{ "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 11-04 18:31:29] 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 11-04 18:31:30] 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 11-04 18:31:30] 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 11-04 18:31:30] 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 11-04 18:31:30] 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 11-04 18:31:30] 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 11-04 18:31:30] 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 11-04 18:31:30] ax.service.managed_loop: Started full optimization with 30 steps.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:31:30] ax.service.managed_loop: Running optimization trial 1...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:31:30] ax.service.managed_loop: Running optimization trial 2...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:31:30] ax.service.managed_loop: Running optimization trial 3...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:31:30] ax.service.managed_loop: Running optimization trial 4...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:31:30] ax.service.managed_loop: Running optimization trial 5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:31:30] ax.service.managed_loop: Running optimization trial 6...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:31:30] ax.service.managed_loop: Running optimization trial 7...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/travis/build/facebook/Ax/ax/models/torch/utils.py:274: UserWarning:\n", "\n", "This overload of nonzero is deprecated:\n", "\tnonzero()\n", "Consider using one of the following signatures instead:\n", "\tnonzero(*, bool as_tuple) (Triggered internally at /pytorch/torch/csrc/utils/python_arg_parser.cpp:882.)\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:31:34] ax.service.managed_loop: Running optimization trial 8...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:31:38] ax.service.managed_loop: Running optimization trial 9...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:31:43] ax.service.managed_loop: Running optimization trial 10...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:31:47] ax.service.managed_loop: Running optimization trial 11...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:31:51] ax.service.managed_loop: Running optimization trial 12...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:31:54] ax.service.managed_loop: Running optimization trial 13...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:31:58] ax.service.managed_loop: Running optimization trial 14...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:32:01] ax.service.managed_loop: Running optimization trial 15...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:32:05] ax.service.managed_loop: Running optimization trial 16...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:32:09] ax.service.managed_loop: Running optimization trial 17...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:32:12] ax.service.managed_loop: Running optimization trial 18...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:32:15] ax.service.managed_loop: Running optimization trial 19...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:32:19] ax.service.managed_loop: Running optimization trial 20...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:32:21] ax.service.managed_loop: Running optimization trial 21...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:32:25] ax.service.managed_loop: Running optimization trial 22...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:32:28] ax.service.managed_loop: Running optimization trial 23...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:32:31] ax.service.managed_loop: Running optimization trial 24...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:32:35] ax.service.managed_loop: Running optimization trial 25...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:32:38] ax.service.managed_loop: Running optimization trial 26...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:32:40] ax.service.managed_loop: Running optimization trial 27...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:32:43] ax.service.managed_loop: Running optimization trial 28...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:32:46] ax.service.managed_loop: Running optimization trial 29...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-04 18:32:47] 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': 0.2111869947334017,\n", " 'x2': 0.12748418169666575,\n", " 'x3': 0.4592005157915138,\n", " 'x4': 0.26838440377634537,\n", " 'x5': 0.3245911165689549,\n", " 'x6': 0.664586136373199}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_parameters" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'l2norm': 0.9438129560665687, 'hartmann6': -3.3011967181996513}" ] }, "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. 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