def test_optimizer_params_passed_through(self):
"""Test that the optimizer parameters get passed through to the endpoint."""
test_case = self.gp_test_environments[0]
python_domain, python_gp = test_case
python_cov, historical_data = python_gp.get_core_data_copy()
# Test default test parameters get passed through
json_payload = json.loads(self._build_json_payload(python_domain, python_cov, historical_data))
request = pyramid.testing.DummyRequest(post=json_payload)
request.json_body = json_payload
view = GpHyperOptView(request)
params = view.get_params_from_request()
_, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(params)
assert optimizer_parameters.num_multistarts == TEST_OPTIMIZER_MULTISTARTS
assert optimizer_parameters._python_max_num_steps == TEST_GRADIENT_DESCENT_PARAMETERS.max_num_steps
# Test arbitrary parameters get passed through
json_payload['optimizer_info']['num_multistarts'] = TEST_OPTIMIZER_MULTISTARTS + 5
json_payload['optimizer_info']['optimizer_parameters']['max_num_steps'] = TEST_GRADIENT_DESCENT_PARAMETERS.max_num_steps + 10
request = pyramid.testing.DummyRequest(post=json_payload)
request.json_body = json_payload
view = GpHyperOptView(request)
params = view.get_params_from_request()
_, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(params)
assert optimizer_parameters.num_multistarts == TEST_OPTIMIZER_MULTISTARTS + 5
assert optimizer_parameters._python_max_num_steps == TEST_GRADIENT_DESCENT_PARAMETERS.max_num_steps + 10
def test_optimizer_params_passed_through(self):
"""Test that the optimizer parameters get passed through to the endpoint."""
# TODO(GH-305): turn this into a unit test by going through OptimizableGpPrettyView
# and mocking out dependencies (instead of awkwardly constructing a more complex object).
test_case = self.gp_test_environments[0]
num_to_sample = 1
python_domain, python_gp = test_case
python_cov, historical_data = python_gp.get_core_data_copy()
# Test default test parameters get passed through
json_payload = json.loads(
self._build_json_payload(python_domain, python_cov,
historical_data, num_to_sample))
request = pyramid.testing.DummyRequest(post=json_payload)
request.json_body = json_payload
view = GpNextPointsPrettyView(request)
# get_params_from_request() requires this field is set. value is arbitrary for now.
# TODO(GH-305): mock out this and other members
view._route_name = GP_NEXT_POINTS_CONSTANT_LIAR_ROUTE_NAME
params = view.get_params_from_request()
_, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(
params)
T.assert_equal(
optimizer_parameters.num_multistarts,
TEST_OPTIMIZER_MULTISTARTS,
)
T.assert_equal(
optimizer_parameters._python_max_num_steps,
TEST_GRADIENT_DESCENT_PARAMETERS.max_num_steps,
)
# Test arbitrary parameters get passed through
json_payload['optimizer_info'][
'num_multistarts'] = TEST_OPTIMIZER_MULTISTARTS + 5
json_payload['optimizer_info']['optimizer_parameters'][
'max_num_steps'] = TEST_GRADIENT_DESCENT_PARAMETERS.max_num_steps + 10
request = pyramid.testing.DummyRequest(post=json_payload)
request.json_body = json_payload
view = GpNextPointsPrettyView(request)
# get_params_from_request() requires this field is set. value is arbitrary for now.
# TODO(GH-305): mock out this and other members
view._route_name = GP_NEXT_POINTS_CONSTANT_LIAR_ROUTE_NAME
params = view.get_params_from_request()
_, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(
params)
T.assert_equal(
optimizer_parameters.num_multistarts,
TEST_OPTIMIZER_MULTISTARTS + 5,
)
T.assert_equal(
optimizer_parameters._python_max_num_steps,
TEST_GRADIENT_DESCENT_PARAMETERS.max_num_steps + 10,
)
def test_optimizer_params_passed_through(self):
"""Test that the optimizer parameters get passed through to the endpoint."""
# TODO(GH-305): turn this into a unit test by going through OptimizableGpPrettyView
# and mocking out dependencies (instead of awkwardly constructing a more complex object).
test_case = self.gp_test_environments[0]
num_to_sample = 1
python_domain, python_gp = test_case
python_cov, historical_data = python_gp.get_core_data_copy()
# Test default test parameters get passed through
json_payload = json.loads(self._build_json_payload(python_domain, python_cov, historical_data, num_to_sample))
request = pyramid.testing.DummyRequest(post=json_payload)
request.json_body = json_payload
view = GpNextPointsPrettyView(request)
# get_params_from_request() requires this field is set. value is arbitrary for now.
# TODO(GH-305): mock out this and other members
view._route_name = GP_NEXT_POINTS_CONSTANT_LIAR_ROUTE_NAME
params = view.get_params_from_request()
_, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(params)
T.assert_equal(
optimizer_parameters.num_multistarts,
TEST_OPTIMIZER_MULTISTARTS,
)
T.assert_equal(
optimizer_parameters._python_max_num_steps,
TEST_GRADIENT_DESCENT_PARAMETERS.max_num_steps,
)
# Test arbitrary parameters get passed through
json_payload['optimizer_info']['num_multistarts'] = TEST_OPTIMIZER_MULTISTARTS + 5
json_payload['optimizer_info']['optimizer_parameters']['max_num_steps'] = TEST_GRADIENT_DESCENT_PARAMETERS.max_num_steps + 10
request = pyramid.testing.DummyRequest(post=json_payload)
request.json_body = json_payload
view = GpNextPointsPrettyView(request)
# get_params_from_request() requires this field is set. value is arbitrary for now.
# TODO(GH-305): mock out this and other members
view._route_name = GP_NEXT_POINTS_CONSTANT_LIAR_ROUTE_NAME
params = view.get_params_from_request()
_, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(params)
T.assert_equal(
optimizer_parameters.num_multistarts,
TEST_OPTIMIZER_MULTISTARTS + 5,
)
T.assert_equal(
optimizer_parameters._python_max_num_steps,
TEST_GRADIENT_DESCENT_PARAMETERS.max_num_steps + 10,
)
def test_optimizer_params_passed_through(self):
"""Test that the optimizer parameters get passed through to the endpoint."""
test_case = self.gp_test_environments[0]
python_domain, python_gp = test_case
python_cov, historical_data = python_gp.get_core_data_copy()
# Test default test parameters get passed through
json_payload = json.loads(
self._build_json_payload(python_domain, python_cov,
historical_data))
request = pyramid.testing.DummyRequest(post=json_payload)
request.json_body = json_payload
view = GpHyperOptView(request)
params = view.get_params_from_request()
_, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(
params)
T.assert_equal(
optimizer_parameters.num_multistarts,
TEST_OPTIMIZER_MULTISTARTS,
)
T.assert_equal(
optimizer_parameters._python_max_num_steps,
TEST_GRADIENT_DESCENT_PARAMETERS.max_num_steps,
)
# Test arbitrary parameters get passed through
json_payload['optimizer_info'][
'num_multistarts'] = TEST_OPTIMIZER_MULTISTARTS + 5
json_payload['optimizer_info']['optimizer_parameters'][
'max_num_steps'] = TEST_GRADIENT_DESCENT_PARAMETERS.max_num_steps + 10
request = pyramid.testing.DummyRequest(post=json_payload)
request.json_body = json_payload
view = GpHyperOptView(request)
params = view.get_params_from_request()
_, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(
params)
T.assert_equal(
optimizer_parameters.num_multistarts,
TEST_OPTIMIZER_MULTISTARTS + 5,
)
T.assert_equal(
optimizer_parameters._python_max_num_steps,
TEST_GRADIENT_DESCENT_PARAMETERS.max_num_steps + 10,
)
def gp_hyper_opt_view(self):
"""Endpoint for gp_hyper_opt POST requests.
.. http:post:: /gp/hyper_opt
Calculates the optimal hyperparameters for a gaussian process, given historical data.
:input: :class:`moe.views.schemas.rest.gp_hyper_opt.GpHyperOptRequest`
:output: :class:`moe.views.schemas.rest.gp_hyper_opt.GpHyperOptResponse`
:status 200: returns a response
:status 500: server error
"""
params = self.get_params_from_request()
max_num_threads = params.get('max_num_threads')
hyperparameter_domain = _make_domain_from_params(params, domain_info_key='hyperparameter_domain_info')
gaussian_process = _make_gp_from_params(params)
covariance_of_process, historical_data = gaussian_process.get_core_data_copy()
optimizer_class, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(params)
log_likelihood_type = params.get('log_likelihood_info')
log_likelihood_eval = LOG_LIKELIHOOD_TYPES_TO_LOG_LIKELIHOOD_METHODS[log_likelihood_type].log_likelihood_class(
covariance_of_process,
historical_data,
)
log_likelihood_optimizer = optimizer_class(
hyperparameter_domain,
log_likelihood_eval,
optimizer_parameters,
num_random_samples=num_random_samples,
)
hyperopt_status = {}
with timing_context(MODEL_SELECTION_TIMING_LABEL):
optimized_hyperparameters = multistart_hyperparameter_optimization(
log_likelihood_optimizer,
optimizer_parameters.num_multistarts,
max_num_threads=max_num_threads,
status=hyperopt_status,
)
covariance_of_process.hyperparameters = optimized_hyperparameters
log_likelihood_eval.current_point = optimized_hyperparameters
return self.form_response({
'endpoint': self._route_name,
'covariance_info': covariance_of_process.get_json_serializable_info(),
'status': {
'log_likelihood': log_likelihood_eval.compute_log_likelihood(),
'grad_log_likelihood': log_likelihood_eval.compute_grad_log_likelihood().tolist(),
'optimizer_success': hyperopt_status,
},
})
def test_optimizer_params_passed_through(self):
"""Test that the optimizer parameters get passed through to the endpoint."""
# TODO(GH-305): turn this into a unit test by going through OptimizableGpPrettyView
# and mocking out dependencies (instead of awkwardly constructing a more complex object).
test_case = self.gp_test_environments[0]
num_to_sample = 1
python_domain, python_gp = test_case
python_cov, historical_data = python_gp.get_core_data_copy()
# Test default test parameters get passed through
json_payload = json.loads(self._build_json_payload(python_domain, python_cov, historical_data, num_to_sample))
request = pyramid.testing.DummyRequest(post=json_payload)
request.json_body = json_payload
view = GpNextPointsPrettyView(request)
# get_params_from_request() requires this field is set. value is arbitrary for now.
# TODO(GH-305): mock out this and other members
view._route_name = GP_NEXT_POINTS_CONSTANT_LIAR_ROUTE_NAME
params = view.get_params_from_request()
_, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(params)
test_param_dict = TEST_GRADIENT_DESCENT_PARAMETERS._asdict()
test_param_dict["num_multistarts"] = TEST_OPTIMIZER_MULTISTARTS
assert optimizer_parameters._get_member_dict() == test_param_dict
# Test arbitrary parameters get passed through
for i, key in enumerate(test_param_dict.iterkeys()):
test_param_dict[key] /= 2
test_num_multistarts = test_param_dict.pop("num_multistarts")
json_payload["optimizer_info"]["num_multistarts"] = test_num_multistarts
json_payload["optimizer_info"]["optimizer_parameters"] = test_param_dict
request = pyramid.testing.DummyRequest(post=json_payload)
request.json_body = json_payload
view = GpNextPointsPrettyView(request)
# get_params_from_request() requires this field is set. value is arbitrary for now.
# TODO(GH-305): mock out this and other members
view._route_name = GP_NEXT_POINTS_CONSTANT_LIAR_ROUTE_NAME
params = view.get_params_from_request()
_, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(params)
test_param_dict["num_multistarts"] = test_num_multistarts
assert optimizer_parameters._get_member_dict() == test_param_dict
def test_optimizer_params_passed_through(self):
"""Test that the optimizer parameters get passed through to the endpoint."""
test_case = self.gp_test_environments[0]
python_domain, python_gp = test_case
python_cov, historical_data = python_gp.get_core_data_copy()
# Test default test parameters get passed through
json_payload = json.loads(
self._build_json_payload(python_domain, python_cov,
historical_data))
request = pyramid.testing.DummyRequest(post=json_payload)
request.json_body = json_payload
view = GpHyperOptView(request)
params = view.get_params_from_request()
_, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(
params)
test_param_dict = TEST_GRADIENT_DESCENT_PARAMETERS._asdict()
test_param_dict['num_multistarts'] = TEST_OPTIMIZER_MULTISTARTS
assert optimizer_parameters._get_member_dict() == test_param_dict
# Test arbitrary parameters get passed through
for i, key in enumerate(iter(test_param_dict.keys())):
test_param_dict[key] /= 2
test_num_multistarts = test_param_dict.pop('num_multistarts')
json_payload['optimizer_info'][
'num_multistarts'] = test_num_multistarts
json_payload['optimizer_info'][
'optimizer_parameters'] = test_param_dict
request = pyramid.testing.DummyRequest(post=json_payload)
request.json_body = json_payload
view = GpHyperOptView(request)
params = view.get_params_from_request()
_, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(
params)
test_param_dict['num_multistarts'] = test_num_multistarts
assert optimizer_parameters._get_member_dict() == test_param_dict
def test_optimizer_params_passed_through(self):
"""Test that the optimizer parameters get passed through to the endpoint."""
test_case = self.gp_test_environments[0]
python_domain, python_gp = test_case
python_cov, historical_data = python_gp.get_core_data_copy()
# Test default test parameters get passed through
json_payload = json.loads(self._build_json_payload(python_domain, python_cov, historical_data))
request = pyramid.testing.DummyRequest(post=json_payload)
request.json_body = json_payload
view = GpHyperOptView(request)
params = view.get_params_from_request()
_, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(params)
test_param_dict = TEST_GRADIENT_DESCENT_PARAMETERS._asdict()
test_param_dict['num_multistarts'] = TEST_OPTIMIZER_MULTISTARTS
assert optimizer_parameters._get_member_dict() == test_param_dict
# Test arbitrary parameters get passed through
for i, key in enumerate(test_param_dict.iterkeys()):
test_param_dict[key] /= 2
test_num_multistarts = test_param_dict.pop('num_multistarts')
json_payload['optimizer_info']['num_multistarts'] = test_num_multistarts
json_payload['optimizer_info']['optimizer_parameters'] = test_param_dict
request = pyramid.testing.DummyRequest(post=json_payload)
request.json_body = json_payload
view = GpHyperOptView(request)
params = view.get_params_from_request()
_, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(params)
test_param_dict['num_multistarts'] = test_num_multistarts
assert optimizer_parameters._get_member_dict() == test_param_dict
def compute_next_points_to_sample_response(self, params, optimizer_method_name, route_name, *args, **kwargs):
"""Compute the next points to sample (and their expected improvement) using optimizer_method_name from params in the request.
.. Warning:: Attempting to find ``num_to_sample`` optimal points with
``num_sampled < num_to_sample`` historical points sampled can cause matrix issues under
some conditions. Try requesting ``num_to_sample < num_sampled`` points for better
performance. To bootstrap more points try sampling at random, or from a grid.
:param request_params: the deserialized REST request, containing ei_optimizer_parameters and gp_historical_info
:type request_params: a deserialized self.request_schema object as a dict
:param optimizer_method_name: the optimization method to use
:type optimizer_method_name: string in :const:`moe.views.constant.NEXT_POINTS_OPTIMIZER_METHOD_NAMES`
:param route_name: name of the route being called
:type route_name: string in :const:`moe.views.constant.ALL_REST_ROUTES_ROUTE_NAME_TO_ENDPOINT`
:param ``*args``: extra args to be passed to optimization method
:param ``**kwargs``: extra kwargs to be passed to optimization method
"""
points_being_sampled = numpy.array(params.get('points_being_sampled'))
num_to_sample = params.get('num_to_sample')
num_mc_iterations = params.get('mc_iterations')
max_num_threads = params.get('max_num_threads')
gaussian_process = _make_gp_from_params(params)
ei_opt_status = {}
# TODO(GH-89): Make the optimal_learning library handle this case 'organically' with
# reasonable default behavior and remove hacks like this one.
if gaussian_process.num_sampled == 0:
# If there is no initial data we bootstrap with random points
py_domain = _make_domain_from_params(params, python_version=True)
next_points = py_domain.generate_uniform_random_points_in_domain(num_to_sample)
ei_opt_status['found_update'] = True
expected_improvement_evaluator = PythonExpectedImprovement(
gaussian_process,
points_being_sampled=points_being_sampled,
num_mc_iterations=num_mc_iterations,
)
else:
# Calculate the next best points to sample given the historical data
optimizer_class, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(params)
if optimizer_class == python_optimization.LBFGSBOptimizer:
domain = RepeatedDomain(num_to_sample, _make_domain_from_params(params, python_version=True))
expected_improvement_evaluator = PythonExpectedImprovement(
gaussian_process,
points_being_sampled=points_being_sampled,
num_mc_iterations=num_mc_iterations,
mvndst_parameters=_make_mvndst_parameters_from_params(params)
)
opt_method = getattr(moe.optimal_learning.python.python_version.expected_improvement, optimizer_method_name)
else:
domain = _make_domain_from_params(params, python_version=False)
expected_improvement_evaluator = ExpectedImprovement(
gaussian_process,
points_being_sampled=points_being_sampled,
num_mc_iterations=num_mc_iterations,
)
opt_method = getattr(moe.optimal_learning.python.cpp_wrappers.expected_improvement, optimizer_method_name)
expected_improvement_optimizer = optimizer_class(
domain,
expected_improvement_evaluator,
optimizer_parameters,
num_random_samples=num_random_samples,
)
with timing_context(EPI_OPTIMIZATION_TIMING_LABEL):
next_points = opt_method(
expected_improvement_optimizer,
params.get('optimizer_info')['num_multistarts'], # optimizer_parameters.num_multistarts,
num_to_sample,
max_num_threads=max_num_threads,
status=ei_opt_status,
*args,
**kwargs
)
# TODO(GH-285): Use analytic q-EI here
# TODO(GH-314): Need to resolve poential issue with NaNs before using q-EI here
# It may be sufficient to check found_update == False in ei_opt_status
# and then use q-EI, else set EI = 0.
expected_improvement_evaluator.current_point = next_points
# The C++ may fail to compute EI with some ``next_points`` inputs (e.g.,
# ``points_to_sample`` and ``points_begin_sampled`` are too close
# together or too close to ``points_sampled``). We catch the exception when this happens
# and attempt a more numerically robust option.
try:
expected_improvement = expected_improvement_evaluator.compute_expected_improvement()
except Exception as exception:
self.log.info('EI computation failed, probably b/c GP-variance matrix is singular. Error: {0:s}'.format(exception))
# ``_compute_expected_improvement_monte_carlo`` in
# :class:`moe.optimal_learning.python.python_version.expected_improvement.ExpectedImprovement`
# has a more reliable (but very expensive) way to deal with singular variance matrices.
python_ei_eval = PythonExpectedImprovement(
expected_improvement_evaluator._gaussian_process,
points_to_sample=next_points,
points_being_sampled=points_being_sampled,
num_mc_iterations=num_mc_iterations,
)
expected_improvement = python_ei_eval.compute_expected_improvement(force_monte_carlo=True)
return self.form_response({
'endpoint': route_name,
'points_to_sample': next_points.tolist(),
'status': {
'expected_improvement': expected_improvement,
'optimizer_success': ei_opt_status,
},
})
def compute_next_points_to_sample_response(self, params,
optimizer_method_name,
route_name, *args, **kwargs):
"""Compute the next points to sample (and their expected improvement) using optimizer_method_name from params in the request.
.. Warning:: Attempting to find ``num_to_sample`` optimal points with
``num_sampled < num_to_sample`` historical points sampled can cause matrix issues under
some conditions. Try requesting ``num_to_sample < num_sampled`` points for better
performance. To bootstrap more points try sampling at random, or from a grid.
:param request_params: the deserialized REST request, containing ei_optimizer_parameters and gp_historical_info
:type request_params: a deserialized self.request_schema object as a dict
:param optimizer_method_name: the optimization method to use
:type optimizer_method_name: string in :const:`moe.views.constant.NEXT_POINTS_OPTIMIZER_METHOD_NAMES`
:param route_name: name of the route being called
:type route_name: string in :const:`moe.views.constant.ALL_REST_ROUTES_ROUTE_NAME_TO_ENDPOINT`
:param ``*args``: extra args to be passed to optimization method
:param ``**kwargs``: extra kwargs to be passed to optimization method
"""
points_being_sampled = numpy.array(params.get('points_being_sampled'))
num_to_sample = params.get('num_to_sample')
num_mc_iterations = params.get('mc_iterations')
max_num_threads = params.get('max_num_threads')
gaussian_process = _make_gp_from_params(params)
ei_opt_status = {}
# TODO(GH-89): Make the optimal_learning library handle this case 'organically' with
# reasonable default behavior and remove hacks like this one.
if gaussian_process.num_sampled == 0:
# If there is no initial data we bootstrap with random points
py_domain = _make_domain_from_params(params, python_version=True)
next_points = py_domain.generate_uniform_random_points_in_domain(
num_to_sample)
ei_opt_status['found_update'] = True
expected_improvement_evaluator = PythonExpectedImprovement(
gaussian_process,
points_being_sampled=points_being_sampled,
num_mc_iterations=num_mc_iterations,
)
else:
# Calculate the next best points to sample given the historical data
optimizer_class, optimizer_parameters, num_random_samples = _make_optimizer_parameters_from_params(
params)
if optimizer_class == python_optimization.LBFGSBOptimizer:
domain = RepeatedDomain(
num_to_sample,
_make_domain_from_params(params, python_version=True))
expected_improvement_evaluator = PythonExpectedImprovement(
gaussian_process,
points_being_sampled=points_being_sampled,
num_mc_iterations=num_mc_iterations,
mvndst_parameters=_make_mvndst_parameters_from_params(
params))
opt_method = getattr(
moe.optimal_learning.python.python_version.
expected_improvement, optimizer_method_name)
else:
domain = _make_domain_from_params(params, python_version=False)
expected_improvement_evaluator = ExpectedImprovement(
gaussian_process,
points_being_sampled=points_being_sampled,
num_mc_iterations=num_mc_iterations,
)
opt_method = getattr(
moe.optimal_learning.python.cpp_wrappers.
expected_improvement, optimizer_method_name)
expected_improvement_optimizer = optimizer_class(
domain,
expected_improvement_evaluator,
optimizer_parameters,
num_random_samples=num_random_samples,
)
with timing_context(EPI_OPTIMIZATION_TIMING_LABEL):
next_points = opt_method(
expected_improvement_optimizer,
params.get('optimizer_info')
['num_multistarts'], # optimizer_parameters.num_multistarts,
num_to_sample,
max_num_threads=max_num_threads,
status=ei_opt_status,
*args,
**kwargs)
# TODO(GH-285): Use analytic q-EI here
# TODO(GH-314): Need to resolve poential issue with NaNs before using q-EI here
# It may be sufficient to check found_update == False in ei_opt_status
# and then use q-EI, else set EI = 0.
expected_improvement_evaluator.current_point = next_points
# The C++ may fail to compute EI with some ``next_points`` inputs (e.g.,
# ``points_to_sample`` and ``points_begin_sampled`` are too close
# together or too close to ``points_sampled``). We catch the exception when this happens
# and attempt a more numerically robust option.
try:
expected_improvement = expected_improvement_evaluator.compute_expected_improvement(
)
except Exception as exception:
self.log.info(
'EI computation failed, probably b/c GP-variance matrix is singular. Error: {0:s}'
.format(exception))
# ``_compute_expected_improvement_monte_carlo`` in
# :class:`moe.optimal_learning.python.python_version.expected_improvement.ExpectedImprovement`
# has a more reliable (but very expensive) way to deal with singular variance matrices.
python_ei_eval = PythonExpectedImprovement(
expected_improvement_evaluator._gaussian_process,
points_to_sample=next_points,
points_being_sampled=points_being_sampled,
num_mc_iterations=num_mc_iterations,
)
expected_improvement = python_ei_eval.compute_expected_improvement(
force_monte_carlo=True)
return self.form_response({
'endpoint': route_name,
'points_to_sample': next_points.tolist(),
'status': {
'expected_improvement': expected_improvement,
'optimizer_success': ei_opt_status,
},
})
