def test_create_dir(self):
path = self.get_random_path()
create_dir(path)
assert_true(os.path.exists(path))
# should not raise OSError if directory already exists
create_dir(path)
assert_true(os.path.exists(path))
def test_exception_class_hierarchy(self):
"""Test that the C++-defined Python exception type objects have the right class hiearchy."""
# Base class inherits from Exception
T.assert_true(issubclass(C_GP.OptimalLearningException, Exception))
type_objects = (C_GP.BoundsException, C_GP.InvalidValueException, C_GP.SingularMatrixException)
for type_object in type_objects:
T.assert_true(issubclass(type_object, C_GP.OptimalLearningException))
def test_move_file(self):
src = self.get_random_file()
dst = self.get_random_path()
assert_true(os.path.exists(src))
assert_false(os.path.exists(dst))
move_file(src, dst)
assert_false(os.path.exists(src))
assert_true(os.path.exists(dst))
def test_msg_servlet_for_watchers(self, _):
resp = self.fetch("/msg", method="POST", body="state=added&message=foo&id=1")
T.assert_is(resp.error, None)
call_arguments = self.call_mock.call_args[0][0]
T.assert_true("/nail/sys/bin/nodebot" in call_arguments[0])
T.assert_true("-i" in call_arguments[1])
T.assert_true("testuser" in call_arguments[4])
T.assert_true("testwatcher_1" in call_arguments[4])
T.assert_true("foo" in call_arguments[4])
def test_msg_servlet_for_watchers(self, _):
resp = self.fetch('/msg', method='POST', body='state=added&message=foo&id=1')
T.assert_is(resp.error, None)
call_arguments = self.call_mock.call_args[0][0]
T.assert_true('/nail/sys/bin/nodebot' in call_arguments[0])
T.assert_true('-i' in call_arguments[1])
T.assert_true('testuser' in call_arguments[4])
T.assert_true('testwatcher_1' in call_arguments[4])
T.assert_true('foo' in call_arguments[4])
def test_exception_class_hierarchy(self):
"""Test that the C++-defined Python exception type objects have the right class hiearchy."""
# Base class inherits from Exception
T.assert_true(issubclass(C_GP.OptimalLearningException, Exception))
type_objects = (C_GP.BoundsException, C_GP.InvalidValueException,
C_GP.SingularMatrixException)
for type_object in type_objects:
T.assert_true(
issubclass(type_object, C_GP.OptimalLearningException))
def test_multistart_qei_expected_improvement_dfo(self):
"""Check that multistart optimization (BFGS) can find the optimum point to sample (using 2-EI)."""
numpy.random.seed(7860)
index = numpy.argmax(numpy.greater_equal(self.num_sampled_list, 20))
domain, gaussian_process = self.gp_test_environments[index]
tolerance = 6.0e-5
num_multistarts = 3
# Expand the domain so that we are definitely not doing constrained optimization
expanded_domain = TensorProductDomain([ClosedInterval(-4.0, 3.0)] * self.dim)
num_to_sample = 2
repeated_domain = RepeatedDomain(num_to_sample, expanded_domain)
num_mc_iterations = 100000
# Just any random point that won't be optimal
points_to_sample = repeated_domain.generate_random_point_in_domain()
ei_eval = ExpectedImprovement(gaussian_process, points_to_sample, num_mc_iterations=num_mc_iterations)
# Compute EI and its gradient for the sake of comparison
ei_initial = ei_eval.compute_expected_improvement()
ei_optimizer = LBFGSBOptimizer(repeated_domain, ei_eval, self.BFGS_parameters)
best_point = multistart_expected_improvement_optimization(ei_optimizer, num_multistarts, num_to_sample)
# Check that gradients are "small" or on border. MC is very inaccurate near 0, so use finite difference
# gradient instead.
ei_eval.current_point = best_point
ei_final = ei_eval.compute_expected_improvement()
finite_diff_grad = numpy.zeros(best_point.shape)
h_value = 0.00001
for i in range(best_point.shape[0]):
for j in range(best_point.shape[1]):
best_point[i, j] += h_value
ei_eval.current_point = best_point
ei_upper = ei_eval.compute_expected_improvement()
best_point[i, j] -= 2 * h_value
ei_eval.current_point = best_point
ei_lower = ei_eval.compute_expected_improvement()
best_point[i, j] += h_value
finite_diff_grad[i, j] = (ei_upper - ei_lower) / (2 * h_value)
self.assert_vector_within_relative(finite_diff_grad, numpy.zeros(finite_diff_grad.shape), tolerance)
# Check that output is in the domain
T.assert_true(repeated_domain.check_point_inside(best_point))
# Since we didn't really converge to the optimal EI (too costly), do some other sanity checks
# EI should have improved
T.assert_gt(ei_final, ei_initial)
def test_normalize_buffer_to_unicode(self):
b = buffer("this is buffer")
val = osxcollector._normalize_val(b)
T.assert_true(isinstance(val, unicode))
def test_normalize_unicode(self):
u = '\u20AC'
val = osxcollector._normalize_val(u)
T.assert_true(isinstance(val, unicode))
def test_normalize_buffer_to_unicode(self):
b = buffer("this is buffer")
val = osxcollector._normalize_val(b)
T.assert_true(isinstance(val, unicode))
def test_normalize_unicode(self):
u = '\u20AC'
val = osxcollector._normalize_val(u)
T.assert_true(isinstance(val, unicode))
def test_assert_true(self):
assert_true(1)
assert_true('False')
assert_true([0])
assert_true([''])
assert_true({'a': 0})
def test_has_uppercase(self):
assert_true(has_uppercase('Foo'))
assert_true(has_uppercase('foO'))
assert_false(has_uppercase('foo'))
assert_false(has_uppercase(''))
def test(self):
content, content_type = self.form.get_value()
lines = content.split('\n')
assert_true(lines[0].startswith('--'))
assert_true(content_type.startswith("multipart/form-data"))
def test_in_bash(self):
os.environ['SHELL'] = '/bin/bash'
assert_true(in_bash())
os.environ['SHELL'] = '/usr/bin/zsh'
assert_false(in_bash())
def test(self):
url = self.request.url
assert_equal(url, "http://localhost:8888")
body = self.request.body
assert_true(body is None)
def test_assert_true(self):
assert_true(1)
assert_true('False')
assert_true([0])
assert_true([''])
assert_true({'a': 0})