message: Further speed up statistical_testing_test by breaking up DKWM test.
diff: @@ -141,16 +141,16 @@ class StatisticalTestingTest(test.TestCase):
  def test_dkwm_mean_two_sample_assertion(self):
  rng = np.random.RandomState(seed=0)
  - num_samples = 15000
  + num_samples = 4000
  - # 15000 samples is chosen to be enough to find discrepancies of
  - # size 0.1 or more with assurance 1e-6, as confirmed here:
  + # 4000 samples is chosen to be enough to find discrepancies of
  + # size 0.2 or more with assurance 1e-6, as confirmed here:
  with self.test_session() as sess:
  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 = sess.run(d)
  - self.assertLess(d, 0.1)
  + self.assertLess(d, 0.2)
  # Test that the test assertion agrees that the standard
  # uniform distribution has the same mean as itself.
  @@ -160,6 +160,15 @@ class StatisticalTestingTest(test.TestCase):
  sess.run(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_2_1_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.
  +
  + with self.test_session() as sess:
  # Test that the test assertion confirms that the mean of the
  # standard uniform distribution is different from the mean of beta(2, 1).
  beta_high_samples = rng.beta(2, 1, size=num_samples).astype(np.float32)
  @@ -169,6 +178,15 @@ class StatisticalTestingTest(test.TestCase):
  beta_high_samples, 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.
  +
  + with self.test_session() as sess:
  # 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)
  