spaCy/spacy/tests/parser/test_arc_eager_oracle.py
Lj Miranda 7d50804644
Migrate regression tests into the main test suite (#9655)
* 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
2021-12-04 20:34:48 +01:00

294 lines
9.8 KiB
Python

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