64 lines
2.7 KiB
Python
64 lines
2.7 KiB
Python
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import numpy as np
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import pytest
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from sklearn.utils._plotting import _interval_max_min_ratio, _validate_score_name
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def metric():
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pass # pragma: no cover
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def neg_metric():
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pass # pragma: no cover
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@pytest.mark.parametrize(
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"score_name, scoring, negate_score, expected_score_name",
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[
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("accuracy", None, False, "accuracy"), # do not transform the name
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(None, "accuracy", False, "Accuracy"), # capitalize the name
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(None, "accuracy", True, "Negative accuracy"), # add "Negative"
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(None, "neg_mean_absolute_error", False, "Negative mean absolute error"),
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(None, "neg_mean_absolute_error", True, "Mean absolute error"), # remove "neg_"
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("MAE", "neg_mean_absolute_error", True, "MAE"), # keep score_name
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(None, None, False, "Score"), # default name
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(None, None, True, "Negative score"), # default name but negated
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("Some metric", metric, False, "Some metric"), # do not transform the name
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("Some metric", metric, True, "Some metric"), # do not transform the name
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(None, metric, False, "Metric"), # default name
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(None, metric, True, "Negative metric"), # default name but negated
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("Some metric", neg_metric, False, "Some metric"), # do not transform the name
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("Some metric", neg_metric, True, "Some metric"), # do not transform the name
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(None, neg_metric, False, "Negative metric"), # default name
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(None, neg_metric, True, "Metric"), # default name but negated
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],
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)
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def test_validate_score_name(score_name, scoring, negate_score, expected_score_name):
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"""Check that we return the right score name."""
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assert (
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_validate_score_name(score_name, scoring, negate_score) == expected_score_name
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)
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# In the following test, we check the value of the max to min ratio
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# for parameter value intervals to check that using a decision threshold
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# of 5. is a good heuristic to decide between linear and log scales on
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# common ranges of parameter values.
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@pytest.mark.parametrize(
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"data, lower_bound, upper_bound",
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[
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# Such a range could be clearly displayed with either log scale or linear
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# scale.
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(np.geomspace(0.1, 1, 5), 5, 6),
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# Checking that the ratio is still positive on a negative log scale.
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(-np.geomspace(0.1, 1, 10), 7, 8),
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# Evenly spaced parameter values lead to a ratio of 1.
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(np.linspace(0, 1, 5), 0.9, 1.1),
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# This is not exactly spaced on a log scale but we will benefit from treating
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# it as such for visualization.
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([1, 2, 5, 10, 20, 50], 20, 40),
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],
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)
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def test_inverval_max_min_ratio(data, lower_bound, upper_bound):
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assert lower_bound < _interval_max_min_ratio(data) < upper_bound
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