import pytest import numpy from numpy.testing import assert_equal, assert_array_equal, assert_almost_equal from thinc.api import get_current_ops from spacy.language import Language from spacy.training import Example from spacy.util import fix_random_seed, registry OPS = get_current_ops() SPAN_KEY = "labeled_spans" TRAIN_DATA = [ ("Who is Shaka Khan?", {"spans": {SPAN_KEY: [(7, 17, "PERSON")]}}), ( "I like London and Berlin.", {"spans": {SPAN_KEY: [(7, 13, "LOC"), (18, 24, "LOC")]}}, ), ] def make_get_examples(nlp): train_examples = [] for t in TRAIN_DATA: eg = Example.from_dict(nlp.make_doc(t[0]), t[1]) train_examples.append(eg) def get_examples(): return train_examples return get_examples def test_no_label(): nlp = Language() nlp.add_pipe("spancat", config={"spans_key": SPAN_KEY}) with pytest.raises(ValueError): nlp.initialize() def test_no_resize(): nlp = Language() spancat = nlp.add_pipe("spancat", config={"spans_key": SPAN_KEY}) spancat.add_label("Thing") spancat.add_label("Phrase") assert spancat.labels == ("Thing", "Phrase") nlp.initialize() assert spancat.model.get_dim("nO") == 2 # this throws an error because the spancat can't be resized after initialization with pytest.raises(ValueError): spancat.add_label("Stuff") def test_implicit_labels(): nlp = Language() spancat = nlp.add_pipe("spancat", config={"spans_key": SPAN_KEY}) assert len(spancat.labels) == 0 train_examples = [] for t in TRAIN_DATA: train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1])) nlp.initialize(get_examples=lambda: train_examples) assert spancat.labels == ("PERSON", "LOC") def test_explicit_labels(): nlp = Language() spancat = nlp.add_pipe("spancat", config={"spans_key": SPAN_KEY}) assert len(spancat.labels) == 0 spancat.add_label("PERSON") spancat.add_label("LOC") nlp.initialize() assert spancat.labels == ("PERSON", "LOC") @pytest.mark.parametrize( "max_positive,nr_results", [(None, 4), (1, 2), (2, 3), (3, 4), (4, 4)] ) def test_make_spangroup(max_positive, nr_results): fix_random_seed(0) nlp = Language() spancat = nlp.add_pipe( "spancat", config={"spans_key": SPAN_KEY, "threshold": 0.5, "max_positive": max_positive}, ) doc = nlp.make_doc("Greater London") ngram_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[1, 2]) indices = ngram_suggester([doc])[0].dataXd assert_array_equal(indices, numpy.asarray([[0, 1], [1, 2], [0, 2]])) labels = ["Thing", "City", "Person", "GreatCity"] scores = numpy.asarray( [[0.2, 0.4, 0.3, 0.1], [0.1, 0.6, 0.2, 0.4], [0.8, 0.7, 0.3, 0.9]], dtype="f" ) spangroup = spancat._make_span_group(doc, indices, scores, labels) assert len(spangroup) == nr_results # first span is always the second token "London" assert spangroup[0].text == "London" assert spangroup[0].label_ == "City" assert_almost_equal(0.6, spangroup.attrs["scores"][0], 5) # second span depends on the number of positives that were allowed assert spangroup[1].text == "Greater London" if max_positive == 1: assert spangroup[1].label_ == "GreatCity" assert_almost_equal(0.9, spangroup.attrs["scores"][1], 5) else: assert spangroup[1].label_ == "Thing" assert_almost_equal(0.8, spangroup.attrs["scores"][1], 5) if nr_results > 2: assert spangroup[2].text == "Greater London" if max_positive == 2: assert spangroup[2].label_ == "GreatCity" assert_almost_equal(0.9, spangroup.attrs["scores"][2], 5) else: assert spangroup[2].label_ == "City" assert_almost_equal(0.7, spangroup.attrs["scores"][2], 5) assert spangroup[-1].text == "Greater London" assert spangroup[-1].label_ == "GreatCity" assert_almost_equal(0.9, spangroup.attrs["scores"][-1], 5) def test_simple_train(): fix_random_seed(0) nlp = Language() spancat = nlp.add_pipe("spancat", config={"spans_key": SPAN_KEY}) get_examples = make_get_examples(nlp) nlp.initialize(get_examples) sgd = nlp.create_optimizer() assert len(spancat.labels) != 0 for i in range(40): losses = {} nlp.update(list(get_examples()), losses=losses, drop=0.1, sgd=sgd) doc = nlp("I like London and Berlin.") assert doc.spans[spancat.key] == doc.spans[SPAN_KEY] assert len(doc.spans[spancat.key]) == 2 assert len(doc.spans[spancat.key].attrs["scores"]) == 2 assert doc.spans[spancat.key][0].text == "London" scores = nlp.evaluate(get_examples()) assert f"spans_{SPAN_KEY}_f" in scores assert scores[f"spans_{SPAN_KEY}_f"] == 1.0 # also test that the spancat works for just a single entity in a sentence doc = nlp("London") assert len(doc.spans[spancat.key]) == 1 def test_ngram_suggester(en_tokenizer): # test different n-gram lengths for size in [1, 2, 3]: ngram_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[size]) docs = [ en_tokenizer(text) for text in [ "a", "a b", "a b c", "a b c d", "a b c d e", "a " * 100, ] ] ngrams = ngram_suggester(docs) # span sizes are correct for s in ngrams.data: assert s[1] - s[0] == size # spans are within docs offset = 0 for i, doc in enumerate(docs): spans = ngrams.dataXd[offset : offset + ngrams.lengths[i]] spans_set = set() for span in spans: assert 0 <= span[0] < len(doc) assert 0 < span[1] <= len(doc) spans_set.add((int(span[0]), int(span[1]))) # spans are unique assert spans.shape[0] == len(spans_set) offset += ngrams.lengths[i] # the number of spans is correct assert_array_equal( OPS.to_numpy(ngrams.lengths), [max(0, len(doc) - (size - 1)) for doc in docs], ) # test 1-3-gram suggestions ngram_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[1, 2, 3]) docs = [ en_tokenizer(text) for text in ["a", "a b", "a b c", "a b c d", "a b c d e"] ] ngrams = ngram_suggester(docs) assert_array_equal(OPS.to_numpy(ngrams.lengths), [1, 3, 6, 9, 12]) assert_array_equal( OPS.to_numpy(ngrams.data), [ # doc 0 [0, 1], # doc 1 [0, 1], [1, 2], [0, 2], # doc 2 [0, 1], [1, 2], [2, 3], [0, 2], [1, 3], [0, 3], # doc 3 [0, 1], [1, 2], [2, 3], [3, 4], [0, 2], [1, 3], [2, 4], [0, 3], [1, 4], # doc 4 [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [0, 2], [1, 3], [2, 4], [3, 5], [0, 3], [1, 4], [2, 5], ], ) # test some empty docs ngram_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[1]) docs = [en_tokenizer(text) for text in ["", "a", ""]] ngrams = ngram_suggester(docs) assert_array_equal(OPS.to_numpy(ngrams.lengths), [len(doc) for doc in docs]) # test all empty docs ngram_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[1]) docs = [en_tokenizer(text) for text in ["", "", ""]] ngrams = ngram_suggester(docs) assert_array_equal(OPS.to_numpy(ngrams.lengths), [len(doc) for doc in docs]) def test_ngram_sizes(en_tokenizer): # test that the range suggester works well size_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[1, 2, 3]) suggester_factory = registry.misc.get("spacy.ngram_range_suggester.v1") range_suggester = suggester_factory(min_size=1, max_size=3) docs = [ en_tokenizer(text) for text in ["a", "a b", "a b c", "a b c d", "a b c d e"] ] ngrams_1 = size_suggester(docs) ngrams_2 = range_suggester(docs) assert_array_equal(OPS.to_numpy(ngrams_1.lengths), [1, 3, 6, 9, 12]) assert_array_equal(OPS.to_numpy(ngrams_1.lengths), OPS.to_numpy(ngrams_2.lengths)) assert_array_equal(OPS.to_numpy(ngrams_1.data), OPS.to_numpy(ngrams_2.data)) # one more variation suggester_factory = registry.misc.get("spacy.ngram_range_suggester.v1") range_suggester = suggester_factory(min_size=2, max_size=4) ngrams_3 = range_suggester(docs) assert_array_equal(OPS.to_numpy(ngrams_3.lengths), [0, 1, 3, 6, 9])