#!/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 __future__ import annotations
from typing import Optional
import numpy as np
import torch
from ax.core.observation import ObservationData, ObservationFeatures
from ax.core.search_space import SearchSpaceDigest
from ax.core.types import TCandidateMetadata
from ax.modelbridge.torch import TorchModelBridge
from ax.utils.common.constants import Keys
from botorch.models.utils.assorted import consolidate_duplicates
from botorch.utils.containers import DenseContainer, SliceContainer
from botorch.utils.datasets import RankingDataset, SupervisedDataset
from torch import Tensor
[docs]class PairwiseModelBridge(TorchModelBridge):
def _convert_observations(
self,
observation_data: list[ObservationData],
observation_features: list[ObservationFeatures],
outcomes: list[str],
parameters: list[str],
search_space_digest: Optional[SearchSpaceDigest],
) -> tuple[
list[SupervisedDataset], list[str], Optional[list[list[TCandidateMetadata]]]
]:
"""Converts observations to a dictionary of `Dataset` containers and (optional)
candidate metadata.
"""
if len(observation_features) != len(observation_data):
raise ValueError("Observation features and data must have the same length!")
ordered_idx = np.argsort([od.trial_index for od in observation_features])
observation_features = [observation_features[i] for i in ordered_idx]
observation_data = [observation_data[i] for i in ordered_idx]
(
Xs,
Ys,
Yvars,
candidate_metadata_dict,
any_candidate_metadata_is_not_none,
) = self._extract_observation_data(
observation_data, observation_features, parameters
)
datasets: list[SupervisedDataset] = []
candidate_metadata = []
for outcome in outcomes:
X = torch.stack(Xs[outcome], dim=0)
Y = torch.tensor(Ys[outcome], dtype=torch.long).unsqueeze(-1)
if outcome == Keys.PAIRWISE_PREFERENCE_QUERY.value:
dataset = _prep_pairwise_data(
X=X, Y=Y, outcome=outcome, parameters=parameters
)
else: # pragma: no cover
event_shape = torch.Size([X.shape[-1]])
dataset_X = DenseContainer(X, event_shape=event_shape)
dataset = SupervisedDataset(
X=dataset_X,
Y=Y,
feature_names=parameters,
outcome_names=[outcome],
)
datasets.append(dataset)
candidate_metadata.append(candidate_metadata_dict[outcome])
if not any_candidate_metadata_is_not_none:
return datasets, outcomes, None
return datasets, outcomes, candidate_metadata
def _predict(
self, observation_features: list[ObservationFeatures]
) -> list[ObservationData]:
# TODO: Implement `_predict` to enable examining predicted effects
raise NotImplementedError
def _prep_pairwise_data(
X: Tensor,
Y: Tensor,
outcome: str,
parameters: list[str],
) -> SupervisedDataset:
"""Prep data for pairwise modeling."""
# Update Xs and Ys shapes for PairwiseGP
Y = _binary_pref_to_comp_pair(Y=Y)
X, Y = _consolidate_comparisons(X=X, Y=Y)
datapoints, comparisons = X, Y.long()
event_shape = torch.Size([2 * datapoints.shape[-1]])
# pyre-fixme[6]: For 2nd param expected `LongTensor` but
dataset_X = SliceContainer(datapoints, comparisons, event_shape=event_shape)
dataset_Y = torch.tensor([[0, 1]]).expand(comparisons.shape)
dataset = RankingDataset(
X=dataset_X,
Y=dataset_Y,
feature_names=parameters,
outcome_names=[outcome],
)
return dataset
def _binary_pref_to_comp_pair(Y: Tensor) -> Tensor:
"""Convert Y from binary indicator pair to index pair comparisons
Convert Y from binary indicator pair such as [[0, 1], [1, 0], ...]
to index comparisons like [[1, 0], [2, 3], ...]
"""
Y_shape = Y.shape[:-2] + (-1, 2)
Y = Y.reshape(Y_shape)
_validate_Y_values(Y)
idx_shift = (torch.arange(0, Y.shape[-2]) * 2).unsqueeze(-1).expand_as(Y)
comparison_pairs = idx_shift + (1 - Y)
return comparison_pairs
def _consolidate_comparisons(X: Tensor, Y: Tensor) -> tuple[Tensor, Tensor]:
"""Drop duplicated Xs and update the indices in Ys accordingly"""
if Y.shape[-1] != 2:
raise ValueError(
"The last dimension of Y must contain 2 elements "
"representing the pairwise comparison."
)
if len(Y.shape) != 2:
raise ValueError("Y must have 2 dimensions.")
X, Y, _ = consolidate_duplicates(X, Y)
return X, Y
def _validate_Y_values(Y: Tensor) -> None:
"""Check if Ys have valid values"""
# Y must have even number of elements
if Y.shape[-1] != 2:
raise ValueError(
f"Trailing dimension of `Y` should be size 2 but is {Y.shape[-1]}"
)
# all adjacent pairs must have exactly a 0 and a 1
if not (Y.min(dim=-1).values.eq(0).all() and Y.max(dim=-1).values.eq(1).all()):
raise ValueError("`Y` values must be `{0, 1}.`")
