def test_dynamic_resampling_effect_threshold(self):
"""Tests that the resampling behavior depends on the error of the IC.
"""
test_dynamic_resampling = DynamicResamplingNonParametric(
0.2, threshold=0.95)
history = {
"fitness":
np.array([10, 5, 4, 8, 10, 12, 16, 15, 10, 12, 16, 14]),
"parameters":
np.array([
[1, 2],
[2, 3],
[1, 3],
[2, 1],
[2, 1],
[2, 1],
[2, 1],
[2, 1],
[2, 1],
[2, 1],
[2, 1],
[2, 1],
]),
}
self.assertTrue(test_dynamic_resampling.resample(history))
test_dynamic_resampling = DynamicResamplingNonParametric(0.2,
threshold=0.1)
self.assertFalse(test_dynamic_resampling.resample(history))
def test_dynamic_resampling_non_parametric_ci_bounds(self):
"""Tests that the CI bounds of the non parametric intervals are properly computed.
"""
test_dynamic_resampling = DynamicResamplingNonParametric(
0.2, resampling_schedule="constant")
history = {
"fitness":
np.array([10, 5, 4, 12, 4, 5]),
"parameters":
np.array([[1, 2], [2, 3], [1, 3], [2, 1], [2, 1], [2, 1]]),
}
test_dynamic_resampling.resample(history)
print(f"Interval ranks: {test_dynamic_resampling.ic_length()}")
def test_dynamic_resampling_non_parametric_no_resample(self):
"""Tests that there is no resampling when the median of the resampled data point is higher
than the current median.
"""
test_dynamic_resampling = DynamicResamplingNonParametric(
0.2, resampling_schedule="constant")
history = {
"fitness":
np.array([10, 5, 4, 12, 12, 12, 12]),
"parameters":
np.array([[1, 2], [2, 3], [1, 3], [2, 1], [2, 1], [2, 1], [2, 1]]),
}
self.assertFalse(test_dynamic_resampling.resample(history))
test_dynamic_resampling.resample(history)
def test_dynamic_resampling_non_parametric_one_resample(self):
"""Tests that when there is only one resampling for a parametrization the parametrization
is automatically repeated.
"""
test_dynamic_resampling = DynamicResamplingNonParametric(
0.2, resampling_schedule="constant")
history = {
"fitness": np.array([10, 5, 4, 12]),
"parameters": np.array([[1, 2], [2, 3], [1, 3], [2, 1]]),
}
self.assertTrue(test_dynamic_resampling.resample(history))
def test_dynamic_resampling_non_parametric_resample(self):
"""Tests that there is resampling when the size of the IC is higher than the threshold.
"""
test_dynamic_resampling = DynamicResamplingNonParametric(0.2,
threshold=0.9)
history = {
"fitness":
np.array([10, 5, 4, 8, 10, 12, 16]),
"parameters":
np.array([[1, 2], [2, 3], [1, 3], [2, 1], [2, 1], [2, 1], [2, 1]]),
}
self.assertTrue(test_dynamic_resampling.resample(history))
def test_dynamic_resampling_non_parametric_resample2(self):
"""Tests that there is resampling when the size of the IC is higher than the threshold and
the median of the resampled data point lower than the current median.
"""
test_dynamic_resampling = DynamicResamplingNonParametric(
0.2, resampling_schedule="constant")
history = {
"fitness":
np.array([10, 5, 4, 2, 5, 3, 7]),
"parameters":
np.array([[1, 2], [2, 3], [1, 3], [2, 1], [2, 1], [2, 1], [2, 1]]),
}
self.assertTrue(test_dynamic_resampling.resample(history))