import pytest from spacy.lang.en import English from ..util import get_doc, apply_transition_sequence, make_tempdir from ... import util from ...training import Example TRAIN_DATA = [ ( "They trade mortgage-backed securities.", { "heads": [1, 1, 4, 4, 5, 1, 1], "deps": ["nsubj", "ROOT", "compound", "punct", "nmod", "dobj", "punct"], }, ), ( "I like London and Berlin.", { "heads": [1, 1, 1, 2, 2, 1], "deps": ["nsubj", "ROOT", "dobj", "cc", "conj", "punct"], }, ), ] def test_parser_root(en_tokenizer): text = "i don't have other assistance" heads = [3, 2, 1, 0, 1, -2] deps = ["nsubj", "aux", "neg", "ROOT", "amod", "dobj"] tokens = en_tokenizer(text) doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads, deps=deps) for t in doc: assert t.dep != 0, t.text @pytest.mark.skip( reason="The step_through API was removed (but should be brought back)" ) @pytest.mark.parametrize("text", ["Hello"]) def test_parser_parse_one_word_sentence(en_tokenizer, en_parser, text): tokens = en_tokenizer(text) doc = get_doc( tokens.vocab, words=[t.text for t in tokens], heads=[0], deps=["ROOT"] ) assert len(doc) == 1 with en_parser.step_through(doc) as _: # noqa: F841 pass assert doc[0].dep != 0 @pytest.mark.skip( reason="The step_through API was removed (but should be brought back)" ) def test_parser_initial(en_tokenizer, en_parser): text = "I ate the pizza with anchovies." # heads = [1, 0, 1, -2, -3, -1, -5] transition = ["L-nsubj", "S", "L-det"] tokens = en_tokenizer(text) apply_transition_sequence(en_parser, tokens, transition) assert tokens[0].head.i == 1 assert tokens[1].head.i == 1 assert tokens[2].head.i == 3 assert tokens[3].head.i == 3 def test_parser_parse_subtrees(en_tokenizer, en_parser): text = "The four wheels on the bus turned quickly" heads = [2, 1, 4, -1, 1, -2, 0, -1] tokens = en_tokenizer(text) doc = get_doc(tokens.vocab, words=[t.text for t in tokens], heads=heads) assert len(list(doc[2].lefts)) == 2 assert len(list(doc[2].rights)) == 1 assert len(list(doc[2].children)) == 3 assert len(list(doc[5].lefts)) == 1 assert len(list(doc[5].rights)) == 0 assert len(list(doc[5].children)) == 1 assert len(list(doc[2].subtree)) == 6 def test_parser_merge_pp(en_tokenizer): text = "A phrase with another phrase occurs" heads = [1, 4, -1, 1, -2, 0] deps = ["det", "nsubj", "prep", "det", "pobj", "ROOT"] pos = ["DET", "NOUN", "ADP", "DET", "NOUN", "VERB"] tokens = en_tokenizer(text) doc = get_doc( tokens.vocab, words=[t.text for t in tokens], deps=deps, heads=heads, pos=pos ) with doc.retokenize() as retokenizer: for np in doc.noun_chunks: retokenizer.merge(np, attrs={"lemma": np.lemma_}) assert doc[0].text == "A phrase" assert doc[1].text == "with" assert doc[2].text == "another phrase" assert doc[3].text == "occurs" @pytest.mark.skip( reason="The step_through API was removed (but should be brought back)" ) def test_parser_arc_eager_finalize_state(en_tokenizer, en_parser): text = "a b c d e" # right branching transition = ["R-nsubj", "D", "R-nsubj", "R-nsubj", "D", "R-ROOT"] tokens = en_tokenizer(text) apply_transition_sequence(en_parser, tokens, transition) assert tokens[0].n_lefts == 0 assert tokens[0].n_rights == 2 assert tokens[0].left_edge.i == 0 assert tokens[0].right_edge.i == 4 assert tokens[0].head.i == 0 assert tokens[1].n_lefts == 0 assert tokens[1].n_rights == 0 assert tokens[1].left_edge.i == 1 assert tokens[1].right_edge.i == 1 assert tokens[1].head.i == 0 assert tokens[2].n_lefts == 0 assert tokens[2].n_rights == 2 assert tokens[2].left_edge.i == 2 assert tokens[2].right_edge.i == 4 assert tokens[2].head.i == 0 assert tokens[3].n_lefts == 0 assert tokens[3].n_rights == 0 assert tokens[3].left_edge.i == 3 assert tokens[3].right_edge.i == 3 assert tokens[3].head.i == 2 assert tokens[4].n_lefts == 0 assert tokens[4].n_rights == 0 assert tokens[4].left_edge.i == 4 assert tokens[4].right_edge.i == 4 assert tokens[4].head.i == 2 # left branching transition = ["S", "S", "S", "L-nsubj", "L-nsubj", "L-nsubj", "L-nsubj"] tokens = en_tokenizer(text) apply_transition_sequence(en_parser, tokens, transition) assert tokens[0].n_lefts == 0 assert tokens[0].n_rights == 0 assert tokens[0].left_edge.i == 0 assert tokens[0].right_edge.i == 0 assert tokens[0].head.i == 4 assert tokens[1].n_lefts == 0 assert tokens[1].n_rights == 0 assert tokens[1].left_edge.i == 1 assert tokens[1].right_edge.i == 1 assert tokens[1].head.i == 4 assert tokens[2].n_lefts == 0 assert tokens[2].n_rights == 0 assert tokens[2].left_edge.i == 2 assert tokens[2].right_edge.i == 2 assert tokens[2].head.i == 4 assert tokens[3].n_lefts == 0 assert tokens[3].n_rights == 0 assert tokens[3].left_edge.i == 3 assert tokens[3].right_edge.i == 3 assert tokens[3].head.i == 4 assert tokens[4].n_lefts == 4 assert tokens[4].n_rights == 0 assert tokens[4].left_edge.i == 0 assert tokens[4].right_edge.i == 4 assert tokens[4].head.i == 4 def test_parser_set_sent_starts(en_vocab): # fmt: off words = ['Ein', 'Satz', '.', 'Außerdem', 'ist', 'Zimmer', 'davon', 'überzeugt', ',', 'dass', 'auch', 'epige-', '\n', 'netische', 'Mechanismen', 'eine', 'Rolle', 'spielen', ',', 'also', 'Vorgänge', ',', 'die', '\n', 'sich', 'darauf', 'auswirken', ',', 'welche', 'Gene', 'abgelesen', 'werden', 'und', '\n', 'welche', 'nicht', '.', '\n'] heads = [1, 0, -1, 27, 0, -1, 1, -3, -1, 8, 4, 3, -1, 1, 3, 1, 1, -11, -1, 1, -9, -1, 4, -1, 2, 1, -6, -1, 1, 2, 1, -6, -1, -1, -17, -31, -32, -1] deps = ['nk', 'ROOT', 'punct', 'mo', 'ROOT', 'sb', 'op', 'pd', 'punct', 'cp', 'mo', 'nk', '', 'nk', 'sb', 'nk', 'oa', 're', 'punct', 'mo', 'app', 'punct', 'sb', '', 'oa', 'op', 'rc', 'punct', 'nk', 'sb', 'oc', 're', 'cd', '', 'oa', 'ng', 'punct', ''] # fmt: on doc = get_doc(en_vocab, words=words, deps=deps, heads=heads) for i in range(len(words)): if i == 0 or i == 3: assert doc[i].is_sent_start is True else: assert doc[i].is_sent_start is None for sent in doc.sents: for token in sent: assert token.head in sent def test_overfitting_IO(): # Simple test to try and quickly overfit the dependency parser - ensuring the ML models work correctly nlp = English() parser = nlp.add_pipe("parser") train_examples = [] for text, annotations in TRAIN_DATA: train_examples.append(Example.from_dict(nlp.make_doc(text), annotations)) for dep in annotations.get("deps", []): parser.add_label(dep) optimizer = nlp.begin_training() for i in range(100): losses = {} nlp.update(train_examples, sgd=optimizer, losses=losses) assert losses["parser"] < 0.0001 # test the trained model test_text = "I like securities." doc = nlp(test_text) assert doc[0].dep_ is "nsubj" assert doc[2].dep_ is "dobj" assert doc[3].dep_ is "punct" # 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 doc2[0].dep_ is "nsubj" assert doc2[2].dep_ is "dobj" assert doc2[3].dep_ is "punct"