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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Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/finetune_src/reverie/model_navref.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import (PretrainedConfig, AutoTokenizer)
from utils.misc import length2mask
from reverie.vlnbert_navref import NavRefCMT
def get_tokenizer(args):
if args.tokenizer == 'bert':
tokenizer = AutoTokenizer... | 5,576 | 36.938776 | 98 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/finetune_src/reverie/vlnbert_navref.py |
import torch
import torch.nn as nn
from transformers import BertPreTrainedModel
from models.vilmodel_cmt import (
BertLayerNorm, BertEmbeddings, ImageEmbeddings,
HistoryEmbeddings, LxmertEncoder, NextActionPrediction,
)
class ObjectEmbeddings(nn.Module):
"""Construct the embeddings from image, spati... | 7,456 | 45.60625 | 124 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/finetune_src/reverie/main_navref.py | import os
import json
import time
import numpy as np
from collections import defaultdict
import torch
from tensorboardX import SummaryWriter
from utils.misc import set_random_seed
from utils.logger import write_to_record_file, print_progress, timeSince
from utils.distributed import init_distributed, is_default_gpu
fr... | 11,533 | 40.192857 | 148 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/finetune_src/reverie/agent.py | import json
import os
import sys
import numpy as np
import random
import math
import time
from collections import defaultdict
import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
from utils.misc import length2mask
from r2r.agent_cmt import Seq2SeqCMTAgent
from reverie.model_navr... | 20,684 | 44.065359 | 140 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/finetune_src/utils/parser.py | import argparse
import os
import torch
def parse_args():
parser = argparse.ArgumentParser(description="")
parser.add_argument('--root_dir', type=str, default='/sequoia/data1/shichen/datasets')
parser.add_argument(
'--dataset', type=str, default='r2r',
choices=['r2r', 'r4r', 'r2r_back', '... | 7,866 | 43.954286 | 113 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/finetune_src/utils/misc.py | import random
import numpy as np
import torch
def set_random_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
random.seed(seed)
np.random.seed(seed)
def length2mask(length, size=None):
batch_size = len(length)
size = int(max(length)) if size... | 510 | 27.388889 | 84 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/finetune_src/utils/distributed.py | """
Distributed tools
"""
import os
from pathlib import Path
from pprint import pformat
import pickle
import torch
import torch.distributed as dist
def load_init_param(opts):
"""
Load parameters for the rendezvous distributed procedure
"""
# sync file
if opts.output_dir != "":
sync_dir = ... | 4,908 | 28.751515 | 94 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/preprocess/precompute_img_features_vit.py | #!/usr/bin/env python3
''' Script to precompute image features using a Pytorch ResNet CNN, using 36 discretized views
at each viewpoint in 30 degree increments, and the provided camera WIDTH, HEIGHT
and VFOV parameters. '''
import os
import sys
import MatterSim
import argparse
import numpy as np
import jso... | 6,102 | 32.168478 | 105 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/main_r2r.py | import os
import sys
import json
import argparse
import time
from collections import defaultdict
from easydict import EasyDict
from tqdm import tqdm
import torch
import torch.nn.functional as F
import torch.distributed as dist
from transformers import AutoTokenizer, PretrainedConfig
from transformers import AutoModel... | 20,591 | 37.779661 | 129 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/main_r2r_image.py | import os
import sys
import json
import argparse
from collections import abc
import time
from collections import defaultdict
from easydict import EasyDict
from tqdm import tqdm
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, PretrainedConfig
from utils.logger import LOGGER, TB_LOGGE... | 21,279 | 35.313993 | 88 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/optim/rangerlars.py | import torch, math
from torch.optim.optimizer import Optimizer
import itertools as it
from .lookahead import *
from .ralamb import *
# RAdam + LARS + LookAHead
# Lookahead implementation from https://github.com/lonePatient/lookahead_pytorch/blob/master/optimizer.py
# RAdam + LARS implementation from https://gist.git... | 519 | 33.666667 | 105 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/optim/radam.py | # from https://github.com/LiyuanLucasLiu/RAdam/blob/master/radam.py
import math
import torch
from torch.optim.optimizer import Optimizer, required
class RAdam(Optimizer):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
defaults = dict(lr=lr, betas=betas, eps=eps, weight... | 8,100 | 37.57619 | 185 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/optim/misc.py | """
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.
Misc lr helper
"""
from torch.optim import Adam, Adamax
from .adamw import AdamW
from .rangerlars import RangerLars
def build_optimizer(model, opts):
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias',... | 1,138 | 28.973684 | 65 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/optim/adamw.py | """
AdamW optimizer (weight decay fix)
copied from hugginface (https://github.com/huggingface/transformers).
"""
import math
from typing import Callable, Iterable, Tuple
import torch
from torch.optim import Optimizer
class AdamW(Optimizer):
"""
Implements Adam algorithm with weight decay fix as introduced i... | 4,887 | 42.256637 | 116 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/optim/ralamb.py | import torch, math
from torch.optim.optimizer import Optimizer
# RAdam + LARS
class Ralamb(Optimizer):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
self.buffer = [[None, None, None] for in... | 4,050 | 39.51 | 181 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/optim/lookahead.py | # Lookahead implementation from https://github.com/rwightman/pytorch-image-models/blob/master/timm/optim/lookahead.py
""" Lookahead Optimizer Wrapper.
Implementation modified from: https://github.com/alphadl/lookahead.pytorch
Paper: `Lookahead Optimizer: k steps forward, 1 step back` - https://arxiv.org/abs/1907.08610... | 4,076 | 40.602041 | 117 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/utils/misc.py | import random
import numpy as np
from typing import Tuple, Union, Dict, Any
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from .distributed import init_distributed
from .logger import LOGGER
def set_random_seed(seed):
random.seed(seed)
np.random.s... | 2,165 | 27.5 | 89 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/utils/save.py | """
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.
saving utilities
"""
import json
import os
import torch
def save_training_meta(args):
os.makedirs(os.path.join(args.output_dir, 'logs'), exist_ok=True)
os.makedirs(os.path.join(args.output_dir, 'ckpts'), exist_ok=True)
with open(os... | 1,606 | 33.191489 | 90 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/utils/distributed.py | """
Distributed tools
"""
import os
from pathlib import Path
from pprint import pformat
import pickle
import torch
import torch.distributed as dist
def load_init_param(opts):
"""
Load parameters for the rendezvous distributed procedure
"""
# sync file
if opts.output_dir != "":
sync_dir = ... | 4,851 | 29.136646 | 94 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/data/r2r_tasks.py | import random
import math
import numpy as np
import torch
from torch.utils.data import Dataset
from torch.nn.utils.rnn import pad_sequence
from .common import pad_tensors, gen_seq_masks
############### Masked Language Modeling ###############
def random_word(tokens, vocab_range, mask):
"""
Masking some rando... | 24,879 | 40.605351 | 112 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/data/image_loader.py | """
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.
A prefetch loader to speedup data loading
Modified from Nvidia Deep Learning Examples
(https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch).
"""
import random
from typing import List, Dict, Tuple, Union, Iterator
import torch
from ... | 5,236 | 29.447674 | 86 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/data/common.py | import numpy as np
import torch
def pad_tensors(tensors, lens=None, pad=0):
"""B x [T, ...]"""
if lens is None:
lens = [t.size(0) for t in tensors]
max_len = max(lens)
bs = len(tensors)
hid = list(tensors[0].size()[1:])
size = [bs, max_len] + hid
dtype = tensors[0].dtype
outpu... | 810 | 26.033333 | 73 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/data/image_tasks.py | import random
import numpy as np
import torch
from torch.nn.utils.rnn import pad_sequence
from .data import pad_tensors, gen_seq_masks
from .mlm import random_word, MlmDataset
from .mrc import _get_img_mask, MrcDataset
from .sap import SapDataset
from .sar import SarDataset
from .sprel import SprelDataset
from .itm i... | 18,514 | 35.375246 | 88 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/data/image_data.py | """
R2R-style dataset: load images
"""
import os
import json
import jsonlines
import numpy as np
import h5py
import math
import lmdb
import networkx as nx
from PIL import Image
import torch
from timm.data.transforms_factory import create_transform
from .data import angle_feature, softmax, MultiStepNavData
HEIGHT = ... | 9,046 | 32.63197 | 88 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/data/loader.py | """
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.
A prefetch loader to speedup data loading
Modified from Nvidia Deep Learning Examples
(https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch).
"""
import random
from typing import List, Dict, Tuple, Union, Iterator
import torch
from... | 5,220 | 30.642424 | 103 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/model/vilmodel.py | import json
import logging
import math
import os
import sys
from io import open
from typing import Callable, List, Tuple
import numpy as np
import copy
import torch
from torch import nn
from torch import Tensor, dtype
from transformers import BertPreTrainedModel
from transformers.modeling_utils import get_parameter_d... | 32,060 | 43.161157 | 137 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/model/vision_transformer.py | """ Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929
The official jax code is released and available at https://github.com/google-research/vision_transformer
... | 33,731 | 45.785021 | 140 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/model/image_vilmodel.py | import json
import logging
import math
import os
import sys
from io import open
from typing import Callable, List, Tuple
import numpy as np
import copy
import torch
from torch import nn
from torch import Tensor, device, dtype
from transformers import BertPreTrainedModel
from .vision_transformer import vit_base_patch... | 9,606 | 44.747619 | 137 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/model/image_pretrain.py | from collections import defaultdict
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import BertPreTrainedModel
from .vilmodel import BertLayerNorm, BertOnlyMLMHead
from .pretrain import (NextActionPrediction, NextActionRegression,
SpatialRelRegression, Reg... | 10,924 | 51.272727 | 128 | py |
Diagnose_VLN | Diagnose_VLN-master/rxr/model/VLN-HAMT/pretrain_src/model/pretrain_cmt.py | from collections import defaultdict
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import BertPreTrainedModel
from .vilmodel import BertLayerNorm, BertOnlyMLMHead
from .vilmodel import NavPreTrainedModel
class NextActionPrediction(nn.Module):
def __init__(self, hidden_size... | 12,828 | 47.779468 | 129 | py |
GNG-ODE | GNG-ODE-main/src/models/gng_ode.py | import math
from rdflib import Graph
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import dgl
import dgl.ops as F
import dgl.function as fn
from dgl.nn.pytorch import GraphConv, GATConv
from torchdiffeq import odeint
from torch.autograd import Variable
class GraphGRUODE(nn.Module):
... | 12,193 | 38.71987 | 152 | py |
GNG-ODE | GNG-ODE-main/src/scripts/main_ode.py | import argparse
import sys
import torch
import random
import numpy as np
import os
def seed_torch(seed=42):
seed = int(seed)
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)... | 4,932 | 28.538922 | 157 | py |
GNG-ODE | GNG-ODE-main/src/utils/train.py | import time
import numpy as np
import torch as th
from sklearn.metrics import accuracy_score
from torch import nn, optim
from tqdm import tqdm
# ignore weight decay for parameters in bias, batch norm and activation
def fix_weight_decay(model):
decay = []
no_decay = []
for name, param in model.named_param... | 4,717 | 34.208955 | 156 | py |
GNG-ODE | GNG-ODE-main/src/utils/data/collate.py | from collections import Counter
import numpy as np
import torch as th
import torch.nn.functional as F
import dgl
import pickle
def label_last(g, last_nid):
is_last = th.zeros(g.num_nodes(), dtype=th.int32)
is_last[last_nid] = 1
g.ndata['last'] = is_last
return g
def label_last_ccs(g, last_nid):
f... | 4,868 | 31.898649 | 150 | py |
GBST | GBST-master/gbst_src/rabit/python/rabit.py | """
Reliable Allreduce and Broadcast Library.
Author: Tianqi Chen
"""
# pylint: disable=unused-argument,invalid-name,global-statement,dangerous-default-value,
import pickle
import ctypes
import os
import platform
import sys
import warnings
import numpy as np
# version information about the doc
__version__ = '1.0'
_L... | 10,642 | 28.158904 | 89 | py |
GBST | GBST-master/gbst_src/rabit/doc/conf.py | # -*- coding: utf-8 -*-
#
# documentation build configuration file, created by
# sphinx-quickstart on Thu Jul 23 19:40:08 2015.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All confi... | 6,362 | 33.394595 | 88 | py |
GBST | GBST-master/gbst_src/tests/ci_build/insert_vcomp140.py | import sys
import re
import zipfile
import glob
if len(sys.argv) != 2:
print('Usage: {} [wheel]'.format(sys.argv[0]))
sys.exit(1)
vcomp140_path = 'C:\\Windows\\System32\\vcomp140.dll'
for wheel_path in sorted(glob.glob(sys.argv[1])):
m = re.search(r'xgboost-(.*)-py2.py3', wheel_path)
assert m
ver... | 479 | 24.263158 | 91 | py |
GBST | GBST-master/gbst_src/tests/ci_build/tidy.py | #!/usr/bin/env python
import subprocess
import yaml
import json
from multiprocessing import Pool, cpu_count
import shutil
import os
import sys
import re
import argparse
def call(args):
'''Subprocess run wrapper.'''
completed = subprocess.run(args,
stdout=subprocess.PIPE,
... | 9,066 | 34.280156 | 76 | py |
GBST | GBST-master/gbst_src/tests/python/test_dmatrix.py | # -*- coding: utf-8 -*-
import numpy as np
import xgboost as xgb
import unittest
import scipy.sparse
from scipy.sparse import rand
rng = np.random.RandomState(1)
dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestDMatrix(unittest.TestCase):
def test_dmatrix_numpy_init(self):
data = np.rand... | 6,189 | 34.988372 | 80 | py |
GBST | GBST-master/gbst_src/tests/python/test_interaction_constraints.py | # -*- coding: utf-8 -*-
import numpy as np
import xgboost
import unittest
import testing as tm
import pytest
dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestInteractionConstraints(unittest.TestCase):
def run_interaction_constraints(self, tree_method):
x1 = np.random.normal(loc=1.0, scale... | 3,396 | 35.526882 | 78 | py |
GBST | GBST-master/gbst_src/tests/python/test_plotting.py | # -*- coding: utf-8 -*-
import numpy as np
import xgboost as xgb
import testing as tm
import unittest
import pytest
try:
import matplotlib
matplotlib.use('Agg')
from matplotlib.axes import Axes
from graphviz import Source
except ImportError:
pass
pytestmark = pytest.mark.skipif(**tm.no_matplotli... | 2,422 | 30.467532 | 72 | py |
GBST | GBST-master/gbst_src/tests/python/test_tracker.py | import time
from xgboost import RabitTracker
import xgboost as xgb
def test_rabit_tracker():
tracker = RabitTracker(hostIP='127.0.0.1', nslave=1)
tracker.start(1)
rabit_env = [
str.encode('DMLC_TRACKER_URI=127.0.0.1'),
str.encode('DMLC_TRACKER_PORT=9091'),
str.encode('DMLC_TASK_ID... | 460 | 24.611111 | 56 | py |
GBST | GBST-master/gbst_src/tests/python/test_ranking.py | import numpy as np
from scipy.sparse import csr_matrix
import xgboost
import os
import unittest
import itertools
import shutil
import urllib.request
import zipfile
def test_ranking_with_unweighted_data():
Xrow = np.array([1, 2, 6, 8, 11, 14, 16, 17])
Xcol = np.array([0, 0, 1, 1, 2, 2, 3, 3])
X = csr_m... | 7,180 | 38.674033 | 117 | py |
GBST | GBST-master/gbst_src/tests/python/test_with_sklearn.py | import numpy as np
import xgboost as xgb
import testing as tm
import tempfile
import os
import shutil
import pytest
rng = np.random.RandomState(1994)
pytestmark = pytest.mark.skipif(**tm.no_sklearn())
class TemporaryDirectory(object):
"""Context manager for tempfile.mkdtemp()"""
def __enter__(self):
... | 25,149 | 35.031519 | 81 | py |
GBST | GBST-master/gbst_src/tests/python/test_basic.py | # -*- coding: utf-8 -*-
import sys
from contextlib import contextmanager
try:
# python 2
from StringIO import StringIO
except ImportError:
# python 3
from io import StringIO
import numpy as np
import xgboost as xgb
import unittest
import json
from pathlib import Path
dpath = 'demo/data/'
rng = np.rando... | 11,472 | 34.853125 | 113 | py |
GBST | GBST-master/gbst_src/tests/python/test_dt.py | # -*- coding: utf-8 -*-
import unittest
import pytest
import testing as tm
import xgboost as xgb
try:
import datatable as dt
import pandas as pd
except ImportError:
pass
pytestmark = pytest.mark.skipif(
tm.no_dt()['condition'] or tm.no_pandas()['condition'],
reason=tm.no_dt()['reason'] + ' or ' +... | 1,592 | 29.634615 | 68 | py |
GBST | GBST-master/gbst_src/tests/python/test_pickling.py | import pickle
import numpy as np
import xgboost as xgb
import os
import unittest
kRows = 100
kCols = 10
def generate_data():
X = np.random.randn(kRows, kCols)
y = np.random.randn(kRows)
return X, y
class TestPickling(unittest.TestCase):
def run_model_pickling(self, xgb_params):
X, y = gene... | 1,349 | 21.5 | 58 | py |
GBST | GBST-master/gbst_src/tests/python/test_with_pandas.py | # -*- coding: utf-8 -*-
import numpy as np
import xgboost as xgb
import testing as tm
import unittest
import pytest
try:
import pandas as pd
except ImportError:
pass
pytestmark = pytest.mark.skipif(**tm.no_pandas())
dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestPandas(unittest.TestCase... | 7,788 | 38.338384 | 77 | py |
GBST | GBST-master/gbst_src/tests/python/testing.py | # coding: utf-8
from xgboost.compat import SKLEARN_INSTALLED, PANDAS_INSTALLED, DT_INSTALLED
from xgboost.compat import CUDF_INSTALLED, DASK_INSTALLED
def no_sklearn():
return {'condition': not SKLEARN_INSTALLED,
'reason': 'Scikit-Learn is not installed'}
def no_dask():
return {'condition': not ... | 1,492 | 24.741379 | 76 | py |
GBST | GBST-master/gbst_src/tests/python/test_with_dask.py | import testing as tm
import pytest
import xgboost as xgb
import sys
import numpy as np
if sys.platform.startswith("win"):
pytest.skip("Skipping dask tests on Windows", allow_module_level=True)
pytestmark = pytest.mark.skipif(**tm.no_dask())
try:
from distributed.utils_test import client, loop, cluster_fixtur... | 3,449 | 26.6 | 74 | py |
GBST | GBST-master/gbst_src/tests/python/test_openmp.py | # -*- coding: utf-8 -*-
import xgboost as xgb
import unittest
import numpy as np
class TestOMP(unittest.TestCase):
def test_omp(self):
dpath = 'demo/data/'
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
param = {'booster': 'gbtr... | 2,357 | 30.44 | 85 | py |
GBST | GBST-master/gbst_src/tests/python/test_eval_metrics.py | import xgboost as xgb
import testing as tm
import numpy as np
import unittest
import pytest
rng = np.random.RandomState(1337)
class TestEvalMetrics(unittest.TestCase):
xgb_params_01 = {
'verbosity': 0,
'nthread': 1,
'eval_metric': 'error'
}
xgb_params_02 = {
'verbosity': ... | 4,075 | 37.093458 | 78 | py |
GBST | GBST-master/gbst_src/tests/python/test_training_continuation.py | import xgboost as xgb
import testing as tm
import numpy as np
import unittest
import pytest
rng = np.random.RandomState(1337)
class TestTrainingContinuation(unittest.TestCase):
num_parallel_tree = 3
def generate_parameters(self, use_json):
xgb_params_01_binary = {
'nthread': 1,
}... | 6,555 | 38.02381 | 76 | py |
GBST | GBST-master/gbst_src/tests/python/test_linear.py | from __future__ import print_function
import numpy as np
import testing as tm
import unittest
import pytest
import xgboost as xgb
try:
from sklearn.linear_model import ElasticNet
from sklearn.preprocessing import scale
from regression_test_utilities import run_suite, parameter_combinations
except ImportE... | 3,397 | 36.755556 | 79 | py |
GBST | GBST-master/gbst_src/tests/python/test_shap.py | # -*- coding: utf-8 -*-
import numpy as np
import xgboost as xgb
import unittest
import itertools
import re
import scipy
import scipy.special
dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestSHAP(unittest.TestCase):
def test_feature_importances(self):
data = np.random.randn(100, 5)
... | 10,198 | 38.839844 | 104 | py |
GBST | GBST-master/gbst_src/tests/python/test_tree_regularization.py | import numpy as np
import unittest
import xgboost as xgb
from numpy.testing import assert_approx_equal
train_data = xgb.DMatrix(np.array([[1]]), label=np.array([1]))
class TestTreeRegularization(unittest.TestCase):
def test_alpha(self):
params = {
'tree_method': 'exact', 'verbosity': 0,
... | 1,843 | 27.8125 | 78 | py |
GBST | GBST-master/gbst_src/tests/python/test_basic_models.py | import numpy as np
import xgboost as xgb
import unittest
import os
import json
dpath = 'demo/data/'
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
rng = np.random.RandomState(1994)
class TestModels(unittest.TestCase):
def test_glm(self):
param = {'ver... | 9,141 | 39.631111 | 107 | py |
GBST | GBST-master/gbst_src/tests/python/test_monotone_constraints.py | import numpy as np
import xgboost as xgb
import unittest
import testing as tm
import pytest
dpath = 'demo/data/'
def is_increasing(y):
return np.count_nonzero(np.diff(y) < 0.0) == 0
def is_decreasing(y):
return np.count_nonzero(np.diff(y) > 0.0) == 0
def is_correctly_constrained(learner):
n = 100
... | 4,196 | 33.121951 | 79 | py |
GBST | GBST-master/gbst_src/tests/python/test_early_stopping.py | import xgboost as xgb
import testing as tm
import numpy as np
import unittest
import pytest
rng = np.random.RandomState(1994)
class TestEarlyStopping(unittest.TestCase):
@pytest.mark.skipif(**tm.no_sklearn())
def test_early_stopping_nonparallel(self):
from sklearn.datasets import load_digits
... | 4,319 | 38.633028 | 90 | py |
GBST | GBST-master/gbst_src/tests/python/test_updaters.py | import testing as tm
import unittest
import pytest
import xgboost as xgb
import numpy as np
try:
from regression_test_utilities import run_suite, parameter_combinations, \
assert_results_non_increasing
except ImportError:
None
class TestUpdaters(unittest.TestCase):
@pytest.mark.skipif(**tm.no_skl... | 3,102 | 39.298701 | 78 | py |
GBST | GBST-master/gbst_src/tests/python/test_parse_tree.py | import xgboost as xgb
import unittest
import numpy as np
import pytest
import testing as tm
pytestmark = pytest.mark.skipif(**tm.no_pandas())
dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestTreesToDataFrame(unittest.TestCase):
def build_model(self, max_depth, num_round):
dtrain = xgb... | 1,834 | 34.288462 | 76 | py |
GBST | GBST-master/gbst_src/tests/python/regression_test_utilities.py | from __future__ import print_function
import glob
import itertools as it
import numpy as np
import os
import sys
import xgboost as xgb
try:
from sklearn import datasets
from sklearn.preprocessing import scale
except ImportError:
None
class Dataset:
def __init__(self, name, get_dataset, objective, me... | 5,046 | 29.587879 | 104 | py |
GBST | GBST-master/gbst_src/tests/python-gpu/load_pickle.py | '''Loading a pickled model generated by test_pickling.py, only used by
`test_gpu_with_dask.py`'''
import unittest
import os
import xgboost as xgb
import json
from test_gpu_pickling import build_dataset, model_path, load_pickle
class TestLoadPickle(unittest.TestCase):
def test_load_pkl(self):
'''Test whet... | 1,413 | 34.35 | 75 | py |
GBST | GBST-master/gbst_src/tests/python-gpu/test_monotonic_constraints.py | from __future__ import print_function
import numpy as np
from sklearn.datasets import make_regression
import unittest
import pytest
import xgboost as xgb
rng = np.random.RandomState(1994)
def non_decreasing(L):
return all((x - y) < 0.001 for x, y in zip(L, L[1:]))
def non_increasing(L):
return all((y - ... | 1,172 | 23.957447 | 78 | py |
GBST | GBST-master/gbst_src/tests/python-gpu/test_gpu_updaters.py | import numpy as np
import sys
import unittest
import pytest
import xgboost
sys.path.append("tests/python")
from regression_test_utilities import run_suite, parameter_combinations, \
assert_results_non_increasing
def assert_gpu_results(cpu_results, gpu_results):
for cpu_res, gpu_res in zip(cpu_results, gpu_re... | 3,390 | 35.858696 | 99 | py |
GBST | GBST-master/gbst_src/tests/python-gpu/test_large_sizes.py | from __future__ import print_function
import sys
import time
import pytest
sys.path.append("../../tests/python")
import xgboost as xgb
import numpy as np
import unittest
def eprint(*args, **kwargs):
print(*args, file=sys.stderr, **kwargs)
sys.stderr.flush()
print(*args, file=sys.stdout, **kwargs)
sy... | 2,863 | 31.91954 | 81 | py |
GBST | GBST-master/gbst_src/tests/python-gpu/test_gpu_with_sklearn.py | import xgboost as xgb
import pytest
import sys
import numpy as np
sys.path.append("tests/python")
import testing as tm
pytestmark = pytest.mark.skipif(**tm.no_sklearn())
rng = np.random.RandomState(1994)
def test_gpu_binary_classification():
from sklearn.datasets import load_digits
from sklearn.model_selec... | 999 | 30.25 | 79 | py |
GBST | GBST-master/gbst_src/tests/python-gpu/test_gpu_training_continuation.py | import unittest
import numpy as np
import xgboost as xgb
import json
rng = np.random.RandomState(1994)
class TestGPUTrainingContinuation(unittest.TestCase):
def run_training_continuation(self, use_json):
kRows = 64
kCols = 32
X = np.random.randn(kRows, kCols)
y = np.random.randn(k... | 2,179 | 36.586207 | 78 | py |
GBST | GBST-master/gbst_src/tests/python-gpu/test_gpu_pickling.py | '''Test model IO with pickle.'''
import pickle
import unittest
import numpy as np
import subprocess
import os
import json
import xgboost as xgb
from xgboost import XGBClassifier
model_path = './model.pkl'
def build_dataset():
N = 10
x = np.linspace(0, N*N, N*N)
x = x.reshape((N, N))
y = np.linspace(0... | 3,826 | 27.774436 | 78 | py |
GBST | GBST-master/gbst_src/tests/python-gpu/test_gpu_ranking.py | import numpy as np
from scipy.sparse import csr_matrix
import xgboost
import os
import math
import unittest
import itertools
import shutil
import urllib.request
import zipfile
class TestRanking(unittest.TestCase):
@classmethod
def setUpClass(cls):
"""
Download and setup the test fixtures
... | 5,913 | 40.069444 | 98 | py |
GBST | GBST-master/gbst_src/tests/python-gpu/test_gpu_prediction.py | from __future__ import print_function
import numpy as np
import unittest
import xgboost as xgb
import pytest
rng = np.random.RandomState(1994)
@pytest.mark.gpu
class TestGPUPredict(unittest.TestCase):
def test_predict(self):
iterations = 10
np.random.seed(1)
test_num_rows = [10, 1000, 50... | 4,499 | 39.178571 | 79 | py |
GBST | GBST-master/gbst_src/tests/python-gpu/test_from_columnar.py | import numpy as np
import xgboost as xgb
import sys
import pytest
sys.path.append("tests/python")
import testing as tm
def dmatrix_from_cudf(input_type, missing=np.NAN):
'''Test constructing DMatrix from cudf'''
import cudf
import pandas as pd
kRows = 80
kCols = 3
na = np.random.randn(kRows,... | 2,688 | 29.213483 | 75 | py |
GBST | GBST-master/gbst_src/tests/python-gpu/test_gpu_with_dask.py | import sys
import pytest
import numpy as np
import unittest
if sys.platform.startswith("win"):
pytest.skip("Skipping dask tests on Windows", allow_module_level=True)
try:
import dask.dataframe as dd
from xgboost import dask as dxgb
from dask_cuda import LocalCUDACluster
from dask.distributed impor... | 3,550 | 36.776596 | 74 | py |
GBST | GBST-master/gbst_src/tests/distributed/test_basic.py | #!/usr/bin/python
import xgboost as xgb
# Always call this before using distributed module
xgb.rabit.init()
# Load file, file will be automatically sharded in distributed mode.
dtrain = xgb.DMatrix('../../demo/data/agaricus.txt.train')
dtest = xgb.DMatrix('../../demo/data/agaricus.txt.test')
# Specify parameters via... | 1,074 | 34.833333 | 84 | py |
GBST | GBST-master/gbst_src/tests/distributed/distributed_gpu.py | """Distributed GPU tests."""
import sys
import time
import xgboost as xgb
import os
def run_test(name, params_fun):
"""Runs a distributed GPU test."""
# Always call this before using distributed module
xgb.rabit.init()
rank = xgb.rabit.get_rank()
world = xgb.rabit.get_world_size()
# Load file... | 2,703 | 29.044444 | 88 | py |
GBST | GBST-master/gbst_src/tests/distributed/test_issue3402.py | #!/usr/bin/python
import xgboost as xgb
import numpy as np
xgb.rabit.init()
X = [
[15.00,28.90,29.00,3143.70,0.00,0.10,69.90,90.00,13726.07,0.00,2299.70,0.00,0.05,
4327.03,0.00,24.00,0.18,3.00,0.41,3.77,0.00,0.00,4.00,0.00,150.92,0.00,2.00,0.00,
0.01,138.00,1.00,0.02,69.90,0.00,0.83,5.00,0.01,0.12,47.30,0.00,... | 5,298 | 65.2375 | 91 | py |
GBST | GBST-master/gbst_src/tests/benchmark/benchmark_linear.py | #pylint: skip-file
import sys, argparse
import xgboost as xgb
import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import time
import ast
rng = np.random.RandomState(1994)
def run_benchmark(args):
try:
dtest = xgb.DMatrix('dtest.dm')
... | 2,921 | 40.742857 | 143 | py |
GBST | GBST-master/gbst_src/tests/benchmark/benchmark_tree.py | """Run benchmark on the tree booster."""
import argparse
import ast
import time
import numpy as np
import xgboost as xgb
RNG = np.random.RandomState(1994)
def run_benchmark(args):
"""Runs the benchmark."""
try:
dtest = xgb.DMatrix('dtest.dm')
dtrain = xgb.DMatrix('dtrain.dm')
if no... | 3,021 | 33.735632 | 100 | py |
GBST | GBST-master/gbst_src/demo/multiclass_classification/train.py | #!/usr/bin/python
from __future__ import division
import numpy as np
import xgboost as xgb
# label need to be 0 to num_class -1
data = np.loadtxt('./dermatology.data', delimiter=',',
converters={33: lambda x:int(x == '?'), 34: lambda x:int(x) - 1})
sz = data.shape
train = data[:int(sz[0] * 0.7), :]
test = d... | 1,572 | 29.25 | 73 | py |
GBST | GBST-master/gbst_src/demo/gpu_acceleration/memory.py | import xgboost as xgb
import numpy as np
import time
import pickle
import GPUtil
n = 10000
m = 1000
X = np.random.random((n, m))
y = np.random.random(n)
param = {'objective': 'binary:logistic',
'tree_method': 'gpu_hist'
}
iterations = 5
dtrain = xgb.DMatrix(X, label=y)
# High memory usage
# active ... | 1,186 | 21.826923 | 97 | py |
GBST | GBST-master/gbst_src/demo/gpu_acceleration/cover_type.py | import xgboost as xgb
import numpy as np
from sklearn.datasets import fetch_covtype
from sklearn.model_selection import train_test_split
import time
# Fetch dataset using sklearn
cov = fetch_covtype()
X = cov.data
y = cov.target
# Create 0.75/0.25 train/test split
X_train, X_test, y_train, y_test = train_test_split(X... | 1,350 | 31.95122 | 90 | py |
GBST | GBST-master/gbst_src/demo/dask/sklearn_cpu_training.py | '''Dask interface demo:
Use scikit-learn regressor interface with CPU histogram tree method.'''
from dask.distributed import Client
from dask.distributed import LocalCluster
from dask import array as da
import xgboost
def main(client):
# generate some random data for demonstration
n = 100
m = 10000
p... | 1,182 | 28.575 | 74 | py |
GBST | GBST-master/gbst_src/demo/dask/sklearn_gpu_training.py | '''Dask interface demo:
Use scikit-learn regressor interface with GPU histogram tree method.'''
from dask.distributed import Client
# It's recommended to use dask_cuda for GPU assignment
from dask_cuda import LocalCUDACluster
from dask import array as da
import xgboost
def main(client):
# generate some random d... | 1,302 | 29.302326 | 73 | py |
GBST | GBST-master/gbst_src/demo/dask/gpu_training.py | from dask_cuda import LocalCUDACluster
from dask.distributed import Client
from dask import array as da
import xgboost as xgb
from xgboost.dask import DaskDMatrix
def main(client):
# generate some random data for demonstration
m = 100000
n = 100
X = da.random.random(size=(m, n), chunks=100)
y = da... | 1,699 | 35.170213 | 79 | py |
GBST | GBST-master/gbst_src/demo/dask/cpu_training.py | import xgboost as xgb
from xgboost.dask import DaskDMatrix
from dask.distributed import Client
from dask.distributed import LocalCluster
from dask import array as da
def main(client):
# generate some random data for demonstration
m = 100000
n = 100
X = da.random.random(size=(m, n), chunks=100)
y =... | 1,462 | 33.023256 | 73 | py |
GBST | GBST-master/gbst_src/demo/guide-python/predict_first_ntree.py | #!/usr/bin/python
import numpy as np
import xgboost as xgb
### load data in do training
dtrain = xgb.DMatrix('../data/agaricus.txt.train')
dtest = xgb.DMatrix('../data/agaricus.txt.test')
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_ro... | 776 | 36 | 83 | py |
GBST | GBST-master/gbst_src/demo/guide-python/external_memory.py | #!/usr/bin/python
import numpy as np
import scipy.sparse
import xgboost as xgb
### simple example for using external memory version
# this is the only difference, add a # followed by a cache prefix name
# several cache file with the prefix will be generated
# currently only support convert from libsvm file
dtrain = x... | 886 | 33.115385 | 107 | py |
GBST | GBST-master/gbst_src/demo/guide-python/generalized_linear_model.py | #!/usr/bin/python
import xgboost as xgb
##
# this script demonstrate how to fit generalized linear model in xgboost
# basically, we are using linear model, instead of tree for our boosters
##
dtrain = xgb.DMatrix('../data/agaricus.txt.train')
dtest = xgb.DMatrix('../data/agaricus.txt.test')
# change booster to gbline... | 1,231 | 38.741935 | 111 | py |
GBST | GBST-master/gbst_src/demo/guide-python/custom_rmsle.py | '''Demo for defining customized metric and objective. Notice that for
simplicity reason weight is not used in following example. In this
script, we implement the Squared Log Error (SLE) objective and RMSLE metric as customized
functions, then compare it with native implementation in XGBoost.
See doc/tutorials/custom_... | 5,845 | 31.477778 | 89 | py |
GBST | GBST-master/gbst_src/demo/guide-python/sklearn_examples.py | #!/usr/bin/python
'''
Created on 1 Apr 2015
@author: Jamie Hall
'''
import pickle
import xgboost as xgb
import numpy as np
from sklearn.model_selection import KFold, train_test_split, GridSearchCV
from sklearn.metrics import confusion_matrix, mean_squared_error
from sklearn.datasets import load_iris, load_digits, loa... | 2,431 | 30.584416 | 73 | py |
GBST | GBST-master/gbst_src/demo/guide-python/custom_objective.py | #!/usr/bin/python
import numpy as np
import xgboost as xgb
###
# advanced: customized loss function
#
print('start running example to used customized objective function')
dtrain = xgb.DMatrix('../data/agaricus.txt.train')
dtest = xgb.DMatrix('../data/agaricus.txt.test')
# note: for customized objective function, we l... | 1,913 | 37.28 | 78 | py |
GBST | GBST-master/gbst_src/demo/guide-python/gamma_regression.py | #!/usr/bin/python
import xgboost as xgb
import numpy as np
# this script demonstrates how to fit gamma regression model (with log link function)
# in xgboost, before running the demo you need to generate the autoclaims dataset
# by running gen_autoclaims.R located in xgboost/demo/data.
data = np.genfromtxt('../dat... | 1,067 | 40.076923 | 99 | py |
GBST | GBST-master/gbst_src/demo/guide-python/boost_from_prediction.py | #!/usr/bin/python
import numpy as np
import xgboost as xgb
dtrain = xgb.DMatrix('../data/agaricus.txt.train')
dtest = xgb.DMatrix('../data/agaricus.txt.test')
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
###
# advanced: start from a initial base prediction
#
print ('start running example to start from a initial pr... | 996 | 38.88 | 103 | py |
GBST | GBST-master/gbst_src/demo/guide-python/evals_result.py | ##
# This script demonstrate how to access the eval metrics in xgboost
##
import xgboost as xgb
dtrain = xgb.DMatrix('../data/agaricus.txt.train', silent=True)
dtest = xgb.DMatrix('../data/agaricus.txt.test', silent=True)
param = [('max_depth', 2), ('objective', 'binary:logistic'), ('eval_metric', 'logloss'), ('eval... | 938 | 29.290323 | 114 | py |
GBST | GBST-master/gbst_src/demo/guide-python/sklearn_parallel.py | import os
if __name__ == "__main__":
# NOTE: on posix systems, this *has* to be here and in the
# `__name__ == "__main__"` clause to run XGBoost in parallel processes
# using fork, if XGBoost was built with OpenMP support. Otherwise, if you
# build XGBoost without OpenMP support, you can use fork, whic... | 1,301 | 35.166667 | 78 | py |
GBST | GBST-master/gbst_src/demo/guide-python/predict_leaf_indices.py | #!/usr/bin/python
import xgboost as xgb
### load data in do training
dtrain = xgb.DMatrix('../data/agaricus.txt.train')
dtest = xgb.DMatrix('../data/agaricus.txt.test')
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
num_round = 3
bst = xgb.t... | 637 | 30.9 | 75 | py |
GBST | GBST-master/gbst_src/demo/guide-python/basic_walkthrough.py | #!/usr/bin/python
import numpy as np
import scipy.sparse
import pickle
import xgboost as xgb
### simple example
# load file from text file, also binary buffer generated by xgboost
dtrain = xgb.DMatrix('../data/agaricus.txt.train')
dtest = xgb.DMatrix('../data/agaricus.txt.test')
# specify parameters via map, definiti... | 2,719 | 32.580247 | 111 | py |
GBST | GBST-master/gbst_src/demo/guide-python/sklearn_evals_result.py | ##
# This script demonstrate how to access the xgboost eval metrics by using sklearn
##
import xgboost as xgb
import numpy as np
from sklearn.datasets import make_hastie_10_2
X, y = make_hastie_10_2(n_samples=2000, random_state=42)
# Map labels from {-1, 1} to {0, 1}
labels, y = np.unique(y, return_inverse=True)
X... | 1,218 | 26.704545 | 82 | py |
GBST | GBST-master/gbst_src/demo/guide-python/cross_validation.py | #!/usr/bin/python
import numpy as np
import xgboost as xgb
### load data in do training
dtrain = xgb.DMatrix('../data/agaricus.txt.train')
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
num_round = 2
print('running cross validation')
# do cross validation, this will print result out as
# ... | 2,329 | 35.984127 | 75 | py |
GBST | GBST-master/gbst_src/demo/rank/rank.py | #!/usr/bin/python
import xgboost as xgb
from xgboost import DMatrix
from sklearn.datasets import load_svmlight_file
# This script demonstrate how to do ranking with xgboost.train
x_train, y_train = load_svmlight_file("mq2008.train")
x_valid, y_valid = load_svmlight_file("mq2008.vali")
x_test, y_test = load_svmlight_... | 1,302 | 30.02381 | 65 | py |
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