Source code for ax.models.random.base

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

from typing import Any, Callable, Dict, List, Optional, Tuple

import numpy as np
from ax.core.types import TConfig
from ax.models.base import Model
from ax.models.model_utils import (
from ax.utils.common.docutils import copy_doc

[docs]class RandomModel(Model): """This class specifies the basic skeleton for a random model. As random generators do not make use of models, they do not implement the fit or predict methods. These models do not need data, or optimization configs. To satisfy search space parameter constraints, these models can use rejection sampling. To enable rejection sampling for a subclass, only only `_gen_samples` needs to be implemented, or alternatively, `_gen_unconstrained`/`gen` can be directly implemented. Attributes: deduplicate: If True (defaults to True), a single instantiation of the model will not return the same point twice. This flag is used in rejection sampling. scramble: If True, permutes the parameter values among the elements of the Sobol sequence. Default is True. seed: An optional seed value for scrambling. """ def __init__( self, deduplicate: bool = True, seed: Optional[int] = None, generated_points: Optional[np.ndarray] = None, ) -> None: super().__init__() self.deduplicate = deduplicate self.seed = seed # Used for deduplication. self.generated_points = generated_points
[docs] def gen( self, n: int, bounds: List[Tuple[float, float]], linear_constraints: Optional[Tuple[np.ndarray, np.ndarray]] = None, fixed_features: Optional[Dict[int, float]] = None, model_gen_options: Optional[TConfig] = None, rounding_func: Optional[Callable[[np.ndarray], np.ndarray]] = None, ) -> Tuple[np.ndarray, np.ndarray]: """Generate new candidates. Args: n: Number of candidates to generate. bounds: A list of (lower, upper) tuples for each column of X. Defined on [0, 1]^d. linear_constraints: A tuple of (A, b). For k linear constraints on d-dimensional x, A is (k x d) and b is (k x 1) such that A x <= b. fixed_features: A map {feature_index: value} for features that should be fixed to a particular value during generation. model_gen_options: A config dictionary that is passed along to the model. rounding_func: A function that rounds an optimization result appropriately (e.g., according to `round-trip` transformations). Returns: 2-element tuple containing - (n x d) array of generated points. - Uniform weights, an n-array of ones for each point. """ tf_indices = tunable_feature_indices( bounds=bounds, fixed_features=fixed_features ) if fixed_features: fixed_feature_indices = np.array(list(fixed_features.keys())) else: fixed_feature_indices = np.array([]) validate_bounds(bounds=bounds, fixed_feature_indices=fixed_feature_indices) attempted_draws = 0 max_draws = None if model_gen_options: max_draws = model_gen_options.get("max_rs_draws") if max_draws is not None: # pyre-fixme[6]: Expected `Union[bytes, str, typing.SupportsInt]` # for 1st param but got # `Union[botorch.acquisition.acquisition.AcquisitionFunction, float, # int, str]`. # pyre-fixme[35]: Target cannot be annotated. max_draws: int = int(max_draws) # Always rejection sample, but this only rejects if there are # constraints or actual duplicates and deduplicate is specified. points, attempted_draws = rejection_sample( gen_unconstrained=self._gen_unconstrained, n=n, d=len(bounds), tunable_feature_indices=tf_indices, linear_constraints=linear_constraints, deduplicate=self.deduplicate, max_draws=max_draws, fixed_features=fixed_features, rounding_func=rounding_func, existing_points=self.generated_points, ) # pyre-fixme[16]: `RandomModel` has no attribute `attempted_draws`. self.attempted_draws = attempted_draws if self.deduplicate: if self.generated_points is None: self.generated_points = points else: self.generated_points = np.vstack([self.generated_points, points]) return (points, np.ones(len(points)))
@copy_doc(Model._get_state) def _get_state(self) -> Dict[str, Any]: state = super()._get_state() if not self.deduplicate: return state state.update({"generated_points": self.generated_points}) return state def _gen_unconstrained( self, n: int, d: int, tunable_feature_indices: np.ndarray, fixed_features: Optional[Dict[int, float]] = None, ) -> np.ndarray: """Generate n points, from an unconstrained parameter space, using _gen_samples. Args: n: Number of points to generate. d: Dimension of parameter space. fixed_features: A map {feature_index: value} for features that should be fixed to a particular value during generation. tunable_feature_indices: Parameter indices (in d) which are tunable. Returns: An (n x d) array of generated points. """ tunable_points = self._gen_samples(n=n, tunable_d=len(tunable_feature_indices)) points = add_fixed_features( tunable_points=tunable_points, d=d, tunable_feature_indices=tunable_feature_indices, fixed_features=fixed_features, ) return points def _gen_samples(self, n: int, tunable_d: int) -> np.ndarray: """Generate n samples on [0, 1]^d. Args: n: Number of points to generate. Returns: (n x d) array of generated points. """ raise NotImplementedError("Base RandomModel can't generate samples.")