import pytest from numpy.testing import assert_equal from spacy import util from spacy.training import Example from spacy.lang.en import English from spacy.language import Language from spacy.tests.util import make_tempdir from spacy.morphology import Morphology from spacy.attrs import MORPH def test_label_types(): nlp = Language() morphologizer = nlp.add_pipe("morphologizer") morphologizer.add_label("Feat=A") with pytest.raises(ValueError): morphologizer.add_label(9) TRAIN_DATA = [ ( "I like green eggs", { "morphs": ["Feat=N", "Feat=V", "Feat=J", "Feat=N"], "pos": ["NOUN", "VERB", "ADJ", "NOUN"], }, ), # test combinations of morph+POS ("Eat blue ham", {"morphs": ["Feat=V", "", ""], "pos": ["", "ADJ", ""]}), ] def test_no_label(): nlp = Language() nlp.add_pipe("morphologizer") with pytest.raises(ValueError): nlp.initialize() def test_implicit_label(): nlp = Language() nlp.add_pipe("morphologizer") 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) def test_no_resize(): nlp = Language() morphologizer = nlp.add_pipe("morphologizer") morphologizer.add_label("POS" + Morphology.FIELD_SEP + "NOUN") morphologizer.add_label("POS" + Morphology.FIELD_SEP + "VERB") nlp.initialize() # this throws an error because the morphologizer can't be resized after initialization with pytest.raises(ValueError): morphologizer.add_label("POS" + Morphology.FIELD_SEP + "ADJ") def test_initialize_examples(): nlp = Language() morphologizer = nlp.add_pipe("morphologizer") morphologizer.add_label("POS" + Morphology.FIELD_SEP + "NOUN") train_examples = [] for t in TRAIN_DATA: train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1])) # you shouldn't really call this more than once, but for testing it should be fine nlp.initialize() nlp.initialize(get_examples=lambda: train_examples) with pytest.raises(TypeError): nlp.initialize(get_examples=lambda: None) with pytest.raises(TypeError): nlp.initialize(get_examples=train_examples) def test_overfitting_IO(): # Simple test to try and quickly overfit the morphologizer - ensuring the ML models work correctly nlp = English() nlp.add_pipe("morphologizer") train_examples = [] for inst in TRAIN_DATA: train_examples.append(Example.from_dict(nlp.make_doc(inst[0]), inst[1])) optimizer = nlp.initialize(get_examples=lambda: train_examples) for i in range(50): losses = {} nlp.update(train_examples, sgd=optimizer, losses=losses) assert losses["morphologizer"] < 0.00001 # test the trained model test_text = "I like blue ham" doc = nlp(test_text) gold_morphs = ["Feat=N", "Feat=V", "", ""] gold_pos_tags = ["NOUN", "VERB", "ADJ", ""] assert [str(t.morph) for t in doc] == gold_morphs assert [t.pos_ for t in doc] == gold_pos_tags # Also test the results are still the same after IO with make_tempdir() as tmp_dir: nlp.to_disk(tmp_dir) nlp2 = util.load_model_from_path(tmp_dir) doc2 = nlp2(test_text) assert [str(t.morph) for t in doc2] == gold_morphs assert [t.pos_ for t in doc2] == gold_pos_tags # Make sure that running pipe twice, or comparing to call, always amounts to the same predictions texts = [ "Just a sentence.", "Then one more sentence about London.", "Here is another one.", "I like London.", ] batch_deps_1 = [doc.to_array([MORPH]) for doc in nlp.pipe(texts)] batch_deps_2 = [doc.to_array([MORPH]) for doc in nlp.pipe(texts)] no_batch_deps = [doc.to_array([MORPH]) for doc in [nlp(text) for text in texts]] assert_equal(batch_deps_1, batch_deps_2) assert_equal(batch_deps_1, no_batch_deps) # Test without POS nlp.remove_pipe("morphologizer") nlp.add_pipe("morphologizer") for example in train_examples: for token in example.reference: token.pos_ = "" optimizer = nlp.initialize(get_examples=lambda: train_examples) for i in range(50): losses = {} nlp.update(train_examples, sgd=optimizer, losses=losses) assert losses["morphologizer"] < 0.00001 # Test the trained model test_text = "I like blue ham" doc = nlp(test_text) gold_morphs = ["Feat=N", "Feat=V", "", ""] gold_pos_tags = ["", "", "", ""] assert [str(t.morph) for t in doc] == gold_morphs assert [t.pos_ for t in doc] == gold_pos_tags # Test with unset morph and partial POS nlp.remove_pipe("morphologizer") nlp.add_pipe("morphologizer") for example in train_examples: for token in example.reference: if token.text == "ham": token.pos_ = "NOUN" else: token.pos_ = "" token.set_morph(None) optimizer = nlp.initialize(get_examples=lambda: train_examples) print(nlp.get_pipe("morphologizer").labels) for i in range(50): losses = {} nlp.update(train_examples, sgd=optimizer, losses=losses) assert losses["morphologizer"] < 0.00001 # Test the trained model test_text = "I like blue ham" doc = nlp(test_text) gold_morphs = ["", "", "", ""] gold_pos_tags = ["NOUN", "NOUN", "NOUN", "NOUN"] assert [str(t.morph) for t in doc] == gold_morphs assert [t.pos_ for t in doc] == gold_pos_tags