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"source": [
"# Visualizations\n",
"\n",
"This tutorial illustrates the core visualization utilities available in Ax."
]
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""
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"text": [
"[INFO 01-31 05:15:43] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.\n"
]
},
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"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:43] 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"
]
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"text/html": [
" \n",
" \n",
" "
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"source": [
"import numpy as np\n",
"\n",
"from ax.modelbridge.cross_validation import cross_validate\n",
"from ax.plot.contour import interact_contour\n",
"from ax.plot.diagnostic import interact_cross_validation\n",
"from ax.plot.scatter import interact_fitted, plot_objective_vs_constraints, tile_fitted\n",
"from ax.plot.slice import plot_slice\n",
"from ax.service.ax_client import AxClient, ObjectiveProperties\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()"
]
},
{
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"id": "d12e03a6",
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"showInput": true,
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"source": [
"## 1. Create experiment and run optimization\n",
"\n",
"The vizualizations require an experiment object and a model fit on the evaluated data. The routine below is a copy of the Service API tutorial, so the explanation here is omitted. Retrieving the experiment and model objects for each API paradigm is shown in the respective tutorials"
]
},
{
"cell_type": "markdown",
"id": "bfa8ad37",
"metadata": {
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"tags": []
},
"source": [
"#### 1a. Define search space and evaluation function"
]
},
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"id": "aef67086",
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"source": [
"noise_sd = 0.1\n",
"param_names = [f\"x{i+1}\" for i in range(6)] # x1, x2, ..., x6\n",
"\n",
"\n",
"def noisy_hartmann_evaluation_function(parameterization):\n",
" x = np.array([parameterization.get(p_name) for p_name in param_names])\n",
" noise1, noise2 = np.random.normal(0, noise_sd, 2)\n",
"\n",
" return {\n",
" \"hartmann6\": (hartmann6(x) + noise1, noise_sd),\n",
" \"l2norm\": (np.sqrt((x**2).sum()) + noise2, noise_sd),\n",
" }"
]
},
{
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"id": "568c62c5",
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"tags": []
},
"source": [
"#### 1b. Create Experiment"
]
},
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"text": [
"[INFO 01-31 05:15:44] ax.service.ax_client: Starting optimization with verbose logging. To disable logging, set the `verbose_logging` argument to `False`. Note that float values in the logs are rounded to 6 decimal points.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:44] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. 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 01-31 05:15:44] 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 01-31 05:15:44] 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 01-31 05:15:44] 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 01-31 05:15:44] 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 01-31 05:15:44] 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 01-31 05:15:44] 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=[]).\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:44] 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 01-31 05:15:44] 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 01-31 05:15:44] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:44] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:44] 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 01-31 05:15:44] 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"
]
}
],
"source": [
"ax_client = AxClient()\n",
"ax_client.create_experiment(\n",
" name=\"test_visualizations\",\n",
" parameters=[\n",
" {\n",
" \"name\": p_name,\n",
" \"type\": \"range\",\n",
" \"bounds\": [0.0, 1.0],\n",
" }\n",
" for p_name in param_names\n",
" ],\n",
" objectives={\"hartmann6\": ObjectiveProperties(minimize=True)},\n",
" outcome_constraints=[\"l2norm <= 1.25\"],\n",
")"
]
},
{
"cell_type": "markdown",
"id": "a6621702",
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"showInput": false,
"tags": []
},
"source": [
"#### 1c. Run the optimization and fit a GP on all data"
]
},
{
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"requestMsgId": "c7a4dea8-fd6d-4e1a-84de-ad973ede0cd7",
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.8SPGnWMU26/Ax-main/ax/modelbridge/cross_validation.py:439: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 01-31 05:15:44] ax.service.ax_client: Generated new trial 0 with parameters {'x1': 0.131491, 'x2': 0.483592, 'x3': 0.053841, 'x4': 0.807448, 'x5': 0.661372, 'x6': 0.134992} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:44] ax.service.ax_client: Completed trial 0 with data: {'hartmann6': (-0.22552, 0.1), 'l2norm': (1.154633, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.8SPGnWMU26/Ax-main/ax/modelbridge/cross_validation.py:439: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 01-31 05:15:44] ax.service.ax_client: Generated new trial 1 with parameters {'x1': 0.930106, 'x2': 0.891206, 'x3': 0.756407, 'x4': 0.099624, 'x5': 0.123986, 'x6': 0.97789} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:44] ax.service.ax_client: Completed trial 1 with data: {'hartmann6': (-0.022299, 0.1), 'l2norm': (1.828465, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.8SPGnWMU26/Ax-main/ax/modelbridge/cross_validation.py:439: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 01-31 05:15:44] ax.service.ax_client: Generated new trial 2 with parameters {'x1': 0.689473, 'x2': 0.079633, 'x3': 0.479976, 'x4': 0.649376, 'x5': 0.496123, 'x6': 0.525501} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:44] ax.service.ax_client: Completed trial 2 with data: {'hartmann6': (-0.058407, 0.1), 'l2norm': (1.345988, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.8SPGnWMU26/Ax-main/ax/modelbridge/cross_validation.py:439: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 01-31 05:15:44] ax.service.ax_client: Generated new trial 3 with parameters {'x1': 0.496879, 'x2': 0.54505, 'x3': 0.713697, 'x4': 0.44155, 'x5': 0.788877, 'x6': 0.368639} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:44] ax.service.ax_client: Completed trial 3 with data: {'hartmann6': (-0.124713, 0.1), 'l2norm': (1.364751, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.8SPGnWMU26/Ax-main/ax/modelbridge/cross_validation.py:439: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 01-31 05:15:44] ax.service.ax_client: Generated new trial 4 with parameters {'x1': 0.26477, 'x2': 0.155562, 'x3': 0.967302, 'x4': 0.26509, 'x5': 0.946099, 'x6': 0.098987} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:44] ax.service.ax_client: Completed trial 4 with data: {'hartmann6': (-0.005791, 0.1), 'l2norm': (1.2374, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.8SPGnWMU26/Ax-main/ax/modelbridge/cross_validation.py:439: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 01-31 05:15:44] ax.service.ax_client: Generated new trial 5 with parameters {'x1': 0.548672, 'x2': 0.719609, 'x3': 0.233558, 'x4': 0.581679, 'x5': 0.268581, 'x6': 0.75609} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:44] ax.service.ax_client: Completed trial 5 with data: {'hartmann6': (-0.425211, 0.1), 'l2norm': (1.496162, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.8SPGnWMU26/Ax-main/ax/modelbridge/cross_validation.py:439: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 01-31 05:15:44] ax.service.ax_client: Generated new trial 6 with parameters {'x1': 0.823973, 'x2': 0.28315, 'x3': 0.50278, 'x4': 0.169272, 'x5': 0.142546, 'x6': 0.739729} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:44] ax.service.ax_client: Completed trial 6 with data: {'hartmann6': (-0.440787, 0.1), 'l2norm': (1.12813, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.8SPGnWMU26/Ax-main/ax/modelbridge/cross_validation.py:439: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 01-31 05:15:44] ax.service.ax_client: Generated new trial 7 with parameters {'x1': 0.114714, 'x2': 0.842136, 'x3': 0.300251, 'x4': 0.985862, 'x5': 0.572509, 'x6': 0.396591} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:44] ax.service.ax_client: Completed trial 7 with data: {'hartmann6': (0.011023, 0.1), 'l2norm': (1.503865, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.8SPGnWMU26/Ax-main/ax/modelbridge/cross_validation.py:439: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 01-31 05:15:44] ax.service.ax_client: Generated new trial 8 with parameters {'x1': 0.046407, 'x2': 0.044498, 'x3': 0.58763, 'x4': 0.533496, 'x5': 0.053691, 'x6': 0.303075} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:44] ax.service.ax_client: Completed trial 8 with data: {'hartmann6': (-0.151621, 0.1), 'l2norm': (0.79458, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.8SPGnWMU26/Ax-main/ax/modelbridge/cross_validation.py:439: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 01-31 05:15:44] ax.service.ax_client: Generated new trial 9 with parameters {'x1': 0.766898, 'x2': 0.5803, 'x3': 0.351647, 'x4': 0.373645, 'x5': 0.731638, 'x6': 0.583227} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:44] ax.service.ax_client: Completed trial 9 with data: {'hartmann6': (-0.093786, 0.1), 'l2norm': (1.478072, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.8SPGnWMU26/Ax-main/ax/modelbridge/cross_validation.py:439: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 01-31 05:15:44] ax.service.ax_client: Generated new trial 10 with parameters {'x1': 0.604634, 'x2': 0.393712, 'x3': 0.880766, 'x4': 0.875407, 'x5': 0.859146, 'x6': 0.912592} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:44] ax.service.ax_client: Completed trial 10 with data: {'hartmann6': (-0.064969, 0.1), 'l2norm': (1.995085, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.8SPGnWMU26/Ax-main/ax/modelbridge/cross_validation.py:439: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 01-31 05:15:44] ax.service.ax_client: Generated new trial 11 with parameters {'x1': 0.333915, 'x2': 0.980972, 'x3': 0.176067, 'x4': 0.215556, 'x5': 0.425824, 'x6': 0.192498} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:44] ax.service.ax_client: Completed trial 11 with data: {'hartmann6': (-0.567738, 0.1), 'l2norm': (1.125026, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:48] ax.service.ax_client: Generated new trial 12 with parameters {'x1': 0.418084, 'x2': 0.759913, 'x3': 0.150699, 'x4': 0.264122, 'x5': 0.297788, 'x6': 0.279296} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:48] ax.service.ax_client: Completed trial 12 with data: {'hartmann6': (-0.744165, 0.1), 'l2norm': (1.057256, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:50] ax.service.ax_client: Generated new trial 13 with parameters {'x1': 0.550333, 'x2': 0.564418, 'x3': 0.13, 'x4': 0.084936, 'x5': 0.162165, 'x6': 0.346114} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:50] ax.service.ax_client: Completed trial 13 with data: {'hartmann6': (-0.080572, 0.1), 'l2norm': (0.888512, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:59] ax.service.ax_client: Generated new trial 14 with parameters {'x1': 0.332407, 'x2': 0.433507, 'x3': 0.316151, 'x4': 0.247231, 'x5': 0.268843, 'x6': 0.371148} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:15:59] ax.service.ax_client: Completed trial 14 with data: {'hartmann6': (-1.062543, 0.1), 'l2norm': (0.772986, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:16:03] ax.service.ax_client: Generated new trial 15 with parameters {'x1': 0.083242, 'x2': 0.2452, 'x3': 0.432486, 'x4': 0.254658, 'x5': 0.254921, 'x6': 0.387337} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:16:03] ax.service.ax_client: Completed trial 15 with data: {'hartmann6': (-1.514199, 0.1), 'l2norm': (0.594865, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:16:05] ax.service.ax_client: Generated new trial 16 with parameters {'x1': 0.0, 'x2': 0.02209, 'x3': 0.603081, 'x4': 0.262333, 'x5': 0.236095, 'x6': 0.522272} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:16:05] ax.service.ax_client: Completed trial 16 with data: {'hartmann6': (-2.106559, 0.1), 'l2norm': (0.905218, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:16:07] ax.service.ax_client: Generated new trial 17 with parameters {'x1': 0.0, 'x2': 0.0, 'x3': 0.789902, 'x4': 0.269476, 'x5': 0.214676, 'x6': 0.683809} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:16:07] ax.service.ax_client: Completed trial 17 with data: {'hartmann6': (-1.612032, 0.1), 'l2norm': (1.130916, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:16:10] ax.service.ax_client: Generated new trial 18 with parameters {'x1': 0.0, 'x2': 0.0, 'x3': 0.80206, 'x4': 0.246761, 'x5': 0.156097, 'x6': 0.491904} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:16:10] ax.service.ax_client: Completed trial 18 with data: {'hartmann6': (-1.020042, 0.1), 'l2norm': (1.053136, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:16:15] ax.service.ax_client: Generated new trial 19 with parameters {'x1': 0.0, 'x2': 0.0, 'x3': 0.475494, 'x4': 0.123269, 'x5': 0.274337, 'x6': 0.290761} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:16:15] ax.service.ax_client: Completed trial 19 with data: {'hartmann6': (-0.713683, 0.1), 'l2norm': (0.602178, 0.1)}.\n"
]
}
],
"source": [
"for i in range(20):\n",
" parameters, trial_index = ax_client.get_next_trial()\n",
" # Local evaluation here can be replaced with deployment to external system.\n",
" ax_client.complete_trial(\n",
" trial_index=trial_index, raw_data=noisy_hartmann_evaluation_function(parameters)\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "3ae76e16",
"metadata": {
"originalKey": "72f4d3e7-fa04-43d0-8451-ded292e705df",
"papermill": {
"duration": 0.043704,
"end_time": "2025-01-31T05:16:15.531006",
"exception": false,
"start_time": "2025-01-31T05:16:15.487302",
"status": "completed"
},
"tags": []
},
"source": [
"## 2. Contour plots\n",
"\n",
"The plot below shows the response surface for `hartmann6` metric as a function of the `x1`, `x2` parameters.\n",
"\n",
"The other parameters are fixed in the middle of their respective ranges, which in this example is 0.5 for all of them."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "85caacc5",
"metadata": {
"code_folding": [],
"execution": {
"iopub.execute_input": "2025-01-31T05:16:15.621005Z",
"iopub.status.busy": "2025-01-31T05:16:15.620396Z",
"iopub.status.idle": "2025-01-31T05:16:16.264156Z",
"shell.execute_reply": "2025-01-31T05:16:16.263135Z"
},
"executionStartTime": 1627654870209,
"executionStopTime": 1627654871972,
"hidden_ranges": [],
"originalKey": "843df85c-965d-4a83-9fe1-696225d81c0f",
"papermill": {
"duration": 0.712765,
"end_time": "2025-01-31T05:16:16.287317",
"exception": false,
"start_time": "2025-01-31T05:16:15.574552",
"status": "completed"
},
"requestMsgId": "4a643541-867c-46b6-868d-64337920c2a3",
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 01-31 05:16:15] ax.service.ax_client: Retrieving contour plot with parameter 'x1' on X-axis and 'x2' on Y-axis, for metric 'hartmann6'. Remaining parameters are affixed to the middle of their range.\n"
]
},
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