repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/dccp-master/build/lib/dccp/problem.py | __author__ = "Xinyue"
import numpy as np
import cvxpy as cvx
import logging
from dccp.objective import convexify_obj
from dccp.objective import convexify_para_obj
from dccp.constraint import convexify_para_constr
from dccp.constraint import convexify_constr
logger = logging.getLogger("dccp")
logger.addHandler(loggin... | 12,115 | 35.059524 | 101 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/dccp-master/build/lib/dccp/constraint.py | __author__ = "Xinyue"
from dccp.linearize import linearize, linearize_para
import cvxpy as cvx
# from dccp.linearize import linearize_para
def convexify_para_constr(self):
"""
input:
self: a constraint of a problem
return:
if the constraint is dcp, return itself;
otherwise, return
... | 2,925 | 32.632184 | 109 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/dccp-master/build/lib/dccp/linearize.py | __author__ = "Xinyue"
import numpy as np
import cvxpy as cvx
def linearize_para(expr):
"""
input:
expr: an expression
return:
linear_expr: linearized expression
zero_order: zero order parameter
linear_dictionary: {variable: [value parameter, [gradient parameter]]}
d... | 3,432 | 36.725275 | 117 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/dccp-master/build/lib/dccp/__init__.py | from dccp.problem import is_dccp
from dccp.linearize import linearize
from dccp.objective import convexify_obj
from dccp.constraint import convexify_constr
__author__ = "Xinyue Shen"
__version__ = "1.0.3"
| 206 | 24.875 | 44 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/Fair_KDE/fairness_metrics.py | import torch
import cvxpy as cp
import numpy as np
# +------------------------------------------+
# | Metric 1: Energy Distance |
# +------------------------------------------+
def energy_distance(y1, y2):
'''
Compute energy distance between empirical distance y1 and y2, each 1 dimensional
... | 8,196 | 30.771318 | 100 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/Fair_KDE/load_data_.py | import numpy as np
import pandas as pd
import sklearn.preprocessing as preprocessing
from collections import namedtuple
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt # for plotting stuff
import os
import collections
def load_compas_data(COMPAS_INPUT_FILE):
FEATURES_CLASSIFICATI... | 15,165 | 43.737463 | 207 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/Fair_KDE/algorithm.py | import random
import IPython
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from dataloader import CustomDataset
from utils import measures_from_Yhat
tau = 0.5
# Approximation of Q-function given by López-Benítez & Cas... | 7,375 | 41.390805 | 141 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/Fair_KDE/dataloader.py | import os
import copy
import torch
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import data_loader
from tempeh.configurations import datasets
from sklearn.datasets import make_moons
from sklearn.preprocessing import LabelEncoder, StandardScaler
def arrays_to_tensor(X, Y, Z, XZ,... | 11,649 | 40.459075 | 159 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/Fair_KDE/data_loader_or.py | # data_loader.py
# utilities for loading data
import torch
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from load_data import *
# TODO: possibly some form of (cross) validation
def to_tensor(data, device):
D = data
if type(data) == pd.core... | 11,586 | 38.546075 | 121 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/Fair_KDE/utils.py | import numpy as np
import pandas as pd
def measures_from_Yhat(Y, Z, Yhat=None, threshold=0.5):
assert isinstance(Y, np.ndarray)
assert isinstance(Z, np.ndarray)
assert Yhat is not None
assert isinstance(Yhat, np.ndarray)
if Yhat is not None:
Ytilde = (Yhat >= threshold).astype(np.floa... | 1,205 | 29.923077 | 70 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/Fair_KDE/models.py | import torch
import torch.nn as nn
class Classifier(nn.Module):
def __init__(self, n_layers, n_inputs, n_hidden_units):
super(Classifier, self).__init__()
layers = []
if n_layers == 1: # Logistic Regression
layers.append(nn.Linear(n_inputs, 1))
layers.append... | 829 | 33.583333 | 72 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/Fair_KDE/fair_KDE_.py | # Baseline Fair KDE : https://proceedings.neurips.cc//paper/2020/file/ac3870fcad1cfc367825cda0101eee62-Paper.pdf
import cvxpy as cp
import numpy as np
import argparse
import pandas as pd
import torch
import fairness_metrics
import data_loader
from tqdm import tqdm
from collections import namedtuple
from sklearn.metrics... | 9,960 | 34.830935 | 136 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/zafar_method/funcs_disp_mist.py | from __future__ import division
import os, sys
import traceback
import numpy as np
from random import seed, shuffle
from collections import defaultdict
from copy import deepcopy
import cvxpy
import dccp
from dccp.problem import is_dccp
from zafar_method import utils as ut
def train_model_disp_mist(x, y, x_control, lo... | 18,367 | 49.185792 | 206 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/zafar_method/loss_funcs.py | import sys
import os
import numpy as np
import scipy.special
from collections import defaultdict
import traceback
from copy import deepcopy
def _hinge_loss(w, X, y):
yz = y * np.dot(X,w) # y * (x.w)
yz = np.maximum(np.zeros_like(yz), (1-yz)) # hinge function
return sum(yz)
def _logistic_loss(... | 2,268 | 22.884211 | 82 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/zafar_method/loss_funcs_after.py | import sys
import os
import numpy as np
import scipy.special
from collections import defaultdict
import traceback
from copy import deepcopy
def _hinge_loss(w, X, y):
yz = y * np.dot(X, w) # y * (x.w)
yz = np.maximum(np.zeros_like(yz), (1 - yz)) # hinge function
return sum(yz)
def _logistic_loss(w, X,... | 2,356 | 27.743902 | 82 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/zafar_method/utils.py | import numpy as np
from random import seed, shuffle
from zafar_method import loss_funcs as lf # our implementation of loss funcs
from scipy.optimize import minimize # for loss func minimization
from multiprocessing import Pool, Process, Queue
from collections import defaultdict
from copy import deepcopy
import matplotl... | 27,182 | 41.606583 | 357 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/zafar_method/funcs_disp_mist_after.py | from __future__ import division
import os, sys
import traceback
import numpy as np
from random import seed, shuffle
from collections import defaultdict
from copy import deepcopy
import cvxpy
import dccp
from dccp.problem import is_dccp
import utils as ut
SEED = 1122334455
seed(SEED) # set the random seed so that the ... | 18,388 | 49.798343 | 202 | py |
Metrizing-Fairness | Metrizing-Fairness-main/offline_experiments/src/zafar_method/utils_after.py | import numpy as np
from random import seed, shuffle
import loss_funcs as lf # our implementation of loss funcs
from scipy.optimize import minimize # for loss func minimization
from multiprocessing import Pool, Process, Queue
from collections import defaultdict
from copy import deepcopy
import matplotlib.pyplot as plt... | 26,695 | 44.094595 | 291 | py |
Metrizing-Fairness | Metrizing-Fairness-main/online_classification/fair_training.py | # fair_training.py
# training methods for fair regression
import torch
from torch.autograd import Variable
import torch.optim as optim
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This script provides imple... | 5,881 | 45.314961 | 189 | py |
Metrizing-Fairness | Metrizing-Fairness-main/online_classification/fairness_metrics.py | import torch
#import ot
import cvxpy as cp
import numpy as np
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This script provides implementations of the fairness metrics (e.g. energy distance, Sinkhorn diverg... | 10,438 | 34.266892 | 133 | py |
Metrizing-Fairness | Metrizing-Fairness-main/online_classification/data_loader.py | # data_loader.py
# utilities for loading data
import torch
import numpy as np
import pandas as pd
import copy
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from load_data import *
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn import preprocessing
"""
% Metrizi... | 16,842 | 39.585542 | 159 | py |
Metrizing-Fairness | Metrizing-Fairness-main/online_classification/run.py | import bias_eval
from pickle import dump
from tqdm import tqdm
for seed in tqdm(range(20)):
for target_batchsize in [4, 8, 16, 32, 64, 128, 256, 512]:
results, path,_ = bias_eval.train_metrics_debiased(target_batchsize, seed=seed)
with open(f'results\\results_debiased_seed{seed}_bs{target_batchsize... | 1,246 | 53.217391 | 103 | py |
Metrizing-Fairness | Metrizing-Fairness-main/online_classification/bias_eval.py | import torch
import data_loader
import models
import fairness_metrics
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
def find_batchsize(N_target, A):
candidate_1 = torch.argmax((A.flatten().cumsum(0)==2).int()).item() + 1
candidate_0 = torch.argmax(((1-A).flatten().cumsum(0)==2).int()).item(... | 9,003 | 36.991561 | 150 | py |
Metrizing-Fairness | Metrizing-Fairness-main/online_classification/models.py | # models.py
# models for regression
import torch
import torch.nn as nn
import torch.nn.functional as F
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script provides models for MFL and Oneta et al.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... | 2,012 | 29.5 | 97 | py |
Metrizing-Fairness | Metrizing-Fairness-main/online_classification/load_data.py | import numpy as np
import pandas as pd
import sklearn.preprocessing as preprocessing
from collections import namedtuple
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt # for plotting stuff
import os
import collections
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... | 11,006 | 46.038462 | 207 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/fair_training.py | # fair_training.py
# training methods for fair regression
import torch
from torch.autograd import Variable
import torch.optim as optim
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This script provides imple... | 6,302 | 45.688889 | 189 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/fairness_metrics.py | import torch
import ot
import cvxpy as cp
import numpy as np
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This script provides implementations of the fairness metrics (e.g. energy distance, Sinkhorn diverge... | 10,671 | 34.221122 | 133 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/benchmark.py | # benchmark.py
# file with functions for running experiment
import fair_training
import numpy as np
import torch
import matplotlib.pyplot as plt
from scipy.spatial import ConvexHull
import time
def convergence_plotter(regloss, fairloss, lambda_):
plt.figure(figsize=(16,5))
plt.subplot(131)
plt.plot(regloss... | 7,159 | 40.149425 | 206 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/setup.py | from setuptools import setup
setup(
name="dccp",
version="1.0.3",
author="Xinyue Shen, Steven Diamond, Stephen Boyd",
author_email="xinyues@stanford.edu, diamond@cs.stanford.edu, boyd@stanford.edu",
packages=["dccp"],
license="GPLv3",
zip_safe=False,
install_requires=["cvxpy >= 0.3.5"],... | 456 | 27.5625 | 84 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/data_loader.py | # data_loader.py
# utilities for loading data
import torch
import numpy as np
import pandas as pd
import copy
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from load_data import *
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn import preprocessing
"""
% Metrizi... | 16,841 | 39.681159 | 159 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/run_benchmark_MMD_simple.py | import models
import fairness_metrics
import benchmark
import data_loader
import pickle
import argparse
import pandas as pd
import numpy as np
import time
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script provides implementatino of ... | 10,851 | 49.240741 | 214 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/MMD_fair_run.py | import models
import fairness_metrics
import data_loader
import MMD_fair
import argparse
import pandas as pd
import numpy as np
import time
import pickle
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script provides an implementation of... | 6,760 | 44.993197 | 158 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/zafar_classification.py | # Baseline 1: https://arxiv.org/pdf/1706.02409.pdf
import cvxpy as cp
import numpy as np
import argparse
import pandas as pd
import torch
from zafar_method import funcs_disp_mist
from zafar_method.utils import *
import fairness_metrics
import data_loader
from zafar_method import utils
import numpy as np
from tqdm impor... | 8,147 | 42.340426 | 195 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/run.py | import os
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This script provides results for Table~7.
Example usage python run.py
The results are saved under ./results folder.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... | 1,317 | 56.304348 | 149 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/MMD_fair.py | # fair_training.py
# training methods for fair regression
import torch
from torch.autograd import Variable
import torch.optim as optim
import time
from tqdm import tqdm
# +---------------------------------+
# | Algorithm 1: Gradient Descent |
# +---------------------------------+
"""
% Metrizing Fairness
%%%%%%%%%%%%... | 6,671 | 44.387755 | 187 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/models.py | # models.py
# models for regression
import torch
import torch.nn as nn
import torch.nn.functional as F
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script provides models for MFL and Oneta et al.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... | 2,012 | 29.5 | 97 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/load_data.py | import numpy as np
import pandas as pd
import sklearn.preprocessing as preprocessing
from collections import namedtuple
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt # for plotting stuff
import os
import collections
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... | 11,005 | 46.034188 | 207 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/fair_KDE.py | # Baseline Fair KDE : https://proceedings.neurips.cc//paper/2020/file/ac3870fcad1cfc367825cda0101eee62-Paper.pdf
import cvxpy as cp
import numpy as np
import argparse
import pandas as pd
import torch
import fairness_metrics
import data_loader
from tqdm import tqdm
from collections import namedtuple
from sklearn.metrics... | 14,571 | 40.280453 | 153 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/run_benchmark.py | import models
import fairness_metrics
import benchmark
import data_loader
import pickle
import argparse
import pandas as pd
import numpy as np
import time
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script provides implementatino of ... | 7,662 | 46.596273 | 214 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/run_benchmark_regression.py | import models
import fairness_metrics
import benchmark
import data_loader
import pickle
import argparse
import pandas as pd
import numpy as np
import time
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This script provides implementatino of ... | 6,191 | 45.208955 | 214 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/Fair_KDE/fairness_metrics.py | import torch
import cvxpy as cp
import numpy as np
# +------------------------------------------+
# | Metric 1: Energy Distance |
# +------------------------------------------+
def energy_distance(y1, y2):
'''
Compute energy distance between empirical distance y1 and y2, each 1 dimensional
... | 8,196 | 30.771318 | 100 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/Fair_KDE/load_data_.py | import numpy as np
import pandas as pd
import sklearn.preprocessing as preprocessing
from collections import namedtuple
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt # for plotting stuff
import os
import collections
def load_compas_data(COMPAS_INPUT_FILE):
FEATURES_CLASSIFICATI... | 15,165 | 43.737463 | 207 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/Fair_KDE/algorithm.py | import random
import IPython
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from dataloader import CustomDataset
from utils import measures_from_Yhat
tau = 0.5
# Approximation of Q-function given by López-Benítez & Cas... | 7,375 | 41.390805 | 141 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/Fair_KDE/dataloader.py | import os
import copy
import torch
import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import data_loader
#from tempeh.configurations import datasets
from sklearn.datasets import make_moons
from sklearn.preprocessing import LabelEncoder, StandardScaler
def arrays_to_tensor(X, Y, Z, XZ... | 11,650 | 40.462633 | 159 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/Fair_KDE/data_loader_or.py | # data_loader.py
# utilities for loading data
import torch
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from load_data import *
# TODO: possibly some form of (cross) validation
def to_tensor(data, device):
D = data
if type(data) == pd.core... | 11,586 | 38.546075 | 121 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/Fair_KDE/utils.py | import numpy as np
import pandas as pd
def measures_from_Yhat(Y, Z, Yhat=None, threshold=0.5):
assert isinstance(Y, np.ndarray)
assert isinstance(Z, np.ndarray)
assert Yhat is not None
assert isinstance(Yhat, np.ndarray)
if Yhat is not None:
Ytilde = (Yhat >= threshold).astype(np.floa... | 1,205 | 29.923077 | 70 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/Fair_KDE/models.py | import torch
import torch.nn as nn
class Classifier(nn.Module):
def __init__(self, n_layers, n_inputs, n_hidden_units):
super(Classifier, self).__init__()
layers = []
if n_layers == 1: # Logistic Regression
layers.append(nn.Linear(n_inputs, 1))
layers.append... | 829 | 33.583333 | 72 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/Fair_KDE/fair_KDE_.py | # Baseline Fair KDE : https://proceedings.neurips.cc//paper/2020/file/ac3870fcad1cfc367825cda0101eee62-Paper.pdf
import cvxpy as cp
import numpy as np
import argparse
import pandas as pd
import torch
import fairness_metrics
import data_loader
from tqdm import tqdm
from collections import namedtuple
from sklearn.metrics... | 9,960 | 34.830935 | 136 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/zafar_method/funcs_disp_mist.py | from __future__ import division
import os, sys
import traceback
import numpy as np
from random import seed, shuffle
from collections import defaultdict
from copy import deepcopy
import cvxpy
import dccp
from dccp.problem import is_dccp
from zafar_method import utils as ut
def train_model_disp_mist(x, y, x_control, lo... | 18,367 | 49.185792 | 206 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/zafar_method/loss_funcs.py | import sys
import os
import numpy as np
import scipy.special
from collections import defaultdict
import traceback
from copy import deepcopy
def _hinge_loss(w, X, y):
yz = y * np.dot(X,w) # y * (x.w)
yz = np.maximum(np.zeros_like(yz), (1-yz)) # hinge function
return sum(yz)
def _logistic_loss(... | 2,268 | 22.884211 | 82 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/zafar_method/loss_funcs_after.py | import sys
import os
import numpy as np
import scipy.special
from collections import defaultdict
import traceback
from copy import deepcopy
def _hinge_loss(w, X, y):
yz = y * np.dot(X, w) # y * (x.w)
yz = np.maximum(np.zeros_like(yz), (1 - yz)) # hinge function
return sum(yz)
def _logistic_loss(w, X,... | 2,356 | 27.743902 | 82 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/zafar_method/utils.py | import numpy as np
from random import seed, shuffle
from zafar_method import loss_funcs as lf # our implementation of loss funcs
from scipy.optimize import minimize # for loss func minimization
from multiprocessing import Pool, Process, Queue
from collections import defaultdict
from copy import deepcopy
import matplotl... | 27,182 | 41.606583 | 357 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/zafar_method/funcs_disp_mist_after.py | from __future__ import division
import os, sys
import traceback
import numpy as np
from random import seed, shuffle
from collections import defaultdict
from copy import deepcopy
import cvxpy
import dccp
from dccp.problem import is_dccp
import utils as ut
SEED = 1122334455
seed(SEED) # set the random seed so that the ... | 18,388 | 49.798343 | 202 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/zafar_method/utils_after.py | import numpy as np
from random import seed, shuffle
import loss_funcs as lf # our implementation of loss funcs
from scipy.optimize import minimize # for loss func minimization
from multiprocessing import Pool, Process, Queue
from collections import defaultdict
from copy import deepcopy
import matplotlib.pyplot as plt... | 26,695 | 44.094595 | 291 | py |
Metrizing-Fairness | Metrizing-Fairness-main/equal_opportunity/zafar_method/.ipynb_checkpoints/utils-checkpoint.py | import numpy as np
from random import seed, shuffle
from zafar_method import loss_funcs as lf # our implementation of loss funcs
from scipy.optimize import minimize # for loss func minimization
from multiprocessing import Pool, Process, Queue
from collections import defaultdict
from copy import deepcopy
import matplotl... | 27,182 | 41.606583 | 357 | py |
Metrizing-Fairness | Metrizing-Fairness-main/online_regression/fairness_metrics.py | import torch
import ot
import cvxpy as cp
import numpy as np
"""
% Metrizing Fairness
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This script provides implementations of the fairness metrics (e.g. energy distance, Sinkhorn diverge... | 11,133 | 34.012579 | 133 | py |
warcio | warcio-master/setup.py | #!/usr/bin/env python
# vim: set sw=4 et:
from setuptools import setup, find_packages
from setuptools.command.test import test as TestCommand
import glob
__version__ = '1.7.4'
class PyTest(TestCommand):
def finalize_options(self):
TestCommand.finalize_options(self)
# should work with setuptools ... | 1,925 | 26.913043 | 98 | py |
warcio | warcio-master/warcio/bufferedreaders.py | from io import BytesIO
import zlib
import sys
from warcio.utils import BUFF_SIZE
#=================================================================
def gzip_decompressor():
"""
Decompressor which can handle decompress gzip stream
"""
return zlib.decompressobj(16 + zlib.MAX_WBITS)
def deflate_decomp... | 11,987 | 30.382199 | 96 | py |
warcio | warcio-master/warcio/indexer.py | import json
import sys
import os
from collections import OrderedDict
from warcio.archiveiterator import ArchiveIterator
from warcio.utils import open_or_default
# ============================================================================
class Indexer(object):
field_names = {}
def __init__(self, fields, ... | 2,894 | 31.166667 | 83 | py |
warcio | warcio-master/warcio/limitreader.py | # ============================================================================
class LimitReader(object):
"""
A reader which will not read more than specified limit
"""
def __init__(self, stream, limit):
self.stream = stream
self.limit = limit
self._orig_limit = limit
def _... | 1,850 | 25.826087 | 78 | py |
warcio | warcio-master/warcio/exceptions.py | #=================================================================
class ArchiveLoadFailed(Exception):
def __init__(self, reason):
self.msg = str(reason)
super(ArchiveLoadFailed, self).__init__(self.msg)
| 224 | 36.5 | 66 | py |
warcio | warcio-master/warcio/archiveiterator.py | from warcio.bufferedreaders import DecompressingBufferedReader
from warcio.exceptions import ArchiveLoadFailed
from warcio.recordloader import ArcWarcRecordLoader
from warcio.utils import BUFF_SIZE
import sys
import six
# ============================================================================
class UnseekableY... | 8,571 | 28.763889 | 87 | py |
warcio | warcio-master/warcio/checker.py | from __future__ import print_function
from warcio.archiveiterator import ArchiveIterator
from warcio.exceptions import ArchiveLoadFailed
def _read_entire_stream(stream):
while True:
piece = stream.read(1024*1024)
if len(piece) == 0:
break
class Checker(object):
def __init__(self... | 2,675 | 36.690141 | 89 | py |
warcio | warcio-master/warcio/statusandheaders.py | """
Representation and parsing of HTTP-style status + headers
"""
from six.moves import range
from six import iteritems
from warcio.utils import to_native_str, headers_to_str_headers
import uuid
from six.moves.urllib.parse import quote
import re
#=================================================================
cla... | 11,625 | 32.504323 | 94 | py |
warcio | warcio-master/warcio/utils.py | import six
import os
from contextlib import contextmanager
import base64
import hashlib
try:
import collections.abc as collections_abc # only works on python 3.3+
except ImportError: #pragma: no cover
import collections as collections_abc
BUFF_SIZE = 16384
# #==============================================... | 2,745 | 25.921569 | 89 | py |
warcio | warcio-master/warcio/recordbuilder.py | import datetime
import six
import tempfile
from io import BytesIO
from warcio.recordloader import ArcWarcRecord, ArcWarcRecordLoader
from warcio.statusandheaders import StatusAndHeadersParser, StatusAndHeaders
from warcio.timeutils import datetime_to_iso_date
from warcio.utils import to_native_str, BUFF_SIZE, Digeste... | 8,105 | 34.090909 | 114 | py |
warcio | warcio-master/warcio/warcwriter.py | import zlib
from socket import gethostname
from warcio.utils import Digester
from warcio.recordbuilder import RecordBuilder
from warcio.statusandheaders import StatusAndHeadersParser
# ============================================================================
class BaseWARCWriter(RecordBuilder):
def __init_... | 5,316 | 31.820988 | 87 | py |
warcio | warcio-master/warcio/timeutils.py | """
utility functions for converting between
datetime, iso date and 14-digit timestamp
"""
import re
import time
import datetime
import calendar
from email.utils import parsedate, formatdate
#=================================================================
# str <-> datetime conversion
#============================... | 9,303 | 24.56044 | 98 | py |
warcio | warcio-master/warcio/digestverifyingreader.py | import base64
import sys
from warcio.limitreader import LimitReader
from warcio.utils import to_native_str, Digester
from warcio.exceptions import ArchiveLoadFailed
# ============================================================================
class DigestChecker(object):
def __init__(self, kind=None):
s... | 5,500 | 31.94012 | 100 | py |
warcio | warcio-master/warcio/cli.py | from argparse import ArgumentParser, RawTextHelpFormatter
from warcio.indexer import Indexer
from warcio.checker import Checker
from warcio.extractor import Extractor
from warcio.recompressor import Recompressor
import sys
# ============================================================================
def main(args=... | 3,803 | 39.903226 | 144 | py |
warcio | warcio-master/warcio/extractor.py | from warcio.archiveiterator import ArchiveIterator
from warcio.utils import BUFF_SIZE
import sys
# ============================================================================
class Extractor(object):
READ_SIZE = BUFF_SIZE * 4
def __init__(self, filename, offset):
self.filename = filename
se... | 1,400 | 31.581395 | 78 | py |
warcio | warcio-master/warcio/recordloader.py | from warcio.statusandheaders import StatusAndHeaders
from warcio.statusandheaders import StatusAndHeadersParser
from warcio.statusandheaders import StatusAndHeadersParserException
from warcio.exceptions import ArchiveLoadFailed
from warcio.limitreader import LimitReader
from warcio.digestverifyingreader import DigestV... | 13,804 | 35.715426 | 112 | py |
warcio | warcio-master/warcio/__init__.py | from warcio.statusandheaders import StatusAndHeaders
from warcio.archiveiterator import ArchiveIterator
from warcio.warcwriter import WARCWriter
| 145 | 35.5 | 52 | py |
warcio | warcio-master/warcio/recompressor.py | from warcio.archiveiterator import ArchiveIterator
from warcio.exceptions import ArchiveLoadFailed
from warcio.warcwriter import WARCWriter
from warcio.bufferedreaders import DecompressingBufferedReader
import tempfile
import shutil
import traceback
import sys
# =====================================================... | 2,751 | 31.761905 | 100 | py |
warcio | warcio-master/warcio/capture_http.py | import threading
from io import BytesIO
from six.moves import http_client as httplib
from contextlib import contextmanager
from array import array
from warcio.utils import to_native_str, BUFF_SIZE, open
from warcio.warcwriter import WARCWriter, BufferWARCWriter
from tempfile import SpooledTemporaryFile
# ======... | 8,176 | 29.285185 | 87 | py |
warcio | warcio-master/test/test_cli.py | from warcio.cli import main
from . import get_test_file
from contextlib import contextmanager
from io import BytesIO
from warcio.exceptions import ArchiveLoadFailed
import pytest
import sys
import tempfile
import os
def test_index(capsys):
files = ['example.warc.gz', 'example.warc', 'example.arc.gz', 'example... | 19,089 | 71.862595 | 2,603 | py |
warcio | warcio-master/test/test_archiveiterator.py | from warcio.archiveiterator import ArchiveIterator, WARCIterator, ARCIterator
from warcio.exceptions import ArchiveLoadFailed
from warcio.bufferedreaders import DecompressingBufferedReader, BufferedReader
from warcio.warcwriter import BufferWARCWriter
import pytest
from io import BytesIO
import sys
import os
from .... | 14,939 | 41.20339 | 121 | py |
warcio | warcio-master/test/test_digestverifyingreader.py | import pytest
from warcio.digestverifyingreader import _compare_digest_rfc_3548
from warcio.utils import Digester
empty_sha1_b32 = '3I42H3S6NNFQ2MSVX7XZKYAYSCX5QBYJ'
empty_sha1_b64 = '2jmj7l5rSw0yVb/vlWAYkK/YBwk='
empty_sha1_b64_alt = '2jmj7l5rSw0yVb_vlWAYkK_YBwk='
empty_sha1_b16 = 'DA39A3EE5E6B4B0D3255BFEF95601890A... | 1,103 | 39.888889 | 95 | py |
warcio | warcio-master/test/test_bufferedreaders.py | r"""
# DecompressingBufferedReader Tests
#=================================================================
# decompress with on the fly compression, default gzip compression
>>> print_str(DecompressingBufferedReader(BytesIO(compress('ABC\n1234\n'))).read())
'ABC\n1234\n'
# decompress with on the fly compression, def... | 6,214 | 32.235294 | 193 | py |
warcio | warcio-master/test/test_check_digest_examples.py | from warcio.cli import main
from warcio import ArchiveIterator
from warcio.warcwriter import BufferWARCWriter
from . import get_test_file
import os
SKIP = ['example-trunc.warc',
'example-iana.org-chunked.warc',
'example-wrong-chunks.warc.gz',
'example-bad-non-chunked.warc.gz',
'example... | 3,007 | 34.388235 | 105 | py |
warcio | warcio-master/test/test_capture_http.py | import threading
from wsgiref.simple_server import make_server
from io import BytesIO
import time
# must be imported before 'requests'
from warcio.capture_http import capture_http
from pytest import raises
import requests
import json
import os
import tempfile
from warcio.archiveiterator import ArchiveIterator
from w... | 10,071 | 33.258503 | 94 | py |
warcio | warcio-master/test/test_statusandheaders.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
>>> st1 = StatusAndHeadersParser(['HTTP/1.0']).parse(StringIO(status_headers_1))
>>> st1
StatusAndHeaders(protocol = 'HTTP/1.0', statusline = '200 OK', headers = [('Content-Type', 'ABC'), ('Some', 'Value'), ('Multi-Line', 'Value1 Also This')])
# add range (and byte... | 6,990 | 28.129167 | 202 | py |
warcio | warcio-master/test/__init__.py | def get_test_file(filename=''):
import os
return os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data', filename)
| 133 | 32.5 | 86 | py |
warcio | warcio-master/test/test_limitreader.py | from warcio.limitreader import LimitReader
from contextlib import closing
from io import BytesIO
class TestLimitReader(object):
def test_limit_reader_1(self):
assert b'abcdefghji' == LimitReader(BytesIO(b'abcdefghjiklmnopqrstuvwxyz'), 10).read(26)
def test_limit_reader_2(self):
assert b'abcde... | 1,457 | 35.45 | 122 | py |
warcio | warcio-master/test/test_capture_http_proxy.py | from warcio.capture_http import capture_http
import threading
from wsgiref.simple_server import make_server, WSGIServer
import time
import requests
from warcio.archiveiterator import ArchiveIterator
from pytest import raises
# ==================================================================
class TestCaptureHttp... | 7,458 | 43.39881 | 103 | py |
warcio | warcio-master/test/test_writer.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from warcio.statusandheaders import StatusAndHeaders
from warcio.warcwriter import BufferWARCWriter, GzippingWrapper
from warcio.recordbuilder import RecordBuilder
from warcio.recordloader import ArcWarcRecordLoader
from warcio.archiveiterator import ArchiveIterator
from w... | 29,716 | 34.085006 | 119 | py |
warcio | warcio-master/test/test_utils.py | import sys
import pytest
from collections import Counter
from io import BytesIO
import os
import tempfile
import warcio.utils as utils
from . import get_test_file
try:
from multidict import CIMultiDict, MultiDict
except ImportError:
pass
class TestUtils(object):
def test_headers_to_str_headers(self):
... | 3,084 | 31.819149 | 97 | py |
lusol | lusol-master/gen/interface.py | #!/usr/bin/env python3
import io
def parse_org_table(table_lines):
# remove separator row
table_lines.pop(1)
table_list = [[b.strip() for b in a[1:-2].split('|')] for a in table_lines]
# get column list
column_list = table_list.pop(0)
#print(column_names)
# organize table data
table_da... | 4,987 | 31.601307 | 90 | py |
PT-MAP | PT-MAP-master/test_standard.py | import collections
import pickle
import random
import numpy as np
import matplotlib.pyplot as plt
import torch
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.optim as optim
import math
import torch.nn.functional as F
import torch.optim as optim
from numpy imp... | 6,764 | 28.159483 | 122 | py |
PT-MAP | PT-MAP-master/wrn_mixup_model.py | ### dropout has been removed in this code. original code had dropout#####
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.autograd import Variable
import sys, os
import numpy as np
import random
act = torch.nn.ReLU()
import math
from torch.nn.utils.weight_no... | 7,986 | 36.674528 | 206 | py |
PT-MAP | PT-MAP-master/res_mixup_model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import random
from torch.nn.utils.weight_norm import WeightNorm
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
... | 7,280 | 35.58794 | 206 | py |
PT-MAP | PT-MAP-master/save_plk.py | from __future__ import print_function
import argparse
import csv
import os
import collections
import pickle
import random
import numpy as np
import torch
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transfor... | 3,145 | 27.342342 | 108 | py |
PT-MAP | PT-MAP-master/FSLTask.py | import os
import pickle
import numpy as np
import torch
# from tqdm import tqdm
# ========================================================
# Usefull paths
_datasetFeaturesFiles = {"miniimagenet": "./checkpoints/miniImagenet/WideResNet28_10_S2M2_R/last/output.plk",
"cub": "./checkpoints/CUB/W... | 5,459 | 32.090909 | 109 | py |
PT-MAP | PT-MAP-master/train_cifar.py | #!/usr/bin/env python3 -u
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree.
from __future__ import print_function
import argparse
import csv
import os
import numpy as np
import t... | 11,421 | 35.375796 | 199 | py |
PT-MAP | PT-MAP-master/train.py | #!/usr/bin/env python3 -u
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree.
from __future__ import print_function
import argparse
import csv
import os
import numpy as np
import t... | 13,168 | 35.278237 | 199 | py |
PT-MAP | PT-MAP-master/configs.py | save_dir = '.'
data_dir = {}
data_dir['cifar'] = './filelists/cifar/'
data_dir['CUB'] = './filelists/CUB/'
data_dir['miniImagenet'] = './filelists/miniImagenet/'
| 211 | 34.333333 | 58 | py |
PT-MAP | PT-MAP-master/io_utils.py | import numpy as np
import os
import glob
import argparse
import numpy as np
import os
import glob
import argparse
def parse_args(script):
parser = argparse.ArgumentParser(description= 'few-shot script %s' %(script))
parser.add_argument('--dataset' , default='miniImagenet', help='CUB/miniImagenet'... | 2,785 | 46.220339 | 214 | py |
PT-MAP | PT-MAP-master/filelists/cifar/write_cifar_filelist.py | import glob
import json
import os
test = {'label_names': [] , 'image_names':[] , 'image_labels':[]}
pathname = os.getcwd()
#pathname = pathname.split('filelists')[0]
print(pathname)
f = open(pathname + '/cifar-FS/splits/bertinetto/test.txt')
classes = f.readlines()
count = 80
for each in classes:
each = each.stri... | 1,504 | 22.515625 | 65 | py |
PT-MAP | PT-MAP-master/filelists/miniImagenet/write_miniImagenet_filelist.py | import numpy as np
from os import listdir
from os.path import isfile, isdir, join
import os
import json
import random
import re
cwd = os.getcwd()
data_path = join(cwd,'ILSVRC2015/Data/CLS-LOC/train')
#data_path = join('/home/yuqing/phd/code/miniimagenet/images')
savedir = './'
dataset_list = ['base', 'val', 'novel']
... | 2,362 | 31.819444 | 118 | py |
PT-MAP | PT-MAP-master/filelists/CUB/write_CUB_filelist.py | import numpy as np
from os import listdir
from os.path import isfile, isdir, join
import os
import json
import random
cwd = os.getcwd()
data_path = join(cwd,'CUB_200_2011/images')
savedir = './'
dataset_list = ['base','val','novel']
#if not os.path.exists(savedir):
# os.makedirs(savedir)
folder_list = [f for f i... | 2,096 | 30.772727 | 138 | py |
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