def test_interface_returns_same_as_cpp(self):
"""Test that the /gp/ei endpoint does the same thing as the C++ interface."""
tolerance = 1.0e-11
for test_case in self.gp_test_environments:
python_domain, python_gp = test_case
python_cov, historical_data = python_gp.get_core_data_copy()
cpp_cov = SquareExponential(python_cov.hyperparameters)
cpp_gp = GaussianProcess(cpp_cov, historical_data)
points_to_evaluate = python_domain.generate_uniform_random_points_in_domain(10)
# EI from C++
expected_improvement_evaluator = ExpectedImprovement(cpp_gp, None)
# TODO(GH-99): Change test case to have the right shape:
# (num_to_evaluate, num_to_sample, dim)
# Here we assume the shape is (num_to_evaluate, dim) so we insert an axis, making num_to_sample = 1.
# Also might be worth testing more num_to_sample values (will require manipulating C++ RNG state).
cpp_expected_improvement = expected_improvement_evaluator.evaluate_at_point_list(
points_to_evaluate[:, numpy.newaxis, :]
)
# EI from REST
json_payload = self._build_json_payload(
python_domain, python_cov, historical_data, points_to_evaluate.tolist()
)
resp = self.testapp.post(self.endpoint, json_payload)
resp_schema = GpEiResponse()
resp_dict = resp_schema.deserialize(json.loads(resp.body))
rest_expected_improvement = numpy.asarray(resp_dict.get("expected_improvement"))
self.assert_vector_within_relative(rest_expected_improvement, cpp_expected_improvement, tolerance)
def gp_ei_view(self):
"""Endpoint for gp_ei POST requests.
.. http:post:: /gp/ei
Calculates the Expected Improvement (EI) of a set of points, given historical data.
:input: :class:`moe.views.schemas.GpEiRequest`
:output: :class:`moe.views.schemas.GpEiResponse`
:status 201: returns a response
:status 500: server error
"""
params = self.get_params_from_request()
# TODO(GH-99): Change REST interface to give points_to_evaluate with shape
# (num_to_evaluate, num_to_sample, dim)
# Here we assume the shape is (num_to_evaluate, dim) so we insert an axis, making num_to_sample = 1.
points_to_evaluate = numpy.array(params.get('points_to_evaluate'))[:, numpy.newaxis, :]
points_being_sampled = numpy.array(params.get('points_being_sampled'))
num_mc_iterations = params.get('mc_iterations')
max_num_threads = params.get('max_num_threads')
gaussian_process = _make_gp_from_params(params)
expected_improvement_evaluator = ExpectedImprovement(
gaussian_process,
points_being_sampled=points_being_sampled,
num_mc_iterations=num_mc_iterations,
)
with timing_context(EI_COMPUTATION_TIMING_LABEL):
expected_improvement = expected_improvement_evaluator.evaluate_at_point_list(
points_to_evaluate,
max_num_threads=max_num_threads,
)
return self.form_response({
'endpoint': self._route_name,
'expected_improvement': expected_improvement.tolist(),
})
def gp_ei_view(self):
"""Endpoint for gp_ei POST requests.
.. http:post:: /gp/ei
Calculates the Expected Improvement (EI) of a set of points, given historical data.
:input: :class:`moe.views.schemas.rest.GpEiRequest`
:output: :class:`moe.views.schemas.rest.GpEiResponse`
:status 200: returns a response
:status 500: server error
"""
params = self.get_params_from_request()
# TODO(GH-99): Change REST interface to give points_to_evaluate with shape
# (num_to_evaluate, num_to_sample, dim)
# Here we assume the shape is (num_to_evaluate, dim) so we insert an axis, making num_to_sample = 1.
points_to_evaluate = numpy.array(params.get('points_to_evaluate'))[:, numpy.newaxis, :]
points_being_sampled = numpy.array(params.get('points_being_sampled'))
num_mc_iterations = params.get('mc_iterations')
max_num_threads = params.get('max_num_threads')
gaussian_process = _make_gp_from_params(params)
expected_improvement_evaluator = ExpectedImprovement(
gaussian_process,
points_being_sampled=points_being_sampled,
num_mc_iterations=num_mc_iterations,
)
with timing_context(EI_COMPUTATION_TIMING_LABEL):
expected_improvement = expected_improvement_evaluator.evaluate_at_point_list(
points_to_evaluate,
max_num_threads=max_num_threads,
)
return self.form_response({
'endpoint': self._route_name,
'expected_improvement': expected_improvement.tolist(),
})
def test_interface_returns_same_as_cpp(self):
"""Test that the /gp/ei endpoint does the same thing as the C++ interface."""
tolerance = 1.0e-11
for test_case in self.gp_test_environments:
python_domain, python_gp = test_case
python_cov, historical_data = python_gp.get_core_data_copy()
cpp_cov = SquareExponential(python_cov.hyperparameters)
cpp_gp = GaussianProcess(cpp_cov, historical_data)
points_to_evaluate = python_domain.generate_uniform_random_points_in_domain(
10)
# EI from C++
expected_improvement_evaluator = ExpectedImprovement(
cpp_gp,
None,
)
# TODO(GH-99): Change test case to have the right shape:
# (num_to_evaluate, num_to_sample, dim)
# Here we assume the shape is (num_to_evaluate, dim) so we insert an axis, making num_to_sample = 1.
# Also might be worth testing more num_to_sample values (will require manipulating C++ RNG state).
cpp_expected_improvement = expected_improvement_evaluator.evaluate_at_point_list(
points_to_evaluate[:, numpy.newaxis, :], )
# EI from REST
json_payload = self._build_json_payload(
python_domain, python_cov, historical_data,
points_to_evaluate.tolist())
resp = self.testapp.post(self.endpoint, json_payload)
resp_schema = GpEiResponse()
resp_dict = resp_schema.deserialize(json.loads(resp.body))
rest_expected_improvement = numpy.asarray(
resp_dict.get('expected_improvement'))
self.assert_vector_within_relative(
rest_expected_improvement,
cpp_expected_improvement,
tolerance,
)
