Source code for ax.modelbridge.transforms.inverse_gaussian_cdf_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.

# pyre-strict

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.modelbridge.transforms.base import Transform
from ax.modelbridge.transforms.utils import match_ci_width_truncated
from ax.models.types import TConfig
from ax.utils.common.logger import get_logger
from scipy.stats import norm

    # import as module to make sphinx-autodoc-typehints happy
    from ax.modelbridge import base as base_modelbridge  # noqa F401

logger: Logger = get_logger(__name__)

# TODO(jej): Add OptimizationConfig validation - can't transform outcome constraints.
[docs]class InverseGaussianCdfY(Transform): """Apply inverse CDF transform to Y. This means that we model uniform distributions as gaussian-distributed. """ def __init__( self, search_space: Optional[SearchSpace] = None, observations: Optional[List[Observation]] = None, modelbridge: Optional["base_modelbridge.ModelBridge"] = None, config: Optional[TConfig] = None, ) -> None: # pyre-fixme[4]: Attribute must be annotated. self.dist = norm(loc=0, scale=1) def _transform_observation_data( self, observation_data: List[ObservationData], ) -> List[ObservationData]: """Map to inverse Gaussian CDF in place.""" # TODO (jej): Transform covariances. for obsd in observation_data: for idx, _ in enumerate(obsd.metric_names): mean = float(obsd.means[idx]) # Error on out-of-domain values. if mean <= 0.0 or mean >= 1.0: raise ValueError( f"Inverse CDF cannot transform value: {mean} outside (0, 1)" ) var = float(obsd.covariance[idx, idx]) transformed_mean, transformed_var = match_ci_width_truncated( mean, var, self._map, lower_bound=0.0, upper_bound=1.0 ) obsd.means[idx] = transformed_mean obsd.covariance[idx, idx] = transformed_var return observation_data def _map(self, val: float) -> float: mapped_val = self.dist.ppf(val) return mapped_val