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update gen scripts/notebooks
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‎docs/generated_notebooks/ax/objective-multi+model-FULLYBAYESIAN+custom_gen-True+existing_data-False+sum_constraint-False+order_constraint-False+linear_constraint-False+composition_constraint-False+categorical-False+custom_threshold-False+synchrony-batch.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "e1ef5215",
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"metadata": {},
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"source": [
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"<a href=\"https://colab.research.google.com/github/sgbaird/honegumi/blob/main/docs/generated_notebooks/ax/objective-multi%2Bmodel-FULLYBAYESIAN%2Bcustom_gen-True%2Bexisting_data-False%2Bsum_constraint-False%2Border_constraint-False%2Blinear_constraint-False%2Bcomposition_constraint-False%2Bcategorical-False%2Bcustom_threshold-False%2Bsynchrony-batch.ipynb\"><img alt=\"Open In Colab\" src=\"https://colab.research.google.com/assets/colab-badge.svg\"></a>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c788e0b6",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install ax-platform"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "748e6dd2",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from ax.service.ax_client import AxClient, ObjectiveProperties\n",
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"\n",
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"from ax.modelbridge.factory import Models\n",
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"from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy\n",
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"\n",
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"\n",
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"obj1_name = \"branin\"\n",
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"obj2_name = \"branin_swapped\"\n",
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"\n",
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"\n",
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"def branin_moo(x1, x2):\n",
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" y = float(\n",
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" (x2 - 5.1 / (4 * np.pi**2) * x1**2 + 5.0 / np.pi * x1 - 6.0) ** 2\n",
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" + 10 * (1 - 1.0 / (8 * np.pi)) * np.cos(x1)\n",
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" + 10\n",
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" )\n",
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"\n",
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" # second objective has x1 and x2 swapped\n",
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" y2 = float(\n",
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" (x1 - 5.1 / (4 * np.pi**2) * x2**2 + 5.0 / np.pi * x2 - 6.0) ** 2\n",
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" + 10 * (1 - 1.0 / (8 * np.pi)) * np.cos(x2)\n",
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" + 10\n",
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" )\n",
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"\n",
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" return {obj1_name: y, obj2_name: y2}\n",
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"\n",
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"\n",
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"gs = GenerationStrategy(\n",
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" steps=[\n",
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" GenerationStep(\n",
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" model=Models.SOBOL,\n",
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" num_trials=4, # https://github.com/facebook/Ax/issues/922\n",
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" min_trials_observed=3,\n",
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" max_parallelism=5,\n",
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" model_kwargs={\"seed\": 999},\n",
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" model_gen_kwargs={},\n",
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" ),\n",
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" GenerationStep(\n",
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" model=Models.FULLYBAYESIANMOO,\n",
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" num_trials=-1,\n",
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" max_parallelism=3,\n",
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" model_kwargs={\"num_samples\": 256, \"warmup_steps\": 512},\n",
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" ),\n",
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" ]\n",
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")\n",
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"\n",
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"ax_client = AxClient(generation_strategy=gs)\n",
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"\n",
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"ax_client.create_experiment(\n",
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" parameters=[\n",
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" {\"name\": \"x1\", \"type\": \"range\", \"bounds\": [-5.0, 10.0]},\n",
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" {\"name\": \"x2\", \"type\": \"range\", \"bounds\": [0.0, 10.0]},\n",
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" ],\n",
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" objectives={\n",
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" obj1_name: ObjectiveProperties(minimize=True),\n",
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" obj2_name: ObjectiveProperties(minimize=True),\n",
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" },\n",
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")\n",
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"\n",
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"\n",
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"batch_size = 2\n",
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"\n",
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"\n",
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"for _ in range(19):\n",
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"\n",
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" parameterizations, optimization_complete = ax_client.get_next_trials(batch_size)\n",
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" for trial_index, parameterization in list(parameterizations.items()):\n",
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" # extract parameters\n",
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" x1 = parameterization[\"x1\"]\n",
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" x2 = parameterization[\"x2\"]\n",
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"\n",
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" results = branin_moo(x1, x2)\n",
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" ax_client.complete_trial(trial_index=trial_index, raw_data=results)\n",
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"\n",
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"pareto_results = ax_client.get_pareto_optimal_parameters()"
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]
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}
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],
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"metadata": {
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"jupytext": {
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"cell_metadata_filter": "-all",
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"main_language": "python",
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"notebook_metadata_filter": "-all"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎docs/generated_notebooks/ax/objective-multi+model-FULLYBAYESIAN+custom_gen-True+existing_data-False+sum_constraint-False+order_constraint-False+linear_constraint-False+composition_constraint-False+categorical-False+custom_threshold-False+synchrony-single.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "8a726861",
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"metadata": {},
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"source": [
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"<a href=\"https://colab.research.google.com/github/sgbaird/honegumi/blob/main/docs/generated_notebooks/ax/objective-multi%2Bmodel-FULLYBAYESIAN%2Bcustom_gen-True%2Bexisting_data-False%2Bsum_constraint-False%2Border_constraint-False%2Blinear_constraint-False%2Bcomposition_constraint-False%2Bcategorical-False%2Bcustom_threshold-False%2Bsynchrony-single.ipynb\"><img alt=\"Open In Colab\" src=\"https://colab.research.google.com/assets/colab-badge.svg\"></a>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9e8904fa",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install ax-platform"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c73576d7",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from ax.service.ax_client import AxClient, ObjectiveProperties\n",
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"\n",
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"from ax.modelbridge.factory import Models\n",
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"from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy\n",
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"\n",
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"\n",
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"obj1_name = \"branin\"\n",
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"obj2_name = \"branin_swapped\"\n",
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"\n",
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"\n",
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"def branin_moo(x1, x2):\n",
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" y = float(\n",
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" (x2 - 5.1 / (4 * np.pi**2) * x1**2 + 5.0 / np.pi * x1 - 6.0) ** 2\n",
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" + 10 * (1 - 1.0 / (8 * np.pi)) * np.cos(x1)\n",
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" + 10\n",
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" )\n",
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"\n",
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" # second objective has x1 and x2 swapped\n",
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" y2 = float(\n",
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" (x1 - 5.1 / (4 * np.pi**2) * x2**2 + 5.0 / np.pi * x2 - 6.0) ** 2\n",
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" + 10 * (1 - 1.0 / (8 * np.pi)) * np.cos(x2)\n",
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" + 10\n",
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" )\n",
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"\n",
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" return {obj1_name: y, obj2_name: y2}\n",
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"\n",
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"\n",
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"gs = GenerationStrategy(\n",
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" steps=[\n",
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" GenerationStep(\n",
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" model=Models.SOBOL,\n",
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" num_trials=4, # https://github.com/facebook/Ax/issues/922\n",
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" min_trials_observed=3,\n",
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" max_parallelism=5,\n",
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" model_kwargs={\"seed\": 999},\n",
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" model_gen_kwargs={},\n",
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" ),\n",
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" GenerationStep(\n",
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" model=Models.FULLYBAYESIANMOO,\n",
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" num_trials=-1,\n",
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" max_parallelism=3,\n",
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" model_kwargs={\"num_samples\": 256, \"warmup_steps\": 512},\n",
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" ),\n",
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" ]\n",
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")\n",
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"\n",
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"ax_client = AxClient(generation_strategy=gs)\n",
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"\n",
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"ax_client.create_experiment(\n",
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" parameters=[\n",
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" {\"name\": \"x1\", \"type\": \"range\", \"bounds\": [-5.0, 10.0]},\n",
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" {\"name\": \"x2\", \"type\": \"range\", \"bounds\": [0.0, 10.0]},\n",
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" ],\n",
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" objectives={\n",
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" obj1_name: ObjectiveProperties(minimize=True),\n",
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" obj2_name: ObjectiveProperties(minimize=True),\n",
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" },\n",
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")\n",
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"\n",
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"\n",
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"for _ in range(19):\n",
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"\n",
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" parameterization, trial_index = ax_client.get_next_trial()\n",
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"\n",
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" # extract parameters\n",
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" x1 = parameterization[\"x1\"]\n",
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" x2 = parameterization[\"x2\"]\n",
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"\n",
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" results = branin_moo(x1, x2)\n",
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" ax_client.complete_trial(trial_index=trial_index, raw_data=results)\n",
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"\n",
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"pareto_results = ax_client.get_pareto_optimal_parameters()"
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]
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}
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],
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"metadata": {
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"jupytext": {
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"cell_metadata_filter": "-all",
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"main_language": "python",
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"notebook_metadata_filter": "-all"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎docs/generated_notebooks/ax/objective-multi+model-FULLYBAYESIAN+custom_gen-True+existing_data-False+sum_constraint-False+order_constraint-False+linear_constraint-False+composition_constraint-False+categorical-False+custom_threshold-True+synchrony-batch.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "01163a12",
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"metadata": {},
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"source": [
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"<a href=\"https://colab.research.google.com/github/sgbaird/honegumi/blob/main/docs/generated_notebooks/ax/objective-multi%2Bmodel-FULLYBAYESIAN%2Bcustom_gen-True%2Bexisting_data-False%2Bsum_constraint-False%2Border_constraint-False%2Blinear_constraint-False%2Bcomposition_constraint-False%2Bcategorical-False%2Bcustom_threshold-True%2Bsynchrony-batch.ipynb\"><img alt=\"Open In Colab\" src=\"https://colab.research.google.com/assets/colab-badge.svg\"></a>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "32409e12",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install ax-platform"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0109f7ee",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from ax.service.ax_client import AxClient, ObjectiveProperties\n",
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"\n",
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"from ax.modelbridge.factory import Models\n",
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"from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy\n",
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"\n",
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"\n",
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"obj1_name = \"branin\"\n",
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"obj2_name = \"branin_swapped\"\n",
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"\n",
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"\n",
39+
"def branin_moo(x1, x2):\n",
40+
" y = float(\n",
41+
" (x2 - 5.1 / (4 * np.pi**2) * x1**2 + 5.0 / np.pi * x1 - 6.0) ** 2\n",
42+
" + 10 * (1 - 1.0 / (8 * np.pi)) * np.cos(x1)\n",
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" + 10\n",
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" )\n",
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"\n",
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" # second objective has x1 and x2 swapped\n",
47+
" y2 = float(\n",
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" (x1 - 5.1 / (4 * np.pi**2) * x2**2 + 5.0 / np.pi * x2 - 6.0) ** 2\n",
49+
" + 10 * (1 - 1.0 / (8 * np.pi)) * np.cos(x2)\n",
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" + 10\n",
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" )\n",
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"\n",
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" return {obj1_name: y, obj2_name: y2}\n",
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"\n",
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"\n",
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"gs = GenerationStrategy(\n",
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" steps=[\n",
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" GenerationStep(\n",
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" model=Models.SOBOL,\n",
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" num_trials=4, # https://github.com/facebook/Ax/issues/922\n",
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" min_trials_observed=3,\n",
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" max_parallelism=5,\n",
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" model_kwargs={\"seed\": 999},\n",
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" model_gen_kwargs={},\n",
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" ),\n",
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" GenerationStep(\n",
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" model=Models.FULLYBAYESIANMOO,\n",
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" num_trials=-1,\n",
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" max_parallelism=3,\n",
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" model_kwargs={\"num_samples\": 256, \"warmup_steps\": 512},\n",
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" ),\n",
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" ]\n",
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")\n",
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"\n",
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"ax_client = AxClient(generation_strategy=gs)\n",
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"\n",
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"ax_client.create_experiment(\n",
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" parameters=[\n",
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" {\"name\": \"x1\", \"type\": \"range\", \"bounds\": [-5.0, 10.0]},\n",
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" {\"name\": \"x2\", \"type\": \"range\", \"bounds\": [0.0, 10.0]},\n",
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" ],\n",
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" objectives={\n",
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" obj1_name: ObjectiveProperties(minimize=True, threshold=25.0),\n",
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" obj2_name: ObjectiveProperties(minimize=True, threshold=15.0),\n",
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" },\n",
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")\n",
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"\n",
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"\n",
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"batch_size = 2\n",
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"\n",
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"\n",
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"for _ in range(19):\n",
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"\n",
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" parameterizations, optimization_complete = ax_client.get_next_trials(batch_size)\n",
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" for trial_index, parameterization in list(parameterizations.items()):\n",
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" # extract parameters\n",
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" x1 = parameterization[\"x1\"]\n",
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" x2 = parameterization[\"x2\"]\n",
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"\n",
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" results = branin_moo(x1, x2)\n",
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" ax_client.complete_trial(trial_index=trial_index, raw_data=results)\n",
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"\n",
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"pareto_results = ax_client.get_pareto_optimal_parameters()"
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]
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}
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],
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"metadata": {
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"jupytext": {
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"cell_metadata_filter": "-all",
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"main_language": "python",
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"notebook_metadata_filter": "-all"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
116+
}

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