#!/usr/bin/env python3 # 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. from __future__ import annotations from typing import TYPE_CHECKING, Dict, List, Optional from ax.core.arm import Arm from ax.core.base_trial import BaseTrial, immutable_once_run from ax.core.generator_run import GeneratorRun, GeneratorRunType from ax.core.types import TCandidateMetadata from ax.utils.common.docutils import copy_doc from ax.utils.common.typeutils import not_none if TYPE_CHECKING: # import as module to make sphinx-autodoc-typehints happy from ax import core # noqa F401 # pragma: no cover class Trial(BaseTrial): """Trial that only has one attached arm and no arm weights. Args: experiment: Experiment, to which this trial is attached. generator_run: GeneratorRun, associated with this trial. Trial has only one generator run (of just one arm) attached to it. This can also be set later through `add_arm` or `add_generator_run`, but a trial's associated genetor run is immutable once set. trial_type: Type of this trial, if used in MultiTypeExperiment. ttl_seconds: If specified, trials will be considered failed after this many seconds since the time the trial was ran, unless the trial is completed before then. Meant to be used to detect 'dead' trials, for which the evaluation process might have crashed etc., and which should be considered failed after their 'time to live' has passed. index: If specified, the trial's index will be set accordingly. This should generally not be specified, as in the index will be automatically determined based on the number of existing trials. This is only used for the purpose of loading from storage. """ def __init__( self, experiment: core.experiment.Experiment, generator_run: Optional[GeneratorRun] = None, trial_type: Optional[str] = None, ttl_seconds: Optional[int] = None, index: Optional[int] = None, ) -> None: super().__init__( experiment=experiment, trial_type=trial_type, ttl_seconds=ttl_seconds, index=index, ) self._generator_run = None if generator_run is not None: self.add_generator_run(generator_run=generator_run) @property def generator_run(self) -> Optional[GeneratorRun]: """Generator run attached to this trial.""" return self._generator_run # pyre-ignore[6]: T77111662. @copy_doc(BaseTrial.generator_runs) @property def generator_runs(self) -> List[GeneratorRun]: gr = self._generator_run return [gr] if gr is not None else [] @property def arm(self) -> Optional[Arm]: """The arm associated with this batch.""" if self.generator_run is None: return None generator_run = not_none(self.generator_run) if len(generator_run.arms) == 0: return None elif len(generator_run.arms) > 1: raise ValueError( # pragma: no cover "Generator run associated with this trial included multiple " "arms, but trial expects only one." ) return generator_run.arms[0] @immutable_once_run def add_arm(self, arm: Arm) -> Trial: """Add arm to the trial. Returns: The trial instance. """ return self.add_generator_run( generator_run=GeneratorRun(arms=[arm], type=GeneratorRunType.MANUAL.name) ) @immutable_once_run def add_generator_run( self, generator_run: GeneratorRun, multiplier: float = 1.0 ) -> Trial: """Add a generator run to the trial. Note: since trial includes only one arm, this will raise a ValueError if the generator run includes multiple arms. Returns: The trial instance. """ if len(generator_run.arms) > 1: raise ValueError( "Trial includes only one arm, but this generator run " "included multiple." ) self.experiment.search_space.check_types( generator_run.arms[0].parameters, raise_error=True ) self._check_existing_and_name_arm(generator_run.arms[0]) self._generator_run = generator_run generator_run.index = 0 self._set_generation_step_index( generation_step_index=generator_run._generation_step_index ) return self @property def arms(self) -> List[Arm]: """All arms attached to this trial. Returns: arms: list of a single arm attached to this trial if there is one, else None. """ return [self.arm] if self.arm is not None else [] @property def arms_by_name(self) -> Dict[str, Arm]: """Dictionary of all arms attached to this trial with their names as keys. Returns: arms: dictionary of a single arm name to arm if one is attached to this trial, else None. """ return {self.arm.name: self.arm} if self.arm is not None else {} @property def abandoned_arms(self) -> List[Arm]: """Abandoned arms attached to this trial.""" return ( [not_none(self.arm)] if self.generator_run is not None and self.arm is not None and self.is_abandoned else [] ) @property def objective_mean(self) -> float: """Objective mean for the arm attached to this trial, retrieved from the latest data available for the objective for the trial. Note: the retrieved objective is the experiment-level objective at the time of the call to `objective_mean`, which is not necessarily the objective that was set at the time the trial was created or ran. """ # For SimpleExperiment, fetch_data just executes eval_trial. df = self.lookup_data().df if df.empty: raise ValueError(f"No data was retrieved for trial {self.index}.") opt_config = self.experiment.optimization_config if opt_config is None: raise ValueError( # pragma: no cover "Experiment optimization config (and thus the objective) is not set." ) return self.get_metric_mean(metric_name=opt_config.objective.metric.name) def get_metric_mean(self, metric_name: str) -> float: """Metric mean for the arm attached to this trial, retrieved from the latest data available for the metric for the trial. """ # For SimpleExperiment, fetch_data just executes eval_trial. df = self.lookup_data().df try: return df.loc[df["metric_name"] == metric_name].iloc[0]["mean"] except IndexError: # pragma: no cover raise ValueError(f"Metric {metric_name} not yet in data for trial.") def __repr__(self) -> str: return ( "Trial(" f"experiment_name='{self._experiment._name}', " f"index={self._index}, " f"status={self._status}, " f"arm={self.arm})" ) def _get_candidate_metadata_from_all_generator_runs( self, ) -> Dict[str, TCandidateMetadata]: """Retrieves candidate metadata from the generator run on this batch trial in the form of { arm name -> candidate metadata} mapping. """ gr = self.generator_run if gr is None or gr.candidate_metadata_by_arm_signature is None: return {} cand_metadata = not_none(gr.candidate_metadata_by_arm_signature) return {a.name: cand_metadata.get(a.signature) for a in gr.arms} def _get_candidate_metadata(self, arm_name: str) -> TCandidateMetadata: """Retrieves candidate metadata for a specific arm.""" gr = self.generator_run if gr is None or gr.arms[0].name != arm_name: raise ValueError( f"Arm by name {arm_name} is not part of trial #{self.index}." ) if gr.candidate_metadata_by_arm_signature is None: return None arm = gr.arms[0] return not_none(gr.candidate_metadata_by_arm_signature).get(arm.signature)