{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Loop API Example on Hartmann6\n", "\n", "The loop API is the most lightweight way to do optimization in Ax. The user makes one call to `optimize`, which performs all of the optimization under the hood and returns the optimized parameters.\n", "\n", "For more customizability of the optimization procedure, consider the Service or Developer API." ] }, { "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 16:25:07] 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", "\n", "from ax.plot.contour import plot_contour\n", "from ax.plot.trace import optimization_trace_single_method\n", "from ax.service.managed_loop import optimize\n", "from ax.metrics.branin import branin\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. Define evaluation function\n", "\n", "First, we define an evaluation function that is able to compute all the metrics needed for this experiment. This function needs to accept a set of parameter values and can also accept a weight. It should produce a dictionary of metric names to tuples of mean and standard error for those metrics." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def hartmann_evaluation_function(parameterization):\n", " x = np.array([parameterization.get(f\"x{i+1}\") for i in range(6)])\n", " # In our case, standard error is 0, since we are computing a synthetic function.\n", " return {\"hartmann6\": (hartmann6(x), 0.0), \"l2norm\": (np.sqrt((x ** 2).sum()), 0.0)}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If there is only one metric in the experiment – the objective – then evaluation function can return a single tuple of mean and SEM, in which case Ax will assume that evaluation corresponds to the objective. It can also return only the mean as a float, in which case Ax will treat SEM as unknown and use a model that can infer it. For more details on evaluation function, refer to the \"Trial Evaluation\" section in the docs." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Run optimization\n", "The setup for the loop is fully compatible with JSON. The optimization algorithm is selected based on the properties of the problem search space." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:07] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x2. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:07] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x3. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:07] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x4. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:07] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x5. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:07] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x6. If that is not the expected value type, you can explicity specify 'value_type' ('int', 'float', 'bool' or 'str') in parameter dict.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:07] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+GPEI', steps=[Sobol for 6 trials, GPEI for subsequent trials]). Iterations after 6 will take longer to generate due to model-fitting.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:07] ax.service.managed_loop: Started full optimization with 30 steps.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:07] ax.service.managed_loop: Running optimization trial 1...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:07] ax.service.managed_loop: Running optimization trial 2...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:07] ax.service.managed_loop: Running optimization trial 3...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:07] ax.service.managed_loop: Running optimization trial 4...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:07] ax.service.managed_loop: Running optimization trial 5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:07] ax.service.managed_loop: Running optimization trial 6...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:08] ax.service.managed_loop: Running optimization trial 7...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:14] ax.service.managed_loop: Running optimization trial 8...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:18] ax.service.managed_loop: Running optimization trial 9...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:23] ax.service.managed_loop: Running optimization trial 10...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:27] ax.service.managed_loop: Running optimization trial 11...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:32] ax.service.managed_loop: Running optimization trial 12...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:35] ax.service.managed_loop: Running optimization trial 13...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:39] ax.service.managed_loop: Running optimization trial 14...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:42] ax.service.managed_loop: Running optimization trial 15...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:45] ax.service.managed_loop: Running optimization trial 16...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:48] ax.service.managed_loop: Running optimization trial 17...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:51] ax.service.managed_loop: Running optimization trial 18...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:56] ax.service.managed_loop: Running optimization trial 19...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:25:59] ax.service.managed_loop: Running optimization trial 20...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:26:01] ax.service.managed_loop: Running optimization trial 21...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:26:05] ax.service.managed_loop: Running optimization trial 22...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:26:07] ax.service.managed_loop: Running optimization trial 23...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:26:11] ax.service.managed_loop: Running optimization trial 24...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:26:14] ax.service.managed_loop: Running optimization trial 25...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:26:15] ax.service.managed_loop: Running optimization trial 26...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:26:17] ax.service.managed_loop: Running optimization trial 27...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:26:19] ax.service.managed_loop: Running optimization trial 28...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:26:21] ax.service.managed_loop: Running optimization trial 29...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 02-24 16:26:21] ax.service.managed_loop: Running optimization trial 30...\n" ] } ], "source": [ "best_parameters, values, experiment, model = optimize(\n", " parameters=[\n", " {\n", " \"name\": \"x1\",\n", " \"type\": \"range\",\n", " \"bounds\": [0.0, 1.0],\n", " \"value_type\": \"float\", # Optional, defaults to inference from type of \"bounds\".\n", " \"log_scale\": False, # Optional, defaults to False.\n", " },\n", " {\n", " \"name\": \"x2\",\n", " \"type\": \"range\",\n", " \"bounds\": [0.0, 1.0],\n", " },\n", " {\n", " \"name\": \"x3\",\n", " \"type\": \"range\",\n", " \"bounds\": [0.0, 1.0],\n", " },\n", " {\n", " \"name\": \"x4\",\n", " \"type\": \"range\",\n", " \"bounds\": [0.0, 1.0],\n", " },\n", " {\n", " \"name\": \"x5\",\n", " \"type\": \"range\",\n", " \"bounds\": [0.0, 1.0],\n", " },\n", " {\n", " \"name\": \"x6\",\n", " \"type\": \"range\",\n", " \"bounds\": [0.0, 1.0],\n", " },\n", " ],\n", " experiment_name=\"test\",\n", " objective_name=\"hartmann6\",\n", " evaluation_function=hartmann_evaluation_function,\n", " minimize=True, # Optional, defaults to False.\n", " parameter_constraints=[\"x1 + x2 <= 20\"], # Optional.\n", " outcome_constraints=[\"l2norm <= 1.25\"], # Optional.\n", " total_trials=30, # Optional.\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we can introspect optimization results:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'x1': 0.20109392908724574,\n", " 'x2': 0.14921405976742266,\n", " 'x3': 0.4779772800404153,\n", " 'x4': 0.2743511135683329,\n", " 'x5': 0.3127381213703998,\n", " 'x6': 0.6601532096925483}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_parameters" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'l2norm': 0.948703937628249, 'hartmann6': -3.3220387849132145}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "means, covariances = values\n", "means" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For comparison, minimum of Hartmann6 is:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-3.32237" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hartmann6.fmin" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Plot results\n", "Here we arbitrarily select \"x1\" and \"x2\" as the two parameters to plot for both metrics, \"hartmann6\" and \"l2norm\"." ] }, { "cell_type": "code", "execution_count": 7, "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": "contour", "x": [ 0.0, 0.02040816326530612, 0.04081632653061224, 0.061224489795918366, 0.08163265306122448, 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