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
value |
|---|---|---|---|---|---|---|
auto_eqf | auto_eqf-main/sphere_example.py | """
This file is part of auto_eqf.
auto_eqf is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
auto_eqf is distributed in the hope that it wil... | 3,658 | 27.364341 | 76 | py |
auto_eqf | auto_eqf-main/automatic_eqf.py | """
This file is part of auto_eqf.
auto_eqf is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
auto_eqf is distributed in the hope that it wil... | 6,543 | 36.609195 | 130 | py |
machine-learning-applied-to-cfd | machine-learning-applied-to-cfd-master/notebooks/helper_module.py | '''Module containing function that are too large to be included in the notebooks.'''
import torch
import numpy as np
class SimpleMLP(torch.nn.Module):
def __init__(self, n_inputs=1, n_outputs=1, n_layers=1, n_neurons=10, activation=torch.sigmoid, batch_norm=False):
super().__init__()
self.n_inputs... | 6,317 | 44.128571 | 118 | py |
adapt | adapt-master/setup.py | from setuptools import setup, find_packages
from pathlib import Path
this_directory = Path(__file__).parent
long_description = (this_directory / "README.md").read_text()
setup(
name='adapt',
version='0.4.3',
description='Awesome Domain Adaptation Python Toolbox for Tensorflow and Scikit-learn',
url='h... | 729 | 33.761905 | 108 | py |
adapt | adapt-master/examples/transfertree.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 3 11:23:55 2022
@author: mounir
"""
import sys
import copy
import numpy as np
import matplotlib.pyplot as plt
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from adapt.parameter_based import Tr... | 10,131 | 37.819923 | 153 | py |
adapt | adapt-master/tests/test_ccsa.py | import numpy as np
import tensorflow as tf
from adapt.utils import make_classification_da
from adapt.feature_based import CCSA
from tensorflow.keras.initializers import GlorotUniform
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.optimizers import Adam
np.random.seed(0)... | 1,311 | 36.485714 | 98 | py |
adapt | adapt-master/tests/test_tradaboost.py | """
Test functions for tradaboost module.
"""
import copy
import numpy as np
import scipy
from sklearn.linear_model import LinearRegression, LogisticRegression, Ridge, RidgeClassifier
from sklearn.metrics import r2_score, accuracy_score
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Ad... | 7,644 | 34.55814 | 93 | py |
adapt | adapt-master/tests/test_nnw.py | import numpy as np
from adapt.instance_based import NearestNeighborsWeighting
np.random.seed(0)
n = 50
m = 50
p = 6
Xs = np.concatenate((np.random.randn(int(m/2), p)*0.1,
2+np.random.randn(int(m/2), p)*0.1))
Xt = np.random.randn(n, p)*0.1
def test_nnw():
nnw = NearestNeighborsWeighting(n_n... | 585 | 25.636364 | 66 | py |
adapt | adapt-master/tests/test_fmmd.py | import pytest
import numpy as np
import tensorflow as tf
from adapt.feature_based import fMMD
from adapt.feature_based._fmmd import _get_optim_function
np.random.seed(0)
n = 50
m = 50
p = 6
Xs = np.random.randn(m, p)*0.1 + np.array([0.]*(p-2) + [2., 2.])
Xt = np.random.randn(n, p)*0.1
def test_fmmd():
fmmd = f... | 2,054 | 31.619048 | 83 | py |
adapt | adapt-master/tests/test_linint.py | from sklearn.linear_model import Ridge
from adapt.utils import make_regression_da
from adapt.parameter_based import LinInt
Xs, ys, Xt, yt = make_regression_da()
def test_linint():
model = LinInt(Ridge(), Xt=Xt[:6], yt=yt[:6],
verbose=0, random_state=0)
model.fit(Xs, ys)
mode... | 395 | 29.461538 | 63 | py |
adapt | adapt-master/tests/test_iwc.py | """
Test functions for iwn module.
"""
import numpy as np
from sklearn.linear_model import RidgeClassifier
from adapt.utils import make_classification_da
from adapt.instance_based import IWC
from adapt.utils import get_default_discriminator
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from ... | 1,428 | 27.58 | 83 | py |
adapt | adapt-master/tests/test_adda.py | """
Test functions for adda module.
"""
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense
from tensorflow.keras.initializers import GlorotUniform
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.k... | 3,128 | 31.59375 | 83 | py |
adapt | adapt-master/tests/test_dann.py | """
Test functions for dann module.
"""
import pytest
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.optimizers import Adam
from adapt.fea... | 4,469 | 33.651163 | 91 | py |
adapt | adapt-master/tests/test_kmm.py | """
Test functions for kmm module.
"""
import os
import numpy as np
from sklearn.linear_model import LinearRegression
from adapt.instance_based import KMM
np.random.seed(0)
Xs = np.concatenate((
np.random.randn(50)*0.1,
np.random.randn(50)*0.1 + 1.,
)).reshape(-1, 1)
Xt = (np.random.randn(100) * 0.1).reshape... | 2,254 | 29.472973 | 77 | py |
adapt | adapt-master/tests/test_mdd.py | """
Test functions for dann module.
"""
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.initial... | 3,369 | 30.495327 | 87 | py |
adapt | adapt-master/tests/test_ulsif.py | from sklearn.linear_model import RidgeClassifier
from adapt.utils import make_classification_da
from adapt.instance_based import ULSIF, RULSIF
Xs, ys, Xt, yt = make_classification_da()
def test_ulsif():
model = ULSIF(RidgeClassifier(0.), Xt=Xt[:73], kernel="rbf",
lambdas=[0.1, 1., 10.], gamma=... | 1,512 | 35.902439 | 80 | py |
adapt | adapt-master/tests/test_iwn.py | """
Test functions for iwn module.
"""
from sklearn.linear_model import RidgeClassifier
from adapt.utils import make_classification_da
from adapt.instance_based import IWN
from adapt.utils import get_default_task
from sklearn.neighbors import KNeighborsClassifier
try:
from tensorflow.keras.optimizers.legacy import... | 1,349 | 31.142857 | 78 | py |
adapt | adapt-master/tests/test_sa.py | import numpy as np
from adapt.metrics import normalized_linear_discrepancy
from adapt.feature_based import SA
np.random.seed(0)
n = 50
m = 50
p = 6
Xs = np.random.randn(m, p)*0.1 + np.array([0.]*(p-2) + [2., 2.])
Xt = np.random.randn(n, p)*0.1
def test_sa():
sa = SA(n_components=2)
Xst = sa.fit_transform(X... | 538 | 24.666667 | 69 | py |
adapt | adapt-master/tests/test_fa.py | """
Test functions for fe module.
"""
import numpy as np
import pytest
from sklearn.linear_model import Ridge, LinearRegression
from adapt.feature_based import FA
Xs = np.ones((75, 10))
Xt = np.ones((25, 10))
ys = np.ones(75)
yt = np.zeros(25)
domains = np.concatenate((np.ones(25)*0, np.ones(25), np.ones(25)*2))
... | 1,767 | 26.2 | 69 | py |
adapt | adapt-master/tests/test_kliep.py | """
Test functions for kliep module.
"""
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.base import BaseEstimator
from adapt.instance_based import KLIEP
import pytest
import warnings
class DummyEstimator(BaseEstimator):
def __init__(self):
pass
def fit(se... | 5,600 | 30.644068 | 103 | py |
adapt | adapt-master/tests/test_coral.py | """
Test functions for coral module.
"""
import numpy as np
from sklearn.linear_model import LogisticRegression
from scipy import linalg
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense
from tensorflow.keras.initializers import GlorotUniform
from adapt.f... | 2,957 | 30.806452 | 73 | py |
adapt | adapt-master/tests/test_mcd.py | """
Test functions for dann module.
"""
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.initial... | 2,758 | 30 | 66 | py |
adapt | adapt-master/tests/test_regular.py | """
Test functions for regular module.
"""
import pytest
import numpy as np
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.gaussian_process import GaussianProcessRegressor, GaussianProcessClassifier
from sklearn.gaussian_process.kernels import Matern, WhiteKernel
from sklearn.base i... | 8,066 | 32.473029 | 88 | py |
adapt | adapt-master/tests/test_treeutils.py | import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
import adapt._tree_utils as ut
np.random.seed(0)
# Generate training source data
ns = 200
ns_perclass = ns // 2
mean_1 = (1, 1)
var_1 = np.diag([1, 1])
mean_2 = (3, 3)
var_2 = np.diag([2, 2])
Xs = ... | 4,776 | 30.846667 | 106 | py |
adapt | adapt-master/tests/test_pred.py | from sklearn.linear_model import RidgeClassifier
from adapt.utils import make_classification_da
from adapt.feature_based import PRED
Xs, ys, Xt, yt = make_classification_da()
def test_pred():
model = PRED(RidgeClassifier(), pretrain=True, Xt=Xt[:3], yt=yt[:3],
verbose=0, random_state... | 773 | 32.652174 | 72 | py |
adapt | adapt-master/tests/__init__.py | """
Tests for adapt package.
"""
| 33 | 7.5 | 24 | py |
adapt | adapt-master/tests/test_metrics.py | """
Test base
"""
import os
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.model_selection import GridSearchCV
from adapt.base import BaseAdaptDeep, BaseAdaptEstimator
from adapt.metrics import *
from adapt.instance_based import KMM
from adapt.feature_based import CORAL
Xs = np.ran... | 1,828 | 32.87037 | 76 | py |
adapt | adapt-master/tests/test_tca.py | import numpy as np
from adapt.metrics import normalized_linear_discrepancy
from adapt.feature_based import TCA
np.random.seed(0)
n = 50
m = 50
p = 6
Xs = np.random.randn(m, p)*0.1 + np.array([0.]*(p-2) + [2., 2.])
Xt = np.random.randn(n, p)*0.1
def test_tca():
tca = TCA(n_components=2, kernel="rbf", gamma=0.01... | 590 | 27.142857 | 71 | py |
adapt | adapt-master/tests/test_wann.py | """
Test functions for wann module.
"""
import numpy as np
from sklearn.linear_model import LinearRegression
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.optimizers import Adam
import tensorflow as tf
from adapt.instance_based import WANN
np.random.seed(0)
Xs = np.con... | 871 | 27.129032 | 59 | py |
adapt | adapt-master/tests/test_cdan.py | """
Test functions for cdan module.
"""
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.initial... | 3,932 | 32.330508 | 89 | py |
adapt | adapt-master/tests/test_base.py | """
Test base
"""
import copy
import shutil
import numpy as np
import pytest
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.optimizers import Adam
from sklear... | 9,849 | 31.943144 | 99 | py |
adapt | adapt-master/tests/test_finetuning.py | import numpy as np
import tensorflow as tf
from sklearn.base import clone
from adapt.utils import make_classification_da
from adapt.parameter_based import FineTuning
from tensorflow.keras.initializers import GlorotUniform
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.op... | 3,453 | 41.121951 | 102 | py |
adapt | adapt-master/tests/test_ldm.py | import numpy as np
import os
from adapt.instance_based import LDM
np.random.seed(0)
n = 50
m = 50
p = 6
Xs = np.concatenate((np.random.randn(int(m/2), p)*0.1,
2+np.random.randn(int(m/2), p)*0.1))
Xt = np.random.randn(n, p)*0.1
ys = Xs[:,0]
ys[Xs[:,0]>1] = 2.
yt = Xt[:,0]
def test_ldm():
if... | 746 | 21.636364 | 61 | py |
adapt | adapt-master/tests/test_wdgrl.py | """
Test functions for wdgrl module.
"""
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.initia... | 2,835 | 31.227273 | 78 | py |
adapt | adapt-master/tests/test_transfertree.py | import copy
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from adapt.utils import make_classification_da
from adapt.parameter_based import TransferTreeClassifier, TransferForestClassifier
from adapt.parameter_based import TransferTreeSelector, Tr... | 11,236 | 40.3125 | 146 | py |
adapt | adapt-master/tests/test_balancedweighting.py | from sklearn.linear_model import RidgeClassifier
from adapt.utils import make_classification_da
from adapt.instance_based import BalancedWeighting
Xs, ys, Xt, yt = make_classification_da()
def test_good_ratio():
model = BalancedWeighting(RidgeClassifier(), gamma=0.5, Xt=Xt[:3], yt=yt[:3],
... | 868 | 35.208333 | 82 | py |
adapt | adapt-master/tests/test_utils.py | """
Test functions for utils module.
"""
import copy
import numpy as np
import pytest
import tensorflow as tf
import tensorflow.keras.backend as K
from sklearn.linear_model import LinearRegression, LogisticRegression, Ridge
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
fro... | 16,182 | 34.567033 | 88 | py |
adapt | adapt-master/src_docs/conf.py | # -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
... | 4,081 | 29.691729 | 83 | py |
adapt | adapt-master/adapt/base.py | """
Base for adapt
"""
import warnings
import inspect
from copy import deepcopy
import numpy as np
import tensorflow as tf
from sklearn.base import BaseEstimator
from sklearn.utils import check_array
from sklearn.metrics.pairwise import KERNEL_PARAMS
from sklearn.exceptions import NotFittedError
from tensorflow.keras... | 60,667 | 35.111905 | 106 | py |
adapt | adapt-master/adapt/_tree_utils.py | import copy
import numpy as np
def _bootstrap_(size,class_wise=False,y=None):
if class_wise:
if y is None:
print("Error : need labels to apply class wise bootstrap.")
else:
inds = []
oob_inds = []
classes_ = set(y)
ind_classes_ = np.ze... | 17,013 | 29.327986 | 236 | py |
adapt | adapt-master/adapt/utils.py | """
Utility functions for adapt package.
"""
import warnings
import inspect
from copy import deepcopy
import numpy as np
from sklearn.datasets import make_classification
from sklearn.utils import check_array
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.base import BaseEstimator, ... | 20,777 | 30.52959 | 95 | py |
adapt | adapt-master/adapt/metrics.py | import inspect
import copy
import numpy as np
import tensorflow as tf
from tensorflow.keras.optimizers import Adam
from scipy import linalg
from sklearn.metrics import pairwise
from sklearn.base import clone
from sklearn.model_selection import train_test_split
from sklearn.utils import check_array
from adapt.utils imp... | 17,730 | 28.01964 | 95 | py |
adapt | adapt-master/adapt/__init__.py | """
ADAPT: Awesome Domain Adaptation Package Toolbox
"""
from adapt import feature_based
from adapt import instance_based
from adapt import parameter_based
from adapt import utils
from adapt import metrics
from adapt import base
__all__ = ["feature_based",
"instance_based",
"parameter_based",
... | 379 | 20.111111 | 48 | py |
adapt | adapt-master/adapt/instance_based/_ldm.py | import numpy as np
from sklearn.base import check_array
from cvxopt import solvers, matrix
from adapt.base import BaseAdaptEstimator, make_insert_doc
from adapt.metrics import linear_discrepancy
from adapt.utils import set_random_seed
@make_insert_doc()
class LDM(BaseAdaptEstimator):
"""
LDM : Linear Discrep... | 4,735 | 25.757062 | 81 | py |
adapt | adapt-master/adapt/instance_based/_rulsif.py | """
Kullback-Leibler Importance Estimation Procedure
"""
import itertools
import warnings
import numpy as np
from sklearn.metrics import pairwise
from sklearn.exceptions import NotFittedError
from sklearn.utils import check_array
from sklearn.metrics.pairwise import KERNEL_PARAMS
from adapt.base import BaseAdaptEstim... | 14,091 | 34.94898 | 165 | py |
adapt | adapt-master/adapt/instance_based/_kmm.py | """
Kernel Mean Matching
"""
import numpy as np
from sklearn.metrics import pairwise
from sklearn.utils import check_array
from sklearn.exceptions import NotFittedError
from sklearn.metrics.pairwise import KERNEL_PARAMS
from cvxopt import matrix, solvers
from adapt.base import BaseAdaptEstimator, make_insert_doc
from... | 8,619 | 28.930556 | 96 | py |
adapt | adapt-master/adapt/instance_based/_balancedweighting.py | import numpy as np
from sklearn.base import check_array
from adapt.base import BaseAdaptEstimator, make_insert_doc
from adapt.utils import set_random_seed
@make_insert_doc(supervised=True)
class BalancedWeighting(BaseAdaptEstimator):
"""
BW : Balanced Weighting
Fit the estimator :math:`h` on source ... | 3,925 | 27.244604 | 89 | py |
adapt | adapt-master/adapt/instance_based/_nearestneighborsweighting.py | import numpy as np
from sklearn.neighbors import NearestNeighbors
from sklearn.base import check_array
from adapt.base import BaseAdaptEstimator, make_insert_doc
from adapt.utils import set_random_seed
@make_insert_doc()
class NearestNeighborsWeighting(BaseAdaptEstimator):
"""
NNW : Nearest Neighbors Weighti... | 5,922 | 32.653409 | 98 | py |
adapt | adapt-master/adapt/instance_based/_wann.py | """
Weighting Adversarial Neural Network (WANN)
"""
import numpy as np
import tensorflow as tf
from adapt.base import BaseAdaptDeep, make_insert_doc
from adapt.utils import check_network, get_default_task
EPS = np.finfo(np.float32).eps
@make_insert_doc(["task", "weighter"], supervised=True)
class WANN(BaseAdaptDeep... | 9,168 | 33.996183 | 114 | py |
adapt | adapt-master/adapt/instance_based/_iwc.py | """
IWC
"""
import inspect
import numpy as np
from sklearn.utils import check_array
from sklearn.linear_model import LogisticRegression
from sklearn.base import BaseEstimator
from sklearn.exceptions import NotFittedError
from adapt.base import BaseAdaptEstimator, make_insert_doc
from adapt.utils import check_arrays,... | 6,855 | 31.187793 | 91 | py |
adapt | adapt-master/adapt/instance_based/__init__.py | """
Instance-Based Methods Module
"""
from ._kliep import KLIEP
from ._kmm import KMM
from ._tradaboost import TrAdaBoost, TrAdaBoostR2, TwoStageTrAdaBoostR2
from ._wann import WANN
from ._ldm import LDM
from ._nearestneighborsweighting import NearestNeighborsWeighting
from ._balancedweighting import BalancedWeighting... | 620 | 31.684211 | 71 | py |
adapt | adapt-master/adapt/instance_based/_kliep.py | """
Kullback-Leibler Importance Estimation Procedure
"""
import itertools
import warnings
import numpy as np
from sklearn.metrics import pairwise
from sklearn.exceptions import NotFittedError
from sklearn.utils import check_array
from sklearn.metrics.pairwise import KERNEL_PARAMS
from adapt.base import BaseAdaptEstim... | 19,504 | 34.528233 | 138 | py |
adapt | adapt-master/adapt/instance_based/_iwn.py | """
Importance Weighting Network (IWN)
"""
import warnings
import inspect
from copy import deepcopy
import numpy as np
from sklearn.utils import check_array
import tensorflow as tf
from tensorflow.keras import Model
from adapt.base import BaseAdaptDeep, make_insert_doc
from adapt.utils import (check_arrays, check_net... | 13,414 | 31.639903 | 89 | py |
adapt | adapt-master/adapt/instance_based/_ulsif.py | import itertools
import warnings
import numpy as np
from sklearn.metrics import pairwise
from sklearn.exceptions import NotFittedError
from sklearn.utils import check_array
from sklearn.metrics.pairwise import KERNEL_PARAMS
from adapt.base import BaseAdaptEstimator, make_insert_doc
from adapt.utils import set_random_... | 13,517 | 34.203125 | 114 | py |
adapt | adapt-master/adapt/instance_based/_tradaboost.py | """
Transfer Adaboost
"""
import numpy as np
import tensorflow as tf
from sklearn.base import BaseEstimator
from sklearn.exceptions import NotFittedError
from sklearn.utils import check_array
from sklearn.metrics import r2_score, accuracy_score
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from scipy.s... | 36,756 | 34.207854 | 106 | py |
adapt | adapt-master/adapt/feature_based/_fa.py | """
Frustratingly Easy Domain Adaptation module.
"""
import warnings
import numpy as np
from sklearn.utils import check_array
from sklearn.exceptions import NotFittedError
from adapt.base import BaseAdaptEstimator, make_insert_doc
from adapt.utils import check_arrays
@make_insert_doc(supervised=True)
class FA(Base... | 8,172 | 31.43254 | 88 | py |
adapt | adapt-master/adapt/feature_based/_cdan.py | """
CDAN
"""
import numpy as np
import tensorflow as tf
from adapt.base import BaseAdaptDeep, make_insert_doc
from tensorflow.keras.initializers import GlorotUniform
from adapt.utils import (check_network,
get_default_encoder,
get_default_discriminator)
EPS = np.finf... | 15,371 | 40.433962 | 118 | py |
adapt | adapt-master/adapt/feature_based/_mcd.py | """
DANN
"""
import numpy as np
import tensorflow as tf
from adapt.base import BaseAdaptDeep, make_insert_doc
from adapt.utils import check_network, get_default_encoder, get_default_task
EPS = np.finfo(np.float32).eps
@make_insert_doc(["encoder", "task"])
class MCD(BaseAdaptDeep):
"""
MCD: Maximum Classifi... | 10,716 | 37.412186 | 123 | py |
adapt | adapt-master/adapt/feature_based/_pred.py | """
Frustratingly Easy Domain Adaptation module.
"""
import warnings
import numpy as np
from sklearn.utils import check_array
from sklearn.exceptions import NotFittedError
from adapt.base import BaseAdaptEstimator, make_insert_doc
from adapt.utils import check_arrays, check_estimator
@make_insert_doc(supervised=Tr... | 8,284 | 29.237226 | 82 | py |
adapt | adapt-master/adapt/feature_based/_tca.py | """
TCA
"""
import numpy as np
from sklearn.utils import check_array
from sklearn.metrics import pairwise
from sklearn.metrics.pairwise import KERNEL_PARAMS
from scipy import linalg
from adapt.base import BaseAdaptEstimator, make_insert_doc
from adapt.utils import set_random_seed
@make_insert_doc()
class TCA(BaseAd... | 4,802 | 27.420118 | 89 | py |
adapt | adapt-master/adapt/feature_based/_deepcoral.py | """
DANN
"""
import numpy as np
import tensorflow as tf
from adapt.base import BaseAdaptDeep, make_insert_doc
from adapt.utils import check_network, get_default_encoder, get_default_task
EPS = np.finfo(np.float32).eps
@make_insert_doc(["encoder", "task"])
class DeepCORAL(BaseAdaptDeep):
"""
DeepCORAL: Deep... | 7,369 | 33.600939 | 88 | py |
adapt | adapt-master/adapt/feature_based/_coral.py | """
Correlation Alignement Module.
"""
import numpy as np
from scipy import linalg
from sklearn.utils import check_array
from adapt.base import BaseAdaptEstimator, make_insert_doc
from adapt.utils import set_random_seed
@make_insert_doc()
class CORAL(BaseAdaptEstimator):
"""
CORAL: CORrelation ALignment
... | 5,861 | 28.457286 | 85 | py |
adapt | adapt-master/adapt/feature_based/_adda.py | """
DANN
"""
import numpy as np
import tensorflow as tf
from adapt.base import BaseAdaptDeep, make_insert_doc
from adapt.utils import check_network
EPS = np.finfo(np.float32).eps
# class SetEncoder(tf.keras.callbacks.Callback):
# def __init__(self):
# self.pretrain = True
# def on_epoch_e... | 11,893 | 32.694051 | 102 | py |
adapt | adapt-master/adapt/feature_based/_dann.py | """
DANN
"""
import warnings
import numpy as np
import tensorflow as tf
from adapt.base import BaseAdaptDeep, make_insert_doc
EPS = np.finfo(np.float32).eps
@make_insert_doc(["encoder", "task", "discriminator"])
class DANN(BaseAdaptDeep):
"""
DANN: Discriminative Adversarial Neural Network
DANN is... | 6,809 | 34.842105 | 107 | py |
adapt | adapt-master/adapt/feature_based/_fmmd.py | import numpy as np
import tensorflow as tf
from sklearn.base import check_array
from cvxopt import solvers, matrix
from adapt.base import BaseAdaptEstimator, make_insert_doc
from adapt.utils import set_random_seed
def pairwise_X(X, Y):
X2 = tf.tile(tf.reduce_sum(tf.square(X), axis=1, keepdims=True), [1, tf.shape... | 8,657 | 30.143885 | 90 | py |
adapt | adapt-master/adapt/feature_based/_sa.py | from sklearn.decomposition import PCA
from sklearn.base import check_array
from adapt.base import BaseAdaptEstimator, make_insert_doc
from adapt.utils import set_random_seed
@make_insert_doc()
class SA(BaseAdaptEstimator):
"""
SA : Subspace Alignment
Linearly align the source domain to the target do... | 3,902 | 27.079137 | 78 | py |
adapt | adapt-master/adapt/feature_based/__init__.py | """
Feature-Based Methods Module
"""
from ._fa import FA
from ._coral import CORAL
from ._dann import DANN
from ._adda import ADDA
from ._deepcoral import DeepCORAL
from ._mcd import MCD
from ._mdd import MDD
from ._wdgrl import WDGRL
from ._cdan import CDAN
from ._sa import SA
from ._fmmd import fMMD
from ._ccsa impo... | 509 | 22.181818 | 78 | py |
adapt | adapt-master/adapt/feature_based/_mdd.py | """
DANN
"""
import numpy as np
import tensorflow as tf
from adapt.base import BaseAdaptDeep, make_insert_doc
from adapt.utils import check_network, get_default_encoder, get_default_task
EPS = np.finfo(np.float32).eps
@make_insert_doc(["encoder", "task"])
class MDD(BaseAdaptDeep):
"""
MDD: Margin Disparity... | 7,767 | 37.078431 | 107 | py |
adapt | adapt-master/adapt/feature_based/_wdgrl.py | """
WDGRL
"""
import numpy as np
import tensorflow as tf
from adapt.base import BaseAdaptDeep, make_insert_doc
EPS = np.finfo(np.float32).eps
@make_insert_doc(["encoder", "task", "discriminator"])
class WDGRL(BaseAdaptDeep):
"""
WDGRL: Wasserstein Distance Guided Representation Learning
WDGRL is a... | 7,667 | 36.960396 | 118 | py |
adapt | adapt-master/adapt/feature_based/_ccsa.py | import numpy as np
import tensorflow as tf
from adapt.base import BaseAdaptDeep, make_insert_doc
from adapt.utils import set_random_seed
EPS = np.finfo(np.float32).eps
def pairwise_y(X, Y):
batch_size_x = tf.shape(X)[0]
batch_size_y = tf.shape(Y)[0]
dim = tf.reduce_prod(tf.shape(X)[1:])
X = tf.resha... | 6,943 | 34.070707 | 129 | py |
adapt | adapt-master/adapt/parameter_based/_linint.py | """
Frustratingly Easy Domain Adaptation module.
"""
import warnings
import numpy as np
from sklearn.utils import check_array
from sklearn.exceptions import NotFittedError
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from adapt.base import BaseAdaptEstimator, make_insert_doc... | 5,268 | 26.586387 | 75 | py |
adapt | adapt-master/adapt/parameter_based/_transfer_tree.py | #from adapt.utils import (check_arrays,set_random_seed,check_estimator)
import copy
import numpy as np
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
from sklearn.metrics import roc_auc_score as _auc_
from adapt.base imp... | 68,889 | 40.177525 | 179 | py |
adapt | adapt-master/adapt/parameter_based/_regular.py | """
Regular Transfer
"""
import numpy as np
from sklearn.preprocessing import LabelBinarizer
from scipy.sparse.linalg import lsqr
from sklearn.gaussian_process import GaussianProcessRegressor, GaussianProcessClassifier
from sklearn.linear_model import LinearRegression
import tensorflow as tf
from tensorflow.keras impo... | 22,633 | 33.821538 | 105 | py |
adapt | adapt-master/adapt/parameter_based/__init__.py | """
Parameter-Based Methods Module
"""
from ._regular import RegularTransferLR, RegularTransferLC, RegularTransferNN, RegularTransferGP
from ._finetuning import FineTuning
from ._transfer_tree import TransferTreeClassifier
from ._transfer_tree import TransferForestClassifier
from ._transfer_tree import TransferTreeSel... | 728 | 30.695652 | 96 | py |
adapt | adapt-master/adapt/parameter_based/_finetuning.py | import tensorflow as tf
from adapt.base import BaseAdaptDeep, make_insert_doc
from adapt.utils import check_fitted_network
@make_insert_doc(["encoder", "task"], supervised=True)
class FineTuning(BaseAdaptDeep):
"""
FineTuning : finetunes pretrained networks on target data.
A pretrained source encoder... | 7,669 | 34.022831 | 102 | py |
pulearn | pulearn-master/setup.py | """Setup for the pulearn package."""
# !/usr/bin/env python
# -*- coding: utf-8 -*-
try:
from setuptools import setup
except ImportError:
from distutils.core import setup
import versioneer
README_RST = ''
with open('README.rst', encoding="utf-8") as f:
README_RST = f.read()
INSTALL_REQUIRES = [
's... | 1,821 | 27.46875 | 71 | py |
pulearn | pulearn-master/versioneer.py |
# Version: 0.18
"""The Versioneer - like a rocketeer, but for versions.
The Versioneer
==============
* like a rocketeer, but for versions!
* https://github.com/warner/python-versioneer
* Brian Warner
* License: Public Domain
* Compatible With: python2.6, 2.7, 3.2, 3.3, 3.4, 3.5, 3.6, and pypy
* [![Latest Version]
... | 68,611 | 36.636862 | 79 | py |
pulearn | pulearn-master/examples/BreastCancerElkanotoExample.py | """A usage example for both Elkan & Noto PU learning methods on breast cancer
data."""
import os
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import precision_recall_fscore_support
from pulearn import (
ElkanotoPuClassifier,
# Wei... | 4,971 | 37.542636 | 78 | py |
pulearn | pulearn-master/examples/ElkanotoPuClassifierExample.py | """A simple usage example for the ElkanotoPuClassifier."""
import numpy as np
from sklearn.svm import SVC
from sklearn.datasets import make_classification
from pulearn import ElkanotoPuClassifier
if __name__ == '__main__':
X, y = make_classification(
n_samples=3000,
n_features=20,
n_info... | 1,328 | 25.58 | 87 | py |
pulearn | pulearn-master/tests/test_bagging.py | """Testing the elkanoto classifiers."""
import numpy as np
import pytest
from sklearn.datasets import make_classification
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linea... | 5,533 | 28.280423 | 74 | py |
pulearn | pulearn-master/tests/test_elkanoto.py | """Testing the elkanoto classifiers."""
import numpy as np
import pytest
from sklearn.datasets import make_classification
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.exceptions import NotFittedError
from pulearn import (
ElkanotoPuClassifier,
WeightedElkanotoPu... | 3,155 | 25.3 | 65 | py |
pulearn | pulearn-master/tests/__init__.py | 0 | 0 | 0 | py | |
pulearn | pulearn-master/tests/test_metrics.py | import numpy as np
from pulearn.metrics import lee_liu_score, recall
def test_recall():
# all correct
y_true = np.array([1, 1, 1, 1, 1])
y_pred = np.array([1, 1, 1, 1, 1])
assert recall(y_true, y_pred) == 1.0
# all wrong
y_true = np.array([1, 1, 1, 1, 1])
y_pred = np.array([-1, -1, -1, -... | 1,289 | 28.318182 | 59 | py |
pulearn | pulearn-master/pulearn/_version.py |
# This file helps to compute a version number in source trees obtained from
# git-archive tarball (such as those provided by githubs download-from-tag
# feature). Distribution tarballs (built by setup.py sdist) and build
# directories (produced by setup.py build) will contain a much shorter file
# that just contains t... | 18,501 | 34.512476 | 79 | py |
pulearn | pulearn-master/pulearn/elkanoto.py | """Both PU classification methods from the Elkan & Noto paper."""
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.exceptions import NotFittedError
from sklearn.utils import check_random_state
class ElkanotoPuClassifier(BaseEstimator, ClassifierMixin):
"""Positive-unlabeled... | 9,371 | 33.32967 | 93 | py |
pulearn | pulearn-master/pulearn/metrics.py | """Implement metrics that are useful for PU learning.
For more background, consult
- Bekker, J.; Davis, J. Learning from Positive and Unlabeled Data: A Survey.
Mach Learn 2020, 109 (4), 719–760.
https://doi.org/10.1007/s10994-020-05877-5.
- Claesen, M.; Davis, J.; De Smet, F.; De Moor, B.
Assessing Binary... | 3,279 | 31.156863 | 76 | py |
pulearn | pulearn-master/pulearn/__init__.py | """
The `pulearn` Python package provide a collection of scikit-learn wrappers to
several positive-unlabled learning (PU-learning) methods.
.. include:: ./documentation.md
"""
from .elkanoto import ( # noqa: F401
ElkanotoPuClassifier,
WeightedElkanotoPuClassifier,
)
from .bagging import ( # noqa: F401
B... | 435 | 21.947368 | 77 | py |
pulearn | pulearn-master/pulearn/bagging.py | """Bagging meta-estimator for PU learning.
Any scikit-learn estimator should work as the base estimator.
This implementation is fully compatible with scikit-learn, and is in fact based
on the code of the sklearn.ensemble.BaggingClassifier class with very minor
changes.
"""
# Author: Gilles Louppe <g.louppe@gmail.com... | 30,122 | 37.178707 | 89 | py |
internalblue | internalblue-master/setup.py | #!/usr/bin/env python2
from setuptools import setup
setup(
name="internalblue",
version="0.4",
description="A Bluetooth Experimentation Framework based on the Broadcom Bluetooth Controller Family.",
url="http://github.com/seemoo-lab/internalblue",
author="The InternalBlue Team",
author_email="... | 874 | 30.25 | 107 | py |
internalblue | internalblue-master/examples/nexus6p/KNOB_PoC.py | #!/usr/bin/python3
# Jiska Classen, Secure Mobile Networking Lab
import sys
import argparse
from argparse import Namespace
import cmd2
from cmd2 import CommandSet
from internalblue.adbcore import ADBCore
import internalblue.hci as hci
from internalblue.utils.packing import p16, u16
from internalblue.cli import auto_... | 3,507 | 34.795918 | 141 | py |
internalblue | internalblue-master/examples/nexus6p/randp.py | #!/usr/bin/python2
# Jiska Classen, Secure Mobile Networking Lab
import sys
from argparse import Namespace
from pwnlib import adb
from pwnlib.asm import asm
from internalblue.adbcore import ADBCore
import internalblue.hci as hci
import numpy as np
from internalblue.cli import InternalBlueCLI
"""
Measure the RNG of ... | 5,961 | 26.859813 | 107 | py |
internalblue | internalblue-master/examples/nexus6p/rand.py | #!/usr/bin/python2
# Jiska Classen, Secure Mobile Networking Lab
import sys
from argparse import Namespace
from pwnlib import adb
from pwnlib.asm import asm
from internalblue.adbcore import ADBCore
import internalblue.hci as hci
import numpy as np
from datetime import datetime
from internalblue.cli import Internal... | 6,707 | 27.666667 | 109 | py |
internalblue | internalblue-master/examples/iphone6/randp.py | #!/usr/bin/python2
# Jiska Classen, Secure Mobile Networking Lab
import sys
from argparse import Namespace
import numpy as np
from pwnlib.asm import asm
import internalblue.hci as hci
from internalblue.cli import InternalBlueCLI
from internalblue.ioscore import iOSCore
"""
Measure the RNG of the iPhone 6.
Similar ... | 5,971 | 27.169811 | 107 | py |
internalblue | internalblue-master/examples/iphone6/rand.py | #!/usr/bin/python2
# Jiska Classen, Secure Mobile Networking Lab
import sys
from argparse import Namespace
from datetime import datetime
import numpy as np
from pwnlib.asm import asm
import internalblue.hci as hci
from internalblue.cli import InternalBlueCLI
from internalblue.ioscore import iOSCore
"""
Measure the... | 6,313 | 26.938053 | 115 | py |
internalblue | internalblue-master/examples/magicpairing/mp_pocs.py | import binascii
import sys
import time
import InternalBlueL2CAP
from BTConnection import BluetoothConnection
from pwnlib import log
from pwnlib.ui import options
from internalblue.ioscore import iOSCore
VULNS = [{
"description": "[MP1]: iOS RatchetAESSIV Crash (0xa8)",
"tech": 0,
"payload": "020102800036... | 5,984 | 34 | 91 | py |
internalblue | internalblue-master/examples/magicpairing/InternalBlueL2CAP.py | #!/usr/bin/python2
# Dennis Heinze
import binascii
import struct
from pwnlib import log
from internalblue.utils.packing import p16
class L2CAPManager:
def __init__(self, btconn, mtu=0x30):
self.connection = btconn
self.connection.registerACLHandler(self._receptionHandler)
# cidHandle... | 2,147 | 28.424658 | 91 | py |
internalblue | internalblue-master/examples/magicpairing/BTConnection.py | # This class can be used to create a bluetooth connection
# to a remote device. currently it only supports unauthenticated
# connections. in general, it is very basic and offers the bare minimum
# to semi-reliably hold an active l2cap channel.
import binascii
import struct
import threading
import time
from pwnlib imp... | 10,091 | 40.191837 | 119 | py |
internalblue | internalblue-master/examples/nexus5/CVE_2018_19860_Crash_on_Connect.py | #!/usr/bin/python3
# Jiska Classen, Secure Mobile Networking Lab
from pwnlib.asm import asm
from internalblue.adbcore import ADBCore
from internalblue.utils.packing import p32
"""
This is a crash only test for CVE-2018-19860. Install this patch and connect
to any device. If the target device Bluetooth chip crashes u... | 5,691 | 34.798742 | 168 | py |
internalblue | internalblue-master/examples/nexus5/BLE_Reception_PoC.py | #!/usr/bin/env python3
# Jiska Classen
# Get receive statistics on a Nexus 5 for BLE connection events
import sys
from argparse import Namespace
from builtins import range
import internalblue.hci as hci
from internalblue.adbcore import ADBCore
from internalblue.cli import InternalBlueCLI
from internalblue.utils.packi... | 6,066 | 33.668571 | 135 | py |
internalblue | internalblue-master/examples/nexus5/KNOB_PoC.py | #!/usr/bin/python3
# Jiska Classen, Secure Mobile Networking Lab
import sys
import argparse
from argparse import Namespace
import cmd2
from cmd2 import CommandSet
from internalblue import Address
from internalblue.adbcore import ADBCore
import internalblue.hci as hci
from internalblue.utils.packing import p16, u16
f... | 3,483 | 34.55102 | 141 | py |
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