Source code for ax.benchmark.problems.hd_embedding

# 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

import copy
from typing import TypeVar

from ax.benchmark.benchmark_problem import BenchmarkProblem
from ax.core.parameter import ParameterType, RangeParameter
from ax.core.search_space import SearchSpace

TProblem = TypeVar("TProblem", bound=BenchmarkProblem)


[docs] def embed_higher_dimension(problem: TProblem, total_dimensionality: int) -> TProblem: """ Return a new `BenchmarkProblem` with enough `RangeParameter`s added to the search space to make its total dimensionality equal to `total_dimensionality` and add `total_dimensionality` to its name. The search space of the original `problem` is within the search space of the new problem, and the constraints are copied from the original problem. """ num_dummy_dimensions = total_dimensionality - len(problem.search_space.parameters) search_space = SearchSpace( parameters=[ *problem.search_space.parameters.values(), *[ RangeParameter( name=f"embedding_dummy_{i}", parameter_type=ParameterType.FLOAT, lower=0, upper=1, ) for i in range(num_dummy_dimensions) ], ], parameter_constraints=problem.search_space.parameter_constraints, ) # if problem name already has dimensionality in it, strip it def _is_dim_suffix(s: str) -> bool: return s[-1] == "d" and all(char in "0123456789" for char in s[:-1]) orig_name_without_dimensionality = "_".join( [substr for substr in problem.name.split("_") if not _is_dim_suffix(substr)] ) new_name = f"{orig_name_without_dimensionality}_{total_dimensionality}d" new_problem = copy.copy(problem) new_problem.name = new_name new_problem.search_space = search_space return new_problem