def test_lower_censored_2(self):
low_censor = 1.6
y = t.tensor([low_censor, low_censor, 2])
y_pred = t.tensor([1.3, 1.7, 2.3])
expected_loss = t.tensor((.01 + .09) / 3)
loss = censored_mse_with_truncation_penalty(
y_pred=y_pred,
y=y,
lower_censoring_bound=low_censor,
clamp_all=False)
self.assertTrue(t.allclose(loss, expected_loss))
def test_truncated_3(self):
upper_truncation = 1.
y = t.tensor([.7, .8, .9])
y_pred = t.tensor([.7, .9, 1.2])
expected_loss = t.tensor((0 + 0.01 + 0.09 + 0.04) / 3)
loss = censored_mse_with_truncation_penalty(
y_pred=y_pred,
y=y,
upper_truncation_limit=upper_truncation,
clamp_all=False)
self.assertTrue(t.allclose(loss, expected_loss))
def test_truncated_2(self):
lower_truncation = 1.
y = t.tensor([1.1, 1.2, 1.3])
y_pred = t.tensor([.9, .9, 1.3])
expected_loss = t.tensor((0.04 + 0.01 + 0.09 + 0.01 + 0) / 3)
loss = censored_mse_with_truncation_penalty(
y_pred=y_pred,
y=y,
lower_truncation_limit=lower_truncation,
clamp_all=False)
self.assertTrue(t.allclose(loss, expected_loss))
def test_upper_censored_3(self):
upper_censor = 1.6
y = t.tensor([1.2, 1.5, upper_censor])
y_pred = t.tensor([1.3, 2.0, 2.3])
expected_loss = t.tensor((.01 + .01) / 3)
loss = censored_mse_with_truncation_penalty(
y_pred=y_pred,
y=y,
upper_censoring_bound=upper_censor,
clamp_all=True)
self.assertTrue(t.allclose(loss, expected_loss))
def test_combined_1(self):
lower_censor = 1.3
upper_censor = 1.7
y = t.tensor(
[lower_censor, lower_censor, 1.4, 1.5, upper_censor, upper_censor])
y_pred = t.tensor([1.0, 1.4, 1.6, 2.0, 1.6, 1.8])
expected_loss = t.tensor((0 + .01 + .04 + .25 + .01 + 0) / 6)
loss = censored_mse_with_truncation_penalty(
y_pred=y_pred,
y=y,
lower_censoring_bound=lower_censor,
upper_censoring_bound=upper_censor,
clamp_all=False)
self.assertTrue(t.allclose(loss, expected_loss))
def test_combined_2(self):
lower_censor = 1.3
upper_censor = 1.7
lower_truncation = 1.1
upper_truncation = 1.8
y = t.tensor(
[lower_censor, lower_censor, 1.4, 1.5, upper_censor, upper_censor])
y_pred = t.tensor([1.0, 1.4, 1.6, 2.0, 1.6, 1.9])
expected_loss = t.tensor((.01 + .01 + .04 + .04 + .04 + .01 + .01) / 6)
loss = censored_mse_with_truncation_penalty(
y_pred=y_pred,
y=y,
lower_censoring_bound=lower_censor,
upper_censoring_bound=upper_censor,
lower_truncation_limit=lower_truncation,
upper_truncation_limit=upper_truncation,
clamp_all=True)
self.assertTrue(t.allclose(loss, expected_loss))
