# -*- coding: utf-8 -*-
# Copyright 2022 PyePAL authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Callable, Optional, Tuple
import numpy as np
import torch
from botorch.models import MultiTaskGP, SingleTaskGP
from botorch.models.transforms.input import ChainedInputTransform, Normalize, Warp
from gpytorch.mlls import ExactMarginalLogLikelihood
from torch.nn import Module
[docs]def build_model(
X: np.ndarray,
y: np.ndarray,
warped: bool = True,
input_scaled: bool = True,
wrap_indices: Optional[Tuple[int]] = None,
scaling_indices: Optional[Tuple[int]] = None,
covar_module: Optional[Module] = None,
) -> Callable:
"""Build a BoTorch model for a single output.
Args:
X (np.ndarray): features
y (np.ndarray): targets
warped (bool, optional): If true, apply Kumaraswamy warping. Defaults to True.
input_scaled (bool, optional): If true, scale features to unit cube. Defaults to True.
wrap_indices (Optional[Tuple[int]], optional): Indices to which warping is applied. Defaults to None.
scaling_indices (Optional[Tuple[int]], optional): Indices to which scaling is applied. Defaults to None.
covar_module (Optional[Module], optional): Coregionalization model. Defaults to None.
Returns:
Callable: Function that return model and likelihood when provided with x and y
"""
input_transformations = {}
if input_scaled:
if scaling_indices is None:
scaling_indices = tuple(range(X.shape[1]))
input_transformations["norm"] = Normalize(
d=X.shape[1],
indices=scaling_indices,
)
if warped:
if wrap_indices is None:
wrap_indices = np.arange(X.shape[1])
input_transformations["warp"] = Warp(wrap_indices)
my_input_transformations = ChainedInputTransform(**input_transformations)
def model_creator(x, y, old_state_dict=None):
x = torch.tensor(x)
y = torch.tensor(y)
gp = SingleTaskGP(x, y, covar_module=covar_module, input_transform=my_input_transformations)
if old_state_dict is not None:
gp.load_state_dict(old_state_dict)
mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
return gp, mll
return model_creator
[docs]def build_multioutput_model(
X: np.ndarray,
y: np.ndarray,
warped: bool = True,
input_scaled: bool = True,
wrap_indices: Optional[Tuple[int]] = None,
scaling_indices: Optional[Tuple[int]] = None,
covar_module: Optional[Module] = None,
) -> Callable:
"""Build a BoTorch model for multiple outputs.
Args:
X (np.ndarray): features
y (np.ndarray): targets
warped (bool, optional): If true, apply Kumaraswamy warping. Defaults to True.
input_scaled (bool, optional): If true, scale features to unit cube. Defaults to True.
wrap_indices (Optional[Tuple[int]], optional): Indices to which warping is applied. Defaults to None.
scaling_indices (Optional[Tuple[int]], optional): Indices to which scaling is applied. Defaults to None.
covar_module (Optional[Module], optional): Coregionalization model. Defaults to None.
Returns:
Callable: Function that return model and likelihood when provided with x and y
"""
num_targets = y.shape[1]
input_transformations = {}
if input_scaled:
if scaling_indices is None:
scaling_indices = tuple(range(X.shape[1]))
input_transformations["norm"] = Normalize(
d=X.shape[1] + 1,
indices=scaling_indices,
)
if warped:
if wrap_indices is None:
wrap_indices = np.arange(X.shape[1])
input_transformations["warp"] = Warp(wrap_indices)
my_input_transformations = ChainedInputTransform(**input_transformations)
def model_creator(x, y, old_state_dict=None):
y_stacked = np.vstack([y[:, i].reshape(-1, 1) for i in range(num_targets)])
x = np.hstack(
[
np.vstack([x] * num_targets),
np.vstack([np.array([i] * x.shape[0]).reshape(-1, 1) for i in range(num_targets)]),
]
)
x = torch.tensor(x)
y = torch.tensor(y_stacked)
gp = MultiTaskGP(
x,
y,
covar_module=covar_module,
input_transform=my_input_transformations,
task_feature=-1,
)
if old_state_dict is not None:
gp.load_state_dict(old_state_dict)
mll = ExactMarginalLogLikelihood(gp.likelihood, gp)
return gp, mll
return model_creator
