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"source": [
"# Visualizations\n",
"\n",
"This tutorial illustrates the core visualization utilities available in Ax."
]
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"[ERROR 11-13 05:15:23] ax.storage.sqa_store.encoder: ATTENTION: The Ax team is considering deprecating SQLAlchemy storage. If you are currently using SQLAlchemy storage, please reach out to us via GitHub Issues here: https://github.com/facebook/Ax/issues/2975\n"
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""
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"text": [
"[INFO 11-13 05:15:23] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:23] 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": "a15c0006",
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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": "d592eba5",
"metadata": {
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"tags": []
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"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": "d6ee82a7",
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"source": [
"#### 1b. Create Experiment"
]
},
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"text": [
"[INFO 11-13 05:15:24] 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 11-13 05:15:24] 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 11-13 05:15:24] 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 11-13 05:15:24] 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 11-13 05:15:24] 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 11-13 05:15:24] 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 11-13 05:15:24] 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 11-13 05:15:24] 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 11-13 05:15:24] 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 11-13 05:15:24] 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 11-13 05:15:24] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:24] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:24] 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 11-13 05:15:24] 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": "8ed89dab",
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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",
"tags": []
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.jh7tLjWjTJ/Ax-main/ax/modelbridge/cross_validation.py:464: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 11-13 05:15:24] ax.service.ax_client: Generated new trial 0 with parameters {'x1': 0.32258, 'x2': 0.860162, 'x3': 0.941987, 'x4': 0.847485, 'x5': 0.589424, 'x6': 0.467366} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:24] ax.service.ax_client: Completed trial 0 with data: {'hartmann6': (-0.289996, 0.1), 'l2norm': (1.830424, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.jh7tLjWjTJ/Ax-main/ax/modelbridge/cross_validation.py:464: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 11-13 05:15:24] ax.service.ax_client: Generated new trial 1 with parameters {'x1': 0.535038, 'x2': 0.492588, 'x3': 0.214804, 'x4': 0.498385, 'x5': 0.101773, 'x6': 0.834754} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:24] ax.service.ax_client: Completed trial 1 with data: {'hartmann6': (-0.296554, 0.1), 'l2norm': (1.361296, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.jh7tLjWjTJ/Ax-main/ax/modelbridge/cross_validation.py:464: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 11-13 05:15:24] ax.service.ax_client: Generated new trial 2 with parameters {'x1': 0.781111, 'x2': 0.52478, 'x3': 0.64435, 'x4': 0.699243, 'x5': 0.495303, 'x6': 0.682004} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:24] ax.service.ax_client: Completed trial 2 with data: {'hartmann6': (-0.090606, 0.1), 'l2norm': (1.282694, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.jh7tLjWjTJ/Ax-main/ax/modelbridge/cross_validation.py:464: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 11-13 05:15:24] ax.service.ax_client: Generated new trial 3 with parameters {'x1': 0.115632, 'x2': 0.142553, 'x3': 0.38744, 'x4': 0.079643, 'x5': 0.945886, 'x6': 0.048842} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:24] ax.service.ax_client: Completed trial 3 with data: {'hartmann6': (-0.089207, 0.1), 'l2norm': (1.026607, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.jh7tLjWjTJ/Ax-main/ax/modelbridge/cross_validation.py:464: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 11-13 05:15:24] ax.service.ax_client: Generated new trial 4 with parameters {'x1': 0.153482, 'x2': 0.680642, 'x3': 0.028476, 'x4': 0.225479, 'x5': 0.828344, 'x6': 0.332626} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:24] ax.service.ax_client: Completed trial 4 with data: {'hartmann6': (-0.11413, 0.1), 'l2norm': (1.128938, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.jh7tLjWjTJ/Ax-main/ax/modelbridge/cross_validation.py:464: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 11-13 05:15:24] ax.service.ax_client: Generated new trial 5 with parameters {'x1': 0.988866, 'x2': 0.047228, 'x3': 0.752218, 'x4': 0.620516, 'x5': 0.347288, 'x6': 0.965604} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:24] ax.service.ax_client: Completed trial 5 with data: {'hartmann6': (-0.033261, 0.1), 'l2norm': (1.72209, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.jh7tLjWjTJ/Ax-main/ax/modelbridge/cross_validation.py:464: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 11-13 05:15:24] ax.service.ax_client: Generated new trial 6 with parameters {'x1': 0.695935, 'x2': 0.954512, 'x3': 0.322634, 'x4': 0.321497, 'x5': 0.234956, 'x6': 0.547187} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:24] ax.service.ax_client: Completed trial 6 with data: {'hartmann6': (-0.0768, 0.1), 'l2norm': (1.320479, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.jh7tLjWjTJ/Ax-main/ax/modelbridge/cross_validation.py:464: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 11-13 05:15:24] ax.service.ax_client: Generated new trial 7 with parameters {'x1': 0.407288, 'x2': 0.337697, 'x3': 0.583061, 'x4': 0.957753, 'x5': 0.690669, 'x6': 0.179738} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:24] ax.service.ax_client: Completed trial 7 with data: {'hartmann6': (-0.028314, 0.1), 'l2norm': (1.746991, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.jh7tLjWjTJ/Ax-main/ax/modelbridge/cross_validation.py:464: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 11-13 05:15:24] ax.service.ax_client: Generated new trial 8 with parameters {'x1': 0.489464, 'x2': 0.624292, 'x3': 0.263571, 'x4': 0.535867, 'x5': 0.006377, 'x6': 0.120232} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:24] ax.service.ax_client: Completed trial 8 with data: {'hartmann6': (-1.441806, 0.1), 'l2norm': (0.925363, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.jh7tLjWjTJ/Ax-main/ax/modelbridge/cross_validation.py:464: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 11-13 05:15:24] ax.service.ax_client: Generated new trial 9 with parameters {'x1': 0.652852, 'x2': 0.242066, 'x3': 0.518098, 'x4': 0.180854, 'x5': 0.551885, 'x6': 0.73756} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:24] ax.service.ax_client: Completed trial 9 with data: {'hartmann6': (-0.476061, 0.1), 'l2norm': (1.338895, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.jh7tLjWjTJ/Ax-main/ax/modelbridge/cross_validation.py:464: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 11-13 05:15:24] ax.service.ax_client: Generated new trial 10 with parameters {'x1': 0.906979, 'x2': 0.772305, 'x3': 0.088551, 'x4': 0.886166, 'x5': 0.912256, 'x6': 0.781084} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:24] ax.service.ax_client: Completed trial 10 with data: {'hartmann6': (-0.002224, 0.1), 'l2norm': (1.903786, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/tmp.jh7tLjWjTJ/Ax-main/ax/modelbridge/cross_validation.py:464: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"[INFO 11-13 05:15:24] ax.service.ax_client: Generated new trial 11 with parameters {'x1': 0.196336, 'x2': 0.40473, 'x3': 0.818117, 'x4': 0.272357, 'x5': 0.395998, 'x6': 0.397982} using model Sobol.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:24] ax.service.ax_client: Completed trial 11 with data: {'hartmann6': (-0.840343, 0.1), 'l2norm': (1.283196, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:37] ax.service.ax_client: Generated new trial 12 with parameters {'x1': 0.440903, 'x2': 0.369174, 'x3': 0.388924, 'x4': 0.462979, 'x5': 0.0, 'x6': 0.067009} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:37] ax.service.ax_client: Completed trial 12 with data: {'hartmann6': (-0.272835, 0.1), 'l2norm': (0.995956, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:43] ax.service.ax_client: Generated new trial 13 with parameters {'x1': 0.444001, 'x2': 0.636952, 'x3': 0.444504, 'x4': 0.450684, 'x5': 0.013502, 'x6': 0.134684} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:43] ax.service.ax_client: Completed trial 13 with data: {'hartmann6': (-1.583432, 0.1), 'l2norm': (0.962306, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:51] ax.service.ax_client: Generated new trial 14 with parameters {'x1': 0.444838, 'x2': 0.689076, 'x3': 0.587226, 'x4': 0.443133, 'x5': 0.0, 'x6': 0.045937} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:51] ax.service.ax_client: Completed trial 14 with data: {'hartmann6': (-1.715079, 0.1), 'l2norm': (1.141092, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:56] ax.service.ax_client: Generated new trial 15 with parameters {'x1': 0.224982, 'x2': 0.677578, 'x3': 0.57727, 'x4': 0.187769, 'x5': 0.0, 'x6': 0.057943} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:15:56] ax.service.ax_client: Completed trial 15 with data: {'hartmann6': (-0.311039, 0.1), 'l2norm': (0.991609, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:16:01] ax.service.ax_client: Generated new trial 16 with parameters {'x1': 0.469436, 'x2': 0.690199, 'x3': 0.430384, 'x4': 0.147157, 'x5': 0.0, 'x6': 0.0} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:16:01] ax.service.ax_client: Completed trial 16 with data: {'hartmann6': (-0.177642, 0.1), 'l2norm': (0.957635, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:16:07] ax.service.ax_client: Generated new trial 17 with parameters {'x1': 0.418802, 'x2': 0.709348, 'x3': 0.546385, 'x4': 0.503416, 'x5': 0.0, 'x6': 0.0} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:16:07] ax.service.ax_client: Completed trial 17 with data: {'hartmann6': (-2.267456, 0.1), 'l2norm': (1.228191, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:16:14] ax.service.ax_client: Generated new trial 18 with parameters {'x1': 0.437804, 'x2': 0.77066, 'x3': 0.455527, 'x4': 0.538772, 'x5': 0.0, 'x6': 0.002496} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:16:14] ax.service.ax_client: Completed trial 18 with data: {'hartmann6': (-2.613806, 0.1), 'l2norm': (1.344416, 0.1)}.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:16:29] ax.service.ax_client: Generated new trial 19 with parameters {'x1': 0.474746, 'x2': 0.783261, 'x3': 0.343669, 'x4': 0.530149, 'x5': 0.0, 'x6': 0.175634} using model BoTorch.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:16:29] ax.service.ax_client: Completed trial 19 with data: {'hartmann6': (-2.015509, 0.1), 'l2norm': (1.110386, 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": "d0d0b813",
"metadata": {
"originalKey": "72f4d3e7-fa04-43d0-8451-ded292e705df",
"papermill": {
"duration": 0.04214,
"end_time": "2024-11-13T05:16:29.542931",
"exception": false,
"start_time": "2024-11-13T05:16:29.500791",
"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": "41e63543",
"metadata": {
"code_folding": [],
"execution": {
"iopub.execute_input": "2024-11-13T05:16:29.629283Z",
"iopub.status.busy": "2024-11-13T05:16:29.628675Z",
"iopub.status.idle": "2024-11-13T05:16:30.299480Z",
"shell.execute_reply": "2024-11-13T05:16:30.298771Z"
},
"executionStartTime": 1627654870209,
"executionStopTime": 1627654871972,
"hidden_ranges": [],
"originalKey": "843df85c-965d-4a83-9fe1-696225d81c0f",
"papermill": {
"duration": 0.718785,
"end_time": "2024-11-13T05:16:30.303995",
"exception": false,
"start_time": "2024-11-13T05:16:29.585210",
"status": "completed"
},
"requestMsgId": "4a643541-867c-46b6-868d-64337920c2a3",
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 05:16:29] 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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"# this could alternately be done with `ax.plot.contour.plot_contour`\n",
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},
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"Arm 0_0
hartmann6: -0.289996 (SEM: 0.1)
x1: 0.32258
x2: 0.860162
x3: 0.941987
x4: 0.847485
x5: 0.589424
x6: 0.467366",
"Arm 10_0
hartmann6: -0.00222435 (SEM: 0.1)
x1: 0.906979
x2: 0.772305
x3: 0.088551
x4: 0.886166
x5: 0.912256
x6: 0.781084",
"Arm 11_0
hartmann6: -0.840343 (SEM: 0.1)
x1: 0.196336
x2: 0.40473
x3: 0.818117
x4: 0.272357
x5: 0.395998
x6: 0.397982",
"Arm 12_0
hartmann6: -0.272835 (SEM: 0.1)
x1: 0.440903
x2: 0.369174
x3: 0.388924
x4: 0.462979
x5: 0
x6: 0.0670093",
"Arm 13_0
hartmann6: -1.58343 (SEM: 0.1)
x1: 0.444001
x2: 0.636952
x3: 0.444504
x4: 0.450684
x5: 0.0135021
x6: 0.134684",
"Arm 14_0
hartmann6: -1.71508 (SEM: 0.1)
x1: 0.444838
x2: 0.689076
x3: 0.587226
x4: 0.443133
x5: 0
x6: 0.0459372",
"Arm 15_0
hartmann6: -0.311039 (SEM: 0.1)
x1: 0.224982
x2: 0.677578
x3: 0.57727
x4: 0.187769
x5: 0
x6: 0.0579427",
"Arm 16_0
hartmann6: -0.177642 (SEM: 0.1)
x1: 0.469436
x2: 0.690199
x3: 0.430384
x4: 0.147157
x5: 0
x6: 0",
"Arm 17_0
hartmann6: -2.26746 (SEM: 0.1)
x1: 0.418802
x2: 0.709348
x3: 0.546385
x4: 0.503416
x5: 0
x6: 0",
"Arm 18_0
hartmann6: -2.61381 (SEM: 0.1)
x1: 0.437804
x2: 0.77066
x3: 0.455527
x4: 0.538772
x5: 0
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hartmann6: -0.289996 (SEM: 0.1)
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