Source code for ax.modelbridge.transforms.percentile_y

#!/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.

import math
from logging import Logger
from typing import List, Optional, TYPE_CHECKING

from ax.core.observation import Observation, ObservationData
from ax.core.search_space import SearchSpace
from ax.exceptions.core import DataRequiredError
from ax.modelbridge.transforms.base import Transform
from ax.modelbridge.transforms.utils import get_data
from ax.models.types import TConfig
from ax.utils.common.logger import get_logger
from ax.utils.common.typeutils import checked_cast
from scipy import stats

    # import as module to make sphinx-autodoc-typehints happy
    from ax import modelbridge as modelbridge_module  # noqa F401  # pragma: no cover

logger: Logger = get_logger(__name__)

# TODO(jej): Add OptimizationConfig validation - can't transform outcome constraints.
[docs]class PercentileY(Transform): """Map Y values to percentiles based on their empirical CDF.""" def __init__( self, search_space: Optional[SearchSpace] = None, observations: Optional[List[Observation]] = None, modelbridge: Optional["modelbridge_module.base.ModelBridge"] = None, config: Optional[TConfig] = None, ) -> None: assert observations is not None, "PercentileY requires observations" if len(observations) == 0: raise DataRequiredError("Percentile transform requires non-empty data.") observation_data = [ for obs in observations] metric_values = get_data(observation_data=observation_data) # pyre-fixme[4]: Attribute must be annotated. self.percentiles = { metric_name: vals for metric_name, vals in metric_values.items() } if config is not None and "winsorize" in config: # pyre-fixme[4]: Attribute must be annotated. self.winsorize = checked_cast(bool, (config.get("winsorize") or False)) else: self.winsorize = False def _transform_observation_data( self, observation_data: List[ObservationData], ) -> List[ObservationData]: """Map observation data to empirical CDF quantiles in place.""" # TODO (jej): Transform covariances. if self.winsorize: winsorization_rates = {} for metric_name, vals in self.percentiles.items(): n = len(vals) # Calculate winsorization rate based on number of observations # using formula from [Salinas, Shen, Perrone 2020] # winsorization_rates[metric_name] = ( 1.0 / (4 * math.pow(n, 0.25) * math.pow(math.pi * math.log(n), 0.5)) if n > 1 else 0.25 ) else: winsorization_rates = { metric_name: 0 for metric_name in self.percentiles.keys() } for obsd in observation_data: for idx, metric_name in enumerate(obsd.metric_names): if metric_name not in self.percentiles: # pragma: no cover raise ValueError( f"Cannot map value to percentile" f" for unknown metric {metric_name}" ) # apply map function percentile = self._map(obsd.means[idx], metric_name) # apply winsorization. If winsorization_rate is 0, has no effect. metric_wr = winsorization_rates[metric_name] percentile = max(metric_wr, percentile) percentile = min((1 - metric_wr), percentile) obsd.means[idx] = percentile obsd.covariance.fill(float("nan")) return observation_data def _map(self, val: float, metric_name: str) -> float: vals = self.percentiles[metric_name] mapped_val = stats.percentileofscore(vals, val, kind="weak") / 100.0 return mapped_val