mirror of https://github.com/explosion/spaCy.git
197 lines
6.6 KiB
Python
197 lines
6.6 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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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.gold import GoldParse
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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 .util import get_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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{"heads": [2, 2, 2, 2, 3, 6, 4, 4, 10, 10, 7],
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"deps": ['nsubj', 'aux', 'ROOT', 'prep', 'pcomp', 'compound', 'dobj', 'prep', 'quantmod', 'compound', 'pobj']},
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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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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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for input_, annot in test_las_apple:
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doc = get_doc(
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en_vocab,
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words=input_.split(" "),
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heads=([h - i for i, h in enumerate(annot["heads"])]),
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deps=annot["deps"],
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)
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gold = GoldParse(doc, heads=annot["heads"], deps=annot["deps"])
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scorer.score(doc, gold)
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results = scorer.scores
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assert results["uas"] == 100
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assert results["las"] == 100
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assert results["las_per_type"]["nsubj"]["p"] == 100
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assert results["las_per_type"]["nsubj"]["r"] == 100
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assert results["las_per_type"]["nsubj"]["f"] == 100
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assert results["las_per_type"]["compound"]["p"] == 100
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assert results["las_per_type"]["compound"]["r"] == 100
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assert results["las_per_type"]["compound"]["f"] == 100
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# One dep is incorrect in Doc
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scorer = Scorer()
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for input_, annot in test_las_apple:
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doc = get_doc(
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en_vocab,
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words=input_.split(" "),
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heads=([h - i for i, h in enumerate(annot["heads"])]),
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deps=annot["deps"]
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)
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gold = GoldParse(doc, heads=annot["heads"], deps=annot["deps"])
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doc[0].dep_ = "compound"
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scorer.score(doc, gold)
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results = scorer.scores
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assert results["uas"] == 100
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assert_almost_equal(results["las"], 90.9090909)
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assert results["las_per_type"]["nsubj"]["p"] == 0
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assert results["las_per_type"]["nsubj"]["r"] == 0
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assert results["las_per_type"]["nsubj"]["f"] == 0
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assert_almost_equal(results["las_per_type"]["compound"]["p"], 66.6666666)
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assert results["las_per_type"]["compound"]["r"] == 100
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assert results["las_per_type"]["compound"]["f"] == 80
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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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for input_, annot in test_ner_cardinal:
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doc = get_doc(
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en_vocab,
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words=input_.split(" "),
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ents=[[0, 1, "CARDINAL"], [2, 3, "CARDINAL"]],
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)
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gold = GoldParse(doc, entities=annot["entities"])
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scorer.score(doc, gold)
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results = scorer.scores
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assert results["ents_p"] == 100
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assert results["ents_f"] == 100
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assert results["ents_r"] == 100
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assert results["ents_per_type"]["CARDINAL"]["p"] == 100
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assert results["ents_per_type"]["CARDINAL"]["f"] == 100
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assert results["ents_per_type"]["CARDINAL"]["r"] == 100
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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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for input_, annot in test_ner_apple:
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doc = get_doc(
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en_vocab,
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words=input_.split(" "),
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ents=[[0, 1, "ORG"], [5, 6, "GPE"], [6, 7, "ORG"]],
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)
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gold = GoldParse(doc, entities=annot["entities"])
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scorer.score(doc, gold)
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results = scorer.scores
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assert results["ents_p"] == approx(66.66666)
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assert results["ents_r"] == approx(66.66666)
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assert results["ents_f"] == approx(66.66666)
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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"] == 100
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assert results["ents_per_type"]["GPE"]["r"] == 100
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assert results["ents_per_type"]["GPE"]["f"] == 100
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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"] == 50
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assert results["ents_per_type"]["ORG"]["r"] == 100
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assert results["ents_per_type"]["ORG"]["f"] == approx(66.66666)
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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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