333 lines
14 KiB
Python
333 lines
14 KiB
Python
|
|
import numpy as np
|
|
from numpy.testing import assert_allclose
|
|
from itertools import product
|
|
import pytest
|
|
|
|
from sklearn.utils._testing import assert_almost_equal
|
|
from sklearn.utils._testing import assert_array_equal
|
|
from sklearn.utils._testing import assert_array_almost_equal
|
|
|
|
from sklearn.metrics import explained_variance_score
|
|
from sklearn.metrics import mean_absolute_error
|
|
from sklearn.metrics import mean_squared_error
|
|
from sklearn.metrics import mean_squared_log_error
|
|
from sklearn.metrics import median_absolute_error
|
|
from sklearn.metrics import mean_absolute_percentage_error
|
|
from sklearn.metrics import max_error
|
|
from sklearn.metrics import r2_score
|
|
from sklearn.metrics import mean_tweedie_deviance
|
|
|
|
from sklearn.metrics._regression import _check_reg_targets
|
|
|
|
from ...exceptions import UndefinedMetricWarning
|
|
|
|
|
|
def test_regression_metrics(n_samples=50):
|
|
y_true = np.arange(n_samples)
|
|
y_pred = y_true + 1
|
|
|
|
assert_almost_equal(mean_squared_error(y_true, y_pred), 1.)
|
|
assert_almost_equal(mean_squared_log_error(y_true, y_pred),
|
|
mean_squared_error(np.log(1 + y_true),
|
|
np.log(1 + y_pred)))
|
|
assert_almost_equal(mean_absolute_error(y_true, y_pred), 1.)
|
|
assert_almost_equal(median_absolute_error(y_true, y_pred), 1.)
|
|
mape = mean_absolute_percentage_error(y_true, y_pred)
|
|
assert np.isfinite(mape)
|
|
assert mape > 1e6
|
|
assert_almost_equal(max_error(y_true, y_pred), 1.)
|
|
assert_almost_equal(r2_score(y_true, y_pred), 0.995, 2)
|
|
assert_almost_equal(explained_variance_score(y_true, y_pred), 1.)
|
|
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=0),
|
|
mean_squared_error(y_true, y_pred))
|
|
|
|
# Tweedie deviance needs positive y_pred, except for p=0,
|
|
# p>=2 needs positive y_true
|
|
# results evaluated by sympy
|
|
y_true = np.arange(1, 1 + n_samples)
|
|
y_pred = 2 * y_true
|
|
n = n_samples
|
|
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=-1),
|
|
5/12 * n * (n**2 + 2 * n + 1))
|
|
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=1),
|
|
(n + 1) * (1 - np.log(2)))
|
|
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=2),
|
|
2 * np.log(2) - 1)
|
|
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=3/2),
|
|
((6 * np.sqrt(2) - 8) / n) * np.sqrt(y_true).sum())
|
|
assert_almost_equal(mean_tweedie_deviance(y_true, y_pred, power=3),
|
|
np.sum(1 / y_true) / (4 * n))
|
|
|
|
|
|
def test_mean_squared_error_multioutput_raw_value_squared():
|
|
# non-regression test for
|
|
# https://github.com/scikit-learn/scikit-learn/pull/16323
|
|
mse1 = mean_squared_error(
|
|
[[1]], [[10]], multioutput="raw_values", squared=True
|
|
)
|
|
mse2 = mean_squared_error(
|
|
[[1]], [[10]], multioutput="raw_values", squared=False
|
|
)
|
|
assert np.sqrt(mse1) == pytest.approx(mse2)
|
|
|
|
|
|
def test_multioutput_regression():
|
|
y_true = np.array([[1, 0, 0, 1], [0, 1, 1, 1], [1, 1, 0, 1]])
|
|
y_pred = np.array([[0, 0, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]])
|
|
|
|
error = mean_squared_error(y_true, y_pred)
|
|
assert_almost_equal(error, (1. / 3 + 2. / 3 + 2. / 3) / 4.)
|
|
|
|
error = mean_squared_error(y_true, y_pred, squared=False)
|
|
assert_almost_equal(error, 0.454, decimal=2)
|
|
|
|
error = mean_squared_log_error(y_true, y_pred)
|
|
assert_almost_equal(error, 0.200, decimal=2)
|
|
|
|
# mean_absolute_error and mean_squared_error are equal because
|
|
# it is a binary problem.
|
|
error = mean_absolute_error(y_true, y_pred)
|
|
assert_almost_equal(error, (1. + 2. / 3) / 4.)
|
|
|
|
error = np.around(mean_absolute_percentage_error(y_true, y_pred),
|
|
decimals=2)
|
|
assert np.isfinite(error)
|
|
assert error > 1e6
|
|
error = median_absolute_error(y_true, y_pred)
|
|
assert_almost_equal(error, (1. + 1.) / 4.)
|
|
|
|
error = r2_score(y_true, y_pred, multioutput='variance_weighted')
|
|
assert_almost_equal(error, 1. - 5. / 2)
|
|
error = r2_score(y_true, y_pred, multioutput='uniform_average')
|
|
assert_almost_equal(error, -.875)
|
|
|
|
|
|
def test_regression_metrics_at_limits():
|
|
assert_almost_equal(mean_squared_error([0.], [0.]), 0.00, 2)
|
|
assert_almost_equal(mean_squared_error([0.], [0.], squared=False), 0.00, 2)
|
|
assert_almost_equal(mean_squared_log_error([0.], [0.]), 0.00, 2)
|
|
assert_almost_equal(mean_absolute_error([0.], [0.]), 0.00, 2)
|
|
assert_almost_equal(mean_absolute_percentage_error([0.], [0.]), 0.00, 2)
|
|
assert_almost_equal(median_absolute_error([0.], [0.]), 0.00, 2)
|
|
assert_almost_equal(max_error([0.], [0.]), 0.00, 2)
|
|
assert_almost_equal(explained_variance_score([0.], [0.]), 1.00, 2)
|
|
assert_almost_equal(r2_score([0., 1], [0., 1]), 1.00, 2)
|
|
err_msg = ("Mean Squared Logarithmic Error cannot be used when targets "
|
|
"contain negative values.")
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
mean_squared_log_error([-1.], [-1.])
|
|
err_msg = ("Mean Squared Logarithmic Error cannot be used when targets "
|
|
"contain negative values.")
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
mean_squared_log_error([1., 2., 3.], [1., -2., 3.])
|
|
err_msg = ("Mean Squared Logarithmic Error cannot be used when targets "
|
|
"contain negative values.")
|
|
with pytest.raises(ValueError, match=err_msg):
|
|
mean_squared_log_error([1., -2., 3.], [1., 2., 3.])
|
|
|
|
# Tweedie deviance error
|
|
power = -1.2
|
|
assert_allclose(mean_tweedie_deviance([0], [1.], power=power),
|
|
2 / (2 - power), rtol=1e-3)
|
|
with pytest.raises(ValueError,
|
|
match="can only be used on strictly positive y_pred."):
|
|
mean_tweedie_deviance([0.], [0.], power=power)
|
|
assert_almost_equal(mean_tweedie_deviance([0.], [0.], power=0), 0.00, 2)
|
|
|
|
msg = "only be used on non-negative y and strictly positive y_pred."
|
|
with pytest.raises(ValueError, match=msg):
|
|
mean_tweedie_deviance([0.], [0.], power=1.0)
|
|
|
|
power = 1.5
|
|
assert_allclose(mean_tweedie_deviance([0.], [1.], power=power),
|
|
2 / (2 - power))
|
|
msg = "only be used on non-negative y and strictly positive y_pred."
|
|
with pytest.raises(ValueError, match=msg):
|
|
mean_tweedie_deviance([0.], [0.], power=power)
|
|
power = 2.
|
|
assert_allclose(mean_tweedie_deviance([1.], [1.], power=power), 0.00,
|
|
atol=1e-8)
|
|
msg = "can only be used on strictly positive y and y_pred."
|
|
with pytest.raises(ValueError, match=msg):
|
|
mean_tweedie_deviance([0.], [0.], power=power)
|
|
power = 3.
|
|
assert_allclose(mean_tweedie_deviance([1.], [1.], power=power),
|
|
0.00, atol=1e-8)
|
|
|
|
msg = "can only be used on strictly positive y and y_pred."
|
|
with pytest.raises(ValueError, match=msg):
|
|
mean_tweedie_deviance([0.], [0.], power=power)
|
|
|
|
with pytest.raises(ValueError,
|
|
match="is only defined for power<=0 and power>=1"):
|
|
mean_tweedie_deviance([0.], [0.], power=0.5)
|
|
|
|
|
|
def test__check_reg_targets():
|
|
# All of length 3
|
|
EXAMPLES = [
|
|
("continuous", [1, 2, 3], 1),
|
|
("continuous", [[1], [2], [3]], 1),
|
|
("continuous-multioutput", [[1, 1], [2, 2], [3, 1]], 2),
|
|
("continuous-multioutput", [[5, 1], [4, 2], [3, 1]], 2),
|
|
("continuous-multioutput", [[1, 3, 4], [2, 2, 2], [3, 1, 1]], 3),
|
|
]
|
|
|
|
for (type1, y1, n_out1), (type2, y2, n_out2) in product(EXAMPLES,
|
|
repeat=2):
|
|
|
|
if type1 == type2 and n_out1 == n_out2:
|
|
y_type, y_check1, y_check2, multioutput = _check_reg_targets(
|
|
y1, y2, None)
|
|
assert type1 == y_type
|
|
if type1 == 'continuous':
|
|
assert_array_equal(y_check1, np.reshape(y1, (-1, 1)))
|
|
assert_array_equal(y_check2, np.reshape(y2, (-1, 1)))
|
|
else:
|
|
assert_array_equal(y_check1, y1)
|
|
assert_array_equal(y_check2, y2)
|
|
else:
|
|
with pytest.raises(ValueError):
|
|
_check_reg_targets(y1, y2, None)
|
|
|
|
|
|
def test__check_reg_targets_exception():
|
|
invalid_multioutput = 'this_value_is_not_valid'
|
|
expected_message = ("Allowed 'multioutput' string values are.+"
|
|
"You provided multioutput={!r}".format(
|
|
invalid_multioutput))
|
|
with pytest.raises(ValueError, match=expected_message):
|
|
_check_reg_targets([1, 2, 3], [[1], [2], [3]], invalid_multioutput)
|
|
|
|
|
|
def test_regression_multioutput_array():
|
|
y_true = [[1, 2], [2.5, -1], [4.5, 3], [5, 7]]
|
|
y_pred = [[1, 1], [2, -1], [5, 4], [5, 6.5]]
|
|
|
|
mse = mean_squared_error(y_true, y_pred, multioutput='raw_values')
|
|
mae = mean_absolute_error(y_true, y_pred, multioutput='raw_values')
|
|
mape = mean_absolute_percentage_error(y_true, y_pred,
|
|
multioutput='raw_values')
|
|
r = r2_score(y_true, y_pred, multioutput='raw_values')
|
|
evs = explained_variance_score(y_true, y_pred, multioutput='raw_values')
|
|
|
|
assert_array_almost_equal(mse, [0.125, 0.5625], decimal=2)
|
|
assert_array_almost_equal(mae, [0.25, 0.625], decimal=2)
|
|
assert_array_almost_equal(mape, [0.0778, 0.2262], decimal=2)
|
|
assert_array_almost_equal(r, [0.95, 0.93], decimal=2)
|
|
assert_array_almost_equal(evs, [0.95, 0.93], decimal=2)
|
|
|
|
# mean_absolute_error and mean_squared_error are equal because
|
|
# it is a binary problem.
|
|
y_true = [[0, 0]]*4
|
|
y_pred = [[1, 1]]*4
|
|
mse = mean_squared_error(y_true, y_pred, multioutput='raw_values')
|
|
mae = mean_absolute_error(y_true, y_pred, multioutput='raw_values')
|
|
r = r2_score(y_true, y_pred, multioutput='raw_values')
|
|
assert_array_almost_equal(mse, [1., 1.], decimal=2)
|
|
assert_array_almost_equal(mae, [1., 1.], decimal=2)
|
|
assert_array_almost_equal(r, [0., 0.], decimal=2)
|
|
|
|
r = r2_score([[0, -1], [0, 1]], [[2, 2], [1, 1]], multioutput='raw_values')
|
|
assert_array_almost_equal(r, [0, -3.5], decimal=2)
|
|
assert np.mean(r) == r2_score([[0, -1], [0, 1]], [[2, 2], [1, 1]],
|
|
multioutput='uniform_average')
|
|
evs = explained_variance_score([[0, -1], [0, 1]], [[2, 2], [1, 1]],
|
|
multioutput='raw_values')
|
|
assert_array_almost_equal(evs, [0, -1.25], decimal=2)
|
|
|
|
# Checking for the condition in which both numerator and denominator is
|
|
# zero.
|
|
y_true = [[1, 3], [-1, 2]]
|
|
y_pred = [[1, 4], [-1, 1]]
|
|
r2 = r2_score(y_true, y_pred, multioutput='raw_values')
|
|
assert_array_almost_equal(r2, [1., -3.], decimal=2)
|
|
assert np.mean(r2) == r2_score(y_true, y_pred,
|
|
multioutput='uniform_average')
|
|
evs = explained_variance_score(y_true, y_pred, multioutput='raw_values')
|
|
assert_array_almost_equal(evs, [1., -3.], decimal=2)
|
|
assert np.mean(evs) == explained_variance_score(y_true, y_pred)
|
|
|
|
# Handling msle separately as it does not accept negative inputs.
|
|
y_true = np.array([[0.5, 1], [1, 2], [7, 6]])
|
|
y_pred = np.array([[0.5, 2], [1, 2.5], [8, 8]])
|
|
msle = mean_squared_log_error(y_true, y_pred, multioutput='raw_values')
|
|
msle2 = mean_squared_error(np.log(1 + y_true), np.log(1 + y_pred),
|
|
multioutput='raw_values')
|
|
assert_array_almost_equal(msle, msle2, decimal=2)
|
|
|
|
|
|
def test_regression_custom_weights():
|
|
y_true = [[1, 2], [2.5, -1], [4.5, 3], [5, 7]]
|
|
y_pred = [[1, 1], [2, -1], [5, 4], [5, 6.5]]
|
|
|
|
msew = mean_squared_error(y_true, y_pred, multioutput=[0.4, 0.6])
|
|
rmsew = mean_squared_error(y_true, y_pred, multioutput=[0.4, 0.6],
|
|
squared=False)
|
|
maew = mean_absolute_error(y_true, y_pred, multioutput=[0.4, 0.6])
|
|
mapew = mean_absolute_percentage_error(y_true, y_pred,
|
|
multioutput=[0.4, 0.6])
|
|
rw = r2_score(y_true, y_pred, multioutput=[0.4, 0.6])
|
|
evsw = explained_variance_score(y_true, y_pred, multioutput=[0.4, 0.6])
|
|
|
|
assert_almost_equal(msew, 0.39, decimal=2)
|
|
assert_almost_equal(rmsew, 0.59, decimal=2)
|
|
assert_almost_equal(maew, 0.475, decimal=3)
|
|
assert_almost_equal(mapew, 0.1668, decimal=2)
|
|
assert_almost_equal(rw, 0.94, decimal=2)
|
|
assert_almost_equal(evsw, 0.94, decimal=2)
|
|
|
|
# Handling msle separately as it does not accept negative inputs.
|
|
y_true = np.array([[0.5, 1], [1, 2], [7, 6]])
|
|
y_pred = np.array([[0.5, 2], [1, 2.5], [8, 8]])
|
|
msle = mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7])
|
|
msle2 = mean_squared_error(np.log(1 + y_true), np.log(1 + y_pred),
|
|
multioutput=[0.3, 0.7])
|
|
assert_almost_equal(msle, msle2, decimal=2)
|
|
|
|
|
|
@pytest.mark.parametrize('metric', [r2_score])
|
|
def test_regression_single_sample(metric):
|
|
y_true = [0]
|
|
y_pred = [1]
|
|
warning_msg = 'not well-defined with less than two samples.'
|
|
|
|
# Trigger the warning
|
|
with pytest.warns(UndefinedMetricWarning, match=warning_msg):
|
|
score = metric(y_true, y_pred)
|
|
assert np.isnan(score)
|
|
|
|
|
|
def test_tweedie_deviance_continuity():
|
|
n_samples = 100
|
|
|
|
y_true = np.random.RandomState(0).rand(n_samples) + 0.1
|
|
y_pred = np.random.RandomState(1).rand(n_samples) + 0.1
|
|
|
|
assert_allclose(mean_tweedie_deviance(y_true, y_pred, power=0 - 1e-10),
|
|
mean_tweedie_deviance(y_true, y_pred, power=0))
|
|
|
|
# Ws we get closer to the limit, with 1e-12 difference the absolute
|
|
# tolerance to pass the below check increases. There are likely
|
|
# numerical precision issues on the edges of different definition
|
|
# regions.
|
|
assert_allclose(mean_tweedie_deviance(y_true, y_pred, power=1 + 1e-10),
|
|
mean_tweedie_deviance(y_true, y_pred, power=1),
|
|
atol=1e-6)
|
|
|
|
assert_allclose(mean_tweedie_deviance(y_true, y_pred, power=2 - 1e-10),
|
|
mean_tweedie_deviance(y_true, y_pred, power=2),
|
|
atol=1e-6)
|
|
|
|
assert_allclose(mean_tweedie_deviance(y_true, y_pred, power=2 + 1e-10),
|
|
mean_tweedie_deviance(y_true, y_pred, power=2),
|
|
atol=1e-6)
|
|
|
|
|
|
def test_mean_absolute_percentage_error():
|
|
random_number_generator = np.random.RandomState(42)
|
|
y_true = random_number_generator.exponential(size=100)
|
|
y_pred = 1.2 * y_true
|
|
assert mean_absolute_percentage_error(y_true, y_pred) == pytest.approx(0.2)
|