wehub-resource-sync
58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
from cleanlab.experimental.span_classification import (
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find_label_issues,
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display_issues,
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get_label_quality_scores,
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)
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import numpy as np
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import pytest
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import warnings
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warnings.filterwarnings("ignore")
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words = [["I", "love", "Cleanlab", "Inc"], ["A", "new", "park"]]
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pred_probs = [
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np.array([0.3, 0.2, 0.9, 0.1]),
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np.array([0.1, 0.1, 0.9]),
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]
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labels = [[0, 0, 1, 1], [0, 0, 1]]
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class_names = ["O", "Span"]
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@pytest.mark.parametrize(
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"test_labels",
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[labels, [np.array(l) for l in labels]],
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ids=["list labels", "np.array labels"],
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)
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@pytest.mark.filterwarnings("ignore::DeprecationWarning")
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def test_find_label_issues(test_labels):
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issues = find_label_issues(test_labels, pred_probs)
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assert isinstance(issues, list)
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assert len(issues) == 1
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assert issues[0] == (0, 3)
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issues = find_label_issues(labels, pred_probs)
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def test_display_issues():
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display_issues(issues, words)
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display_issues(issues, tokens=words, labels=labels)
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display_issues(issues, words, pred_probs=pred_probs)
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display_issues(issues, words, pred_probs=pred_probs, labels=labels)
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display_issues(issues, words, pred_probs=pred_probs, labels=labels, class_names=class_names)
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@pytest.fixture(name="label_quality_scores")
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def fixture_label_quality_scores():
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sentence_scores, token_info = get_label_quality_scores(labels, pred_probs)
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return sentence_scores, token_info
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def test_get_label_quality_scores(label_quality_scores):
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sentence_scores, token_info = label_quality_scores
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assert len(sentence_scores) == 2
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assert np.allclose(sentence_scores, [0.1, 0.9])
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assert len(token_info) == 2
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assert np.allclose(token_info[0], [0.7, 0.8, 0.9, 0.1])
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