{ "cells": [ { "cell_type": "markdown", "id": "eb59c558", "metadata": { "papermill": { "duration": 0.003058, "end_time": "2024-05-07T05:10:39.733551", "exception": false, "start_time": "2024-05-07T05:10:39.730493", "status": "completed" }, "tags": [] }, "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, "id": "c1857dd2", "metadata": { "execution": { "iopub.execute_input": "2024-05-07T05:10:39.740364Z", "iopub.status.busy": "2024-05-07T05:10:39.739650Z", "iopub.status.idle": "2024-05-07T05:10:43.176182Z", "shell.execute_reply": "2024-05-07T05:10:43.175263Z" }, "papermill": { "duration": 3.481419, "end_time": "2024-05-07T05:10:43.217763", "exception": false, "start_time": "2024-05-07T05:10:39.736344", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] 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" ] }, { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from ax.metrics.branin import branin\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.utils.measurement.synthetic_functions import hartmann6\n", "from ax.utils.notebook.plotting import init_notebook_plotting, render\n", "\n", "init_notebook_plotting()" ] }, { "cell_type": "markdown", "id": "93e524ed", "metadata": { "papermill": { "duration": 0.036596, "end_time": "2024-05-07T05:10:43.289076", "exception": false, "start_time": "2024-05-07T05:10:43.252480", "status": "completed" }, "tags": [] }, "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, "id": "f5478126", "metadata": { "execution": { "iopub.execute_input": "2024-05-07T05:10:43.364325Z", "iopub.status.busy": "2024-05-07T05:10:43.363684Z", "iopub.status.idle": "2024-05-07T05:10:43.368245Z", "shell.execute_reply": "2024-05-07T05:10:43.367580Z" }, "papermill": { "duration": 0.043676, "end_time": "2024-05-07T05:10:43.369596", "exception": false, "start_time": "2024-05-07T05:10:43.325920", "status": "completed" }, "tags": [] }, "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", "id": "79ef92fd", "metadata": { "papermill": { "duration": 0.036582, "end_time": "2024-05-07T05:10:43.442821", "exception": false, "start_time": "2024-05-07T05:10:43.406239", "status": "completed" }, "tags": [] }, "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", "id": "8e866c52", "metadata": { "papermill": { "duration": 0.036805, "end_time": "2024-05-07T05:10:43.516278", "exception": false, "start_time": "2024-05-07T05:10:43.479473", "status": "completed" }, "tags": [] }, "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, "id": "33ff0b78", "metadata": { "execution": { "iopub.execute_input": "2024-05-07T05:10:43.591952Z", "iopub.status.busy": "2024-05-07T05:10:43.591309Z", "iopub.status.idle": "2024-05-07T05:12:55.860040Z", "shell.execute_reply": "2024-05-07T05:12:55.859263Z" }, "papermill": { "duration": 132.309124, "end_time": "2024-05-07T05:12:55.862304", "exception": false, "start_time": "2024-05-07T05:10:43.553180", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] 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 05-07 05:10:43] 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 05-07 05:10:43] 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 05-07 05:10:43] 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 05-07 05:10:43] 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 05-07 05:10:43] 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=[ParameterConstraint(1.0*x1 + 1.0*x2 <= 20.0)]).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] 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 05-07 05:10:43] 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 05-07 05:10:43] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] 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 05-07 05:10:43] 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" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] ax.service.managed_loop: Started full optimization with 30 steps.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 1...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 2...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 3...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 4...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 6...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 7...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 8...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 9...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 10...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 11...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 12...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:43] ax.service.managed_loop: Running optimization trial 13...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:10:51] ax.service.managed_loop: Running optimization trial 14...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:11:00] ax.service.managed_loop: Running optimization trial 15...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:11:12] ax.service.managed_loop: Running optimization trial 16...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:11:20] ax.service.managed_loop: Running optimization trial 17...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:11:27] ax.service.managed_loop: Running optimization trial 18...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:11:36] ax.service.managed_loop: Running optimization trial 19...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:11:44] ax.service.managed_loop: Running optimization trial 20...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:11:50] ax.service.managed_loop: Running optimization trial 21...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:11:56] ax.service.managed_loop: Running optimization trial 22...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:12:03] ax.service.managed_loop: Running optimization trial 23...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:12:09] ax.service.managed_loop: Running optimization trial 24...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:12:16] ax.service.managed_loop: Running optimization trial 25...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:12:22] ax.service.managed_loop: Running optimization trial 26...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:12:28] ax.service.managed_loop: Running optimization trial 27...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:12:35] ax.service.managed_loop: Running optimization trial 28...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:12:42] ax.service.managed_loop: Running optimization trial 29...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 05-07 05:12:48] 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", "id": "cc1c4c1e", "metadata": { "papermill": { "duration": 0.05322, "end_time": "2024-05-07T05:12:55.976591", "exception": false, "start_time": "2024-05-07T05:12:55.923371", "status": "completed" }, "tags": [] }, "source": [ "And we can introspect optimization results:" ] }, { "cell_type": "code", "execution_count": 4, "id": "f84884d0", "metadata": { "execution": { "iopub.execute_input": "2024-05-07T05:12:56.057239Z", "iopub.status.busy": "2024-05-07T05:12:56.056712Z", "iopub.status.idle": "2024-05-07T05:12:56.063495Z", "shell.execute_reply": "2024-05-07T05:12:56.062896Z" }, "papermill": { "duration": 0.049454, "end_time": "2024-05-07T05:12:56.064919", "exception": false, "start_time": "2024-05-07T05:12:56.015465", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "{'x1': 0.20171028229718294,\n", " 'x2': 0.15512007263899935,\n", " 'x3': 0.4569584217338681,\n", " 'x4': 0.2796334237557797,\n", " 'x5': 0.3059130225074349,\n", " 'x6': 0.6559523770668575}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_parameters" ] }, { "cell_type": "code", "execution_count": 5, "id": "23e23efe", "metadata": { "execution": { "iopub.execute_input": "2024-05-07T05:12:56.145776Z", "iopub.status.busy": "2024-05-07T05:12:56.145118Z", "iopub.status.idle": "2024-05-07T05:12:56.150486Z", "shell.execute_reply": "2024-05-07T05:12:56.149900Z" }, "papermill": { "duration": 0.047087, "end_time": "2024-05-07T05:12:56.151874", "exception": false, "start_time": "2024-05-07T05:12:56.104787", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "{'hartmann6': -3.3165222416355187, 'l2norm': 0.9357442004534721}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "means, covariances = values\n", "means" ] }, { "cell_type": "markdown", "id": "a3bfc93a", "metadata": { "papermill": { "duration": 0.03936, "end_time": "2024-05-07T05:12:56.230288", "exception": false, "start_time": "2024-05-07T05:12:56.190928", "status": "completed" }, "tags": [] }, "source": [ "For comparison, minimum of Hartmann6 is:" ] }, { "cell_type": "code", "execution_count": 6, "id": "20a818e8", "metadata": { "execution": { "iopub.execute_input": "2024-05-07T05:12:56.311976Z", "iopub.status.busy": "2024-05-07T05:12:56.311417Z", "iopub.status.idle": "2024-05-07T05:12:56.316098Z", "shell.execute_reply": "2024-05-07T05:12:56.315393Z" }, "papermill": { "duration": 0.04743, "end_time": "2024-05-07T05:12:56.317482", "exception": false, "start_time": "2024-05-07T05:12:56.270052", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "-3.32237" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hartmann6.fmin" ] }, { "cell_type": "markdown", "id": "8b7ebd84", "metadata": { "papermill": { "duration": 0.040225, "end_time": "2024-05-07T05:12:56.397658", "exception": false, "start_time": "2024-05-07T05:12:56.357433", "status": "completed" }, "tags": [] }, "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, "id": "ed1aa216", "metadata": { "execution": { "iopub.execute_input": "2024-05-07T05:12:56.479167Z", "iopub.status.busy": "2024-05-07T05:12:56.478657Z", "iopub.status.idle": "2024-05-07T05:12:57.328402Z", "shell.execute_reply": "2024-05-07T05:12:57.327618Z" }, "papermill": { "duration": 0.895793, "end_time": "2024-05-07T05:12:57.333573", "exception": false, "start_time": "2024-05-07T05:12:56.437780", "status": "completed" }, "tags": [] }, "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, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# `plot_single_method` expects a 2-d array of means, because it expects to average means from multiple\n", "# optimization runs, so we wrap out best objectives array in another array.\n", "best_objectives = np.array(\n", " [[trial.objective_mean for trial in experiment.trials.values()]]\n", ")\n", "best_objective_plot = optimization_trace_single_method(\n", " y=np.minimum.accumulate(best_objectives, axis=1),\n", " optimum=hartmann6.fmin,\n", " title=\"Model performance vs. # of iterations\",\n", " ylabel=\"Hartmann6\",\n", ")\n", "render(best_objective_plot)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.14" }, "papermill": { "default_parameters": {}, "duration": 142.292543, "end_time": "2024-05-07T05:13:01.098756", "environment_variables": {}, "exception": null, "input_path": "/tmp/tmp.7QtOT7RD5f/Ax-main/tutorials/gpei_hartmann_loop.ipynb", "output_path": "/tmp/tmp.7QtOT7RD5f/Ax-main/tutorials/gpei_hartmann_loop.ipynb", "parameters": {}, "start_time": "2024-05-07T05:10:38.806213", "version": "2.6.0" } }, "nbformat": 4, "nbformat_minor": 5 }