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Use assertIn instead of assertContainsSubsequence for substring checks.

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A contained subsequence must not necessarily be continuous, so the check only
checked if the individual letters were appearing in order within the message,
possibly interspersed with other characters.

PiperOrigin-RevId: 690977874
Change-Id: I36bd5a0105b979a9b0d6d04ca5239245d4f8d041
This commit is contained in:
Google DeepMind
2024-10-29 05:20:07 -07:00
committed by Copybara-Service
parent 6703a40fcf
commit 71cfcc5432
1 changed files with 12 additions and 14 deletions
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python/mujoco/minimize_test.py
+12 -14
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@@ -32,7 +32,7 @@ class MinimizeTest(absltest.TestCase):
x, _ = minimize.least_squares(x0, residual, output=out)
expected_x = np.array((1.0, 1.0))
np.testing.assert_array_almost_equal(x, expected_x)
self.assertContainsSubsequence(out.getvalue(), 'norm(dx) < tol')
self.assertIn('norm(dx) < tol', out.getvalue())
def test_start_at_minimum(self) -> None:
def residual(x):
@@ -43,8 +43,8 @@ class MinimizeTest(absltest.TestCase):
x, _ = minimize.least_squares(x0, residual, output=out)
expected_x = np.array((1.0, 1.0))
np.testing.assert_array_almost_equal(x, expected_x)
self.assertContainsSubsequence(out.getvalue(), 'norm(dx) < tol')
self.assertContainsSubsequence(out.getvalue(), 'exact minimum found')
self.assertIn('norm(dx) < tol', out.getvalue())
self.assertIn('exact minimum found', out.getvalue())
def test_jac_callback(self) -> None:
def residual(x):
@@ -60,8 +60,8 @@ class MinimizeTest(absltest.TestCase):
check_derivatives=True)
expected_x = np.array((1.0, 1.0))
np.testing.assert_array_almost_equal(x, expected_x)
self.assertContainsSubsequence(out.getvalue(), 'norm(dx) < tol')
self.assertContainsSubsequence(out.getvalue(), 'Jacobian matches')
self.assertIn('norm(dx) < tol', out.getvalue())
self.assertIn('Jacobian matches', out.getvalue())
# Try with bad Jacobian, ask least_squares to check it.
def bad_jacobian(x, r):
@@ -83,7 +83,7 @@ class MinimizeTest(absltest.TestCase):
x0 = np.zeros(dim)
out = io.StringIO()
minimize.least_squares(x0, residual, max_iter=20, output=out)
self.assertContainsSubsequence(out.getvalue(), 'maximum iterations')
self.assertIn('maximum iterations', out.getvalue())
# Succeed after 100 iterations (default).
x, _ = minimize.least_squares(x0, residual)
@@ -106,7 +106,7 @@ class MinimizeTest(absltest.TestCase):
x, _ = minimize.least_squares(x0, residual, bounds=bounds_types['inbounds'],
output=out)
np.testing.assert_array_almost_equal(x, expected_x)
self.assertContainsSubsequence(out.getvalue(), 'norm(dx) < tol')
self.assertIn('norm(dx) < tol', out.getvalue())
# Test different bounds conditions.
for bounds in bounds_types.values():
@@ -118,7 +118,7 @@ class MinimizeTest(absltest.TestCase):
output=out,
verbose=minimize.Verbosity.FULLITER,
)
self.assertContainsSubsequence(out.getvalue(), ' < tol')
self.assertIn(' < tol', out.getvalue())
grad = trace[-2].jacobian.T @ trace[-2].residual
# If x_i is on the boundary, gradient points out, otherwise it is 0.
for i, xi in enumerate(x):
@@ -161,7 +161,7 @@ class MinimizeTest(absltest.TestCase):
iter_callback=iter_callback)
expected_x = np.array((1.0, 1.0))
np.testing.assert_array_almost_equal(x, expected_x)
self.assertContainsSubsequence(out.getvalue(), 'Hello iteration 3!')
self.assertIn('Hello iteration 3!', out.getvalue())
def test_norm(self) -> None:
def residual(x):
@@ -187,11 +187,9 @@ class MinimizeTest(absltest.TestCase):
check_derivatives=True)
expected_x = np.array((1.0, 1.0))
np.testing.assert_array_almost_equal(x, expected_x)
self.assertContainsSubsequence(out.getvalue(), 'norm(dx) < tol')
self.assertContainsSubsequence(out.getvalue(),
'User-provided norm gradient matches')
self.assertContainsSubsequence(out.getvalue(),
'User-provided norm Hessian matches')
self.assertIn('norm(dx) < tol', out.getvalue())
self.assertIn('User-provided norm gradient matches', out.getvalue())
self.assertIn('User-provided norm Hessian matches', out.getvalue())
class SmoothL2BadGrad(minimize.Norm):
def value(self, r):