310 lines
13 KiB
Python
310 lines
13 KiB
Python
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import numpy as np
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from numpy.testing import assert_allclose
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from itertools import product
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import pytest
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from sklearn.utils._testing import assert_almost_equal
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from sklearn.utils._testing import assert_array_equal
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from sklearn.utils._testing import assert_array_almost_equal
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from sklearn.metrics import explained_variance_score
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from sklearn.metrics import mean_absolute_error
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from sklearn.metrics import mean_squared_error
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from sklearn.metrics import mean_squared_log_error
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from sklearn.metrics import median_absolute_error
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from sklearn.metrics import max_error
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from sklearn.metrics import r2_score
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from sklearn.metrics import mean_tweedie_deviance
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from sklearn.metrics._regression import _check_reg_targets
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from ...exceptions import UndefinedMetricWarning
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def test_regression_metrics(n_samples=50):
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y_true = np.arange(n_samples)
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y_pred = y_true + 1
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assert_almost_equal(mean_squared_error(y_true, y_pred), 1.)
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assert_almost_equal(mean_squared_log_error(y_true, y_pred),
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mean_squared_error(np.log(1 + y_true),
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np.log(1 + y_pred)))
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assert_almost_equal(mean_absolute_error(y_true, y_pred), 1.)
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assert_almost_equal(median_absolute_error(y_true, y_pred), 1.)
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assert_almost_equal(max_error(y_true, y_pred), 1.)
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assert_almost_equal(r2_score(y_true, y_pred), 0.995, 2)
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assert_almost_equal(explained_variance_score(y_true, y_pred), 1.)
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assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=0),
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mean_squared_error(y_true, y_pred))
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# Tweedie deviance needs positive y_pred, except for p=0,
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# p>=2 needs positive y_true
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# results evaluated by sympy
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y_true = np.arange(1, 1 + n_samples)
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y_pred = 2 * y_true
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n = n_samples
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assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=-1),
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5/12 * n * (n**2 + 2 * n + 1))
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assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=1),
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(n + 1) * (1 - np.log(2)))
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assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=2),
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2 * np.log(2) - 1)
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assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=3/2),
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((6 * np.sqrt(2) - 8) / n) * np.sqrt(y_true).sum())
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assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=3),
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np.sum(1 / y_true) / (4 * n))
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def test_mean_squared_error_multioutput_raw_value_squared():
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# non-regression test for
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# https://github.com/scikit-learn/scikit-learn/pull/16323
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mse1 = mean_squared_error(
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[[1]], [[10]], multioutput="raw_values", squared=True
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)
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mse2 = mean_squared_error(
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[[1]], [[10]], multioutput="raw_values", squared=False
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)
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assert np.sqrt(mse1) == pytest.approx(mse2)
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def test_multioutput_regression():
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y_true = np.array([[1, 0, 0, 1], [0, 1, 1, 1], [1, 1, 0, 1]])
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y_pred = np.array([[0, 0, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]])
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error = mean_squared_error(y_true, y_pred)
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assert_almost_equal(error, (1. / 3 + 2. / 3 + 2. / 3) / 4.)
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error = mean_squared_error(y_true, y_pred, squared=False)
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assert_almost_equal(error, 0.454, decimal=2)
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error = mean_squared_log_error(y_true, y_pred)
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assert_almost_equal(error, 0.200, decimal=2)
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# mean_absolute_error and mean_squared_error are equal because
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# it is a binary problem.
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error = mean_absolute_error(y_true, y_pred)
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assert_almost_equal(error, (1. + 2. / 3) / 4.)
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error = median_absolute_error(y_true, y_pred)
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assert_almost_equal(error, (1. + 1.) / 4.)
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error = r2_score(y_true, y_pred, multioutput='variance_weighted')
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assert_almost_equal(error, 1. - 5. / 2)
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error = r2_score(y_true, y_pred, multioutput='uniform_average')
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assert_almost_equal(error, -.875)
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def test_regression_metrics_at_limits():
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assert_almost_equal(mean_squared_error([0.], [0.]), 0.00, 2)
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assert_almost_equal(mean_squared_error([0.], [0.], squared=False), 0.00, 2)
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assert_almost_equal(mean_squared_log_error([0.], [0.]), 0.00, 2)
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assert_almost_equal(mean_absolute_error([0.], [0.]), 0.00, 2)
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assert_almost_equal(median_absolute_error([0.], [0.]), 0.00, 2)
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assert_almost_equal(max_error([0.], [0.]), 0.00, 2)
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assert_almost_equal(explained_variance_score([0.], [0.]), 1.00, 2)
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assert_almost_equal(r2_score([0., 1], [0., 1]), 1.00, 2)
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err_msg = ("Mean Squared Logarithmic Error cannot be used when targets "
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"contain negative values.")
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with pytest.raises(ValueError, match=err_msg):
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mean_squared_log_error([-1.], [-1.])
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err_msg = ("Mean Squared Logarithmic Error cannot be used when targets "
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"contain negative values.")
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with pytest.raises(ValueError, match=err_msg):
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mean_squared_log_error([1., 2., 3.], [1., -2., 3.])
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err_msg = ("Mean Squared Logarithmic Error cannot be used when targets "
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"contain negative values.")
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with pytest.raises(ValueError, match=err_msg):
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mean_squared_log_error([1., -2., 3.], [1., 2., 3.])
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# Tweedie deviance error
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power = -1.2
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assert_allclose(mean_tweedie_deviance([0], [1.], power=power),
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2 / (2 - power), rtol=1e-3)
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with pytest.raises(ValueError,
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match="can only be used on strictly positive y_pred."):
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mean_tweedie_deviance([0.], [0.], power=power)
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assert_almost_equal(mean_tweedie_deviance([0.], [0.], power=0), 0.00, 2)
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msg = "only be used on non-negative y and strictly positive y_pred."
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with pytest.raises(ValueError, match=msg):
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mean_tweedie_deviance([0.], [0.], power=1.0)
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power = 1.5
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assert_allclose(mean_tweedie_deviance([0.], [1.], power=power),
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2 / (2 - power))
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msg = "only be used on non-negative y and strictly positive y_pred."
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with pytest.raises(ValueError, match=msg):
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mean_tweedie_deviance([0.], [0.], power=power)
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power = 2.
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assert_allclose(mean_tweedie_deviance([1.], [1.], power=power), 0.00,
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atol=1e-8)
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msg = "can only be used on strictly positive y and y_pred."
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with pytest.raises(ValueError, match=msg):
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mean_tweedie_deviance([0.], [0.], power=power)
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power = 3.
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assert_allclose(mean_tweedie_deviance([1.], [1.], power=power),
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0.00, atol=1e-8)
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msg = "can only be used on strictly positive y and y_pred."
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with pytest.raises(ValueError, match=msg):
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mean_tweedie_deviance([0.], [0.], power=power)
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with pytest.raises(ValueError,
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match="is only defined for power<=0 and power>=1"):
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mean_tweedie_deviance([0.], [0.], power=0.5)
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def test__check_reg_targets():
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# All of length 3
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EXAMPLES = [
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("continuous", [1, 2, 3], 1),
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("continuous", [[1], [2], [3]], 1),
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("continuous-multioutput", [[1, 1], [2, 2], [3, 1]], 2),
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("continuous-multioutput", [[5, 1], [4, 2], [3, 1]], 2),
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("continuous-multioutput", [[1, 3, 4], [2, 2, 2], [3, 1, 1]], 3),
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]
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for (type1, y1, n_out1), (type2, y2, n_out2) in product(EXAMPLES,
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repeat=2):
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if type1 == type2 and n_out1 == n_out2:
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y_type, y_check1, y_check2, multioutput = _check_reg_targets(
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y1, y2, None)
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assert type1 == y_type
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if type1 == 'continuous':
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assert_array_equal(y_check1, np.reshape(y1, (-1, 1)))
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assert_array_equal(y_check2, np.reshape(y2, (-1, 1)))
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else:
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assert_array_equal(y_check1, y1)
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assert_array_equal(y_check2, y2)
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else:
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with pytest.raises(ValueError):
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_check_reg_targets(y1, y2, None)
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def test__check_reg_targets_exception():
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invalid_multioutput = 'this_value_is_not_valid'
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expected_message = ("Allowed 'multioutput' string values are.+"
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"You provided multioutput={!r}".format(
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invalid_multioutput))
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with pytest.raises(ValueError, match=expected_message):
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_check_reg_targets([1, 2, 3], [[1], [2], [3]], invalid_multioutput)
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def test_regression_multioutput_array():
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y_true = [[1, 2], [2.5, -1], [4.5, 3], [5, 7]]
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y_pred = [[1, 1], [2, -1], [5, 4], [5, 6.5]]
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mse = mean_squared_error(y_true, y_pred, multioutput='raw_values')
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mae = mean_absolute_error(y_true, y_pred, multioutput='raw_values')
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r = r2_score(y_true, y_pred, multioutput='raw_values')
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evs = explained_variance_score(y_true, y_pred, multioutput='raw_values')
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assert_array_almost_equal(mse, [0.125, 0.5625], decimal=2)
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assert_array_almost_equal(mae, [0.25, 0.625], decimal=2)
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assert_array_almost_equal(r, [0.95, 0.93], decimal=2)
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assert_array_almost_equal(evs, [0.95, 0.93], decimal=2)
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# mean_absolute_error and mean_squared_error are equal because
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# it is a binary problem.
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y_true = [[0, 0]]*4
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y_pred = [[1, 1]]*4
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mse = mean_squared_error(y_true, y_pred, multioutput='raw_values')
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mae = mean_absolute_error(y_true, y_pred, multioutput='raw_values')
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r = r2_score(y_true, y_pred, multioutput='raw_values')
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assert_array_almost_equal(mse, [1., 1.], decimal=2)
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assert_array_almost_equal(mae, [1., 1.], decimal=2)
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assert_array_almost_equal(r, [0., 0.], decimal=2)
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r = r2_score([[0, -1], [0, 1]], [[2, 2], [1, 1]], multioutput='raw_values')
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assert_array_almost_equal(r, [0, -3.5], decimal=2)
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assert np.mean(r) == r2_score([[0, -1], [0, 1]], [[2, 2], [1, 1]],
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multioutput='uniform_average')
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evs = explained_variance_score([[0, -1], [0, 1]], [[2, 2], [1, 1]],
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multioutput='raw_values')
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assert_array_almost_equal(evs, [0, -1.25], decimal=2)
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# Checking for the condition in which both numerator and denominator is
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# zero.
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y_true = [[1, 3], [-1, 2]]
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y_pred = [[1, 4], [-1, 1]]
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r2 = r2_score(y_true, y_pred, multioutput='raw_values')
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assert_array_almost_equal(r2, [1., -3.], decimal=2)
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assert np.mean(r2) == r2_score(y_true, y_pred,
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multioutput='uniform_average')
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evs = explained_variance_score(y_true, y_pred, multioutput='raw_values')
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assert_array_almost_equal(evs, [1., -3.], decimal=2)
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assert np.mean(evs) == explained_variance_score(y_true, y_pred)
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# Handling msle separately as it does not accept negative inputs.
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y_true = np.array([[0.5, 1], [1, 2], [7, 6]])
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y_pred = np.array([[0.5, 2], [1, 2.5], [8, 8]])
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msle = mean_squared_log_error(y_true, y_pred, multioutput='raw_values')
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msle2 = mean_squared_error(np.log(1 + y_true), np.log(1 + y_pred),
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multioutput='raw_values')
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assert_array_almost_equal(msle, msle2, decimal=2)
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def test_regression_custom_weights():
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y_true = [[1, 2], [2.5, -1], [4.5, 3], [5, 7]]
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y_pred = [[1, 1], [2, -1], [5, 4], [5, 6.5]]
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msew = mean_squared_error(y_true, y_pred, multioutput=[0.4, 0.6])
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rmsew = mean_squared_error(y_true, y_pred, multioutput=[0.4, 0.6],
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squared=False)
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maew = mean_absolute_error(y_true, y_pred, multioutput=[0.4, 0.6])
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rw = r2_score(y_true, y_pred, multioutput=[0.4, 0.6])
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evsw = explained_variance_score(y_true, y_pred, multioutput=[0.4, 0.6])
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assert_almost_equal(msew, 0.39, decimal=2)
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assert_almost_equal(rmsew, 0.59, decimal=2)
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assert_almost_equal(maew, 0.475, decimal=3)
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assert_almost_equal(rw, 0.94, decimal=2)
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assert_almost_equal(evsw, 0.94, decimal=2)
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# Handling msle separately as it does not accept negative inputs.
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y_true = np.array([[0.5, 1], [1, 2], [7, 6]])
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y_pred = np.array([[0.5, 2], [1, 2.5], [8, 8]])
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msle = mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7])
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msle2 = mean_squared_error(np.log(1 + y_true), np.log(1 + y_pred),
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multioutput=[0.3, 0.7])
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assert_almost_equal(msle, msle2, decimal=2)
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@pytest.mark.parametrize('metric', [r2_score])
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def test_regression_single_sample(metric):
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y_true = [0]
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y_pred = [1]
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warning_msg = 'not well-defined with less than two samples.'
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# Trigger the warning
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with pytest.warns(UndefinedMetricWarning, match=warning_msg):
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score = metric(y_true, y_pred)
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assert np.isnan(score)
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def test_tweedie_deviance_continuity():
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n_samples = 100
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y_true = np.random.RandomState(0).rand(n_samples) + 0.1
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y_pred = np.random.RandomState(1).rand(n_samples) + 0.1
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assert_allclose(mean_tweedie_deviance(y_true, y_pred, power=0 - 1e-10),
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mean_tweedie_deviance(y_true, y_pred, power=0))
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# Ws we get closer to the limit, with 1e-12 difference the absolute
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# tolerance to pass the below check increases. There are likely
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# numerical precision issues on the edges of different definition
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# regions.
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assert_allclose(mean_tweedie_deviance(y_true, y_pred, power=1 + 1e-10),
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mean_tweedie_deviance(y_true, y_pred, power=1),
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atol=1e-6)
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assert_allclose(mean_tweedie_deviance(y_true, y_pred, power=2 - 1e-10),
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mean_tweedie_deviance(y_true, y_pred, power=2),
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atol=1e-6)
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assert_allclose(mean_tweedie_deviance(y_true, y_pred, power=2 + 1e-10),
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mean_tweedie_deviance(y_true, y_pred, power=2),
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atol=1e-6)
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