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"##characteristics#run#vqe_input#minimizer_options#maxfev#range": 0, "##characteristics#run#vqe_input#minimizer_options#maxfev#range_percent": 0.0, "##characteristics#run#vqe_input#minimizer_options#maxfev#repeats": 1, "##characteristics#run#vqe_input#minimizer_src#all": [ "def my_minimizer( func, x0, my_args=(), my_options=None ):\n \"Your own attempt at writing a stochastic minimizer\"\n global invocations\n print(\"x0 is\", x0)\n print(\"func is\", inspect.getsource(func))\n para_func = mapf(func)\n invocations = 0\n def wp(*test):\n global invocations\n args = test[0][0]\n print(\"invoking with\", args)\n ret = func(args, my_args)\n print(\"evaluated as\", ret)\n invocations += SAMPLE_SZ\n return ret\n \n myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n acquisition_type = 'MPI',\n initial_design_numdata = 20,\n Initial_design_type=\"latin\",\n evaluator_type = 'local_penalization',\n num_cores=1, batch_size=1)\n myProblem.run_optimization(20)\n myProblem.plot_acquisition()\n\n current_func_value = func(myProblem.x_opt, my_args)\n minimizer_output = { 'fun' : current_func_value, 'nfev' : invocations, 'nit' : 1, 'x' : myProblem.x_opt }\n return minimizer_output\n" ], "##characteristics#run#vqe_input#minimizer_src#all_unique": [ "def my_minimizer( func, x0, my_args=(), my_options=None ):\n \"Your own attempt at writing a stochastic minimizer\"\n global invocations\n print(\"x0 is\", x0)\n print(\"func is\", inspect.getsource(func))\n para_func = mapf(func)\n invocations = 0\n def wp(*test):\n global invocations\n args = test[0][0]\n print(\"invoking with\", args)\n ret = func(args, my_args)\n print(\"evaluated as\", ret)\n invocations += SAMPLE_SZ\n return ret\n \n myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n acquisition_type = 'MPI',\n initial_design_numdata = 20,\n Initial_design_type=\"latin\",\n evaluator_type = 'local_penalization',\n num_cores=1, batch_size=1)\n myProblem.run_optimization(20)\n myProblem.plot_acquisition()\n\n current_func_value = func(myProblem.x_opt, my_args)\n minimizer_output = { 'fun' : current_func_value, 'nfev' : invocations, 'nit' : 1, 'x' : myProblem.x_opt }\n return minimizer_output\n" ], "##characteristics#run#vqe_input#minimizer_src#min": "def my_minimizer( func, x0, my_args=(), my_options=None ):\n \"Your own attempt at writing a stochastic minimizer\"\n global invocations\n print(\"x0 is\", x0)\n print(\"func is\", inspect.getsource(func))\n para_func = mapf(func)\n invocations = 0\n def wp(*test):\n global invocations\n args = test[0][0]\n print(\"invoking with\", args)\n ret = func(args, my_args)\n print(\"evaluated as\", ret)\n invocations += SAMPLE_SZ\n return ret\n \n myProblem = GPyOpt.methods.BayesianOptimization(wp, domain=bounds,\n acquisition_type = 'MPI',\n initial_design_numdata = 20,\n Initial_design_type=\"latin\",\n evaluator_type = 'local_penalization',\n num_cores=1, batch_size=1)\n myProblem.run_optimization(20)\n 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