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 |
|---|---|---|---|---|---|---|
StatisticalClearSky | StatisticalClearSky-master/statistical_clear_sky/algorithm/plot/__init__.py | 0 | 0 | 0 | py | |
StatisticalClearSky | StatisticalClearSky-master/statistical_clear_sky/algorithm/serialization/state_data.py | """
This module defines a class that holds the current state of algorithm object.
"""
import numpy as np
class StateData(object):
"""
Holds the data to be serialized.
"""
def __init__(self):
self._auto_fix_time_shifts = True
self._power_signals_d = None
self._rank_k = None
... | 4,291 | 21.010256 | 77 | py |
StatisticalClearSky | StatisticalClearSky-master/statistical_clear_sky/algorithm/serialization/serialization_mixin.py | """
This module defines Mixin for serialization.
"""
import json
import numpy as np
from statistical_clear_sky.algorithm.serialization.state_data import StateData
class SerializationMixin(object):
"""
Mixin for IterativeClearSky, taking care of serialization.
"""
def save_instance(self, filepath):
... | 3,634 | 43.329268 | 87 | py |
StatisticalClearSky | StatisticalClearSky-master/statistical_clear_sky/algorithm/serialization/__init__.py | 0 | 0 | 0 | py | |
StatisticalClearSky | StatisticalClearSky-master/tests/__init__.py | 0 | 0 | 0 | py | |
StatisticalClearSky | StatisticalClearSky-master/tests/statistical_clear_sky/__init__.py | 0 | 0 | 0 | py | |
StatisticalClearSky | StatisticalClearSky-master/tests/statistical_clear_sky/algorithm/test_iterative_fitting_execute.py | import unittest
from unittest.mock import Mock
import os
import numpy as np
import cvxpy as cvx
from statistical_clear_sky.algorithm.iterative_fitting import IterativeFitting
from statistical_clear_sky.algorithm.initialization.linearization_helper\
import LinearizationHelper
from statistical_clear_sky.algorithm.initia... | 7,472 | 46 | 80 | py |
StatisticalClearSky | StatisticalClearSky-master/tests/statistical_clear_sky/algorithm/__init__.py | 0 | 0 | 0 | py | |
StatisticalClearSky | StatisticalClearSky-master/tests/statistical_clear_sky/algorithm/test_iterative_fitting.py | import unittest
import os
import numpy as np
import cvxpy as cvx
from statistical_clear_sky.algorithm.iterative_fitting import IterativeFitting
class TestIterativeFitting(unittest.TestCase):
def test_calculate_objective(self):
input_power_signals_file_path = os.path.abspath(
os.path.join(os.p... | 2,327 | 37.163934 | 78 | py |
StatisticalClearSky | StatisticalClearSky-master/tests/statistical_clear_sky/algorithm/minimization/test_left_matrix_minimization.py | import unittest
import os
import numpy as np
import cvxpy as cvx
from statistical_clear_sky.algorithm.minimization.left_matrix\
import LeftMatrixMinimization
class TestLeftMatrixMinimization(unittest.TestCase):
def test_minimize(self):
power_signals_d = np.array([[0.0, 0.0, 0.0, 0.0],
... | 7,344 | 45.783439 | 79 | py |
StatisticalClearSky | StatisticalClearSky-master/tests/statistical_clear_sky/algorithm/minimization/__init__.py | 0 | 0 | 0 | py | |
StatisticalClearSky | StatisticalClearSky-master/tests/statistical_clear_sky/algorithm/minimization/test_right_matrix_minimization.py | import unittest
import os
import numpy as np
import cvxpy as cvx
from statistical_clear_sky.algorithm.minimization.right_matrix\
import RightMatrixMinimization
class TestRightMatrixMinimization(unittest.TestCase):
def test_minimize_with_large_data(self):
input_power_signals_file_path = os.path.abspath(
... | 4,006 | 42.086022 | 78 | py |
StatisticalClearSky | StatisticalClearSky-master/tests/statistical_clear_sky/algorithm/initialization/test_linearization_helper.py | import unittest
import numpy as np
# import os
from statistical_clear_sky.algorithm.initialization.linearization_helper\
import LinearizationHelper
class TestLinealizationHelper(unittest.TestCase):
'''
Unit test for obtaining initial data of Right Vectors component r0,
which is used as a denomoniator of n... | 1,804 | 40.022727 | 80 | py |
StatisticalClearSky | StatisticalClearSky-master/tests/statistical_clear_sky/algorithm/initialization/test_weight_setting.py | import unittest
import os
import numpy as np
import cvxpy as cvx
from statistical_clear_sky.algorithm.initialization.weight_setting\
import WeightSetting
class TestWeightSetting(unittest.TestCase):
def test_obtain_weights(self):
power_signals_d = np.array([[3.65099996e-01, 0.00000000e+00,
... | 2,150 | 41.176471 | 83 | py |
StatisticalClearSky | StatisticalClearSky-master/tests/statistical_clear_sky/algorithm/initialization/test_singular_value_decomposition.py | import unittest
import numpy as np
from\
statistical_clear_sky.algorithm.initialization.singular_value_decomposition\
import SingularValueDecomposition
class TestSingularValueDecomposition(unittest.TestCase):
def test_adjust_singular_vectors(self):
power_signals_d = np.array([[3.65099996e-01, 0.0000000... | 3,272 | 55.431034 | 80 | py |
StatisticalClearSky | StatisticalClearSky-master/tests/statistical_clear_sky/algorithm/initialization/__init__.py | 0 | 0 | 0 | py | |
StatisticalClearSky | StatisticalClearSky-master/tests/statistical_clear_sky/algorithm/serialization/__init__.py | 0 | 0 | 0 | py | |
StatisticalClearSky | StatisticalClearSky-master/tests/statistical_clear_sky/algorithm/serialization/test_serialization_mixin.py | import unittest
import numpy as np
import tempfile
import shutil
import os
from statistical_clear_sky.algorithm.iterative_fitting import IterativeFitting
class TestSerializationMixin(unittest.TestCase):
def setUp(self):
self._temp_directory = tempfile.mkdtemp()
self._filepath = os.path.join(self._... | 1,594 | 37.902439 | 78 | py |
StatisticalClearSky | StatisticalClearSky-master/docs/source/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 1,991 | 32.762712 | 79 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/experiment_scripts/normalization-ablation/experiments.py | """Biggest batch size that would fit in one GPU."""
import explib
from explib.expmaker import PROB_CIFAR10_RESNET18 as C10_R18
from explib.expmaker import PROB_DB_SQD as DB_SQD
from explib.expmaker import PROB_MNIST_LENET5 as MNI_LN5
from explib.expmaker import PROB_PTB_TENC as PTB_TEC
from explib.expmaker import PROB... | 9,273 | 31.770318 | 78 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/experiment_scripts/no_dropout/experiment.py | import explib
from explib.expmaker import PROB_PTB_TENC_DET as PTB_TEC
from explib.expmaker import PROB_WT2_TXL_DET as WT2_TXL
from explib.expmaker import merge_dicts, merge_sets, nice_logspace
from explib.expmaker.slurm_configs import DEFAULT_GPU_12H, DEFAULT_GPU_16H
from explib.optim import NORMALIZED_GD, RESCALED_SI... | 5,188 | 28.482955 | 77 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/experiment_scripts/full-batch-training/experiments.py | """
Sanity checks for the full runs,
checking runtime and memory consumption of various configurations-
"""
import explib
from explib.expmaker.slurm_configs import (
DEFAULT_GPU_12H,
LARGE_GPU_24H,
DEFAULT_GPU_16H,
)
from explib.expmaker import merge_dicts, nice_logspace, merge_sets
from explib.expmaker i... | 5,443 | 26.917949 | 73 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/experiment_scripts/longer_wt2/incr_batch.py | """Sanity checks for the full runs, checking runtime and memory consumption of
various configurations-"""
import explib
from explib.expmaker.slurm_configs import (
SMALL_GPU_4H,
SMALL_GPU_12H,
SMALL_GPU_8H,
LARGE_GPU_24H,
LARGE_GPU_12H,
)
from explib.expmaker import merge_dicts, nice_logspace, merg... | 2,558 | 23.605769 | 78 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/experiment_scripts/longer_wt2/longer_wt2.py | import explib
from explib.expmaker import PROB_PTB_TENC_DET as PTB_TEC
from explib.expmaker import PROB_WT2_TXL_DET as WT2_TXL
from explib.expmaker import merge_dicts, merge_sets, nice_logspace
from explib.expmaker.slurm_configs import DEFAULT_GPU_12H, DEFAULT_GPU_16H
from explib.optim import NORMALIZED_GD, RESCALED_SI... | 4,456 | 28.322368 | 77 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/experiment_scripts/increasing-batch-size/experiments.py | """
Sanity checks for the full runs,
checking runtime and memory consumption of various configurations-
"""
import explib
from explib.expmaker.slurm_configs import (
SMALL_GPU_4H,
SMALL_GPU_12H,
SMALL_GPU_8H,
LARGE_GPU_24H,
LARGE_GPU_12H,
)
from explib.expmaker import merge_dicts, nice_logspace, me... | 5,746 | 29.247368 | 75 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/experiment_scripts/hist_maker/make_wt2_hists.py | import explib
from explib.expmaker import slurm_configs, BASE_PROBLEMS
EXPERIMENTS = [
{
**BASE_PROBLEMS["WT2_TRANSFORMERXL"],
"batch_size": bs,
"max_epoch": 0,
"seed": seed,
"opt": {
"name": "Adam",
"alpha": 0.001,
"b1": 0.99,
... | 729 | 22.548387 | 56 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/experiment_scripts/hist_maker/make_squad_hists.py | import numpy as np
import explib
from explib.expmaker import slurm_configs
def merge_grids(*grids):
return sorted(list(set.union(*[set(grid) for grid in grids])))
EXPERIMENTS = []
EXPERIMENTS_ADAM = [
{
"dataset": dataset,
"model": "distilbert_base_pretrained",
"batch_size": bs,
... | 1,008 | 22.465116 | 66 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/experiment_scripts/hist_maker/make_image_hists.py | import numpy as np
import explib
from explib.expmaker import slurm_configs
def merge_grids(*grids):
return sorted(list(set.union(*[set(grid) for grid in grids])))
EXPERIMENTS = []
EXPERIMENTS_MNIST = [
{
"loss_func": "logloss",
"metrics": ["accuracy"],
"dataset": "mnist",
"... | 1,480 | 20.779412 | 66 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/experiment_scripts/hist_maker/make_ptb_hists.py | import numpy as np
import explib
from explib.expmaker import slurm_configs
def merge_grids(*grids):
return sorted(list(set.union(*[set(grid) for grid in grids])))
EXPERIMENTS = []
EXPERIMENTS_SGD = [
{
"loss_func": "logloss",
"dataset": "ptb",
"model": "transformer_encoder",
... | 1,001 | 20.319149 | 66 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/experiment_scripts/fix-full-batch-squad/experiment.py | """Sanity checks for the full runs, checking runtime and memory consumption of
various configurations-"""
import explib
from explib.expmaker import PROB_DB_SQD as DB_SQD
from explib.expmaker import merge_dicts, nice_logspace
from explib.expmaker.slurm_configs import DEFAULT_GPU_36H, LARGE_GPU_36H
from explib.optim imp... | 2,935 | 28.36 | 87 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/experiment_scripts/full-batch-training-normalized-optimizers/experiments.py | """Sanity checks for the full runs, checking runtime and memory consumption of
various configurations-"""
import explib
from explib.expmaker import PROB_CIFAR10_RESNET18 as C10_R18
from explib.expmaker import PROB_DB_SQD as DB_SQD
from explib.expmaker import PROB_MNIST_LENET5 as MNI_LN5
from explib.expmaker import PRO... | 8,479 | 30.176471 | 109 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/plots/final_perf.py | import importlib
import os
from pathlib import Path
import explib.results.cleanup as cleanh
import explib.results.data as data_h
import explib.results.data as datah
import explib.results.data_caching as data_cache
import explib.results.experiment_groups as expdef
import explib.results.plotting as plth
import matplotli... | 8,058 | 32.164609 | 87 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/plots/histograms_and_small_training.py | import importlib
import os
from pathlib import Path
import explib.results.data as data_h
import explib.results.experiment_groups as expdef
import explib.results.plotting as helpers
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from explib.results import data_caching
from matplotlib import gridsp... | 10,301 | 31.913738 | 87 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/plots/data_preprocessing_for_additional_plot.py | import os
import pdb
import pickle
import explib.results.cleanup as cleanh
import explib.results.data as datah
import explib.results.plotting as plth
import numpy as np
from explib import config
from tqdm import tqdm
def standard_gridsearch():
df, runs = datah.get_summary(), datah.get_all_runs()
df, runs = c... | 50,314 | 37.973664 | 88 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/plots/full_performance_for_each_batch_size.py | """Attempt at a figure that would show."""
import cmd
import importlib
import os
from pathlib import Path
import explib.results.cleanup as cleanh
import explib.results.data as data_h
import explib.results.data as datah
import explib.results.data_caching as data_cache
import explib.results.experiment_groups as expdef... | 5,946 | 31.856354 | 88 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/plots/grid_search_best_runs.py | import importlib
import os
from pathlib import Path
import explib.results.cleanup as cleanh
import explib.results.data as data_h
import explib.results.data as datah
import explib.results.data_caching as data_cache
import explib.results.experiment_groups as expdef
import explib.results.plotting as plth
import matplotli... | 5,424 | 33.775641 | 87 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/plots/legends.py | """Attempt at a figure that would show."""
import importlib
import os
from pathlib import Path
import explib.results.cleanup as cleanh
import explib.results.data as datah
import explib.results.experiment_groups as expdef
import explib.results.plotting as plth
import matplotlib
import matplotlib.pyplot as plt
import ... | 3,186 | 25.781513 | 84 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/plots/convergence_full_batch.py | import importlib
import os
from pathlib import Path
import explib.results.data as data_h
import explib.results.experiment_groups as expdef
import explib.results.plotting as plth
import matplotlib
import matplotlib.pyplot as plt
from explib.results import data_caching
def load_data():
runs_at_last_epoch, best_run... | 3,655 | 29.466667 | 83 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/plots/perf_vs_batchsize_at_comparable_iter.py | import cmd
import os
from pathlib import Path
import explib.results.data as data_h
import explib.results.data_caching as data_cache
import explib.results.experiment_groups as expdef
import explib.results.plotting as plth
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from explib.results.cleanup... | 5,251 | 33.552632 | 88 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/plots/table_of_comparable_iter.py | import cmd
import explib.results.data as data_h
import explib.results.data_caching as data_cache
import explib.results.experiment_groups as expdef
import explib.results.plotting as plth
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from explib.results.cleanup import clean_data
def load_data(... | 3,385 | 26.088 | 87 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/plots/grid_search.py | import importlib
import os
from pathlib import Path
import explib.results.cleanup as cleanh
import explib.results.data as data_h
import explib.results.data as datah
import explib.results.data_caching as data_cache
import explib.results.experiment_groups as expdef
import explib.results.plotting as plth
import matplotli... | 7,557 | 33.669725 | 87 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/plots/no_dropout.py | import importlib
import os
from pathlib import Path
import explib.results.data as data_h
import explib.results.experiment_groups as expdef
import explib.results.plotting as plth
import matplotlib
import matplotlib.pyplot as plt
from explib.results import data_caching
def load_data():
runs_at_last_epoch, best_run... | 2,749 | 29.555556 | 85 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/setup.py | """Setup file for ExpLib."""
from setuptools import find_packages, setup
with open("requirements.txt") as f:
requirements = f.read().splitlines()
with open("requirements-nocc.txt") as f:
requirements += f.read().splitlines()
setup(
author="Author",
name="ExpLib",
version="0.1.0",
description="... | 634 | 25.458333 | 50 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/__main__.py | import json
import os
from explib import cli_helper
from .experiment import Experiment
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Experiment runner")
parser.add_argument(
"experiment_file",
type=str,
help="Experiment file",
def... | 1,383 | 25.113208 | 86 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/experiment.py | import time
import torch
import math
import os
import random
import datetime
from pathlib import Path
import numpy as np
from explib import config
from explib.expmaker.experiment_defs import make_wuuid, exp_dict_to_str
from . import logging, problem
class Experiment:
def __init__(
self,
exp_dict,... | 5,622 | 34.815287 | 88 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/cli_helper.py | from explib import config
def add_dotenv_option(parser):
parser.add_argument(
"--dotenv",
type=str,
help=".env file to override local environment variables (including workspace)",
default=None,
)
return parser
def load_dotenv_if_required(args):
if getattr(args, "doten... | 391 | 22.058824 | 87 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/config.py | import os
from dotenv import load_dotenv
def load_dotenv_file(path=None):
"""Load a dotenv file from path (defaults to cwd if None)"""
if path is None:
load_dotenv(verbose=True, override=True)
else:
load_dotenv_file(path, verbose=True, override=True)
def get_workspace():
return os.pa... | 1,368 | 22.603448 | 65 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/util.py | import torch
import torch.nn as nn
def get_grads(model):
res = []
for p in model.parameters():
if p.requires_grad:
res.append(p.grad.view(-1))
grad_flat = torch.cat(res)
return grad_flat
INIT_STD = 0.02
PROJ_INIT_STD = 0.01
def init_weight(weight):
nn.init.normal_(weight, 0... | 2,310 | 30.22973 | 73 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/__init__.py | """ExpLib"""
import json
from . import dataset, expmaker, logging, model, optim
from .experiment import Experiment
| 116 | 18.5 | 54 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/dataset/language_loader.py | import os, sys
import glob
from collections import Counter, OrderedDict
import numpy as np
import torch
import subprocess
# Code copied from https://github.com/kimiyoung/transformer-xl
from explib import config
class Vocab(object):
def __init__(
self,
special=[],
min_freq=0,
max_... | 17,819 | 31.459016 | 88 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/dataset/glue_loader.py | import os
import random
from explib import config
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
PretrainedConfig,
)
MAX_LENGTH = 128
EVAL_BAS... | 6,484 | 34.828729 | 117 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/dataset/torchvision_loader.py | import os
import torch
import torchvision
from explib import config
from torchvision import transforms
from torchvision.datasets import MNIST, USPS
def torchvision_loader(dataset_name, batch_size, drop_last=False, shuffle=True):
if dataset_name == "mnist":
loader = MNIST
elif dataset_name == "usps":
... | 1,511 | 26.490909 | 80 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/dataset/squad_loader.py | import tokenize
import datasets
import os
from datasets import load_dataset
from accelerate import Accelerator
from explib import config
from transformers import (
CONFIG_MAPPING,
MODEL_MAPPING,
AutoTokenizer,
DataCollatorWithPadding,
)
from torch.utils.data.dataloader import DataLoader
import numpy as... | 11,801 | 38.209302 | 118 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/dataset/cifar_loader.py | import os
import torch
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from explib import config
def cifar_loader(
batch_size,
load_100=False,
drop_last=False,
fake_full_batch_mode=False,
shuffle=True,
):
data_class = "CIFAR100" if load_100 else "CIFAR10"
... | 1,561 | 23.40625 | 78 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/dataset/__init__.py | """Datasets.
General interface to load a dataset
"""
import os
from pathlib import Path
from explib import config
from .cifar_loader import cifar_loader
from .glue_loader import glue_loader
from .language_loader import ptb_loader, wikitext2_loader
from .squad_loader import squad_loader
from .torchvision_loader impor... | 2,805 | 26.242718 | 79 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/results/experiment_groups.py | """Definition of "groups" of experiments mapping (dataset x batch size) to keys
to be selected from the experiment dataframe.
Simplified from explib/results/plotting.py
"""
##
# Dataset names
MNIST = "mnist"
WT2 = "wikitext2"
PTB = "ptb"
CIFAR10 = "cifar10"
SQUAD = "squad"
ALL_DS = [MNIST, CIFAR10, PTB, WT2, SQUAD]
... | 8,066 | 25.800664 | 86 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/results/__main__.py | import json
import os
import sys
import explib.results.wandb_cleanups
from explib.results import data
from explib import cli_helper
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Tools to download results")
parser.add_argument(
"--download",
actio... | 2,251 | 28.246753 | 98 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/results/plotting.py | import os
import pickle
import warnings
from datetime import datetime
from math import atan2, degrees
from pathlib import Path
import numpy
import numpy as np
import pandas as pd
# Label line with line2D label data
from explib import config
from explib.results import cleanup as datacleaning
from explib.results import... | 44,397 | 27.961513 | 97 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/results/cleanup.py | import json
import re
from datetime import datetime, timedelta
import explib.results.data as data_h
import numpy as np
import warnings
import pandas as pd
from explib import logging
def clean_data(summary, runs):
"""
All the data cleanup such that the summary and run data can be plotted.
Expects summary... | 9,029 | 31.135231 | 106 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/results/data.py | import json
import os
import warnings
from pathlib import Path
from typing import Any, Dict, List
import numpy as np
import pandas as pd
import wandb
from explib import config
from explib.results import experiment_groups as expdef
from tqdm import tqdm
class WandbAPI:
"""Static class to provide a singleton handl... | 14,209 | 26.917485 | 87 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/results/data_caching.py | import os.path
import pickle as pk
from pathlib import Path
import explib.results.data as data_h
from explib import config
from explib.results import experiment_groups as expdef
from explib.results.cleanup import clean_data
from explib.results.data import get_all_runs, get_summary, gridsearch_for
CACHE_DIR = os.path.... | 4,602 | 29.686667 | 88 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/results/__init__.py | from . import cleanup
from . import data
from . import plotting
from . import wandb_cleanups
| 93 | 17.8 | 28 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/results/wandb_cleanups.py | import pdb
from explib import config
from explib.expmaker.experiment_defs import (
exp_dict_from_str,
exp_dict_to_str,
make_uuid,
make_wuuid,
)
from explib.results.data import WandbAPI
from tqdm import tqdm
##
# Helper functions
def get_logs(run):
"""Downloads the .log file for the run and retur... | 7,692 | 28.250951 | 161 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/logging/__init__.py | import logging
import os
from pathlib import Path
import wandb
from wandb.util import generate_id
from dotenv import load_dotenv
import sys
import datetime
from explib import config
base_logger = None
wandb_is_enabled = True
def log_data(dict, commit=True):
if wandb_is_enabled:
wandb.log(dict, commit=co... | 3,276 | 27.008547 | 85 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/optim/signum.py | import torch
from torch.optim import Optimizer
class Signum(Optimizer):
r"""
Code taken from https://github.com/jiaweizzhao/Signum_pytorch/blob/master/Example/signum.py
Implements Signum optimizer that takes the sign of gradient or momentum.
See details in the original paper at:https://arxiv.org/abs/... | 3,282 | 36.735632 | 95 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/optim/normalized_gd.py | import torch
from torch import Tensor
from torch.optim.optimizer import Optimizer, required
from torch.nn.utils import parameters_to_vector as p2v
from typing import List, Optional
class CopyOfSGD(Optimizer):
def __init__(
self,
params,
lr=required,
momentum=0,
dampening=0,... | 7,502 | 30.004132 | 101 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/optim/modified_adam.py | from torch.optim import Optimizer
import math
import torch
from torch import Tensor
from typing import List, Optional
def f_modifiedadam(
params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
max_exp_avg_sqs: List[Tensor],
state_steps: List[int],
*,
... | 6,017 | 34.192982 | 104 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/optim/__init__.py | """Optimizers
Generic interface to build optimizers by name,
possibly interfacing with pytorch
"""
import json
import torch
from .signum import Signum
from .modified_adam import ModifiedAdam
from .normalized_gd import (
PlainSGD,
NormalizedSGD,
BlockNormalizedSGD,
SignSGD,
RescaledSignDescent,
)
... | 2,628 | 24.77451 | 87 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/optim/clipped_sgd.py | import itertools
import torch
from torch import Tensor
from torch.optim import SGD
from torch.optim.optimizer import Optimizer, required
from torch.nn.utils import parameters_to_vector as p2v
from typing import List, Optional
class ClippedGD(SGD):
def __init__(
self,
params,
lr=required,
... | 1,091 | 24.395349 | 94 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/problem/problem.py | import torch
from torch.nn.utils import parameters_to_vector as p2v
from abc import ABCMeta, abstractmethod
from explib import config
from ..util import get_grads, enable_running_stats, disable_running_stats
import os
import numpy as np
from pathlib import Path
import csv
from ..dataset import *
class Problem(metacla... | 6,790 | 32.78607 | 84 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/problem/bert_squad_prob.py | import csv
import torch
from accelerate import Accelerator
from datasets import load_metric
from .. import dataset, model, optim
from .problem import Problem
class BertSquadProb(Problem):
def __init__(self, exp_dict):
super().__init__(exp_dict)
(
self.train_dataloader,
s... | 4,505 | 31.185714 | 87 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/problem/image_prob.py | import torch
import torch.nn.functional as F
from .. import dataset, model, optim
from .problem import Problem
class ImageProb(Problem):
def __init__(self, exp_dict):
super().__init__(exp_dict)
self.train_dataloader, self.valid_dataloader = dataset.init(
self.dataset_name,
... | 2,823 | 29.042553 | 86 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/problem/simple_prob.py | from .problem import Problem
from .. import dataset, model, optim
import torch
class SimpleProb(Problem):
def __init__(self, exp_dict):
super().__init__(exp_dict)
self.train_dataloader, self.valid_dataloader = dataset.init(
self.dataset_name,
self.batch_size,
s... | 2,272 | 28.141026 | 86 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/problem/transformer_prob.py | import csv
import math
import torch
from .. import dataset, model, optim
from .problem import Problem
class TransformerProb(Problem):
def __init__(self, exp_dict):
super().__init__(exp_dict)
init_outputs = dataset.init(
self.dataset_name,
self.batch_size,
sel... | 4,995 | 31.025641 | 86 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/problem/__init__.py | from ..model import *
from .bert_squad_prob import BertSquadProb
from .image_prob import ImageProb
from .simple_prob import SimpleProb
from .transformer_prob import TransformerProb
image_models = [
LENET5,
RESNET18,
RESNET34,
RESNET50,
RESNET101,
]
simple_models = [
LIN_REG,
LOG_REG,
F... | 950 | 20.133333 | 64 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/model/full_connected.py | import torch
from torch import nn
import copy
class FullyConnected(nn.Module):
def __init__(self, input_dim=3 * 32 * 32, width=100, depth=3, num_classes=10):
super(FullyConnected, self).__init__()
self.input_dim = input_dim
self.width = width
self.depth = depth
self.num_cla... | 935 | 26.529412 | 82 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/model/transformer_xl.py | import math
import functools
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class PositionalEmbedding(nn.Module):
def __init__(self, demb):
super(PositionalEmbedding, self).__init__()
self.demb = demb
inv_freq = 1 / (10000 ** (torch.arange(0.0, de... | 38,100 | 33.356177 | 119 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/model/resnet.py | import torchvision.models as models
def getResNet(size, pretrained=False):
if size == 50:
return models.resnet50(pretrained=pretrained)
elif size == 34:
return models.resnet34(pretrained=pretrained)
elif size == 101:
return models.resnet101(pretrained=pretrained)
elif size == 1... | 377 | 28.076923 | 54 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/model/bert_glue.py | import os
import random
from explib import config
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
PretrainedConfig,
)
def get_bert_glue(model_... | 1,138 | 28.205128 | 77 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/model/transformer_encoder.py | """
Simple transformer architecture used as introduction by the pytorch team
https://pytorch.org/tutorials/beginner/transformer_tutorial.html
Version used
https://github.com/pytorch/tutorials/blob/a981886fd8f1793ac5808b26e75dd50b788eb4e5/beginner_source/transformer_tutorial.py
Code covered by
See pytorch_
Copyright ... | 2,568 | 33.253333 | 122 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/model/letnet5.py | import torch
from torch import nn
class LeNet5(nn.Module):
def __init__(self, n_classes, in_channels=3):
super(LeNet5, self).__init__()
self.feature_extractor = nn.Sequential(
nn.Conv2d(in_channels=in_channels, out_channels=6, kernel_size=5, stride=1),
nn.Tanh(),
... | 961 | 30.032258 | 88 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/model/__init__.py | from .letnet5 import LeNet5
from .linear_model import LinearModel
from .transformer_encoder import TransformerEncoderModel
from .resnet import getResNet
from .full_connected import FullyConnected
from .transformer_xl import MemTransformerLM
from .bert_base_pretrained import (
get_bert_base_pretrained,
get_disti... | 3,881 | 27.544118 | 85 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/model/bert_base_pretrained.py | from datasets import load_metric
import numpy as np
from typing import Optional, Tuple
import json
import collections
import os
import torch
from transformers import (
AutoConfig,
AutoModelForQuestionAnswering,
EvalPrediction,
)
from .. import logging
def get_bert_base_pretrained():
config = AutoConfi... | 18,940 | 39.997835 | 119 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/model/linear_model.py | import torch
class LinearModel(torch.nn.Module):
def __init__(self, inputSize, outputSize):
super(LinearModel, self).__init__()
self.linear = torch.nn.Linear(inputSize, outputSize)
def forward(self, X):
out = self.linear(X)
return out
| 278 | 22.25 | 60 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/expmaker/sbatch_writers.py | import os
import textwrap
from explib import config, logging
from explib.expmaker import (
slurm_configs,
get_jobs_folder,
get_exp_full_path_json,
load_summary,
get_job_path,
)
from explib.expmaker.experiment_defs import make_uuid, make_wuuid
from explib.expmaker.slurm_configs import SlurmConfigIss... | 8,757 | 32.684615 | 88 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/expmaker/slurm_configs.py | from functools import partial
def set_config(time, gpu, mem, cpus):
return {
"gpu": gpu,
"mem": mem,
"time": time,
"cpus-per-task": cpus,
}
small_cpu = partial(set_config, gpu=None, mem="12000M", cpus=2)
medium_cpu = partial(set_config, gpu=None, mem="32000M", cpus=8)
small_g... | 3,536 | 32.685714 | 66 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/expmaker/wandb_reporting.py | from explib.expmaker.experiment_defs import load_summary
from explib.expmaker.experiment_defs import make_wuuid
from explib.results.cleanup import process_tags
import explib.results.data as data_h
import pandas as pd
from functools import lru_cache
import json
def wuuid_to_successful_run(exp_name):
"""Returns a d... | 2,946 | 34.083333 | 94 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/expmaker/__init__.py | """Helpers to create experiments."""
import argparse
import numpy as np
from explib import cli_helper, config, logging
from explib.expmaker import wandb_reporting
from explib.expmaker.experiment_defs import (
create_experiment_definitions,
exp_dict_from_str,
exp_dict_to_str,
get_exp_def_folder,
ge... | 10,448 | 29.642229 | 114 | py |
noise-sgd-adam-sign | noise-sgd-adam-sign-main/explib/explib/expmaker/experiment_defs.py | import base64
import hashlib
import json
import os
from pathlib import Path
from explib import config, logging
def exp_dict_from_str(exp_dict_str):
return json.loads(exp_dict_str)
def exp_dict_to_str(exp_dict, remove_keys=None):
"""String version of the experiment dictionary"""
if remove_keys is not Non... | 3,190 | 29.980583 | 82 | py |
pairwiseMKL | pairwiseMKL-master/main.py | import numpy as np
import copy
from math import sqrt
from sklearn import preprocessing, metrics
from pairwisemkl.learner.compute_M import *
from pairwisemkl.learner.compute_a_regression import *
from pairwisemkl.learner.optimize_kernel_weights import *
from pairwisemkl.learner.cg_kron_rls import CGKronRLS
data_path =... | 8,753 | 37.563877 | 168 | py |
pairwiseMKL | pairwiseMKL-master/main_precalculate_M_arrayjob.py | from sys import argv, exit
import os
import numpy as np
from pairwisemkl.learner.compute_M__arrayjob import *
try:
id_in = int(argv[1])
except:
exit()
data_path = './drug_response_data'
# Drug kernels
# Read file names of drug kernels
fn_kd = open(data_path + '/Drug_kernels/Drug_kernel_file_names.... | 1,409 | 25.111111 | 78 | py |
pairwiseMKL | pairwiseMKL-master/setup.py | from setuptools import setup, find_packages
from setuptools.extension import Extension
import numpy as np
USE_CYTHON = False
ext = '.pyx' if USE_CYTHON else '.c'
#sys.argv[1:] = ['build_ext', '--inplace']
ext_modules = [
Extension("pairwisemkl.utilities._sampled_kronecker_products",["pairwisemkl/utilities/_sampl... | 735 | 23.533333 | 124 | py |
pairwiseMKL | pairwiseMKL-master/main_arrayjob_using_precalculated_M.py | from sys import argv, exit
import os
import numpy as np
import copy
from math import sqrt
from sklearn import preprocessing, metrics
from pairwisemkl.learner.compute_a_regression import *
from pairwisemkl.learner.optimize_kernel_weights import *
from pairwisemkl.learner.cg_kron_rls import CGKronRLS
try:
i_out = i... | 8,644 | 34.871369 | 164 | py |
pairwiseMKL | pairwiseMKL-master/cython_setup.py | from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
import numpy as np
ext_modules = [
Extension("pairwisemkl.utilities._sampled_kronecker_products",["pairwisemkl/utilities/_sampled_kronecker_products.pyx"], include_dirs=[np.get_include()])
]
setup... | 424 | 25.5625 | 157 | py |
pairwiseMKL | pairwiseMKL-master/pairwisemkl/predictor/pairwise_predictor.py | #
# The MIT License (MIT)
#
# This file is part of pairwiseMKL and RLScore
#
# Copyright (c) 2018 Tapio Pahikkala, Antti Airola
#
# 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 restr... | 9,761 | 43.575342 | 142 | py |
pairwiseMKL | pairwiseMKL-master/pairwisemkl/utilities/array_tools.py | #
# The MIT License (MIT)
#
# This file is part of pairwiseMKL and RLScore
#
# Copyright (c) 2018 Tapio Pahikkala, Antti Airola
#
# 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 restr... | 4,189 | 29.143885 | 117 | py |
pairwiseMKL | pairwiseMKL-master/pairwisemkl/utilities/sampled_kronecker_products.py | #
# The MIT License (MIT)
#
# This file is part of pairwiseMKL and RLScore
#
# Copyright (c) 2018 Tapio Pahikkala, Antti Airola
#
# 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 restr... | 6,300 | 39.651613 | 130 | py |
pairwiseMKL | pairwiseMKL-master/pairwisemkl/learner/compute_M.py | #
# The MIT License (MIT)
#
# This file is part of pairwiseMKL
#
# Copyright (c) 2018 Anna Cichonska
#
# 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 l... | 4,219 | 37.018018 | 112 | py |
pairwiseMKL | pairwiseMKL-master/pairwisemkl/learner/compute_a_regression.py | #
# The MIT License (MIT)
#
# This file is part of pairwiseMKL
#
# Copyright (c) 2018 Anna Cichonska
#
# 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 l... | 5,993 | 37.423077 | 119 | py |
pairwiseMKL | pairwiseMKL-master/pairwisemkl/learner/cg_kron_rls.py | #
# The MIT License (MIT)
#
# This file is part of pairwiseMKL and RLScore
#
# Copyright (c) 2018 Tapio Pahikkala, Antti Airola
#
# 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 restr... | 11,138 | 43.378486 | 165 | py |
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