Calibrate Delft3D-FLOW Problem with PODS

This is an demo to use the PODS in parallel for the calibraiton of the Delft3D problem with Single Objective Problem. The experiment setting is the same with example in Calibrate Delft3D-FLOW Problem with DYCORS in serial. Users just need to change the parameter nthreads to be the number of parallel processors that users want to use.

def main():
        # -----------Initilizae logging-----------------#
        if not os.path.exists("./logfiles"):
                os.makedirs("logfiles")
        if os.path.exists("./logfiles/test_simple.log"):
                os.remove("./logfiles/test_simple.log")
        logging.basicConfig(filename="./logfiles/test_simple.log",
                                                level=logging.INFO)

        # -----------Initilizae result saving-----------------#
        if not os.path.exists("./result"):
                os.makedirs("result")

                """ histroy_data folder is needed when you need to
                save the simultion output of each evaluation"""
        if os.path.exists("./result/history_data"):
                os.rmdir("./result/history_data")
        if not os.path.exists("./result/history_data"):
                os.makedirs("./result/history_data")

                """ pysot_tesult.txt file is for saving the objective
                function value and parameter vector of each evaluations"""
        if os.path.exists("./result/pysot_result.txt"):
                os.remove("./result/pysot_result.txt")

        fp = open("./result/pysot_result.txt", "a")
        fp.write("Iteration\tSimID\tObj\tParmaters\n")
        fp.close()

        # -----------set the threads and budget-----------------#
        nthreads = 4 # set this to the nubmer of parallel processors that users want to use.
        maxeval = 80
        nsamples = nthreads

        # (1) Initilize the Optimization problem
        data = delft3d_flow(dim=4)
        logging.info(data.info)
        data.home_dir = '/Users/xiawei/Desktop/opdelft/examples/'

        # (2) Experimental design
        # Use a symmetric Latin hypercube with 2d + 1 samples
        exp_des = SymmetricLatinHypercube(dim=data.dim, npts=12)

        # (3) Surrogate model
        # Use a cubic RBF interpolant with a linear tail
        surrogate = RBFInterpolant(kernel=CubicKernel, tail=LinearTail, maxp=maxeval)

        # (4) Adaptive sampling
        adapt_samp = CandidateDYCORS(data=data, numcand=1000 * data.dim)

        # (5) Use the multiprocessing-based sychronous strategy without non-bound constraints
        strategy = SyncStrategyNoConstraintsMutipro(obj_func,
                                                                                                worker_id=0, data=data, maxeval=maxeval, nsamples=nsamples,
                                                                                                exp_design=exp_des, response_surface=surrogate,
                                                                                                sampling_method=adapt_samp)

        # (6) Use the multiprocessing-based sychronous controller
        controller = MultiproController()
        controller.strategy = strategy

        # Run the optimization strategy
        result = controller.run()
        print "result", result



if __name__ == "__main__":
   main()