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test_calibration_metrics.py
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233 lines (200 loc) · 8.46 KB
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from MetricsReloaded.metrics.calibration_measures import CalibrationMeasures
from numpy.testing import assert_allclose
import numpy as np
from scipy.special import gamma
from MetricsReloaded.utility.utils import median_heuristic
pred_224 = [[1-0.22, 0.22 ],
[1-0.48, 0.48],
[0.51,0.49],
[0.04, 0.96],
[0.45, 0.55],
[0.36, 0.64],
[0.22, 0.78],
[0.18, 0.82],
[0.66, 0.34],
[0.13, 0.87]]
#f40_pred = [0.22, 0.48, 0.49, 0.96, 0.55, 0.64, 0.78, 0.82, 0.34, 0.87]
ref_224 = [0, 1, 0, 0, 1, 1, 1, 1, 1, 0]
def test_expected_calibration_error():
"""
Using as reference SN 2.24 p67
"""
ppm1 = CalibrationMeasures(pred_224, ref_224, dict_args={"bins_ece": 2})
ppm2 = CalibrationMeasures(pred_224, ref_224, dict_args={'bins_ece':5})
ppm3 = CalibrationMeasures(pred_224, ref_224)
value_test1 = ppm1.expectation_calibration_error()
value_test2 = ppm2.expectation_calibration_error()
value_test3 = ppm3.expectation_calibration_error()
expected_ece1 = 0.11
expected_ece2 = 0.32
expected_ece3 = 0.36
assert_allclose(value_test1, expected_ece1, atol=0.01)
assert_allclose(value_test2, expected_ece2, atol=0.01)
assert_allclose(value_test3, expected_ece3, atol=0.01)
def test_maximum_calibration_error():
"""
Using figure 2.24 p67 of pitfalls as reference
"""
ppm1 = CalibrationMeasures(pred_224, ref_224, dict_args={"bins_mce": 2})
ppm2 = CalibrationMeasures(pred_224, ref_224, dict_args={'bins_mce':5})
ppm3 = CalibrationMeasures(pred_224, ref_224)
value_test1 = ppm1.maximum_calibration_error()
value_test2 = ppm2.maximum_calibration_error()
value_test3 = ppm3.maximum_calibration_error()
expected_ece1 = 0.12
expected_ece2 = 0.55
expected_ece3 = 0.96
assert_allclose(value_test1, expected_ece1, atol=0.01)
assert_allclose(value_test2, expected_ece2, atol=0.01)
assert_allclose(value_test3, expected_ece3, atol=0.01)
def test_logarithmic_score():
ref_ls = [1, 0]
pred_ls = [[0.2,0.8],
[0.4,0.6]]
ppm = CalibrationMeasures(np.asarray(pred_ls), np.asarray(ref_ls))
value_test = ppm.logarithmic_score()
expected_ls = -0.57
assert_allclose(expected_ls, value_test, atol=0.01)
def test_brier_score():
ref_bs = [1, 0]
pred_bs = [[0.2,0.8],
[0.4,0.6]]
ppm = CalibrationMeasures(np.asarray(pred_bs), np.asarray(ref_bs))
value_test = ppm.brier_score()
expected_bs = 0.4
assert_allclose(expected_bs, value_test, atol=0.01)
#To use SN 2.14 p 99 of Metrics Reloaded
def test_top_label_classification_error():
ref_tce = [1, 0, 2, 1]
pred_tce = [[0.1, 0.8, 0, 0.1], [0.6, 0.1, 0, 0.7], [0.3, 0.1, 1, 0.2]]
pred_tce = np.asarray(pred_tce).T
ref_tce = np.asarray(ref_tce)
expected_prob = [0.5, 0.25, 0.25, 0.5]
best_prob = [0.6, 0.8, 1, 0.7]
pred_class = [1, 0, 2, 1]
expected_tce = 0.478
cm = CalibrationMeasures(pred_tce, ref_tce)
value_test = cm.top_label_classification_error()
assert_allclose(value_test, expected_tce, atol=0.001)
def test_negative_log_likelihood():
ref_nll = [1, 0, 2, 1]
pred_nll = [[0.1, 0.8, 0.05, 0.1], [0.6, 0.1, 0, 0.7], [0.3, 0.1, 0.95, 0.2]]
ref_nll = np.asarray(ref_nll)
pred_nll = np.asarray(pred_nll).T
expected_nll = -1/4 * (np.log(0.8) + np.log(0.6) + np.log(0.7) + np.log(0.95))
cm = CalibrationMeasures(pred_nll, ref_nll)
value_test = cm.negative_log_likelihood()
assert_allclose(value_test, expected_nll)
def test_class_wise_expectation_calibration_error():
ref_cwece = [1, 0, 2, 1]
pred_cwece = [[0.1, 0.8, 0, 0.1], [0.6, 0.1, 0, 0.7], [0.3, 0.1, 1, 0.2]]
# 0.06 * 3
# 0.2 * 1
# 0.05 * 2
# 0.35 * 2
# 0.2 * 3
# 0 * 1
ref_cwece = np.asarray(ref_cwece)
pred_cwece = np.asarray(pred_cwece).T
dict_args = {"bins_ece": 2}
cm = CalibrationMeasures(pred_cwece, ref_cwece, dict_args=dict_args)
value_test = cm.class_wise_expectation_calibration_error()
expected_cwece = 0.150
assert_allclose(value_test, expected_cwece, atol=0.001)
def test_gamma_ik():
pred = [[0.1, 0.8, 0, 0.1], [0.6, 0.1, 0, 0.7], [0.3, 0.1, 1, 0.2]]
pred = np.asarray(pred).T
ref = np.asarray([1, 0, 2, 1])
cm = CalibrationMeasures(pred, ref)
value_test = cm.gamma_ik(0, 0)
expected_gamma = gamma(1.2)
assert_allclose(value_test, expected_gamma, atol=0.001)
def test_dirichlet_kernel():
pred = [[0.1, 0.8, 0, 0.1], [0.6, 0.1, 0, 0.7], [0.3, 0.1, 1, 0.2]]
pred = np.asarray(pred).T
ref = np.asarray([1, 0, 2, 1])
cm = CalibrationMeasures(pred, ref)
numerator = gamma(1.2 + 2.2 + 1.6)
denominator = gamma(1.2) * gamma(2.2) * gamma(1.6)
prod = np.power(0.8, 0.2) * np.power(0.1, 1.2) * np.power(0.1, 0.6)
value_test = cm.dirichlet_kernel(1, 0)
expected_dir = numerator * prod / denominator
assert_allclose(value_test, expected_dir, atol=0.001)
def test_kernel_calibration_error():
pred = [[0.1, 0.8, 0, 0.1], [0.6, 0.1, 0, 0.7], [0.3, 0.1, 1, 0.2]]
pred = np.asarray(pred).T
ref = np.asarray([1, 0, 2, 1])
expected_median_heuristic = 0.90
value_median = median_heuristic(pred)
assert_allclose(value_median, expected_median_heuristic, atol = 0.01)
kernel_01 = np.exp(-np.sqrt(0.78)/value_median) * np.eye(3)
kernel_02 = np.exp(-np.sqrt(0.86)/value_median) * np.eye(3)
kernel_03 = np.exp(-np.sqrt(0.02)/value_median) * np.eye(3)
kernel_12 = np.exp(-np.sqrt(1.26)/value_median) * np.eye(3)
kernel_13 = np.exp(-np.sqrt(0.86)/value_median) * np.eye(3)
kernel_23 = np.exp(-np.sqrt(1.14)/value_median) * np.eye(3)
vect_0 = np.asarray([-0.1, 0.4, -0.3])
vect_1 = np.asarray([0.2, -0.1, -0.1])
vect_2 = np.asarray([0, 0, 0])
vect_3 = np.asarray([-0.1, 0.3, -0.2])
val_01 = np.matmul(vect_0, np.matmul(kernel_01, vect_1.T))
val_02 = np.matmul(vect_0, np.matmul(kernel_02, vect_2.T))
val_03 = np.matmul(vect_0, np.matmul(kernel_03, vect_3.T))
val_12 = np.matmul(vect_1, np.matmul(kernel_12, vect_2.T))
val_13 = np.matmul(vect_1, np.matmul(kernel_13, vect_3.T))
val_23 = np.matmul(vect_2, np.matmul(kernel_23, vect_3.T))
sum_tot = val_01 + val_02 + val_03 + val_12 + val_13 + val_23
mult = 1/6
expected_kce = sum_tot * mult
cm = CalibrationMeasures(pred, ref)
value_test = cm.kernel_calibration_error()
assert_allclose(value_test, expected_kce, atol=0.01)
# 0.1 0.6 0.3
# 0.8 0.1 0.1
# 0 0 1
# 0.1 0.7 0.2
# 0.49+0.25+0.04
# 0.01 + 0.36 + 0.49
# 0.7^2 + 0.5^2 + 0.2^2 = 0.78 0.88
# 0.1^2 + 0.6^2 + 0.7^2 = 0.86 0.92
# 0 + 0.1^2 + 0.1^2 = 0.02
# 0.8^2 + 0.1^2 + 0.9^2 = 1.26
# 0.7^2 + 0.6^2 + 0.1^2 = 0.86
# 0.1^2 + 0.7^2 + 0.8^2 = 1.14
# 0 0 0.02 0.78 0.86 0.86 1.14 1.26
def test_ece_kde():
pred = [[0.1, 0.8, 0, 0.1], [0.6, 0.1, 0, 0.7], [0.3, 0.1, 1, 0.2]]
pred = np.asarray(pred).T
ref = np.asarray([1, 0, 2, 1])
cm = CalibrationMeasures(pred, ref)
dir_01 = cm.dirichlet_kernel(0, 1)
dir_02 = cm.dirichlet_kernel(0, 2)
dir_03 = cm.dirichlet_kernel(0, 3)
dir_10 = cm.dirichlet_kernel(1, 0)
dir_12 = cm.dirichlet_kernel(1, 2)
dir_13 = cm.dirichlet_kernel(1, 3)
dir_20 = cm.dirichlet_kernel(2, 0)
dir_21 = cm.dirichlet_kernel(2, 1)
dir_23 = cm.dirichlet_kernel(2, 3)
dir_30 = cm.dirichlet_kernel(3, 0)
dir_31 = cm.dirichlet_kernel(3, 1)
dir_32 = cm.dirichlet_kernel(3, 2)
den_0 = dir_01 + dir_02 + dir_03
vect_0 = dir_01 * pred[1, :] + dir_02 * pred[2, :] + dir_03 * pred[3, :]
vect_0norm = vect_0 / den_0 - pred[0, :]
den_1 = dir_10 + dir_12 + dir_13
vect_1 = dir_10 * pred[0, :] + dir_12 * pred[2, :] + dir_13 * pred[3, :]
vect_1norm = vect_1 / den_1 - pred[1, :]
den_2 = dir_20 + dir_21 + dir_23
vect_2 = dir_20 * pred[0, :] + dir_21 * pred[1, :] + dir_23 * pred[3, :]
vect_2norm = vect_2 / den_2 - pred[2, :]
den_3 = dir_30 + dir_31 + dir_32
vect_3 = dir_30 * pred[0, :] + dir_31 * pred[1, :] + dir_32 * pred[2, :]
vect_3norm = vect_3 / den_3 - pred[3, :]
norm_v0 = np.sqrt(np.sum(np.square(vect_0norm)))
norm_v1 = np.sqrt(np.sum(np.square(vect_1norm)))
norm_v2 = np.sqrt(np.sum(np.square(vect_2norm)))
norm_v3 = np.sqrt(np.sum(np.square(vect_3norm)))
expected_ece_kde = np.mean([norm_v0, norm_v1, norm_v2, norm_v3])
value_test = cm.kernel_based_ece()
assert_allclose(value_test, expected_ece_kde, atol=0.001)