import pytest import numpy from spacy.training import Example from spacy.lang.en import English from spacy.pipeline import AttributeRuler from spacy import util, registry from ..util import get_doc, make_tempdir @pytest.fixture def nlp(): return English() @pytest.fixture def pattern_dicts(): return [ { "patterns": [[{"ORTH": "a"}], [{"ORTH": "irrelevant"}]], "attrs": {"LEMMA": "the", "MORPH": "Case=Nom|Number=Plur"}, }, # one pattern sets the lemma {"patterns": [[{"ORTH": "test"}]], "attrs": {"LEMMA": "cat"}}, # another pattern sets the morphology { "patterns": [[{"ORTH": "test"}]], "attrs": {"MORPH": "Case=Nom|Number=Sing"}, "index": 0, }, ] @registry.misc("attribute_ruler_patterns") def attribute_ruler_patterns(): return [ { "patterns": [[{"ORTH": "a"}], [{"ORTH": "irrelevant"}]], "attrs": {"LEMMA": "the", "MORPH": "Case=Nom|Number=Plur"}, }, # one pattern sets the lemma {"patterns": [[{"ORTH": "test"}]], "attrs": {"LEMMA": "cat"}}, # another pattern sets the morphology { "patterns": [[{"ORTH": "test"}]], "attrs": {"MORPH": "Case=Nom|Number=Sing"}, "index": 0, }, ] @pytest.fixture def tag_map(): return { ".": {"POS": "PUNCT", "PunctType": "peri"}, ",": {"POS": "PUNCT", "PunctType": "comm"}, } @pytest.fixture def morph_rules(): return {"DT": {"the": {"POS": "DET", "LEMMA": "a", "Case": "Nom"}}} def test_attributeruler_init(nlp, pattern_dicts): a = nlp.add_pipe("attribute_ruler") for p in pattern_dicts: a.add(**p) doc = nlp("This is a test.") assert doc[2].lemma_ == "the" assert doc[2].morph_ == "Case=Nom|Number=Plur" assert doc[3].lemma_ == "cat" assert doc[3].morph_ == "Case=Nom|Number=Sing" def test_attributeruler_init_patterns(nlp, pattern_dicts): # initialize with patterns nlp.add_pipe("attribute_ruler", config={"pattern_dicts": pattern_dicts}) doc = nlp("This is a test.") assert doc[2].lemma_ == "the" assert doc[2].morph_ == "Case=Nom|Number=Plur" assert doc[3].lemma_ == "cat" assert doc[3].morph_ == "Case=Nom|Number=Sing" nlp.remove_pipe("attribute_ruler") # initialize with patterns from asset nlp.add_pipe( "attribute_ruler", config={"pattern_dicts": {"@misc": "attribute_ruler_patterns"}}, ) doc = nlp("This is a test.") assert doc[2].lemma_ == "the" assert doc[2].morph_ == "Case=Nom|Number=Plur" assert doc[3].lemma_ == "cat" assert doc[3].morph_ == "Case=Nom|Number=Sing" def test_attributeruler_score(nlp, pattern_dicts): # initialize with patterns nlp.add_pipe("attribute_ruler", config={"pattern_dicts": pattern_dicts}) doc = nlp("This is a test.") assert doc[2].lemma_ == "the" assert doc[2].morph_ == "Case=Nom|Number=Plur" assert doc[3].lemma_ == "cat" assert doc[3].morph_ == "Case=Nom|Number=Sing" dev_examples = [ Example.from_dict( nlp.make_doc("This is a test."), {"lemmas": ["this", "is", "a", "cat", "."]} ) ] scores = nlp.evaluate(dev_examples) # "cat" is the only correct lemma assert scores["lemma_acc"] == pytest.approx(0.2) # the empty morphs are correct assert scores["morph_acc"] == pytest.approx(0.6) def test_attributeruler_rule_order(nlp): a = AttributeRuler(nlp.vocab) patterns = [ {"patterns": [[{"TAG": "VBZ"}]], "attrs": {"POS": "VERB"}}, {"patterns": [[{"TAG": "VBZ"}]], "attrs": {"POS": "NOUN"}}, ] a.add_patterns(patterns) doc = get_doc( nlp.vocab, words=["This", "is", "a", "test", "."], tags=["DT", "VBZ", "DT", "NN", "."], ) doc = a(doc) assert doc[1].pos_ == "NOUN" def test_attributeruler_tag_map(nlp, tag_map): a = AttributeRuler(nlp.vocab) a.load_from_tag_map(tag_map) doc = get_doc( nlp.vocab, words=["This", "is", "a", "test", "."], tags=["DT", "VBZ", "DT", "NN", "."], ) doc = a(doc) for i in range(len(doc)): if i == 4: assert doc[i].pos_ == "PUNCT" assert doc[i].morph_ == "PunctType=peri" else: assert doc[i].pos_ == "" assert doc[i].morph_ == "" def test_attributeruler_morph_rules(nlp, morph_rules): a = AttributeRuler(nlp.vocab) a.load_from_morph_rules(morph_rules) doc = get_doc( nlp.vocab, words=["This", "is", "the", "test", "."], tags=["DT", "VBZ", "DT", "NN", "."], ) doc = a(doc) for i in range(len(doc)): if i != 2: assert doc[i].pos_ == "" assert doc[i].morph_ == "" else: assert doc[2].pos_ == "DET" assert doc[2].lemma_ == "a" assert doc[2].morph_ == "Case=Nom" def test_attributeruler_indices(nlp): a = nlp.add_pipe("attribute_ruler") a.add( [[{"ORTH": "a"}, {"ORTH": "test"}]], {"LEMMA": "the", "MORPH": "Case=Nom|Number=Plur"}, index=0, ) a.add( [[{"ORTH": "This"}, {"ORTH": "is"}]], {"LEMMA": "was", "MORPH": "Case=Nom|Number=Sing"}, index=1, ) a.add([[{"ORTH": "a"}, {"ORTH": "test"}]], {"LEMMA": "cat"}, index=-1) text = "This is a test." doc = nlp(text) for i in range(len(doc)): if i == 1: assert doc[i].lemma_ == "was" assert doc[i].morph_ == "Case=Nom|Number=Sing" elif i == 2: assert doc[i].lemma_ == "the" assert doc[i].morph_ == "Case=Nom|Number=Plur" elif i == 3: assert doc[i].lemma_ == "cat" else: assert doc[i].morph_ == "" # raises an error when trying to modify a token outside of the match a.add([[{"ORTH": "a"}, {"ORTH": "test"}]], {"LEMMA": "cat"}, index=2) with pytest.raises(ValueError): doc = nlp(text) # raises an error when trying to modify a token outside of the match a.add([[{"ORTH": "a"}, {"ORTH": "test"}]], {"LEMMA": "cat"}, index=10) with pytest.raises(ValueError): doc = nlp(text) def test_attributeruler_patterns_prop(nlp, pattern_dicts): a = nlp.add_pipe("attribute_ruler") a.add_patterns(pattern_dicts) for p1, p2 in zip(pattern_dicts, a.patterns): assert p1["patterns"] == p2["patterns"] assert p1["attrs"] == p2["attrs"] if p1.get("index"): assert p1["index"] == p2["index"] def test_attributeruler_serialize(nlp, pattern_dicts): a = nlp.add_pipe("attribute_ruler") a.add_patterns(pattern_dicts) text = "This is a test." attrs = ["ORTH", "LEMMA", "MORPH"] doc = nlp(text) # bytes roundtrip a_reloaded = AttributeRuler(nlp.vocab).from_bytes(a.to_bytes()) assert a.to_bytes() == a_reloaded.to_bytes() doc1 = a_reloaded(nlp.make_doc(text)) numpy.array_equal(doc.to_array(attrs), doc1.to_array(attrs)) assert a.patterns == a_reloaded.patterns # disk roundtrip with make_tempdir() as tmp_dir: nlp.to_disk(tmp_dir) nlp2 = util.load_model_from_path(tmp_dir) doc2 = nlp2(text) assert nlp2.get_pipe("attribute_ruler").to_bytes() == a.to_bytes() assert numpy.array_equal(doc.to_array(attrs), doc2.to_array(attrs)) assert a.patterns == nlp2.get_pipe("attribute_ruler").patterns