ax.telemetry

Experiment

class ax.telemetry.experiment.ExperimentCompletedRecord(num_initialization_trials: int, num_bayesopt_trials: int, num_other_trials: int, num_completed_trials: int, num_failed_trials: int, num_abandoned_trials: int, num_early_stopped_trials: int, total_fit_time: int, total_gen_time: int)[source]

Bases: object

Record of the Experiment completion event. This can be used for telemetry in settings where many Experiments are being created either manually or programatically. In order to facilitate easy serialization only include simple types: numbers, strings, bools, and None.

classmethod from_experiment(experiment: Experiment) → ExperimentCompletedRecord[source]
num_abandoned_trials: int
num_bayesopt_trials: int
num_completed_trials: int
num_early_stopped_trials: int
num_failed_trials: int
num_initialization_trials: int
num_other_trials: int
total_fit_time: int
total_gen_time: int
class ax.telemetry.experiment.ExperimentCreatedRecord(experiment_name: Optional[str], experiment_type: Optional[str], num_continuous_range_parameters: int, num_int_range_parameters_small: int, num_int_range_parameters_medium: int, num_int_range_parameters_large: int, num_log_scale_range_parameters: int, num_unordered_choice_parameters_small: int, num_unordered_choice_parameters_medium: int, num_unordered_choice_parameters_large: int, num_fixed_parameters: int, dimensionality: int, hierarchical_tree_height: int, num_parameter_constraints: int, num_objectives: int, num_tracking_metrics: int, num_outcome_constraints: int, num_map_metrics: int, metric_cls_to_quantity: Dict[str, int], runner_cls: str)[source]

Bases: object

Record of the Experiment creation event. This can be used for telemetry in settings where many Experiments are being created either manually or programatically. In order to facilitate easy serialization only include simple types: numbers, strings, bools, and None.

dimensionality: int
experiment_name: Optional[str]
experiment_type: Optional[str]
classmethod from_experiment(experiment: Experiment) → ExperimentCreatedRecord[source]
hierarchical_tree_height: int
metric_cls_to_quantity: Dict[str, int]
num_continuous_range_parameters: int
num_fixed_parameters: int
num_int_range_parameters_large: int
num_int_range_parameters_medium: int
num_int_range_parameters_small: int
num_log_scale_range_parameters: int
num_map_metrics: int
num_objectives: int
num_outcome_constraints: int
num_parameter_constraints: int
num_tracking_metrics: int
num_unordered_choice_parameters_large: int
num_unordered_choice_parameters_medium: int
num_unordered_choice_parameters_small: int
runner_cls: str

Generation Strategy

class ax.telemetry.generation_strategy.GenerationStrategyCreatedRecord(generation_strategy_name: str, num_requested_initialization_trials: int, num_requested_bayesopt_trials: int, num_requested_other_trials: int, max_parallelism: int)[source]

Bases: object

Record of the GenerationStrategy creation event. This can be used for telemetry in settings where many GenerationStrategy are being created either manually or programatically. In order to facilitate easy serialization only include simple types: numbers, strings, bools, and None.

classmethod from_generation_strategy(generation_strategy: GenerationStrategy) → GenerationStrategyCreatedRecord[source]
generation_strategy_name: str
max_parallelism: int
num_requested_bayesopt_trials: int
num_requested_initialization_trials: int
num_requested_other_trials: int

Scheduler

class ax.telemetry.scheduler.SchedulerCompletedRecord(experiment_completed_record: ExperimentCompletedRecord, best_point_quality: float, model_fit_quality: float, model_std_quality: float, model_fit_generalization: float, model_std_generalization: float, improvement_over_baseline: float, num_metric_fetch_e_encountered: int, num_trials_bad_due_to_err: int)[source]

Bases: object

Record of the Scheduler completion event. This will have information only available after the optimization has completed.

best_point_quality: float
experiment_completed_record: ExperimentCompletedRecord
flatten() → Dict[str, Any][source]

Flatten into an appropriate format for logging to a tabular database.

classmethod from_scheduler(scheduler: Scheduler) → SchedulerCompletedRecord[source]
improvement_over_baseline: float
model_fit_generalization: float
model_fit_quality: float
model_std_generalization: float
model_std_quality: float
num_metric_fetch_e_encountered: int
num_trials_bad_due_to_err: int
class ax.telemetry.scheduler.SchedulerCreatedRecord(experiment_created_record: ExperimentCreatedRecord, generation_strategy_created_record: GenerationStrategyCreatedRecord, scheduler_total_trials: Optional[int], scheduler_max_pending_trials: int, arms_per_trial: int, early_stopping_strategy_cls: Optional[str], global_stopping_strategy_cls: Optional[str], transformed_dimensionality: int)[source]

Bases: object

Record of the Scheduler creation event. This can be used for telemetry in settings where many Schedulers are being created either manually or programatically. In order to facilitate easy serialization only include simple types: numbers, strings, bools, and None.

arms_per_trial: int
early_stopping_strategy_cls: Optional[str]
experiment_created_record: ExperimentCreatedRecord
flatten() → Dict[str, Any][source]

Flatten into an appropriate format for logging to a tabular database.

classmethod from_scheduler(scheduler: Scheduler) → SchedulerCreatedRecord[source]
generation_strategy_created_record: GenerationStrategyCreatedRecord
global_stopping_strategy_cls: Optional[str]
scheduler_max_pending_trials: int
scheduler_total_trials: Optional[int]
transformed_dimensionality: int