#!/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 Callable, Dict, List, Optional, Tuple
import torch
from ax.core.types import TCandidateMetadata, TConfig, TGenMetadata
from ax.models.base import Model
from torch import Tensor
# pyre-fixme[13]: Attribute `device` is never initialized.
# pyre-fixme[13]: Attribute `dtype` is never initialized.
[docs]class TorchModel(Model):
"""This class specifies the interface for a torch-based model.
These methods should be implemented to have access to all of the features
of Ax.
"""
dtype: Optional[torch.dtype]
device: Optional[torch.device]
[docs] def fit(
self,
Xs: List[Tensor],
Ys: List[Tensor],
Yvars: List[Tensor],
bounds: List[Tuple[float, float]],
task_features: List[int],
feature_names: List[str],
metric_names: List[str],
fidelity_features: List[int],
candidate_metadata: Optional[List[List[TCandidateMetadata]]] = None,
) -> None:
"""Fit model to m outcomes.
Args:
Xs: A list of m (k_i x d) feature tensors X. Number of rows k_i can
vary from i=1,...,m.
Ys: The corresponding list of m (k_i x 1) outcome tensors Y, for
each outcome.
Yvars: The variances of each entry in Ys, same shape.
bounds: A list of d (lower, upper) tuples for each column of X.
task_features: Columns of X that take integer values and should be
treated as task parameters.
feature_names: Names of each column of X.
metric_names: Names of each outcome Y in Ys.
fidelity_features: Columns of X that should be treated as fidelity
parameters.
candidate_metadata: Model-produced metadata for candidates, in
the order corresponding to the Xs.
"""
pass
[docs] def predict(self, X: Tensor) -> Tuple[Tensor, Tensor]:
"""Predict
Args:
X: (j x d) tensor of the j points at which to make predictions.
Returns:
2-element tuple containing
- (j x m) tensor of outcome predictions at X.
- (j x m x m) tensor of predictive covariances at X.
cov[j, m1, m2] is Cov[m1@j, m2@j].
"""
raise NotImplementedError
[docs] def gen(
self,
n: int,
bounds: List[Tuple[float, float]],
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
pending_observations: Optional[List[Tensor]] = None,
model_gen_options: Optional[TConfig] = None,
rounding_func: Optional[Callable[[Tensor], Tensor]] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> Tuple[Tensor, Tensor, TGenMetadata, Optional[List[TCandidateMetadata]]]:
"""
Generate new candidates.
Args:
n: Number of candidates to generate.
bounds: A list of (lower, upper) tuples for each column of X.
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights.
outcome_constraints: A tuple of (A, b). For k outcome constraints
and m outputs at f(x), A is (k x m) and b is (k x 1) such that
A f(x) <= b.
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.
pending_observations: A list of m (k_i x d) feature tensors X
for m outcomes and k_i pending observations for outcome i.
model_gen_options: A config dictionary that can contain
model-specific options.
rounding_func: A function that rounds an optimization result
appropriately (i.e., according to `round-trip` transformations).
target_fidelities: A map {feature_index: value} of fidelity feature
column indices to their respective target fidelities. Used for
multi-fidelity optimization.
Returns:
4-element tuple containing
- (n x d) tensor of generated points.
- n-tensor of weights for each point.
- Generation metadata
- Dictionary of model-specific metadata for the given
generation candidates
"""
raise NotImplementedError
[docs] def best_point(
self,
bounds: List[Tuple[float, float]],
objective_weights: Tensor,
outcome_constraints: Optional[Tuple[Tensor, Tensor]] = None,
linear_constraints: Optional[Tuple[Tensor, Tensor]] = None,
fixed_features: Optional[Dict[int, float]] = None,
model_gen_options: Optional[TConfig] = None,
target_fidelities: Optional[Dict[int, float]] = None,
) -> Optional[Tensor]:
"""
Identify the current best point, satisfying the constraints in the same
format as to gen.
Return None if no such point can be identified.
Args:
bounds: A list of (lower, upper) tuples for each column of X.
objective_weights: The objective is to maximize a weighted sum of
the columns of f(x). These are the weights.
outcome_constraints: A tuple of (A, b). For k outcome constraints
and m outputs at f(x), A is (k x m) and b is (k x 1) such that
A f(x) <= b.
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 in the best point.
model_gen_options: A config dictionary that can contain
model-specific options.
target_fidelities: A map {feature_index: value} of fidelity feature
column indices to their respective target fidelities. Used for
multi-fidelity optimization.
Returns:
d-tensor of the best point.
"""
return None
[docs] def cross_validate(
self,
Xs_train: List[Tensor],
Ys_train: List[Tensor],
Yvars_train: List[Tensor],
X_test: Tensor,
) -> Tuple[Tensor, Tensor]:
"""Do cross validation with the given training and test sets.
Training set is given in the same format as to fit. Test set is given
in the same format as to predict.
Args:
Xs_train: A list of m (k_i x d) feature tensors X. Number of rows
k_i can vary from i=1,...,m.
Ys_train: The corresponding list of m (k_i x 1) outcome tensors Y,
for each outcome.
Yvars_train: The variances of each entry in Ys, same shape.
X_test: (j x d) tensor of the j points at which to make predictions.
Returns:
2-element tuple containing
- (j x m) tensor of outcome predictions at X.
- (j x m x m) tensor of predictive covariances at X.
cov[j, m1, m2] is Cov[m1@j, m2@j].
"""
raise NotImplementedError
[docs] def update(
self,
Xs: List[Tensor],
Ys: List[Tensor],
Yvars: List[Tensor],
candidate_metadata: Optional[List[List[TCandidateMetadata]]] = None,
) -> None:
"""Update the model.
Updating the model requires both existing and additional data.
The data passed into this method will become the new training data.
Args:
Xs: Existing + additional data for the model,
in the same format as for `fit`.
Ys: Existing + additional data for the model,
in the same format as for `fit`.
Yvars: Existing + additional data for the model,
in the same format as for `fit`.
candidate_metadata: Model-produced metadata for candidates, in
the order corresponding to the Xs.
"""
raise NotImplementedError
[docs] def evaluate_acquisition_function(self, X: Tensor) -> Tensor:
"""Evaluate the acquisition function on the candidate set `X`.
Args:
X: (j x d) tensor of the j points at which to evaluate the acquisition
function.
Returns:
A single-element tensor with the acquisition value for these points.
"""
raise NotImplementedError
