from numpy.testing import assert_almost_equal, assert_array_almost_equal import pytest from pytest import approx from spacy.training import Example from spacy.training.iob_utils import offsets_to_biluo_tags from spacy.scorer import Scorer, ROCAUCScore from spacy.scorer import _roc_auc_score, _roc_curve from spacy.lang.en import English from spacy.tokens import Doc test_las_apple = [ [ "Apple is looking at buying U.K. startup for $ 1 billion", { "heads": [2, 2, 2, 2, 3, 6, 4, 4, 10, 10, 7], "deps": [ "nsubj", "aux", "ROOT", "prep", "pcomp", "compound", "dobj", "prep", "quantmod", "compound", "pobj", ], }, ] ] test_ner_cardinal = [ ["100 - 200", {"entities": [[0, 3, "CARDINAL"], [6, 9, "CARDINAL"]]}] ] test_ner_apple = [ [ "Apple is looking at buying U.K. startup for $1 billion", {"entities": [(0, 5, "ORG"), (27, 31, "GPE"), (44, 54, "MONEY")]}, ] ] @pytest.fixture def tagged_doc(): text = "Sarah's sister flew to Silicon Valley via London." tags = ["NNP", "POS", "NN", "VBD", "IN", "NNP", "NNP", "IN", "NNP", "."] pos = [ "PROPN", "PART", "NOUN", "VERB", "ADP", "PROPN", "PROPN", "ADP", "PROPN", "PUNCT", ] morphs = [ "NounType=prop|Number=sing", "Poss=yes", "Number=sing", "Tense=past|VerbForm=fin", "", "NounType=prop|Number=sing", "NounType=prop|Number=sing", "", "NounType=prop|Number=sing", "PunctType=peri", ] nlp = English() doc = nlp(text) for i in range(len(tags)): doc[i].tag_ = tags[i] doc[i].pos_ = pos[i] doc[i].set_morph(morphs[i]) if i > 0: doc[i].is_sent_start = False return doc @pytest.fixture def sented_doc(): text = "One sentence. Two sentences. Three sentences." nlp = English() doc = nlp(text) for i in range(len(doc)): if i % 3 == 0: doc[i].is_sent_start = True else: doc[i].is_sent_start = False return doc def test_tokenization(sented_doc): scorer = Scorer() gold = {"sent_starts": [t.sent_start for t in sented_doc]} example = Example.from_dict(sented_doc, gold) scores = scorer.score([example]) assert scores["token_acc"] == 1.0 nlp = English() example.predicted = Doc( nlp.vocab, words=["One", "sentence.", "Two", "sentences.", "Three", "sentences."], spaces=[True, True, True, True, True, False], ) example.predicted[1].is_sent_start = False scores = scorer.score([example]) assert scores["token_acc"] == approx(0.66666666) assert scores["token_p"] == 0.5 assert scores["token_r"] == approx(0.33333333) assert scores["token_f"] == 0.4 def test_sents(sented_doc): scorer = Scorer() gold = {"sent_starts": [t.sent_start for t in sented_doc]} example = Example.from_dict(sented_doc, gold) scores = scorer.score([example]) assert scores["sents_f"] == 1.0 # One sentence start is moved gold["sent_starts"][3] = 0 gold["sent_starts"][4] = 1 example = Example.from_dict(sented_doc, gold) scores = scorer.score([example]) assert scores["sents_f"] == approx(0.3333333) def test_las_per_type(en_vocab): # Gold and Doc are identical scorer = Scorer() examples = [] for input_, annot in test_las_apple: doc = Doc( en_vocab, words=input_.split(" "), heads=annot["heads"], deps=annot["deps"] ) gold = {"heads": annot["heads"], "deps": annot["deps"]} example = Example.from_dict(doc, gold) examples.append(example) results = scorer.score(examples) assert results["dep_uas"] == 1.0 assert results["dep_las"] == 1.0 assert results["dep_las_per_type"]["nsubj"]["p"] == 1.0 assert results["dep_las_per_type"]["nsubj"]["r"] == 1.0 assert results["dep_las_per_type"]["nsubj"]["f"] == 1.0 assert results["dep_las_per_type"]["compound"]["p"] == 1.0 assert results["dep_las_per_type"]["compound"]["r"] == 1.0 assert results["dep_las_per_type"]["compound"]["f"] == 1.0 # One dep is incorrect in Doc scorer = Scorer() examples = [] for input_, annot in test_las_apple: doc = Doc( en_vocab, words=input_.split(" "), heads=annot["heads"], deps=annot["deps"], ) gold = {"heads": annot["heads"], "deps": annot["deps"]} doc[0].dep_ = "compound" example = Example.from_dict(doc, gold) examples.append(example) results = scorer.score(examples) assert results["dep_uas"] == 1.0 assert_almost_equal(results["dep_las"], 0.9090909) assert results["dep_las_per_type"]["nsubj"]["p"] == 0 assert results["dep_las_per_type"]["nsubj"]["r"] == 0 assert results["dep_las_per_type"]["nsubj"]["f"] == 0 assert_almost_equal(results["dep_las_per_type"]["compound"]["p"], 0.666666666) assert results["dep_las_per_type"]["compound"]["r"] == 1.0 assert results["dep_las_per_type"]["compound"]["f"] == 0.8 def test_ner_per_type(en_vocab): # Gold and Doc are identical scorer = Scorer() examples = [] for input_, annot in test_ner_cardinal: doc = Doc( en_vocab, words=input_.split(" "), ents=["B-CARDINAL", "O", "B-CARDINAL"], ) entities = offsets_to_biluo_tags(doc, annot["entities"]) example = Example.from_dict(doc, {"entities": entities}) # a hack for sentence boundaries example.predicted[1].is_sent_start = False example.reference[1].is_sent_start = False examples.append(example) results = scorer.score(examples) assert results["ents_p"] == 1.0 assert results["ents_r"] == 1.0 assert results["ents_f"] == 1.0 assert results["ents_per_type"]["CARDINAL"]["p"] == 1.0 assert results["ents_per_type"]["CARDINAL"]["r"] == 1.0 assert results["ents_per_type"]["CARDINAL"]["f"] == 1.0 # Doc has one missing and one extra entity # Entity type MONEY is not present in Doc scorer = Scorer() examples = [] for input_, annot in test_ner_apple: doc = Doc( en_vocab, words=input_.split(" "), ents=["B-ORG", "O", "O", "O", "O", "B-GPE", "B-ORG", "O", "O", "O"], ) entities = offsets_to_biluo_tags(doc, annot["entities"]) example = Example.from_dict(doc, {"entities": entities}) # a hack for sentence boundaries example.predicted[1].is_sent_start = False example.reference[1].is_sent_start = False examples.append(example) results = scorer.score(examples) assert results["ents_p"] == approx(0.6666666) assert results["ents_r"] == approx(0.6666666) assert results["ents_f"] == approx(0.6666666) assert "GPE" in results["ents_per_type"] assert "MONEY" in results["ents_per_type"] assert "ORG" in results["ents_per_type"] assert results["ents_per_type"]["GPE"]["p"] == 1.0 assert results["ents_per_type"]["GPE"]["r"] == 1.0 assert results["ents_per_type"]["GPE"]["f"] == 1.0 assert results["ents_per_type"]["MONEY"]["p"] == 0 assert results["ents_per_type"]["MONEY"]["r"] == 0 assert results["ents_per_type"]["MONEY"]["f"] == 0 assert results["ents_per_type"]["ORG"]["p"] == 0.5 assert results["ents_per_type"]["ORG"]["r"] == 1.0 assert results["ents_per_type"]["ORG"]["f"] == approx(0.6666666) def test_tag_score(tagged_doc): # Gold and Doc are identical scorer = Scorer() gold = { "tags": [t.tag_ for t in tagged_doc], "pos": [t.pos_ for t in tagged_doc], "morphs": [str(t.morph) for t in tagged_doc], "sent_starts": [1 if t.is_sent_start else -1 for t in tagged_doc], } example = Example.from_dict(tagged_doc, gold) results = scorer.score([example]) assert results["tag_acc"] == 1.0 assert results["pos_acc"] == 1.0 assert results["morph_acc"] == 1.0 assert results["morph_per_feat"]["NounType"]["f"] == 1.0 # Gold annotation is modified scorer = Scorer() tags = [t.tag_ for t in tagged_doc] tags[0] = "NN" pos = [t.pos_ for t in tagged_doc] pos[1] = "X" morphs = [str(t.morph) for t in tagged_doc] morphs[1] = "Number=sing" morphs[2] = "Number=plur" gold = { "tags": tags, "pos": pos, "morphs": morphs, "sent_starts": gold["sent_starts"], } example = Example.from_dict(tagged_doc, gold) results = scorer.score([example]) assert results["tag_acc"] == 0.9 assert results["pos_acc"] == 0.9 assert results["morph_acc"] == approx(0.8) assert results["morph_per_feat"]["NounType"]["f"] == 1.0 assert results["morph_per_feat"]["Poss"]["f"] == 0.0 assert results["morph_per_feat"]["Number"]["f"] == approx(0.72727272) def test_roc_auc_score(): # Binary classification, toy tests from scikit-learn test suite y_true = [0, 1] y_score = [0, 1] tpr, fpr, _ = _roc_curve(y_true, y_score) roc_auc = _roc_auc_score(y_true, y_score) assert_array_almost_equal(tpr, [0, 0, 1]) assert_array_almost_equal(fpr, [0, 1, 1]) assert_almost_equal(roc_auc, 1.0) y_true = [0, 1] y_score = [1, 0] tpr, fpr, _ = _roc_curve(y_true, y_score) roc_auc = _roc_auc_score(y_true, y_score) assert_array_almost_equal(tpr, [0, 1, 1]) assert_array_almost_equal(fpr, [0, 0, 1]) assert_almost_equal(roc_auc, 0.0) y_true = [1, 0] y_score = [1, 1] tpr, fpr, _ = _roc_curve(y_true, y_score) roc_auc = _roc_auc_score(y_true, y_score) assert_array_almost_equal(tpr, [0, 1]) assert_array_almost_equal(fpr, [0, 1]) assert_almost_equal(roc_auc, 0.5) y_true = [1, 0] y_score = [1, 0] tpr, fpr, _ = _roc_curve(y_true, y_score) roc_auc = _roc_auc_score(y_true, y_score) assert_array_almost_equal(tpr, [0, 0, 1]) assert_array_almost_equal(fpr, [0, 1, 1]) assert_almost_equal(roc_auc, 1.0) y_true = [1, 0] y_score = [0.5, 0.5] tpr, fpr, _ = _roc_curve(y_true, y_score) roc_auc = _roc_auc_score(y_true, y_score) assert_array_almost_equal(tpr, [0, 1]) assert_array_almost_equal(fpr, [0, 1]) assert_almost_equal(roc_auc, 0.5) # same result as above with ROCAUCScore wrapper score = ROCAUCScore() score.score_set(0.5, 1) score.score_set(0.5, 0) assert_almost_equal(score.score, 0.5) # check that errors are raised in undefined cases and score is -inf y_true = [0, 0] y_score = [0.25, 0.75] with pytest.raises(ValueError): _roc_auc_score(y_true, y_score) score = ROCAUCScore() score.score_set(0.25, 0) score.score_set(0.75, 0) assert score.score == -float("inf") y_true = [1, 1] y_score = [0.25, 0.75] with pytest.raises(ValueError): _roc_auc_score(y_true, y_score) score = ROCAUCScore() score.score_set(0.25, 1) score.score_set(0.75, 1) assert score.score == -float("inf")