{ "cells": [ { "cell_type": "markdown", "id": "9e00c61c", "metadata": { "papermill": { "duration": 0.002776, "end_time": "2024-09-23T20:26:50.322994", "exception": false, "start_time": "2024-09-23T20:26:50.320218", "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": "d3b2f09f", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T20:26:50.329100Z", "iopub.status.busy": "2024-09-23T20:26:50.328607Z", "iopub.status.idle": "2024-09-23T20:26:53.861036Z", "shell.execute_reply": "2024-09-23T20:26:53.860293Z" }, "papermill": { "duration": 3.558784, "end_time": "2024-09-23T20:26:53.884086", "exception": false, "start_time": "2024-09-23T20:26:50.325302", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:26:53] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:26:53] 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": "94c579aa", "metadata": { "papermill": { "duration": 0.04733, "end_time": "2024-09-23T20:26:53.972292", "exception": false, "start_time": "2024-09-23T20:26:53.924962", "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": "da0de0e8", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T20:26:54.060668Z", "iopub.status.busy": "2024-09-23T20:26:54.060397Z", "iopub.status.idle": "2024-09-23T20:26:54.064426Z", "shell.execute_reply": "2024-09-23T20:26:54.063914Z" }, "papermill": { "duration": 0.050298, "end_time": "2024-09-23T20:26:54.065790", "exception": false, "start_time": "2024-09-23T20:26:54.015492", "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": "0b34b2af", "metadata": { "papermill": { "duration": 0.043157, "end_time": "2024-09-23T20:26:54.152430", "exception": false, "start_time": "2024-09-23T20:26:54.109273", "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": "86e77bd9", "metadata": { "papermill": { "duration": 0.043746, "end_time": "2024-09-23T20:26:54.239698", "exception": false, "start_time": "2024-09-23T20:26:54.195952", "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": "6dfe28a8", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T20:26:54.329638Z", "iopub.status.busy": "2024-09-23T20:26:54.328992Z", "iopub.status.idle": "2024-09-23T20:29:10.860025Z", "shell.execute_reply": "2024-09-23T20:29:10.858631Z" }, "papermill": { "duration": 136.579554, "end_time": "2024-09-23T20:29:10.863411", "exception": false, "start_time": "2024-09-23T20:26:54.283857", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:26:54] 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 09-23 20:26:54] 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 09-23 20:26:54] 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 09-23 20:26:54] 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 09-23 20:26:54] 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 09-23 20:26:54] 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 09-23 20:26:54] 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 09-23 20:26:54] 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 09-23 20:26:54] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=12\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:26:54] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=12\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:26:54] 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 09-23 20:26:54] 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 09-23 20:26:54] ax.service.managed_loop: Started full optimization with 30 steps.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:26:54] ax.service.managed_loop: Running optimization trial 1...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/tmp.QqcA7fo0ui/Ax-main/ax/modelbridge/cross_validation.py:463: UserWarning:\n", "\n", "Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n", "\n", "[INFO 09-23 20:26:54] ax.service.managed_loop: Running optimization trial 2...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/tmp.QqcA7fo0ui/Ax-main/ax/modelbridge/cross_validation.py:463: UserWarning:\n", "\n", "Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n", "\n", "[INFO 09-23 20:26:54] ax.service.managed_loop: Running optimization trial 3...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/tmp.QqcA7fo0ui/Ax-main/ax/modelbridge/cross_validation.py:463: UserWarning:\n", "\n", "Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n", "\n", "[INFO 09-23 20:26:54] ax.service.managed_loop: Running optimization trial 4...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/tmp.QqcA7fo0ui/Ax-main/ax/modelbridge/cross_validation.py:463: UserWarning:\n", "\n", "Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n", "\n", "[INFO 09-23 20:26:54] ax.service.managed_loop: Running optimization trial 5...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/tmp.QqcA7fo0ui/Ax-main/ax/modelbridge/cross_validation.py:463: UserWarning:\n", "\n", "Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n", "\n", "[INFO 09-23 20:26:54] ax.service.managed_loop: Running optimization trial 6...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/tmp.QqcA7fo0ui/Ax-main/ax/modelbridge/cross_validation.py:463: UserWarning:\n", "\n", "Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n", "\n", "[INFO 09-23 20:26:54] ax.service.managed_loop: Running optimization trial 7...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/tmp.QqcA7fo0ui/Ax-main/ax/modelbridge/cross_validation.py:463: UserWarning:\n", "\n", "Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n", "\n", "[INFO 09-23 20:26:54] ax.service.managed_loop: Running optimization trial 8...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/tmp.QqcA7fo0ui/Ax-main/ax/modelbridge/cross_validation.py:463: UserWarning:\n", "\n", "Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n", "\n", "[INFO 09-23 20:26:54] ax.service.managed_loop: Running optimization trial 9...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/tmp.QqcA7fo0ui/Ax-main/ax/modelbridge/cross_validation.py:463: UserWarning:\n", "\n", "Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n", "\n", "[INFO 09-23 20:26:54] ax.service.managed_loop: Running optimization trial 10...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/tmp.QqcA7fo0ui/Ax-main/ax/modelbridge/cross_validation.py:463: UserWarning:\n", "\n", "Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n", "\n", "[INFO 09-23 20:26:54] ax.service.managed_loop: Running optimization trial 11...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/tmp.QqcA7fo0ui/Ax-main/ax/modelbridge/cross_validation.py:463: UserWarning:\n", "\n", "Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:26:54] ax.service.managed_loop: Running optimization trial 12...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/tmp.QqcA7fo0ui/Ax-main/ax/modelbridge/cross_validation.py:463: UserWarning:\n", "\n", "Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:26:54] ax.service.managed_loop: Running optimization trial 13...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:27:04] ax.service.managed_loop: Running optimization trial 14...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:27:12] ax.service.managed_loop: Running optimization trial 15...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:27:18] ax.service.managed_loop: Running optimization trial 16...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:27:24] ax.service.managed_loop: Running optimization trial 17...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:27:32] ax.service.managed_loop: Running optimization trial 18...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:27:40] ax.service.managed_loop: Running optimization trial 19...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:27:48] ax.service.managed_loop: Running optimization trial 20...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:27:56] ax.service.managed_loop: Running optimization trial 21...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:28:05] ax.service.managed_loop: Running optimization trial 22...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:28:11] ax.service.managed_loop: Running optimization trial 23...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:28:18] ax.service.managed_loop: Running optimization trial 24...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:28:25] ax.service.managed_loop: Running optimization trial 25...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:28:32] ax.service.managed_loop: Running optimization trial 26...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:28:40] ax.service.managed_loop: Running optimization trial 27...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:28:45] ax.service.managed_loop: Running optimization trial 28...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:28:54] ax.service.managed_loop: Running optimization trial 29...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 09-23 20:29:01] 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": "f23e76fd", "metadata": { "papermill": { "duration": 0.057643, "end_time": "2024-09-23T20:29:10.994893", "exception": false, "start_time": "2024-09-23T20:29:10.937250", "status": "completed" }, "tags": [] }, "source": [ "And we can introspect optimization results:" ] }, { "cell_type": "code", "execution_count": 4, "id": "c452b80b", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T20:29:11.089345Z", "iopub.status.busy": "2024-09-23T20:29:11.088835Z", "iopub.status.idle": "2024-09-23T20:29:11.095394Z", "shell.execute_reply": "2024-09-23T20:29:11.094716Z" }, "papermill": { "duration": 0.05531, "end_time": "2024-09-23T20:29:11.096722", "exception": false, "start_time": "2024-09-23T20:29:11.041412", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "{'x1': 0.23705465638804576,\n", " 'x2': 0.0,\n", " 'x3': 0.40656427611782464,\n", " 'x4': 0.2697953479080321,\n", " 'x5': 0.30915548621736666,\n", " 'x6': 0.6897464923126132}" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_parameters" ] }, { "cell_type": "code", "execution_count": 5, "id": "b3a31837", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T20:29:11.191225Z", "iopub.status.busy": "2024-09-23T20:29:11.190619Z", "iopub.status.idle": "2024-09-23T20:29:11.195320Z", "shell.execute_reply": "2024-09-23T20:29:11.194733Z" }, "papermill": { "duration": 0.053302, "end_time": "2024-09-23T20:29:11.196645", "exception": false, "start_time": "2024-09-23T20:29:11.143343", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "{'l2norm': 0.930325614554449, 'hartmann6': -3.008878560482145}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "means, covariances = values\n", "means" ] }, { "cell_type": "markdown", "id": "ca211973", "metadata": { "papermill": { "duration": 0.047341, "end_time": "2024-09-23T20:29:11.290737", "exception": false, "start_time": "2024-09-23T20:29:11.243396", "status": "completed" }, "tags": [] }, "source": [ "For comparison, minimum of Hartmann6 is:" ] }, { "cell_type": "code", "execution_count": 6, "id": "0644b412", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T20:29:11.386762Z", "iopub.status.busy": "2024-09-23T20:29:11.386407Z", "iopub.status.idle": "2024-09-23T20:29:11.390978Z", "shell.execute_reply": "2024-09-23T20:29:11.390385Z" }, "papermill": { "duration": 0.054108, "end_time": "2024-09-23T20:29:11.392392", "exception": false, "start_time": "2024-09-23T20:29:11.338284", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "-3.32237" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hartmann6.fmin" ] }, { "cell_type": "markdown", "id": "54f20d39", "metadata": { "papermill": { "duration": 0.046487, "end_time": "2024-09-23T20:29:11.484500", "exception": false, "start_time": "2024-09-23T20:29:11.438013", "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": "9e852d70", "metadata": { "execution": { "iopub.execute_input": "2024-09-23T20:29:11.578812Z", "iopub.status.busy": "2024-09-23T20:29:11.578281Z", "iopub.status.idle": "2024-09-23T20:29:12.110469Z", "shell.execute_reply": "2024-09-23T20:29:12.109766Z" }, "papermill": { "duration": 0.583952, "end_time": "2024-09-23T20:29:12.114663", "exception": false, "start_time": "2024-09-23T20:29:11.530711", "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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