num_samples = 5000
# 5000 samples is chosen to be enough to find discrepancies of
# size 0.1 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(
- num_samples, 0., 1., false_fail_rate=1e-6, false_pass_rate=1e-6)
- d = sess.run(d)
- self.assertLess(d, 0.1)
+ d = st.min_discrepancy_of_true_means_detectable_by_dkwm(
+ num_samples, 0., 1., false_fail_rate=1e-6, false_pass_rate=1e-6)
+ d = self.evaluate(d)
+ self.assertLess(d, 0.1)
# Test that the confidence interval computed for the mean includes
# 0.5 and excludes 0.4 and 0.6.
- with self.test_session() as sess:
- samples = rng.uniform(size=num_samples).astype(np.float32)
- (low, high) = st.true_mean_confidence_interval_by_dkwm(
- samples, 0., 1., error_rate=1e-6)
- low, high = sess.run([low, high])
- self.assertGreater(low, 0.4)
- self.assertLess(low, 0.5)
- self.assertGreater(high, 0.5)
- self.assertLess(high, 0.6)
+ samples = rng.uniform(size=num_samples).astype(np.float32)
+ (low, high) = st.true_mean_confidence_interval_by_dkwm(
+ samples, 0., 1., error_rate=1e-6)
+ low, high = self.evaluate([low, high])
+ self.assertGreater(low, 0.4)
+ self.assertLess(low, 0.5)
+ self.assertGreater(high, 0.5)
+ self.assertLess(high, 0.6)
def test_dkwm_mean_one_sample_assertion(self):
rng = np.random.RandomState(seed=0)
# Test that the test assertion agrees that the mean of the standard
# uniform distribution is 0.5.
samples = rng.uniform(size=num_samples).astype(np.float32)
- with self.test_session() as sess:
- sess.run(st.assert_true_mean_equal_by_dkwm(
- samples, 0., 1., 0.5, false_fail_rate=1e-6))
-
- # Test that the test assertion confirms that the mean of the
- # standard uniform distribution is not 0.4.
- with self.assertRaisesOpError("true mean greater than expected"):
- sess.run(st.assert_true_mean_equal_by_dkwm(
- samples, 0., 1., 0.4, false_fail_rate=1e-6))
-
- # Test that the test assertion confirms that the mean of the
- # standard uniform distribution is not 0.6.
- with self.assertRaisesOpError("true mean smaller than expected"):
- sess.run(st.assert_true_mean_equal_by_dkwm(
- samples, 0., 1., 0.6, false_fail_rate=1e-6))
+ self.evaluate(st.assert_true_mean_equal_by_dkwm(
+ samples, 0., 1., 0.5, false_fail_rate=1e-6))
+
+ # Test that the test assertion confirms that the mean of the
+ # standard uniform distribution is not 0.4.
+ with self.assertRaisesOpError("true mean greater than expected"):
+ self.evaluate(st.assert_true_mean_equal_by_dkwm(
+ samples, 0., 1., 0.4, false_fail_rate=1e-6))
+
+ # Test that the test assertion confirms that the mean of the
+ # standard uniform distribution is not 0.6.
+ with self.assertRaisesOpError("true mean smaller than expected"):
+ self.evaluate(st.assert_true_mean_equal_by_dkwm(
+ samples, 0., 1., 0.6, false_fail_rate=1e-6))
def test_dkwm_mean_in_interval_one_sample_assertion(self):
rng = np.random.RandomState(seed=0)
# 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, 0.2)
+ 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)
- with self.test_session() as sess:
- sess.run(st.assert_true_mean_equal_by_dkwm_two_sample(
- samples1, 0., 1., samples2, 0., 1., false_fail_rate=1e-6))
+ 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_2_1_false(self):
rng = np.random.RandomState(seed=0)
# 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)
- with self.assertRaisesOpError("true mean smaller than expected"):
- sess.run(st.assert_true_mean_equal_by_dkwm_two_sample(
- samples1, 0., 1.,
- beta_high_samples, 0., 1.,
- false_fail_rate=1e-6))
+ # 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)
+ with self.assertRaisesOpError("true mean smaller than expected"):
+ self.evaluate(st.assert_true_mean_equal_by_dkwm_two_sample(
+ samples1, 0., 1.,
+ 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)
# 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)
- with self.assertRaisesOpError("true mean greater than expected"):
- sess.run(st.assert_true_mean_equal_by_dkwm_two_sample(
- samples1, 0., 1.,
- beta_low_samples, 0., 1.,
- false_fail_rate=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_argument_validity_checking(self):
rng = np.random.RandomState(seed=0)
# Test that the test library complains if the given samples fall
# outside the purported bounds.
- with self.test_session() as sess:
- with self.assertRaisesOpError("maximum value exceeds expectations"):
- sess.run(st.true_mean_confidence_interval_by_dkwm(
- samples, [[0., 1.]], [[0.5, 1.5]], error_rate=0.5))
- with self.assertRaisesOpError("minimum value falls below expectations"):
- sess.run(st.true_mean_confidence_interval_by_dkwm(
- samples, [[0.5, 1.5]], [[1., 2.]], error_rate=0.5))
-
- # But doesn't complain if they don't.
- op = st.true_mean_confidence_interval_by_dkwm(
- samples, [[0., 1.]], [[1., 2.]], error_rate=0.5)
- _ = sess.run(op)
+ with self.assertRaisesOpError("maximum value exceeds expectations"):
+ self.evaluate(st.true_mean_confidence_interval_by_dkwm(
+ samples, [[0., 1.]], [[0.5, 1.5]], error_rate=0.5))
+ with self.assertRaisesOpError("minimum value falls below expectations"):
+ self.evaluate(st.true_mean_confidence_interval_by_dkwm(
+ samples, [[0.5, 1.5]], [[1., 2.]], error_rate=0.5))
+
+ # But doesn't complain if they don't.
+ op = st.true_mean_confidence_interval_by_dkwm(
+ samples, [[0., 1.]], [[1., 2.]], error_rate=0.5)
+ _ = self.evaluate(op)
def test_do_maximum_mean(self):
n = 117
samples = rng.uniform(size=n).astype(np.float32)
# Compute the answer in TF using the code under test
- with self.test_session() as sess:
- envelope_t = ops.convert_to_tensor(envelope)
- max_mean = st._do_maximum_mean(samples, envelope_t, 1)
- max_mean = sess.run(max_mean)
+ envelope_t = ops.convert_to_tensor(envelope)
+ max_mean = st._do_maximum_mean(samples, envelope_t, 1)
+ max_mean = self.evaluate(max_mean)
# Compute the correct answer for this case in numpy. In this
# example, `n` and `envelope` are such that `samples[2]` is the
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