python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
|---|---|---|
import logging
import os
import json
import time
from copy import deepcopy
from collections import defaultdict
import torch
import numpy as np
from torch.utils.data import DataLoader
from torch.utils.data.sampler import WeightedRandomSampler
from stratification.utils.utils import NumpyEncoder, get_unique_str, keys_to... | hidden-stratification-master | stratification/harness.py |
import os
import torch
from stratification.harness import GEORGEHarness
from stratification.utils.utils import set_seed, init_cuda
from stratification.utils.parse_args import get_config
from stratification.cluster.models.cluster import GaussianMixture
from stratification.cluster.models.reduction import UMAPReducer
d... | hidden-stratification-master | stratification/demo.py |
import os
import logging
from functools import partial
import numpy as np
import sklearn.metrics
import torch
import torch.optim as optimizers
import torch.optim.lr_scheduler as schedulers
import torch.nn.functional as F
from progress.bar import IncrementalBar as ProgressBar
from stratification.classification.utils i... | hidden-stratification-master | stratification/classification/george_classification.py |
hidden-stratification-master | stratification/classification/__init__.py | |
import torch
from sklearn.metrics import roc_auc_score
class AverageMeter:
"""Computes and stores the average and current value
Imported from https://github.com/pytorch/examples/blob/master/imagenet/main.py#L247-L262
"""
def __init__(self):
self.reset()
def reset(self):
self.va... | hidden-stratification-master | stratification/classification/utils.py |
import logging
import torch
import numpy as np
class LossComputer:
def __init__(self, criterion, is_robust, n_groups, group_counts, robust_step_size, stable=True,
size_adjustments=None, auroc_version=False, class_map=None, use_cuda=True):
self.criterion = criterion
self.is_robust... | hidden-stratification-master | stratification/classification/losses/loss_computer.py |
from .loss_computer import init_criterion
| hidden-stratification-master | stratification/classification/losses/__init__.py |
import os
import logging
import random
from collections import defaultdict, Counter
import itertools
from PIL import Image
import numpy as np
import pandas as pd
import torch
from torchvision import transforms
from stratification.classification.datasets.base import GEORGEDataset
class ISICDataset(GEORGEDataset):
... | hidden-stratification-master | stratification/classification/datasets/isic.py |
from .base import GEORGEDataset, DATA_SPLITS, LABEL_TYPES
from .celebA import CelebADataset
from .isic import ISICDataset
from .waterbirds import WaterbirdsDataset
from .mnist import MNISTDataset
| hidden-stratification-master | stratification/classification/datasets/__init__.py |
import os
import torch
import pandas as pd
from PIL import Image
import logging
import numpy as np
import torchvision.transforms as transforms
from .base import GEORGEDataset
class CelebADataset(GEORGEDataset):
"""
CelebA dataset (already cropped and centered).
Note: idx and filenames are off by one.
... | hidden-stratification-master | stratification/classification/datasets/celebA.py |
import argparse
import collections
import os
import random
import shutil
import pandas as pd
import requests
from PIL import Image
from tqdm import tqdm
from stratification.utils.utils import flatten_dict
def main():
parser = argparse.ArgumentParser(description='Downloads the ISIC dataset')
parser.add_argum... | hidden-stratification-master | stratification/classification/datasets/isic_download.py |
import itertools
import os
import logging
from collections import Counter
from PIL import Image
import numpy as np
import pandas as pd
import torch
from torchvision import transforms
from stratification.classification.datasets.base import GEORGEDataset
class WaterbirdsDataset(GEORGEDataset):
"""Waterbirds Datas... | hidden-stratification-master | stratification/classification/datasets/waterbirds.py |
import logging
import os
import torch
from torch.utils.data import Dataset
import numpy as np
import random
DATA_SPLITS = ['train', 'train_clean', 'val', 'test']
LABEL_TYPES = ['superclass', 'subclass', 'true_subclass', 'alt_subclass']
class GEORGEDataset(Dataset):
"""
Lightweight class that enforces design... | hidden-stratification-master | stratification/classification/datasets/base.py |
import os
import logging
import codecs
import random
from collections import defaultdict
from PIL import Image
import numpy as np
import pandas as pd
import torch
from torchvision import transforms
from torchvision.datasets.utils import download_and_extract_archive
from .base import GEORGEDataset
class MNISTDataset... | hidden-stratification-master | stratification/classification/datasets/mnist.py |
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | hidden-stratification-master | stratification/classification/models/bit_pytorch_models.py |
from .lenet import LeNet4
from .shallow_cnn import ShallowCNN
from .pt_resnet import PyTorchResNet
from .bit_pytorch_models import BiTResNet
| hidden-stratification-master | stratification/classification/models/__init__.py |
import torch.nn as nn
from collections import OrderedDict
class LeNet4(nn.Module):
"""
Adapted from https://github.com/activatedgeek/LeNet-5
"""
def __init__(self, **kwargs):
super().__init__()
in_channels = kwargs.get('num_channels', 1)
classes = kwargs.get('num_classes', 10)... | hidden-stratification-master | stratification/classification/models/lenet.py |
import torch.nn as nn
from collections import OrderedDict
class ShallowCNN(nn.Module):
def __init__(self, **kwargs):
super().__init__()
in_channels = kwargs.get('num_channels', 1)
classes = kwargs.get('num_classes', 10)
self.convnet = nn.Sequential(
OrderedDict([('c1',... | hidden-stratification-master | stratification/classification/models/shallow_cnn.py |
import logging
import torch
import torch.nn as nn
from torchvision.models.utils import load_state_dict_from_url
__all__ = ['PyTorchResNet']
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
}
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 con... | hidden-stratification-master | stratification/classification/models/pt_resnet.py |
from copy import deepcopy
import os
from collections import defaultdict
import logging
import torch
import numpy as np
import stratification.cluster.models.reduction as reduction_models
from stratification.utils.logger import init_logger
class GEORGEReducer:
"""Executes the cluster stage of the GEORGE algorith... | hidden-stratification-master | stratification/cluster/george_reduce.py |
hidden-stratification-master | stratification/cluster/__init__.py | |
from collections import Counter
import numpy as np
from stratification.cluster.fast_sil import silhouette_samples
def get_k_from_model(model):
if hasattr(model, 'n_clusters'):
return model.n_clusters
elif hasattr(model, 'n_components'):
return model.n_components
else:
raise NotImpl... | hidden-stratification-master | stratification/cluster/utils.py |
from copy import deepcopy
import os
from collections import defaultdict
import json
import logging
import torch
import numpy as np
from stratification.cluster.models.cluster import DummyClusterer
from stratification.cluster.utils import get_cluster_mean_loss, get_cluster_composition, get_k_from_model
from stratifica... | hidden-stratification-master | stratification/cluster/george_cluster.py |
'''The functions in this file are adapted from scikit-learn
(https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/metrics/cluster/_unsupervised.py)
to use CUDA for Silhouette score computation.'''
import numpy as np
from sklearn.utils import gen_batches, get_chunk_n_rows
from sklearn.metrics.cluster._unsup... | hidden-stratification-master | stratification/cluster/fast_sil.py |
from numba.core.errors import NumbaWarning
import numpy as np
from sklearn.decomposition import PCA
from umap import UMAP
import warnings
__all__ = ['HardnessAugmentedReducer', 'NoOpReducer', 'PCAReducer', 'UMAPReducer']
class Reducer:
def __init__(self, **kwargs):
raise NotImplementedError()
def fi... | hidden-stratification-master | stratification/cluster/models/reduction.py |
try:
from libKMCUDA import kmeans_cuda
_LIBKMCUDA_FOUND = True
except ModuleNotFoundError:
_LIBKMCUDA_FOUND = False
from functools import partial
import logging
import numpy as np
from sklearn.cluster import KMeans
from sklearn.mixture import GaussianMixture
from stratification.cluster.utils import silhou... | hidden-stratification-master | stratification/cluster/models/cluster.py |
import os
import sys
import logging
from collections import defaultdict
from datetime import datetime
import pandas as pd
from .utils import flatten_dict
class EpochCSVLogger:
'''Save training process without relying on fixed column names'''
def __init__(self, fpath, title=None, resume=False):
self.f... | hidden-stratification-master | stratification/utils/logger.py |
import os
import random
from collections import defaultdict
import json
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn.metrics as skl
import pickle
import torch
def visualize_clusters_by_group(activations, cluster_assignments, group_assignments,
... | hidden-stratification-master | stratification/utils/visualization.py |
import ast
import uuid
import datetime
import subprocess
import random
import time
import json
from functools import singledispatch
from datetime import datetime, timedelta
from collections import MutableMapping
import numpy as np
import torch
tenmin_td = timedelta(minutes=10)
hour_td = timedelta(hours=1)
def forma... | hidden-stratification-master | stratification/utils/utils.py |
import json
import argparse
from jsonargparse import ActionJsonSchema, namespace_to_dict
from .utils import ScientificNotationDecoder, convert_value, set_by_dotted_path
from .schema import schema
def get_config(args_list=None):
"""
"""
# load and validate config file
parser = argparse.ArgumentParser(... | hidden-stratification-master | stratification/utils/parse_args.py |
schema = {
'type':
'object',
'required':
['exp_dir', 'mode', 'dataset', 'classification_config', 'reduction_config', 'cluster_config'],
'properties': {
'seed': {
'type': 'number',
'default': -1
},
'deterministic': {
'type': 'boolean',
... | hidden-stratification-master | stratification/utils/schema.py |
import warnings
warnings.filterwarnings("ignore") # Suppress warnings from FlyingSquid
import numpy as np
from flyingsquid.label_model import LabelModel
def run_embroid(votes, nn_info, knn=10, thresholds=[[0.5, 0.5]]):
"""
Implements Embroid.
Parameters
----------
votes : ndarray of shape (n_s... | embroid-main | embroid.py |
from distutils.util import convert_path
from setuptools import find_packages, setup
main_ns = {}
ver_path = convert_path("bootleg/_version.py")
with open(ver_path) as ver_file:
exec(ver_file.read(), main_ns)
NAME = "bootleg"
DESCRIPTION = "Bootleg NED System"
URL = "https://github.com/HazyResearch/bootleg"
EMAIL... | bootleg-master | setup.py |
"""Emmental task constants."""
CANDGEN_TASK = "CANDGEN"
BATCH_CANDS_LABEL = "gold_unq_eid_idx"
| bootleg-master | cand_gen/task_config.py |
import logging
import multiprocessing
import os
import re
import shutil
import tempfile
import time
import traceback
import warnings
import numpy as np
import torch
import ujson
from tqdm.auto import tqdm
from bootleg import log_rank_0_debug, log_rank_0_info
from bootleg.dataset import convert_examples_to_features_an... | bootleg-master | cand_gen/dataset.py |
"""Bootleg run command."""
import argparse
import logging
import os
import subprocess
import sys
from copy import copy
import emmental
import torch
from emmental.learner import EmmentalLearner
from emmental.model import EmmentalModel
from rich.logging import RichHandler
from transformers import AutoTokenizer
from bo... | bootleg-master | cand_gen/train.py |
"""Bootleg run command."""
import argparse
import logging
import os
import subprocess
import sys
from collections import defaultdict
from copy import copy
from pathlib import Path
import emmental
import faiss
import numpy as np
import torch
import ujson
from emmental.model import EmmentalModel
from rich.logging impor... | bootleg-master | cand_gen/eval.py |
"""Data"""
import logging
import os
from emmental import Meta
from emmental.data import EmmentalDataLoader, emmental_collate_fn
from torch.utils.data import DistributedSampler, RandomSampler
from bootleg import log_rank_0_info
from bootleg.data import bootleg_collate_fn
from cand_gen.dataset import CandGenContextData... | bootleg-master | cand_gen/data.py |
import torch
import torch.nn.functional as F
from emmental.scorer import Scorer
from emmental.task import Action, EmmentalTask
from torch import nn
from transformers import AutoModel
from bootleg.layers.bert_encoder import Encoder
from bootleg.scorer import BootlegSlicedScorer
from cand_gen.task_config import CANDGEN_... | bootleg-master | cand_gen/tasks/candgen_task.py |
import torch.nn.functional as F
from emmental.scorer import Scorer
from emmental.task import Action, EmmentalTask
from torch import nn
from transformers import AutoModel
from bootleg.layers.bert_encoder import Encoder
from cand_gen.task_config import CANDGEN_TASK
class ContextGenOutput:
"""Context gen for output... | bootleg-master | cand_gen/tasks/context_gen_task.py |
import torch.nn.functional as F
from emmental.scorer import Scorer
from emmental.task import Action, EmmentalTask
from torch import nn
from transformers import AutoModel
from bootleg.layers.bert_encoder import Encoder
from cand_gen.task_config import CANDGEN_TASK
class EntityGenOutput:
"""Entity gen for output."... | bootleg-master | cand_gen/tasks/entity_gen_task.py |
"""
Merge contextual candidates for NED.
This file
1. Reads in raw wikipedia sentences from /lfs/raiders7/0/lorr1/sentences
2. Reads in map of WPID-Title-QID from /lfs/raiders7/0/lorr1/title_to_all_ids.jsonl
3. Computes frequencies for alias-QID over Wikipedia. Keeps only alias-QID mentions which occur > args.min_freq... | bootleg-master | cand_gen/utils/merge_contextual_cands.py |
"""Parses a Booleg input config into a DottedDict of config values (with
defaults filled in) for running a model."""
import argparse
import os
from bootleg.utils.classes.dotted_dict import create_bool_dotted_dict
from bootleg.utils.parser.emm_parse_args import (
parse_args as emm_parse_args,
parse_args_to_con... | bootleg-master | cand_gen/utils/parser/parser_utils.py |
"""Bootleg default configuration parameters.
In the json file, everything is a string or number. In this python file,
if the default is a boolean, it will be parsed as such. If the default
is a dictionary, True and False strings will become booleans. Otherwise
they will stay string.
"""
import multiprocessing
config_... | bootleg-master | cand_gen/utils/parser/candgen_args.py |
"""Test entity."""
import os
import shutil
import unittest
from pathlib import Path
import torch
from bootleg.layers.alias_to_ent_encoder import AliasEntityTable
from bootleg.symbols.entity_symbols import EntitySymbols
from bootleg.symbols.kg_symbols import KGSymbols
from bootleg.symbols.type_symbols import TypeSymbo... | bootleg-master | tests/test_entity/test_entity.py |
"""Test entity profile."""
import os
import shutil
import unittest
from pathlib import Path
import emmental
import numpy as np
import torch
import ujson
from pydantic import ValidationError
from bootleg.run import run_model
from bootleg.symbols.entity_profile import EntityProfile
from bootleg.utils.parser import pars... | bootleg-master | tests/test_entity/test_entity_profile.py |
"""End2end test."""
import os
import shutil
import unittest
import emmental
import ujson
from bootleg.run import run_model
from bootleg.utils import utils
from bootleg.utils.parser import parser_utils
class TestEnd2End(unittest.TestCase):
"""Test end to end."""
def setUp(self) -> None:
"""Set up.""... | bootleg-master | tests/test_end_to_end/test_end_to_end.py |
"""Test mention extraction."""
import os
import tempfile
import unittest
from pathlib import Path
import ujson
from bootleg.symbols.entity_symbols import EntitySymbols
class MentionExtractionTest(unittest.TestCase):
"""Mention extraction test."""
def setUp(self) -> None:
"""Set up."""
self.... | bootleg-master | tests/test_end_to_end/test_mention_extraction.py |
"""Test generate entities."""
import os
import shutil
import unittest
import emmental
import numpy as np
import torch
import ujson
import bootleg.extract_all_entities as extract_all_entities
import bootleg.run as run
from bootleg.utils import utils
from bootleg.utils.parser import parser_utils
class TestGenEntities... | bootleg-master | tests/test_end_to_end/test_gen_entities.py |
"""Test annotator."""
import os
import shutil
import unittest
import emmental
import torch
from bootleg import extract_all_entities
from bootleg.end2end.bootleg_annotator import BootlegAnnotator
from bootleg.run import run_model
from bootleg.utils import utils
from bootleg.utils.parser import parser_utils
class Tes... | bootleg-master | tests/test_end_to_end/test_annotator.py |
"""Test scorer."""
import unittest
import numpy as np
from bootleg.scorer import BootlegSlicedScorer
class BootlegMockScorer(BootlegSlicedScorer):
"""Bootleg mock scorer class."""
def __init__(self, train_in_candidates):
"""Mock initializer."""
self.mock_slices = {
0: {"all": [1... | bootleg-master | tests/test_scorer/test_scorer.py |
"""Test eval utils."""
import os
import shutil
import tempfile
import unittest
import jsonlines
import numpy as np
import torch
import ujson
from bootleg.symbols.entity_symbols import EntitySymbols
from bootleg.utils import eval_utils
from bootleg.utils.classes.nested_vocab_tries import (
TwoLayerVocabularyScoreT... | bootleg-master | tests/test_utils/test_eval_utils.py |
"""Test preprocessing utils."""
import os
import tempfile
import unittest
from pathlib import Path
import ujson
from bootleg.symbols.entity_symbols import EntitySymbols
class PreprocessingUtils(unittest.TestCase):
"""Preprocessing utils test."""
def setUp(self) -> None:
"""Set up."""
self.t... | bootleg-master | tests/test_utils/test_preprocessing.py |
"""Test class utils."""
import tempfile
import unittest
from bootleg.end2end.annotator_utils import DownloadProgressBar
from bootleg.utils.classes.nested_vocab_tries import (
ThreeLayerVocabularyTrie,
TwoLayerVocabularyScoreTrie,
VocabularyTrie,
)
class UtilClasses(unittest.TestCase):
"""Class util t... | bootleg-master | tests/test_utils/test_util_classes.py |
"""Test entity dataset."""
import os
import shutil
import unittest
import ujson
from transformers import AutoTokenizer
from bootleg.dataset import BootlegDataset
from bootleg.symbols.constants import SPECIAL_TOKENS
from bootleg.symbols.entity_symbols import EntitySymbols
from bootleg.symbols.type_symbols import TypeS... | bootleg-master | tests/test_data/test_entity_data.py |
"""Test slice data."""
import os
import shutil
import unittest
import numpy as np
import torch
from bootleg.slicing.slice_dataset import BootlegSliceDataset
from bootleg.symbols.constants import FINAL_LOSS
from bootleg.symbols.entity_symbols import EntitySymbols
from bootleg.utils import utils
from bootleg.utils.pars... | bootleg-master | tests/test_data/test_slice_data.py |
"""Test data."""
import os
import shutil
import unittest
from collections import defaultdict
import numpy as np
import torch
from transformers import AutoTokenizer
from bootleg.dataset import BootlegDataset, extract_context
from bootleg.symbols.constants import SPECIAL_TOKENS
from bootleg.symbols.entity_symbols impor... | bootleg-master | tests/test_data/test_data.py |
"""Test entity embedding generation."""
import os
import shutil
import unittest
import emmental
import torch
import cand_gen.eval as eval
import cand_gen.train as train
from bootleg.utils import utils
from cand_gen.utils.parser import parser_utils
class TestGenEntities(unittest.TestCase):
"""Test entity generat... | bootleg-master | tests/test_cand_gen/test_eval.py |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | bootleg-master | docs/source/conf.py |
"""Bootleg run command."""
import argparse
import itertools
import logging
import os
import shutil
import subprocess
import sys
import warnings
from copy import copy
import emmental
import numpy as np
import torch
from emmental.learner import EmmentalLearner
from emmental.model import EmmentalModel
from rich.logging ... | bootleg-master | bootleg/run.py |
"""Bootleg version."""
__version__ = "1.1.1dev0"
| bootleg-master | bootleg/_version.py |
"""Emmental task constants."""
NED_TASK = "NED"
BATCH_CANDS_LABEL = "gold_unq_eid_idx"
CANDS_LABEL = "gold_cand_K_idx"
| bootleg-master | bootleg/task_config.py |
"""Print functions for distributed computation."""
import torch
def log_rank_0_info(logger, message):
"""If distributed is initialized log info only on rank 0."""
if torch.distributed.is_initialized():
if torch.distributed.get_rank() == 0:
logger.info(message)
else:
logger.info... | bootleg-master | bootleg/__init__.py |
"""Bootleg NED Dataset."""
import logging
import multiprocessing
import os
import re
import shutil
import sys
import time
import traceback
import warnings
from collections import defaultdict
import numpy as np
import torch
import ujson
from emmental.data import EmmentalDataset
from tqdm.auto import tqdm
from bootleg ... | bootleg-master | bootleg/dataset.py |
"""Bootleg run command."""
import argparse
import logging
import os
import subprocess
import sys
from copy import copy
import emmental
import numpy as np
import torch
from emmental.model import EmmentalModel
from rich.logging import RichHandler
from transformers import AutoTokenizer
from bootleg import log_rank_0_in... | bootleg-master | bootleg/extract_all_entities.py |
"""Bootleg data creation."""
import copy
import logging
import os
from collections import defaultdict
from typing import Any, Dict, List, Tuple, Union
import torch
from emmental import Meta
from emmental.data import EmmentalDataLoader, emmental_collate_fn
from emmental.utils.utils import list_to_tensor
from torch.util... | bootleg-master | bootleg/data.py |
"""Bootleg scorer."""
import logging
from collections import Counter
from typing import Dict, List, Optional
from numpy import ndarray
logger = logging.getLogger(__name__)
class BootlegSlicedScorer:
"""Sliced NED scorer init.
Args:
train_in_candidates: are we training assuming that all gold qids ar... | bootleg-master | bootleg/scorer.py |
"""Task init."""
| bootleg-master | bootleg/tasks/__init__.py |
"""NED task definitions."""
import torch
import torch.nn.functional as F
from emmental.scorer import Scorer
from emmental.task import Action, EmmentalTask
from torch import nn
from transformers import AutoModel
from bootleg.layers.bert_encoder import Encoder
from bootleg.layers.static_entity_embeddings import EntityEm... | bootleg-master | bootleg/tasks/ned_task.py |
"""Entity gen task definitions."""
import torch.nn.functional as F
from emmental.scorer import Scorer
from emmental.task import Action, EmmentalTask
from torch import nn
from transformers import AutoModel
from bootleg.layers.bert_encoder import Encoder
from bootleg.task_config import NED_TASK
class EntityGenOutput:
... | bootleg-master | bootleg/tasks/entity_gen_task.py |
"""AliasEntityTable class."""
import logging
import os
import time
import numpy as np
import torch
import torch.nn as nn
from tqdm.auto import tqdm
from bootleg import log_rank_0_debug
from bootleg.utils import data_utils, utils
from bootleg.utils.model_utils import get_max_candidates
logger = logging.getLogger(__na... | bootleg-master | bootleg/layers/alias_to_ent_encoder.py |
"""Entity embeddings."""
import logging
import numpy as np
import torch
logger = logging.getLogger(__name__)
class EntityEmbedding(torch.nn.Module):
"""Static entity embeddings class.
Args:
entity_emb_file: numpy file of entity embeddings
"""
def __init__(self, entity_emb_file):
""... | bootleg-master | bootleg/layers/static_entity_embeddings.py |
"""Layer init."""
| bootleg-master | bootleg/layers/__init__.py |
"""BERT encoder."""
import torch
from torch import nn
class Encoder(nn.Module):
"""
Encoder module.
Return the CLS token of Transformer.
Args:
transformer: transformer
out_dim: out dimension to project to
"""
def __init__(self, transformer, out_dim):
"""BERT Encoder ... | bootleg-master | bootleg/layers/bert_encoder.py |
"""Model utils."""
import logging
from bootleg import log_rank_0_debug
logger = logging.getLogger(__name__)
def count_parameters(model, requires_grad, logger):
"""Count the number of parameters.
Args:
model: model to count
requires_grad: whether to look at grad or no grad params
log... | bootleg-master | bootleg/utils/model_utils.py |
"""Util init."""
| bootleg-master | bootleg/utils/__init__.py |
"""Bootleg data utils."""
import os
from bootleg.symbols.constants import FINAL_LOSS, SPECIAL_TOKENS
from bootleg.utils import utils
def correct_not_augmented_dict_values(gold, dict_values):
"""
Correct gold label dict values in data prep.
Modifies the dict_values to only contain those mentions that are... | bootleg-master | bootleg/utils/data_utils.py |
"""Bootleg utils."""
import collections
import json
import logging
import math
import os
import pathlib
import shutil
import time
import unicodedata
from itertools import chain, islice
import marisa_trie
import ujson
import yaml
from bootleg import log_rank_0_info
from bootleg.symbols.constants import USE_LOWER, USE_... | bootleg-master | bootleg/utils/utils.py |
import logging
import string
from collections import namedtuple
from typing import List, Tuple, Union
import nltk
import spacy
from spacy.cli.download import download as spacy_download
from bootleg.symbols.constants import LANG_CODE
from bootleg.utils.utils import get_lnrm
logger = logging.getLogger(__name__)
span_... | bootleg-master | bootleg/utils/mention_extractor_utils.py |
"""Bootleg eval utils."""
import glob
import logging
import math
import multiprocessing
import os
import shutil
import time
from collections import defaultdict
import emmental
import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
import ujson
from emmental.utils.utils import array_to_nump... | bootleg-master | bootleg/utils/eval_utils.py |
"""Classes init."""
| bootleg-master | bootleg/utils/classes/__init__.py |
"""Dotted dict class."""
import keyword
import re
import string
import ujson
class DottedDict(dict):
"""
Dotted dictionary.
Override for the dict object to allow referencing of keys as attributes, i.e. dict.key.
"""
def __init__(self, *args, **kwargs):
"""Dotted dict initializer."""
... | bootleg-master | bootleg/utils/classes/dotted_dict.py |
"""Nested vocab tries."""
import itertools
import logging
import os
from pathlib import Path
from typing import Any, Callable, Dict, List, Set, Tuple, Union
import marisa_trie
import numpy as np
import ujson
from numba import njit
from tqdm.auto import tqdm
from bootleg.utils.utils import dump_json_file, load_json_fi... | bootleg-master | bootleg/utils/classes/nested_vocab_tries.py |
"""
JSON with comments class.
An example of how to remove comments and trailing commas from JSON before
parsing. You only need the two functions below, `remove_comments()` and
`remove_trailing_commas()` to accomplish this. This script serves as an
example of how to use them but feel free to just copy & paste them in... | bootleg-master | bootleg/utils/classes/comment_json.py |
"""Emmental dataset and dataloader."""
import logging
from typing import Any, Dict, Optional, Tuple, Union
from emmental import EmmentalDataset
from torch import Tensor
logger = logging.getLogger(__name__)
class RangedEmmentalDataset(EmmentalDataset):
"""
RangedEmmentalDataset dataset.
An advanced data... | bootleg-master | bootleg/utils/classes/emmental_data.py |
"""
Bootleg parser utils.
Parses a Booleg input config into a DottedDict of config values (with
defaults filled in) for running a model.
"""
import argparse
import fileinput
import os
import ujson
import bootleg.utils.classes.comment_json as comment_json
from bootleg.utils.classes.dotted_dict import DottedDict, cre... | bootleg-master | bootleg/utils/parser/parser_utils.py |
"""Parser init."""
| bootleg-master | bootleg/utils/parser/__init__.py |
"""Overrides the Emmental parse_args."""
import argparse
from argparse import ArgumentParser
from typing import Any, Dict, Optional, Tuple
from emmental.utils.utils import (
nullable_float,
nullable_int,
nullable_string,
str2bool,
str2dict,
)
from bootleg.utils.classes.dotted_dict import DottedDic... | bootleg-master | bootleg/utils/parser/emm_parse_args.py |
"""Bootleg default configuration parameters.
In the json file, everything is a string or number. In this python file,
if the default is a boolean, it will be parsed as such. If the default
is a dictionary, True and False strings will become booleans. Otherwise
they will stay string.
"""
import multiprocessing
config_... | bootleg-master | bootleg/utils/parser/bootleg_args.py |
"""
Compute statistics over data.
Helper file for computing various statistics over our data such as mention
frequency, mention text frequency in the data (even if not labeled as an
anchor), ...
etc.
"""
import argparse
import logging
import multiprocessing
import os
import time
from collections import Counter
impo... | bootleg-master | bootleg/utils/preprocessing/compute_statistics.py |
"""Preprocessing init."""
| bootleg-master | bootleg/utils/preprocessing/__init__.py |
"""
Sample eval data.
This will sample a jsonl train or eval data based on the slices in the data.
This is useful for subsampling a smaller eval dataset.py.
The output of this file is a files with a subset of sentences from the
input file samples such that for each slice in --args.slice, a minimum
of args.min_sample_... | bootleg-master | bootleg/utils/preprocessing/sample_eval_data.py |
"""
Compute QID counts.
Helper function that computes a dictionary of QID -> count in training data.
If a QID is not in this dictionary, it has a count of zero.
"""
import argparse
import multiprocessing
import os
import shutil
import tempfile
from collections import defaultdict
from pathlib import Path
import ujso... | bootleg-master | bootleg/utils/preprocessing/convert_to_char_spans.py |
"""
Compute QID counts.
Helper function that computes a dictionary of QID -> count in training data.
If a QID is not in this dictionary, it has a count of zero.
"""
import argparse
import multiprocessing
from collections import defaultdict
import ujson
from tqdm.auto import tqdm
from bootleg.utils import utils
d... | bootleg-master | bootleg/utils/preprocessing/get_train_qid_counts.py |
"""BootlegAnnotator."""
import logging
import os
import tarfile
import urllib
from pathlib import Path
from typing import Any, Dict, Union
import emmental
import numpy as np
import torch
from emmental.model import EmmentalModel
from tqdm.auto import tqdm
from transformers import AutoTokenizer
from bootleg.dataset imp... | bootleg-master | bootleg/end2end/bootleg_annotator.py |
"""End2End init."""
| bootleg-master | bootleg/end2end/__init__.py |
"""Annotator utils."""
import progressbar
class DownloadProgressBar:
"""Progress bar."""
def __init__(self):
"""Progress bar initializer."""
self.pbar = None
def __call__(self, block_num, block_size, total_size):
"""Call."""
if not self.pbar:
self.pbar = prog... | bootleg-master | bootleg/end2end/annotator_utils.py |
"""
Extract mentions.
This file takes in a jsonlines file with sentences
and extract aliases and spans using a pre-computed alias table.
"""
import argparse
import logging
import multiprocessing
import os
import time
import jsonlines
import numpy as np
from tqdm.auto import tqdm
from bootleg.symbols.constants import... | bootleg-master | bootleg/end2end/extract_mentions.py |
"""KG symbols class."""
import copy
import os
import re
from typing import Dict, List, Optional, Set, Union
from tqdm.auto import tqdm
from bootleg.symbols.constants import edit_op
from bootleg.utils import utils
from bootleg.utils.classes.nested_vocab_tries import ThreeLayerVocabularyTrie
def _convert_to_trie(qid2... | bootleg-master | bootleg/symbols/kg_symbols.py |
"""Entity profile."""
import logging
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import ujson
from pydantic import BaseModel, ValidationError
from tqdm.auto import tqdm
from bootleg.symbols.constants import check_qid_exists, edit_op
from bootleg.symbols.entity_symbols import EntitySymbols
... | bootleg-master | bootleg/symbols/entity_profile.py |
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