# Yelp/MOE

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 # -*- coding: utf-8 -*- """An example for accessing the gp_next_points_* endpoints. :func:`moe.easy_interface.simple_endpoint.gp_next_points` The function requires some historical information to inform the Gaussian Process The result is the next best set of point(s) to sample .. Note:: This file is copied in ``README.md`` and ``docs/index.rst``. Any changes here should be made there as well. """ import math import random from moe.easy_interface.experiment import Experiment from moe.easy_interface.simple_endpoint import gp_next_points from moe.optimal_learning.python.data_containers import SamplePoint # Note: this function can be anything, the output of a batch, results of an A/B experiment, the value of a physical experiment etc. def function_to_minimize(x): """Calculate an aribitrary 2-d function with some noise with minimum near [1, 2.6].""" return math.sin(x[0]) * math.cos(x[1]) + math.cos(x[0] + x[1]) + random.uniform(-0.02, 0.02) def run_example(num_points_to_sample=20, verbose=True, **kwargs): """Run the example, aksing MOE for ``num_points_to_sample`` optimal points to sample.""" exp = Experiment([[0, 2], [0, 4]]) # 2D experiment, we build a tensor product domain # Bootstrap with some known or already sampled point(s) exp.historical_data.append_sample_points([ SamplePoint([0, 0], function_to_minimize([0, 0]), 0.05), # Iterables of the form [point, f_val, f_var] are also allowed ]) # Sample num_points_to_sample points for _ in range(num_points_to_sample): # Use MOE to determine what is the point with highest Expected Improvement to use next next_point_to_sample = gp_next_points(exp, **kwargs)[0] # By default we only ask for one point # Sample the point from our objective function, we can replace this with any function value_of_next_point = function_to_minimize(next_point_to_sample) if verbose: print "Sampled f({0:s}) = {1:.18E}".format(str(next_point_to_sample), value_of_next_point) # Add the information about the point to the experiment historical data to inform the GP exp.historical_data.append_sample_points([SamplePoint(next_point_to_sample, value_of_next_point, 0.01)]) # We can add some noise if __name__ == '__main__': run_example()