wehub-resource-sync
89 lines
2.3 KiB
Python
89 lines
2.3 KiB
Python
from __future__ import division
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import numpy as np
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import pytest
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from numpy.testing import assert_almost_equal
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from mla.metrics.base import check_data, validate_input
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from mla.metrics.metrics import get_metric
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def test_data_validation():
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with pytest.raises(ValueError):
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check_data([], 1)
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with pytest.raises(ValueError):
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check_data([1, 2, 3], [3, 2])
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a, b = check_data([1, 2, 3], [3, 2, 1])
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assert np.all(a == np.array([1, 2, 3]))
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assert np.all(b == np.array([3, 2, 1]))
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def metric(name):
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return validate_input(get_metric(name))
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def test_classification_error():
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f = metric("classification_error")
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assert f([1, 2, 3, 4], [1, 2, 3, 4]) == 0
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assert f([1, 2, 3, 4], [1, 2, 3, 5]) == 0.25
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assert f([1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 0, 0]) == (1.0 / 6)
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def test_absolute_error():
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f = metric("absolute_error")
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assert f([3], [5]) == [2]
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assert f([-1], [-4]) == [3]
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def test_mean_absolute_error():
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f = metric("mean_absolute_error")
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assert f([1, 2, 3], [1, 2, 3]) == 0
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assert f([1, 2, 3], [3, 2, 1]) == 4 / 3
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def test_squared_error():
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f = metric("squared_error")
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assert f([1], [1]) == [0]
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assert f([3], [1]) == [4]
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def test_squared_log_error():
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f = metric("squared_log_error")
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assert f([1], [1]) == [0]
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assert f([3], [1]) == [np.log(2) ** 2]
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assert f([np.exp(2) - 1], [np.exp(1) - 1]) == [1.0]
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def test_mean_squared_log_error():
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f = metric("mean_squared_log_error")
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assert f([1, 2, 3], [1, 2, 3]) == 0
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assert f([1, 2, 3, np.exp(1) - 1], [1, 2, 3, np.exp(2) - 1]) == 0.25
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def test_root_mean_squared_log_error():
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f = metric("root_mean_squared_log_error")
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assert f([1, 2, 3], [1, 2, 3]) == 0
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assert f([1, 2, 3, np.exp(1) - 1], [1, 2, 3, np.exp(2) - 1]) == 0.5
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def test_mean_squared_error():
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f = metric("mean_squared_error")
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assert f([1, 2, 3], [1, 2, 3]) == 0
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assert f(range(1, 5), [1, 2, 3, 6]) == 1
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def test_root_mean_squared_error():
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f = metric("root_mean_squared_error")
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assert f([1, 2, 3], [1, 2, 3]) == 0
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assert f(range(1, 5), [1, 2, 3, 5]) == 0.5
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def test_multiclass_logloss():
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f = metric("logloss")
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assert_almost_equal(f([1], [1]), 0)
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assert_almost_equal(f([1, 1], [1, 1]), 0)
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assert_almost_equal(f([1], [0.5]), -np.log(0.5))
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