{
"cells": [
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"source": [
"# Loop API Example on Hartmann6\n",
"\n",
"The loop API is the most lightweight way to do optimization in Ax. The user makes one call to `optimize`, which performs all of the optimization under the hood and returns the optimized parameters.\n",
"\n",
"For more customizability of the optimization procedure, consider the Service or Developer API."
]
},
{
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"id": "7b66783a",
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{
"data": {
"text/html": [
""
]
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"metadata": {},
"output_type": "display_data"
},
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"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.utils.notebook.plotting: Please see\n",
" (https://ax.dev/tutorials/visualizations.html#Fix-for-plots-that-are-not-rendering)\n",
" if visualizations are not rendering.\n"
]
},
{
"data": {
"text/html": [
" \n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"from ax.metrics.branin import branin\n",
"\n",
"from ax.plot.contour import plot_contour\n",
"from ax.plot.trace import optimization_trace_single_method\n",
"from ax.service.managed_loop import optimize\n",
"from ax.utils.measurement.synthetic_functions import hartmann6\n",
"from ax.utils.notebook.plotting import init_notebook_plotting, render\n",
"\n",
"init_notebook_plotting()"
]
},
{
"cell_type": "markdown",
"id": "375c77c4",
"metadata": {
"papermill": {
"duration": 0.038883,
"end_time": "2024-03-01T16:31:48.252115",
"exception": false,
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"status": "completed"
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"tags": []
},
"source": [
"## 1. Define evaluation function\n",
"\n",
"First, we define an evaluation function that is able to compute all the metrics needed for this experiment. This function needs to accept a set of parameter values and can also accept a weight. It should produce a dictionary of metric names to tuples of mean and standard error for those metrics."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "480d4528",
"metadata": {
"execution": {
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"tags": []
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"outputs": [],
"source": [
"def hartmann_evaluation_function(parameterization):\n",
" x = np.array([parameterization.get(f\"x{i+1}\") for i in range(6)])\n",
" # In our case, standard error is 0, since we are computing a synthetic function.\n",
" return {\"hartmann6\": (hartmann6(x), 0.0), \"l2norm\": (np.sqrt((x**2).sum()), 0.0)}"
]
},
{
"cell_type": "markdown",
"id": "0b219166",
"metadata": {
"papermill": {
"duration": 0.040606,
"end_time": "2024-03-01T16:31:48.421479",
"exception": false,
"start_time": "2024-03-01T16:31:48.380873",
"status": "completed"
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"tags": []
},
"source": [
"If there is only one metric in the experiment – the objective – then evaluation function can return a single tuple of mean and SEM, in which case Ax will assume that evaluation corresponds to the objective. It can also return only the mean as a float, in which case Ax will treat SEM as unknown and use a model that can infer it. For more details on evaluation function, refer to the \"Trial Evaluation\" section in the docs."
]
},
{
"cell_type": "markdown",
"id": "8d182fe8",
"metadata": {
"papermill": {
"duration": 0.040502,
"end_time": "2024-03-01T16:31:48.502654",
"exception": false,
"start_time": "2024-03-01T16:31:48.462152",
"status": "completed"
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"tags": []
},
"source": [
"## 2. Run optimization\n",
"The setup for the loop is fully compatible with JSON. The optimization algorithm is selected based on the properties of the problem search space."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f4ecaa04",
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"shell.execute_reply": "2024-03-01T16:33:49.976824Z"
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"exception": false,
"start_time": "2024-03-01T16:31:48.543245",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicitly specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x3', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x4', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x5', parameter_type=FLOAT, range=[0.0, 1.0]), RangeParameter(name='x6', parameter_type=FLOAT, range=[0.0, 1.0])], parameter_constraints=[ParameterConstraint(1.0*x1 + 1.0*x2 <= 20.0)]).\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.modelbridge.dispatch_utils: Using Models.BOTORCH_MODULAR since there is at least one ordered parameter and there are no unordered categorical parameters.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=6 num_trials=None use_batch_trials=False\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.modelbridge.dispatch_utils: `verbose`, `disable_progbar`, and `jit_compile` are not yet supported when using `choose_generation_strategy` with ModularBoTorchModel, dropping these arguments.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+BoTorch', steps=[Sobol for 12 trials, BoTorch for subsequent trials]). Iterations after 12 will take longer to generate due to model-fitting.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.managed_loop: Started full optimization with 30 steps.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.managed_loop: Running optimization trial 1...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.managed_loop: Running optimization trial 2...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.managed_loop: Running optimization trial 3...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.managed_loop: Running optimization trial 4...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.managed_loop: Running optimization trial 5...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.managed_loop: Running optimization trial 6...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.managed_loop: Running optimization trial 7...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.managed_loop: Running optimization trial 8...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.managed_loop: Running optimization trial 9...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.managed_loop: Running optimization trial 10...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.managed_loop: Running optimization trial 11...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.managed_loop: Running optimization trial 12...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:48] ax.service.managed_loop: Running optimization trial 13...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:31:55] ax.service.managed_loop: Running optimization trial 14...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:32:04] ax.service.managed_loop: Running optimization trial 15...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:32:11] ax.service.managed_loop: Running optimization trial 16...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:32:18] ax.service.managed_loop: Running optimization trial 17...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:32:24] ax.service.managed_loop: Running optimization trial 18...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:32:31] ax.service.managed_loop: Running optimization trial 19...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:32:38] ax.service.managed_loop: Running optimization trial 20...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:32:44] ax.service.managed_loop: Running optimization trial 21...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:32:50] ax.service.managed_loop: Running optimization trial 22...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:32:55] ax.service.managed_loop: Running optimization trial 23...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:33:01] ax.service.managed_loop: Running optimization trial 24...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:33:09] ax.service.managed_loop: Running optimization trial 25...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:33:15] ax.service.managed_loop: Running optimization trial 26...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:33:21] ax.service.managed_loop: Running optimization trial 27...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:33:27] ax.service.managed_loop: Running optimization trial 28...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:33:34] ax.service.managed_loop: Running optimization trial 29...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 03-01 16:33:42] ax.service.managed_loop: Running optimization trial 30...\n"
]
}
],
"source": [
"best_parameters, values, experiment, model = optimize(\n",
" parameters=[\n",
" {\n",
" \"name\": \"x1\",\n",
" \"type\": \"range\",\n",
" \"bounds\": [0.0, 1.0],\n",
" \"value_type\": \"float\", # Optional, defaults to inference from type of \"bounds\".\n",
" \"log_scale\": False, # Optional, defaults to False.\n",
" },\n",
" {\n",
" \"name\": \"x2\",\n",
" \"type\": \"range\",\n",
" \"bounds\": [0.0, 1.0],\n",
" },\n",
" {\n",
" \"name\": \"x3\",\n",
" \"type\": \"range\",\n",
" \"bounds\": [0.0, 1.0],\n",
" },\n",
" {\n",
" \"name\": \"x4\",\n",
" \"type\": \"range\",\n",
" \"bounds\": [0.0, 1.0],\n",
" },\n",
" {\n",
" \"name\": \"x5\",\n",
" \"type\": \"range\",\n",
" \"bounds\": [0.0, 1.0],\n",
" },\n",
" {\n",
" \"name\": \"x6\",\n",
" \"type\": \"range\",\n",
" \"bounds\": [0.0, 1.0],\n",
" },\n",
" ],\n",
" experiment_name=\"test\",\n",
" objective_name=\"hartmann6\",\n",
" evaluation_function=hartmann_evaluation_function,\n",
" minimize=True, # Optional, defaults to False.\n",
" parameter_constraints=[\"x1 + x2 <= 20\"], # Optional.\n",
" outcome_constraints=[\"l2norm <= 1.25\"], # Optional.\n",
" total_trials=30, # Optional.\n",
")"
]
},
{
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"id": "ce5e8b5b",
"metadata": {
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},
"tags": []
},
"source": [
"And we can introspect optimization results:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "85ea93c9",
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-01T16:33:50.195248Z",
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"status": "completed"
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"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"{'x1': 0.19539133116559393,\n",
" 'x2': 0.13076645318512956,\n",
" 'x3': 0.47342175790484636,\n",
" 'x4': 0.2680870659744522,\n",
" 'x5': 0.30936593249631217,\n",
" 'x6': 0.6574812241032494}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"best_parameters"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1478cdd9",
"metadata": {
"execution": {
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},
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"start_time": "2024-03-01T16:33:50.245979",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"{'hartmann6': -3.3157006195668597, 'l2norm': 0.937697332732986}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"means, covariances = values\n",
"means"
]
},
{
"cell_type": "markdown",
"id": "edafc0be",
"metadata": {
"papermill": {
"duration": 0.042805,
"end_time": "2024-03-01T16:33:50.382374",
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"status": "completed"
},
"tags": []
},
"source": [
"For comparison, minimum of Hartmann6 is:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "30655fb0",
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-01T16:33:50.472836Z",
"iopub.status.busy": "2024-03-01T16:33:50.472165Z",
"iopub.status.idle": "2024-03-01T16:33:50.476929Z",
"shell.execute_reply": "2024-03-01T16:33:50.476361Z"
},
"papermill": {
"duration": 0.050577,
"end_time": "2024-03-01T16:33:50.478296",
"exception": false,
"start_time": "2024-03-01T16:33:50.427719",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"-3.32237"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hartmann6.fmin"
]
},
{
"cell_type": "markdown",
"id": "066b0948",
"metadata": {
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"duration": 0.042675,
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"tags": []
},
"source": [
"## 3. Plot results\n",
"Here we arbitrarily select \"x1\" and \"x2\" as the two parameters to plot for both metrics, \"hartmann6\" and \"l2norm\"."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7e22957d",
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"status": "completed"
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},
"outputs": [
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hartmann6: -0.0165024 (SEM: 0)
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hartmann6: -2.36956 (SEM: 0)
x1: 0.0163226
x2: 0.329739
x3: 0.644698
x4: 0.241429
x5: 0.340149
x6: 0.66561",
"Arm 11_0
hartmann6: -0.465148 (SEM: 0)
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x3: 0.00295743
x4: 0.551849
x5: 0.162423
x6: 0.932842",
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hartmann6: -1.97223 (SEM: 0)
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x3: 0.638862
x4: 0.240118
x5: 0.394105
x6: 0.547353",
"Arm 13_0
hartmann6: -2.09307 (SEM: 0)
x1: 2.38953e-16
x2: 0.26318
x3: 0.675059
x4: 0.175802
x5: 0.364154
x6: 0.738206",
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hartmann6: -2.02469 (SEM: 0)
x1: 9.08834e-18
x2: 0.355257
x3: 0.677436
x4: 0.297335
x5: 0.22865
x6: 0.667765",
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hartmann6: -1.83982 (SEM: 0)
x1: 0.0161322
x2: 0.430962
x3: 0.599003
x4: 0.267143
x5: 0.418351
x6: 0.68729",
"Arm 16_0
hartmann6: -2.8036 (SEM: 0)
x1: 0.0834731
x2: 0.259304
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x4: 0.223776
x5: 0.309589
x6: 0.646376",
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hartmann6: -2.5628 (SEM: 0)
x1: 0.110299
x2: 0.209659
x3: 0.582877
x4: 0.132389
x5: 0.277396
x6: 0.635667",
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hartmann6: -2.82601 (SEM: 0)
x1: 0.116671
x2: 0.199314
x3: 0.662457
x4: 0.282671
x5: 0.340721
x6: 0.649146",
"Arm 19_0
hartmann6: -3.06193 (SEM: 0)
x1: 0.0891991
x2: 0.187592
x3: 0.560033
x4: 0.292964
x5: 0.33199
x6: 0.650988",
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hartmann6: -2.26344 (SEM: 0)
x1: 0.413824
x2: 0.709716
x3: 0.65934
x4: 0.486394
x5: 0.644617
x6: 0.0478884",
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hartmann6: -3.141 (SEM: 0)
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x2: 0.194576
x3: 0.483965
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x6: 0.646308",
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hartmann6: -2.80998 (SEM: 0)
x1: 0.117678
x2: 0.183252
x3: 0.44669
x4: 0.273578
x5: 0.407767
x6: 0.656578",
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hartmann6: -3.10277 (SEM: 0)
x1: 0.139702
x2: 0.16501
x3: 0.479932
x4: 0.339857
x5: 0.297946
x6: 0.618955",
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hartmann6: -3.19941 (SEM: 0)
x1: 0.150209
x2: 0.185686
x3: 0.500008
x4: 0.321413
x5: 0.306202
x6: 0.673256",
"Arm 24_0
hartmann6: -3.23199 (SEM: 0)
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x2: 0.209314
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x4: 0.307743
x5: 0.305093
x6: 0.654379",
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hartmann6: -3.19553 (SEM: 0)
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x3: 0.517618
x4: 0.330489
x5: 0.317811
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hartmann6: -3.31224 (SEM: 0)
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x5: 0.309913
x6: 0.659809",
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hartmann6: -3.28582 (SEM: 0)
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x2: 0.1654
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x4: 0.267075
x5: 0.308045
x6: 0.659495",
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hartmann6: -3.3157 (SEM: 0)
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x5: 0.309366
x6: 0.657481",
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hartmann6: -0.531326 (SEM: 0)
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hartmann6: -1.62979 (SEM: 0)
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hartmann6: -0.166905 (SEM: 0)
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hartmann6: -0.502685 (SEM: 0)
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hartmann6: -0.0165024 (SEM: 0)
x1: 0.761784
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hartmann6: -0.465148 (SEM: 0)
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x6: 0.932842",
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hartmann6: -1.97223 (SEM: 0)
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x6: 0.547353",
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hartmann6: -2.09307 (SEM: 0)
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hartmann6: -2.02469 (SEM: 0)
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x6: 0.667765",
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hartmann6: -1.83982 (SEM: 0)
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hartmann6: -2.8036 (SEM: 0)
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hartmann6: -2.5628 (SEM: 0)
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x5: 0.277396
x6: 0.635667",
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hartmann6: -2.82601 (SEM: 0)
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x5: 0.340721
x6: 0.649146",
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hartmann6: -3.06193 (SEM: 0)
x1: 0.0891991
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x5: 0.33199
x6: 0.650988",
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hartmann6: -2.26344 (SEM: 0)
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x3: 0.65934
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x6: 0.0478884",
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hartmann6: -3.141 (SEM: 0)
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x6: 0.646308",
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hartmann6: -2.80998 (SEM: 0)
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x6: 0.656578",
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hartmann6: -3.10277 (SEM: 0)
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x4: 0.339857
x5: 0.297946
x6: 0.618955",
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hartmann6: -3.19941 (SEM: 0)
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hartmann6: -3.23199 (SEM: 0)
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x5: 0.305093
x6: 0.654379",
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hartmann6: -3.19553 (SEM: 0)
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hartmann6: -3.31224 (SEM: 0)
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x6: 0.659809",
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hartmann6: -3.28582 (SEM: 0)
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x4: 0.267075
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x6: 0.659495",
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