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 gp_hyper_opt(points_sampled,
rest_host=DEFAULT_HOST,
rest_port=DEFAULT_PORT,
testapp=None,
**kwargs):
"""Hit the rest endpoint for optimizing the hyperparameters of a gaussian process, given points already sampled."""
endpoint = ALL_REST_ROUTES_ROUTE_NAME_TO_ENDPOINT[GP_HYPER_OPT_ROUTE_NAME]
# This will fail if len(points_sampled) == 0; but then again this endpoint doesn't make sense with 0 historical data
gp_dim = len(points_sampled[0][0])
raw_payload = kwargs.copy()
# Sanitize input points
points_sampled_clean = [
SamplePoint._make(point) for point in points_sampled
]
historical_data = HistoricalData(
gp_dim,
sample_points=points_sampled_clean,
)
if 'domain_info' not in raw_payload:
raw_payload['domain_info'] = {'dim': gp_dim}
if 'gp_historical_info' not in raw_payload:
raw_payload['gp_historical_info'] = historical_data.json_payload()
if 'hyperparameter_domain_info' not in raw_payload:
hyper_dim = gp_dim + 1 # default covariance has this many parameters
raw_payload['hyperparameter_domain_info'] = {
'dim': hyper_dim,
'domain_bounds': [{
'min': 0.1,
'max': 2.0
}] * hyper_dim,
}
json_payload = json.dumps(raw_payload)
json_response = call_endpoint_with_payload(rest_host, rest_port, endpoint,
json_payload, testapp)
output = GpHyperOptResponse().deserialize(json_response)
return output['covariance_info']
def gp_hyper_opt(
points_sampled,
rest_host=DEFAULT_HOST,
rest_port=DEFAULT_PORT,
testapp=None,
**kwargs
):
"""Hit the rest endpoint for optimizing the hyperparameters of a gaussian process, given points already sampled."""
endpoint = ALL_REST_ROUTES_ROUTE_NAME_TO_ENDPOINT[GP_HYPER_OPT_ROUTE_NAME]
# This will fail if len(points_sampled) == 0; but then again this endpoint doesn't make sense with 0 historical data
gp_dim = len(points_sampled[0][0])
raw_payload = kwargs.copy()
# Sanitize input points
points_sampled_clean = [SamplePoint._make(point) for point in points_sampled]
historical_data = HistoricalData(
gp_dim,
sample_points=points_sampled_clean,
)
if 'domain_info' not in raw_payload:
raw_payload['domain_info'] = {'dim': gp_dim}
if 'gp_historical_info' not in raw_payload:
raw_payload['gp_historical_info'] = historical_data.json_payload()
if 'hyperparameter_domain_info' not in raw_payload:
hyper_dim = gp_dim + 1 # default covariance has this many parameters
raw_payload['hyperparameter_domain_info'] = {
'dim': hyper_dim,
'domain_bounds': [{'min': 0.1, 'max': 2.0}] * hyper_dim,
}
json_payload = json.dumps(raw_payload)
json_response = call_endpoint_with_payload(rest_host, rest_port, endpoint, json_payload, testapp)
output = GpHyperOptResponse().deserialize(json_response)
return output['covariance_info']
