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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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"[INFO 07-23 19:37:59] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.\n"
]
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"[INFO 07-23 19:37:59] 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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" \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": "9bd2ce13",
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"showInput": true,
"tags": []
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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": "e1879901",
"metadata": {
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"tags": []
},
"source": [
"#### 1a. Define search space and evaluation function"
]
},
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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": "8d015186",
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"tags": []
},
"source": [
"#### 1b. Create Experiment"
]
},
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"text": [
"[INFO 07-23 19:37:59] 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 07-23 19:37:59] 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 07-23 19:37:59] 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 07-23 19:37:59] 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 07-23 19:37:59] 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 07-23 19:37:59] 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 07-23 19:37:59] 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 07-23 19:37:59] 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 07-23 19:37:59] 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 07-23 19:37:59] 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 07-23 19:37:59] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:37:59] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:37:59] 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 07-23 19:37:59] 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": "ec43bb69",
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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.DL1QmpHQMI/Ax-main/ax/modelbridge/cross_validation.py:462: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 07-23 19:38:00] ax.service.ax_client: Generated new trial 0 with parameters {'x1': 0.005029, 'x2': 0.536797, 'x3': 0.498193, 'x4': 0.128202, 'x5': 0.24232, 'x6': 0.566737} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:00] ax.service.ax_client: Completed trial 0 with data: {'hartmann6': (-1.314676, 0.1), 'l2norm': (0.954271, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.DL1QmpHQMI/Ax-main/ax/modelbridge/cross_validation.py:462: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 07-23 19:38:00] ax.service.ax_client: Generated new trial 1 with parameters {'x1': 0.645062, 'x2': 0.660466, 'x3': 0.007177, 'x4': 0.899662, 'x5': 0.297496, 'x6': 0.854738} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:00] ax.service.ax_client: Completed trial 1 with data: {'hartmann6': (0.036733, 0.1), 'l2norm': (1.412671, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.DL1QmpHQMI/Ax-main/ax/modelbridge/cross_validation.py:462: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 07-23 19:38:00] ax.service.ax_client: Generated new trial 2 with parameters {'x1': 0.060622, 'x2': 0.488749, 'x3': 0.270709, 'x4': 0.356009, 'x5': 0.179082, 'x6': 0.580092} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:00] ax.service.ax_client: Completed trial 2 with data: {'hartmann6': (-1.297832, 0.1), 'l2norm': (0.899584, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.DL1QmpHQMI/Ax-main/ax/modelbridge/cross_validation.py:462: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 07-23 19:38:00] ax.service.ax_client: Generated new trial 3 with parameters {'x1': 0.55685, 'x2': 0.804798, 'x3': 0.189441, 'x4': 0.989962, 'x5': 0.853408, 'x6': 0.140541} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:00] ax.service.ax_client: Completed trial 3 with data: {'hartmann6': (-0.135698, 0.1), 'l2norm': (1.645901, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.DL1QmpHQMI/Ax-main/ax/modelbridge/cross_validation.py:462: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 07-23 19:38:00] ax.service.ax_client: Generated new trial 4 with parameters {'x1': 0.635358, 'x2': 0.661891, 'x3': 0.69756, 'x4': 0.971525, 'x5': 0.8347, 'x6': 0.443203} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:00] ax.service.ax_client: Completed trial 4 with data: {'hartmann6': (0.012793, 0.1), 'l2norm': (1.862247, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.DL1QmpHQMI/Ax-main/ax/modelbridge/cross_validation.py:462: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 07-23 19:38:00] ax.service.ax_client: Generated new trial 5 with parameters {'x1': 0.079007, 'x2': 0.266237, 'x3': 0.240404, 'x4': 0.507038, 'x5': 0.077789, 'x6': 0.899208} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:00] ax.service.ax_client: Completed trial 5 with data: {'hartmann6': (-0.364899, 0.1), 'l2norm': (1.146802, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.DL1QmpHQMI/Ax-main/ax/modelbridge/cross_validation.py:462: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 07-23 19:38:00] ax.service.ax_client: Generated new trial 6 with parameters {'x1': 0.582654, 'x2': 0.655124, 'x3': 0.98322, 'x4': 0.281674, 'x5': 0.841391, 'x6': 0.104157} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:00] ax.service.ax_client: Completed trial 6 with data: {'hartmann6': (-0.313359, 0.1), 'l2norm': (1.676689, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.DL1QmpHQMI/Ax-main/ax/modelbridge/cross_validation.py:462: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 07-23 19:38:00] ax.service.ax_client: Generated new trial 7 with parameters {'x1': 0.426719, 'x2': 0.3738, 'x3': 0.616366, 'x4': 0.81264, 'x5': 0.030149, 'x6': 0.307442} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:00] ax.service.ax_client: Completed trial 7 with data: {'hartmann6': (-0.190222, 0.1), 'l2norm': (1.252231, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.DL1QmpHQMI/Ax-main/ax/modelbridge/cross_validation.py:462: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 07-23 19:38:00] ax.service.ax_client: Generated new trial 8 with parameters {'x1': 0.893668, 'x2': 0.138902, 'x3': 0.648711, 'x4': 0.62237, 'x5': 0.441383, 'x6': 0.49616} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:00] ax.service.ax_client: Completed trial 8 with data: {'hartmann6': (-0.004672, 0.1), 'l2norm': (1.410809, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.DL1QmpHQMI/Ax-main/ax/modelbridge/cross_validation.py:462: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 07-23 19:38:00] ax.service.ax_client: Generated new trial 9 with parameters {'x1': 0.90315, 'x2': 0.070489, 'x3': 0.406913, 'x4': 0.864679, 'x5': 0.156146, 'x6': 0.075863} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:00] ax.service.ax_client: Completed trial 9 with data: {'hartmann6': (0.144427, 0.1), 'l2norm': (1.476797, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.DL1QmpHQMI/Ax-main/ax/modelbridge/cross_validation.py:462: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 07-23 19:38:00] ax.service.ax_client: Generated new trial 10 with parameters {'x1': 0.120365, 'x2': 0.691714, 'x3': 0.19489, 'x4': 0.534393, 'x5': 0.104493, 'x6': 0.134331} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:00] ax.service.ax_client: Completed trial 10 with data: {'hartmann6': (-0.417549, 0.1), 'l2norm': (1.079256, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.DL1QmpHQMI/Ax-main/ax/modelbridge/cross_validation.py:462: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 07-23 19:38:00] ax.service.ax_client: Generated new trial 11 with parameters {'x1': 0.047774, 'x2': 0.213587, 'x3': 0.856849, 'x4': 0.797774, 'x5': 0.389709, 'x6': 0.100644} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:00] ax.service.ax_client: Completed trial 11 with data: {'hartmann6': (-0.119663, 0.1), 'l2norm': (1.207949, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:01] ax.service.ax_client: Generated new trial 12 with parameters {'x1': 0.014492, 'x2': 0.532679, 'x3': 0.347245, 'x4': 0.222015, 'x5': 0.207501, 'x6': 0.556295} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:01] ax.service.ax_client: Completed trial 12 with data: {'hartmann6': (-1.205324, 0.1), 'l2norm': (1.003972, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:03] ax.service.ax_client: Generated new trial 13 with parameters {'x1': 0.076827, 'x2': 0.484831, 'x3': 0.49298, 'x4': 0.279434, 'x5': 0.225764, 'x6': 0.590385} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:03] ax.service.ax_client: Completed trial 13 with data: {'hartmann6': (-1.813216, 0.1), 'l2norm': (0.947904, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:05] ax.service.ax_client: Generated new trial 14 with parameters {'x1': 0.125147, 'x2': 0.482602, 'x3': 0.562263, 'x4': 0.320569, 'x5': 0.248091, 'x6': 0.597693} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:05] ax.service.ax_client: Completed trial 14 with data: {'hartmann6': (-2.093941, 0.1), 'l2norm': (0.859925, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:06] ax.service.ax_client: Generated new trial 15 with parameters {'x1': 0.128409, 'x2': 0.523032, 'x3': 0.654072, 'x4': 0.360235, 'x5': 0.255691, 'x6': 0.620561} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:06] ax.service.ax_client: Completed trial 15 with data: {'hartmann6': (-1.727028, 0.1), 'l2norm': (1.33117, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:09] ax.service.ax_client: Generated new trial 16 with parameters {'x1': 0.172432, 'x2': 0.429106, 'x3': 0.544233, 'x4': 0.29239, 'x5': 0.271603, 'x6': 0.577619} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:09] ax.service.ax_client: Completed trial 16 with data: {'hartmann6': (-2.330821, 0.1), 'l2norm': (0.834112, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:11] ax.service.ax_client: Generated new trial 17 with parameters {'x1': 0.182029, 'x2': 0.403679, 'x3': 0.527473, 'x4': 0.276148, 'x5': 0.352344, 'x6': 0.578771} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:11] ax.service.ax_client: Completed trial 17 with data: {'hartmann6': (-2.508585, 0.1), 'l2norm': (1.027769, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:13] ax.service.ax_client: Generated new trial 18 with parameters {'x1': 0.234979, 'x2': 0.413404, 'x3': 0.519195, 'x4': 0.215853, 'x5': 0.343164, 'x6': 0.594287} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:13] ax.service.ax_client: Completed trial 18 with data: {'hartmann6': (-2.449358, 0.1), 'l2norm': (1.018855, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:17] ax.service.ax_client: Generated new trial 19 with parameters {'x1': 0.19953, 'x2': 0.336787, 'x3': 0.566451, 'x4': 0.235734, 'x5': 0.335288, 'x6': 0.579376} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:17] ax.service.ax_client: Completed trial 19 with data: {'hartmann6': (-2.809778, 0.1), 'l2norm': (0.925198, 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": "2aaeed12",
"metadata": {
"originalKey": "72f4d3e7-fa04-43d0-8451-ded292e705df",
"papermill": {
"duration": 0.036801,
"end_time": "2024-07-23T19:38:17.715609",
"exception": false,
"start_time": "2024-07-23T19:38:17.678808",
"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": "043d84a8",
"metadata": {
"code_folding": [],
"execution": {
"iopub.execute_input": "2024-07-23T19:38:17.790427Z",
"iopub.status.busy": "2024-07-23T19:38:17.789881Z",
"iopub.status.idle": "2024-07-23T19:38:18.578971Z",
"shell.execute_reply": "2024-07-23T19:38:18.578252Z"
},
"executionStartTime": 1627654870209,
"executionStopTime": 1627654871972,
"hidden_ranges": [],
"originalKey": "843df85c-965d-4a83-9fe1-696225d81c0f",
"papermill": {
"duration": 0.830848,
"end_time": "2024-07-23T19:38:18.582996",
"exception": false,
"start_time": "2024-07-23T19:38:17.752148",
"status": "completed"
},
"requestMsgId": "4a643541-867c-46b6-868d-64337920c2a3",
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 07-23 19:38:17] 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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