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Creating preference testing stub (facebook#2443)
Summary: Adding `preference_stubs.py` and migrate `PairwiseModelBridgeTest` logic to it. Reviewed By: esantorella Differential Revision: D57131012
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| # Copyright (c) Meta Platforms, Inc. and affiliates. | ||
| # | ||
| # This source code is licensed under the MIT license found in the | ||
| # LICENSE file in the root directory of this source tree. | ||
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| from typing import Any, Callable, Dict, List, Optional | ||
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| import numpy as np | ||
| from ax.core import Arm, GeneratorRun | ||
| from ax.core.experiment import Experiment | ||
| from ax.core.parameter import RangeParameter | ||
| from ax.core.types import TEvaluationOutcome, TParameterization | ||
| from ax.service.utils.instantiation import InstantiationBase | ||
| from ax.utils.common.constants import Keys | ||
| from ax.utils.common.typeutils import checked_cast | ||
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| # from ExperimentType in ae/lazarus/fb/utils/if/ae.thrift | ||
| PBO_EXPERIMENT_TYPE: str = "PREFERENCE_LEARNING" | ||
| PE_EXPERIMENT_TYPE: str = "PREFERENCE_EXPLORATION" | ||
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| def sum_utility(parameters: TParameterization) -> float: | ||
| """Test utility function that sums over parameter values""" | ||
| values = [checked_cast(float, v) for v in parameters.values()] | ||
| return sum(values) | ||
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| def pairwise_pref_metric_eval( | ||
| parameters: Dict[str, TParameterization], | ||
| utility_func: Callable[[TParameterization], float] = sum_utility, | ||
| ) -> Dict[str, TEvaluationOutcome]: | ||
| """evaluating pairwise comparisons using utility_func""" | ||
| assert len(parameters.keys()) == 2 | ||
| arm1, arm2 = list(parameters.keys()) | ||
| arm1_sum, arm2_sum = sum_utility(parameters[arm1]), sum_utility(parameters[arm2]) | ||
| is_arm1_preferred = int(arm1_sum - arm2_sum > 0) | ||
| return { | ||
| arm1: {Keys.PAIRWISE_PREFERENCE_QUERY.value: is_arm1_preferred}, | ||
| arm2: {Keys.PAIRWISE_PREFERENCE_QUERY.value: 1 - is_arm1_preferred}, | ||
| } | ||
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| def experimental_metric_eval( | ||
| parameters: Dict[str, Any], metric_names: List[str] | ||
| ) -> Dict[str, Any]: | ||
| return { | ||
| arm_name: {metric_name: np.random.random() for metric_name in metric_names} | ||
| for arm_name, _ in parameters.items() | ||
| } | ||
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| def get_pbo_experiment( | ||
| num_parameters: int = 2, | ||
| num_experimental_metrics: int = 3, | ||
| tracking_metric_names: Optional[List[str]] = None, | ||
| num_experimental_trials: int = 3, | ||
| num_preference_trials: int = 3, | ||
| num_preference_trials_w_repeated_arm: int = 5, | ||
| include_sq: bool = True, | ||
| partial_data: bool = False, | ||
| ) -> Experiment: | ||
| """Create synthetic preferential BO (not preference exploration) experiment""" | ||
| tracking_metric_names = [ | ||
| f"metric{i}" for i in range(1, num_experimental_metrics + 1) | ||
| ] | ||
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| sq = {f"x{i}": 0.0 for i in range(1, num_parameters + 1)} if include_sq else None | ||
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| parameters = [ | ||
| { | ||
| "name": f"x{i}", | ||
| "type": "range", | ||
| "bounds": [0.0, 1.0], | ||
| } | ||
| for i in range(1, num_parameters + 1) | ||
| ] | ||
| experiment = InstantiationBase.make_experiment( | ||
| name="pref_experiment", | ||
| # pyre-ignore: Incompatible parameter type [6] | ||
| parameters=parameters, | ||
| objectives={Keys.PAIRWISE_PREFERENCE_QUERY.value: "maximize"}, | ||
| tracking_metric_names=tracking_metric_names, | ||
| is_test=True, | ||
| # pyre-ignore: Incompatible parameter type [6] | ||
| status_quo=sq, | ||
| ) | ||
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| # Adding arms with experimental metrics | ||
| for _ in range(num_experimental_trials): | ||
| arm = {} | ||
| for param_name, param in experiment.search_space.parameters.items(): | ||
| lb = checked_cast(RangeParameter, param).lower | ||
| ub = checked_cast(RangeParameter, param).upper | ||
| arm[param_name] = np.random.uniform(low=lb, high=ub) | ||
| gr = ( | ||
| # pyre-ignore: Incompatible parameter type [6] | ||
| GeneratorRun([Arm(arm), Arm(sq)]) | ||
| if include_sq | ||
| else GeneratorRun([Arm(arm)]) | ||
| ) | ||
| trial = experiment.new_batch_trial(generator_run=gr) | ||
| raw_data = experimental_metric_eval( | ||
| parameters={a.name: a.parameters for a in trial.arms}, | ||
| metric_names=tracking_metric_names, | ||
| ) | ||
| # create incomplete data by dropping the first metric | ||
| if partial_data: | ||
| for v in raw_data.values(): | ||
| del v[tracking_metric_names[-1]] | ||
| trial.attach_batch_trial_data(raw_data=raw_data) | ||
| trial.mark_running(no_runner_required=True) | ||
| trial.mark_completed() | ||
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| # Adding arms with preferential queries | ||
| for _ in range(num_preference_trials): | ||
| gr = GeneratorRun( | ||
| [ | ||
| Arm( | ||
| { | ||
| pn: np.random.uniform( | ||
| low=checked_cast(RangeParameter, p).lower, | ||
| high=checked_cast(RangeParameter, p).upper, | ||
| ) | ||
| for pn, p in experiment.search_space.parameters.items() | ||
| } | ||
| ) | ||
| for _ in range(2) | ||
| ] | ||
| ) | ||
| trial = experiment.new_batch_trial(generator_run=gr) | ||
| trial.attach_batch_trial_data( | ||
| raw_data=pairwise_pref_metric_eval( | ||
| parameters={a.name: a.parameters for a in trial.arms} | ||
| ) | ||
| ) | ||
| trial.mark_running(no_runner_required=True) | ||
| trial.mark_completed() | ||
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| # Adding preferential queries using previously evaluated arms | ||
| for _ in range(num_preference_trials_w_repeated_arm): | ||
| arms = np.random.choice( | ||
| list(experiment.arms_by_name.values()), 2, replace=False | ||
| ) | ||
| trial = experiment.new_batch_trial() | ||
| trial.add_arms_and_weights(arms=arms) | ||
| trial.attach_batch_trial_data( | ||
| raw_data=pairwise_pref_metric_eval( | ||
| parameters={a.name: a.parameters for a in trial.arms} | ||
| ) | ||
| ) | ||
| trial.mark_running(no_runner_required=True) | ||
| trial.mark_completed() | ||
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| return experiment |