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"This tutorial illustrates use of a Global Stopping Strategy (GSS) in combination with the Service API. For background on the Service API, see the Service API Tutorial: https://ax.dev/tutorials/gpei_hartmann_service.html GSS is also supported in the Scheduler API, where it can be provided as part of `SchedulerOptions`. For more on `Scheduler`, see the Scheduler tutorial: https://ax.dev/tutorials/scheduler.html\n",
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"[ERROR 11-13 07:14:22] ax.storage.sqa_store.encoder: ATTENTION: The Ax team is considering deprecating SQLAlchemy storage. If you are currently using SQLAlchemy storage, please reach out to us via GitHub Issues here: https://github.com/facebook/Ax/issues/2975\n"
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"[INFO 11-13 07:14:22] ax.utils.notebook.plotting: Injecting Plotly library into cell. Do not overwrite or delete cell.\n"
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"[INFO 11-13 07:14:22] ax.utils.notebook.plotting: Please see\n",
" (https://ax.dev/tutorials/visualizations.html#Fix-for-plots-that-are-not-rendering)\n",
" if visualizations are not rendering.\n"
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"import numpy as np\n",
"\n",
"from ax.service.ax_client import AxClient, ObjectiveProperties\n",
"from ax.utils.measurement.synthetic_functions import Branin, branin\n",
"from ax.utils.notebook.plotting import init_notebook_plotting, render\n",
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"init_notebook_plotting()"
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"source": [
"# 1. What happens without global stopping? Optimization can run for too long.\n",
"This example uses the Branin test problem. We run 25 trials, which turns out to be far more than needed, because we get close to the optimum quite quickly."
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"def evaluate(parameters):\n",
" x = np.array([parameters.get(f\"x{i+1}\") for i in range(2)])\n",
" return {\"branin\": (branin(x), 0.0)}"
]
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"params = [\n",
" {\n",
" \"name\": f\"x{i + 1}\",\n",
" \"type\": \"range\",\n",
" \"bounds\": [*Branin._domain[i]],\n",
" \"value_type\": \"float\",\n",
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"[WARNING 11-13 07:14:23] ax.service.ax_client: Random seed set to 0. Note that this setting only affects the Sobol quasi-random generator and BoTorch-powered Bayesian optimization models. For the latter models, setting random seed to the same number for two optimizations will make the generated trials similar, but not exactly the same, and over time the trials will diverge more.\n"
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"[INFO 11-13 07:14:23] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.0, 10.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 15.0])], parameter_constraints=[]).\n"
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"[INFO 11-13 07:14:23] 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"
]
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"[INFO 11-13 07:14:23] ax.modelbridge.dispatch_utils: Using Models.BOTORCH_MODULAR since there is at least one ordered parameter and there are no unordered categorical parameters.\n"
]
},
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"name": "stderr",
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"[INFO 11-13 07:14:23] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False\n"
]
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"[INFO 11-13 07:14:23] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5\n"
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"[INFO 11-13 07:14:23] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5\n"
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"[INFO 11-13 07:14:23] ax.modelbridge.dispatch_utils: `verbose`, `disable_progbar`, and `jit_compile` are not yet supported when using `choose_generation_strategy` with ModularBoTorchModel, dropping these arguments.\n"
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"[INFO 11-13 07:14:23] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+BoTorch', steps=[Sobol for 5 trials, BoTorch for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.\n"
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],
"source": [
"render(ax_client.get_optimization_trace())"
]
},
{
"cell_type": "markdown",
"id": "2e0233f6",
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"tags": []
},
"source": [
"# 2. Optimization with global stopping, with the Service API"
]
},
{
"cell_type": "markdown",
"id": "95c61fb8",
"metadata": {
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"source": [
"Rather than running a fixed number of trials, we can use a GlobalStoppingStrategy (GSS), which checks whether some stopping criteria have been met when `get_next_trial` is called. Here, we use an `ImprovementGlobalStoppingStrategy`, which checks whether the the last `window_size` trials have improved by more than some threshold amount.\n",
"\n",
"For single-objective optimization, which we are doing here, `ImprovementGlobalStoppingStrategy` checks if an improvement is \"significant\" by comparing it to the inter-quartile range (IQR) of the objective values attained so far. \n",
"\n",
"`ImprovementGlobalStoppingStrategy` also supports multi-objective optimization (MOO), in which case it checks whether the percentage improvement in hypervolume over the last `window_size` trials exceeds `improvement_bar`."
]
},
{
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"source": [
"from ax.global_stopping.strategies.improvement import ImprovementGlobalStoppingStrategy\n",
"from ax.exceptions.core import OptimizationShouldStop"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "66a39602",
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"source": [
"# Start considering stopping only after the 5 initialization trials + 5 GPEI trials.\n",
"# Stop if the improvement in the best point in the past 5 trials is less than\n",
"# 1% of the IQR thus far.\n",
"stopping_strategy = ImprovementGlobalStoppingStrategy(\n",
" min_trials=5 + 5, window_size=5, improvement_bar=0.01\n",
")"
]
},
{
"cell_type": "code",
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"status": "completed"
},
"requestMsgId": "2ba6f82b-1443-4274-83d1-03c56f0190d0",
"showInput": true,
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},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[WARNING 11-13 07:14:41] ax.service.ax_client: Random seed set to 0. Note that this setting only affects the Sobol quasi-random generator and BoTorch-powered Bayesian optimization models. For the latter models, setting random seed to the same number for two optimizations will make the generated trials similar, but not exactly the same, and over time the trials will diverge more.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 07:14:41] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.0, 10.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 15.0])], parameter_constraints=[]).\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 07:14:41] 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 11-13 07:14:41] ax.modelbridge.dispatch_utils: Using Models.BOTORCH_MODULAR since there is at least one ordered parameter and there are no unordered categorical parameters.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 07:14:41] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 07:14:41] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 07:14:41] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 07:14:41] ax.modelbridge.dispatch_utils: `verbose`, `disable_progbar`, and `jit_compile` are not yet supported when using `choose_generation_strategy` with ModularBoTorchModel, dropping these arguments.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 07:14:41] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+BoTorch', steps=[Sobol for 5 trials, BoTorch for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.\n"
]
}
],
"source": [
"ax_client_gss = AxClient(\n",
" global_stopping_strategy=stopping_strategy, random_seed=0, verbose_logging=False\n",
")\n",
"\n",
"ax_client_gss.create_experiment(\n",
" name=\"branin_test_experiment\",\n",
" parameters=params,\n",
" objectives={\"branin\": ObjectiveProperties(minimize=True)},\n",
" is_test=True,\n",
")"
]
},
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"source": [
"If there has not been much improvement, `ImprovementGlobalStoppingStrategy` will raise an exception. If the exception is raised, we catch it and terminate optimization."
]
},
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"You can write a custom Global Stopping Strategy by subclassing `BaseGlobalStoppingStrategy` and use it where `ImprovementGlobalStoppingStrategy` was used above."
]
},
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"source": [
"from ax.global_stopping.strategies.base import BaseGlobalStoppingStrategy\n",
"from typing import Tuple\n",
"from ax.core.experiment import Experiment\n",
"from ax.core.base_trial import TrialStatus\n",
"from ax.global_stopping.strategies.improvement import constraint_satisfaction"
]
},
{
"cell_type": "markdown",
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"Here, we define `SimpleThresholdGlobalStoppingStrategy`, which stops when we observe a point better than a provided threshold. This can be useful when there is a known optimum. For example, the Branin function has an optimum of zero. When the optimum is not known, this can still be useful from a satisficing perspective: For example, maybe we need a model to take up less than a certain amount of RAM so it doesn't crash our usual hardware, but there is no benefit to further improvements."
]
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"class SimpleThresholdGlobalStoppingStrategy(BaseGlobalStoppingStrategy):\n",
" \"\"\"\n",
" A GSS that stops when we observe a point better than `threshold`.\n",
" \"\"\"\n",
" def __init__(\n",
" self,\n",
" min_trials: int,\n",
" inactive_when_pending_trials: bool = True,\n",
" threshold: float = 0.1\n",
" ):\n",
" self.threshold = threshold\n",
" super().__init__(\n",
" min_trials=min_trials,\n",
" inactive_when_pending_trials=inactive_when_pending_trials\n",
" )\n",
" \n",
" def _should_stop_optimization(\n",
" self, experiment: Experiment\n",
" ) -> Tuple[bool, str]:\n",
" \"\"\"\n",
" Check if the best seen is better than `self.threshold`.\n",
" \"\"\"\n",
" feasible_objectives = [\n",
" trial.objective_mean\n",
" for trial in experiment.trials_by_status[TrialStatus.COMPLETED]\n",
" if constraint_satisfaction(trial)\n",
" ]\n",
"\n",
" # Computing the interquartile for scaling the difference\n",
" if len(feasible_objectives) <= 1:\n",
" message = \"There are not enough feasible arms tried yet.\"\n",
" return False, message\n",
" \n",
" minimize = experiment.optimization_config.objective.minimize\n",
" if minimize:\n",
" best = np.min(feasible_objectives)\n",
" stop = best < self.threshold\n",
" else:\n",
" best = np.max(feasible_objectives)\n",
" stop = best > self.threshold\n",
"\n",
" comparison = \"less\" if minimize else \"greater\"\n",
" if stop:\n",
" message = (\n",
" f\"The best objective seen is {best:.3f}, which is {comparison} \"\n",
" f\"than the threshold of {self.threshold:.3f}.\"\n",
" )\n",
" else:\n",
" message = \"\"\n",
"\n",
" return stop, message"
]
},
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"stopping_strategy = SimpleThresholdGlobalStoppingStrategy(min_trials=5, threshold=1.)"
]
},
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{
"name": "stderr",
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"text": [
"[WARNING 11-13 07:14:49] ax.service.ax_client: Random seed set to 0. Note that this setting only affects the Sobol quasi-random generator and BoTorch-powered Bayesian optimization models. For the latter models, setting random seed to the same number for two optimizations will make the generated trials similar, but not exactly the same, and over time the trials will diverge more.\n"
]
},
{
"name": "stderr",
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"text": [
"[INFO 11-13 07:14:49] ax.service.utils.instantiation: Created search space: SearchSpace(parameters=[RangeParameter(name='x1', parameter_type=FLOAT, range=[-5.0, 10.0]), RangeParameter(name='x2', parameter_type=FLOAT, range=[0.0, 15.0])], parameter_constraints=[]).\n"
]
},
{
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"text": [
"[INFO 11-13 07:14:49] 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 11-13 07:14:49] ax.modelbridge.dispatch_utils: Using Models.BOTORCH_MODULAR since there is at least one ordered parameter and there are no unordered categorical parameters.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 07:14:49] ax.modelbridge.dispatch_utils: Calculating the number of remaining initialization trials based on num_initialization_trials=None max_initialization_trials=None num_tunable_parameters=2 num_trials=None use_batch_trials=False\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 07:14:49] ax.modelbridge.dispatch_utils: calculated num_initialization_trials=5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 07:14:49] ax.modelbridge.dispatch_utils: num_completed_initialization_trials=0 num_remaining_initialization_trials=5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 07:14:49] ax.modelbridge.dispatch_utils: `verbose`, `disable_progbar`, and `jit_compile` are not yet supported when using `choose_generation_strategy` with ModularBoTorchModel, dropping these arguments.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO 11-13 07:14:49] ax.modelbridge.dispatch_utils: Using Bayesian Optimization generation strategy: GenerationStrategy(name='Sobol+BoTorch', steps=[Sobol for 5 trials, BoTorch for subsequent trials]). Iterations after 5 will take longer to generate due to model-fitting.\n"
]
}
],
"source": [
"ax_client_custom_gss = AxClient(\n",
" global_stopping_strategy=stopping_strategy,\n",
" random_seed=0,\n",
" verbose_logging=False,\n",
")\n",
"\n",
"ax_client_custom_gss.create_experiment(\n",
" name=\"branin_test_experiment\",\n",
" parameters=params,\n",
" objectives={\"branin\": ObjectiveProperties(minimize=True)},\n",
" is_test=True,\n",
")"
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"/tmp/tmp.jh7tLjWjTJ/Ax-main/ax/modelbridge/cross_validation.py:464: UserWarning:\n",
"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
"\n",
"/tmp/tmp.jh7tLjWjTJ/Ax-main/ax/modelbridge/cross_validation.py:464: UserWarning:\n",
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"\n",
"Encountered exception in computing model fit quality: RandomModelBridge does not support prediction.\n",
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"text": [
"The best objective seen is 0.401, which is less than the threshold of 1.000.\n"
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}
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"for i in range(25):\n",
" try:\n",
" parameters, trial_index = ax_client_custom_gss.get_next_trial()\n",
" except OptimizationShouldStop as exc:\n",
" print(exc.message)\n",
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" ax_client_custom_gss.complete_trial(\n",
" trial_index=trial_index, raw_data=evaluate(parameters)\n",
" )"
]
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