cleanlab--cleanlab
Jonas Mueller
a95a447010
* issuemanager.get_summary->make_summary * test(datalab): ♻️ move tests for issue managers into their respective test modules * test(datalab): ✅ add tests for custom issue manager - Validate scores provided to IssueManager.make_summary - Move fixture for custom issue manager to conftest.py - Test make_summary on a "custom" IssueManager * test(datalab): add __init__.py for discoverability of issue manager tests --------- Co-authored-by: Elías Snorrason <eliassno@gmail.com>
106 行
4.2 KiB
Python
106 行
4.2 KiB
Python
import numpy as np
|
|
import pandas as pd
|
|
import pytest
|
|
|
|
from cleanlab.datalab.issue_manager.label import LabelIssueManager
|
|
|
|
|
|
class TestLabelIssueManager:
|
|
@pytest.fixture
|
|
def issue_manager(self, lab):
|
|
return LabelIssueManager(datalab=lab)
|
|
|
|
def test_find_issues(self, pred_probs, issue_manager):
|
|
"""Test that the find_issues method works."""
|
|
issue_manager.find_issues(pred_probs=pred_probs)
|
|
issues, summary, info = issue_manager.issues, issue_manager.summary, issue_manager.info
|
|
assert isinstance(issues, pd.DataFrame), "Issues should be a dataframe"
|
|
|
|
assert isinstance(summary, pd.DataFrame), "Summary should be a dataframe"
|
|
assert summary["issue_type"].values[0] == "label"
|
|
assert pytest.approx(summary["score"].values[0]) == 0.6
|
|
|
|
assert isinstance(info, dict), "Info should be a dict"
|
|
info_keys = info.keys()
|
|
expected_keys = [
|
|
"num_label_issues",
|
|
"average_label_quality",
|
|
"confident_joint",
|
|
"classes_by_label_quality",
|
|
"overlapping_classes",
|
|
"py",
|
|
"noise_matrix",
|
|
"inverse_noise_matrix",
|
|
]
|
|
assert all(
|
|
[key in info_keys for key in expected_keys]
|
|
), f"Info should have the right keys, but is missing {set(expected_keys) - set(info_keys)}"
|
|
|
|
def test_find_issues_with_kwargs(self, pred_probs, issue_manager):
|
|
issue_manager.find_issues(pred_probs=pred_probs, thresholds=[0.2, 0.3, 0.1])
|
|
|
|
def test_init_with_clean_learning_kwargs(self, lab, issue_manager):
|
|
"""Test that the init method can provide kwargs to the CleanLearning constructor."""
|
|
new_issue_manager = LabelIssueManager(
|
|
datalab=lab,
|
|
clean_learning_kwargs={"cv_n_folds": 10},
|
|
)
|
|
cv_n_folds = [im.cl.cv_n_folds for im in [issue_manager, new_issue_manager]]
|
|
assert cv_n_folds == [5, 10], "Issue manager should have the right attributes"
|
|
|
|
def test_get_summary_parameters(self, issue_manager, monkeypatch):
|
|
mock_health_summary_parameters = {
|
|
"labels": [1, 0, 2],
|
|
"asymmetric": False,
|
|
"class_names": ["a", "b", "c"],
|
|
"num_examples": 3,
|
|
"joint": [1 / 3, 1 / 3, 1 / 3],
|
|
"confident_joint": [1 / 3, 1 / 3, 1 / 3],
|
|
"multi_label": False,
|
|
}
|
|
monkeypatch.setattr(
|
|
issue_manager.datalab, "_labels", mock_health_summary_parameters["labels"]
|
|
)
|
|
pred_probs = np.random.rand(3, 3)
|
|
monkeypatch.setattr(
|
|
issue_manager, "health_summary_parameters", mock_health_summary_parameters
|
|
)
|
|
summary_parameters = issue_manager._get_summary_parameters(pred_probs=pred_probs)
|
|
expected_parameters = {
|
|
"confident_joint": [1 / 3, 1 / 3, 1 / 3],
|
|
"asymmetric": False,
|
|
"class_names": ["a", "b", "c"],
|
|
}
|
|
assert summary_parameters == expected_parameters
|
|
|
|
# Test missing "confident_joint" key
|
|
mock_health_summary_parameters.pop("confident_joint")
|
|
monkeypatch.setattr(
|
|
issue_manager, "health_summary_parameters", mock_health_summary_parameters
|
|
)
|
|
summary_parameters = issue_manager._get_summary_parameters(pred_probs=pred_probs)
|
|
expected_parameters = {
|
|
"joint": [1 / 3, 1 / 3, 1 / 3],
|
|
"num_examples": 3,
|
|
"asymmetric": False,
|
|
"class_names": ["a", "b", "c"],
|
|
}
|
|
assert summary_parameters == expected_parameters
|
|
|
|
# Test missing "joint" key
|
|
mock_health_summary_parameters.pop("joint")
|
|
monkeypatch.setattr(
|
|
issue_manager, "health_summary_parameters", mock_health_summary_parameters
|
|
)
|
|
summary_parameters = issue_manager._get_summary_parameters(pred_probs=pred_probs)
|
|
expected_parameters = {
|
|
"pred_probs": pred_probs,
|
|
"labels": [1, 0, 2],
|
|
"asymmetric": False,
|
|
"class_names": ["a", "b", "c"],
|
|
}
|
|
assert np.all(summary_parameters["pred_probs"] == expected_parameters["pred_probs"])
|
|
summary_parameters.pop("pred_probs")
|
|
expected_parameters.pop("pred_probs")
|
|
assert summary_parameters == expected_parameters
|