def gp_mean_var_response_dict(self, compute_mean=False, compute_var=False, var_diag=False): """Produce a response dict (to be serialized) for gp_mean, gp_var, gp_mean_var, and _diag POST requests. :param compute_mean: whether to compute the GP mean :type compute_mean: bool :param compute_var: whether to compute the GP variance or covariance :type compute_var: bool :param var_diag: whether to compute the full GP covariance or just the variance terms :type var_diag: bool :return: dict with 'endpoint' and optionally 'mean' and 'var' keys depending on inputs :rtype: dict """ params = self.get_params_from_request() points_to_evaluate = numpy.array(params.get('points_to_evaluate')) gaussian_process = _make_gp_from_params(params) response_dict = {} response_dict['endpoint'] = self._route_name with timing_context(MEAN_VAR_COMPUTATION_TIMING_LABEL): if compute_mean: response_dict['mean'] = gaussian_process.compute_mean_of_points(points_to_evaluate).tolist() if compute_var: if var_diag: response_dict['var'] = numpy.diag( gaussian_process.compute_variance_of_points(points_to_evaluate) ).tolist() else: response_dict['var'] = gaussian_process.compute_variance_of_points(points_to_evaluate).tolist() return response_dict
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 gp_mean_var_response_dict(self, compute_mean=False, compute_var=False, var_diag=False): """Produce a response dict (to be serialized) for gp_mean, gp_var, gp_mean_var, and _diag POST requests. :param compute_mean: whether to compute the GP mean :type compute_mean: bool :param compute_var: whether to compute the GP variance or covariance :type compute_var: bool :param var_diag: whether to compute the full GP covariance or just the variance terms :type var_diag: bool :return: dict with 'endpoint' and optionally 'mean' and 'var' keys depending on inputs :rtype: dict """ params = self.get_params_from_request() points_to_evaluate = numpy.array(params.get('points_to_evaluate')) gaussian_process = _make_gp_from_params(params) response_dict = {} response_dict['endpoint'] = self._route_name with timing_context(MEAN_VAR_COMPUTATION_TIMING_LABEL): if compute_mean: response_dict[ 'mean'] = gaussian_process.compute_mean_of_points( points_to_evaluate).tolist() if compute_var: if var_diag: response_dict['var'] = numpy.diag( gaussian_process.compute_variance_of_points( points_to_evaluate)).tolist() else: response_dict[ 'var'] = gaussian_process.compute_variance_of_points( points_to_evaluate).tolist() return response_dict
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 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, }, })