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86c3ec9c2b
* Refactor Token morph setting * Remove `Token.morph_` * Add `Token.set_morph()` * `0` resets `token.c.morph` to unset * Any other values are passed to `Morphology.add` * Add token.morph setter to set from MorphAnalysis
350 lines
11 KiB
Python
350 lines
11 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
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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
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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,
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words=input_.split(" "),
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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_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_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_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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assert score.score == -float("inf")
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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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assert score.score == -float("inf")
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