def test_dkwm_mean_two_sample_assertion(self):
rng = np.random.RandomState(seed=0)
num_samples = 4000
# 4000 samples is chosen to be enough to find discrepancies of
# size 0.2 or more with assurance 1e-6, as confirmed here:
d = st.min_discrepancy_of_true_means_detectable_by_dkwm_two_sample(
num_samples,
0.,
1.,
num_samples,
0.,
1.,
false_fail_rate=1e-6,
false_pass_rate=1e-6)
d = self.evaluate(d)
self.assertLess(d, 0.2)
# Test that the test assertion agrees that the standard
# uniform distribution has the same mean as itself.
samples1 = rng.uniform(size=num_samples).astype(np.float32)
samples2 = rng.uniform(size=num_samples).astype(np.float32)
self.evaluate(
st.assert_true_mean_equal_by_dkwm_two_sample(samples1,
0.,
1.,
samples2,
0.,
1.,
false_fail_rate=1e-6))
def test_dkwm_mean_two_sample_assertion_beta_1_2_false(self):
rng = np.random.RandomState(seed=0)
num_samples = 4000
samples1 = rng.uniform(size=num_samples).astype(np.float32)
# As established above, 4000 samples is enough to find discrepancies
# of size 0.2 or more with assurance 1e-6.
# Test that the test assertion confirms that the mean of the
# standard uniform distribution is different from the mean of beta(1, 2).
beta_low_samples = rng.beta(1, 2, size=num_samples).astype(np.float32)
with self.assertRaisesOpError("true mean greater than expected"):
self.evaluate(st.assert_true_mean_equal_by_dkwm_two_sample(
samples1, 0., 1.,
beta_low_samples, 0., 1.,
false_fail_rate=1e-6))
def test_dkwm_mean_two_sample_assertion_beta_1_2_false(self, dtype):
rng = np.random.RandomState(seed=0)
num_samples = 4000
samples1 = rng.uniform(size=num_samples).astype(dtype=dtype)
# As established above, 4000 samples is enough to find discrepancies
# of size 0.2 or more with assurance 1e-6.
# Test that the test assertion confirms that the mean of the
# standard uniform distribution is different from the mean of beta(1, 2).
beta_low_samples = rng.beta(1, 2, size=num_samples).astype(dtype=dtype)
with self.assertRaisesOpError('true mean greater than expected'):
self.evaluate(st.assert_true_mean_equal_by_dkwm_two_sample(
samples1, 0., 1.,
beta_low_samples, 0., 1.,
false_fail_rate=1e-6))
def test_dkwm_mean_two_sample_assertion(self):
rng = np.random.RandomState(seed=0)
num_samples = 4000
# 4000 samples is chosen to be enough to find discrepancies of
# size 0.2 or more with assurance 1e-6, as confirmed here:
d = st.min_discrepancy_of_true_means_detectable_by_dkwm_two_sample(
num_samples, 0., 1., num_samples, 0., 1.,
false_fail_rate=1e-6, false_pass_rate=1e-6)
d = self.evaluate(d)
self.assertLess(d, 0.2)
# Test that the test assertion agrees that the standard
# uniform distribution has the same mean as itself.
samples1 = rng.uniform(size=num_samples).astype(np.float32)
samples2 = rng.uniform(size=num_samples).astype(np.float32)
self.evaluate(st.assert_true_mean_equal_by_dkwm_two_sample(
samples1, 0., 1., samples2, 0., 1., false_fail_rate=1e-6))
