repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/utils.py | """ Code for various 1-off functions """
import argparse
import functools
import glob
import gzip
import os
import pickle
import sys
from abc import ABC, abstractmethod
from typing import List, Union, Optional, Dict, Any
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn as nn
import torch.... | 15,038 | 32.871622 | 117 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/models.py | """ code for base VAE model """
import argparse
import math
from typing import Optional
import pytorch_lightning as pl
import torch
import torch.nn.functional as torch_func
from torch import nn, Tensor
from torch.nn import functional as F
from utils.utils_cmd import parse_dict, parse_list
from weighted_retraining.we... | 18,899 | 37.809035 | 120 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/metrics.py | # Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions... | 10,528 | 41.800813 | 757 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/expr/expr_model.py | """ Contains code for the arithmetic expression model (Grammar VAE) """
from keras import backend as K
import weighted_retraining.weighted_retraining.expr.eq_grammar as G
masks_K = K.variable(G.masks)
ind_of_ind_K = K.variable(G.ind_of_ind)
MAX_LEN = 15
DIM = G.D
| 269 | 19.769231 | 71 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/expr/equation_vae.py | import collections
import re
from typing import List, Union
import nltk
import numpy as np
import torch
from torch import Tensor
from weighted_retraining.weighted_retraining.expr import eq_grammar, expr_model_pt
from weighted_retraining.weighted_retraining.expr.expr_model_pt import EquationVaeTorch
def tokenize(s):... | 6,693 | 37.471264 | 119 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/expr/expr_model_pt.py | """ Contains code for the shapes model """
from typing import Optional
import torch
from torch import nn, Tensor
import weighted_retraining.weighted_retraining.expr.eq_grammar as eq_gram
from weighted_retraining.weighted_retraining.models import BaseVAE, MLPRegressor
masks_t = torch.tensor(eq_gram.masks)
ind_of_ind... | 8,201 | 37.688679 | 120 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/expr/expr_data.py | """ Code for loading and manipulating the arithmetic expression data """
import os
from typing import Sequence, Any, Dict, Optional, Iterable, Collection
import h5py
import numpy as np
import torch
# noinspection PyUnresolvedReferences
from numpy import exp, sin
from torch import Tensor
from torch.utils.data import D... | 15,446 | 37.90932 | 119 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/expr/expr_data_pt.py | from typing import Optional
import numpy as np
import pytorch_lightning as pl
import torch
from torch.utils.data import DataLoader, TensorDataset, WeightedRandomSampler
from weighted_retraining.weighted_retraining.utils import print_flush
NUM_WORKERS = 3
class WeightedExprDataset(pl.LightningDataModule):
""" I... | 8,218 | 37.406542 | 122 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/shapes/shapes_data.py | import pytorch_lightning as pl
from torch.utils.data import TensorDataset, WeightedRandomSampler
NUM_WORKERS = 0
from torch.utils.data.dataloader import DataLoader, _SingleProcessDataLoaderIter, _MultiProcessingDataLoaderIter
from torch.utils.data import _utils
from torchvision import transforms as transforms
import... | 16,487 | 39.214634 | 122 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/shapes/shapes_model.py | """ Contains code for the shapes model """
import itertools
from typing import Union, Optional
import numpy as np
import torch
from torch import nn, distributions, Tensor
from torchvision.utils import make_grid
# My imports
from weighted_retraining.weighted_retraining.models import BaseCLR, BaseVAE, UnFlatten, MLPRe... | 9,584 | 29.623003 | 110 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/shapes/shapes_utils.py | from typing import Dict, Any
import torch
from tqdm import tqdm
from weighted_retraining.weighted_retraining.shapes.shapes_model import ShapesVAE
from weighted_retraining.weighted_retraining.utils import print_flush
import numpy as np
def get_latent_encodings(use_test_set: bool, use_full_data_for_gp: bool, model: ... | 3,392 | 35.483871 | 125 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/train_scripts/train_shapes.py | """ Trains a convnet for the shapes task """
import argparse
import os
import shutil
import sys
from typing import Any, Optional, Dict, List
from pathlib import Path
ROOT_PROJECT = str(Path(os.path.realpath(__file__)).parent.parent.parent.parent)
sys.path[0] = ROOT_PROJECT
import pytorch_lightning as pl
from pytorch_... | 5,000 | 37.767442 | 108 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/train_scripts/train_chem.py | """ Script to train chem model """
import argparse
import os
import sys
from pathlib import Path
ROOT_PROJECT = str(Path(os.path.realpath(__file__)).parent.parent.parent.parent)
sys.path[-1] = ROOT_PROJECT
import pytorch_lightning as pl
# My imports
from pytorch_lightning.callbacks import LearningRateMonitor
from ... | 2,770 | 33.209877 | 90 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/train_scripts/train_expr_pt.py | """ Trains a VAE for the equation task """
import argparse
import os
import shutil
import sys
from pathlib import Path
ROOT_PROJECT = str(Path(os.path.realpath(__file__)).parent.parent.parent.parent)
sys.path[0] = ROOT_PROJECT
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import Learnin... | 5,722 | 37.409396 | 111 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/topology/topology_data.py | import pytorch_lightning as pl
from torch.utils.data import TensorDataset, WeightedRandomSampler, Dataset
NUM_WORKERS = 0
from torch.utils.data.dataloader import DataLoader
from torch.utils.data import _utils
from torchvision import transforms as transforms
import numpy as np
import numbers
from collections.abc impo... | 8,784 | 37.530702 | 122 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/topology/topology_utils.py | from typing import Any, Dict, Optional
import numpy as np
import torch
from torch import Tensor
from tqdm import tqdm
from torch.utils.data import DataLoader, TensorDataset
from weighted_retraining.weighted_retraining.utils import print_flush
from weighted_retraining.weighted_retraining.topology.topology_model import ... | 10,106 | 39.590361 | 119 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/topology/topology_model.py | """ Contains code for the shapes model """
import itertools
from typing import Union, Optional
import numpy as np
import torch
from torch import nn, distributions, Tensor
from torchvision.utils import make_grid
# My imports
from weighted_retraining.weighted_retraining.models import BaseVAE, UnFlatten, MLPRegressor
... | 13,003 | 34.336957 | 112 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/topology/topology_dataset.py | import os
from typing import Optional
import torch
from torch import Tensor
from utils.utils_save import get_data_root
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
from sklearn.preprocessing import MaxAbsScaler, StandardScaler
def get_topology_dataset_path():
return os.path.join(get_d... | 4,185 | 38.490566 | 117 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/chem/chem_data.py | """ Code for chem datasets """
import pickle
from typing import Any, Optional, Dict, List, Iterable
import numpy as np
import pytorch_lightning as pl
import torch
from torch import Tensor
from torch.utils.data import DataLoader
from tqdm import trange
from tqdm.auto import tqdm
import weighted_retraining.weighted_re... | 22,741 | 36.71476 | 118 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/chem/chem_model.py | """ Contains code for the chem model (JT-VAE) """
import argparse
import torch
from torch import nn, Tensor
import torch.nn.functional as torch_func
# My imports
from typing import List, Any, Optional
from weighted_retraining.weighted_retraining import utils
from weighted_retraining.weighted_retraining.models import... | 6,034 | 34.292398 | 109 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/chem/jtnn/jtnn_enc.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import deque
from .mol_tree import Vocab, MolTree
from .nnutils import index_select_ND
class JTNNEncoder(nn.Module):
def __init__(self, hidden_size, depth, embedding):
super(JTNNEncoder, self).__init__()
self.hidde... | 4,824 | 34.477941 | 85 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/chem/jtnn/datautils.py | import torch
from torch.utils.data import Dataset, DataLoader, IterableDataset
from weighted_retraining.weighted_retraining.chem.jtnn.mol_tree import MolTree
import numpy as np
from weighted_retraining.weighted_retraining.chem.jtnn.jtnn_enc import JTNNEncoder
from weighted_retraining.weighted_retraining.chem.jtnn.mpn i... | 7,051 | 29.929825 | 93 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/chem/jtnn/nnutils.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
def index_select_ND(source, dim, index):
index_size = index.size()
suffix_dim = source.size()[1:]
final_size = index_size + suffix_dim
target = source.index_select(dim, index.view(-1))
return tar... | 1,863 | 29.064516 | 59 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/chem/jtnn/mpn.py | import torch
import torch.nn as nn
import rdkit.Chem as Chem
import torch.nn.functional as F
from .nnutils import index_select_ND
from .chemutils import get_mol
ELEM_LIST = [
"C",
"N",
"O",
"S",
"F",
"Si",
"P",
"Cl",
"Br",
"Mg",
"Na",
"Ca",
"Fe",
"Al",
"I",
... | 4,785 | 28.726708 | 77 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/chem/jtnn/jtnn_vae.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import pytorch_lightning as pl
from .mol_tree import Vocab, MolTree
from .nnutils import flatten_tensor, avg_pool
from .jtnn_enc import JTNNEncoder
from .jtnn_dec import JTNNDecoder
from .mpn import MPN
from .jtmpn import JTMPN
from .datautils import te... | 13,943 | 34.212121 | 97 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/chem/jtnn/jtmpn.py | import rdkit.Chem as Chem
import torch
import torch.nn as nn
import torch.nn.functional as F
from .nnutils import index_select_ND
ELEM_LIST = [
"C",
"N",
"O",
"S",
"F",
"Si",
"P",
"Cl",
"Br",
"Mg",
"Na",
"Ca",
"Fe",
"Al",
"I",
"B",
"K",
"Se",
... | 5,718 | 30.949721 | 89 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/chem/jtnn/jtnn_dec.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .mol_tree import Vocab, MolTree, MolTreeNode
from .nnutils import GRU
from .chemutils import enum_assemble, set_atommap
import copy
MAX_NB = 15
MAX_DECODE_LEN = 100
class JTNNDecoder(nn.Module):
def __init__(self, vocab, hidden_size, latent_... | 15,937 | 35.63908 | 90 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/robust_opt_scripts/robust_opt_topology.py | import argparse
import gc
import glob
import logging
import os
import sys
import time
import traceback
from pathlib import Path
from typing import Dict, Any, List, Optional, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from botorch.models.transforms import Standardize
from botorch.utils.transfo... | 67,867 | 43.127438 | 143 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/robust_opt_scripts/robust_opt_expr.py | import argparse
import gc
import glob
import os
import sys
import time
import traceback
from pathlib import Path
from typing import Dict, Any, Tuple, List, Optional
import numpy as np
import torch
from botorch.models.transforms import Standardize
from botorch.utils.transforms import normalize, unnormalize
from gpytorc... | 65,068 | 43.204484 | 143 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/robust_opt_scripts/robust_opt_chem.py | import argparse
import logging
import os
import shutil
import subprocess
import sys
import time
import traceback
from pathlib import Path
from typing import Dict, Any, Optional
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.loggers import TensorBoardLogger
from tqdm.auto import t... | 43,950 | 38.382616 | 120 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/robust_opt_scripts/utils.py | # Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions... | 2,187 | 77.142857 | 757 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/bo_torch/mo_acquisition.py | # Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions... | 5,172 | 54.623656 | 757 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/bo_torch/fit.py | from __future__ import annotations
import time
from typing import Any, Callable, Dict, List, NamedTuple, Optional, Set, Tuple, Union
import numpy as np
import torch
from botorch.optim.numpy_converter import (
TorchAttr,
)
from botorch.optim.stopping import ExpMAStoppingCriterion
from botorch.optim.utils import (
... | 7,430 | 40.747191 | 120 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/bo_torch/utils.py | # Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions... | 2,232 | 56.25641 | 757 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/bo_torch/optimize.py | r"""
Methods for optimizing acquisition functions.
"""
from __future__ import annotations
from typing import Callable, Dict, List, Optional, Tuple, Type, Union, Any, Iterable
import torch
from botorch.acquisition.acquisition import (
AcquisitionFunction,
OneShotAcquisitionFunction,
)
from botorch.acquisition... | 10,502 | 40.027344 | 106 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/bo_torch/__init__.py | # Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions... | 1,772 | 83.428571 | 757 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/bo_torch/mo_acq_func.py | # Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions... | 14,091 | 51.192593 | 757 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/bo_torch/gp_torch.py | # Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions... | 34,040 | 52.947702 | 757 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/partial_train_scripts/partial_train_topology.py | """ Trains a convnet for the Topology task """
import argparse
import os
import shutil
import sys
from typing import Any, Optional, Dict, List
from pathlib import Path
from torchvision.transforms import transforms
ROOT_PROJECT = str(Path(os.path.realpath(__file__)).parent.parent.parent.parent)
sys.path.insert(0, ROOT... | 7,144 | 41.029412 | 112 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/partial_train_scripts/partial_train_shapes.py | """ Trains a convnet for the shapes task """
import argparse
import os
import shutil
import sys
from pathlib import Path
from typing import Any, Optional, Dict, List
ROOT_PROJECT = str(Path(os.path.realpath(__file__)).parent.parent.parent.parent)
sys.path[0] = ROOT_PROJECT
import pytorch_lightning as pl
from pytorch_... | 4,481 | 36.663866 | 108 | py |
HEBO | HEBO-master/T-LBO/weighted_retraining/weighted_retraining/partial_train_scripts/partial_train_expr.py | """ Trains a VAE for the equation task """
import argparse
import os
import shutil
import sys
from pathlib import Path
ROOT_PROJECT = str(Path(os.path.realpath(__file__)).parent.parent.parent.parent)
sys.path[0] = ROOT_PROJECT
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import Learnin... | 6,848 | 39.288235 | 108 | py |
HEBO | HEBO-master/BOiLS/core/algos/GRiLLS/grills_env.py | # 2021.11.10-modified the reward function
# Huawei Technologies Co., Ltd. <foss@huawei.com>
import os
from typing import List
import abc_py
import numpy as np
import torch
from dgl import DGLGraph
from resources.abcRL.graphExtractor import extract_dgl_graph
from core.action_space import Action
from core.s... | 8,638 | 36.724891 | 120 | py |
HEBO | HEBO-master/BOiLS/core/algos/GRiLLS/multi_grills_exp.py | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. Redistribution and use in source and binary
# forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of condition... | 12,615 | 39.828479 | 117 | py |
HEBO | HEBO-master/BOiLS/core/algos/bo/hebo/multi_hebo_exp.py | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. Redistribution and use in source and binary
# forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of condition... | 19,855 | 43.420582 | 118 | py |
HEBO | HEBO-master/BOiLS/core/algos/bo/combo/combo_exp.py | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. Redistribution and use in source and binary
# forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of condition... | 9,633 | 40.347639 | 117 | py |
HEBO | HEBO-master/BOiLS/core/algos/bo/combo/main_multi_combo.py | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. Redistribution and use in source and binary
# forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of condition... | 11,926 | 44.522901 | 120 | py |
HEBO | HEBO-master/BOiLS/core/algos/bo/combo/multi_combo_exp.py | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. Redistribution and use in source and binary
# forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of condition... | 16,989 | 42.121827 | 118 | py |
HEBO | HEBO-master/BOiLS/core/algos/bo/combo/main_combo.py | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. Redistribution and use in source and binary
# forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of condition... | 13,005 | 46.467153 | 120 | py |
HEBO | HEBO-master/BOiLS/core/algos/bo/boils/multi_boils_exp.py | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. Redistribution and use in source and binary
# forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of condition... | 21,768 | 42.889113 | 117 | py |
HEBO | HEBO-master/BOiLS/core/algos/bo/boils/utils.py | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. Redistribution and use in source and binary
# forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions ... | 2,861 | 47.508475 | 117 | py |
HEBO | HEBO-master/BOiLS/core/algos/bo/boils/multiseq_boils_exp.py | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. Redistribution and use in source and binary
# forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of condition... | 19,873 | 43.164444 | 119 | py |
HEBO | HEBO-master/BOiLS/DRiLLS/drills/exps/exp_gym.py | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. Redistribution and use in source and binary
# forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions ... | 8,380 | 41.979487 | 119 | py |
HEBO | HEBO-master/BOiLS/resources/casmopolitan/main.py | import argparse
import logging
import pandas as pd
import time
from resources.casmopolitan.bo.optimizer import Optimizer
from resources.casmopolitan.bo.optimizer_mixed import MixedOptimizer
from resources.casmopolitan.mixed_test_func import *
from resources.casmopolitan.test_funcs import *
from resources.casmopolitan.... | 6,169 | 42.146853 | 120 | py |
HEBO | HEBO-master/BOiLS/resources/casmopolitan/utils.py | from torch import Tensor
def spearman(pred, target) -> float:
"""Compute the spearman correlation coefficient between prediction and target"""
from scipy import stats
coef_val, p_val = stats.spearmanr(pred, target)
return coef_val
def pearson(pred, target) -> float:
from scipy import stats
c... | 1,711 | 28.016949 | 84 | py |
HEBO | HEBO-master/BOiLS/resources/casmopolitan/bo/kernels.py | # Implementation of various kernels
import torch
from gpytorch.constraints import Interval
from gpytorch.kernels import Kernel
from gpytorch.kernels.matern_kernel import MaternKernel
from gpytorch.kernels.rbf_kernel import RBFKernel
from torch import Tensor
from resources.casmopolitan.utils import normalize
class M... | 15,169 | 41.732394 | 120 | py |
HEBO | HEBO-master/BOiLS/resources/casmopolitan/bo/seq_kernel_fast.py | # Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. Redistribution and use in source and binary
# forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions ... | 8,327 | 42.375 | 118 | py |
HEBO | HEBO-master/BOiLS/resources/casmopolitan/bo/localbo_utils.py | # 2021.11.10-Add support for ssk
# Huawei Technologies Co., Ltd. <foss@huawei.com>
import random
from collections import Callable
from copy import deepcopy
import logging
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import ScaleKernel
from gpytorch.likelihoods import Gaussian... | 20,669 | 40.011905 | 123 | py |
HEBO | HEBO-master/BOiLS/resources/casmopolitan/bo/optimizer_mixed.py | # 2021.11.10-Minor refactoring
# Huawei Technologies Co., Ltd. <foss@huawei.com>
from torch.quasirandom import SobolEngine
from resources.casmopolitan.bo.localbo_mixed import CASMOPOLITANMixed
from resources.casmopolitan.bo.localbo_utils import to_unit_cube
from resources.casmopolitan.bo.optimizer import *... | 9,565 | 50.15508 | 120 | py |
HEBO | HEBO-master/BOiLS/resources/casmopolitan/bo/localbo_mixed.py | # 2021.11.10-Specify `tr` in adjust_length
# Huawei Technologies Co., Ltd. <foss@huawei.com>
from copy import deepcopy
import gpytorch
import math
import numpy as np
import torch
from torch.quasirandom import SobolEngine
from resources.casmopolitan.bo.localbo_cat import CASMOPOLITANCat
from resources.casm... | 12,976 | 49.104247 | 117 | py |
HEBO | HEBO-master/BOiLS/resources/casmopolitan/bo/localbo_cat.py | # 2021.11.10-Add support for ssk
# Huawei Technologies Co., Ltd. <foss@huawei.com>
from copy import deepcopy
from typing import Optional
import gpytorch
import math
import numpy as np
import sys
import torch
from core.algos.bo.boils.utils import InputTransformation, SentenceBertInputTransform
from resourc... | 15,807 | 44.82029 | 120 | py |
HEBO | HEBO-master/BOiLS/resources/casmopolitan/bo/optimizer.py | # 2021.11.10-Add support for ssk
# Huawei Technologies Co., Ltd. <foss@huawei.com>
from copy import deepcopy
from typing import Optional, List, Union
import numpy as np
import torch
import scipy.stats as ss
from gpytorch.utils.errors import NotPSDError, NanError
from core.algos.bo.boils.utils import Input... | 12,947 | 48.992278 | 146 | py |
HEBO | HEBO-master/BOiLS/resources/abcRL/reinforce.py | ##
# @file reinforce.py
# @author Keren Zhu
# @date 10/30/2019
# @brief The REINFORCE algorithm
#
import torch
from torch import nn
import torch.nn.functional as F
from torch.distributions import Categorical
import bisect
import random
from dgl.nn.pytorch import GraphConv
import dgl
from resources.abcRL.env import En... | 8,581 | 30.903346 | 105 | py |
HEBO | HEBO-master/BOiLS/resources/abcRL/graphExtractor.py | ##
# @file graphExtractor.py
# @author Keren Zhu
# @date 11/16/2019
# @brief The functions and classes for processing the graph
#
import warnings
import dgl
import numpy as np
import torch
from dgl.base import DGLWarning
from numpy import linalg
def symmetricLaplacian(abc):
numNodes = abc.numNodes()
L = np.... | 1,813 | 26.074627 | 62 | py |
HEBO | HEBO-master/BOiLS/resources/abcRL/env.py | ##
# @file env.py
# @author Keren Zhu
# @date 10/25/2019
# @brief The environment classes
#
# 2021.11.10-Remove one comment
# Huawei Technologies Co., Ltd. <foss@huawei.com>
import abc_py as abcPy
import numpy as np
from resources import abcRL as GE
import torch
class EnvNaive2(object):
"""
@brie... | 10,913 | 34.093248 | 110 | py |
HEBO | HEBO-master/CompBO/core/bayes_opt.py | import time
from typing import Type, Optional, List, Any, Dict, Union, Tuple
import numpy as np
import torch
from botorch.acquisition import qUpperConfidenceBound, qExpectedImprovement, \
qProbabilityOfImprovement, AcquisitionFunction, OneShotAcquisitionFunction, qNoisyExpectedImprovement
from botorch.fit import f... | 37,539 | 46.943806 | 134 | py |
HEBO | HEBO-master/CompBO/core/es/evolution_opt.py | from typing import Dict
import numpy as np
import torch
from botorch.utils import draw_sobol_samples
from gpytorch.utils.errors import NotPSDError
from pymoo.algorithms.so_cmaes import CMAES
from pymoo.algorithms.so_de import DE
from pymoo.configuration import Configuration
from pymoo.model.problem import get_problem... | 6,344 | 41.3 | 122 | py |
HEBO | HEBO-master/CompBO/core/comp_acquisition/compositional_acquisition.py | from abc import ABC, abstractmethod
from typing import Optional, Tuple, Union, Callable
import numpy as np
import torch
from botorch.utils import draw_sobol_normal_samples
from torch import Tensor
class CompositionalAcquisition(ABC):
"""
Abstract class for Compositional Acquisition Functions: alpha(x) = f(E[... | 5,500 | 40.674242 | 120 | py |
HEBO | HEBO-master/CompBO/core/comp_acquisition/mc_compositional_acquisition.py | from typing import Union, Optional
import torch
from botorch.acquisition import qExpectedImprovement, MCAcquisitionObjective, qProbabilityOfImprovement, qSimpleRegret, \
qUpperConfidenceBound
from botorch.models.model import Model
from botorch.sampling import MCSampler
from botorch.utils import draw_sobol_normal_s... | 19,880 | 43.979638 | 121 | py |
HEBO | HEBO-master/CompBO/core/comp_acquisition/mc_fs_acquisition.py | import math
from typing import Union, Optional
from botorch.acquisition import qExpectedImprovement, qSimpleRegret, qProbabilityOfImprovement, qUpperConfidenceBound, \
MCAcquisitionObjective
from botorch.models.model import Model
from botorch.sampling import MCSampler
import torch
from botorch.utils.transforms imp... | 11,339 | 40.386861 | 120 | py |
HEBO | HEBO-master/CompBO/core/gp/custom_gp.py | import math
from typing import Optional, List
import numpy as np
import torch
from gpytorch.constraints.constraints import GreaterThan
from gpytorch.distributions.multivariate_normal import MultivariateNormal
from gpytorch.functions import MaternCovariance
from gpytorch.kernels import Kernel
from gpytorch.kernels.scal... | 12,730 | 47.041509 | 129 | py |
HEBO | HEBO-master/CompBO/core/utils/utils_query.py | from typing import Type, List, Optional
import torch
from botorch import test_functions, acquisition
from botorch.acquisition import MCAcquisitionFunction
from botorch.test_functions import SyntheticTestFunction
from gpytorch.kernels import MaternKernel, RBFKernel, ScaleKernel, Kernel
from gpytorch.priors import Gamma... | 5,545 | 38.056338 | 110 | py |
HEBO | HEBO-master/CompBO/custom_optimizer/cadam.py | import math
from typing import Optional, Callable
import torch
from torch.autograd.functional import vjp
from torch.optim.optimizer import required
from custom_optimizer.comp_opt import CompositionalOptimizer
class CAdam(CompositionalOptimizer):
r"""Implements Compositional Adam algorithm.
Arguments:
... | 7,148 | 46.344371 | 118 | py |
HEBO | HEBO-master/CompBO/custom_optimizer/scgd.py | from typing import Optional
import torch
from torch.autograd.functional import vjp
from torch.optim.optimizer import required
from custom_optimizer.comp_opt import CompositionalOptimizer
class SCGD(CompositionalOptimizer):
r"""Implements Stochastic Compositional Gradient Descent. `https://arxiv.org/pdf/1411.380... | 4,114 | 43.247312 | 137 | py |
HEBO | HEBO-master/CompBO/custom_optimizer/adamos.py | import math
import torch
from torch.optim.optimizer import Optimizer
class Adamos(Optimizer):
r"""Implements Adam-like optimization steps with CAdam scheduling.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional)... | 4,927 | 43.396396 | 114 | py |
HEBO | HEBO-master/CompBO/custom_optimizer/ascgd.py | from typing import Optional
import torch
from torch.autograd.functional import vjp
from torch.optim.optimizer import required
from custom_optimizer.comp_opt import CompositionalOptimizer
class ASCGD(CompositionalOptimizer):
r"""Implements Accelerated Stochastic Compositional Gradient Descent. `https://arxiv.org... | 4,300 | 42.444444 | 136 | py |
HEBO | HEBO-master/CompBO/custom_optimizer/nasa.py | import math
from typing import Optional, Callable
import torch
from torch.autograd.functional import vjp
from torch.optim.optimizer import Optimizer, required
from custom_optimizer.comp_opt import CompositionalOptimizer
class NASA(CompositionalOptimizer):
r"""Implements Nested Averaged Stochastic Approximation.... | 4,531 | 40.962963 | 114 | py |
HEBO | HEBO-master/CompBO/custom_optimizer/comp_opt.py | from torch.optim import Optimizer
class CompositionalOptimizer(Optimizer):
pass
| 86 | 13.5 | 40 | py |
HEBO | HEBO-master/CompBO/custom_optimizer/utils/utils.py | from botorch.optim.utils import columnwise_clamp
from torch import Tensor
from typing import Optional, Union
def columnwise_clamp_(
X: Tensor,
lower: Optional[Union[float, Tensor]] = None,
upper: Optional[Union[float, Tensor]] = None,
raise_on_violation: bool = False,
) -> Tensor:
r"""Clamp values ... | 1,131 | 39.428571 | 85 | py |
HEBO | HEBO-master/CompBO/utils/utils_gp.py | import matplotlib.pyplot as plt
import numpy as np
import torch
from gpytorch import ExactMarginalLogLikelihood
from botorch import fit_gpytorch_model
from botorch.models import SingleTaskGP
def test_gp_fit(x: np.ndarray, y: np.ndarray, ax=None, title='GP fit'):
n_held_out = int(.25 * len(y))
indices = np.ra... | 1,390 | 38.742857 | 87 | py |
HEBO | HEBO-master/SIMMER/envs/utils.py | import torch
import numpy as np
from typing import Union
Array = Union[torch.Tensor, np.ndarray]
def angle_normalize(theta:Array, is_tensor:bool=True) -> Array:
"""Normalizes an angle theta to be between -pi and pi."""
if is_tensor:
torch_pi = torch.Tensor(np.asarray(np.pi))
return ((theta + t... | 422 | 29.214286 | 63 | py |
HEBO | HEBO-master/SIMMER/envs/pendula/single_pendulum.py | import gym
from gym import spaces
from gym.utils import seeding
import numpy as np
from typing import Callable, List, Dict, Tuple
import torch
from os import path
from envs.utils import angle_normalize, Array
from envs.wrappers.safe_env import SafeEnv
from envs.wrappers.simmer_env import simmer_env
from envs.wrappers... | 10,248 | 44.14978 | 166 | py |
HEBO | HEBO-master/SIMMER/envs/wrappers/saute_env.py | import numpy as np
import torch
from gym import Env
from gym import spaces
from envs.utils import Array
class SauteBaseEnv(Env):
def __init__(
self,
saute_discount_factor:float=0.99,
max_ep_len:int=200,
unsafe_reward:float=0,
use_reward_shaping:bool=True,... | 5,996 | 41.835714 | 165 | py |
HEBO | HEBO-master/SIMMER/envs/wrappers/simmer_env.py | import numpy as np
import torch
from gym import Env
from gym import spaces
from envs.utils import Array
class SimmerBaseEnv(Env):
def __init__(
self,
saute_discount_factor:float=0.99,
max_ep_len:int=200,
unsafe_reward:float=0,
use_reward_shaping:bool=True... | 6,501 | 41.220779 | 144 | py |
HEBO | HEBO-master/SIMMER/tf_algos/safety_starter_agents/run_agents.py | """
Main run file copied from safety starter agents with the following modifications:
- added a capability for Sauteing Vanilla and Lagrangian methods
- added a capability to use CVaR constraints
"""
import numpy as np
from tensorboardX.writer import SummaryWriter
import tensorflow as tf
import pandas as pd
import time... | 31,506 | 42.759722 | 141 | py |
VSR-Transformer | VSR-Transformer-main/setup.py | #!/usr/bin/env python
from setuptools import find_packages, setup
import os
import subprocess
import sys
import time
import torch
from torch.utils.cpp_extension import (BuildExtension, CppExtension,
CUDAExtension)
version_file = 'basicsr/version.py'
def readme():
with ope... | 5,365 | 29.662857 | 77 | py |
VSR-Transformer | VSR-Transformer-main/scripts/publish_models.py | import glob
import subprocess
import torch
from os import path as osp
from torch.serialization import _is_zipfile, _open_file_like
def update_sha(paths):
print('# Update sha ...')
for idx, path in enumerate(paths):
print(f'{idx+1:03d}: Processing {path}')
net = torch.load(path, map_location=to... | 2,463 | 40.066667 | 79 | py |
VSR-Transformer | VSR-Transformer-main/scripts/model_conversion/convert_ridnet.py | import torch
from collections import OrderedDict
from basicsr.models.archs.ridnet_arch import RIDNet
if __name__ == '__main__':
ori_net_checkpoint = torch.load(
'experiments/pretrained_models/RIDNet/RIDNet_official_original.pt',
map_location=lambda storage, loc: storage)
rid_net = RIDNet(3, 64... | 768 | 29.76 | 75 | py |
VSR-Transformer | VSR-Transformer-main/scripts/model_conversion/convert_models.py | import torch
def convert_edvr():
ori_net = torch.load('experiments/pretrained_models/EDVR_REDS_SR_M.pth')
crt_net = torch.load('xxx/net_g_8.pth')
save_path = './edvr_medium_x4_reds_sr_official.pth'
# for k, v in ori_net.items():
# print(k)
# print('*****')
# for k, v in crt_net.items... | 17,432 | 41.938424 | 79 | py |
VSR-Transformer | VSR-Transformer-main/scripts/model_conversion/convert_dfdnet.py | import torch
from basicsr.models.archs.dfdnet_arch import DFDNet
from basicsr.models.archs.vgg_arch import NAMES
def convert_net(ori_net, crt_net):
for crt_k, crt_v in crt_net.items():
# vgg feature extractor
if 'vgg_extractor' in crt_k:
ori_k = crt_k.replace('vgg_extractor',
... | 3,023 | 36.8 | 77 | py |
VSR-Transformer | VSR-Transformer-main/scripts/model_conversion/convert_stylegan.py | import torch
from basicsr.models.archs.stylegan2_arch import (StyleGAN2Discriminator,
StyleGAN2Generator)
def convert_net_g(ori_net, crt_net):
"""Convert network generator."""
for crt_k, crt_v in crt_net.items():
if 'style_mlp' in crt_k:
o... | 3,612 | 35.13 | 119 | py |
VSR-Transformer | VSR-Transformer-main/scripts/metrics/calculate_fid_stats_from_datasets.py | import argparse
import math
import numpy as np
import torch
from torch.utils.data import DataLoader
from basicsr.data import create_dataset
from basicsr.metrics.fid import (extract_inception_features,
load_patched_inception_v3)
def calculate_stats_from_dataset():
device = torch.d... | 2,294 | 30.438356 | 76 | py |
VSR-Transformer | VSR-Transformer-main/scripts/metrics/calculate_fid_folder.py | import argparse
import math
import numpy as np
import torch
from torch.utils.data import DataLoader
from basicsr.data import create_dataset
from basicsr.metrics.fid import (calculate_fid, extract_inception_features,
load_patched_inception_v3)
def calculate_fid_folder():
device = ... | 2,643 | 30.47619 | 76 | py |
VSR-Transformer | VSR-Transformer-main/scripts/metrics/calculate_stylegan2_fid.py | import argparse
import math
import numpy as np
import torch
from torch import nn
from basicsr.metrics.fid import (calculate_fid, extract_inception_features,
load_patched_inception_v3)
from basicsr.models.archs.stylegan2_arch import StyleGAN2Generator
def calculate_stylegan2_fid():
... | 2,858 | 34.7375 | 76 | py |
VSR-Transformer | VSR-Transformer-main/scripts/metrics/calculate_lpips.py | import cv2
import glob
import numpy as np
import os.path as osp
from torchvision.transforms.functional import normalize
from basicsr.utils import img2tensor
try:
import lpips
except ImportError:
print('Please install lpips: pip install lpips')
def main():
# Configurations
# -------------------------... | 1,854 | 31.54386 | 79 | py |
VSR-Transformer | VSR-Transformer-main/tests/test_ffhq_dataset.py | import math
import os
import torch
import torchvision.utils
from basicsr.data import create_dataloader, create_dataset
def main():
"""Test FFHQ dataset."""
opt = {}
opt['dist'] = False
opt['gpu_ids'] = [0]
opt['phase'] = 'train'
opt['name'] = 'FFHQ'
opt['type'] = 'FFHQDataset'
opt['... | 1,402 | 21.629032 | 64 | py |
VSR-Transformer | VSR-Transformer-main/tests/test_vimeo90k_dataset.py | import math
import os
import torchvision.utils
from basicsr.data import create_dataloader, create_dataset
def main(mode='lmdb'):
"""Test vimeo90k dataset.
Args:
mode: There are two modes: 'lmdb', 'folder'.
"""
opt = {}
opt['dist'] = False
opt['phase'] = 'train'
opt['name'] = 'Vi... | 2,951 | 30.741935 | 115 | py |
VSR-Transformer | VSR-Transformer-main/tests/test_discriminator_backward.py | import copy
import random
import torch
from torch import nn as nn
class ToyDiscriminator(nn.Module):
def __init__(self):
super(ToyDiscriminator, self).__init__()
self.conv0 = nn.Conv2d(3, 4, 3, 1, 1, bias=True)
self.bn0 = nn.BatchNorm2d(4, affine=True)
self.conv1 = nn.Conv2d(4, 4,... | 2,923 | 25.825688 | 70 | py |
VSR-Transformer | VSR-Transformer-main/tests/test_paired_image_dataset.py | import math
import os
import torchvision.utils
from basicsr.data import create_dataloader, create_dataset
def main(mode='folder'):
"""Test paired image dataset.
Args:
mode: There are three modes: 'lmdb', 'folder', 'meta_info_file'.
"""
opt = {}
opt['dist'] = False
opt['phase'] = 'tra... | 2,396 | 27.879518 | 98 | py |
VSR-Transformer | VSR-Transformer-main/tests/test_lr_scheduler.py | import torch
from basicsr.models.lr_scheduler import CosineAnnealingRestartLR
try:
import matplotlib as mpl
from matplotlib import pyplot as plt
from matplotlib import ticker as mtick
except ImportError:
print('Please install matplotlib.')
mpl.use('Agg')
def main():
optim_params = [
{
... | 1,880 | 24.418919 | 79 | py |
VSR-Transformer | VSR-Transformer-main/tests/test_reds_dataset.py | import math
import os
import torchvision.utils
import time
from basicsr.data import create_dataloader, create_dataset
def main(mode='lmdb'):
"""Test reds dataset.
Args:
mode: There are two modes: 'lmdb', 'folder'.
"""
opt = {}
opt['dist'] = False
opt['phase'] = 'train'
opt['name'... | 2,955 | 28.858586 | 102 | py |
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