Source code for ax.metrics.dict_lookup

#!/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 Any, Dict, List, Optional, Tuple, Union

import numpy as np
import pandas as pd
from ax.core.base_trial import BaseTrial
from import Data
from ax.core.metric import Metric, MetricFetchE, MetricFetchResult
from ax.utils.common.result import Err, Ok
from ax.utils.common.typeutils import not_none

[docs]class DictLookupMetric(Metric): """A metric defined by a dictionary mapping parameter values to the corresponding metric values. This provides an option to add normal noise with mean 0 and mean_sd scale to the given metric values. """ def __init__( self, name: str, param_names: List[str], lookup_dict: Dict[Tuple[Union[str, float, int, bool], ...], float], noise_sd: Optional[float] = 0.0, lower_is_better: Optional[bool] = None, ) -> None: """Metric is computed via a dictionary look up using a tuple of parameter values, constructed based on the ordering of parameter names given in `param_names`. Args: name: Name of the metric. param_names: An ordered list of names of parameters to be used to construct the dictionary key. lookup_dict: A dictionary mapping a tuple of parameter values to the metric values. noise_sd: Scale of normal noise added to the function result. If None, interpret the function as noisy with unknown noise level. lower_is_better: Flag for metrics which should be minimized. """ self.param_names = param_names self.lookup_dict = lookup_dict self.noise_sd = noise_sd super().__init__(name=name, lower_is_better=lower_is_better)
[docs] @classmethod def is_available_while_running(cls) -> bool: return True
[docs] def clone(self) -> DictLookupMetric: return self.__class__( name=self._name, param_names=self.param_names, lookup_dict=self.lookup_dict, noise_sd=self.noise_sd, lower_is_better=self.lower_is_better, )
[docs] def fetch_trial_data(self, trial: BaseTrial, **kwargs: Any) -> MetricFetchResult: try: noise_sd = self.noise_sd arm_names = [] mean = [] for name, arm in trial.arms_by_name.items(): arm_names.append(name) lookup_key = tuple( not_none(arm.parameters[p]) for p in self.param_names ) try: val = self.lookup_dict[lookup_key] except KeyError: raise KeyError( "Got a KeyError while attempting to retrieve the " f"parameterization {arm.parameters} from the lookup dict. " f"This parameterization corresponds to {lookup_key=}." ) if noise_sd: val = val + noise_sd * np.random.randn() mean.append(val) # Indicate unknown noise level in data. if noise_sd is None: noise_sd = float("nan") df = pd.DataFrame( { "arm_name": arm_names, "metric_name":, "mean": mean, "sem": noise_sd, "trial_index": trial.index, } ) return Ok(value=Data(df=df)) except Exception as e: return Err( MetricFetchE(message=f"Failed to fetch {}", exception=e) )