)
sentence = Sentence("Humboldt Universität zu Berlin is located in Berlin .")
sentence[0:4].add_label("ner", "Organization")
sentence[0:4].add_label("orgtype", "University")
sentence[7:8].add_label("ner", "City")
# check if there are three labels with correct text and values
labels: List[Label] = sentence.get_labels("ner")
assert len(labels) == 2
assert labels[0].data_point.text == "Humboldt Universität zu Berlin"
assert labels[0].value == "Organization"
assert labels[1].data_point.text == "Berlin"
assert labels[1].value == "City"
# check if there are two spans with correct text and values
spans: List[Span] = sentence.get_spans("ner")
assert len(spans) == 2
assert spans[0].text == "Humboldt Universität zu Berlin"
assert spans[0].get_label("ner").value == "Organization"
assert spans[0].get_label("orgtype").value == "University"
assert len(spans[0].get_labels("ner")) == 1
assert spans[1].text == "Berlin"
assert spans[1].get_label("ner").value == "City"
# now delete the NER tags of "Humboldt-Universität zu Berlin"
sentence[0:4].remove_labels("ner")
# should be only one NER label left
labels: List[Label] = sentence.get_labels("ner")
assert len(labels) == 1
assert labels[0].data_point.text == "Berlin"
assert labels[0].value == "City"
# and only one NER span
spans: List[Span] = sentence.get_spans("ner")
assert len(spans) == 1
assert spans[0].text == "Berlin"
assert spans[0].get_label("ner").value == "City"
# but there is also one orgtype span and label
labels: List[Label] = sentence.get_labels("orgtype")
assert len(labels) == 1
assert labels[0].data_point.text == "Humboldt Universität zu Berlin"
assert labels[0].value == "University"
# and only one NER span
spans: List[Span] = sentence.get_spans("orgtype")
assert len(spans) == 1
assert spans[0].text == "Humboldt Universität zu Berlin"
assert spans[0].get_label("orgtype").value == "University"
# let's add the NER tag back
sentence[0:4].add_label("ner", "Organization")
# check if there are three labels with correct text and values
labels: List[Label] = sentence.get_labels("ner")
print(labels)
assert len(labels) == 2
assert labels[0].data_point.text == "Humboldt Universität zu Berlin"
assert labels[0].value == "Organization"
assert labels[1].data_point.text == "Berlin"
assert labels[1].value == "City"
# check if there are two spans with correct text and values
spans: List[Span] = sentence.get_spans("ner")
assert len(spans) == 2
assert spans[0].text == "Humboldt Universität zu Berlin"
assert spans[0].get_label("ner").value == "Organization"
assert spans[0].get_label("orgtype").value == "University"
assert len(spans[0].get_labels("ner")) == 1
assert spans[1].text == "Berlin"
assert spans[1].get_label("ner").value == "City"
# now remove all NER tags
sentence.remove_labels("ner")
assert len(sentence.get_labels("ner")) == 0
assert len(sentence.get_spans("ner")) == 0
assert len(sentence.get_spans("orgtype")) == 1
assert len(sentence.get_labels("orgtype")) == 1
assert len(sentence.labels) == 1
assert len(sentence[0:4].get_labels("ner")) == 0
assert len(sentence[0:4].get_labels("orgtype")) == 1
def test_relation_tags():
# set 3 labels for 2 spans (HU is tagged twice with different tags)
sentence = Sentence("Humboldt Universität zu Berlin is located in Berlin .")
# create two relation label
Relation(sentence[0:4], sentence[7:8]).add_label("rel", "located in")
Relation(sentence[0:2], sentence[3:4]).add_label("rel", "university of")
Relation(sentence[0:2], sentence[3:4]).add_label("syntactic", "apposition")
# there should be two relation labels
labels: List[Label] = sentence.get_labels("rel")
assert len(labels) == 2
assert labels[0].value == "located in"
assert labels[1].value == "university of"
# there should be one syntactic labels
labels: List[Label] = sentence.get_labels("syntactic")
assert len(labels) == 1
# there should be two relations, one with two and one with one label
relations: List[Relation] = sentence.get_relations("rel")
assert len(relations) == 2
assert len(relations[0].labels) == 1
assert len(relations[1].labels) == 2
def test_sentence_labels():
# example sentence
sentence = Sentence("I love Berlin")
sentence.add_label("sentiment", "positive")
sentence.add_label("topic", "travelling")
assert len(sentence.labels) == 2
assert len(sentence.get_labels("sentiment")) == 1
assert len(sentence.get_labels("topic")) == 1
# add another topic label
sentence.add_label("topic", "travelling")
assert len(sentence.labels) == 3
assert len(sentence.get_labels("sentiment")) == 1
assert len(sentence.get_labels("topic")) == 2
sentence.remove_labels("topic")
assert len(sentence.labels) == 1
assert len(sentence.get_labels("sentiment")) == 1
assert len(sentence.get_labels("topic")) == 0
def test_mixed_labels():
# example sentence
sentence = Sentence("I love New York")
# has sentiment value
sentence.add_label("sentiment", "positive")
# has 4 part of speech tags
sentence[1].add_label("pos", "verb")
sentence[2].add_label("pos", "proper noun")
sentence[3].add_label("pos", "proper noun")
sentence[0].add_label("pos", "pronoun")
# has 1 NER tag
sentence[2:4].add_label("ner", "City")
# should be in total 6 labels
assert len(sentence.labels) == 6
assert len(sentence.get_labels("pos")) == 4
assert len(sentence.get_labels("sentiment")) == 1
assert len(sentence.get_labels("ner")) == 1
def test_data_point_equality():
# example sentence
sentence = Sentence("George Washington went to Washington .")
# add two NER labels
sentence[0:2].add_label("span_ner", "PER")
sentence[0:2].add_label("span_other", "Politician")
sentence[4].add_label("ner", "LOC")
sentence[4].add_label("other", "Village")
# get the four labels
ner_label = sentence.get_label("ner")
other_label = sentence.get_label("other")
span_ner_label = sentence.get_label("span_ner")
span_other_label = sentence.get_label("span_other")
# check that only two of the respective data points are equal
assert ner_label.data_point == other_label.data_point
assert span_ner_label.data_point == span_other_label.data_point
assert ner_label.data_point != span_other_label.data_point
assert other_label.data_point != span_ner_label.data_point