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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SOLikeT | SOLikeT-master/soliket/gaussian_data.py | import numpy as np
try:
import holoviews as hv
except ImportError:
pass
from scipy.linalg import cholesky, LinAlgError
def multivariate_normal_logpdf(theory, data, cov, inv_cov, log_det):
const = np.log(2 * np.pi) * (-len(data) / 2) + log_det * (-1 / 2)
delta = data - theory
#print(const,delta,np... | 4,889 | 28.281437 | 84 | py |
SOLikeT | SOLikeT-master/soliket/poisson_data.py | import numpy as np
import pandas as pd
class PoissonData:
"""Poisson-process-generated data.
Parameters
----------
catalog : pd.DataFrame
Catalog of observed data.
columns : list
Columns of catalog relevant for computing poisson rate.
samples : dict, optional
Each entr... | 2,851 | 34.209877 | 87 | py |
SOLikeT | SOLikeT-master/soliket/utils.py | from importlib import import_module
from scipy.stats import binned_statistic as binnedstat
import numpy as np
from cobaya.likelihood import Likelihood
from cobaya.likelihoods.one import one
def binner(ls, cls, bin_edges):
x = ls.copy()
y = cls.copy()
cents = (bin_edges[:-1] + bin_edges[1:]) / 2.0
bi... | 1,140 | 26.166667 | 73 | py |
SOLikeT | SOLikeT-master/soliket/bandpass.py | r"""
.. module:: bandpass
This module computes the bandpass transmission based on the inputs from
the parameter file ``BandPass.yaml``. There are three possibilities:
* reading the passband :math:`\tau(\nu)` stored in a sacc file
(which is the default now, being the mflike default)
* building the passb... | 13,271 | 39.711656 | 91 | py |
SOLikeT | SOLikeT-master/soliket/gaussian.py | import numpy as np
from typing import Optional, Sequence
from cobaya.likelihood import Likelihood
from cobaya.input import merge_info
from cobaya.tools import recursive_update
from cobaya.typing import empty_dict
from .gaussian_data import GaussianData, MultiGaussianData
from .utils import get_likelihood
class Gaus... | 3,654 | 31.061404 | 88 | py |
SOLikeT | SOLikeT-master/soliket/poisson.py | import pandas as pd
from cobaya.likelihood import Likelihood
from .poisson_data import PoissonData
class PoissonLikelihood(Likelihood):
name = "Poisson"
data_path = None
columns = None
def initialize(self):
catalog = self._get_catalog()
if self.columns is None:
self.colu... | 1,084 | 26.125 | 83 | py |
SOLikeT | SOLikeT-master/soliket/__init__.py | from .lensing import LensingLiteLikelihood, LensingLikelihood # noqa: F401
from .gaussian import GaussianLikelihood, MultiGaussianLikelihood # noqa: F401
# from .studentst import StudentstLikelihood # noqa: F401
from .ps import PSLikelihood, BinnedPSLikelihood # noqa: F401
from .mflike import MFLike # noqa: F401
f... | 953 | 37.16 | 98 | py |
SOLikeT | SOLikeT-master/soliket/cosmopower.py | """
.. module:: soliket.cosmopower
:Synopsis: Simple CosmoPower theory wrapper for Cobaya.
:Author: Hidde T. Jense
.. |br| raw:: html
<br />
.. note::
**If you use this cosmological code, please cite:**
|br|
A. Spurio Mancini et al.
*CosmoPower: emulating cosmological power spectra for accelerated B... | 12,879 | 32.541667 | 106 | py |
SOLikeT | SOLikeT-master/soliket/bias.py | """
.. module:: soliket.bias
:Synopsis: Class to calculate bias models for haloes and galaxies as cobaya
Theory classes.
:author: Ian Harrison
Usage
-----
To use the Linear Bias model, simply add it as a theory code alongside camb in
your run settings, e.g.:
.. code-block:: yaml
theory:
camb:
soliket.bia... | 2,862 | 26.528846 | 87 | py |
SOLikeT | SOLikeT-master/soliket/lensing/lensing.py | import os
from pkg_resources import resource_filename
import numpy as np
import sacc
from cobaya.likelihoods.base_classes import InstallableLikelihood
from cobaya.model import get_model
from cobaya.log import LoggedError
# from cobaya.install import NotInstalledError
from ..ps import BinnedPSLikelihood
class Lensin... | 6,836 | 34.42487 | 87 | py |
SOLikeT | SOLikeT-master/soliket/lensing/__init__.py | from .lensing import LensingLiteLikelihood, LensingLikelihood # noqa: F401
| 76 | 37.5 | 75 | py |
SOLikeT | SOLikeT-master/soliket/tests/test_xcorr.py | # pytest -k xcorr -v --pdb .
import pytest
import numpy as np
from cobaya.yaml import yaml_load
from cobaya.model import get_model
import os
import pdb
def get_demo_xcorr_model(theory):
if theory == "camb":
info_yaml = r"""
likelihood:
soliket.XcorrLikelihood:
stop_a... | 5,744 | 32.208092 | 90 | py |
SOLikeT | SOLikeT-master/soliket/tests/test_bias.py | # pytest -k bias -v .
import pytest
import numpy as np
from cobaya.model import get_model
from cobaya.run import run
info = {"params": {
"b_lin": 1.1,
"H0": 70.,
"ombh2": 0.0245,
"omch2": 0.1225,
"ns": 0.96,
... | 1,999 | 26.027027 | 85 | py |
SOLikeT | SOLikeT-master/soliket/tests/test_gaussian.py | # import unittest
import numpy as np
from sklearn.datasets import make_spd_matrix
from soliket.gaussian import GaussianData, MultiGaussianData, CrossCov
def toy_data():
name1 = "A"
n1 = 10
x1 = np.arange(n1)
y1 = np.random.random(n1)
name2 = "B"
n2 = 20
x2 = np.arange(n2)
y2 = np.ran... | 2,674 | 28.395604 | 89 | py |
SOLikeT | SOLikeT-master/soliket/tests/test_cash.py | import numpy as np
from soliket.cash import CashCData
def toy_data():
x = np.arange(20)
y = np.arange(20)
xx, yy = np.meshgrid(x, y)
return x, y, xx, yy
def test_cash():
data1d, theory1d, data2d, theory2d = toy_data()
cashdata1d = CashCData("toy 1d", data1d)
cashdata2d = CashCData("... | 484 | 19.208333 | 73 | py |
SOLikeT | SOLikeT-master/soliket/tests/test_lensing.py | import os
import tempfile
import pytest
import numpy as np
from cobaya.yaml import yaml_load
from cobaya.model import get_model
packages_path = os.environ.get("COBAYA_PACKAGES_PATH") or os.path.join(
tempfile.gettempdir(), "lensing_packages"
)
# Cosmological parameters for the test data from SO sims
# See https:/... | 1,256 | 26.326087 | 79 | py |
SOLikeT | SOLikeT-master/soliket/tests/test_ccl.py | """
Check that CCL works correctly.
"""
import pytest
import numpy as np
from cobaya.model import get_model
from cobaya.likelihood import Likelihood
class CheckLike(Likelihood):
"""
This is a mock likelihood that simply forces soliket.CCL to calculate
a CCL object.
"""
def logp(self, **params_valu... | 2,033 | 20.638298 | 80 | py |
SOLikeT | SOLikeT-master/soliket/tests/test_cosmopower.py | """
Check that CosmoPower gives the correct Planck CMB power spectrum.
"""
import os
import pytest
import numpy as np
import matplotlib.pyplot as plt
from cobaya.model import get_model
from soliket.cosmopower import HAS_COSMOPOWER
fiducial_params = {
"ombh2": 0.0224,
"omch2": 0.122,
"h": 0.67,
"tau": ... | 4,311 | 30.474453 | 84 | py |
SOLikeT | SOLikeT-master/soliket/tests/test_mflike.py | """
Make sure that this returns the same result as original mflike.MFLike from LAT_MFlike repo
"""
import os
import tempfile
import unittest
import pytest
from packaging.version import Version
import camb
import soliket # noqa
from soliket.mflike import TestMFLike
import numpy as np
packages_path = os.environ.get("... | 5,764 | 29.664894 | 90 | py |
SOLikeT | SOLikeT-master/soliket/tests/test_ps.py | import os
from tempfile import gettempdir
import numpy as np
from sklearn.datasets import make_spd_matrix
# from scipy.stats import multivariate_normal
from soliket.gaussian import GaussianData, CrossCov
from soliket import MultiGaussianLikelihood
from soliket import PSLikelihood
from soliket.utils import get_likeli... | 2,554 | 30.158537 | 88 | py |
SOLikeT | SOLikeT-master/soliket/tests/test_poisson.py | import numpy as np
import pandas as pd
from functools import partial
from soliket.poisson_data import PoissonData
x_min = 0
x_max = 10
def rate_density(x, a):
"""simple linear rate density
"""
return a * x
def n_expected(a):
return 0.5 * a * (x_max ** 2 - x_min ** 2) # integral(rate_density, x_mi... | 1,639 | 28.285714 | 87 | py |
SOLikeT | SOLikeT-master/soliket/tests/test_cross_correlation.py | import numpy as np
import os
import pytest
from soliket.ccl import CCL
from cobaya.model import get_model
auto_file = 'soliket/data/xcorr_simulated/clgg_noiseless.txt'
cross_file = 'soliket/data/xcorr_simulated/clkg_noiseless.txt'
dndz_file = 'soliket/data/xcorr_simulated/dndz.txt'
sacc_file = 'soliket/tests/data/des_... | 8,759 | 32.822394 | 88 | py |
SOLikeT | SOLikeT-master/soliket/tests/test_foreground.py | # pytest -k bandpass -v .
import pytest
import numpy as np
import os
from cobaya.model import get_model
from cobaya.run import run
info = {"params": {
"a_tSZ": 3.3044404448917724,
"a_kSZ": 1.6646620740058649,
"a_p": 6.912474322461401,
"beta_... | 11,069 | 46.922078 | 90 | py |
SOLikeT | SOLikeT-master/soliket/tests/test_lensing_lite.py | import pytest
import numpy as np
from cobaya.yaml import yaml_load
from cobaya.model import get_model
try:
import classy # noqa F401
except ImportError:
boltzmann_codes = ["camb"]
else:
boltzmann_codes = ["camb", "classy"]
def get_demo_lensing_model(theory):
if theory == "camb":
info_yaml =... | 1,586 | 20.739726 | 55 | py |
SOLikeT | SOLikeT-master/soliket/tests/__init__.py | 0 | 0 | 0 | py | |
SOLikeT | SOLikeT-master/soliket/tests/test_multi.py | import numpy as np
import pytest
from soliket.tests.test_mflike import cosmo_params, nuisance_params
@pytest.mark.xfail(reason="lensing lhood install failure")
def test_multi():
lensing_options = {"theory_lmax": 5000}
pre = "data_sacc_"
mflike_options = {
"input_file": pre + "00000.fits",
... | 3,066 | 34.252874 | 88 | py |
SOLikeT | SOLikeT-master/soliket/tests/test_clusters.py | import numpy as np
import pytest
from cobaya.model import get_model
fiducial_params = {
"ombh2": 0.02225,
"omch2": 0.1198,
"H0": 67.3,
"tau": 0.06,
"As": 2.2e-9,
"ns": 0.96,
"mnu": 0.06,
"nnu": 3.046,
}
info_fiducial = {
"params": fiducial_params,
"likelihood": {"soliket.Clust... | 1,322 | 21.05 | 73 | py |
SOLikeT | SOLikeT-master/soliket/tests/test_bandpass.py | # pytest -k bandpass -v .
import pytest
import numpy as np
from cobaya.model import get_model
from cobaya.run import run
from ..constants import T_CMB, h_Planck, k_Boltzmann
info = {"params": {
"bandint_shift_LAT_93": 0.0,
"bandint_shift_LAT_145": 0.0,
"bandin... | 5,237 | 30.178571 | 90 | py |
SOLikeT | SOLikeT-master/soliket/tests/test_runs.py | import pkgutil
import pytest
import tempfile
from cobaya.yaml import yaml_load
from cobaya.run import run
import os
packages_path = os.environ.get("COBAYA_PACKAGES_PATH") or os.path.join(
tempfile.gettempdir(), "lensing_packages"
)
import os
packages_path = os.environ.get("COBAYA_PACKAGES_PATH") or os.path.join... | 2,146 | 27.25 | 77 | py |
SOLikeT | SOLikeT-master/soliket/xcorr/limber.py | import numpy as np
import pdb
from ..constants import C_HMPC
oneover_chmpc = 1. / C_HMPC
def mag_bias_kernel(provider, dndz, s1, zatchi, chi_arr, chiprime_arr, zprime_arr):
'''Calculates magnification bias kernel.
'''
dndzprime = np.interp(zprime_arr, dndz[:, 0], dndz[:, 1], left=0, right=0)
norm =... | 4,606 | 36.762295 | 87 | py |
SOLikeT | SOLikeT-master/soliket/xcorr/xcorr.py | r""" Likelihood for cross-correlation of CMB lensing and galaxy clustering probes.
"""
import numpy as np
import sacc
import pdb
from scipy.interpolate import InterpolatedUnivariateSpline as Spline
from ..gaussian import GaussianData, GaussianLikelihood
from .. import utils
from .limber import do_limber
class Xco... | 9,916 | 36.422642 | 110 | py |
SOLikeT | SOLikeT-master/soliket/xcorr/__init__.py | from .xcorr import XcorrLikelihood
| 35 | 17 | 34 | py |
SOLikeT | SOLikeT-master/soliket/mflike/mflike.py | r"""
.. module:: mflike
:Synopsis: Multi frequency likelihood for TTTEEE CMB power spectra for Simons Observatory
:Authors: Thibaut Louis, Xavier Garrido, Max Abitbol,
Erminia Calabrese, Antony Lewis, David Alonso.
MFLike is a multi frequency likelihood code interfaced with the Cobaya
sampler and a theory ... | 17,618 | 39.226027 | 90 | py |
SOLikeT | SOLikeT-master/soliket/mflike/theoryforge_MFLike.py | """
.. module:: theoryforge
The ``TheoryForge_MFLike`` class applies the foreground spectra and systematic
effects to the theory spectra provided by ``MFLike``. To do that, ``MFLike``
provides ``TheoryForge_MFLike`` with the appropriate list of channels, the
requested temperature/polarization fields, the
:math:`\ell`... | 13,738 | 38.366762 | 88 | py |
SOLikeT | SOLikeT-master/soliket/mflike/__init__.py | from .mflike import MFLike, TestMFLike
from .theoryforge_MFLike import TheoryForge_MFLike
| 90 | 29.333333 | 50 | py |
SOLikeT | SOLikeT-master/soliket/clusters/massfunc.py | import numpy as np
from scipy.interpolate import RegularGridInterpolator
from .tinker import dn_dlogM
from ..constants import MSUN_CGS, G_CGS, MPC2CM
np.seterr(divide='ignore', invalid='ignore')
class HMF:
def __init__(self, om, Ez, pk=None, kh=None, zarr=None):
# Initialize redshift and mass ranges
... | 2,360 | 30.065789 | 85 | py |
SOLikeT | SOLikeT-master/soliket/clusters/tinker.py | from builtins import zip
import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline as iuSpline
from scipy.integrate import simps
# Tinker stuff
tinker_data = np.transpose([[float(x) for x in line.split()]
for line in
"""200 0.186 1.47 2.57 1.... | 5,114 | 29.813253 | 77 | py |
SOLikeT | SOLikeT-master/soliket/clusters/survey.py | import os
import numpy as np
from scipy import interpolate
import astropy.io.fits as pyfits
# from astLib import astWCS
from astropy.wcs import WCS
from astropy.io import fits
import astropy.table as atpy
def read_clust_cat(fitsfile, qmin):
list = fits.open(fitsfile)
data = list[1].data
SNR = data.field... | 6,963 | 31.849057 | 88 | py |
SOLikeT | SOLikeT-master/soliket/clusters/sz_utils.py | import numpy as np
from scipy import interpolate
# from astropy.cosmology import FlatLambdaCDM
# from nemo import signals
from ..constants import MPC2CM, MSUN_CGS, G_CGS, C_M_S, T_CMB
from ..constants import h_Planck, k_Boltzmann, electron_mass_kg, elementary_charge
# from .clusters import C_KM_S as C_in_kms
rho_cri... | 13,757 | 32.474453 | 90 | py |
SOLikeT | SOLikeT-master/soliket/clusters/clusters.py | """
requires extra: astlib
"""
import numpy as np
import pandas as pd
from scipy.interpolate import interp1d
from pkg_resources import resource_filename
import pyccl as ccl
from ..poisson import PoissonLikelihood
from . import massfunc as mf
from .survey import SurveyData
from .sz_utils import szutils
C_KM_S = 2.997... | 7,397 | 29.697095 | 89 | py |
SOLikeT | SOLikeT-master/soliket/clusters/__init__.py | from .clusters import ClusterLikelihood # noqa: F401
| 54 | 26.5 | 53 | py |
SOLikeT | SOLikeT-master/docs/conf.py | # Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# Import SOLikeT (for autodoc)
import sys
sys.path.insert(0, "..")
# Create some mock imports
import mock
MOCK_MODUL... | 1,440 | 31.022222 | 87 | py |
studd | studd-main/utils.py | import numpy as np
import pandas as pd
import copy
def num_cols(df):
types = [type(df[col].values[1]) for col in df.columns]
is_num = [not int(x is str) for x in types]
is_num_idx = np.flatnonzero(is_num)
return is_num_idx
def any_str_col(df):
types = [type(df[col].values[0]) for col in df.colu... | 1,397 | 18.150685 | 63 | py |
studd | studd-main/__init__.py | # | 1 | 1 | 1 | py |
studd | studd-main/workflows.py | import pandas as pd
import numpy as np
from studd.studd_batch import STUDD
from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier as RF
from skmultiflow.drift_detection.page_hinkley import PageHinkley as PHT
def Workflow(X, y, delta, window_size):
ucdd = STUDD(X=X, y=y, n_train=window_s... | 6,327 | 40.090909 | 73 | py |
studd | studd-main/studd/studd_batch.py | from skmultiflow.data.data_stream import DataStream
from skmultiflow.drift_detection.page_hinkley import PageHinkley as PHT
from ht_detectors.tracker_output import HypothesisTestDetector
import copy
import numpy as np
class STUDD:
def __init__(self, X, y, n_train):
"""
:param X:
:param y... | 16,337 | 28.813869 | 92 | py |
studd | studd-main/studd/__init__.py | #
| 2 | 0.5 | 1 | py |
studd | studd-main/ht_detectors/tracker_output.py | import numpy as np
from scipy import stats
class HypothesisTestDetector(object):
METHOD = "ks"
def __init__(self, method, window, thr):
assert method in ["ks", "wrs", "tt"]
if method == "ks":
# Two-sample Kolmogorov-Smirnov test
m = stats.ks_2samp
elif method ... | 2,561 | 24.366337 | 67 | py |
studd | studd-main/ht_detectors/__init__.py | # | 1 | 1 | 1 | py |
studd | studd-main/ht_detectors/tracker_covariates.py | import numpy as np
import pandas as pd
from scipy.stats import ks_2samp
class FeatTracker(object):
def __init__(self, ref_window, thr, window_size):
self.method = ks_2samp
self.alarm_list = []
self.data = []
self.ref_window = np.array(ref_window)
self.window_size = window_... | 2,842 | 23.508621 | 67 | py |
studd | studd-main/experiments/main.py | import pickle
from workflows import Workflow
with open('data/real_datasets.pkl', 'rb') as fp:
datasets = pickle.load(fp)
delta = 0.002
results = dict()
for df in datasets:
y = data.target.values
X = data.drop(['target'], axis=1)
small_data_streams = ['AbruptInsects',
... | 1,142 | 24.4 | 72 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 16/PIPO-FAN-master/pipo_fan/resample.py | import numpy as np
import SimpleITK as sitk
import os
import argparse
parser = argparse.ArgumentParser(description='Resampling CT img or seg to 256 or 512')
parser.add_argument('-p1', '--path1_filename', default=None, type=str,
metavar='path1_filename',
help='Raw file folder ')
... | 2,903 | 28.333333 | 119 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 16/PIPO-FAN-master/pipo_fan/train_sf_partial.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 5 16:00:33 2017
@author: yan
"""
# %% train the network
import argparse
import datetime
import math
import numpy as np
import os
from os import path
import shutil
import time
import torch
from torch import cuda
from torch import optim
#from torch.... | 24,733 | 35.480826 | 110 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 16/PIPO-FAN-master/pipo_fan/train_concave0.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 5 16:00:33 2017
@author: yan
"""
# %% train the network
import argparse
import datetime
import math
import numpy as np
import os
from os import path
import shutil
import time
import torch
from torch import cuda
from torch import optim
#from torch.... | 24,809 | 35.485294 | 131 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 16/PIPO-FAN-master/pipo_fan/segment_sf_partial.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 28 16:24:59 2017
@author: yan
Load pre-trained network to segment a new image
Code v0.01
"""
# %% Resnet blocks in U-net
import argparse
import datetime
import nibabel as nib
import numpy as np
import os
from os import path
from scipy import ndi... | 17,990 | 34.001946 | 126 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 16/PIPO-FAN-master/pipo_fan/dataset/dataset_liverCT_2D.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 22 14:10:33 2017
@author: yanrpi
"""
# %%
import glob
import numpy as np
import nibabel as nib
import random
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from os import path
# from scipy.misc impo... | 16,145 | 31.552419 | 99 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 16/PIPO-FAN-master/pipo_fan/dataset/dataset_muor_2D.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 22 14:10:33 2017
@author: yanrpi
"""
# %%
import glob
import numpy as np
import nibabel as nib
import random
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from os import path
# from scipy.misc impo... | 15,824 | 31.428279 | 93 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 16/model/denseu_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
class _DenseLayer(nn.Sequential):
def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
super(_DenseLayer, self).__init__()
self.add_module('norm1', nn.BatchNorm2d(num_input_fea... | 7,830 | 42.505556 | 114 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 16/model/unet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class double_conv(nn.Module):
'''(conv => BN => ReLU) * 2'''
def __init__(self, in_ch, out_ch):
super(double_conv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, padding=1),
nn.B... | 2,971 | 26.266055 | 86 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 16/model/resu_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class double_conv(nn.Module):
'''(conv => BN => ReLU) * 2'''
def __init__(self, in_ch, out_ch):
super(double_conv, self).__init__()
self.conv = nn.Sequential(
nn.BatchNorm2d(in_ch),
nn.ReLU(inplace=True)... | 3,831 | 27.176471 | 86 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 16/model/concave_dps.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class double_conv(nn.Module):
'''(conv => BN => ReLU) * 2'''
def __init__(self, in_ch, out_ch):
super(double_conv, self).__init__()
self.conv = nn.Sequential(
nn.BatchNorm2d(in_ch),
nn.ReLU(inplace=True)... | 8,568 | 29.386525 | 86 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 16/model/concave_dps_w.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .concave_dps import ResUNet as ResUNet_0
class attention(nn.Module):
def __init__(self, in_ch, out_ch):
super(attention, self).__init__()
self.conv = nn.Sequential(
nn.BatchNorm2d(in_ch),
nn.ReLU(inplace... | 1,754 | 26.421875 | 54 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 5/main.py | print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.neural_network import MLPRegressor
from sklearn.datasets.california_housing import fetch_california_housing
import math
from itertools import combinations
import shap
## Use House Dataset
cal_hous... | 2,784 | 28.62766 | 142 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 6/Gradient_DeepTaylorLRP.py | import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
from tqdm import tqdm
import lrp
import pandas as pd
from pylab import rcParams
### Import Data From TensorFlow
rcParams['figure.figsize'] = 8, 10
mnist = input_data.read_data_sets('/... | 3,595 | 32.607477 | 119 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 6/Gradient_Sundararajan.py | import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
from tqdm import tqdm
import lrp
import pandas as pd
from pylab import rcParams
### Import Data From TensorFlow
rcParams['figure.figsize'] = 8, 10
mnist = input_data.read_data_sets('/t... | 3,861 | 30.655738 | 92 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 6/Gradient_Simonyan.py | import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
from tqdm import tqdm
import pandas as pd
from pylab import rcParams
### Import Data From TensorFlow
rcParams['figure.figsize'] = 8, 10
mnist = input_data.read_data_sets('/tmp/tensorf... | 3,554 | 31.614679 | 92 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 6/Gradient_Smilkov.py | import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
from tqdm import tqdm
import pandas as pd
from pylab import rcParams
### Import Data From TensorFlow
rcParams['figure.figsize'] = 8, 10
mnist = input_data.read_data_sets('/tmp/tensorf... | 3,777 | 30.22314 | 92 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 6/ShowSaliency.py | import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
from tqdm import tqdm
import lrp
import pandas as pd
from pylab import rcParams
import numpy as np
import tensorflow as tf
import h5py
import os
import matplotlib.pyplot as plt
import ... | 7,312 | 25.305755 | 88 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 6/lrp.py | import sys
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import numpy as np
from tqdm import tqdm
from tensorflow.python.ops import nn_ops, gen_nn_ops
import matplotlib.pyplot as plt
from scipy.stats.mstats import zscore
#Helper Method for
def lrp(F, lowest, highest, graph=None, ... | 6,252 | 35.354651 | 109 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 3/main.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals
import abc
import sys
import numpy as np
import pandas as pd
from sklearn import linear_model, preprocessing, cluster
import scipy.linalg as slin
import scipy.sparse.... | 12,510 | 35.263768 | 168 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 3/influence/dataset_poisoning.py | import IPython
import numpy as np
import os
import time
from shutil import copyfile
from influence.inceptionModel import BinaryInceptionModel
from influence.binaryLogisticRegressionWithLBFGS import BinaryLogisticRegressionWithLBFGS
import influence.experiments
from influence.dataset import DataSet
import tensorflow ... | 8,970 | 40.532407 | 188 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 3/influence/logisticRegressionWithLBFGS.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals
import abc
import sys
import numpy as np
import pandas as pd
from sklearn import linear_model, preprocessing, cluster
import scipy.linalg as slin
import scipy.sparse.l... | 6,886 | 33.094059 | 114 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 3/influence/image_utils.py | import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, model_from_json
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers ... | 1,739 | 30.636364 | 99 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 3/influence/all_CNN_c.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals
import abc
import sys
import numpy as np
import pandas as pd
from sklearn import linear_model, preprocessing, cluster
import matplotlib.pyplot as plt
import seaborn as... | 5,279 | 36.183099 | 137 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 3/influence/experiments.py | import numpy as np
import os
import time
import IPython
from scipy.stats import pearsonr
def get_try_check(model, X_train, Y_train, Y_train_flipped, X_test, Y_test):
def try_check(idx_to_check, label):
Y_train_fixed = np.copy(Y_train_flipped)
Y_train_fixed[idx_to_check] = Y_train[idx_to_check]
... | 7,208 | 40.67052 | 125 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 3/influence/smooth_hinge.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals
import abc
import sys
import numpy as np
import pandas as pd
from sklearn import linear_model, preprocessing, cluster, svm
import matplotlib.pyplot as plt
import seabo... | 11,722 | 32.686782 | 114 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 3/influence/dataset.py | # Adapted from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/learn/python/learn/datasets/mnist.py
import numpy as np
class DataSet(object):
def __init__(self, x, labels):
if len(x.shape) > 2:
x = np.reshape(x, [x.shape[0], -1])
assert(x.shape[0] == labels.s... | 2,619 | 27.172043 | 123 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 3/influence/imagenet_utils.py | # Taken from https://github.com/fchollet/keras/blob/master/keras/applications/imagenet_utils.py
import numpy as np
import json
from keras.utils.data_utils import get_file
from keras import backend as K
CLASS_INDEX = None
CLASS_INDEX_PATH = 'https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_in... | 1,663 | 31.627451 | 105 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 3/influence/nlprocessor.py | # from spacy.en import English
# import spacy
from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
import en_core_web_sm
class NLProcessor(object):
def __init__(self):
# self.nlp = English()
self.nlp = en_core_web_sm.load()
# self.nlp = spacy.load('en_core_web_sm-... | 3,105 | 33.131868 | 109 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 3/influence/inception_v3.py | # -*- coding: utf-8 -*-
"""Inception V3 model for Keras.
Note that the input image format for this model is different than for
the VGG16 and ResNet models (299x299 instead of 224x224),
and that the input preprocessing function is also different (same as Xception).
# Reference
- [Rethinking the Inception Architecture... | 15,178 | 35.753027 | 152 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 3/influence/genericNeuralNet.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals
import abc
import sys
import numpy as np
import pandas as pd
from sklearn import linear_model, preprocessing, cluster
import scipy.linalg as slin
import scipy.sparse.... | 33,790 | 39.565426 | 158 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 3/influence/binaryLogisticRegressionWithLBFGS.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals
import abc
import sys
import numpy as np
import pandas as pd
from sklearn import linear_model, preprocessing, cluster
import scipy.linalg as slin
import scipy.sparse.l... | 5,853 | 34.695122 | 142 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 3/influence/inceptionModel.py | from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals
import abc
import sys
import numpy as np
import pandas as pd
from sklearn import linear_model, preprocessing, cluster
import scipy.linalg as slin
import scipy.sparse.l... | 11,113 | 34.059937 | 165 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 3/influence/hessians.py | ### Adapted from TF repo
import tensorflow as tf
from tensorflow import gradients
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
def hessian_vector_product(ys, xs, v):
"""Multiply the Hessian of `ys` wrt `xs` by `v`.
This is an e... | 5,137 | 40.772358 | 83 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 17/main_BioLearning.py | import scipy.io
import numpy as np
import matplotlib.pyplot as plt
import pickle
### Loading CIFAR100
def load_cifar100(filename):
with open(filename, 'rb')as f:
datadict = pickle.load(f, encoding='latin1')
images = datadict['data']
labels = datadict['fine_labels']
labels = np.arr... | 3,108 | 23.674603 | 107 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 7/main_rule_extraction.py | import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
import numpy as np
import tensorflow as tf
### Import Iris Data to play with
# Sepal Length, Sepal Width, Petal Length and Petal Width
iris = datasets.load_iris()
X = iris.data # we only take the first two features... | 6,727 | 27.033333 | 96 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 9/main_lime.py | from __future__ import print_function
import sklearn
import sklearn.datasets
import sklearn.ensemble
import numpy as np
import lime
import lime.lime_tabular
### Before running this code, you need to install package lime
np.random.seed(1)
iris = sklearn.datasets.load_iris()
train, test, labels_train, labels_test = s... | 864 | 28.827586 | 150 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 10/main_adjointMethods.py | import numpy as np
import numpy.random as npr
import tensorflow as tf
from tqdm import tqdm
import matplotlib.pyplot as plt
import tensorflow.contrib.eager as tfe
from main_neural_ode import NeuralODE
### tf.enable_eager_execution must be called at program startup. Please restart your kernel.
plt.rcParams["font.f... | 2,914 | 25.026786 | 92 | py |
IndependentEvaluation | IndependentEvaluation-main/Code For Figure 10/main_neural_ode.py | from typing import Optional, List
import numpy as np
import tensorflow as tf
from tensorflow.python.framework.ops import EagerTensor
import tensorflow.contrib.eager as tfe
keras = tf.keras
def zip_map(zipped, update_op):
return [update_op(*elems) for elems in zipped]
def euler_update(h_list, dh_list, dt):
... | 4,469 | 28.215686 | 85 | py |
enpheeph | enpheeph-main/.noxfile.py | # -*- coding: utf-8 -*-
# enpheeph - Neural Fault Injection Framework
# Copyright (C) 2020-2023 Alessio "Alexei95" Colucci
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version... | 4,819 | 27.52071 | 94 | py |
enpheeph | enpheeph-main/src/enpheeph/__init__.py | # -*- coding: utf-8 -*-
# enpheeph - Neural Fault Injection Framework
# Copyright (C) 2020-2023 Alessio "Alexei95" Colucci
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version... | 12,581 | 37.477064 | 88 | py |
enpheeph | enpheeph-main/src/enpheeph/__about__.py | # -*- coding: utf-8 -*-
# enpheeph - Neural Fault Injection Framework
# Copyright (C) 2020-2023 Alessio "Alexei95" Colucci
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version... | 2,648 | 37.955882 | 77 | py |
enpheeph | enpheeph-main/src/enpheeph/abc/libraryhandlerpluginabc.py | # -*- coding: utf-8 -*-
# enpheeph - Neural Fault Injection Framework
# Copyright (C) 2020-2023 Alessio "Alexei95" Colucci
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version... | 2,244 | 35.803279 | 77 | py |
enpheeph | enpheeph-main/src/enpheeph/abc/lowleveltorchmaskpluginabc.py | # -*- coding: utf-8 -*-
# enpheeph - Neural Fault Injection Framework
# Copyright (C) 2020-2023 Alessio "Alexei95" Colucci
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version... | 3,069 | 35.117647 | 85 | py |
enpheeph | enpheeph-main/src/enpheeph/abc/indexingpluginabc.py | # -*- coding: utf-8 -*-
# enpheeph - Neural Fault Injection Framework
# Copyright (C) 2020-2023 Alessio "Alexei95" Colucci
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version... | 3,462 | 37.910112 | 86 | py |
enpheeph | enpheeph-main/src/enpheeph/abc/sqlstoragepluginabc.py | # -*- coding: utf-8 -*-
# enpheeph - Neural Fault Injection Framework
# Copyright (C) 2020-2023 Alessio "Alexei95" Colucci
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version... | 14,542 | 37.070681 | 88 | py |
enpheeph | enpheeph-main/src/enpheeph/abc/pytorchinjectionabc.py | # -*- coding: utf-8 -*-
# enpheeph - Neural Fault Injection Framework
# Copyright (C) 2020-2023 Alessio "Alexei95" Colucci
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version... | 2,730 | 34.934211 | 86 | py |
enpheeph | enpheeph-main/src/enpheeph/abc/pytorchsparseinterfacepluginabc.py | # -*- coding: utf-8 -*-
# enpheeph - Neural Fault Injection Framework
# Copyright (C) 2020-2023 Alessio "Alexei95" Colucci
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version... | 3,162 | 35.356322 | 85 | py |
enpheeph | enpheeph-main/src/enpheeph/abc/injectionabc.py | # -*- coding: utf-8 -*-
# enpheeph - Neural Fault Injection Framework
# Copyright (C) 2020-2023 Alessio "Alexei95" Colucci
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version... | 2,066 | 35.910714 | 77 | py |
enpheeph | enpheeph-main/src/enpheeph/abc/faultabc.py | # -*- coding: utf-8 -*-
# enpheeph - Neural Fault Injection Framework
# Copyright (C) 2020-2023 Alessio "Alexei95" Colucci
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version... | 1,662 | 41.641026 | 77 | py |
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