python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
|---|---|---|
import pandas as pd
import numpy as np
__all__ = ['csv_node']
class csv_node(object):
def __init__(self, DataFrame, seed):
self.dataframe = DataFrame
self.seed = int(seed)
self.build_node()
@classmethod
def from_csv(klass, csv, seed=None):
dataframe = pd.read_csv(csv)
... | ukb-cardiac-mri-master | ukb/ensemble/csv_reader.py |
from .csv_reader import *
from .voting import *
| ukb-cardiac-mri-master | ukb/ensemble/__init__.py |
import glob
import numpy as np
import pandas as pd
from collections import OrderedDict
#from . import metrics
import metrics
from .csv_reader import csv_node
__all__ = ['tune_threshold',
'assemble_node',
'assemble_dev_threshold',
'metric_reading',
'Ensemble']
def tune_thres... | ukb-cardiac-mri-master | ukb/ensemble/voting.py |
from .ukbb import UKBBCardiacMRI
from .ukbb import UKBBCardiacMRIMeta
from .ukbb import UKBBCardiacMRICache
from .ukbb import stratified_sample_dataset
from .cifar10 import CIFAR10
| ukb-cardiac-mri-master | ukb/dataloaders/__init__.py |
from __future__ import print_function, division
import os
import sys
import logging
import numpy as np
import pandas as pd
from skimage.color import grey2rgb
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import StratifiedKFold
from collections import OrderedDict
from sklearn.preprocessin... | ukb-cardiac-mri-master | ukb/dataloaders/ukbb.py |
from __future__ import print_function, division
import os
import logging
import numpy as np
import pandas as pd
import torchvision.datasets
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import StratifiedKFold
logger = logging.getLogger(__name__)
class CIFAR10(Dataset):
"""
""... | ukb-cardiac-mri-master | ukb/dataloaders/cifar10.py |
import sys
import json
import models
import logging
from collections import OrderedDict
logger = logging.getLogger(__name__)
def convert_param_string(s):
"""
Convert string of hyperparamters into typed dictionary
e.g., `lr=0.001,rebalance=False,attention=True`
This is used to parse paramaters specifi... | ukb-cardiac-mri-master | ukb/utils/config_parser.py |
from .utils import *
from .config_parser import * | ukb-cardiac-mri-master | ukb/utils/__init__.py |
import time
import logging
import numpy as np
from skimage import draw
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import matplotlib.animation as animation
logger = logging.getLogger(__name__)
def print_key_pairs(v, title="Parameters"):
"""
Print python dictionary key/value pairs... | ukb-cardiac-mri-master | ukb/utils/utils.py |
import torch
import numpy as np
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from sklearn.manifold import TSNE
from sklearn.metrics import auc, roc_curve, precision_recall_curve
import seaborn as sns
sns.set_style("d... | ukb-cardiac-mri-master | ukb/utils/viz.py |
from .mri import *
from .trainer import *
| ukb-cardiac-mri-master | ukb/models/__init__.py |
import torch
import logging
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from .frame import LeNetFrameEncoder, FNNFrameEncoder, DenseNet121, vgg16_bn, densenet121, densenet_40_12_bc
from .sequence import RNN, MetaRNN, SeqSumPoolingEncoder
logger = logging... | ukb-cardiac-mri-master | ukb/models/mri.py |
"""
Simple random grid search
"""
import os
import sys
import glob
import copy
import time
import torch
import logging
import numpy as np
import pandas as pd
from itertools import product
import metrics
from metrics import *
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
fro... | ukb-cardiac-mri-master | ukb/models/trainer.py |
#!/usr/bin/env python
"""
vgg.py
- VGG11/13/16/19 and VGG11_bn/VGG13_bn/VGG16_bn/VGG19_bn in torchvision
- load the features layers weights only (without the classifier)
"""
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import math
from torchvision.models.vgg import model_urls, make_layers, c... | ukb-cardiac-mri-master | ukb/models/frame/vgg.py |
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from os.path import dirname, join
#Implementation based on https://github.com/andreasveit/densenet-pytorch
densenet_40_12_bc_weights_path = join(dirname(__file__), "pretrained_densenet_4012BC.pth.tar")
def densenet_40_12_bc(pretrained=Fal... | ukb-cardiac-mri-master | ukb/models/frame/densenet_av.py |
'''
DenseNet in PyTorch.
From: https://github.com/kuangliu/pytorch-cifar
https://arxiv.org/pdf/1608.06993.pdf
'''
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class Bottleneck(nn.Module):
def __init__(self, in_planes, growth_rate):
... | ukb-cardiac-mri-master | ukb/models/frame/densenet.py |
from .fnn import *
from .vgg import *
from .lenet import *
from .densenet import *
from .densenet_pretrained import *
from .densenet_av import *
| ukb-cardiac-mri-master | ukb/models/frame/__init__.py |
import torch.nn as nn
import torch.nn.functional as F
################################################################################
# Simple CNN Models
################################################################################
class LeNetFrameEncoder(nn.Module):
def __init__(self, input_shape=(1, 32, 3... | ukb-cardiac-mri-master | ukb/models/frame/lenet.py |
import torch.nn as nn
import torch.nn.functional as F
################################################################################
# FNN Models
################################################################################
class FNNFrameEncoder(nn.Module):
def __init__(self, input_size=1024, layers=[64, 32... | ukb-cardiac-mri-master | ukb/models/frame/fnn.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from collections import OrderedDict
from torchvision.models.densenet import model_urls, _DenseLayer, _DenseBlock, _Transition
__all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161']
... | ukb-cardiac-mri-master | ukb/models/frame/densenet_pretrained.py |
from .pooled import *
from .rnn import * | ukb-cardiac-mri-master | ukb/models/sequence/__init__.py |
"""
Simple models for encoding dense representations of sequences
"""
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
################################################################################
# Summing Models
###############################################... | ukb-cardiac-mri-master | ukb/models/sequence/pooled.py |
"""
Simple models for encoding dense representations of sequences
"""
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
################################################################################
# Recurrent Neural Network Models
##############################... | ukb-cardiac-mri-master | ukb/models/sequence/rnn.py |
from .ukbb import *
from .augmentations import *
from .multi_series import *
from torchvision.transforms import Compose
class RandomTransforms(object):
"""Base class for a list of transformations with randomness
Args:
transforms (list or tuple): list of transformations
"""
def __init__(self,... | ukb-cardiac-mri-master | ukb/transforms/__init__.py |
"""
Custom preprocessing transformation functions for video/sequential frame
MRI data from the UK Biobank
"""
import numpy as np
from skimage.exposure import rescale_intensity
from torchvision.transforms import Lambda
class NullTransform(Lambda):
"""
Create a null transformation.
This is to be used whe... | ukb-cardiac-mri-master | ukb/transforms/ukbb.py |
"""
Custom preprocessing transformation functions for video/sequential frame
MRI data from the UK Biobank
TO BE USED ON MULTI SERIES INPUTS
"""
import numpy as np
from skimage.exposure import rescale_intensity
class NullTransformMulti():
"""
Create a null transformation (for multiple series inputs).
Th... | ukb-cardiac-mri-master | ukb/transforms/multi_series.py |
ukb-cardiac-mri-master | ukb/transforms/debug.py | |
"""
Custom augmentation transform functions for video/sequential frame
MRI data from the UK Biobank
"""
import math
import random
import numbers
import collections
import numpy as np
from PIL import Image
from torchvision.transforms import functional as F
from torchvision.transforms import Lambda
##################... | ukb-cardiac-mri-master | ukb/transforms/augmentations.py |
ukb-cardiac-mri-master | ukb/weak_supervision/coral/coral/__init__.py | |
import coral_sa as csa
def is_square(width, height):
if width == height:
return True
else:
return False
def is_large(width, height):
if width * height > 100:
return True
else:
return False
def is_small(width, height):
if width * height < 10:
return True
... | ukb-cardiac-mri-master | ukb/weak_supervision/coral/coral/old_sa/tests.py |
import coral_types as ct
# A basic node in an AST.
# This is meant to be subclassed.
class Expression(object):
def __repr__(self):
return str(self)
def children(self):
return NotImplementedError
def walk(self, f):
"""
Walks the AST, applying the function f to each node in... | ukb-cardiac-mri-master | ukb/weak_supervision/coral/coral/old_sa/coral_sa/coral_ast.py |
import ast
import meta
import coral_ast as cast
import coral_types as ct
# Public API
# Turn this on for printing.
verbose = False
def vprint(a):
global verbose
if verbose:
print a
def convert_to_coral_ast(func):
"""
Converts a Python function to a Coral AST.
"""
# A Python AST.
... | ukb-cardiac-mri-master | ukb/weak_supervision/coral/coral/old_sa/coral_sa/coral_funcs.py |
# These are directly accessible.
from coral_funcs import *
| ukb-cardiac-mri-master | ukb/weak_supervision/coral/coral/old_sa/coral_sa/__init__.py |
class Type(object):
pass
class LabelingFunctionType(Type):
def __str__(self):
return "LF"
class VocabType(Type):
def __init__(self, deps):
# This vocabulary is dependent on the given list of vocabulary types.
self.deps = list(set(deps))
def __str__(self):
return "|".j... | ukb-cardiac-mri-master | ukb/weak_supervision/coral/coral/old_sa/coral_sa/coral_types.py |
"""
Subpackage for learning the structures of models.
"""
from .gen_learning import CoralModel
from .structure_learning import CoralDependencySelector | ukb-cardiac-mri-master | ukb/weak_supervision/coral/coral/learning/__init__.py |
from numbskull import NumbSkull
from numbskull.inference import *
from numbskull.numbskulltypes import Weight, Variable, Factor, FactorToVar
from numbskull.udf import *
import numpy as np
import random
class CoralModel(object):
def __init__(self, class_prior=False, lf_prior=False, lf_propensity=False, lf_class_pro... | ukb-cardiac-mri-master | ukb/weak_supervision/coral/coral/learning/gen_learning.py |
from numba import jit
import numpy as np
import random
from numbskull.udf import *
from numbskull.numbskulltypes import *
class CoralDependencySelector(object):
"""
Fast method for identifying dependencies among labeling functions.
:param seed: seed for initializing state of Numbskull variables
"""
... | ukb-cardiac-mri-master | ukb/weak_supervision/coral/coral/learning/structure_learning.py |
import numpy as np
from scipy import sparse
def log_odds(p):
"""This is the logit function"""
return np.log(p / (1.0 - p))
def odds_to_prob(l):
"""
This is the inverse logit function logit^{-1}:
l = \log\frac{p}{1-p}
\exp(l) = \frac{p}{1-p}
p = \frac{\exp(l)}{1 + \exp(l)}
"""
... | ukb-cardiac-mri-master | ukb/weak_supervision/coral/coral/learning/indep_learning.py |
from inspect import getsourcelines
| ukb-cardiac-mri-master | ukb/weak_supervision/coral/coral/static_analysis/__init__.py |
from inspect import getsourcelines
import numpy as np
def find_dependencies(L_names, primitive_names):
LFs = []
for lf in L_names:
LFs.append(getsourcelines(lf)[0])
L_deps = []
for lf_idx, lf in enumerate(LFs):
L_dep = []
for line in lf:
if len(line.strip()) > 0:
... | ukb-cardiac-mri-master | ukb/weak_supervision/coral/coral/static_analysis/dependency_learning.py |
import json
import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import skimage.io as io
class DataLoader(object):
def __init__(self, data_path='/tutorial_data/'):
current_dir = os.getcwd()
self.data_path = current_dir + data_path
def load... | ukb-cardiac-mri-master | ukb/weak_supervision/coral/tutorials/data_loader.py |
import numpy as np
class PrimitiveObject(object):
def save_primitive_matrix(self,primitive_mtx):
self.primitive_mtx = primitive_mtx
self.discrete_primitive_mtx = primitive_mtx
self.num_primitives = np.shape(self.primitive_mtx)[1]
def save_primitive_names(self,names):
self.... | ukb-cardiac-mri-master | ukb/weak_supervision/coral/tutorials/primitive_helpers.py |
#!/usr/bin/env python
"""TODO."""
from __future__ import print_function
import numbskull
from numbskull.numbskulltypes import *
import numpy as np
def factor(f, args):
"""THIS IS A DOCSTRING."""
if f == FUNC_IMPLY_NATURAL:
# TODO
pass
elif f == FUNC_OR:
return 1 if any(args) else... | ukb-cardiac-mri-master | ukb/weak_supervision/numbskull/loadfg.py |
#!/usr/bin/env python
"""TODO."""
from numbskull import numbskull
args = ['test',
'-l', '100',
'-i', '100',
'-t', '10',
'-s', '0.01',
'--regularization', '2',
'-r', '0.1',
'--quiet']
ns = numbskull.load(args)
ns.learning()
ns.inference()
print(ns.factorGraphs[0... | ukb-cardiac-mri-master | ukb/weak_supervision/numbskull/test.py |
#!/usr/bin/env python
"""This tests learning for labelling functions."""
from __future__ import print_function, absolute_import
import numpy as np
import numbskull
from numbskull.numbskulltypes import *
import math
def index_to_values(index, num_lf):
value = [0] * (1 + num_lf)
value[0] = index % 2
index... | ukb-cardiac-mri-master | ukb/weak_supervision/numbskull/test_lf_learning.py |
"""For pip."""
from setuptools import setup, find_packages
setup(
name='numbskull',
version='0.0',
description='sample away',
packages=find_packages(),
install_requires=[],
entry_points={
'console_scripts': [
'numbskull = numbskull.numbskull:main',
],
},
)
| ukb-cardiac-mri-master | ukb/weak_supervision/numbskull/setup.py |
# -*- coding: utf-8 -*-
#
# Numbskull documentation build configuration file, created by
# sphinx-quickstart on Fri Aug 26 17:55:24 2016.
#
# 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.
#
#... | ukb-cardiac-mri-master | ukb/weak_supervision/numbskull/docs/source/conf.py |
"""TODO."""
from __future__ import print_function, absolute_import
import numba
from numba import jit
import numpy as np
# HELPER METHODS #
def dataType(i):
"""TODO."""
return {0: "Boolean",
1: "Categorical"}.get(i, "Unknown")
@jit(nopython=True, cache=True)
def compute_var_map(variables, facto... | ukb-cardiac-mri-master | ukb/weak_supervision/numbskull/numbskull/dataloading.py |
"""TODO."""
from __future__ import print_function, absolute_import
import sys
import numpy as np
from numbskull.inference import *
from numbskull.learning import *
from numbskull.timer import Timer
import concurrent.futures
from concurrent.futures import ThreadPoolExecutor
def run_pool(threadpool, threads, func, arg... | ukb-cardiac-mri-master | ukb/weak_supervision/numbskull/numbskull/factorgraph.py |
"""TODO."""
from __future__ import print_function, absolute_import
import time
class Timer:
"""TODO."""
def __enter__(self):
"""TODO."""
self.start = time.time()
return self
def __exit__(self, *args):
"""TODO."""
self.end = time.time()
self.interval = sel... | ukb-cardiac-mri-master | ukb/weak_supervision/numbskull/numbskull/timer.py |
"""TODO."""
from .numbskull import NumbSkull
from .numbskull import main
__all__ = ('numbskull', 'factorgraph', 'timer')
| ukb-cardiac-mri-master | ukb/weak_supervision/numbskull/numbskull/__init__.py |
#!/usr/bin/env python
"""TODO: This is a docstring."""
from __future__ import print_function, absolute_import
import os
import sys
import argparse
import numbskull.factorgraph
from numbskull.factorgraph import FactorGraph
from numbskull.dataloading import *
from numbskull.numbskulltypes import *
import numpy as np
... | ukb-cardiac-mri-master | ukb/weak_supervision/numbskull/numbskull/numbskull.py |
#!/usr/bin/env python
"""TODO."""
from __future__ import print_function
import zmq
import sys
import time
import argparse
import gibbs
import numpy as np
def send_array(socket, A, flags=0, copy=True, track=False):
"""TODO: send a numpy array with metadata."""
md = dict(
dtype=str(A.dtype),
s... | ukb-cardiac-mri-master | ukb/weak_supervision/numbskull/numbskull/distributed.py |
"""TODO."""
from __future__ import print_function, absolute_import
import numpy as np
# TODO (shared with DW): space optimization:
# 1. use smaller ints for some fields
# 2. replace a[x].length with a[x+1].offset - a[x].offset
Meta = np.dtype([('weights', np.int64),
('variables', np.int64),
... | ukb-cardiac-mri-master | ukb/weak_supervision/numbskull/numbskull/numbskulltypes.py |
"""TODO."""
from __future__ import print_function, absolute_import
import numba
from numba import jit
import numpy as np
import math
from numbskull.udf import *
@jit(nopython=True, cache=True, nogil=True)
def gibbsthread(shardID, nshards, var_copy, weight_copy, weight, variable,
factor, fmap, vmap, f... | ukb-cardiac-mri-master | ukb/weak_supervision/numbskull/numbskull/inference.py |
"""TODO."""
from __future__ import print_function, absolute_import
import numba
from numba import jit
import numpy as np
import math
import random
from numbskull.inference import draw_sample, eval_factor
@jit(nopython=True, cache=True, nogil=True)
def learnthread(shardID, nshards, step, regularization, reg_param, tr... | ukb-cardiac-mri-master | ukb/weak_supervision/numbskull/numbskull/learning.py |
"""TODO."""
from __future__ import print_function, absolute_import
import numba
from numba import jit
import numpy as np
import math
# Search "USER" to find sections that need to be implemented.
# USER: insert name of UDF and cardinality here
UDF_CARDINALITY = {
# UDFs for toy example
"TOY_OR": 2,
"... | ukb-cardiac-mri-master | ukb/weak_supervision/numbskull/numbskull/udf.py |
"""TODO."""
from .numbskull import main
main()
| ukb-cardiac-mri-master | ukb/weak_supervision/numbskull/numbskull/__main__.py |
import logging
import re
from builtins import chr, range, str
from difflib import SequenceMatcher
from fonduer.candidates import MentionNgrams
from fonduer.candidates.models.implicit_span_mention import TemporaryImplicitSpanMention
logger = logging.getLogger(__name__)
def expand_part_range(text):
"""
Given ... | fonduer-tutorials-master | hardware/hardware_spaces.py |
import codecs
import csv
from builtins import range
from fonduer.candidates.models import Candidate
from fonduer.learning.utils import confusion_matrix
from fonduer.supervision.models import GoldLabel, GoldLabelKey
try:
from IPython import get_ipython
if "IPKernelApp" not in get_ipython().config:
rai... | fonduer-tutorials-master | hardware/hardware_utils.py |
import codecs
import csv
from builtins import range
from fonduer.candidates.models import Candidate
from fonduer.learning.utils import confusion_matrix
from fonduer.supervision.models import GoldLabel, GoldLabelKey
try:
from IPython import get_ipython
if "IPKernelApp" not in get_ipython().config:
rai... | fonduer-tutorials-master | intro/hardware_utils.py |
import codecs
import csv
from builtins import range
from fonduer.candidates.models import Candidate
from fonduer.parser.models import Document, Sentence
from fonduer.learning.utils import confusion_matrix
from fonduer.supervision.models import GoldLabel, GoldLabelKey
try:
from IPython import get_ipython
if "... | fonduer-tutorials-master | wiki/wiki_table_utils.py |
import datasets
import tensorflow as tf
import pandas as pd
from pathlib import Path
import json
from tqdm.auto import tqdm
import os
DATA_DIR = os.environ.get("AMA_DATA", "/home/data")
# Download P3 github data from HF website
'''
git lfs install
git clone https://huggingface.co/datasets/bigscience/P3
'''
import sy... | ama_prompting-main | download_p3.py |
#!/usr/bin/env python
# coding: utf-8
from tqdm.auto import tqdm
import pandas as pd
from sklearn.metrics import classification_report
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputPrompt
synonym = InputOutputPrompt(
input_formatter=lambda x: f"{x['passage'... | ama_prompting-main | tasks/WIC_final.py |
#!/usr/bin/env python
# coding: utf-8
from pathlib import Path
import pandas as pd
import numpy as np
from tqdm.auto import tqdm
from datasets import load_dataset
from decomposition import Decomposition, get_args
from utils import get_response, text_f1, InputOutputPrompt, load_hf_data
extract = InputOutputPrompt(
... | ama_prompting-main | tasks/drop_final.py |
#!/usr/bin/env python
# coding: utf-8
from tqdm.auto import tqdm
import pandas as pd
from sklearn.metrics import classification_report
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputPrompt
questioner = InputOutputPrompt(
input_formatter=lambda x: f"Statement... | ama_prompting-main | tasks/CB_final.py |
#!/usr/bin/env python
# coding: utf-8
import pandas as pd
import numpy as np
import ast
from tqdm.auto import tqdm
from pathlib import Path
from nltk.corpus import stopwords
stops = set(stopwords.words("english"))
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputP... | ama_prompting-main | tasks/NQ_final.py |
#!/usr/bin/env python
# coding: utf-8
from tqdm.auto import tqdm
from pathlib import Path
import pandas as pd
import numpy as np
import random
from sklearn.metrics import classification_report
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response
def format_data(lines):
"""fro... | ama_prompting-main | tasks/SST2_final.py |
#!/usr/bin/env python
# coding: utf-8
import pandas as pd
import numpy as np
from tqdm.auto import tqdm
from sklearn.metrics import classification_report
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputPrompt
summarize = InputOutputPrompt(
input_formatter=la... | ama_prompting-main | tasks/DBPedia_final.py |
#!/usr/bin/env python
# coding: utf-8
import pandas as pd
import numpy as np
from tqdm.auto import tqdm
import random
from sklearn.metrics import classification_report
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputPrompt
########################################... | ama_prompting-main | tasks/ANLIR1_final.py |
#!/usr/bin/env python
# coding: utf-8
from tqdm.auto import tqdm
import pandas as pd
import random
import numpy as np
from pathlib import Path
from datasets import load_dataset
from sklearn.metrics import classification_report
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, ... | ama_prompting-main | tasks/StoryCloze_final.py |
#!/usr/bin/env python
# coding: utf-8
from tqdm.auto import tqdm
from pathlib import Path
from collections import Counter
import re
import pandas as pd
import json
import unicodedata
import string
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputPrompt
cloze_comp... | ama_prompting-main | tasks/ReCoRD_final.py |
#!/usr/bin/env python
# coding: utf-8
import pandas as pd
import numpy as np
import ast
from tqdm.auto import tqdm
from decomposition import Decomposition, get_args
from utils import get_response, InputOutputPrompt, accuracy_span_overlap, load_hf_data
extract = InputOutputPrompt(
input_formatter=lambda x: f"Questi... | ama_prompting-main | tasks/webq_final.py |
#!/usr/bin/env python
# coding: utf-8
from pathlib import Path
from tqdm.auto import tqdm
import pandas as pd
from nltk.corpus import stopwords
from datasets import load_dataset
stops = set(stopwords.words("english"))
from sklearn.metrics import classification_report
from utils import get_response, InputOutputPrompt,... | ama_prompting-main | tasks/WSC_final.py |
from pathlib import Path
from collections import Counter
import json
from datasets import load_dataset
import re
import pandas as pd
from typing import Callable, List
from manifest import Manifest
class InputOutputPrompt:
def __init__(self,
input_formatter: Callable,
output_formatter: Callable,
... | ama_prompting-main | tasks/utils.py |
#!/usr/bin/env python
# coding: utf-8
from tqdm.auto import tqdm
import pandas as pd
from nltk.corpus import stopwords
stops = set(stopwords.words("english"))
from sklearn.metrics import classification_report
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputPrompt... | ama_prompting-main | tasks/RTE_final.py |
#!/usr/bin/env python
# coding: utf-8
from pathlib import Path
import argparse
from typing import Counter
import pandas as pd
import json
import numpy as np
import datetime
import os
import random
from utils import save_log, get_manifest_session
DATA_DIR = os.environ.get("AMA_DATA", "/home/data")
def get_args():
... | ama_prompting-main | tasks/decomposition.py |
#!/usr/bin/env python
# coding: utf-8
from tqdm.auto import tqdm
from collections import Counter
import pandas as pd
import random
import numpy as np
from sklearn.metrics import classification_report
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputPrompt
what_nex... | ama_prompting-main | tasks/COPA_final.py |
#!/usr/bin/env python
# coding: utf-8
import pandas as pd
import numpy as np
from pathlib import Path
from tqdm.auto import tqdm
import random
from datasets import load_dataset
from sklearn.metrics import classification_report
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, ... | ama_prompting-main | tasks/Amazon_final.py |
#!/usr/bin/env python
# coding: utf-8
from collections import defaultdict
from pathlib import Path
import pandas as pd
import numpy as np
from tqdm.auto import tqdm
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputPrompt
answer_prompt = InputOutputPrompt(
inpu... | ama_prompting-main | tasks/MultiRC_final.py |
#!/usr/bin/env python
# coding: utf-8
import pandas as pd
import numpy as np
from tqdm.auto import tqdm
import random
from sklearn.metrics import classification_report
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputPrompt
########################################... | ama_prompting-main | tasks/ANLIR2_final.py |
#!/usr/bin/env python
# coding: utf-8
import pandas as pd
import numpy as np
from tqdm.auto import tqdm
import random
from sklearn.metrics import classification_report
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputPrompt
########################################... | ama_prompting-main | tasks/ANLIR3_final.py |
#!/usr/bin/env python
# coding: utf-8
from pathlib import Path
from tqdm.auto import tqdm
import pandas as pd
import numpy as np
import sys
import json
import string
import datetime
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputPrompt
realtime_qa_path = Path(f"... | ama_prompting-main | tasks/RealtimeQA_final.py |
#!/usr/bin/env python
# coding: utf-8
import pandas as pd
import numpy as np
from pathlib import Path
from tqdm.auto import tqdm
from sklearn.metrics import classification_report
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputPrompt
#############################... | ama_prompting-main | tasks/AGNews_final.py |
#!/usr/bin/env python
# coding: utf-8
from tqdm.auto import tqdm
import pandas as pd
import numpy as np
from tqdm.auto import tqdm
from sklearn.metrics import classification_report
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputPrompt
extract = InputOutputPrompt... | ama_prompting-main | tasks/BoolQ_final.py |
#!/usr/bin/env python
# coding: utf-8
import os
from tqdm.auto import tqdm
import pandas as pd
from sklearn.metrics import classification_report
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputPrompt
from collections import defaultdict, Counter
class CBDecomp(Dec... | ama_prompting-main | ablations/T0_variants/CB_variants.py |
#!/usr/bin/env python
# coding: utf-8
import os
from tqdm.auto import tqdm
import pandas as pd
from sklearn.metrics import classification_report
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputPrompt
from collections import defaultdict, Counter
class RTEDecomp(De... | ama_prompting-main | ablations/T0_variants/WSC_variants.py |
from pathlib import Path
from collections import Counter
import json
from datasets import load_dataset
import re
import pandas as pd
from typing import Callable, List
from manifest import Manifest
class InputOutputPrompt:
def __init__(self,
input_formatter: Callable,
output_formatter: Callable,
... | ama_prompting-main | ablations/T0_variants/utils.py |
#!/usr/bin/env python
# coding: utf-8
from pathlib import Path
import argparse
from typing import Counter
import pandas as pd
import json
import numpy as np
import datetime
import os
import random
from utils import save_log, get_manifest_session
DATA_DIR = os.environ.get("AMA_DATA", "/home/data")
def get_args():
... | ama_prompting-main | ablations/T0_variants/decomposition.py |
#!/usr/bin/env python
# coding: utf-8
import os
from tqdm.auto import tqdm
import pandas as pd
from sklearn.metrics import classification_report
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputPrompt
from collections import defaultdict, Counter
class WICDecomp(De... | ama_prompting-main | ablations/T0_variants/WIC_variants.py |
#!/usr/bin/env python
# coding: utf-8
import os
from tqdm.auto import tqdm
import pandas as pd
from sklearn.metrics import classification_report
from decomposition import Decomposition, get_args, DATA_DIR
from utils import get_response, InputOutputPrompt
from collections import defaultdict, Counter
class RTEDecomp(De... | ama_prompting-main | ablations/T0_variants/RTE_variants.py |
import numpy as np
import itertools
import matplotlib.pyplot as plt
import scipy.stats
class Ising():
def __init__(self, m, potentials, thetas = None, vals = [-1, 1], ) -> None:
self.m = m
self.v = m + 1 # total number of vertices
self.potentials = potentials
self.va... | ama_prompting-main | boosting/pgm.py |
import networkx as nx
import numpy as np
from itertools import chain, product, combinations
from scipy.sparse import issparse
import more_itertools
import torch
class DependentPGM:
"""
This class describes a PGM learned from labeled data with specified edge structure.
Args:
edges: li... | ama_prompting-main | boosting/binary_deps.py |
"""This script contains code to execute different methods"""
from readline import append_history_file
from sklearn.metrics import accuracy_score
import numpy as np
from snorkel.labeling.model import LabelModel
from snorkel.utils import probs_to_preds
import itertools
import math
import torch
import collections
from ... | ama_prompting-main | boosting/methods.py |
import argparse
import numpy as np
import json
import os
import cvxpy as cp
import scipy as sp
import datetime
from methods import Aggregator
from metal.label_model import LabelModel
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--task_name", type=str, required=True)
parser.add_a... | ama_prompting-main | boosting/run_ws.py |
import numpy as np
import itertools
def get_probabilties(num_lfs, num_examples, predictions, label_name_to_int):
lf_array = np.zeros((num_lfs, num_examples))
golds = []
# Collect golds and preds
for i, (k, item) in enumerate(predictions.items()):
preds = item['chos... | ama_prompting-main | boosting/utils.py |
import numpy as np
import itertools
import scipy.stats
import math
import networkx as nx
from itertools import chain
from methods import Aggregator
from binary_deps import structure_learning
from binary_deps import DependentPGM
from sklearn.metrics import log_loss, accuracy_score
class Ising():
def _... | ama_prompting-main | boosting/make_pgm.py |
""" Running and Scoring the AMA Diagnostics """
import os
import json
from collections import Counter
from tqdm import tqdm
import openai
from manifest import Manifest
openai.api_key = "" # Find this on the OpenAI Website
from datasets import load_metric
rouge = load_metric("rouge")
######################### HELPER ... | ama_prompting-main | diagnostics/run_diagnostics.py |
from setuptools import setup, find_packages
setup(name='stratification', version='1.0', packages=find_packages())
| hidden-stratification-master | setup.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
def main():
config = get_config()
use_cuda = config['use_cuda'] and torch.cuda.is_available()
set_seed(config['seed'], ... | hidden-stratification-master | stratification/run.py |
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