def new_batch_trial( self, generator_run: Optional[GeneratorRun] = None, trial_type: Optional[str] = None, optimize_for_power: Optional[bool] = False, ttl_seconds: Optional[int] = None, ) -> BatchTrial: """Create a new batch trial associated with this experiment. Args: generator_run: GeneratorRun, associated with this trial. This can a also be set later through `add_arm` or `add_generator_run`, but a trial's associated generator run is immutable once set. trial_type: Type of this trial, if used in MultiTypeExperiment. optimize_for_power: Whether to optimize the weights of arms in this trial such that the experiment's power to detect effects of certain size is as high as possible. Refer to documentation of `BatchTrial.set_status_quo_and_optimize_power` for more detail. 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. """ if ttl_seconds is not None: self._trials_have_ttl = True return BatchTrial( experiment=self, trial_type=trial_type, generator_run=generator_run, optimize_for_power=optimize_for_power, ttl_seconds=ttl_seconds, )
def new_batch_trial( self, generator_run: Optional[GeneratorRun] = None, trial_type: Optional[str] = None, ) -> BatchTrial: """Create a new batch trial associated with this experiment.""" return BatchTrial(experiment=self, trial_type=trial_type, generator_run=generator_run)
def new_batch_trial( self, generator_run: Optional[GeneratorRun] = None, trial_type: Optional[str] = None, optimize_for_power: Optional[bool] = False, ) -> BatchTrial: """Create a new batch trial associated with this experiment.""" return BatchTrial( experiment=self, trial_type=trial_type, generator_run=generator_run, optimize_for_power=optimize_for_power, )
def new_batch_trial( self, generator_run: Optional[GeneratorRun] = None, trial_type: Optional[str] = None, optimize_for_power: Optional[bool] = False, ) -> BatchTrial: """Create a new batch trial associated with this experiment. Args: generator_run: GeneratorRun, associated with this trial. This can a also be set later through `add_arm` or `add_generator_run`, but a trial's associated generator run is immutable once set. trial_type: Type of this trial, if used in MultiTypeExperiment. optimize_for_power: Whether to optimize the weights of arms in this trial such that the experiment's power to detect effects of certain size is as high as possible. Refer to documentation of `BatchTrial.set_status_quo_and_optimize_power` for more detail. """ return BatchTrial( experiment=self, trial_type=trial_type, generator_run=generator_run, optimize_for_power=optimize_for_power, )
def trial_from_sqa(self, trial_sqa: SQATrial, experiment: Experiment, reduced_state: bool = False) -> BaseTrial: """Convert SQLAlchemy Trial to Ax Trial. Args: trial_sqa: `SQATrial` to decode. reduced_state: Whether to load trial's generator run(s) with a slightly reduced state (without model state, search space, and optimization config). """ if trial_sqa.is_batch: trial = BatchTrial( experiment=experiment, optimize_for_power=trial_sqa.optimize_for_power, ttl_seconds=trial_sqa.ttl_seconds, index=trial_sqa.index, ) generator_run_structs = [ GeneratorRunStruct( generator_run=self.generator_run_from_sqa( generator_run_sqa=generator_run_sqa, reduced_state=reduced_state, ), weight=generator_run_sqa.weight or 1.0, ) for generator_run_sqa in trial_sqa.generator_runs ] if trial_sqa.status_quo_name is not None: new_generator_run_structs = [] for struct in generator_run_structs: if (struct.generator_run.generator_run_type == GeneratorRunType.STATUS_QUO.name): status_quo_weight = struct.generator_run.weights[0] trial._status_quo = struct.generator_run.arms[0] trial._status_quo_weight_override = status_quo_weight else: new_generator_run_structs.append(struct) generator_run_structs = new_generator_run_structs trial._generator_run_structs = generator_run_structs if not reduced_state: trial._abandoned_arms_metadata = { abandoned_arm_sqa.name: self.abandoned_arm_from_sqa( abandoned_arm_sqa=abandoned_arm_sqa) for abandoned_arm_sqa in trial_sqa.abandoned_arms } trial._refresh_arms_by_name() # Trigger cache build else: trial = Trial( experiment=experiment, ttl_seconds=trial_sqa.ttl_seconds, index=trial_sqa.index, ) if trial_sqa.generator_runs: if len(trial_sqa.generator_runs) != 1: raise SQADecodeError( # pragma: no cover "Cannot decode SQATrial to Trial because trial is not batched " "but has more than one generator run.") trial._generator_run = self.generator_run_from_sqa( generator_run_sqa=trial_sqa.generator_runs[0], reduced_state=reduced_state, ) trial._trial_type = trial_sqa.trial_type # Swap `DISPATCHED` for `RUNNING`, since `DISPATCHED` is deprecated and nearly # equivalent to `RUNNING`. trial._status = (trial_sqa.status if trial_sqa.status != TrialStatus.DISPATCHED else TrialStatus.RUNNING) trial._time_created = trial_sqa.time_created trial._time_completed = trial_sqa.time_completed trial._time_staged = trial_sqa.time_staged trial._time_run_started = trial_sqa.time_run_started trial._abandoned_reason = trial_sqa.abandoned_reason # pyre-fixme[9]: _run_metadata has type `Dict[str, Any]`; used as # `Optional[Dict[str, Any]]`. # pyre-fixme[8]: Attribute has type `Dict[str, typing.Any]`; used as # `Optional[typing.Dict[Variable[_KT], Variable[_VT]]]`. trial._run_metadata = ( # pyre-fixme[6]: Expected `Mapping[Variable[_KT], Variable[_VT]]` for # 1st param but got `Optional[Dict[str, typing.Any]]`. dict(trial_sqa.run_metadata) if trial_sqa.run_metadata is not None else None) trial._num_arms_created = trial_sqa.num_arms_created trial._runner = (self.runner_from_sqa(trial_sqa.runner) if trial_sqa.runner else None) trial._generation_step_index = trial_sqa.generation_step_index trial._properties = trial_sqa.properties or {} trial.db_id = trial_sqa.id return trial
def trial_from_sqa(self, trial_sqa: SQATrial, experiment: Experiment) -> BaseTrial: """Convert SQLAlchemy Trial to Ax Trial.""" if trial_sqa.is_batch: trial = BatchTrial( experiment=experiment, optimize_for_power=trial_sqa.optimize_for_power, ttl_seconds=trial_sqa.ttl_seconds, ) generator_run_structs = [ GeneratorRunStruct( generator_run=self.generator_run_from_sqa( generator_run_sqa=generator_run_sqa ), weight=generator_run_sqa.weight or 1.0, ) for generator_run_sqa in trial_sqa.generator_runs ] if trial_sqa.status_quo_name is not None: new_generator_run_structs = [] for struct in generator_run_structs: if ( struct.generator_run.generator_run_type == GeneratorRunType.STATUS_QUO.name ): status_quo_weight = struct.generator_run.weights[0] trial._status_quo = struct.generator_run.arms[0] trial._status_quo_weight_override = status_quo_weight else: new_generator_run_structs.append(struct) generator_run_structs = new_generator_run_structs trial._generator_run_structs = generator_run_structs trial._abandoned_arms_metadata = { abandoned_arm_sqa.name: self.abandoned_arm_from_sqa( abandoned_arm_sqa=abandoned_arm_sqa ) for abandoned_arm_sqa in trial_sqa.abandoned_arms } else: trial = Trial(experiment=experiment, ttl_seconds=trial_sqa.ttl_seconds) if trial_sqa.generator_runs: if len(trial_sqa.generator_runs) != 1: raise SQADecodeError( # pragma: no cover "Cannot decode SQATrial to Trial because trial is not batched " "but has more than one generator run." ) trial._generator_run = self.generator_run_from_sqa( generator_run_sqa=trial_sqa.generator_runs[0] ) trial._index = trial_sqa.index trial._trial_type = trial_sqa.trial_type # Swap `DISPATCHED` for `RUNNING`, since `DISPATCHED` is deprecated and nearly # equivalent to `RUNNING`. trial._status = ( trial_sqa.status if trial_sqa.status != TrialStatus.DISPATCHED else TrialStatus.RUNNING ) trial._time_created = trial_sqa.time_created trial._time_completed = trial_sqa.time_completed trial._time_staged = trial_sqa.time_staged trial._time_run_started = trial_sqa.time_run_started trial._abandoned_reason = trial_sqa.abandoned_reason # pyre-fixme[9]: _run_metadata has type `Dict[str, Any]`; used as # `Optional[Dict[str, Any]]`. trial._run_metadata = ( # pyre-fixme[6]: Expected `Mapping[Variable[_KT], Variable[_VT]]` for # 1st param but got `Optional[Dict[str, typing.Any]]`. dict(trial_sqa.run_metadata) if trial_sqa.run_metadata is not None else None ) trial._num_arms_created = trial_sqa.num_arms_created trial._runner = ( self.runner_from_sqa(trial_sqa.runner) if trial_sqa.runner else None ) trial._generation_step_index = trial_sqa.generation_step_index return trial
def batch_trial_from_json( experiment: "core.experiment.Experiment", index: int, trial_type: Optional[str], status: TrialStatus, time_created: datetime, time_completed: Optional[datetime], time_staged: Optional[datetime], time_run_started: Optional[datetime], abandoned_reason: Optional[str], run_metadata: Optional[Dict[str, Any]], generator_run_structs: List[GeneratorRunStruct], runner: Optional[Runner], abandoned_arms_metadata: Dict[str, AbandonedArm], num_arms_created: int, status_quo: Optional[Arm], status_quo_weight: float, ) -> BatchTrial: """Load Ax BatchTrial from JSON. Other classes don't need explicit deserializers, because we can just use their constructors (see decoder.py). However, the constructor for Batch does not allow us to exactly recreate an existing object. """ batch = BatchTrial(experiment=experiment) batch._index = index batch._trial_type = trial_type batch._status = status batch._time_created = time_created batch._time_completed = time_completed batch._time_staged = time_staged batch._time_run_started = time_run_started batch._abandoned_reason = abandoned_reason batch._run_metadata = run_metadata or {} batch._generator_run_structs = generator_run_structs batch._runner = runner batch._abandoned_arms_metadata = abandoned_arms_metadata batch._num_arms_created = num_arms_created batch._status_quo = status_quo batch._status_quo_weight = status_quo_weight return batch
def trial_from_sqa(self, trial_sqa: SQATrial, experiment: Experiment) -> BaseTrial: """Convert SQLAlchemy Trial to Ax Trial.""" if trial_sqa.is_batch: trial = BatchTrial(experiment=experiment, optimize_for_power=trial_sqa.optimize_for_power) generator_run_structs = [ GeneratorRunStruct( generator_run=self.generator_run_from_sqa( generator_run_sqa=generator_run_sqa), weight=generator_run_sqa.weight or 1.0, ) for generator_run_sqa in trial_sqa.generator_runs ] if trial_sqa.status_quo_name is not None: new_generator_run_structs = [] for struct in generator_run_structs: if (struct.generator_run.generator_run_type == GeneratorRunType.STATUS_QUO.name): status_quo_weight = struct.generator_run.weights[0] trial._status_quo = struct.generator_run.arms[0] trial._status_quo_weight_override = status_quo_weight else: new_generator_run_structs.append(struct) generator_run_structs = new_generator_run_structs trial._generator_run_structs = generator_run_structs trial._abandoned_arms_metadata = { abandoned_arm_sqa.name: self.abandoned_arm_from_sqa( abandoned_arm_sqa=abandoned_arm_sqa) for abandoned_arm_sqa in trial_sqa.abandoned_arms } else: trial = Trial(experiment=experiment) if trial_sqa.generator_runs: if len(trial_sqa.generator_runs) != 1: raise SQADecodeError( # pragma: no cover "Cannot decode SQATrial to Trial because trial is not batched " "but has more than one generator run.") trial._generator_run = self.generator_run_from_sqa( generator_run_sqa=trial_sqa.generator_runs[0]) trial._index = trial_sqa.index trial._trial_type = trial_sqa.trial_type trial._status = trial_sqa.status trial._time_created = trial_sqa.time_created trial._time_completed = trial_sqa.time_completed trial._time_staged = trial_sqa.time_staged trial._time_run_started = trial_sqa.time_run_started trial._abandoned_reason = trial_sqa.abandoned_reason # pyre-fixme[9]: _run_metadata has type `Dict[str, Any]`; used as # `Optional[Dict[str, Any]]`. trial._run_metadata = (dict(trial_sqa.run_metadata) if trial_sqa.run_metadata is not None else None) trial._num_arms_created = trial_sqa.num_arms_created trial._runner = (self.runner_from_sqa(trial_sqa.runner) if trial_sqa.runner else None) return trial
def batch_trial_from_json( experiment: core.experiment.Experiment, index: int, trial_type: Optional[str], status: TrialStatus, time_created: datetime, time_completed: Optional[datetime], time_staged: Optional[datetime], time_run_started: Optional[datetime], abandoned_reason: Optional[str], run_metadata: Optional[Dict[str, Any]], generator_run_structs: List[GeneratorRunStruct], runner: Optional[Runner], abandoned_arms_metadata: Dict[str, AbandonedArm], num_arms_created: int, status_quo: Optional[Arm], status_quo_weight_override: float, optimize_for_power: Optional[bool], # Allowing default values for backwards compatibility with # objects stored before these fields were added. ttl_seconds: Optional[int] = None, generation_step_index: Optional[int] = None, properties: Optional[Dict[str, Any]] = None, stop_metadata: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> BatchTrial: """Load Ax BatchTrial from JSON. Other classes don't need explicit deserializers, because we can just use their constructors (see decoder.py). However, the constructor for Batch does not allow us to exactly recreate an existing object. """ batch = BatchTrial(experiment=experiment, ttl_seconds=ttl_seconds) batch._index = index batch._trial_type = trial_type batch._status = status batch._time_created = time_created batch._time_completed = time_completed batch._time_staged = time_staged batch._time_run_started = time_run_started batch._abandoned_reason = abandoned_reason batch._run_metadata = run_metadata or {} batch._stop_metadata = stop_metadata or {} batch._generator_run_structs = generator_run_structs batch._runner = runner batch._abandoned_arms_metadata = abandoned_arms_metadata batch._num_arms_created = num_arms_created batch._status_quo = status_quo batch._status_quo_weight_override = status_quo_weight_override batch.optimize_for_power = optimize_for_power batch._generation_step_index = generation_step_index batch._properties = properties batch._refresh_arms_by_name() # Trigger cache build warn_on_kwargs(callable_with_kwargs=BatchTrial, **kwargs) return batch
def testObservationsFromDataAbandoned(self): truth = { 0.5: { "arm_name": "0_0", "parameters": {"x": 0, "y": "a", "z": 1}, "mean": 2.0, "sem": 2.0, "trial_index": 0, "metric_name": "a", "updated_parameters": {"x": 0, "y": "a", "z": 0.5}, "mean_t": np.array([2.0]), "covariance_t": np.array([[4.0]]), "z": 0.5, "timestamp": 50, }, 1: { "arm_name": "1_0", "parameters": {"x": 0, "y": "a", "z": 1}, "mean": 4.0, "sem": 4.0, "trial_index": 1, "metric_name": "b", "updated_parameters": {"x": 0, "y": "a", "z": 1}, "mean_t": np.array([4.0]), "covariance_t": np.array([[16.0]]), "z": 1, "timestamp": 100, }, 0.25: { "arm_name": "2_0", "parameters": {"x": 1, "y": "a", "z": 0.5}, "mean": 3.0, "sem": 3.0, "trial_index": 2, "metric_name": "a", "updated_parameters": {"x": 1, "y": "b", "z": 0.25}, "mean_t": np.array([3.0]), "covariance_t": np.array([[9.0]]), "z": 0.25, "timestamp": 25, }, 0.75: { "arm_name": "2_1", "parameters": {"x": 1, "y": "b", "z": 0.75}, "mean": 3.0, "sem": 3.0, "trial_index": 2, "metric_name": "a", "updated_parameters": {"x": 1, "y": "b", "z": 0.75}, "mean_t": np.array([3.0]), "covariance_t": np.array([[9.0]]), "z": 0.75, "timestamp": 25, }, } arms = { obs["arm_name"]: Arm(name=obs["arm_name"], parameters=obs["parameters"]) for _, obs in truth.items() } experiment = Mock() experiment._trial_indices_by_status = {status: set() for status in TrialStatus} trials = { obs["trial_index"]: ( Trial(experiment, GeneratorRun(arms=[arms[obs["arm_name"]]])) ) for _, obs in list(truth.items())[:-1] if not obs["arm_name"].startswith("2") } batch = BatchTrial(experiment, GeneratorRun(arms=[arms["2_0"], arms["2_1"]])) trials.update({2: batch}) trials.get(1).mark_abandoned() trials.get(2).mark_arm_abandoned(arm_name="2_1") type(experiment).arms_by_name = PropertyMock(return_value=arms) type(experiment).trials = PropertyMock(return_value=trials) df = pd.DataFrame(list(truth.values()))[ ["arm_name", "trial_index", "mean", "sem", "metric_name"] ] data = Data(df=df) # 1 arm is abandoned and 1 trial is abandoned, so only 2 observations should be # included. obs_no_abandoned = observations_from_data(experiment, data) self.assertEqual(len(obs_no_abandoned), 2) # 1 arm is abandoned and 1 trial is abandoned, so only 2 observations should be # included. obs_with_abandoned = observations_from_data( experiment, data, include_abandoned=True ) self.assertEqual(len(obs_with_abandoned), 4)