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"source": [
"# Visualizations\n",
"\n",
"This tutorial illustrates the core visualization utilities available in Ax."
]
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"[INFO 06-10 19:22:03] ipy_plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.\n"
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"source": [
"import numpy as np\n",
"from ax import (\n",
" Arm,\n",
" ComparisonOp,\n",
" RangeParameter,\n",
" ParameterType,\n",
" SearchSpace, \n",
" SimpleExperiment, \n",
" OutcomeConstraint, \n",
")\n",
"\n",
"from ax.metrics.l2norm import L2NormMetric\n",
"from ax.modelbridge.cross_validation import cross_validate\n",
"from ax.modelbridge.registry import Models\n",
"from ax.plot.contour import interact_contour, plot_contour\n",
"from ax.plot.diagnostic import interact_cross_validation\n",
"from ax.plot.scatter import(\n",
" interact_fitted,\n",
" plot_objective_vs_constraints,\n",
" tile_fitted,\n",
")\n",
"from ax.plot.slice import plot_slice\n",
"from ax.utils.measurement.synthetic_functions import hartmann6\n",
"from ax.utils.notebook.plotting import render, init_notebook_plotting\n",
"\n",
"init_notebook_plotting()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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 Developer 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",
"metadata": {},
"source": [
"#### 1a. Define search space and evaluation function"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"noise_sd = 0.1\n",
"param_names = [f\"x{i+1}\" for i in range(6)] # x1, x2, ..., x6\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",
" }\n",
"\n",
"hartmann_search_space = SearchSpace(\n",
" parameters=[\n",
" RangeParameter(\n",
" name=p_name, parameter_type=ParameterType.FLOAT, lower=0.0, upper=1.0\n",
" )\n",
" for p_name in param_names\n",
" ]\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1b. Create Experiment"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"exp = SimpleExperiment(\n",
" name=\"test_branin\",\n",
" search_space=hartmann_search_space,\n",
" evaluation_function=noisy_hartmann_evaluation_function,\n",
" objective_name=\"hartmann6\",\n",
" minimize=True,\n",
" outcome_constraints=[\n",
" OutcomeConstraint(\n",
" metric=L2NormMetric(\n",
" name=\"l2norm\", param_names=param_names, noise_sd=0.2\n",
" ),\n",
" op=ComparisonOp.LEQ,\n",
" bound=1.25,\n",
" relative=False,\n",
" )\n",
" ],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1c. Run the optimization and fit a GP on all data\n",
"\n",
"After doing (`N_BATCHES=15`) rounds of optimization, fit final GP using all data to feed into the plots."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"N_RANDOM = 5\n",
"BATCH_SIZE = 1\n",
"N_BATCHES = 15\n",
"\n",
"sobol = Models.SOBOL(exp.search_space)\n",
"exp.new_batch_trial(generator_run=sobol.gen(N_RANDOM))\n",
"\n",
"for i in range(N_BATCHES):\n",
" intermediate_gp = Models.GPEI(experiment=exp, data=exp.eval())\n",
" exp.new_trial(generator_run=intermediate_gp.gen(BATCH_SIZE))\n",
"\n",
"model = Models.GPEI(experiment=exp, data=exp.eval())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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."
]
},
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"cell_type": "code",
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