{
"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": "c1857dd2",
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{
"data": {
"text/html": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] 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": "93e524ed",
"metadata": {
"papermill": {
"duration": 0.036596,
"end_time": "2024-05-07T05:10:43.289076",
"exception": false,
"start_time": "2024-05-07T05:10:43.252480",
"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": "f5478126",
"metadata": {
"execution": {
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"status": "completed"
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"tags": []
},
"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": "79ef92fd",
"metadata": {
"papermill": {
"duration": 0.036582,
"end_time": "2024-05-07T05:10:43.442821",
"exception": false,
"start_time": "2024-05-07T05:10:43.406239",
"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": "8e866c52",
"metadata": {
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"duration": 0.036805,
"end_time": "2024-05-07T05:10:43.516278",
"exception": false,
"start_time": "2024-05-07T05:10:43.479473",
"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": "33ff0b78",
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"shell.execute_reply": "2024-05-07T05:12:55.859263Z"
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"end_time": "2024-05-07T05:12:55.862304",
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"start_time": "2024-05-07T05:10:43.553180",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] 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 05-07 05:10:43] 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 05-07 05:10:43] 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 05-07 05:10:43] 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 05-07 05:10:43] 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 05-07 05:10:43] 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 05-07 05:10:43] 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 05-07 05:10:43] 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 05-07 05:10:43] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] 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 05-07 05:10:43] 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 05-07 05:10:43] ax.service.managed_loop: Started full optimization with 30 steps.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 1...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 2...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 3...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 4...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 5...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 6...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 7...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 8...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 9...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 10...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 11...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 12...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 13...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:10:51] ax.service.managed_loop: Running optimization trial 14...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:11:00] ax.service.managed_loop: Running optimization trial 15...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:11:12] ax.service.managed_loop: Running optimization trial 16...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:11:20] ax.service.managed_loop: Running optimization trial 17...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:11:27] ax.service.managed_loop: Running optimization trial 18...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:11:36] ax.service.managed_loop: Running optimization trial 19...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:11:44] ax.service.managed_loop: Running optimization trial 20...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:11:50] ax.service.managed_loop: Running optimization trial 21...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:11:56] ax.service.managed_loop: Running optimization trial 22...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:12:03] ax.service.managed_loop: Running optimization trial 23...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:12:09] ax.service.managed_loop: Running optimization trial 24...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:12:16] ax.service.managed_loop: Running optimization trial 25...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:12:22] ax.service.managed_loop: Running optimization trial 26...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:12:28] ax.service.managed_loop: Running optimization trial 27...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:12:35] ax.service.managed_loop: Running optimization trial 28...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:12:42] ax.service.managed_loop: Running optimization trial 29...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 05-07 05:12:48] 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",
")"
]
},
{
"cell_type": "markdown",
"id": "cc1c4c1e",
"metadata": {
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"duration": 0.05322,
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},
"tags": []
},
"source": [
"And we can introspect optimization results:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f84884d0",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-07T05:12:56.057239Z",
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"status": "completed"
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"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"{'x1': 0.20171028229718294,\n",
" 'x2': 0.15512007263899935,\n",
" 'x3': 0.4569584217338681,\n",
" 'x4': 0.2796334237557797,\n",
" 'x5': 0.3059130225074349,\n",
" 'x6': 0.6559523770668575}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"best_parameters"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "23e23efe",
"metadata": {
"execution": {
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"shell.execute_reply": "2024-05-07T05:12:56.149900Z"
},
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"end_time": "2024-05-07T05:12:56.151874",
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"start_time": "2024-05-07T05:12:56.104787",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"{'hartmann6': -3.3165222416355187, 'l2norm': 0.9357442004534721}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"means, covariances = values\n",
"means"
]
},
{
"cell_type": "markdown",
"id": "a3bfc93a",
"metadata": {
"papermill": {
"duration": 0.03936,
"end_time": "2024-05-07T05:12:56.230288",
"exception": false,
"start_time": "2024-05-07T05:12:56.190928",
"status": "completed"
},
"tags": []
},
"source": [
"For comparison, minimum of Hartmann6 is:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "20a818e8",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-07T05:12:56.311976Z",
"iopub.status.busy": "2024-05-07T05:12:56.311417Z",
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"shell.execute_reply": "2024-05-07T05:12:56.315393Z"
},
"papermill": {
"duration": 0.04743,
"end_time": "2024-05-07T05:12:56.317482",
"exception": false,
"start_time": "2024-05-07T05:12:56.270052",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"-3.32237"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hartmann6.fmin"
]
},
{
"cell_type": "markdown",
"id": "8b7ebd84",
"metadata": {
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"duration": 0.040225,
"end_time": "2024-05-07T05:12:56.397658",
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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": "ed1aa216",
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"status": "completed"
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"tags": []
},
"outputs": [
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hartmann6: -0.408252 (SEM: 0)
x1: 0.226736
x2: 0.149516
x3: 0.163255
x4: 0.566514
x5: 0.0486936
x6: 0.752692",
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hartmann6: -0.136766 (SEM: 0)
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x6: 0.427005",
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x6: 0.774204",
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hartmann6: -0.14633 (SEM: 0)
x1: 0.53117
x2: 0.639677
x3: 0.443875
x4: 0.187629
x5: 0.953314
x6: 0.3175",
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hartmann6: -0.521667 (SEM: 0)
x1: 0.136019
x2: 0.390944
x3: 0.622159
x4: 0.718975
x5: 0.249928
x6: 0.710206",
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x6: 0.823742",
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hartmann6: -1.71444 (SEM: 0)
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x3: 0.5851
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x6: 0.81713",
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hartmann6: -2.32358 (SEM: 0)
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x3: 0.587811
x4: 0.329031
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x6: 0.816026",
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x2: 0.291526
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x4: 0.34552
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hartmann6: -2.61659 (SEM: 0)
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x2: 0.290325
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x4: 0.327414
x5: 0.333929
x6: 0.741192",
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hartmann6: -2.49674 (SEM: 0)
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hartmann6: -2.21597 (SEM: 0)
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hartmann6: -2.98839 (SEM: 0)
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hartmann6: -3.24395 (SEM: 0)
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hartmann6: -3.29073 (SEM: 0)
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hartmann6: -3.27303 (SEM: 0)
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hartmann6: -0.408252 (SEM: 0)
x1: 0.226736
x2: 0.149516
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x5: 0.0486936
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hartmann6: -0.136766 (SEM: 0)
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hartmann6: -1.90338 (SEM: 0)
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hartmann6: -0.724695 (SEM: 0)
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hartmann6: -0.00540819 (SEM: 0)
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x6: 0.427005",
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hartmann6: -0.019604 (SEM: 0)
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x5: 0.638768
x6: 0.774204",
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hartmann6: -0.289853 (SEM: 0)
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hartmann6: -1.42796 (SEM: 0)
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x5: 0.260958
x6: 0.605042",
"Arm 8_0
hartmann6: -0.930131 (SEM: 0)
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x2: 0.235096
x3: 0.134826
x4: 0.159475
x5: 0.370955
x6: 0.938764",
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hartmann6: -0.14633 (SEM: 0)
x1: 0.53117
x2: 0.639677
x3: 0.443875
x4: 0.187629
x5: 0.953314
x6: 0.3175",
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hartmann6: -0.521667 (SEM: 0)
x1: 0.136019
x2: 0.390944
x3: 0.622159
x4: 0.718975
x5: 0.249928
x6: 0.710206",
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hartmann6: -0.666162 (SEM: 0)
x1: 0.53861
x2: 0.0556451
x3: 0.657503
x4: 0.0727585
x5: 0.482207
x6: 0.493718",
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hartmann6: -2.04364 (SEM: 0)
x1: 0.1237
x2: 0.350541
x3: 0.634667
x4: 0.29518
x5: 0.20386
x6: 0.829148",
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hartmann6: -1.99102 (SEM: 0)
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x6: 0.823742",
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hartmann6: -1.71444 (SEM: 0)
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x2: 0.37776
x3: 0.5851
x4: 0.23997
x5: 0.156023
x6: 0.81713",
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hartmann6: -2.32358 (SEM: 0)
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x2: 0.336769
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x6: 0.816026",
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hartmann6: -2.3897 (SEM: 0)
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x3: 0.57369
x4: 0.34552
x5: 0.334956
x6: 0.820268",
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hartmann6: -2.06082 (SEM: 0)
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x2: 0.332822
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x4: 0.342069
x5: 0.311077
x6: 0.800134",
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hartmann6: -2.61659 (SEM: 0)
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x2: 0.290325
x3: 0.670628
x4: 0.327414
x5: 0.333929
x6: 0.741192",
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hartmann6: -2.49674 (SEM: 0)
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hartmann6: -2.21597 (SEM: 0)
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x6: 0.711958",
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x6: 0.674994",
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