def test_mean_var_interface_returns_same_as_cpp(self):
"""Test that the /gp/mean_var 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)
# mean and var from C++
cpp_mean = cpp_gp.compute_mean_of_points(points_to_evaluate)
cpp_var = cpp_gp.compute_variance_of_points(points_to_evaluate)
# mean and var 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 = GpMeanVarResponse()
resp_dict = resp_schema.deserialize(json.loads(resp.body))
rest_mean = numpy.asarray(resp_dict.get('mean'))
rest_var = numpy.asarray(resp_dict.get('var'))
self.assert_vector_within_relative(rest_mean, cpp_mean, tolerance)
self.assert_vector_within_relative(rest_var, cpp_var, tolerance)
def test_mean_var_interface_returns_same_as_cpp(self):
"""Test that the /gp/mean_var 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)
# mean and var from C++
cpp_mean = cpp_gp.compute_mean_of_points(points_to_evaluate)
cpp_var = cpp_gp.compute_variance_of_points(points_to_evaluate)
# mean and var 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 = GpMeanVarResponse()
resp_dict = resp_schema.deserialize(json.loads(resp.body))
rest_mean = numpy.asarray(resp_dict.get('mean'))
rest_var = numpy.asarray(resp_dict.get('var'))
self.assert_vector_within_relative(rest_mean, cpp_mean, tolerance)
self.assert_vector_within_relative(rest_var, cpp_var, tolerance)
def gp_mean_var(
points_sampled,
points_to_evaluate,
rest_host=DEFAULT_HOST,
rest_port=DEFAULT_PORT,
testapp=None,
**kwargs
):
"""Hit the rest endpoint for calculating the posterior mean and variance of a gaussian process, given points already sampled."""
endpoint = ALL_REST_ROUTES_ROUTE_NAME_TO_ENDPOINT[GP_MEAN_VAR_ROUTE_NAME]
raw_payload = kwargs.copy() # Any options can be set via the kwargs ('covariance_info' etc.)
raw_payload['points_to_evaluate'] = points_to_evaluate
# Sanitize input points
points_sampled_clean = [SamplePoint._make(point) for point in points_sampled]
historical_data = HistoricalData(
len(points_to_evaluate[0]), # The dim of the space
sample_points=points_sampled_clean,
)
if 'gp_historical_info' not in raw_payload:
raw_payload['gp_historical_info'] = historical_data.json_payload()
if 'domain_info' not in raw_payload:
raw_payload['domain_info'] = {'dim': len(points_to_evaluate[0])}
json_payload = json.dumps(raw_payload)
json_response = call_endpoint_with_payload(rest_host, rest_port, endpoint, json_payload, testapp)
output = GpMeanVarResponse().deserialize(json_response)
return output.get('mean'), output.get('var')
def test_interfaces_equivalent(self):
"""Test that the /gp/mean, var, mean_var, etc. endpoints are consistent."""
tolerance = numpy.finfo(numpy.float64).eps
for test_case in self.gp_test_environments:
python_domain, python_gp = test_case
python_cov, historical_data = python_gp.get_core_data_copy()
points_to_evaluate = python_domain.generate_uniform_random_points_in_domain(
10)
# mean and var 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 = GpMeanVarResponse()
resp_dict = resp_schema.deserialize(json.loads(resp.body))
truth_mean = numpy.asarray(resp_dict.get('mean'))
truth_var = numpy.asarray(resp_dict.get('var'))
self._compare_endpoint_mean_var_results(
json_payload,
GP_MEAN_ENDPOINT,
GpMeanResponse(),
tolerance,
truth_mean=truth_mean,
)
self._compare_endpoint_mean_var_results(
json_payload,
GP_VAR_ENDPOINT,
GpVarResponse(),
tolerance,
truth_var=truth_var,
)
self._compare_endpoint_mean_var_results(
json_payload,
GP_VAR_DIAG_ENDPOINT,
GpVarDiagResponse(),
tolerance,
truth_var=numpy.diag(truth_var),
)
self._compare_endpoint_mean_var_results(
json_payload,
GP_MEAN_VAR_DIAG_ENDPOINT,
GpMeanVarDiagResponse(),
tolerance,
truth_mean=truth_mean,
truth_var=numpy.diag(truth_var),
)
def test_interfaces_equivalent(self):
"""Test that the /gp/mean, var, mean_var, etc. endpoints are consistent."""
tolerance = numpy.finfo(numpy.float64).eps
for test_case in self.gp_test_environments:
python_domain, python_gp = test_case
python_cov, historical_data = python_gp.get_core_data_copy()
points_to_evaluate = python_domain.generate_uniform_random_points_in_domain(10)
# mean and var 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 = GpMeanVarResponse()
resp_dict = resp_schema.deserialize(json.loads(resp.body))
truth_mean = numpy.asarray(resp_dict.get('mean'))
truth_var = numpy.asarray(resp_dict.get('var'))
self._compare_endpoint_mean_var_results(
json_payload,
GP_MEAN_ENDPOINT,
GpMeanResponse(),
tolerance,
truth_mean=truth_mean,
)
self._compare_endpoint_mean_var_results(
json_payload,
GP_VAR_ENDPOINT,
GpVarResponse(),
tolerance,
truth_var=truth_var,
)
self._compare_endpoint_mean_var_results(
json_payload,
GP_VAR_DIAG_ENDPOINT,
GpVarDiagResponse(),
tolerance,
truth_var=numpy.diag(truth_var),
)
self._compare_endpoint_mean_var_results(
json_payload,
GP_MEAN_VAR_DIAG_ENDPOINT,
GpMeanVarDiagResponse(),
tolerance,
truth_mean=truth_mean,
truth_var=numpy.diag(truth_var),
)
class GpMeanVarView(GpMeanVarBaseView):
"""Views for gp_mean_var endpoints."""
_route_name = GP_MEAN_VAR_ROUTE_NAME
_pretty_route_name = GP_MEAN_VAR_PRETTY_ROUTE_NAME
response_schema = GpMeanVarResponse()
@view_config(route_name=_pretty_route_name, renderer=PRETTY_RENDERER)
def pretty_view(self):
"""A pretty, browser interactive view for the interface. Includes form request and response.
.. http:get:: /gp/mean_var/pretty
"""
return self.pretty_response()
@view_config(route_name=_route_name,
renderer='json',
request_method='POST')
def gp_mean_var_view(self):
"""Endpoint for gp_mean_var POST requests.
.. http:post:: /gp/mean_var
Calculates the GP mean and covariance of a set of points, given historical data.
:input: :class:`moe.views.schemas.rest.gp_mean_var.GpMeanVarRequest`
:output: :class:`moe.views.schemas.rest.gp_mean_var.GpMeanVarResponse`
:status 200: returns a response
:status 500: server error
"""
return self.form_response(
self.gp_mean_var_response_dict(compute_mean=True,
compute_var=True,
var_diag=False))
