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delete outdated tests
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import pytest
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import spacy
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from spacy.matcher import PhraseMatcher
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from spacy.training import Example
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from spacy.lang.en import English
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from spacy.tests.util import make_tempdir
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from spacy.tokens import Doc
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from spacy.pipeline.coref import DEFAULT_CLUSTERS_PREFIX
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from spacy.pipeline.coref_er import DEFAULT_MENTIONS
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# fmt: off
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TRAIN_DATA = [
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(
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"John Smith told Laura that he was running late and asked her whether she could pick up their kids.",
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{
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"spans": {
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DEFAULT_MENTIONS: [
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(0, 10, "MENTION"),
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(16, 21, "MENTION"),
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(27, 29, "MENTION"),
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(57, 60, "MENTION"),
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(69, 72, "MENTION"),
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(87, 92, "MENTION"),
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(87, 97, "MENTION"),
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],
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f"{DEFAULT_CLUSTERS_PREFIX}_1": [
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(0, 10, "MENTION"), # John
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(27, 29, "MENTION"),
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(87, 92, "MENTION"), # 'their' refers to John and Laur
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],
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f"{DEFAULT_CLUSTERS_PREFIX}_2": [
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(16, 21, "MENTION"), # Laura
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(57, 60, "MENTION"),
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(69, 72, "MENTION"),
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(87, 92, "MENTION"), # 'their' refers to John and Laura
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],
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}
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},
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),
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(
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"Yes, I noticed that many friends around me received it. It seems that almost everyone received this SMS.",
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{
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"spans": {
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DEFAULT_MENTIONS: [
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(5, 6, "MENTION"),
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(40, 42, "MENTION"),
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(52, 54, "MENTION"),
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(95, 103, "MENTION"),
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],
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f"{DEFAULT_CLUSTERS_PREFIX}_1": [
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(5, 6, "MENTION"), # I
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(40, 42, "MENTION"),
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],
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f"{DEFAULT_CLUSTERS_PREFIX}_2": [
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(52, 54, "MENTION"), # SMS
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(95, 103, "MENTION"),
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]
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}
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},
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),
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]
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# fmt: on
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@pytest.fixture
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def nlp():
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return English()
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@pytest.fixture
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def examples(nlp):
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examples = []
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for text, annot in TRAIN_DATA:
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# eg = Example.from_dict(nlp.make_doc(text), annot)
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# if PR #7197 is merged, replace below with above line
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ref_doc = nlp.make_doc(text)
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for key, span_list in annot["spans"].items():
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spans = []
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for span_tuple in span_list:
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start_char = span_tuple[0]
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end_char = span_tuple[1]
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label = span_tuple[2]
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span = ref_doc.char_span(start_char, end_char, label=label)
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spans.append(span)
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ref_doc.spans[key] = spans
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eg = Example(nlp.make_doc(text), ref_doc)
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examples.append(eg)
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return examples
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def test_coref_er_no_POS(nlp):
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doc = nlp("The police woman talked to him.")
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coref_er = nlp.add_pipe("coref_er", last=True)
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with pytest.raises(ValueError):
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coref_er(doc)
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def test_coref_er_with_POS(nlp):
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words = ["The", "police", "woman", "talked", "to", "him", "."]
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pos = ["DET", "NOUN", "NOUN", "VERB", "ADP", "PRON", "PUNCT"]
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doc = Doc(nlp.vocab, words=words, pos=pos)
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coref_er = nlp.add_pipe("coref_er", last=True)
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coref_er(doc)
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assert len(doc.spans[coref_er.span_mentions]) == 1
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mention = doc.spans[coref_er.span_mentions][0]
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assert (mention.text, mention.start, mention.end) == ("him", 5, 6)
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def test_coref_er_custom_POS(nlp):
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words = ["The", "police", "woman", "talked", "to", "him", "."]
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pos = ["DET", "NOUN", "NOUN", "VERB", "ADP", "PRON", "PUNCT"]
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doc = Doc(nlp.vocab, words=words, pos=pos)
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config = {"matcher_key": "POS", "matcher_values": ["NOUN"]}
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coref_er = nlp.add_pipe("coref_er", last=True, config=config)
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coref_er(doc)
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assert len(doc.spans[coref_er.span_mentions]) == 1
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mention = doc.spans[coref_er.span_mentions][0]
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assert (mention.text, mention.start, mention.end) == ("police woman", 1, 3)
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def test_coref_clusters(nlp, examples):
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coref_er = nlp.add_pipe("coref_er", last=True)
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coref = nlp.add_pipe("coref", last=True)
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coref.initialize(lambda: examples)
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words = ["Laura", "walked", "her", "dog", "."]
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pos = ["PROPN", "VERB", "PRON", "NOUN", "PUNCT"]
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doc = Doc(nlp.vocab, words=words, pos=pos)
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coref_er(doc)
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coref(doc)
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assert len(doc.spans[coref_er.span_mentions]) > 0
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found_clusters = 0
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for name, spans in doc.spans.items():
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if name.startswith(coref.span_cluster_prefix):
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found_clusters += 1
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assert found_clusters > 0
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def test_coref_er_score(nlp, examples):
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config = {"matcher_key": "POS", "matcher_values": []}
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coref_er = nlp.add_pipe("coref_er", last=True, config=config)
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coref = nlp.add_pipe("coref", last=True)
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coref.initialize(lambda: examples)
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mentions_key = coref_er.span_mentions
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cluster_prefix_key = coref.span_cluster_prefix
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matcher = PhraseMatcher(nlp.vocab)
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terms_1 = ["Laura", "her", "she"]
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terms_2 = ["it", "this SMS"]
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matcher.add("A", [nlp.make_doc(text) for text in terms_1])
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matcher.add("B", [nlp.make_doc(text) for text in terms_2])
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for eg in examples:
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pred = eg.predicted
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matches = matcher(pred, as_spans=True)
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pred.set_ents(matches)
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coref_er(pred)
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coref(pred)
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eg.predicted = pred
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# TODO: if #7209 is merged, experiment with 'include_label'
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scores = coref_er.score([eg])
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assert f"{mentions_key}_f" in scores
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scores = coref.score([eg])
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assert f"{cluster_prefix_key}_f" in scores
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def test_coref_serialization(nlp):
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# Test that the coref component can be serialized
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config_er = {"matcher_key": "TAG", "matcher_values": ["NN"]}
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nlp.add_pipe("coref_er", last=True, config=config_er)
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nlp.add_pipe("coref", last=True)
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assert "coref_er" in nlp.pipe_names
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assert "coref" in nlp.pipe_names
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with make_tempdir() as tmp_dir:
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nlp.to_disk(tmp_dir)
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nlp2 = spacy.load(tmp_dir)
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assert "coref_er" in nlp2.pipe_names
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assert "coref" in nlp2.pipe_names
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coref_er_2 = nlp2.get_pipe("coref_er")
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assert coref_er_2.matcher_key == "TAG"
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