Source code for ax.telemetry.ax_client

# 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.

# pyre-strict

from __future__ import annotations

from dataclasses import asdict, dataclass
from typing import Any

from ax.service.ax_client import AxClient
from ax.telemetry.common import _get_max_transformed_dimensionality
from ax.telemetry.experiment import ExperimentCompletedRecord, ExperimentCreatedRecord
from ax.telemetry.generation_strategy import GenerationStrategyCreatedRecord


[docs] @dataclass(frozen=True) class AxClientCreatedRecord: """ Record of the AxClient creation event. This can be used for telemetry in settings where many AxClients are being created either manually or programatically. In order to facilitate easy serialization only include simple types: numbers, strings, bools, and None. """ experiment_created_record: ExperimentCreatedRecord generation_strategy_created_record: GenerationStrategyCreatedRecord arms_per_trial: int early_stopping_strategy_cls: str | None global_stopping_strategy_cls: str | None # Dimensionality of transformed SearchSpace can often be much higher due to one-hot # encoding of unordered ChoiceParameters transformed_dimensionality: int | None
[docs] @classmethod def from_ax_client(cls, ax_client: AxClient) -> AxClientCreatedRecord: # Some AxClients may implement `batch_size`, those that do not use # one trial arms. if getattr(ax_client, "batch_size", None) is not None: # pyre-fixme[16] `AxClient` has no attribute `batch_size` arms_per_trial = ax_client.batch_size else: arms_per_trial = 1 return cls( experiment_created_record=ExperimentCreatedRecord.from_experiment( experiment=ax_client.experiment ), generation_strategy_created_record=( GenerationStrategyCreatedRecord.from_generation_strategy( generation_strategy=ax_client.generation_strategy ) ), arms_per_trial=arms_per_trial, early_stopping_strategy_cls=( None if ax_client.early_stopping_strategy is None else ax_client.early_stopping_strategy.__class__.__name__ ), global_stopping_strategy_cls=( None if ax_client.global_stopping_strategy is None else ax_client.global_stopping_strategy.__class__.__name__ ), transformed_dimensionality=_get_max_transformed_dimensionality( search_space=ax_client.experiment.search_space, generation_strategy=ax_client.generation_strategy, ), )
[docs] def flatten(self) -> dict[str, Any]: """ Flatten into an appropriate format for logging to a tabular database. """ self_dict = asdict(self) experiment_created_record_dict = self_dict.pop("experiment_created_record") generation_strategy_created_record_dict = self_dict.pop( "generation_strategy_created_record" ) return { **self_dict, **experiment_created_record_dict, **generation_strategy_created_record_dict, }
[docs] @dataclass(frozen=True) class AxClientCompletedRecord: """ Record of the AxClient completion event. This will have information only available after the optimization has completed. """ experiment_completed_record: ExperimentCompletedRecord best_point_quality: float model_fit_quality: float model_std_quality: float model_fit_generalization: float model_std_generalization: float
[docs] @classmethod def from_ax_client(cls, ax_client: AxClient) -> AxClientCompletedRecord: return cls( experiment_completed_record=ExperimentCompletedRecord.from_experiment( experiment=ax_client.experiment ), best_point_quality=float("nan"), # TODO[T147907632] model_fit_quality=float("nan"), # TODO[T147907632] model_std_quality=float("nan"), model_fit_generalization=float("nan"), model_std_generalization=float("nan"), )
[docs] def flatten(self) -> dict[str, Any]: """ Flatten into an appropriate format for logging to a tabular database. """ self_dict = asdict(self) experiment_completed_record_dict = self_dict.pop("experiment_completed_record") return { **self_dict, **experiment_completed_record_dict, }