{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualizations\n", "\n", "This tutorial illustrates the core visualization utilities available in Ax." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:51:21] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.\n" ] }, { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "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." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "autocolorscale": false, "autocontour": true, "colorbar": { "tickfont": { "size": 8 }, "ticksuffix": "", "x": 0.45, "y": 0.5 }, "colorscale": [ [ 0.0, "rgb(247,252,253)" ], [ 0.125, "rgb(229,245,249)" ], [ 0.25, "rgb(204,236,230)" ], [ 0.375, "rgb(153,216,201)" ], [ 0.5, "rgb(102,194,164)" ], [ 0.625, "rgb(65,174,118)" ], [ 0.75, "rgb(35,139,69)" ], [ 0.875, "rgb(0,109,44)" ], [ 1.0, "rgb(0,68,27)" ] ], "contours": { "coloring": "heatmap" }, "hoverinfo": "x+y+z", "ncontours": 25, "type": 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