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 |
|---|---|---|---|---|---|---|
fl-analysis | fl-analysis-master/src/util.py | import collections
from copy import deepcopy
import numpy as np
import pandas as pd
from os.path import join
from tensorflow.python.keras.layers.convolutional import Conv2D
from tensorflow.python.keras.layers.core import Dense
def log_data(experiment_dir, rounds, accuracy, adv_success):
"""Logs data."""
df ... | 6,070 | 38.679739 | 136 | py |
fl-analysis | fl-analysis-master/src/test_tf_model.py | from unittest import TestCase
from src.tf_model import Model
from src.tf_data import Dataset
from matplotlib import pyplot
import tensorflow as tf
import numpy as np
class TestModel(TestCase):
def test_create_model_weight(self):
model = Model.create_model("dev")
(x_train, y_train), (x_test, y_te... | 3,013 | 36.209877 | 137 | py |
fl-analysis | fl-analysis-master/src/__init__.py | 0 | 0 | 0 | py | |
fl-analysis | fl-analysis-master/src/config_old.py | import sys
import configargparse
import logging
from src.client_attacks import Attack
parser = configargparse.ArgumentParser()
parser.add('-c', '--config_filepath', required=False, is_config_file=True, help='Path to config file.')
# logging configuration
parser.add_argument(
'-d', '--debug',
help="Print deb... | 24,879 | 63.455959 | 313 | py |
fl-analysis | fl-analysis-master/src/hyperparameter_tuning.py | import os
from tensorboard.plugins.hparams import api as hp
import tensorflow as tf
import numpy as np
from src.federated_averaging import FederatedAveraging
from src.tf_model import Model
def load_model(args, config):
if args.load_model is not None:
model = tf.keras.models.load_model(args.load_model) # ... | 6,049 | 52.539823 | 159 | py |
fl-analysis | fl-analysis-master/src/config/definitions.py |
from dataclasses import dataclass, MISSING, field
from typing import Optional, Dict, Any, List
from mashumaro.mixins.yaml import DataClassYAMLMixin
"""
This class defines the configuration schema of the framework.
"""
@dataclass
class Quantization(DataClassYAMLMixin):
"""
Apply quantization to the client up... | 12,695 | 44.342857 | 121 | py |
fl-analysis | fl-analysis-master/src/config/test_config.py | import unittest
from config import load_config
class ConfigTest(unittest.TestCase):
def test_load_config(self):
load_config("example_config.yaml")
if __name__ == '__main__':
unittest.main()
| 212 | 14.214286 | 42 | py |
fl-analysis | fl-analysis-master/src/config/config.py |
from .definitions import Config
def load_config(config_name):
with open(config_name, "rb") as f:
config = Config.from_yaml(f.read())
return config
| 167 | 15.8 | 43 | py |
fl-analysis | fl-analysis-master/src/config/__init__.py |
from .config import load_config
from .definitions import Environment | 69 | 22.333333 | 36 | py |
fl-analysis | fl-analysis-master/src/aggregation/aggregators.py | from copy import deepcopy
import numpy as np
import logging
class Aggregator:
""" Aggregation behavior """
def aggregate(self, global_weights, client_weight_list):
"""
:type client_weight_list: list[np.ndarray]
"""
raise NotImplementedError("Subclass")
class FedAvg(Aggregato... | 4,099 | 35.607143 | 127 | py |
fl-analysis | fl-analysis-master/src/aggregation/trimmed_mean_test.py | import unittest
import numpy as np
from src.aggregation.aggregators import TrimmedMean
class TrimmedMeanTest(unittest.TestCase):
def test_aggregates_properly(self):
w1 = np.array(((1, 5), (1, 5)))
w2 = np.array(((2, 3), (2, 3)))
w3 = np.array(((10, 11), (10, 11)))
average = Trim... | 455 | 21.8 | 84 | py |
fl-analysis | fl-analysis-master/src/aggregation/__init__.py |
from .aggregators import FedAvg, TrimmedMean, Aggregator | 57 | 28 | 56 | py |
fl-analysis | fl-analysis-master/src/attack/targeted_attack.py | from src.attack.attack import LossBasedAttack
import tensorflow as tf
import logging
import numpy as np
from src.data import image_augmentation
logger = logging.getLogger(__name__)
class TargetedAttack(LossBasedAttack):
def generate(self, dataset, model, **kwargs):
self.parse_params(**kwargs)
... | 3,152 | 35.241379 | 135 | py |
fl-analysis | fl-analysis-master/src/attack/attack.py |
from src.data.tf_data import Dataset
from src.attack.evasion.evasion_method import EvasionMethod
class Attack(object):
def __init__(self):
pass
def generate(self, dataset, model, **kwargs):
raise NotImplementedError("Sub-classes must implement generate.")
return x
def _compute_g... | 1,884 | 26.318841 | 92 | py |
fl-analysis | fl-analysis-master/src/attack/framework_attack_wrapper.py |
class FrameworkAttackWrapper(object):
"""Wraps an attack with dict params to invocate later."""
def __init__(self, attack, kwargs):
self.attack = attack
self.kwargs = kwargs | 200 | 24.125 | 61 | py |
fl-analysis | fl-analysis-master/src/attack/anticipate_tf_attack.py | from src.attack.attack import LossBasedAttack
import logging
import numpy as np
import tensorflow as tf
from copy import copy
logger = logging.getLogger(__name__)
# Move this into generate later
# from src.torch_compat.anticipate import train_anticipate
class AnticipateTfAttack(LossBasedAttack):
def generate(... | 7,110 | 40.104046 | 150 | py |
fl-analysis | fl-analysis-master/src/attack/parse_config.py |
def map_objective(name):
"""
:param name: str
:param evasion: EvasionMethod to be added
:return:
"""
from src import attack
cls = getattr(attack, name)
return cls()
# def load_attacks(attack_file_name):
# with open(attack_file_name) as stream:
# yaml = YAML(typ='safe')
# ... | 562 | 19.107143 | 45 | py |
fl-analysis | fl-analysis-master/src/attack/__init__.py |
from .targeted_attack import TargetedAttack
from .untargeted_attack import UntargetedAttack
from .anticipate_tf_attack import AnticipateTfAttack | 145 | 35.5 | 52 | py |
fl-analysis | fl-analysis-master/src/attack/untargeted_attack.py | from src.attack.attack import LossBasedAttack
import tensorflow as tf
import logging
logger = logging.getLogger(__name__)
class UntargetedAttack(LossBasedAttack):
def generate(self, dataset, model, **kwargs):
self.parse_params(**kwargs)
self.weights = model.get_weights()
loss_object_w... | 1,636 | 33.829787 | 107 | py |
fl-analysis | fl-analysis-master/src/attack/test/AttackTest.py | import tensorflow as tf
import numpy as np
from src.data.tf_data_global import IIDGlobalDataset
from src.attack.evasion.norm import NormBoundPGDEvasion
from src.attack.evasion.trimmed_mean import TrimmedMeanEvasion
from src.attack.attack import AttackDatasetBridge
from src.attack.untargeted_attack import UntargetedAtt... | 6,328 | 45.19708 | 138 | py |
fl-analysis | fl-analysis-master/src/attack/test/__init__.py | 0 | 0 | 0 | py | |
fl-analysis | fl-analysis-master/src/attack/evasion/evasion_method.py | import tensorflow as tf
class EvasionMethod(object):
def __init__(self, alpha):
"""
:type alpha: float|None alpha weight of evasion method. The closer to 1 the more we want to evade.
"""
self.alpha = alpha
def loss_term(self, model):
return None
def update_after... | 418 | 17.217391 | 106 | py |
fl-analysis | fl-analysis-master/src/attack/evasion/trimmed_mean.py |
from .evasion_method import EvasionMethod
import numpy as np
import tensorflow as tf
class TrimmedMeanEvasion(EvasionMethod):
def __init__(self, benign_updates_this_round, alpha, n_remove_malicious):
"""
:type benign_updates_this_round: [[np.ndarray]] list of client updates,
:type alpha... | 2,652 | 43.966102 | 132 | py |
fl-analysis | fl-analysis-master/src/attack/evasion/norm.py |
from .evasion_method import EvasionMethod
import logging
import numpy as np
import tensorflow as tf
class NormBoundPGDEvasion(EvasionMethod):
"""
Evades norm bound using PGD.
"""
def __init__(self, old_weights, norm_type, scale_factor, clipping_bound=None, pgd_factor=None,
benign_up... | 5,826 | 42.162963 | 147 | py |
fl-analysis | fl-analysis-master/src/attack/evasion/norm_prob_check.py | from . import NormBoundPGDEvasion
from .evasion_method import EvasionMethod
import logging
import numpy as np
import tensorflow as tf
class NormBoundProbabilisticCheckingEvasion(NormBoundPGDEvasion):
"""
Adaptive attack for probabilistic checking
"""
def __init__(self, old_weights, norm_type, scale_fa... | 6,332 | 43.598592 | 144 | py |
fl-analysis | fl-analysis-master/src/attack/evasion/__init__.py |
from .norm import NormBoundPGDEvasion
from .trimmed_mean import TrimmedMeanEvasion
from .norm_prob_check import NormBoundProbabilisticCheckingEvasion
from .neurotoxin import NeurotoxinEvasion
def construct_evasion(classname, **kwargs):
"""Constructs evasion method"""
import src.attack.evasion as ev
cls = ... | 368 | 29.75 | 66 | py |
fl-analysis | fl-analysis-master/src/attack/evasion/neurotoxin.py | from . import NormBoundPGDEvasion
from .evasion_method import EvasionMethod
import logging
import numpy as np
import tensorflow as tf
class NeurotoxinEvasion(NormBoundPGDEvasion):
"""
Adaptive attack for probabilistic checking
"""
def __init__(self, old_weights, norm_type, scale_factor, topk, last_rou... | 2,523 | 39.063492 | 129 | py |
fl-analysis | fl-analysis-master/src/test/DataLoaderTest.py | import tensorflow as tf
import numpy as np
from src.client_attacks import Attack
from src.data import data_loader
from src.data.tf_data_global import NonIIDGlobalDataset
class DataLoaderTest(tf.test.TestCase):
def setUp(self):
super(DataLoaderTest, self).setUp()
def tearDown(self):
pass
... | 3,093 | 37.197531 | 132 | py |
fl-analysis | fl-analysis-master/src/test/TfDataTest.py | import tensorflow as tf
import numpy as np
from src.data.tf_data import ImageGeneratorDataset, Dataset
class TfDataTest(tf.test.TestCase):
def setUp(self):
super(TfDataTest, self).setUp()
def tearDown(self):
pass
def get_dataset(self, aux_size):
(x_train, y_train), (x_test, y_te... | 1,358 | 32.146341 | 79 | py |
fl-analysis | fl-analysis-master/src/test/__init__.py | 0 | 0 | 0 | py | |
fl-analysis | fl-analysis-master/src/subspace/__init__.py | 0 | 0 | 0 | py | |
fl-analysis | fl-analysis-master/src/subspace/general/tfutil.py | # Copyright (c) 2018 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, pub... | 15,340 | 39.265092 | 157 | py |
fl-analysis | fl-analysis-master/src/subspace/general/image_preproc.py | # Copyright (c) 2018 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, pub... | 3,582 | 37.945652 | 122 | py |
fl-analysis | fl-analysis-master/src/subspace/general/util.py | # Copyright (c) 2018 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, pub... | 4,248 | 27.709459 | 88 | py |
fl-analysis | fl-analysis-master/src/subspace/general/__init__.py | # Copyright (c) 2018 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, pub... | 1,104 | 51.619048 | 80 | py |
fl-analysis | fl-analysis-master/src/subspace/general/stats_buddy.py | # Copyright (c) 2018 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, pub... | 10,845 | 41.03876 | 126 | py |
fl-analysis | fl-analysis-master/src/subspace/builder/resnet.py |
import numpy as np
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.layers import (Flatten, Input, Activation,
Reshape, Dropout, Convolution2D,
MaxPooling2D, BatchNormalization,
Conv2D, GlobalAveragePooling2D... | 27,766 | 57.21174 | 116 | py |
fl-analysis | fl-analysis-master/src/subspace/builder/model_builders.py |
import numpy as np
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.layers import (Dense, Flatten, Input, Activation,
Reshape, Dropout, Convolution2D,
MaxPooling2D, BatchNormalization,
Conv2D, GlobalAveragePo... | 18,121 | 44.762626 | 202 | py |
fl-analysis | fl-analysis-master/src/subspace/builder/test_model_builders.py | from unittest import TestCase
import tensorflow as tf
import numpy as np
from tf_data import Dataset
from tf_model import Model
from .model_builders import build_model_mnist_fc, build_cnn_model_mnist_bhagoji, build_test, build_cnn_model_mnist_dev_conv
from ..keras_ext.rproj_layers_util import ThetaPrime
import resour... | 7,121 | 33.572816 | 123 | py |
fl-analysis | fl-analysis-master/src/subspace/builder/__init__.py | 0 | 0 | 0 | py | |
fl-analysis | fl-analysis-master/src/subspace/keras_ext/test_layers.py | #! /usr/bin/env python
# Copyright (c) 2018 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, ... | 6,558 | 34.074866 | 89 | py |
fl-analysis | fl-analysis-master/src/subspace/keras_ext/engine.py | # Copyright (c) 2018 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, pub... | 1,201 | 51.26087 | 80 | py |
fl-analysis | fl-analysis-master/src/subspace/keras_ext/rproj_layers_util.py | #! /usr/bin/env python
# Copyright (c) 2018 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, ... | 28,439 | 36.970628 | 136 | py |
fl-analysis | fl-analysis-master/src/subspace/keras_ext/engine_training.py | # Copyright (c) 2018 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, pub... | 12,035 | 39.12 | 192 | py |
fl-analysis | fl-analysis-master/src/subspace/keras_ext/engine_topology.py | # Copyright (c) 2018 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, pub... | 3,056 | 45.318182 | 119 | py |
fl-analysis | fl-analysis-master/src/subspace/keras_ext/layers.py | # Copyright (c) 2018 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, pub... | 1,235 | 48.44 | 99 | py |
fl-analysis | fl-analysis-master/src/subspace/keras_ext/util.py | # Copyright (c) 2018 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, pub... | 6,451 | 38.10303 | 109 | py |
fl-analysis | fl-analysis-master/src/subspace/keras_ext/regularizers.py | # Copyright (c) 2018 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, pub... | 3,830 | 30.925 | 80 | py |
fl-analysis | fl-analysis-master/src/subspace/keras_ext/__init__.py | # Copyright (c) 2018 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, pub... | 1,104 | 51.619048 | 80 | py |
fl-analysis | fl-analysis-master/src/subspace/keras_ext/rproj_layers.py | # Copyright (c) 2018 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, pub... | 33,100 | 41.491656 | 116 | py |
fl-analysis | fl-analysis-master/src/data/emnist.py | import os
import h5py
import tensorflow as tf
import numpy as np
def load_data(only_digits=True, cache_dir=None):
"""Loads the Federated EMNIST dataset.
Downloads and caches the dataset locally. If previously downloaded, tries to
load the dataset from cache.
This dataset is derived from the Leaf repository
... | 3,842 | 43.172414 | 99 | py |
fl-analysis | fl-analysis-master/src/data/tf_data.py | import itertools
import math
import numpy as np
import tensorflow as tf
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from src.data import image_augmentation
from src.data import emnist
class Dataset:
def __init__(self, x_train, y_train, batch_size=50, x_test=None, y_test=None):
... | 18,878 | 42.802784 | 135 | py |
fl-analysis | fl-analysis-master/src/data/image_augmentation.py |
import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing.image import apply_affine_transform
def augment(image,label):
image = tf.image.random_flip_left_right(image)
image = tf.numpy_function(shift, [image], tf.float32)
image = normalize(image)
# debug(image, label)
return i... | 3,414 | 28.695652 | 88 | py |
fl-analysis | fl-analysis-master/src/data/data_loader.py | from src.attack_dataset_config import AttackDatasetConfig
from src.backdoor.edge_case_attack import EdgeCaseAttack
from src.client_attacks import Attack
from src.data.tf_data import Dataset
from src.data.tf_data_global import GlobalDataset, IIDGlobalDataset, NonIIDGlobalDataset, DirichletDistributionDivider
from src.co... | 17,418 | 48.768571 | 118 | py |
fl-analysis | fl-analysis-master/src/data/leaf_loader.py |
"""Loads leaf datasets"""
import os
import numpy as np
import pathlib
from src.data.leaf.model_utils import read_data
def load_leaf_dataset(dataset, use_val_set=False):
eval_set = 'test' if not use_val_set else 'val'
base_dir = pathlib.Path(__file__).parent.resolve()
train_data_dir = os.path.join(ba... | 1,726 | 22.657534 | 98 | py |
fl-analysis | fl-analysis-master/src/data/ardis.py | import os
import h5py
import tensorflow as tf
import numpy as np
def load_data():
path = f"{os.path.dirname(os.path.abspath(__file__))}/ARDIS_7.npy"
(x_train, y_train), (x_test, y_test) = np.load(path, allow_pickle=True)
# Normalize
x_train, x_test = x_train / 255.0, x_test / 255.0
x_train, x_test = np.m... | 490 | 27.882353 | 117 | py |
fl-analysis | fl-analysis-master/src/data/__init__.py | 0 | 0 | 0 | py | |
fl-analysis | fl-analysis-master/src/data/tf_data_global.py | from collections import defaultdict
import numpy as np
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
import tensorflow as tf
from src.data import image_augmentation
import logging
class GlobalDataset:
"""
A GlobalDataset represents a dataset as a whole. It has two purposes.
-... | 10,891 | 41.054054 | 146 | py |
fl-analysis | fl-analysis-master/src/data/southwest/__init__.py |
import pickle
import os
import numpy as np
def load_data():
cifar_mean = np.array([0.5125891, 0.5335556, 0.5198208, 0.51035565, 0.5311504, 0.51707786, 0.51392424, 0.5343016, 0.5199328, 0.51595825, 0.535995, 0.5210931, 0.51837546, 0.5381541, 0.5226226, 0.5209901, 0.5406102, 0.52463686, 0.52302873, 0.5422941, 0.52... | 36,589 | 1,260.724138 | 35,628 | py |
fl-analysis | fl-analysis-master/src/data/leaf/model_utils.py | import json
import numpy as np
import os
from collections import defaultdict
def batch_data(data, batch_size, seed):
'''
data is a dict := {'x': [numpy array], 'y': [numpy array]} (on one client)
returns x, y, which are both numpy array of length: batch_size
'''
data_x = data['x']
data_y = dat... | 2,067 | 28.542857 | 78 | py |
fl-analysis | fl-analysis-master/src/data/leaf/__init__.py | 0 | 0 | 0 | py | |
fl-analysis | fl-analysis-master/src/data/leaf/shakespeare/__init__.py | 0 | 0 | 0 | py | |
fl-analysis | fl-analysis-master/src/data/leaf/shakespeare/preprocess/shake_utils.py | '''
helper functions for preprocessing shakespeare data
'''
import json
import os
import re
def __txt_to_data(txt_dir, seq_length=80):
"""Parses text file in given directory into data for next-character model.
Args:
txt_dir: path to text file
seq_length: length of strings in X
"""
raw... | 2,004 | 28.925373 | 78 | py |
fl-analysis | fl-analysis-master/src/data/leaf/shakespeare/preprocess/preprocess_shakespeare.py | """Preprocesses the Shakespeare dataset for federated training.
Copyright 2017 Google Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless requi... | 8,701 | 42.079208 | 94 | py |
fl-analysis | fl-analysis-master/src/data/leaf/shakespeare/preprocess/gen_all_data.py |
import argparse
import json
import os
from shake_utils import parse_data_in
parser = argparse.ArgumentParser()
parser.add_argument('--raw',
help='include users\' raw .txt data in respective .json files',
action="store_true")
parser.set_defaults(raw=False)
args = parser.parse_args()... | 786 | 29.269231 | 92 | py |
fl-analysis | fl-analysis-master/src/data/leaf/shakespeare/preprocess/__init__.py | 0 | 0 | 0 | py | |
fl-analysis | fl-analysis-master/src/data/leaf/utils/sample.py | '''
samples from all raw data;
by default samples in a non-iid manner; namely, randomly selects users from
raw data until their cumulative amount of data exceeds the given number of
datapoints to sample (specified by --fraction argument);
ordering of original data points is not preserved in sampled data
'''
import a... | 6,761 | 32.475248 | 91 | py |
fl-analysis | fl-analysis-master/src/data/leaf/utils/split_data.py | '''
splits data into train and test sets
'''
import argparse
import json
import os
import random
import time
import sys
from collections import OrderedDict
from constants import DATASETS, SEED_FILES
def create_jsons_for(user_files, which_set, max_users, include_hierarchy):
"""used in split-by-user case"""
u... | 9,200 | 35.951807 | 99 | py |
fl-analysis | fl-analysis-master/src/data/leaf/utils/constants.py | DATASETS = ['sent140', 'femnist', 'shakespeare', 'celeba', 'synthetic']
SEED_FILES = { 'sampling': 'sampling_seed.txt', 'split': 'split_seed.txt' }
| 148 | 48.666667 | 75 | py |
fl-analysis | fl-analysis-master/src/data/leaf/utils/stats.py | '''
assumes that the user has already generated .json file(s) containing data
'''
import argparse
import json
import matplotlib.pyplot as plt
import math
import numpy as np
import os
from scipy import io
from scipy import stats
from constants import DATASETS
parser = argparse.ArgumentParser()
parser.add_argument('... | 2,786 | 28.967742 | 139 | py |
fl-analysis | fl-analysis-master/src/data/leaf/utils/remove_users.py |
'''
removes users with less than the given number of samples
'''
import argparse
import json
import os
from constants import DATASETS
parser = argparse.ArgumentParser()
parser.add_argument('--name',
help='name of dataset to parse; default: sent140;',
type=str,
choice... | 2,288 | 27.974684 | 82 | py |
fl-analysis | fl-analysis-master/src/data/leaf/utils/util.py | import pickle
def save_obj(obj, name):
with open(name + '.pkl', 'wb') as f:
pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
def load_obj(name):
with open(name + '.pkl', 'rb') as f:
return pickle.load(f)
def iid_divide(l, g):
'''
divide list l among g groups
each group has either i... | 839 | 25.25 | 70 | py |
fl-analysis | fl-analysis-master/src/data/leaf/utils/__init__.py | 0 | 0 | 0 | py | |
fl-analysis | fl-analysis-master/src/backdoor/edge_case_attack.py |
import numpy as np
import src.data.ardis as ardis
import src.data.southwest as southwest
class EdgeCaseAttack:
def load(self) -> ((np.ndarray, np.ndarray), (np.ndarray, np.ndarray), (np.ndarray, np.ndarray)):
"""Loads training and test set"""
raise NotImplementedError("Do not instantiate supercla... | 10,118 | 62.641509 | 1,003 | py |
fl-analysis | fl-analysis-master/src/backdoor/__init__.py | 0 | 0 | 0 | py | |
fl-analysis | fl-analysis-master/src/model/resnet.py | from __future__ import print_function
import tensorflow as tf
from tensorflow.keras.layers import Dense, Conv2D, BatchNormalization, Activation
from tensorflow.keras.layers import AveragePooling2D, Input, Flatten
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import ModelCheckpoint, Learni... | 15,635 | 36.317422 | 81 | py |
fl-analysis | fl-analysis-master/src/model/modelc.py |
import tensorflow.keras as keras
from tensorflow.keras.regularizers import l2
from tensorflow.keras import layers
def build_modelc(l2_reg):
do = 0.2
model = keras.Sequential()
# model.add(layers.Dropout(0.2, noise_shape=(32, 32, 3)))
model.add(layers.Conv2D(filters=96, kernel_size=3, strides=1, ker... | 2,230 | 73.366667 | 218 | py |
fl-analysis | fl-analysis-master/src/model/lenet.py |
import tensorflow.keras as keras
from tensorflow.keras import layers
from tensorflow.keras.regularizers import l2
def build_lenet5(input_shape=(32, 32, 3), l2_reg=None):
do = 0.0
regularizer = l2(l2_reg) if l2_reg is not None else None
model = keras.Sequential()
model.add(layers.Conv2D(filters=6, ... | 1,288 | 46.740741 | 220 | py |
fl-analysis | fl-analysis-master/src/model/__init__.py | 0 | 0 | 0 | py | |
fl-analysis | fl-analysis-master/src/model/test_model.py |
import tensorflow.keras as keras
from tensorflow.keras import layers
from tensorflow.keras.regularizers import l2
def build_test_model(input_shape=(32, 32, 3), l2_reg=None):
do = 0.0
regularizer = l2(l2_reg) if l2_reg is not None else None
model = keras.Sequential()
model.add(layers.Conv2D(filters... | 848 | 37.590909 | 220 | py |
fl-analysis | fl-analysis-master/src/model/mobilenet.py | # Implementation by https://github.com/ruchi15/CNN-MobileNetV2-Cifar10
import tensorflow as tf
import os
import warnings
import numpy as np
from tensorflow.keras.layers import Input, Activation, Conv2D, Dense, Dropout, BatchNormalization, ReLU, \
DepthwiseConv2D, GlobalAveragePooling2D, GlobalMaxPooling2D, Add
f... | 6,395 | 41.357616 | 129 | py |
fl-analysis | fl-analysis-master/src/model/stacked_lstm.py |
import tensorflow as tf
# class StackedLSTM(tf.keras.Model):
# def __init__(self, vocab_size, embedding_dim, n_hidden):
# super().__init__(self)
# self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
#
# rnn_cells = [tf.keras.layers.LSTMCell(n_hidden) for _ in range(2)]
# stacked_l... | 1,762 | 31.054545 | 77 | py |
fl-analysis | fl-analysis-master/src/error/__init__.py |
class ConfigurationError(Exception):
pass | 47 | 11 | 36 | py |
us_hep_funding | us_hep_funding-main/us_hep_funding/constants.py | """Constants for configuring us_hep_funding"""
import pathlib
RAW_DATA_PATH = pathlib.Path("/workspaces/us_hep_funding/raw_data/")
CLEANED_DBS_PATH = pathlib.Path("/workspaces/us_hep_funding/cleaned_data/")
USASPENDING_BASEURL = "https://files.usaspending.gov/award_data_archive/"
DOE_CONTRACTS_STR = "_089_Contracts_F... | 2,291 | 53.571429 | 98 | py |
us_hep_funding | us_hep_funding-main/us_hep_funding/__init__.py | 0 | 0 | 0 | py | |
us_hep_funding | us_hep_funding-main/us_hep_funding/clients/create_doe_grants.py | import pandas as pd
from us_hep_funding.constants import CLEANED_DBS_PATH, RAW_DATA_PATH
from us_hep_funding.data.cleaners import DoeGrantsCleaner
def run():
doe_grants2012 = DoeGrantsCleaner(
RAW_DATA_PATH / "unzipped" / "DOE-SC_Grants_FY2012.xlsx",
2012,
sheet_name="DOE SC Awards FY 20... | 2,809 | 27.383838 | 87 | py |
us_hep_funding | us_hep_funding-main/us_hep_funding/clients/ad_hoc.py | data2.loc[
data2["Institution"] == "University of Minnesota", "Congressional District"
] = "MN-05"
data4.loc[
data4["Institution"] == "CALIFORNIA INST. OF TECHNOLOGY",
"Congressional District *",
] = "CA-27"
data3.loc[
data3["Institution"] == "California Institute of Technology (CalTech)",
"Congress... | 1,189 | 26.045455 | 79 | py |
us_hep_funding | us_hep_funding-main/us_hep_funding/clients/create_suli_data.py | import pandas as pd
from us_hep_funding.constants import CLEANED_DBS_PATH
from us_hep_funding.data.cleaners import SuliStudentDataCleaner
from us_hep_funding.mapping import SuliStudentMapMaker
def run():
suli2014 = SuliStudentDataCleaner(
"/workspaces/us_hep_funding/raw_data/unzipped/2014-SULI-Terms_Par... | 3,362 | 25.480315 | 94 | py |
us_hep_funding | us_hep_funding-main/us_hep_funding/clients/__init__.py | 0 | 0 | 0 | py | |
us_hep_funding | us_hep_funding-main/us_hep_funding/clients/create_databases.py | """This will be the top-level API for producing updated
data tables."""
from datetime import datetime
from us_hep_funding.data.downloaders import (
UsaSpendingDataDownloader,
DoeDataDownloader,
SuliStudentDataDownloader,
)
from us_hep_funding.data.cleaners import DoeContractDataCleaner, NsfGrantsCleaner
#... | 919 | 29.666667 | 81 | py |
us_hep_funding | us_hep_funding-main/us_hep_funding/clients/create_maps.py | """This will be the top-level API for creating maps
of SULI/CCI/VFP data."""
| 78 | 25.333333 | 52 | py |
us_hep_funding | us_hep_funding-main/us_hep_funding/data/__init__.py | 0 | 0 | 0 | py | |
us_hep_funding | us_hep_funding-main/us_hep_funding/data/cleaners/_nsf_grants_cleaner.py | import pandas as pd
from us_hep_funding.constants import RAW_DATA_PATH, CLEANED_DBS_PATH
class NsfGrantsCleaner:
def __init__(self):
self.contract_file_list = (RAW_DATA_PATH / "unzipped").glob(
"*049_Assistance*.csv"
)
def _load_data(self):
contract_df_list = []
... | 3,888 | 39.092784 | 82 | py |
us_hep_funding | us_hep_funding-main/us_hep_funding/data/cleaners/_doe_contracts_cleaner.py | import pandas as pd
from us_hep_funding.constants import (
CLEANED_DBS_PATH,
RAW_DATA_PATH,
SC_CONTRACTS_OFFICES,
)
class DoeContractDataCleaner:
def __init__(self):
self.contract_file_list = (RAW_DATA_PATH / "unzipped").glob(
"*089_Contracts*.csv"
)
def _load_data(se... | 3,175 | 35.505747 | 88 | py |
us_hep_funding | us_hep_funding-main/us_hep_funding/data/cleaners/_suli_cci_cleaner.py | import camelot
import pandas as pd
_LAB_ABBRS_TO_NAMES = {
"LBNL": "Lawrence Berkeley National Laboratory",
"BNL": "Brookhaven National Laboratory",
"ANL": "Argonne National Laboratory",
"ORNL": "Oak Ridge National Laboratory",
"NREL": "National Renewable Energy Laboratory",
"PNNL": "Pacific No... | 2,950 | 33.313953 | 83 | py |
us_hep_funding | us_hep_funding-main/us_hep_funding/data/cleaners/__init__.py | from ._doe_contracts_cleaner import DoeContractDataCleaner
from ._nsf_grants_cleaner import NsfGrantsCleaner
from ._doe_grants_cleaner import DoeGrantsCleaner
from ._suli_cci_cleaner import SuliStudentDataCleaner
| 213 | 41.8 | 58 | py |
us_hep_funding | us_hep_funding-main/us_hep_funding/data/cleaners/_doe_grants_cleaner.py | import re
import numpy as np
import pandas as pd
from titlecase import titlecase
class DoeGrantsCleaner:
def __init__(
self,
filepath,
fiscal_year,
sheet_name=0,
skiprows=0,
institution_key="Institution",
district_key="Congressional District",
amoun... | 10,169 | 37.089888 | 88 | py |
us_hep_funding | us_hep_funding-main/us_hep_funding/data/downloaders/_doe_downloader.py | import os
import requests
import warnings
import numpy
from us_hep_funding.constants import RAW_DATA_PATH, DOE_GRANTS_URLS
class DoeDataDownloader:
def __init__(self):
self.save_path = RAW_DATA_PATH / "unzipped"
def run(self, fiscal_year: int):
try:
url = DOE_GRANTS_URLS[fiscal... | 1,107 | 26.02439 | 87 | py |
us_hep_funding | us_hep_funding-main/us_hep_funding/data/downloaders/_usa_spending_downloader.py | """Classes that download data from usaspending.gov"""
import pathlib
import warnings
import zipfile
import requests
from us_hep_funding.constants import (
DOE_CONTRACTS_STR,
NSF_GRANTS_STR,
RAW_DATA_PATH,
USASPENDING_BASEURL,
)
class UsaSpendingDataDownloader:
"""A downloader for getting data f... | 1,531 | 26.357143 | 79 | py |
us_hep_funding | us_hep_funding-main/us_hep_funding/data/downloaders/_suli_student_data.py | import os
import requests
import warnings
import numpy
from us_hep_funding.constants import RAW_DATA_PATH, SULI_STUDENT_URLS
class SuliStudentDataDownloader:
def __init__(self):
self.save_path = RAW_DATA_PATH / "unzipped"
def run(self, fiscal_year: int):
try:
url = SULI_STUDENT... | 1,119 | 26.317073 | 87 | py |
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