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12974bf4d9
* Add micro PRF for morph scoring For pipelines where morph features are added by more than one component and a reference training corpus may not contain all features, a micro PRF score is more flexible than a simple accuracy score. An example is the reading and inflection features added by the Japanese tokenizer. * Use `morph_micro_f` as the default morph score for Japanese morphologizers. * Update docstring * Fix typo in docstring * Update Scorer API docs * Fix results type * Organize score list by attribute prefix
477 lines
15 KiB
Python
477 lines
15 KiB
Python
from numpy.testing import assert_almost_equal, assert_array_almost_equal
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import pytest
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from pytest import approx
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from spacy.training import Example
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from spacy.training.iob_utils import offsets_to_biluo_tags
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from spacy.scorer import Scorer, ROCAUCScore, PRFScore
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from spacy.scorer import _roc_auc_score, _roc_curve
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from spacy.lang.en import English
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from spacy.tokens import Doc, Span
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test_las_apple = [
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[
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"Apple is looking at buying U.K. startup for $ 1 billion",
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{
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"heads": [2, 2, 2, 2, 3, 6, 4, 4, 10, 10, 7],
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"deps": [
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"nsubj",
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"aux",
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"ROOT",
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"prep",
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"pcomp",
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"compound",
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"dobj",
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"prep",
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"quantmod",
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"compound",
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"pobj",
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],
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},
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]
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]
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test_ner_cardinal = [
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["100 - 200", {"entities": [[0, 3, "CARDINAL"], [6, 9, "CARDINAL"]]}]
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]
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test_ner_apple = [
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[
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"Apple is looking at buying U.K. startup for $1 billion",
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{"entities": [(0, 5, "ORG"), (27, 31, "GPE"), (44, 54, "MONEY")]},
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]
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]
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@pytest.fixture
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def tagged_doc():
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text = "Sarah's sister flew to Silicon Valley via London."
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tags = ["NNP", "POS", "NN", "VBD", "IN", "NNP", "NNP", "IN", "NNP", "."]
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pos = [
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"PROPN",
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"PART",
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"NOUN",
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"VERB",
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"ADP",
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"PROPN",
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"PROPN",
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"ADP",
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"PROPN",
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"PUNCT",
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]
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morphs = [
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"NounType=prop|Number=sing",
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"Poss=yes",
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"Number=sing",
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"Tense=past|VerbForm=fin",
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"",
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"NounType=prop|Number=sing",
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"NounType=prop|Number=sing",
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"",
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"NounType=prop|Number=sing",
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"PunctType=peri",
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]
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nlp = English()
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doc = nlp(text)
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for i in range(len(tags)):
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doc[i].tag_ = tags[i]
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doc[i].pos_ = pos[i]
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doc[i].set_morph(morphs[i])
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if i > 0:
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doc[i].is_sent_start = False
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return doc
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@pytest.fixture
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def sented_doc():
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text = "One sentence. Two sentences. Three sentences."
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nlp = English()
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doc = nlp(text)
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for i in range(len(doc)):
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if i % 3 == 0:
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doc[i].is_sent_start = True
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else:
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doc[i].is_sent_start = False
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return doc
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def test_tokenization(sented_doc):
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scorer = Scorer()
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gold = {"sent_starts": [t.sent_start for t in sented_doc]}
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example = Example.from_dict(sented_doc, gold)
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scores = scorer.score([example])
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assert scores["token_acc"] == 1.0
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nlp = English()
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example.predicted = Doc(
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nlp.vocab,
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words=["One", "sentence.", "Two", "sentences.", "Three", "sentences."],
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spaces=[True, True, True, True, True, False],
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)
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example.predicted[1].is_sent_start = False
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scores = scorer.score([example])
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assert scores["token_acc"] == approx(0.66666666)
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assert scores["token_p"] == 0.5
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assert scores["token_r"] == approx(0.33333333)
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assert scores["token_f"] == 0.4
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def test_sents(sented_doc):
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scorer = Scorer()
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gold = {"sent_starts": [t.sent_start for t in sented_doc]}
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example = Example.from_dict(sented_doc, gold)
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scores = scorer.score([example])
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assert scores["sents_f"] == 1.0
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# One sentence start is moved
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gold["sent_starts"][3] = 0
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gold["sent_starts"][4] = 1
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example = Example.from_dict(sented_doc, gold)
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scores = scorer.score([example])
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assert scores["sents_f"] == approx(0.3333333)
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def test_las_per_type(en_vocab):
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# Gold and Doc are identical
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scorer = Scorer()
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examples = []
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for input_, annot in test_las_apple:
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doc = Doc(
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en_vocab, words=input_.split(" "), heads=annot["heads"], deps=annot["deps"]
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)
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gold = {"heads": annot["heads"], "deps": annot["deps"]}
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example = Example.from_dict(doc, gold)
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examples.append(example)
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results = scorer.score(examples)
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assert results["dep_uas"] == 1.0
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assert results["dep_las"] == 1.0
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assert results["dep_las_per_type"]["nsubj"]["p"] == 1.0
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assert results["dep_las_per_type"]["nsubj"]["r"] == 1.0
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assert results["dep_las_per_type"]["nsubj"]["f"] == 1.0
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assert results["dep_las_per_type"]["compound"]["p"] == 1.0
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assert results["dep_las_per_type"]["compound"]["r"] == 1.0
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assert results["dep_las_per_type"]["compound"]["f"] == 1.0
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# One dep is incorrect in Doc
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scorer = Scorer()
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examples = []
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for input_, annot in test_las_apple:
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doc = Doc(
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en_vocab, words=input_.split(" "), heads=annot["heads"], deps=annot["deps"]
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)
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gold = {"heads": annot["heads"], "deps": annot["deps"]}
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doc[0].dep_ = "compound"
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example = Example.from_dict(doc, gold)
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examples.append(example)
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results = scorer.score(examples)
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assert results["dep_uas"] == 1.0
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assert_almost_equal(results["dep_las"], 0.9090909)
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assert results["dep_las_per_type"]["nsubj"]["p"] == 0
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assert results["dep_las_per_type"]["nsubj"]["r"] == 0
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assert results["dep_las_per_type"]["nsubj"]["f"] == 0
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assert_almost_equal(results["dep_las_per_type"]["compound"]["p"], 0.666666666)
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assert results["dep_las_per_type"]["compound"]["r"] == 1.0
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assert results["dep_las_per_type"]["compound"]["f"] == 0.8
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def test_ner_per_type(en_vocab):
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# Gold and Doc are identical
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scorer = Scorer()
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examples = []
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for input_, annot in test_ner_cardinal:
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doc = Doc(
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en_vocab, words=input_.split(" "), ents=["B-CARDINAL", "O", "B-CARDINAL"]
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)
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entities = offsets_to_biluo_tags(doc, annot["entities"])
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example = Example.from_dict(doc, {"entities": entities})
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# a hack for sentence boundaries
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example.predicted[1].is_sent_start = False
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example.reference[1].is_sent_start = False
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examples.append(example)
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results = scorer.score(examples)
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assert results["ents_p"] == 1.0
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assert results["ents_r"] == 1.0
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assert results["ents_f"] == 1.0
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assert results["ents_per_type"]["CARDINAL"]["p"] == 1.0
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assert results["ents_per_type"]["CARDINAL"]["r"] == 1.0
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assert results["ents_per_type"]["CARDINAL"]["f"] == 1.0
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# Doc has one missing and one extra entity
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# Entity type MONEY is not present in Doc
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scorer = Scorer()
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examples = []
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for input_, annot in test_ner_apple:
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doc = Doc(
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en_vocab,
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words=input_.split(" "),
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ents=["B-ORG", "O", "O", "O", "O", "B-GPE", "B-ORG", "O", "O", "O"],
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)
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entities = offsets_to_biluo_tags(doc, annot["entities"])
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example = Example.from_dict(doc, {"entities": entities})
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# a hack for sentence boundaries
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example.predicted[1].is_sent_start = False
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example.reference[1].is_sent_start = False
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examples.append(example)
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results = scorer.score(examples)
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assert results["ents_p"] == approx(0.6666666)
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assert results["ents_r"] == approx(0.6666666)
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assert results["ents_f"] == approx(0.6666666)
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assert "GPE" in results["ents_per_type"]
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assert "MONEY" in results["ents_per_type"]
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assert "ORG" in results["ents_per_type"]
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assert results["ents_per_type"]["GPE"]["p"] == 1.0
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assert results["ents_per_type"]["GPE"]["r"] == 1.0
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assert results["ents_per_type"]["GPE"]["f"] == 1.0
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assert results["ents_per_type"]["MONEY"]["p"] == 0
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assert results["ents_per_type"]["MONEY"]["r"] == 0
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assert results["ents_per_type"]["MONEY"]["f"] == 0
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assert results["ents_per_type"]["ORG"]["p"] == 0.5
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assert results["ents_per_type"]["ORG"]["r"] == 1.0
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assert results["ents_per_type"]["ORG"]["f"] == approx(0.6666666)
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def test_tag_score(tagged_doc):
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# Gold and Doc are identical
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scorer = Scorer()
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gold = {
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"tags": [t.tag_ for t in tagged_doc],
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"pos": [t.pos_ for t in tagged_doc],
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"morphs": [str(t.morph) for t in tagged_doc],
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"sent_starts": [1 if t.is_sent_start else -1 for t in tagged_doc],
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}
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example = Example.from_dict(tagged_doc, gold)
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results = scorer.score([example])
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assert results["tag_acc"] == 1.0
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assert results["pos_acc"] == 1.0
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assert results["morph_acc"] == 1.0
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assert results["morph_micro_f"] == 1.0
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assert results["morph_per_feat"]["NounType"]["f"] == 1.0
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# Gold annotation is modified
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scorer = Scorer()
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tags = [t.tag_ for t in tagged_doc]
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tags[0] = "NN"
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pos = [t.pos_ for t in tagged_doc]
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pos[1] = "X"
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morphs = [str(t.morph) for t in tagged_doc]
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morphs[1] = "Number=sing"
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morphs[2] = "Number=plur"
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gold = {
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"tags": tags,
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"pos": pos,
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"morphs": morphs,
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"sent_starts": gold["sent_starts"],
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}
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example = Example.from_dict(tagged_doc, gold)
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results = scorer.score([example])
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assert results["tag_acc"] == 0.9
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assert results["pos_acc"] == 0.9
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assert results["morph_acc"] == approx(0.8)
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assert results["morph_micro_f"] == approx(0.8461538)
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assert results["morph_per_feat"]["NounType"]["f"] == 1.0
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assert results["morph_per_feat"]["Poss"]["f"] == 0.0
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assert results["morph_per_feat"]["Number"]["f"] == approx(0.72727272)
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def test_partial_annotation(en_tokenizer):
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pred_doc = en_tokenizer("a b c d e")
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pred_doc[0].tag_ = "A"
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pred_doc[0].pos_ = "X"
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pred_doc[0].set_morph("Feat=Val")
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pred_doc[0].dep_ = "dep"
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# unannotated reference
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ref_doc = en_tokenizer("a b c d e")
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ref_doc.has_unknown_spaces = True
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example = Example(pred_doc, ref_doc)
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scorer = Scorer()
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scores = scorer.score([example])
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for key in scores:
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# cats doesn't have an unset state
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if key.startswith("cats"):
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continue
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assert scores[key] is None
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# partially annotated reference, not overlapping with predicted annotation
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ref_doc = en_tokenizer("a b c d e")
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ref_doc.has_unknown_spaces = True
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ref_doc[1].tag_ = "A"
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ref_doc[1].pos_ = "X"
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ref_doc[1].set_morph("Feat=Val")
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ref_doc[1].dep_ = "dep"
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example = Example(pred_doc, ref_doc)
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scorer = Scorer()
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scores = scorer.score([example])
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assert scores["token_acc"] is None
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assert scores["tag_acc"] == 0.0
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assert scores["pos_acc"] == 0.0
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assert scores["morph_acc"] == 0.0
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assert scores["dep_uas"] == 1.0
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assert scores["dep_las"] == 0.0
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assert scores["sents_f"] is None
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# partially annotated reference, overlapping with predicted annotation
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ref_doc = en_tokenizer("a b c d e")
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ref_doc.has_unknown_spaces = True
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ref_doc[0].tag_ = "A"
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ref_doc[0].pos_ = "X"
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ref_doc[1].set_morph("Feat=Val")
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ref_doc[1].dep_ = "dep"
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example = Example(pred_doc, ref_doc)
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scorer = Scorer()
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scores = scorer.score([example])
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assert scores["token_acc"] is None
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assert scores["tag_acc"] == 1.0
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assert scores["pos_acc"] == 1.0
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assert scores["morph_acc"] == 0.0
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assert scores["dep_uas"] == 1.0
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assert scores["dep_las"] == 0.0
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assert scores["sents_f"] is None
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def test_roc_auc_score():
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# Binary classification, toy tests from scikit-learn test suite
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y_true = [0, 1]
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y_score = [0, 1]
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tpr, fpr, _ = _roc_curve(y_true, y_score)
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roc_auc = _roc_auc_score(y_true, y_score)
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assert_array_almost_equal(tpr, [0, 0, 1])
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assert_array_almost_equal(fpr, [0, 1, 1])
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assert_almost_equal(roc_auc, 1.0)
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y_true = [0, 1]
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y_score = [1, 0]
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tpr, fpr, _ = _roc_curve(y_true, y_score)
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roc_auc = _roc_auc_score(y_true, y_score)
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assert_array_almost_equal(tpr, [0, 1, 1])
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assert_array_almost_equal(fpr, [0, 0, 1])
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assert_almost_equal(roc_auc, 0.0)
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y_true = [1, 0]
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y_score = [1, 1]
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tpr, fpr, _ = _roc_curve(y_true, y_score)
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roc_auc = _roc_auc_score(y_true, y_score)
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assert_array_almost_equal(tpr, [0, 1])
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assert_array_almost_equal(fpr, [0, 1])
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assert_almost_equal(roc_auc, 0.5)
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y_true = [1, 0]
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y_score = [1, 0]
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tpr, fpr, _ = _roc_curve(y_true, y_score)
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roc_auc = _roc_auc_score(y_true, y_score)
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assert_array_almost_equal(tpr, [0, 0, 1])
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assert_array_almost_equal(fpr, [0, 1, 1])
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assert_almost_equal(roc_auc, 1.0)
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y_true = [1, 0]
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y_score = [0.5, 0.5]
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tpr, fpr, _ = _roc_curve(y_true, y_score)
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roc_auc = _roc_auc_score(y_true, y_score)
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assert_array_almost_equal(tpr, [0, 1])
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assert_array_almost_equal(fpr, [0, 1])
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assert_almost_equal(roc_auc, 0.5)
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# same result as above with ROCAUCScore wrapper
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score = ROCAUCScore()
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score.score_set(0.5, 1)
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score.score_set(0.5, 0)
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assert_almost_equal(score.score, 0.5)
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# check that errors are raised in undefined cases and score is -inf
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y_true = [0, 0]
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y_score = [0.25, 0.75]
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with pytest.raises(ValueError):
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_roc_auc_score(y_true, y_score)
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score = ROCAUCScore()
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score.score_set(0.25, 0)
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score.score_set(0.75, 0)
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with pytest.raises(ValueError):
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_ = score.score # noqa: F841
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y_true = [1, 1]
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y_score = [0.25, 0.75]
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with pytest.raises(ValueError):
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_roc_auc_score(y_true, y_score)
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score = ROCAUCScore()
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score.score_set(0.25, 1)
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score.score_set(0.75, 1)
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with pytest.raises(ValueError):
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_ = score.score # noqa: F841
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def test_score_spans():
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nlp = English()
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text = "This is just a random sentence."
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key = "my_spans"
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gold = nlp.make_doc(text)
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pred = nlp.make_doc(text)
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spans = []
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spans.append(gold.char_span(0, 4, label="PERSON"))
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spans.append(gold.char_span(0, 7, label="ORG"))
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spans.append(gold.char_span(8, 12, label="ORG"))
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gold.spans[key] = spans
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def span_getter(doc, span_key):
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return doc.spans[span_key]
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# Predict exactly the same, but overlapping spans will be discarded
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pred.spans[key] = spans
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eg = Example(pred, gold)
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scores = Scorer.score_spans([eg], attr=key, getter=span_getter)
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assert scores[f"{key}_p"] == 1.0
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assert scores[f"{key}_r"] < 1.0
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# Allow overlapping, now both precision and recall should be 100%
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pred.spans[key] = spans
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eg = Example(pred, gold)
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scores = Scorer.score_spans([eg], attr=key, getter=span_getter, allow_overlap=True)
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assert scores[f"{key}_p"] == 1.0
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assert scores[f"{key}_r"] == 1.0
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# Change the predicted labels
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new_spans = [Span(pred, span.start, span.end, label="WRONG") for span in spans]
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pred.spans[key] = new_spans
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eg = Example(pred, gold)
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|
scores = Scorer.score_spans([eg], attr=key, getter=span_getter, allow_overlap=True)
|
|
assert scores[f"{key}_p"] == 0.0
|
|
assert scores[f"{key}_r"] == 0.0
|
|
assert f"{key}_per_type" in scores
|
|
|
|
# Discard labels from the evaluation
|
|
scores = Scorer.score_spans(
|
|
[eg], attr=key, getter=span_getter, allow_overlap=True, labeled=False
|
|
)
|
|
assert scores[f"{key}_p"] == 1.0
|
|
assert scores[f"{key}_r"] == 1.0
|
|
assert f"{key}_per_type" not in scores
|
|
|
|
|
|
def test_prf_score():
|
|
cand = {"hi", "ho"}
|
|
gold1 = {"yo", "hi"}
|
|
gold2 = set()
|
|
|
|
a = PRFScore()
|
|
a.score_set(cand=cand, gold=gold1)
|
|
assert (a.precision, a.recall, a.fscore) == approx((0.5, 0.5, 0.5))
|
|
|
|
b = PRFScore()
|
|
b.score_set(cand=cand, gold=gold2)
|
|
assert (b.precision, b.recall, b.fscore) == approx((0.0, 0.0, 0.0))
|
|
|
|
c = a + b
|
|
assert (c.precision, c.recall, c.fscore) == approx((0.25, 0.5, 0.33333333))
|
|
|
|
a += b
|
|
assert (a.precision, a.recall, a.fscore) == approx(
|
|
(c.precision, c.recall, c.fscore)
|
|
)
|