import pytest from spacy import util from spacy.gold 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 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_overfitting_IO(): # Simple test to try and quickly overfit the morphologizer - ensuring the ML models work correctly nlp = English() morphologizer = nlp.add_pipe("morphologizer") train_examples = [] for inst in TRAIN_DATA: train_examples.append(Example.from_dict(nlp.make_doc(inst[0]), inst[1])) for morph, pos in zip(inst[1]["morphs"], inst[1]["pos"]): if morph and pos: morphologizer.add_label( morph + Morphology.FEATURE_SEP + "POS" + Morphology.FIELD_SEP + pos ) elif pos: morphologizer.add_label("POS" + Morphology.FIELD_SEP + pos) elif morph: morphologizer.add_label(morph) optimizer = nlp.begin_training() 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 [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 [t.morph_ for t in doc2] == gold_morphs assert [t.pos_ for t in doc2] == gold_pos_tags