spaCy/spacy/tests/parser/test_arc_eager_oracle.py
Ines Montani 43b960c01b
Refactor pipeline components, config and language data (#5759)
* Update with WIP

* Update with WIP

* Update with pipeline serialization

* Update types and pipe factories

* Add deep merge, tidy up and add tests

* Fix pipe creation from config

* Don't validate default configs on load

* Update spacy/language.py

Co-authored-by: Ines Montani <ines@ines.io>

* Adjust factory/component meta error

* Clean up factory args and remove defaults

* Add test for failing empty dict defaults

* Update pipeline handling and methods

* provide KB as registry function instead of as object

* small change in test to make functionality more clear

* update example script for EL configuration

* Fix typo

* Simplify test

* Simplify test

* splitting pipes.pyx into separate files

* moving default configs to each component file

* fix batch_size type

* removing default values from component constructors where possible (TODO: test 4725)

* skip instead of xfail

* Add test for config -> nlp with multiple instances

* pipeline.pipes -> pipeline.pipe

* Tidy up, document, remove kwargs

* small cleanup/generalization for Tok2VecListener

* use DEFAULT_UPSTREAM field

* revert to avoid circular imports

* Fix tests

* Replace deprecated arg

* Make model dirs require config

* fix pickling of keyword-only arguments in constructor

* WIP: clean up and integrate full config

* Add helper to handle function args more reliably

Now also includes keyword-only args

* Fix config composition and serialization

* Improve config debugging and add visual diff

* Remove unused defaults and fix type

* Remove pipeline and factories from meta

* Update spacy/default_config.cfg

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>

* Update spacy/default_config.cfg

* small UX edits

* avoid printing stack trace for debug CLI commands

* Add support for language-specific factories

* specify the section of the config which holds the model to debug

* WIP: add Language.from_config

* Update with language data refactor WIP

* Auto-format

* Add backwards-compat handling for Language.factories

* Update morphologizer.pyx

* Fix morphologizer

* Update and simplify lemmatizers

* Fix Japanese tests

* Port over tagger changes

* Fix Chinese and tests

* Update to latest Thinc

* WIP: xfail first Russian lemmatizer test

* Fix component-specific overrides

* fix nO for output layers in debug_model

* Fix default value

* Fix tests and don't pass objects in config

* Fix deep merging

* Fix lemma lookup data registry

Only load the lookups if an entry is available in the registry (and if spacy-lookups-data is installed)

* Add types

* Add Vocab.from_config

* Fix typo

* Fix tests

* Make config copying more elegant

* Fix pipe analysis

* Fix lemmatizers and is_base_form

* WIP: move language defaults to config

* Fix morphology type

* Fix vocab

* Remove comment

* Update to latest Thinc

* Add morph rules to config

* Tidy up

* Remove set_morphology option from tagger factory

* Hack use_gpu

* Move [pipeline] to top-level block and make [nlp.pipeline] list

Allows separating component blocks from component order – otherwise, ordering the config would mean a changed component order, which is bad. Also allows initial config to define more components and not use all of them

* Fix use_gpu and resume in CLI

* Auto-format

* Remove resume from config

* Fix formatting and error

* [pipeline] -> [components]

* Fix types

* Fix tagger test: requires set_morphology?

Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
2020-07-22 13:42:59 +02:00

230 lines
7.8 KiB
Python

import pytest
from spacy.vocab import Vocab
from spacy import registry
from spacy.gold import Example
from spacy.pipeline import DependencyParser
from spacy.tokens import Doc
from spacy.syntax.nonproj import projectivize
from spacy.syntax.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
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 = ["L-left", "B-ROOT", "L-left"]
state, cost_history = get_sequence_costs(arc_eager, words, heads, deps, actions)
assert state.is_final()
for i, state_costs in enumerate(cost_history):
# Check gold moves is 0 cost
assert state_costs[actions[i]] == 0.0, actions[i]
for other_action, cost in state_costs.items():
if other_action != actions[i]:
assert cost >= 1, (i, other_action)
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])
config = {
"learn_tokens": False,
"min_action_freq": 0,
"update_with_oracle_cut_size": 100,
}
model = registry.make_from_config({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
parser = DependencyParser(doc.vocab, model, **config)
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 'Motor'
"S", # Shift 'Cars'
"L-nn", # Attach 'Cars' to 'Inc.'
"L-nn", # Attach 'Motor' to 'Inc.'
"L-nn", # Attach 'Rolls-Royce' to 'Inc.', force shift
"L-nsubj", # Attach 'Inc.' to '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"
]
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)
ae_oracle_actions = [arc_eager.get_class_name(i) for i in ae_oracle_actions]
assert ae_oracle_actions == expected_transitions