python_code stringlengths 0 229k |
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#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a... |
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
class Sequence(nn.Module):
def __init__(self):
super(Sequence, self).__init__()
self.lstm1 ... |
import numpy as np
import torch
np.random.seed(2)
T = 20
L = 1000
N = 100
x = np.empty((N, L), 'int64')
x[:] = np.array(range(L)) + np.random.randint(-4 * T, 4 * T, N).reshape(N, 1)
data = np.sin(x / 1.0 / T).astype('float64')
torch.save(data, open('traindata.pt', 'wb'))
|
import os
import zipfile
# PyTorch 1.1 moves _download_url_to_file
# from torch.utils.model_zoo to torch.hub
# PyTorch 1.0 exists another _download_url_to_file
# 2 argument
# TODO: If you remove support PyTorch 1.0 or older,
# You should remove torch.utils.model_zoo
# Ref. PyTorch #18758
# http... |
import torch
class TransformerNet(torch.nn.Module):
def __init__(self):
super(TransformerNet, self).__init__()
# Initial convolution layers
self.conv1 = ConvLayer(3, 32, kernel_size=9, stride=1)
self.in1 = torch.nn.InstanceNorm2d(32, affine=True)
self.conv2 = ConvLayer(32, ... |
from collections import namedtuple
import torch
from torchvision import models
class Vgg16(torch.nn.Module):
def __init__(self, requires_grad=False):
super(Vgg16, self).__init__()
vgg_pretrained_features = models.vgg16(pretrained=True).features
self.slice1 = torch.nn.Sequential()
... |
import argparse
import os
import sys
import time
import re
import numpy as np
import torch
from torch.optim import Adam
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision import transforms
import torch.onnx
import utils
from transformer_net import TransformerNet
from vgg import ... |
import torch
from PIL import Image
def load_image(filename, size=None, scale=None):
img = Image.open(filename).convert('RGB')
if size is not None:
img = img.resize((size, size), Image.ANTIALIAS)
elif scale is not None:
img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), ... |
from __future__ import division
from __future__ import print_function
import argparse
import gzip
import os
import sys
import urllib
try:
from urllib.error import URLError
from urllib.request import urlretrieve
except ImportError:
from urllib2 import URLError
from urllib import urlretrieve
RESOURCES ... |
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import matplotlib.pyplot as plt
import torch
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--sample-file", required=True)
parser.add_argument("-o", "--out-file", default="out.png")
parser.add_argument("-d",... |
"""
This python script converts the network into Script Module
"""
import torch
from torchvision import models
# Download and load the pre-trained model
model = models.resnet18(pretrained=True)
# Set upgrading the gradients to False
for param in model.parameters():
param.requires_grad = False
# Save the model excep... |
import argparse
import os
import random
import shutil
import time
import warnings
from enum import Enum
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import ... |
# This code is based on the implementation of Mohammad Pezeshki available at
# https://github.com/mohammadpz/pytorch_forward_forward and licensed under the MIT License.
# Modifications/Improvements to the original code have been made by Vivek V Patel.
import argparse
import torch
import torch.nn as nn
from torchvision... |
from __future__ import print_function
import argparse, random, copy
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from torch.utils.data import Dataset
from torchvision import datasets
from torchvision import transforms as T
from tor... |
import os
import torch
import torch.optim as optim
import torch.nn.functional as F
def train(rank, args, model, device, dataset, dataloader_kwargs):
torch.manual_seed(args.seed + rank)
train_loader = torch.utils.data.DataLoader(dataset, **dataloader_kwargs)
optimizer = optim.SGD(model.parameters(), lr=a... |
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.multiprocessing as mp
from torch.utils.data.sampler import Sampler
from torchvision import datasets, transforms
from train import train, test
# Training settings
parser = argparse.Argu... |
#!/usr/bin/env python
from __future__ import print_function
from itertools import count
import torch
import torch.nn.functional as F
POLY_DEGREE = 4
W_target = torch.randn(POLY_DEGREE, 1) * 5
b_target = torch.randn(1) * 5
def make_features(x):
"""Builds features i.e. a matrix with columns [x, x^2, x^3, x^4]."""... |
import os
import time
import requests
import tarfile
import numpy as np
import argparse
import torch
from torch import nn
import torch.nn.functional as F
from torch.optim import Adam
class GraphConv(nn.Module):
"""
Graph Convolutional Layer described in "Semi-Supervised Classification with Graph Convolut... |
from __future__ import print_function
import argparse
import torch
import torch.utils.data
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
parser = argparse.ArgumentParser(description='VAE MNIST Example')
parser.add... |
#!/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.
import logging
import torch
from botorch import settings
from botorch.logging import LOG_LEVEL_DEFAULT, logger, sha... |
#!/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.
import itertools
import warnings
import torch
from botorch.cross_validation import batch_cross_validation, gen_loo_c... |
#!/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.
import math
from contextlib import ExitStack, nullcontext
from itertools import filterfalse, product
from typing impo... |
#!/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.
|
#!/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.
import math
import warnings
import torch
from botorch.acquisition import ExpectedImprovement, qExpectedImprovement
f... |
#!/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.
r"""
Monolithic CUDA tests. This implements a single monolithic test for all
CUDA functionality. The main reason for ... |
#!/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.
import warnings
import gpytorch.settings as gp_settings
import linear_operator.settings as linop_settings
from botor... |
#!/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.
from botorch.test_functions.multi_fidelity import (
AugmentedBranin,
AugmentedHartmann,
AugmentedRosenbro... |
#!/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.
|
# 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.
import torch
from botorch.test_functions.multi_objective_multi_fidelity import (
MOMFBraninCurrin,
MOMFPark,
)
from botorch.utils.te... |
#!/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.
import torch
from botorch.exceptions.errors import InputDataError
from botorch.test_functions.synthetic import (
... |
#! /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.
import math
from typing import List
import torch
from botorch.exceptions.errors import UnsupportedError
from botorc... |
#!/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.
import torch
from botorch.test_functions.sensitivity_analysis import Gsobol, Ishigami, Morris
from botorch.utils.test... |
#!/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.
import torch
from botorch.test_functions.base import BaseTestProblem, ConstrainedBaseTestProblem
from botorch.utils.t... |
#!/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.
from typing import List
import torch
from botorch.acquisition import LinearMCObjective, ScalarizedPosteriorTransform... |
#!/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.
from itertools import product
import torch
from botorch.acquisition.predictive_entropy_search import qPredictiveEntr... |
#!/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.
import itertools
from unittest import mock
import torch
from botorch.acquisition.objective import GenericMCObjective... |
#!/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.
import torch
from botorch.acquisition import (
ExpectedImprovement,
qExpectedImprovement,
qMultiStepLook... |
#!/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.
import warnings
from copy import deepcopy
from functools import partial
from itertools import product
from math impor... |
#!/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.
from typing import Callable, Optional
from unittest import mock
import torch
from botorch.acquisition.cost_aware imp... |
#!/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.
from itertools import product
import torch
from botorch.acquisition.analytic import ExpectedImprovement
from botorch... |
#!/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.
import warnings
from copy import deepcopy
from itertools import product
from math import pi
from unittest import mock... |
#!/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.
import warnings
import torch
from botorch import settings
from botorch.acquisition.decoupled import DecoupledAcquisi... |
#!/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.
import warnings
import torch
from botorch import settings
from botorch.acquisition.cost_aware import (
CostAwar... |
#!/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.
from __future__ import annotations
import math
from typing import Any, Callable, Sequence, Type
from unittest import... |
#!/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.
import math
from unittest import mock
import torch
from botorch.acquisition import logei, monte_carlo
from botorch.a... |
#!/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.
from typing import Optional
import torch
from botorch.acquisition.objective import LinearMCObjective
from botorch.a... |
#!/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.
|
#!/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.
import torch
from botorch.acquisition.acquisition import (
AcquisitionFunction,
MCSamplerMixin,
MultiMode... |
#!/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.
import math
import torch
from botorch.acquisition import qAnalyticProbabilityOfImprovement
from botorch.acquisition.... |
#!/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.
import itertools
import warnings
from typing import Optional
import torch
from botorch import settings
from botorch.... |
#!/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.
import torch
from botorch.acquisition.analytic import ExpectedImprovement
from botorch.acquisition.monte_carlo import... |
#!/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.
from contextlib import ExitStack
from unittest import mock
import torch
from botorch.acquisition.analytic import Pos... |
#!/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.
import torch
from botorch.acquisition.analytic import ExpectedImprovement
from botorch.acquisition.fixed_feature impo... |
#!/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.
import warnings
from unittest import mock
import torch
from botorch import settings
from botorch.acquisition.cached_... |
#!/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.
import torch
from botorch.acquisition.acquisition import AcquisitionFunction
from botorch.acquisition.preference impo... |
#!/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.
from itertools import product
import torch
from botorch.acquisition.joint_entropy_search import qJointEntropySearch
... |
#!/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.
from unittest import mock
import torch
from botorch.acquisition.active_learning import (
PairwiseMCPosteriorVari... |
#!/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.
from itertools import product
import torch
from botorch.acquisition.multi_objective.predictive_entropy_search import... |
#!/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.
import warnings
from itertools import product
from unittest import mock
import torch
from botorch.acquisition.multi_... |
#!/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.
import warnings
from unittest import mock
import torch
from botorch import settings
from botorch.acquisition.multi_o... |
#!/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.
import warnings
from copy import deepcopy
from itertools import product
from math import pi
from unittest import mock... |
#!/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.
from itertools import product
from unittest import mock
import torch
from botorch.acquisition.max_value_entropy_sear... |
#!/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.
import warnings
from typing import Optional
import torch
from botorch import settings
from botorch.acquisition.multi... |
#!/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.
|
#!/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.
import torch
from botorch.acquisition.multi_objective.analytic import (
ExpectedHypervolumeImprovement,
Mult... |
#!/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.
import itertools
import warnings
import torch
from botorch.acquisition.multi_objective.multi_output_risk_measures im... |
#!/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.
from itertools import product
import torch
from botorch.acquisition.multi_objective.joint_entropy_search import (
... |
#!/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.
import torch
from botorch.sampling.deterministic import DeterministicSampler
from botorch.utils.testing import Botorc... |
#!/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.
from unittest import mock
import torch
from botorch.exceptions.errors import UnsupportedError
from botorch.posterior... |
#!/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.
import torch
from botorch.posteriors.deterministic import DeterministicPosterior
from botorch.posteriors.gpytorch imp... |
#!/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.
from unittest import mock
import torch
from botorch.posteriors.torch import TorchPosterior
from botorch.sampling.sto... |
#!/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.
import torch
from botorch.posteriors import GPyTorchPosterior
from botorch.sampling.pairwise_samplers import (
Pa... |
#!/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.
|
#!/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.
import torch
from botorch.posteriors.ensemble import EnsemblePosterior
from botorch.sampling.index_sampler import Ind... |
#!/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.
import torch
from botorch.exceptions.errors import UnsupportedError
from botorch.posteriors import GPyTorchPosterior
... |
#!/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.
import torch
from botorch.exceptions.errors import InputDataError
from botorch.sampling.base import MCSampler
from bo... |
#!/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.
import itertools
import math
import numpy as np
import torch
from botorch.sampling.qmc import MultivariateNormalQMCE... |
#!/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.
from __future__ import annotations
from unittest.mock import patch
import torch
from botorch.models import SingleTa... |
#!/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.
|
#!/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.
from __future__ import annotations
from collections import defaultdict
from copy import deepcopy
from itertools impo... |
#!/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.
from __future__ import annotations
import torch
from botorch.exceptions.errors import UnsupportedError
from botorch.... |
#!/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.
from __future__ import annotations
from typing import Tuple
from botorch.models.transforms.outcome import Standardi... |
#!/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.
from __future__ import annotations
from collections import defaultdict
from copy import deepcopy
from itertools impo... |
#!/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.
from __future__ import annotations
from collections import defaultdict
from copy import deepcopy
from itertools impo... |
#!/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.
from __future__ import annotations
from unittest.mock import MagicMock, patch
import torch
from botorch.sampling.pa... |
#!/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.
from __future__ import annotations
from math import ceil
from unittest.mock import patch
import torch
from botorch.... |
#!/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.
|
#!/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.
from itertools import product
import numpy as np
import torch
from botorch.exceptions.errors import CandidateGenerat... |
# 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.
import unittest.mock as mock
import torch
from botorch.acquisition import PosteriorMean
from botorch.acquisition.monte_carlo import qExpect... |
#!/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.
import itertools
import warnings
from inspect import signature
from itertools import product
from unittest import moc... |
#!/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.
import warnings
from contextlib import ExitStack
from itertools import product
from random import random
from typing ... |
#!/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.
import time
from functools import partial
from typing import Dict
from unittest.mock import MagicMock, patch
import ... |
#!/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.
import math
import re
from unittest.mock import MagicMock, patch
from warnings import catch_warnings
import torch
fr... |
#!/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.
|
#!/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.
from math import pi
from unittest.mock import MagicMock, patch
from warnings import catch_warnings, simplefilter
imp... |
#!/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.
from __future__ import annotations
import torch
from botorch.optim.stopping import ExpMAStoppingCriterion, StoppingC... |
#!/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.
from __future__ import annotations
import warnings
import torch
from botorch import settings
from botorch.acquisiti... |
#!/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.
from __future__ import annotations
from unittest.mock import MagicMock
import numpy as np
import torch
from botorch... |
#!/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.
from __future__ import annotations
from functools import partial
from warnings import catch_warnings, warn
import n... |
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