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7d50804644
* Migrate regressions 1-1000 * Move serialize test to correct file * Remove tests that won't work in v3 * Migrate regressions 1000-1500 Removed regression test 1250 because v3 doesn't support the old LEX scheme anymore. * Add missing imports in serializer tests * Migrate tests 1500-2000 * Migrate regressions from 2000-2500 * Migrate regressions from 2501-3000 * Migrate regressions from 3000-3501 * Migrate regressions from 3501-4000 * Migrate regressions from 4001-4500 * Migrate regressions from 4501-5000 * Migrate regressions from 5001-5501 * Migrate regressions from 5501 to 7000 * Migrate regressions from 7001 to 8000 * Migrate remaining regression tests * Fixing missing imports * Update docs with new system [ci skip] * Update CONTRIBUTING.md - Fix formatting - Update wording * Remove lemmatizer tests in el lang * Move a few tests into the general tokenizer * Separate Doc and DocBin tests
294 lines
9.8 KiB
Python
294 lines
9.8 KiB
Python
import pytest
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from spacy.vocab import Vocab
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from spacy import registry
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from spacy.training import Example
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from spacy.pipeline import DependencyParser
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from spacy.tokens import Doc
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from spacy.pipeline._parser_internals.nonproj import projectivize
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from spacy.pipeline._parser_internals.arc_eager import ArcEager
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from spacy.pipeline.dep_parser import DEFAULT_PARSER_MODEL
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def get_sequence_costs(M, words, heads, deps, transitions):
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doc = Doc(Vocab(), words=words)
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example = Example.from_dict(doc, {"heads": heads, "deps": deps})
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states, golds, _ = M.init_gold_batch([example])
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state = states[0]
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gold = golds[0]
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cost_history = []
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for gold_action in transitions:
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gold.update(state)
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state_costs = {}
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for i in range(M.n_moves):
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name = M.class_name(i)
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state_costs[name] = M.get_cost(state, gold, i)
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M.transition(state, gold_action)
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cost_history.append(state_costs)
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return state, cost_history
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@pytest.fixture
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def vocab():
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return Vocab()
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@pytest.fixture
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def arc_eager(vocab):
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moves = ArcEager(vocab.strings, ArcEager.get_actions())
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moves.add_action(2, "left")
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moves.add_action(3, "right")
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return moves
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@pytest.mark.issue(7056)
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def test_issue7056():
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"""Test that the Unshift transition works properly, and doesn't cause
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sentence segmentation errors."""
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vocab = Vocab()
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ae = ArcEager(
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vocab.strings, ArcEager.get_actions(left_labels=["amod"], right_labels=["pobj"])
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)
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doc = Doc(vocab, words="Severe pain , after trauma".split())
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state = ae.init_batch([doc])[0]
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ae.apply_transition(state, "S")
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ae.apply_transition(state, "L-amod")
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ae.apply_transition(state, "S")
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ae.apply_transition(state, "S")
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ae.apply_transition(state, "S")
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ae.apply_transition(state, "R-pobj")
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ae.apply_transition(state, "D")
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ae.apply_transition(state, "D")
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ae.apply_transition(state, "D")
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assert not state.eol()
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def test_oracle_four_words(arc_eager, vocab):
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words = ["a", "b", "c", "d"]
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heads = [1, 1, 3, 3]
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deps = ["left", "ROOT", "left", "ROOT"]
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for dep in deps:
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arc_eager.add_action(2, dep) # Left
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arc_eager.add_action(3, dep) # Right
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actions = ["S", "L-left", "B-ROOT", "S", "D", "S", "L-left", "S", "D"]
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state, cost_history = get_sequence_costs(arc_eager, words, heads, deps, actions)
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expected_gold = [
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["S"],
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["B-ROOT", "L-left"],
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["B-ROOT"],
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["S"],
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["D"],
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["S"],
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["L-left"],
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["S"],
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["D"],
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]
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assert state.is_final()
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for i, state_costs in enumerate(cost_history):
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# Check gold moves is 0 cost
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golds = [act for act, cost in state_costs.items() if cost < 1]
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assert golds == expected_gold[i], (i, golds, expected_gold[i])
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annot_tuples = [
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(0, "When", "WRB", 11, "advmod", "O"),
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(1, "Walter", "NNP", 2, "compound", "B-PERSON"),
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(2, "Rodgers", "NNP", 11, "nsubj", "L-PERSON"),
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(3, ",", ",", 2, "punct", "O"),
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(4, "our", "PRP$", 6, "poss", "O"),
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(5, "embedded", "VBN", 6, "amod", "O"),
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(6, "reporter", "NN", 2, "appos", "O"),
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(7, "with", "IN", 6, "prep", "O"),
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(8, "the", "DT", 10, "det", "B-ORG"),
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(9, "3rd", "NNP", 10, "compound", "I-ORG"),
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(10, "Cavalry", "NNP", 7, "pobj", "L-ORG"),
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(11, "says", "VBZ", 44, "advcl", "O"),
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(12, "three", "CD", 13, "nummod", "U-CARDINAL"),
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(13, "battalions", "NNS", 16, "nsubj", "O"),
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(14, "of", "IN", 13, "prep", "O"),
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(15, "troops", "NNS", 14, "pobj", "O"),
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(16, "are", "VBP", 11, "ccomp", "O"),
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(17, "on", "IN", 16, "prep", "O"),
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(18, "the", "DT", 19, "det", "O"),
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(19, "ground", "NN", 17, "pobj", "O"),
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(20, ",", ",", 17, "punct", "O"),
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(21, "inside", "IN", 17, "prep", "O"),
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(22, "Baghdad", "NNP", 21, "pobj", "U-GPE"),
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(23, "itself", "PRP", 22, "appos", "O"),
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(24, ",", ",", 16, "punct", "O"),
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(25, "have", "VBP", 26, "aux", "O"),
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(26, "taken", "VBN", 16, "dep", "O"),
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(27, "up", "RP", 26, "prt", "O"),
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(28, "positions", "NNS", 26, "dobj", "O"),
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(29, "they", "PRP", 31, "nsubj", "O"),
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(30, "'re", "VBP", 31, "aux", "O"),
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(31, "going", "VBG", 26, "parataxis", "O"),
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(32, "to", "TO", 33, "aux", "O"),
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(33, "spend", "VB", 31, "xcomp", "O"),
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(34, "the", "DT", 35, "det", "B-TIME"),
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(35, "night", "NN", 33, "dobj", "L-TIME"),
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(36, "there", "RB", 33, "advmod", "O"),
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(37, "presumably", "RB", 33, "advmod", "O"),
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(38, ",", ",", 44, "punct", "O"),
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(39, "how", "WRB", 40, "advmod", "O"),
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(40, "many", "JJ", 41, "amod", "O"),
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(41, "soldiers", "NNS", 44, "pobj", "O"),
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(42, "are", "VBP", 44, "aux", "O"),
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(43, "we", "PRP", 44, "nsubj", "O"),
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(44, "talking", "VBG", 44, "ROOT", "O"),
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(45, "about", "IN", 44, "prep", "O"),
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(46, "right", "RB", 47, "advmod", "O"),
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(47, "now", "RB", 44, "advmod", "O"),
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(48, "?", ".", 44, "punct", "O"),
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]
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def test_get_oracle_actions():
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ids, words, tags, heads, deps, ents = [], [], [], [], [], []
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for id_, word, tag, head, dep, ent in annot_tuples:
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ids.append(id_)
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words.append(word)
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tags.append(tag)
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heads.append(head)
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deps.append(dep)
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ents.append(ent)
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doc = Doc(Vocab(), words=[t[1] for t in annot_tuples])
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cfg = {"model": DEFAULT_PARSER_MODEL}
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model = registry.resolve(cfg, validate=True)["model"]
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parser = DependencyParser(doc.vocab, model)
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parser.moves.add_action(0, "")
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parser.moves.add_action(1, "")
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parser.moves.add_action(1, "")
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parser.moves.add_action(4, "ROOT")
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heads, deps = projectivize(heads, deps)
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for i, (head, dep) in enumerate(zip(heads, deps)):
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if head > i:
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parser.moves.add_action(2, dep)
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elif head < i:
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parser.moves.add_action(3, dep)
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example = Example.from_dict(
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doc, {"words": words, "tags": tags, "heads": heads, "deps": deps}
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)
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parser.moves.get_oracle_sequence(example)
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def test_oracle_dev_sentence(vocab, arc_eager):
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words_deps_heads = """
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Rolls-Royce nn Inc.
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Motor nn Inc.
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Cars nn Inc.
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Inc. nsubj said
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said ROOT said
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it nsubj expects
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expects ccomp said
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its poss sales
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U.S. nn sales
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sales nsubj steady
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to aux steady
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remain cop steady
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steady xcomp expects
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at prep steady
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about quantmod 1,200
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1,200 num cars
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cars pobj at
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in prep steady
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1990 pobj in
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. punct said
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"""
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expected_transitions = [
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"S", # Shift "Rolls-Royce"
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"S", # Shift 'Motor'
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"S", # Shift 'Cars'
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"L-nn", # Attach 'Cars' to 'Inc.'
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"L-nn", # Attach 'Motor' to 'Inc.'
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"L-nn", # Attach 'Rolls-Royce' to 'Inc.'
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"S", # Shift "Inc."
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"L-nsubj", # Attach 'Inc.' to 'said'
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"S", # Shift 'said'
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"S", # Shift 'it'
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"L-nsubj", # Attach 'it.' to 'expects'
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"R-ccomp", # Attach 'expects' to 'said'
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"S", # Shift 'its'
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"S", # Shift 'U.S.'
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"L-nn", # Attach 'U.S.' to 'sales'
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"L-poss", # Attach 'its' to 'sales'
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"S", # Shift 'sales'
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"S", # Shift 'to'
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"S", # Shift 'remain'
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"L-cop", # Attach 'remain' to 'steady'
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"L-aux", # Attach 'to' to 'steady'
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"L-nsubj", # Attach 'sales' to 'steady'
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"R-xcomp", # Attach 'steady' to 'expects'
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"R-prep", # Attach 'at' to 'steady'
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"S", # Shift 'about'
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"L-quantmod", # Attach "about" to "1,200"
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"S", # Shift "1,200"
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"L-num", # Attach "1,200" to "cars"
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"R-pobj", # Attach "cars" to "at"
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"D", # Reduce "cars"
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"D", # Reduce "at"
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"R-prep", # Attach "in" to "steady"
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"R-pobj", # Attach "1990" to "in"
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"D", # Reduce "1990"
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"D", # Reduce "in"
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"D", # Reduce "steady"
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"D", # Reduce "expects"
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"R-punct", # Attach "." to "said"
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"D", # Reduce "."
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"D", # Reduce "said"
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]
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gold_words = []
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gold_deps = []
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gold_heads = []
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for line in words_deps_heads.strip().split("\n"):
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line = line.strip()
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if not line:
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continue
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word, dep, head = line.split()
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gold_words.append(word)
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gold_deps.append(dep)
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gold_heads.append(head)
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gold_heads = [gold_words.index(head) for head in gold_heads]
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for dep in gold_deps:
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arc_eager.add_action(2, dep) # Left
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arc_eager.add_action(3, dep) # Right
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doc = Doc(Vocab(), words=gold_words)
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example = Example.from_dict(doc, {"heads": gold_heads, "deps": gold_deps})
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ae_oracle_actions = arc_eager.get_oracle_sequence(example, _debug=False)
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ae_oracle_actions = [arc_eager.get_class_name(i) for i in ae_oracle_actions]
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assert ae_oracle_actions == expected_transitions
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def test_oracle_bad_tokenization(vocab, arc_eager):
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words_deps_heads = """
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[catalase] dep is
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: punct is
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that nsubj is
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is root is
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bad comp is
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"""
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gold_words = []
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gold_deps = []
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gold_heads = []
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for line in words_deps_heads.strip().split("\n"):
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line = line.strip()
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if not line:
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continue
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word, dep, head = line.split()
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gold_words.append(word)
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gold_deps.append(dep)
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gold_heads.append(head)
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gold_heads = [gold_words.index(head) for head in gold_heads]
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for dep in gold_deps:
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arc_eager.add_action(2, dep) # Left
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arc_eager.add_action(3, dep) # Right
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reference = Doc(Vocab(), words=gold_words, deps=gold_deps, heads=gold_heads)
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predicted = Doc(
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reference.vocab, words=["[", "catalase", "]", ":", "that", "is", "bad"]
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)
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example = Example(predicted=predicted, reference=reference)
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ae_oracle_actions = arc_eager.get_oracle_sequence(example, _debug=False)
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ae_oracle_actions = [arc_eager.get_class_name(i) for i in ae_oracle_actions]
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assert ae_oracle_actions
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