{ "cells": [ { "cell_type": "markdown", "metadata": { "code_folding": [], "customInput": null, "hidden_ranges": [], "originalKey": "95e7a97a-bf78-48d4-a0c1-c0e8dfc4fed9", "showInput": true }, "source": [ "# Multi-Objective Optimization Ax API\n", "### Using the Service API\n", "For Multi-objective optimization (MOO) in the `AxClient`, objectives are specified through the `ObjectiveProperties` dataclass. An `ObjectiveProperties` requires a boolean `minimize`, and also accepts an optional floating point `threshold`. If a `threshold` is not specified, Ax will infer it through the use of heuristics. If the user knows the region of interest (because they have specs or prior knowledge), then specifying the thresholds is preferable to inferring it. But if the user would need to guess, inferring is preferable.\n", "\n", "\n", "To learn more about how to choose a threshold, see [Set Objective Thresholds to focus candidate generation in a region of interest](#Set-Objective-Thresholds-to-focus-candidate-generation-in-a-region-of-interest). See the [Service API Tutorial](/tutorials/gpei_hartmann_service.html) for more infomation on running experiments with the Service API." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "code_folding": [], "customInput": null, "execution": { "iopub.execute_input": "2022-11-10T21:07:21.908495Z", "iopub.status.busy": "2022-11-10T21:07:21.907994Z", "iopub.status.idle": "2022-11-10T21:07:24.718985Z", "shell.execute_reply": "2022-11-10T21:07:24.718194Z" }, "hidden_ranges": [], "originalKey": "06bf2029-0ea4-40b4-aced-956f1411cb6e", "showInput": true }, "outputs": [ { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:24] 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": [ "from ax.service.ax_client import AxClient\n", "from ax.service.utils.instantiation import ObjectiveProperties\n", "\n", "import torch\n", "\n", "# Plotting imports and initialization\n", "from ax.utils.notebook.plotting import render, init_notebook_plotting\n", "from ax.plot.pareto_utils import compute_posterior_pareto_frontier\n", "from ax.plot.pareto_frontier import plot_pareto_frontier\n", "init_notebook_plotting()\n", "\n", "# Load our sample 2-objective problem\n", "from botorch.test_functions.multi_objective import BraninCurrin\n", "branin_currin = BraninCurrin(negate=True).to(\n", " dtype=torch.double, \n", " device= torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"),\n", ")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "code_folding": [], "customInput": null, "execution": { "iopub.execute_input": "2022-11-10T21:07:24.792399Z", "iopub.status.busy": "2022-11-10T21:07:24.791051Z", "iopub.status.idle": "2022-11-10T21:07:24.805912Z", "shell.execute_reply": "2022-11-10T21:07:24.805068Z" }, "executionStartTime": 1628191188673, "executionStopTime": 1628191188746, "hidden_ranges": [], "originalKey": "c687973d-1b09-4a8f-9108-1f74adf64d4d", "requestMsgId": "ea523260-8896-48e4-a62f-3530d268b209", "showInput": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:24] ax.service.ax_client: Starting optimization with verbose logging. To disable logging, set the `verbose_logging` argument to `False`. Note that float values in the logs are rounded to 6 decimal points.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:24] ax.service.utils.instantiation: Inferred value type of ParameterType.FLOAT for parameter x1. 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 11-10 21:07:24] 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 11-10 21:07:24] 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])], parameter_constraints=[]).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:24] ax.core.experiment: The is_test flag has been set to True. This flag is meant purely for development and integration testing purposes. If you are running a live experiment, please set this flag to False\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:24] ax.modelbridge.dispatch_utils: Using Bayesian optimization since there are more ordered parameters than there are categories for the unordered categorical parameters.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:24] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+MOO', steps=[Sobol for 5 trials, MOO for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.\n" ] } ], "source": [ "ax_client = AxClient()\n", "ax_client.create_experiment(\n", " name=\"moo_experiment\",\n", " parameters=[\n", " {\n", " \"name\": f\"x{i+1}\",\n", " \"type\": \"range\",\n", " \"bounds\": [0.0, 1.0],\n", " }\n", " for i in range(2)\n", " ],\n", " objectives={\n", " # `threshold` arguments are optional\n", " \"a\": ObjectiveProperties(minimize=False, threshold=branin_currin.ref_point[0]), \n", " \"b\": ObjectiveProperties(minimize=False, threshold=branin_currin.ref_point[1])\n", " },\n", " overwrite_existing_experiment=True,\n", " is_test=True,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "customInput": null, "hidden_ranges": [], "originalKey": "70fd45e1-a2ce-4034-bb44-086507833472", "showInput": true }, "source": [ "### Create an Evaluation Function\n", "In the case of MOO experiments, evaluation functions can be any arbitrary function that takes in a `dict` of parameter names mapped to values and returns a `dict` of objective names mapped to a `tuple` of mean and SEM values." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "code_folding": [], "customInput": null, "execution": { "iopub.execute_input": "2022-11-10T21:07:24.809521Z", "iopub.status.busy": "2022-11-10T21:07:24.808993Z", "iopub.status.idle": "2022-11-10T21:07:24.813722Z", "shell.execute_reply": "2022-11-10T21:07:24.812993Z" }, "executionStartTime": 1628191201840, "executionStopTime": 1628191201871, "hidden_ranges": [], "originalKey": "a0e4fa8d-ebc7-4dc6-b370-ed4a83e3208f", "requestMsgId": "9cfd336d-c317-4d1c-a028-42d45903bac6", "showInput": true }, "outputs": [], "source": [ "def evaluate(parameters):\n", " evaluation = branin_currin(torch.tensor([parameters.get(\"x1\"), parameters.get(\"x2\")]))\n", " # In our case, standard error is 0, since we are computing a synthetic function.\n", " # Set standard error to None if the noise level is unknown.\n", " return {\"a\": (evaluation[0].item(), 0.0), \"b\": (evaluation[1].item(), 0.0)}" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "customInput": null, "hidden_ranges": [], "originalKey": "4200cd7c-8e13-4cbf-b0c1-72b52d900aaf", "showInput": true }, "source": [ "### Run Optimization" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "customInput": null, "execution": { "iopub.execute_input": "2022-11-10T21:07:24.817407Z", "iopub.status.busy": "2022-11-10T21:07:24.816888Z", "iopub.status.idle": "2022-11-10T21:07:59.064977Z", "shell.execute_reply": "2022-11-10T21:07:59.064142Z" }, "executionStartTime": 1628191208271, "executionStopTime": 1628191238749, "originalKey": "f91b1a1e-c78a-4262-a211-a13115c007c1", "requestMsgId": "842a1cf8-97a3-43d6-83a3-f258ea96ae20", "showInput": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/core/observation.py:274: FutureWarning:\n", "\n", "In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n", "\n", "[INFO 11-10 21:07:24] ax.service.ax_client: Generated new trial 0 with parameters {'x1': 0.904733, 'x2': 0.900744}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:24] ax.service.ax_client: Completed trial 0 with data: {'a': (-139.696442, 0.0), 'b': (-4.378954, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:24] ax.service.ax_client: Generated new trial 1 with parameters {'x1': 0.971915, 'x2': 0.130264}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:24] ax.service.ax_client: Completed trial 1 with data: {'a': (-0.939132, 0.0), 'b': (-9.983678, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:24] ax.service.ax_client: Generated new trial 2 with parameters {'x1': 0.475125, 'x2': 0.336992}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:24] ax.service.ax_client: Completed trial 2 with data: {'a': (-8.374448, 0.0), 'b': (-9.19126, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:24] ax.service.ax_client: Generated new trial 3 with parameters {'x1': 0.981195, 'x2': 0.55417}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:24] ax.service.ax_client: Completed trial 3 with data: {'a': (-31.934563, 0.0), 'b': (-6.059327, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:24] ax.service.ax_client: Generated new trial 4 with parameters {'x1': 0.489548, 'x2': 0.957582}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:24] ax.service.ax_client: Completed trial 4 with data: {'a': (-132.888977, 0.0), 'b': (-4.79389, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:25] ax.service.ax_client: Generated new trial 5 with parameters {'x1': 0.0, 'x2': 0.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:25] ax.service.ax_client: Completed trial 5 with data: {'a': (-308.129059, 0.0), 'b': (-3.0, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:26] ax.service.ax_client: Generated new trial 6 with parameters {'x1': 0.0, 'x2': 1.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:26] ax.service.ax_client: Completed trial 6 with data: {'a': (-17.508297, 0.0), 'b': (-1.180408, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:27] ax.service.ax_client: Generated new trial 7 with parameters {'x1': 0.0, 'x2': 0.810116}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:27] ax.service.ax_client: Completed trial 7 with data: {'a': (-38.081207, 0.0), 'b': (-1.381634, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:28] ax.service.ax_client: Generated new trial 8 with parameters {'x1': 0.117739, 'x2': 1.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:28] ax.service.ax_client: Completed trial 8 with data: {'a': (-6.698983, 0.0), 'b': (-4.801826, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:30] ax.service.ax_client: Generated new trial 9 with parameters {'x1': 0.053286, 'x2': 0.956613}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:30] ax.service.ax_client: Completed trial 9 with data: {'a': (-5.677922, 0.0), 'b': (-3.332395, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:31] ax.service.ax_client: Generated new trial 10 with parameters {'x1': 0.734744, 'x2': 0.379224}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:31] ax.service.ax_client: Completed trial 10 with data: {'a': (-40.322601, 0.0), 'b': (-7.786732, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:32] ax.service.ax_client: Generated new trial 11 with parameters {'x1': 0.024022, 'x2': 0.9742}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:32] ax.service.ax_client: Completed trial 11 with data: {'a': (-11.711469, 0.0), 'b': (-2.193388, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:34] ax.service.ax_client: Generated new trial 12 with parameters {'x1': 0.090306, 'x2': 0.921506}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:34] ax.service.ax_client: Completed trial 12 with data: {'a': (-1.683393, 0.0), 'b': (-4.546085, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:35] ax.service.ax_client: Generated new trial 13 with parameters {'x1': 0.0, 'x2': 0.424165}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:35] ax.service.ax_client: Completed trial 13 with data: {'a': (-129.901917, 0.0), 'b': (-2.077045, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:38] ax.service.ax_client: Generated new trial 14 with parameters {'x1': 0.038255, 'x2': 0.985463}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:38] ax.service.ax_client: Completed trial 14 with data: {'a': (-7.918545, 0.0), 'b': (-2.722753, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:39] ax.service.ax_client: Generated new trial 15 with parameters {'x1': 0.068927, 'x2': 0.971073}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:39] ax.service.ax_client: Completed trial 15 with data: {'a': (-3.530133, 0.0), 'b': (-3.796133, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:42] ax.service.ax_client: Generated new trial 16 with parameters {'x1': 0.012105, 'x2': 1.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:42] ax.service.ax_client: Completed trial 16 with data: {'a': (-13.798748, 0.0), 'b': (-1.673965, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:44] ax.service.ax_client: Generated new trial 17 with parameters {'x1': 0.028978, 'x2': 1.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:44] ax.service.ax_client: Completed trial 17 with data: {'a': (-9.511669, 0.0), 'b': (-2.342496, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:47] ax.service.ax_client: Generated new trial 18 with parameters {'x1': 0.111208, 'x2': 0.878688}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:47] ax.service.ax_client: Completed trial 18 with data: {'a': (-0.767756, 0.0), 'b': (-5.176579, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:48] ax.service.ax_client: Generated new trial 19 with parameters {'x1': 0.21908, 'x2': 0.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:48] ax.service.ax_client: Completed trial 19 with data: {'a': (-91.569672, 0.0), 'b': (-13.798164, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:50] ax.service.ax_client: Generated new trial 20 with parameters {'x1': 0.045953, 'x2': 1.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:50] ax.service.ax_client: Completed trial 20 with data: {'a': (-6.314091, 0.0), 'b': (-2.969521, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:52] ax.service.ax_client: Generated new trial 21 with parameters {'x1': 0.05963, 'x2': 0.993683}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:52] ax.service.ax_client: Completed trial 21 with data: {'a': (-4.561624, 0.0), 'b': (-3.444644, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:54] ax.service.ax_client: Generated new trial 22 with parameters {'x1': 0.005949, 'x2': 1.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:54] ax.service.ax_client: Completed trial 22 with data: {'a': (-15.621313, 0.0), 'b': (-1.423648, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:56] ax.service.ax_client: Generated new trial 23 with parameters {'x1': 0.07902, 'x2': 0.947881}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:56] ax.service.ax_client: Completed trial 23 with data: {'a': (-2.56357, 0.0), 'b': (-4.159083, 0.0)}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:59] ax.service.ax_client: Generated new trial 24 with parameters {'x1': 0.018447, 'x2': 1.0}.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[INFO 11-10 21:07:59] ax.service.ax_client: Completed trial 24 with data: {'a': (-12.062981, 0.0), 'b': (-1.929054, 0.0)}.\n" ] } ], "source": [ "for i in range(25):\n", " parameters, trial_index = ax_client.get_next_trial()\n", " # Local evaluation here can be replaced with deployment to external system.\n", " ax_client.complete_trial(trial_index=trial_index, raw_data=evaluate(parameters))" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "customInput": null, "hidden_ranges": [], "originalKey": "e0a6feb4-8c38-42e4-9d7c-62b79307e043", "showInput": false }, "source": [ "### Plot Pareto Frontier" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "code_folding": [], "customInput": null, "execution": { "iopub.execute_input": "2022-11-10T21:07:59.069226Z", "iopub.status.busy": "2022-11-10T21:07:59.068399Z", "iopub.status.idle": "2022-11-10T21:08:13.494395Z", "shell.execute_reply": "2022-11-10T21:08:13.493470Z" }, "executionStartTime": 1628191262231, "executionStopTime": 1628191270720, "hidden_ranges": [], "originalKey": "c2c2b222-6b68-4f1a-839f-16b50019ada4", "requestMsgId": "563d345b-573c-4d93-a480-5db88a283250", "showInput": true }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "error_x": { "array": [ 0.050974216532408155, 0.050974216532408155, 0.03503435309646542, 0.05097421662040848, 0.049717646115883724, 0.03646544881070412, 0.05097421659107504, 0.050974216532408155, 0.05097421667907536, 0.05097421662040848, 0.050974216532408155, 0.05097421659107504, 0.050974216532408155, 0.050974216649741916, 0.050974216532408155, 0.04161584268279902, 0.05097421659107504, 0.050974216532408155, 0.05097421667907536, 0.0509742165617416 ], "color": "rgba(128,177,211,0.4)", "thickness": 2, "type": "data" }, "error_y": { "array": [ 0.0022798128840634878, 0.002279812891413774, 0.0015840897723668652, 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a: -17.506 [-17.557, -17.455]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 2.201686790780075e-15
x2: 1.0", "Parameterization 1
a: -17.506 [-17.557, -17.455]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 0.0
x2: 1.0", "Parameterization 2
a: -12.329 [-12.364, -12.294]
b: -1.889 [-1.891, -1.888]

Parameterization:
x1: 0.017448307142270542
x2: 1.0", "Parameterization 3
a: -17.506 [-17.557, -17.455]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 9.278955900190044e-18
x2: 1.0", "Parameterization 4
a: -5.278 [-5.327, -5.228]
b: -3.228 [-3.230, -3.226]

Parameterization:
x1: 0.05351015825595366
x2: 1.0", "Parameterization 5
a: -11.017 [-11.054, -10.981]
b: -2.092 [-2.094, -2.091]

Parameterization:
x1: 0.022562713646356407
x2: 1.0", "Parameterization 6
a: -17.506 [-17.557, -17.455]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 6.2938309131930216e-15
x2: 0.9999999999999996", "Parameterization 7
a: -17.506 [-17.557, -17.455]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 0.0
x2: 1.0", "Parameterization 8
a: -17.506 [-17.557, -17.455]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 6.371636918164192e-16
x2: 0.9999999999999996", "Parameterization 9
a: -17.506 [-17.557, -17.455]
b: -1.18 [-1.182, -1.178]

Parameterization:
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "objectives = ax_client.experiment.optimization_config.objective.objectives\n", "frontier = compute_posterior_pareto_frontier(\n", " experiment=ax_client.experiment,\n", " data=ax_client.experiment.fetch_data(),\n", " primary_objective=objectives[1].metric,\n", " secondary_objective=objectives[0].metric,\n", " absolute_metrics=[\"a\", \"b\"],\n", " num_points=20,\n", ")\n", "render(plot_pareto_frontier(frontier, CI_level=0.90)) " ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "f4f6ce29-4a0c-4ac5-84a7-f83a4de9112c", "showInput": true }, "source": [ "# Deep Dive\n", "\n", "In the rest of this tutorial, we will show two algorithms available in Ax for multi-objective optimization\n", "and visualize how they compare to eachother and to quasirandom search.\n", "\n", "MOO covers the case where we care about multiple\n", "outcomes in our experiment but we do not know before hand a specific weighting of those\n", "objectives (covered by `ScalarizedObjective`) or a specific constraint on one objective \n", "(covered by `OutcomeConstraint`s) that will produce the best result.\n", "\n", "The solution in this case is to find a whole Pareto frontier, a surface in outcome-space\n", "containing points that can't be improved on in every outcome. This shows us the\n", "tradeoffs between objectives that we can choose to make." ] }, { "cell_type": "markdown", "metadata": { "originalKey": "e04a24fa-dcfc-4430-960f-9c0e772fd754", "showInput": true }, "source": [ "### Problem Statement\n", "\n", "Optimize a list of M objective functions $ \\bigl(f^{(1)}( x),..., f^{(M)}( x) \\bigr)$ over a bounded search space $\\mathcal X \\subset \\mathbb R^d$.\n", "\n", "We assume $f^{(i)}$ are expensive-to-evaluate black-box functions with no known analytical expression, and no observed gradients. For instance, a machine learning model where we're interested in maximizing accuracy and minimizing inference time, with $\\mathcal X$ the set of possible configuration spaces" ] }, { "attachments": { "pareto_front%20%281%29.png": { "image/png": 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} }, "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "1842c5bf-4113-406b-b2c7-bc2535e9dd6c", "showInput": false }, "source": [ "### Pareto Optimality\n", "\n", "In a multi-objective optimization problem, there typically is no single best solution. Rather, the *goal* is to identify the set of Pareto optimal solutions such that any improvement in one objective means deteriorating another. Provided with the Pareto set, decision-makers can select an objective trade-off according to their preferences. In the plot below, the red dots are the Pareto optimal solutions (assuming both objectives are to be minimized).\n", "![pareto front](attachment:pareto_front%20%281%29.png)" ] }, { "attachments": { "hv_figure%20%281%29.png": { "image/png": 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} }, "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "cefa89be-ef41-40d9-9458-d6faed3c6c91", "showInput": false }, "source": [ "### Evaluating the Quality of a Pareto Front (Hypervolume)\n", "\n", "Given a reference point $ r \\in \\mathbb R^M$, which we represent as a list of M `ObjectiveThreshold`s, one for each coordinate, the hypervolume (HV) of a Pareto set $\\mathcal P = \\{ f(x_i)\\}_{i=1}^{|\\mathcal P|}$ is the volume of the space dominated (superior in every one of our M objectives) by $\\mathcal P$ and bounded from above by a point $ r$. The reference point should be set to be slightly worse (10% is reasonable) than the worst value of each objective that a decision maker would tolerate. In the figure below, the grey area is the hypervolume in this 2-objective problem.\n", "![hv_figure](attachment:hv_figure%20%281%29.png)" ] }, { "attachments": { "objective_thresholds_comparison.png": { "image/png": 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" } }, "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "1819970e-9b48-4b57-b280-35bf2c4919d2", "showInput": false }, "source": [ "### Set Objective Thresholds to focus candidate generation in a region of interest\n", "\n", "The below plots show three different sets of points generated by the qNEHVI [1] algorithm with different objective thresholds (aka reference points). Note that here we use absolute thresholds, but thresholds can also be relative to a status_quo arm.\n", "\n", "The first plot shows the points without the `ObjectiveThreshold`s visible (they're set far below the origin of the graph).\n", "\n", "The second shows the points generated with (-18, -6) as thresholds. The regions violating the thresholds are greyed out. Only the white region in the upper right exceeds both threshold, points in this region dominate the intersection of these thresholds (this intersection is the reference point). Only points in this region contribute to the hypervolume objective. A few exploration points are not in the valid region, but almost all the rest of the points are.\n", "\n", "The third shows points generated with a very strict pair of thresholds, (-18, -2). Only the white region in the upper right exceeds both thresholds. Many points do not lie in the dominating region, but there are still more focused there than in the second examples.\n", "![objective_thresholds_comparison.png](attachment:objective_thresholds_comparison.png)" ] }, { "cell_type": "markdown", "metadata": { "originalKey": "f2f39a8f-279f-49a1-b645-d51caed24d9c" }, "source": [ "### Further Information\n", "A deeper explanation of our the qNEHVI [1] and qNParEGO [2] algorithms this notebook explores can be found at \n", "\n", "[1] [S. Daulton, M. Balandat, and E. Bakshy. Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement. Advances in Neural Information Processing Systems 34, 2021.](https://arxiv.org/abs/2105.08195)\n", "\n", "[2] [S. Daulton, M. Balandat, and E. Bakshy. Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. Advances in Neural Information Processing Systems 33, 2020.](https://arxiv.org/abs/2006.05078)\n", "\n", "In addition, the underlying BoTorch implementation has a researcher-oriented tutorial at https://botorch.org/tutorials/multi_objective_bo." ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "0ac396dd-8040-4f87-8abe-472127734aef" }, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "code_folding": [], "execution": { "iopub.execute_input": "2022-11-10T21:08:13.499098Z", "iopub.status.busy": "2022-11-10T21:08:13.498321Z", "iopub.status.idle": "2022-11-10T21:08:13.504080Z", "shell.execute_reply": "2022-11-10T21:08:13.503371Z" }, "executionStartTime": 1628191302514, "executionStopTime": 1628191302546, "hidden_ranges": [], "originalKey": "500597fc-a996-48f4-a8fe-defd429162b8", "requestMsgId": "07dd11c9-cd20-4bfa-b2d9-9a7bf70b2e44" }, "outputs": [], "source": [ "import pandas as pd\n", "from ax import *\n", "\n", "import numpy as np\n", "\n", "from ax.core.metric import Metric\n", "from ax.metrics.noisy_function import NoisyFunctionMetric\n", "from ax.service.utils.report_utils import exp_to_df\n", "from ax.runners.synthetic import SyntheticRunner\n", "\n", "# Factory methods for creating multi-objective optimization modesl.\n", "from ax.modelbridge.factory import get_MOO_EHVI, get_MOO_PAREGO\n", "\n", "# Analysis utilities, including a method to evaluate hypervolumes\n", "from ax.modelbridge.modelbridge_utils import observed_hypervolume" ] }, { "cell_type": "markdown", "metadata": { "originalKey": "0b43c263-41da-4aa8-99f3-4a2a7fc49e4b" }, "source": [ "## Define experiment configurations" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "963a036d-a250-4e3c-9570-afe6f2192f9a" }, "source": [ "### Search Space" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "code_folding": [], "execution": { "iopub.execute_input": "2022-11-10T21:08:13.507730Z", "iopub.status.busy": "2022-11-10T21:08:13.507209Z", "iopub.status.idle": "2022-11-10T21:08:13.511980Z", "shell.execute_reply": "2022-11-10T21:08:13.511205Z" }, "executionStartTime": 1628191313915, "executionStopTime": 1628191313944, "hidden_ranges": [], "originalKey": "90637eb4-730f-4f3d-8712-875bf88d6c2d", "requestMsgId": "fbb9db8e-5414-4add-ad10-0bd00583ebf5" }, "outputs": [], "source": [ "x1 = RangeParameter(name=\"x1\", lower=0, upper=1, parameter_type=ParameterType.FLOAT)\n", "x2 = RangeParameter(name=\"x2\", lower=0, upper=1, parameter_type=ParameterType.FLOAT)\n", "\n", "search_space = SearchSpace(\n", " parameters=[x1, x2],\n", ")" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "ac3cf1fe-d39d-48bb-a31d-e3ee0d70418b", "showInput": false }, "source": [ "### MultiObjectiveOptimizationConfig\n", "\n", "To optimize multiple objective we must create a `MultiObjective` containing the metrics we'll optimize and `MultiObjectiveOptimizationConfig` (which contains `ObjectiveThreshold`s) instead of our more typical `Objective` and `OptimizationConfig`\n", "\n", "We define `NoisyFunctionMetric`s to wrap our synthetic Branin-Currin problem's outputs. Add noise to see how robust our different optimization algorithms are." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "code_folding": [], "execution": { "iopub.execute_input": "2022-11-10T21:08:13.515222Z", "iopub.status.busy": "2022-11-10T21:08:13.514792Z", "iopub.status.idle": "2022-11-10T21:08:13.520650Z", "shell.execute_reply": "2022-11-10T21:08:13.519878Z" }, "executionStartTime": 1628191319191, "executionStopTime": 1628191319220, "hidden_ranges": [], "originalKey": "9fdb11b6-7845-4f06-90fd-527fee088d76", "requestMsgId": "febe0d60-fe60-4d55-ba6f-724c8ce7601d" }, "outputs": [], "source": [ "class MetricA(NoisyFunctionMetric):\n", " def f(self, x: np.ndarray) -> float:\n", " return float(branin_currin(torch.tensor(x))[0])\n", " \n", "class MetricB(NoisyFunctionMetric):\n", " def f(self, x: np.ndarray) -> float:\n", " return float(branin_currin(torch.tensor(x))[1])\n", "\n", "metric_a = MetricA(\"a\", [\"x1\", \"x2\"], noise_sd=0.0, lower_is_better=False)\n", "metric_b = MetricB(\"b\", [\"x1\", \"x2\"], noise_sd=0.0, lower_is_better=False)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "code_folding": [], "execution": { "iopub.execute_input": "2022-11-10T21:08:13.524637Z", "iopub.status.busy": "2022-11-10T21:08:13.524040Z", "iopub.status.idle": "2022-11-10T21:08:13.528616Z", "shell.execute_reply": "2022-11-10T21:08:13.527916Z" }, "executionStartTime": 1628191321755, "executionStopTime": 1628191321791, "hidden_ranges": [], "originalKey": "27065b03-7234-49c1-b3ae-f6442ec4e3d6", "requestMsgId": "d4010fca-5cbd-4a41-a779-cfa97ec15cc3" }, "outputs": [], "source": [ "mo = MultiObjective(\n", " objectives=[Objective(metric=metric_a), Objective(metric=metric_b)],\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2022-11-10T21:08:13.531880Z", "iopub.status.busy": "2022-11-10T21:08:13.531473Z", "iopub.status.idle": "2022-11-10T21:08:13.536290Z", "shell.execute_reply": "2022-11-10T21:08:13.535594Z" }, "executionStartTime": 1628191323464, "executionStopTime": 1628191323491, "originalKey": "c58b70de-06b5-4e03-8958-c3c55d4c295a", "requestMsgId": "27e7efe5-d29e-4211-944e-41e6de065299" }, "outputs": [], "source": [ "objective_thresholds = [\n", " ObjectiveThreshold(metric=metric, bound=val, relative=False)\n", " for metric, val in zip(mo.metrics, branin_currin.ref_point)\n", "]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "code_folding": [], "execution": { "iopub.execute_input": "2022-11-10T21:08:13.539490Z", "iopub.status.busy": "2022-11-10T21:08:13.539003Z", "iopub.status.idle": "2022-11-10T21:08:13.543389Z", "shell.execute_reply": "2022-11-10T21:08:13.542689Z" }, "executionStartTime": 1628191325491, "executionStopTime": 1628191325519, "hidden_ranges": [], "originalKey": "4b1ce9ba-e2e5-4a8a-9c15-5d01a2940a55", "requestMsgId": "314ea591-0d2e-4fb5-b091-2aa2ea27f0eb" }, "outputs": [], "source": [ "optimization_config = MultiObjectiveOptimizationConfig(\n", " objective=mo,\n", " objective_thresholds=objective_thresholds,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "3b7b797c-2478-48d6-84ea-c62a886db31f", "showInput": false }, "source": [ "## Define experiment creation utilities\n", "\n", "These construct our experiment, then initialize with Sobol points before we fit a Gaussian Process model to those initial points." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2022-11-10T21:08:13.546941Z", "iopub.status.busy": "2022-11-10T21:08:13.546319Z", "iopub.status.idle": "2022-11-10T21:08:13.550780Z", "shell.execute_reply": "2022-11-10T21:08:13.550023Z" }, "executionStartTime": 1628191328765, "executionStopTime": 1628191328792, "originalKey": "a52ace6c-8144-446b-97d5-2f27879ca187", "requestMsgId": "6a222fb5-231e-4476-86a6-c29ca5113332" }, "outputs": [], "source": [ "# Reasonable defaults for number of quasi-random initialization points and for subsequent model-generated trials.\n", "N_INIT = 6\n", "N_BATCH = 25" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "code_folding": [], "execution": { "iopub.execute_input": "2022-11-10T21:08:13.554796Z", "iopub.status.busy": "2022-11-10T21:08:13.553584Z", "iopub.status.idle": "2022-11-10T21:08:13.558709Z", "shell.execute_reply": "2022-11-10T21:08:13.557993Z" }, "executionStartTime": 1628191330913, "executionStopTime": 1628191330991, "hidden_ranges": [], "originalKey": "9fd6ec68-4c53-4276-a98a-61431cdc05d5", "requestMsgId": "8f659995-6b8f-4544-8392-03daaf8220b8" }, "outputs": [], "source": [ "def build_experiment():\n", " experiment = Experiment(\n", " name=\"pareto_experiment\",\n", " search_space=search_space,\n", " optimization_config=optimization_config,\n", " runner=SyntheticRunner(),\n", " )\n", " return experiment" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "code_folding": [], "execution": { "iopub.execute_input": "2022-11-10T21:08:13.561992Z", "iopub.status.busy": "2022-11-10T21:08:13.561590Z", "iopub.status.idle": "2022-11-10T21:08:13.566713Z", "shell.execute_reply": "2022-11-10T21:08:13.565999Z" }, "executionStartTime": 1628191334273, "executionStopTime": 1628191334299, "hidden_ranges": [], "originalKey": "a8eef6a6-1d53-494a-907f-10ca35492a8c", "requestMsgId": "b207dbd4-0a53-4efd-bbb9-9dee8835d60b" }, "outputs": [], "source": [ "## Initialize with Sobol samples\n", "\n", "def initialize_experiment(experiment):\n", " sobol = Models.SOBOL(search_space=experiment.search_space, seed=1234)\n", "\n", " for _ in range(N_INIT):\n", " experiment.new_trial(sobol.gen(1)).run()\n", "\n", " return experiment.fetch_data()" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "0c918735-9fda-4c36-90b5-163443e66c72", "showInput": false }, "source": [ "# Sobol\n", "We use quasirandom points as a fast baseline for evaluating the quality of our multi-objective optimization algorithms." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "code_folding": [], "execution": { "iopub.execute_input": "2022-11-10T21:08:13.570111Z", "iopub.status.busy": "2022-11-10T21:08:13.569697Z", "iopub.status.idle": "2022-11-10T21:08:13.669850Z", "shell.execute_reply": "2022-11-10T21:08:13.668826Z" }, "executionStartTime": 1628191356513, "executionStopTime": 1628191356896, "hidden_ranges": [], "originalKey": "5ee13832-804a-413f-a6bc-1f8f96a817d8", "requestMsgId": "5b40f1e6-45b9-40e4-8569-9d459e98ca57" }, "outputs": [], "source": [ "sobol_experiment = build_experiment()\n", "sobol_data = initialize_experiment(sobol_experiment)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "code_folding": [], "execution": { "iopub.execute_input": "2022-11-10T21:08:13.673750Z", "iopub.status.busy": "2022-11-10T21:08:13.672979Z", "iopub.status.idle": "2022-11-10T21:08:24.980201Z", "shell.execute_reply": "2022-11-10T21:08:24.979256Z" }, "executionStartTime": 1628191362562, "executionStopTime": 1628191408255, "hidden_ranges": [], "originalKey": "0c6a6d44-29db-43dd-982d-dc664d00b009", "requestMsgId": "8aca7b5b-aab8-4a39-9a49-d7b1e0c714c5" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 0, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 1, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 2, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 3, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 4, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 5, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 6, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 7, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. 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You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 22, HV: 24.273393500395358\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 23, HV: 24.273393500395358\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 24, HV: 24.273393500395358\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] } ], "source": [ "sobol_model = Models.SOBOL(\n", " experiment=sobol_experiment, \n", " data=sobol_data,\n", ")\n", "sobol_hv_list = []\n", "for i in range(N_BATCH):\n", " generator_run = sobol_model.gen(1)\n", " trial = sobol_experiment.new_trial(generator_run=generator_run)\n", " trial.run()\n", " exp_df = exp_to_df(sobol_experiment)\n", " outcomes = np.array(exp_df[['a', 'b']], dtype=np.double)\n", " # Fit a GP-based model in order to calculate hypervolume.\n", " # We will not use this model to generate new points.\n", " dummy_model = get_MOO_EHVI(\n", " experiment=sobol_experiment, \n", " data=sobol_experiment.fetch_data(),\n", " )\n", " try:\n", " hv = observed_hypervolume(modelbridge=dummy_model)\n", " except:\n", " hv = 0\n", " print(\"Failed to compute hv\")\n", " sobol_hv_list.append(hv)\n", " print(f\"Iteration: {i}, HV: {hv}\")\n", "\n", "sobol_outcomes = np.array(exp_to_df(sobol_experiment)[['a', 'b']], dtype=np.double)" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "767a7e9b-8902-424e-bfc4-f7afdba47302" }, "source": [ "## qNEHVI\n", "Noisy Expected Hypervolume Improvement. This is our current recommended algorithm for multi-objective optimization." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "code_folding": [], "execution": { "iopub.execute_input": "2022-11-10T21:08:24.985307Z", "iopub.status.busy": "2022-11-10T21:08:24.983867Z", "iopub.status.idle": "2022-11-10T21:08:25.083873Z", "shell.execute_reply": "2022-11-10T21:08:25.082898Z" }, "executionStartTime": 1628191422463, "executionStopTime": 1628191422803, "hidden_ranges": [], "originalKey": "8fc6bfb4-3012-4ce2-99ed-288378098c50", "requestMsgId": "0fd945a2-ac45-4a74-82cc-7173e15ced85" }, "outputs": [], "source": [ "ehvi_experiment = build_experiment()\n", "ehvi_data = initialize_experiment(ehvi_experiment)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "code_folding": [], "execution": { "iopub.execute_input": "2022-11-10T21:08:25.088187Z", "iopub.status.busy": "2022-11-10T21:08:25.087677Z", "iopub.status.idle": "2022-11-10T21:09:07.197355Z", "shell.execute_reply": "2022-11-10T21:09:07.195070Z" }, "executionStartTime": 1628191425090, "executionStopTime": 1628191500240, "hidden_ranges": [], "originalKey": "27dd9425-b77e-4027-8412-30dd40c5abf1", "requestMsgId": "65430b82-de1e-4946-9d8d-4a75762354c1" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 0, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 1, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 2, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 3, HV: 2.369795709893773\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 4, HV: 2.369795709893773\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 5, HV: 2.369795709893773\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 6, HV: 31.31253035482222\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 7, HV: 43.51095353756711\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 8, HV: 47.48986238535117\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 9, HV: 50.208931124941124\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 10, HV: 51.93897178082902\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 11, HV: 52.56857066045838\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 12, HV: 53.481840071100194\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 13, HV: 54.13209785321424\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 14, HV: 55.10633490985355\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 15, HV: 55.496090562118695\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 16, HV: 55.87235196290518\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 17, HV: 56.09329386785561\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 18, HV: 56.26497311139039\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 19, HV: 56.50228495501194\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 20, HV: 56.65923813212495\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 21, HV: 56.7968729144097\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 22, HV: 57.01824329000972\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 23, HV: 57.09081290099962\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 24, HV: 57.09081290099962\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] } ], "source": [ "ehvi_hv_list = []\n", "ehvi_model = None\n", "for i in range(N_BATCH): \n", " ehvi_model = get_MOO_EHVI(\n", " experiment=ehvi_experiment, \n", " data=ehvi_data,\n", " )\n", " generator_run = ehvi_model.gen(1)\n", " trial = ehvi_experiment.new_trial(generator_run=generator_run)\n", " trial.run()\n", " ehvi_data = Data.from_multiple_data([ehvi_data, Metric._unwrap_trial_data_multi(results=trial.fetch_data())])\n", " \n", " exp_df = exp_to_df(ehvi_experiment)\n", " outcomes = np.array(exp_df[['a', 'b']], dtype=np.double)\n", " try:\n", " hv = observed_hypervolume(modelbridge=ehvi_model)\n", " except:\n", " hv = 0\n", " print(\"Failed to compute hv\")\n", " ehvi_hv_list.append(hv)\n", " print(f\"Iteration: {i}, HV: {hv}\")\n", "\n", "ehvi_outcomes = np.array(exp_to_df(ehvi_experiment)[['a', 'b']], dtype=np.double)" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "e93178b6-5ba4-4c01-b8a2-e05971b7326f", "showInput": false }, "source": [ "## Plot qNEHVI Pareto Frontier based on model posterior \n", "\n", "The plotted points are samples from the fitted model's posterior, not observed samples." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "code_folding": [], "execution": { "iopub.execute_input": "2022-11-10T21:09:07.201489Z", "iopub.status.busy": "2022-11-10T21:09:07.200914Z", "iopub.status.idle": "2022-11-10T21:09:29.535708Z", "shell.execute_reply": "2022-11-10T21:09:29.534966Z" }, "executionStartTime": 1628191505148, "executionStopTime": 1628191521900, "hidden_ranges": [], "originalKey": "71e013c5-638f-4ba4-bb9a-3e4a7d3eb9fa", "requestMsgId": "681433c5-fc21-4699-9fe1-8e444c671153" }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "error_x": { "array": [ 0.02351827032211003, 0.023518270432913064, 0.023518270100503953, 0.023518270155905473, 0.015827895522855327, 0.016937636495875985, 0.02404932412055154, 0.01483190171580389, 0.023518270571416863, 0.02351827026670851, 0.023518270377511547, 0.031526661610920444, 0.02351827026670851, 0.022670202068479785, 0.023518270460613826, 0.02351827026670851, 0.02351827026670851, 0.02351827026670851, 0.02351827026670851, 15.447938820632691 ], "color": "rgba(128,177,211,0.4)", "thickness": 2, "type": "data" }, "error_y": { "array": [ 0.0018925255433208431, 0.0018925255433208431, 0.0018925255409839102, 0.0018925255398154432, 0.0012446412326949342, 0.0013244518105347506, 0.0016507015748156613, 0.0011792233413187793, 0.0018925255351415768, 0.0018925255398154432, 0.0018925255433208431, 0.001905439004507421, 0.0018925255398154432, 0.0016073903635301899, 0.0018925255386469768, 0.0018925255398154432, 0.0018925255398154432, 0.0018925255398154432, 0.0018925255398154432, 0.5503264972270822 ], "color": "rgba(128,177,211,0.4)", "thickness": 2, "type": "data" }, "hoverinfo": "text", "legendgroup": "mean", "marker": { "color": "rgba(128,177,211,1)" }, "mode": "markers", "name": "mean", "text": [ "Parameterization 0
a: -17.507 [-17.530, -17.483]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 3.875514502445762e-15
x2: 0.9999999999999991", "Parameterization 1
a: -17.507 [-17.530, -17.483]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 0.0
x2: 0.9999999999999967", "Parameterization 2
a: -17.507 [-17.530, -17.483]
b: -1.18 [-1.182, -1.178]

Parameterization:
x1: 4.2795529586645886e-14
x2: 0.9999999999999989", "Parameterization 3
a: -17.507 [-17.530, -17.483]
b: -1.18 [-1.182, -1.178]

Parameterization:
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "frontier = compute_posterior_pareto_frontier(\n", " experiment=ehvi_experiment,\n", " data=ehvi_experiment.fetch_data(),\n", " primary_objective=metric_b,\n", " secondary_objective=metric_a,\n", " absolute_metrics=[\"a\", \"b\"],\n", " num_points=20,\n", ")\n", "\n", "render(plot_pareto_frontier(frontier, CI_level=0.90)) " ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "77b2dbce-f1e4-443a-8f81-2e1cbe207301" }, "source": [ "## qNParEGO\n", "This is a good alternative algorithm for multi-objective optimization when qNEHVI runs too slowly." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "code_folding": [], "execution": { "iopub.execute_input": "2022-11-10T21:09:29.540300Z", "iopub.status.busy": "2022-11-10T21:09:29.539717Z", "iopub.status.idle": "2022-11-10T21:09:29.638153Z", "shell.execute_reply": "2022-11-10T21:09:29.637038Z" }, "hidden_ranges": [], "originalKey": "2f796182-558b-47aa-8072-4dbf40123133" }, "outputs": [], "source": [ "parego_experiment = build_experiment()\n", "parego_data = initialize_experiment(parego_experiment)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "code_folding": [], "execution": { "iopub.execute_input": "2022-11-10T21:09:29.643104Z", "iopub.status.busy": "2022-11-10T21:09:29.642600Z", "iopub.status.idle": "2022-11-10T21:09:54.883041Z", "shell.execute_reply": "2022-11-10T21:09:54.882286Z" }, "hidden_ranges": [], "originalKey": "72999188-90f5-43e0-b1d9-d468e7d51191" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 0, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 1, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 2, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 3, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 4, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 5, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 6, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 7, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 8, HV: 0.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 9, HV: 19.480927610607832\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 10, HV: 34.51169014024245\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 11, HV: 35.30098538240324\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 12, HV: 42.48101352728357\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 13, HV: 42.48101352728357\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 14, HV: 43.04858855002238\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 15, HV: 43.04858855002238\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 16, HV: 43.04858855002238\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 17, HV: 43.04858855002238\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 18, HV: 43.04858855002238\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 19, HV: 43.04858855002238\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 20, HV: 43.04858855002238\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 21, HV: 43.04858855002238\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 22, HV: 43.04858855002238\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 23, HV: 43.04858855002238\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Iteration: 24, HV: 43.04858855002238\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/runner/work/Ax/Ax/ax/modelbridge/modelbridge_utils.py:811: UserWarning:\n", "\n", "FYI: The default behavior of `get_pareto_frontier_and_configs` when `transform_outcomes_and_configs` is not specified has changed. Previously, the default was `transform_outcomes_and_configs=True`; now this argument is deprecated and behavior is as if `transform_outcomes_and_configs=False`. You did not specify `transform_outcomes_and_configs`, so this warning requires no action.\n", "\n" ] } ], "source": [ "parego_hv_list = []\n", "parego_model = None\n", "for i in range(N_BATCH): \n", " parego_model = get_MOO_PAREGO(\n", " experiment=parego_experiment, \n", " data=parego_data,\n", " )\n", " generator_run = parego_model.gen(1)\n", " trial = parego_experiment.new_trial(generator_run=generator_run)\n", " trial.run()\n", " parego_data = Data.from_multiple_data([parego_data, Metric._unwrap_trial_data_multi(results=trial.fetch_data())])\n", " \n", " exp_df = exp_to_df(parego_experiment)\n", " outcomes = np.array(exp_df[['a', 'b']], dtype=np.double)\n", " try:\n", " hv = observed_hypervolume(modelbridge=parego_model)\n", " except:\n", " hv = 0\n", " print(\"Failed to compute hv\")\n", " parego_hv_list.append(hv)\n", " print(f\"Iteration: {i}, HV: {hv}\")\n", "\n", "parego_outcomes = np.array(exp_to_df(parego_experiment)[['a', 'b']], dtype=np.double)" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "67ded85f-7c58-4c31-8df5-b0d8d07e4299", "showInput": false }, "source": [ "## Plot qNParEGO Pareto Frontier based on model posterior \n", "\n", "The plotted points are samples from the fitted model's posterior, not observed samples." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "code_folding": [], "execution": { "iopub.execute_input": "2022-11-10T21:09:54.886705Z", "iopub.status.busy": "2022-11-10T21:09:54.886117Z", "iopub.status.idle": "2022-11-10T21:10:11.793820Z", "shell.execute_reply": "2022-11-10T21:10:11.792959Z" }, "hidden_ranges": [], "originalKey": "3b1f39fd-ef75-4ea4-865b-f7b54b90da07" }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "error_x": { "array": [ 0.04491908569979191, 0.04491908582231424, 0.44027991351975265, 0.4456869177167324, 0.04491908569979191, 0.04491908557726959, 0.04491908586315501, 0.4734167194261438, 0.4477812401359329, 0.24982903605283704, 0.47291334229113363, 0.44847992233976447, 0.04491908582231424, 0.43099810792200527, 0.4302456286505691, 0.04491908586315501, 0.40021184956904216, 0.04491908569979191, 0.04491908569979191, 0.4320821014535261 ], "color": "rgba(128,177,211,0.4)", "thickness": 2, "type": "data" }, "error_y": { "array": [ 0.0022377833209810513, 0.0022377833209810513, 0.02526612636763488, 0.025384832492074075, 0.002237783322005557, 0.0022377833199565458, 0.0022377833148340184, 0.0271658013330566, 0.025104509489147997, 0.01487279110376071, 0.027216725139809474, 0.026005754571985354, 0.0022377833209810513, 0.023630639515774578, 0.023555270836501448, 0.0022377833148340184, 0.023181486439706495, 0.0022377833209810513, 0.002237783322005557, 0.02373676429349084 ], "color": "rgba(128,177,211,0.4)", "thickness": 2, "type": "data" }, "hoverinfo": "text", "legendgroup": "mean", "marker": { "color": "rgba(128,177,211,1)" }, "mode": "markers", "name": "mean", "text": [ "Parameterization 0
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15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Pareto Frontier" }, "width": 750, "xaxis": { "ticksuffix": "", "title": { "text": "a" }, "zeroline": true }, "yaxis": { "ticksuffix": "", "title": { "text": "b" }, "zeroline": true } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "frontier = compute_posterior_pareto_frontier(\n", " experiment=parego_experiment,\n", " data=parego_experiment.fetch_data(),\n", " primary_objective=metric_b,\n", " secondary_objective=metric_a,\n", " absolute_metrics=[\"a\", \"b\"],\n", " num_points=20,\n", ")\n", "\n", "render(plot_pareto_frontier(frontier, CI_level=0.90)) " ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "collapsed": true, "hidden_ranges": [], "originalKey": "a67f7345-1777-4372-8704-bb80c4c4e783" }, "source": [ "## Plot empirical data" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "collapsed": true, "hidden_ranges": [], "originalKey": "de878adc-0eb2-4599-8c1b-e0adbc0c0765", "showInput": false }, "source": [ "#### Plot observed hypervolume, with color representing the iteration that a point was generated on.\n", "\n", "To examine optimization process from another perspective, we plot the collected observations under each algorithm where the color corresponds to the BO iteration at which the point was collected. The plot on the right for $q$NEHVI shows that the $q$NEHVI quickly identifies the Pareto frontier and most of its evaluations are very close to the Pareto frontier. $q$NParEGO also identifies has many observations close to the Pareto frontier, but relies on optimizing random scalarizations, which is a less principled way of optimizing the Pareto front compared to $q$NEHVI, which explicitly attempts focuses on improving the Pareto front. Sobol generates random points and has few points close to the Pareto front." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "code_folding": [], "execution": { "iopub.execute_input": "2022-11-10T21:10:11.797699Z", "iopub.status.busy": "2022-11-10T21:10:11.797124Z", "iopub.status.idle": "2022-11-10T21:10:15.166278Z", "shell.execute_reply": "2022-11-10T21:10:15.165265Z" }, "hidden_ranges": [], "originalKey": "c6296697-ef07-422d-b965-35e4e5104a12" }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Iteration')" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", "\n", "from matplotlib.cm import ScalarMappable\n", "fig, axes = plt.subplots(1, 3, figsize=(20,6))\n", "algos = [\"Sobol\", \"qNParEGO\", \"qNEHVI\"]\n", "outcomes_list = [sobol_outcomes, parego_outcomes, ehvi_outcomes]\n", "cm = plt.cm.get_cmap('viridis')\n", "BATCH_SIZE = 1\n", "\n", "n_results = N_BATCH*BATCH_SIZE + N_INIT\n", "batch_number = torch.cat([torch.zeros(N_INIT), torch.arange(1, N_BATCH+1).repeat(BATCH_SIZE, 1).t().reshape(-1)]).numpy()\n", "for i, train_obj in enumerate(outcomes_list):\n", " x = i\n", " sc = axes[x].scatter(train_obj[:n_results, 0], train_obj[:n_results,1], c=batch_number[:n_results], alpha=0.8)\n", " axes[x].set_title(algos[i])\n", " axes[x].set_xlabel(\"Objective 1\")\n", " axes[x].set_xlim(-150, 5)\n", " axes[x].set_ylim(-15, 0)\n", "axes[0].set_ylabel(\"Objective 2\")\n", "norm = plt.Normalize(batch_number.min(), batch_number.max())\n", "sm = ScalarMappable(norm=norm, cmap=cm)\n", "sm.set_array([])\n", "fig.subplots_adjust(right=0.9)\n", "cbar_ax = fig.add_axes([0.93, 0.15, 0.01, 0.7])\n", "cbar = fig.colorbar(sm, cax=cbar_ax)\n", "cbar.ax.set_title(\"Iteration\")" ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "ca12287f-c7b8-4ef8-8eb9-57760eda5fed", "showInput": true }, "source": [ "# Hypervolume statistics\n", "The hypervolume of the space dominated by points that dominate the reference point." ] }, { "cell_type": "markdown", "metadata": { "code_folding": [], "hidden_ranges": [], "originalKey": "ec8b764b-c27d-4722-9e3d-d81cebb3624a" }, "source": [ "#### Plot the results\n", "The plot below shows a common metric of multi-objective optimization performance when the true Pareto frontier is known: the log difference between the hypervolume of the true Pareto front and the hypervolume of the approximate Pareto front identified by each algorithm. The log hypervolume difference is plotted at each step of the optimization for each of the algorithms.\n", "\n", "The plot show that $q$NEHVI vastly outperforms $q$NParEGO which outperforms the Sobol baseline." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "code_folding": [], "execution": { "iopub.execute_input": "2022-11-10T21:10:15.170918Z", "iopub.status.busy": "2022-11-10T21:10:15.170106Z", "iopub.status.idle": "2022-11-10T21:10:15.390854Z", "shell.execute_reply": "2022-11-10T21:10:15.389930Z" }, "hidden_ranges": [], "originalKey": "d50b98bc-5ab1-4826-a5b2-474a13f4bae0" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", "\n", "iters = np.arange(1, N_BATCH + 1)\n", "log_hv_difference_sobol = np.log10(branin_currin.max_hv - np.asarray(sobol_hv_list))[:N_BATCH + 1]\n", "log_hv_difference_parego = np.log10(branin_currin.max_hv - np.asarray(parego_hv_list))[:N_BATCH + 1]\n", "log_hv_difference_ehvi = np.log10(branin_currin.max_hv - np.asarray(ehvi_hv_list))[:N_BATCH + 1]\n", "\n", "fig, ax = plt.subplots(1, 1, figsize=(8, 6))\n", "ax.plot(iters, log_hv_difference_sobol, label=\"Sobol\", linewidth=1.5)\n", "ax.plot(iters, log_hv_difference_parego, label=\"qNParEGO\", linewidth=1.5)\n", "ax.plot(iters, log_hv_difference_ehvi, label=\"qNEHVI\", linewidth=1.5)\n", "ax.set(xlabel='number of observations (beyond initial points)', ylabel='Log Hypervolume Difference')\n", "ax.legend(loc=\"lower right\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "python3", "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": "3.8.14" } }, "nbformat": 4, "nbformat_minor": 2 }