python_code stringlengths 0 1.02M | repo_name stringlengths 9 48 | file_path stringlengths 5 114 |
|---|---|---|
import atexit
import getpass
import os
import pwd
import shutil
import subprocess as sp
import tempfile
import warnings
import genomepy.utils
from pybedtools import BedTool
import pysam
import pandas as pd
def check_path(arg, error_missing=True):
"""Expand all paths. Can check for existence."""
if arg is Non... | ANANSE-master | ananse/utils.py |
#!/usr/bin/env python
# Copyright (c) 2009-2019 Quan Xu <qxuchn@gmail.com>
#
# This module is free software. You can redistribute it and/or modify it under
# the terms of the MIT License, see the file COPYING included with this
# distribution.
"""Build gene regulatory network"""
# Python imports
import os
import mat... | ANANSE-master | ananse/network.py |
import multiprocessing as mp
import os
import tempfile
import shutil
import dask.dataframe as dd
import dask.diagnostics
import genomepy
from gimmemotifs.scanner import scan_regionfile_to_table
from gimmemotifs.utils import pfmfile_location
from loguru import logger
import numpy as np
import pandas as pd
import pickle... | ANANSE-master | ananse/enhancer_binding.py |
ANANSE-master | ananse/db/__init__.py | |
from ananse.commands.binding import binding # noqa
from ananse.commands.influence import influence # noqa
from ananse.commands.network import network # noqa
from ananse.commands.view import view # noqa
| ANANSE-master | ananse/commands/__init__.py |
#!/usr/bin/env python
# Copyright (c) 2009-2019 Quan Xu <qxuchn@gmail.com>
#
# This module is free software. You can redistribute it and/or modify it under
# the terms of the MIT License, see the file COPYING included with this
# distribution.
from __future__ import print_function
import ananse.influence
from ananse.... | ANANSE-master | ananse/commands/influence.py |
#!/usr/bin/env python
# Copyright (c) 2021 Simon van Heeringen
#
# This module is free software. You can redistribute it and/or modify it under
# the terms of the MIT License, see the file COPYING included with this
# distribution.
from ananse.utils import view_h5
import sys
def view(args):
df = view_h5(args.infil... | ANANSE-master | ananse/commands/view.py |
#!/usr/bin/env python
# Copyright (c) 2009-2019 Quan Xu <qxuchn@gmail.com>
#
# This module is free software. You can redistribute it and/or modify it under
# the terms of the MIT License, see the file COPYING included with this
# distribution.
from __future__ import print_function
import os
import ananse.network
from... | ANANSE-master | ananse/commands/network.py |
#!/usr/bin/env python
# Copyright (c) 2009-2019 Quan Xu <qxuchn@gmail.com>
#
# This module is free software. You can redistribute it and/or modify it under
# the terms of the MIT License, see the file COPYING included with this
# distribution.
from ananse.peakpredictor import predict_peaks
from ananse.utils import chec... | ANANSE-master | ananse/commands/binding.py |
import os
import genomepy.utils
from loguru import logger
from ananse.enhancer_binding import (
CombineBedFiles,
ScorePeaks,
ScoreMotifs,
Binding,
)
from ananse.utils import clean_tmp
@logger.catch
def run_binding(
genome,
peakfiles,
bams,
outdir,
peak_width=200,
dist_func="p... | ANANSE-master | ananse/commands/enhancer_binding.py |
ANANSE-master | tests/__init__.py | |
import numpy as np
import pytest
import ananse.distributions
def test_distributions():
d = ananse.distributions.Distributions()
func_list = d.get()
assert isinstance(func_list, list)
for func in func_list:
d.set(func)
scores = np.array([0, 1, 2])
def test_scale_dist():
s = ananse.dis... | ANANSE-master | tests/continuous_integration/test_03_distributions.py |
from collections import namedtuple
from tempfile import NamedTemporaryFile
import numpy as np
import pytest
import pandas as pd
from ananse.network import Network
from ananse.commands import network
@pytest.fixture
def binding_fname():
return "tests/example_data/binding2.tsv"
@pytest.fixture
def network_obj()... | ANANSE-master | tests/continuous_integration/test_05_network.py |
from ananse.influence import read_expression
def test_read_expression():
res = read_expression("tests/data/dge.tsv")
assert set(res.keys()) == {"ANPEP", "CD24", "COL6A3", "DAB2", "DMKN"}
assert res["ANPEP"].score - 7.44242618323665 < 0.001
assert res["ANPEP"].realfc - 7.44242618323665 < 0.001
asse... | ANANSE-master | tests/continuous_integration/test_06_influence.py |
ANANSE-master | tests/continuous_integration/__init__.py | |
import subprocess as sp
# run tests locally with:
# pytest -vv --disable-pytest-warnings
# pytest -vv --disable-pytest-warnings tests/continuous_integration/test_01*
# pytest -vv --disable-pytest-warnings -k [substring]
# TODO: apply to all code --> targets = ["ananse/", "tests/"]
targets = [
"ananse/commands/__i... | ANANSE-master | tests/continuous_integration/test_01_basics.py |
import os
import genomepy.utils
import pytest
import ananse.enhancer_binding
from ananse.commands.enhancer_binding import run_binding
import ananse.utils
from .test_02_utils import write_file, write_bam, h0, h1, line1, line2, line3
# prep
test_dir = os.path.dirname(os.path.dirname(__file__))
outdir = os.path.join(t... | ANANSE-master | tests/continuous_integration/test_04_enhancer_binding.py |
import os
import tempfile
import time
import genomepy.utils
import pysam
import ananse.utils
# prep
test_dir = os.path.dirname(os.path.dirname(__file__))
outdir = os.path.join(test_dir, "output")
genomepy.utils.mkdir_p(outdir)
def write_file(filename, lines):
with open(filename, "w") as f:
for line in... | ANANSE-master | tests/continuous_integration/test_02_utils.py |
# source:
# https://stackoverflow.com/questions/6620471/fitting-empirical-distribution-to-theoretical-ones-with-scipy-python
import warnings
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import scipy.stats as st
from tqdm import tqdm
mpl.rcParams["figure.figsize"] = ... | ANANSE-master | tests/benchmark/find_distribution.py |
import os
# import numpy as np
import pandas as pd
import seaborn as sns
def distplot(infile, score_col=4, show=False):
"""
generate simple distplot from bedfile
"""
# https://stackoverflow.com/questions/18534562/scipy-lognormal-fitting
# https://stackoverflow.com/questions/41940726/scipy-lognorm... | ANANSE-master | tests/benchmark/utils.py |
import os
import genomepy.utils
from ananse.enhancer_binding import (
CombineBedFiles,
ScorePeaks,
ScoreMotifs,
Binding,
)
from tests.benchmark.utils import distplot
# prep
run_gimme = False # takes ages
test_dir = os.path.dirname(os.path.dirname(__file__))
data_dir = os.path.join(test_dir, "data"... | ANANSE-master | tests/benchmark/enhancer_binding.py |
from setuptools import setup, find_packages
setup(
name = 'JEPA-pytorch',
packages = find_packages(exclude=[]),
version = '0.0.1',
license='MIT',
description = 'JEPA - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
long_description_content_type = 'text/markdown',
url = 'https:... | JEPA-pytorch-main | setup.py |
from jepa_pytorch.jepa_pytorch import JEPA
| JEPA-pytorch-main | jepa_pytorch/__init__.py |
import torch
from torch import nn, einsum
from einops import rearrange
class JEPA(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x):
return x
| JEPA-pytorch-main | jepa_pytorch/jepa_pytorch.py |
from setuptools import setup, find_packages
setup(
name = 'compositional-attention-pytorch',
packages = find_packages(exclude=[]),
version = '0.0.1',
license='MIT',
description = 'Compositional Attention - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
url = 'https://github.com/... | compositional-attention-pytorch-main | setup.py |
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange
from einops_exts import rearrange_many
def exists(val):
return val is not None
def stable_softmax(t, dim = -1):
t = t - t.amax(dim = dim, keepdim = True).detach()
return t.softmax(dim = dim)
class Comp... | compositional-attention-pytorch-main | compositional_attention_pytorch/compositional_attention_pytorch.py |
from compositional_attention_pytorch.compositional_attention_pytorch import CompositionalAttention
| compositional-attention-pytorch-main | compositional_attention_pytorch/__init__.py |
from setuptools import setup, find_packages
setup(
name = 'VN-transformer',
packages = find_packages(exclude=[]),
version = '0.1.0',
license='MIT',
description = 'Vector Neuron Transformer (VN-Transformer)',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
long_description_content_type = 't... | VN-transformer-main | setup.py |
import torch
import torch.nn.functional as F
from torch.optim import Adam
from einops import rearrange, repeat
import sidechainnet as scn
from VN_transformer import VNTransformer
BATCH_SIZE = 1
GRADIENT_ACCUMULATE_EVERY = 16
MAX_SEQ_LEN = 256
DEFAULT_TYPE = torch.float64
torch.set_default_dtype(DEFAULT_TYPE)
def c... | VN-transformer-main | denoise.py |
import torch
import torch.nn.functional as F
from torch import nn, einsum, Tensor
from einops import rearrange, repeat, reduce
from einops.layers.torch import Rearrange, Reduce
from VN_transformer.attend import Attend
# helper
def exists(val):
return val is not None
def default(val, d):
return val if exist... | VN-transformer-main | VN_transformer/VN_transformer.py |
from VN_transformer.VN_transformer import (
VNTransformer,
VNLinear,
VNLayerNorm,
VNFeedForward,
VNAttention,
VNWeightedPool,
VNTransformerEncoder,
VNInvariant
)
| VN-transformer-main | VN_transformer/__init__.py |
import torch
from torch import sin, cos, atan2, acos
def rot_z(gamma):
return torch.tensor([
[cos(gamma), -sin(gamma), 0],
[sin(gamma), cos(gamma), 0],
[0, 0, 1]
], dtype=gamma.dtype)
def rot_y(beta):
return torch.tensor([
[cos(beta), 0, sin(beta)],
[0, 1, 0],
... | VN-transformer-main | VN_transformer/rotations.py |
from functools import wraps
from packaging import version
from collections import namedtuple
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, reduce
# constants
FlashAttentionConfig = namedtuple('FlashAttentionConfig', ['enable_flash', 'enable_math', 'enable_me... | VN-transformer-main | VN_transformer/attend.py |
import pytest
import torch
from VN_transformer.VN_transformer import VNTransformer, VNInvariant, VNAttention
from VN_transformer.rotations import rot
torch.set_default_dtype(torch.float64)
# test invariant layers
def test_vn_invariant():
layer = VNInvariant(64)
coors = torch.randn(1, 32, 64, 3)
R = ro... | VN-transformer-main | tests/test.py |
from setuptools import setup, find_packages
setup(
name = 'gated-state-spaces-pytorch',
packages = find_packages(exclude=[]),
version = '0.1.0',
license='MIT',
description = 'Gated State Spaces - GSS - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
long_description_content_type ... | gated-state-spaces-pytorch-main | setup.py |
import gzip
import random
import numpy as np
import torch
import torch.optim as optim
import tqdm
from torch.nn import functional as F
from torch.utils.data import DataLoader, Dataset
from gated_state_spaces_pytorch import GatedStateSpacesLM
from gated_state_spaces_pytorch.autoregressive_wrapper import Autoregressive... | gated-state-spaces-pytorch-main | train.py |
import torch
import torch.nn.functional as F
from torch import nn, einsum
from torch.fft import rfft, irfft
from einops import rearrange
# functions
def exists(val):
return val is not None
# classes
class DSS(nn.Module):
def __init__(
self,
*,
dim,
kernel_N = 512,
ds... | gated-state-spaces-pytorch-main | gated_state_spaces_pytorch/gss.py |
import torch
import torch.nn.functional as F
from einops import rearrange
from torch import nn
# helper function
def exists(val):
return val is not None
def eval_decorator(fn):
def inner(model, *args, **kwargs):
was_training = model.training
model.eval()
out = fn(model, *args, **kwa... | gated-state-spaces-pytorch-main | gated_state_spaces_pytorch/autoregressive_wrapper.py |
import torch
import torch.nn.functional as F
from torch import nn, einsum
from torch.fft import rfft, irfft
from einops import rearrange
from scipy.fftpack import next_fast_len
# functions
def exists(val):
return val is not None
def append_dims(x, num_dims):
if num_dims <= 0:
return x
return x.v... | gated-state-spaces-pytorch-main | gated_state_spaces_pytorch/dsconv.py |
from gated_state_spaces_pytorch.gss import GSS, GatedStateSpacesLM
from gated_state_spaces_pytorch.dsconv import GatedDsConv, GatedDsConvLM
from gated_state_spaces_pytorch.mhesa import GatedExponentialSmoothingLM, GatedMHESA
| gated-state-spaces-pytorch-main | gated_state_spaces_pytorch/__init__.py |
import torch
import torch.nn.functional as F
from torch import nn, einsum
from torch.fft import rfft, irfft
from einops import rearrange
from scipy.fftpack import next_fast_len
# functions
def exists(val):
return val is not None
def append_dims(x, num_dims):
if num_dims <= 0:
return x
return x.v... | gated-state-spaces-pytorch-main | gated_state_spaces_pytorch/mhesa.py |
from setuptools import setup, find_packages
setup(
name = 'product_key_memory',
packages = find_packages(),
version = '0.2.10',
license = 'MIT',
description = 'Product Key Memory',
long_description_content_type = 'text/markdown',
author = 'Aran Komatsuzaki, Phil Wang',
author_email = 'a... | product-key-memory-master | setup.py |
import gzip
import random
import tqdm
import numpy as np
import torch
from torch.optim import Adam
from torch.nn import functional as F
from torch.utils.data import DataLoader, Dataset
from product_key_memory.transformer import Transformer
# constants
NUM_BATCHES = int(1e5)
BATCH_SIZE = 4
GRADIENT_ACCUMULATE_EVERY ... | product-key-memory-master | train.py |
import math
import torch
from torch import nn, einsum
from einops import rearrange
from einops.layers.torch import Rearrange, Reduce
from colt5_attention import topk as coor_descent_topk
# helper functions
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
def lo... | product-key-memory-master | product_key_memory/product_key_memory.py |
from product_key_memory.product_key_memory import PKM, fetch_pkm_value_parameters, fetch_optimizer_parameters
ProductKeyMemory = PKM
| product-key-memory-master | product_key_memory/__init__.py |
import json
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange
from product_key_memory.product_key_memory import PKM
# helper function
def exists(val):
return val is not None
def default(val, d):
return val if exists(val) else d
# sampling helpers
def ... | product-key-memory-master | product_key_memory/transformer.py |
from setuptools import setup, find_packages
setup(
name = 'med-seg-diff-pytorch',
packages = find_packages(exclude=[]),
version = '0.2.6',
license='MIT',
description = 'MedSegDiff - SOTA medical image segmentation - Pytorch',
author = 'Phil Wang',
author_email = 'lucidrains@gmail.com',
long_description... | med-seg-diff-pytorch-main | setup.py |
import os
import argparse
from tqdm import tqdm
import torch
import torchvision.transforms as transforms
from med_seg_diff_pytorch import Unet, MedSegDiff
from med_seg_diff_pytorch.dataset import ISICDataset, GenericNpyDataset
from accelerate import Accelerator
import skimage.io as io
## Parse CLI arguments ##
def p... | med-seg-diff-pytorch-main | sample.py |
import os
import argparse
from tqdm import tqdm
import torch
import numpy as np
import torchvision.transforms as transforms
from torch.optim import AdamW
from lion_pytorch import Lion
from med_seg_diff_pytorch import Unet, MedSegDiff
from med_seg_diff_pytorch.dataset import ISICDataset, GenericNpyDataset
from accelerat... | med-seg-diff-pytorch-main | driver.py |
from med_seg_diff_pytorch.med_seg_diff_pytorch import MedSegDiff, Unet
| med-seg-diff-pytorch-main | med_seg_diff_pytorch/__init__.py |
import os
import numpy as np
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
import torch
from torch.utils.data import Dataset
from PIL import Image
import pandas as pd
import random
import torchvision.transforms.functional as F
class ISICDataset(Dataset):
def __init__(self, data_path, csv_file, img_folder, transform... | med-seg-diff-pytorch-main | med_seg_diff_pytorch/dataset.py |
import math
import copy
from random import random
from functools import partial
from collections import namedtuple
from beartype import beartype
import torch
from torch import nn, einsum
import torch.nn.functional as F
from torch.fft import fft2, ifft2
from einops import rearrange, reduce
from einops.layers.torch im... | med-seg-diff-pytorch-main | med_seg_diff_pytorch/med_seg_diff_pytorch.py |
from setuptools import setup, find_packages
setup(
name = 'nuwa-pytorch',
packages = find_packages(exclude=[]),
include_package_data = True,
version = '0.7.8',
license='MIT',
description = 'NÜWA - Pytorch',
long_description_content_type = 'text/markdown',
author = 'Phil Wang',
author_email = 'lucidra... | nuwa-pytorch-main | setup.py |
import torch
import torchvision.transforms as T
from PIL import Image
# constants
CHANNELS_TO_MODE = {
1 : 'L',
3 : 'RGB',
4 : 'RGBA'
}
def seek_all_images(img, channels = 3):
assert channels in CHANNELS_TO_MODE, f'channels {channels} invalid'
mode = CHANNELS_TO_MODE[channels]
i = 0
whil... | nuwa-pytorch-main | nuwa_pytorch/image_utils.py |
from random import randrange
from pathlib import Path
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torch.nn.utils.rnn import pad_sequence
from einops import rearrange
from tqdm import tqdm
import numpy as np
from shutil import rmtree
from nuwa_pytorch.tokenizer import token... | nuwa-pytorch-main | nuwa_pytorch/train_nuwa.py |
import torch
import torch.nn as nn
from operator import itemgetter
from torch.autograd.function import Function
from torch.utils.checkpoint import get_device_states, set_device_states
# for routing arguments into the functions of the reversible layer
def route_args(router, args, depth):
routed_args = [(dict(), dic... | nuwa-pytorch-main | nuwa_pytorch/reversible.py |
from nuwa_pytorch.nuwa_pytorch import NUWA, NUWASketch, NUWAVideoAudio, Sparse3DNA, CrossModalityCrossAttention
from nuwa_pytorch.vqgan_vae import VQGanVAE
from nuwa_pytorch.train_vqgan_vae import VQGanVAETrainer
from nuwa_pytorch.train_nuwa import NUWATrainer
| nuwa-pytorch-main | nuwa_pytorch/__init__.py |
# take from https://github.com/openai/CLIP/blob/main/clip/simple_tokenizer.py
# to give users a quick easy start to training DALL-E without doing BPE
import torch
import html
import os
from functools import lru_cache
from pathlib import Path
import ftfy
import regex as re
# OpenAI simple tokenizer
@lru_cache()
def ... | nuwa-pytorch-main | nuwa_pytorch/tokenizer.py |
from math import sqrt
import copy
from random import choice
from pathlib import Path
from shutil import rmtree
import torch
from torch import nn
import numpy as np
from PIL import Image
from torchvision.datasets import ImageFolder
import torchvision.transforms as T
from torch.utils.data import Dataset, DataLoader, ra... | nuwa-pytorch-main | nuwa_pytorch/train_vqgan_vae.py |
import torch
import torch.nn as nn
from torch.autograd.function import Function
from contextlib import contextmanager
from nuwa_pytorch.reversible import Deterministic
from einops import reduce
# helpers
def exists(val):
return val is not None
@contextmanager
def null_context():
yield
def split_at_index(d... | nuwa-pytorch-main | nuwa_pytorch/reversible_video_audio.py |
import torch
from torch.optim import AdamW, Adam
# adamw functions
def separate_weight_decayable_params(params):
no_wd_params = set([param for param in params if param.ndim < 2])
wd_params = set(params) - no_wd_params
return wd_params, no_wd_params
def get_optimizer(
params,
lr = 3e-4,
wd = 1... | nuwa-pytorch-main | nuwa_pytorch/optimizer.py |
import copy
import math
from functools import partial, wraps
from math import sqrt
from vector_quantize_pytorch import VectorQuantize as VQ
import torchvision
import torch
from torch import nn, einsum
import torch.nn.functional as F
from torch.autograd import grad as torch_grad
from einops import rearrange, reduce, ... | nuwa-pytorch-main | nuwa_pytorch/vqgan_vae.py |
import functools
from functools import partial
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, reduce, repeat
from einops.layers.torch import Rearrange, Reduce
from nuwa_pytorch.reversible import ReversibleSequence
from nuwa_pytorch.reversible_video_audio impor... | nuwa-pytorch-main | nuwa_pytorch/nuwa_pytorch.py |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
from __future__ import absolute_import, print_function
import io
import os
import sys
from glob import glob
from os.path import basename
from os.path import dirname
from os.path import join
from os.path import splitext
from setuptools import find_packages
from setuptool... | bolt-master | setup.py |
#!/usr/bin/env python
# TODO maybe have sklearn transforms for dot prod and Lp dists
# TODO add L1 distance
from . import bolt # inner bolt because of SWIG
import kmc2 # state-of-the-art kmeans initialization (as of NIPS 2016)
import numpy as np
from sklearn import cluster, exceptions
# =========================... | bolt-master | python/bolt/bolt_api.py |
#!/usr/bin/env python
# note that we import module generate py file, not the generated
# wrapper so (which is _bolt)
from .bolt_api import * # noqa
| bolt-master | python/bolt/__init__.py |
#!/usr/bin/env python
# from future import absolute_import, division, print_function
import os
import numpy as np
# import pathlib as pl
from sklearn import linear_model
from scipy import signal
from python.datasets import caltech, sharee, incart, ucr
from python import misc_algorithms as algo
from python import win... | bolt-master | experiments/python/matmul_datasets.py |
#!/bin/env python
import numpy as np
import matplotlib.pyplot as plt
def main():
UNDEFINED = 7
M = 40000
# M = 500
# M = 2
# K = 16
# C = 64
try_Cs = np.array([2, 4, 8, 16, 32, 64, 128])
try_Us = np.array([2, 4, 8, 16, 32, 64, 128])
biases = np.zeros((try_Cs.size, try_Us.size))... | bolt-master | experiments/python/debias_scratch.py |
#!/bin/env/python
import os
import shutil
def ls(dir='.'):
return os.listdir(dir)
def is_hidden(path):
return os.path.basename(path).startswith('.')
def is_visible(path):
return not is_hidden(path)
def join_paths(dir, contents):
return [os.path.join(dir, f) for f in contents]
def files_matchi... | bolt-master | experiments/python/files.py |
import os
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
import results
from files import ensure_dir_exists
# CAMERA_READY_FONT = 'Calibri'
CAMERA_READY_FONT = 'DejaVu Sans'
SAVE_DIR = os.path.expanduser('~/Desktop/bolt/figs/')
ensure_dir_exists(SAVE_DI... | bolt-master | experiments/python/figs.py |
#!/bin/env/python
from __future__ import division
import numpy as np
import numba
from .utils import top_k_idxs
# ================================================================ eigenvecs
# @numba.jit(nopython=True) # don't jit since take like 2.5s
# def top_principal_component(X, niters=50, return_eigenval=Fal... | bolt-master | experiments/python/subspaces.py |
# first 3 functions taken from:
# http://www.johnvinyard.com/blog/?p=268
import numpy as np
from numpy.lib.stride_tricks import as_strided as ast
# from .arrays import normalizeMat
def norm_shape(shape):
'''
Normalize numpy array shapes so they're always expressed as a tuple,
even for one-dimensional s... | bolt-master | experiments/python/window.py |
#!#!/bin/env/python
from __future__ import print_function
import numpy as np
import torch
import torch.nn.functional as F
import torch.optim as optim
from .utils import kmeans
from joblib import Memory
_memory = Memory('.', verbose=0)
def _to_np(A):
return A.cpu().detach().numpy()
def _class_balanced_sampli... | bolt-master | experiments/python/misc_algorithms.py |
#!/usr/bin/env python
import os
import numpy as np
import pandas as pd
# TODO this file is hideous (but necessarily so for deadline purposes...)
#
# Also, this file is tightly coupled to figs.py; it basically has a func
# for each figure func that spits out data in exactly the required form
MCQ_RESULTS_DIR = '../re... | bolt-master | experiments/python/results.py |
#!/bin/env/python
import functools
import numpy as np
import pprint
import scipy
import time
from . import amm
from . import matmul_datasets as md
from . import pyience as pyn
from . import compress
from . import amm_methods as methods
from joblib import Memory
_memory = Memory('.', verbose=0)
# NUM_TRIALS = 1
NU... | bolt-master | experiments/python/amm_main.py |
#!/bin/env/python
import abc
import numpy as np
# from sklearn.decomposition import PCA, SparsePCA
from sklearn import decomposition
from sklearn.decomposition import PCA, SparsePCA, MiniBatchSparsePCA
from sklearn.utils.extmath import randomized_svd
import numba # conda install numba
# import ffht # https://github... | bolt-master | experiments/python/amm.py |
bolt-master | experiments/python/vq.py | |
#!/bin/env/python
import numpy as np
def energy(A):
if A.ndim < 2 or len(A) < 2:
return 0
diffs = A - A.mean(axis=0)
return np.sum(diffs * diffs)
def run_trial(N=100, D=3, seed=None):
if seed is not None:
np.random.seed(seed)
w0, w = np.random.randn(2, D)
X = np.random.ran... | bolt-master | experiments/python/submodular_scratch.py |
#!/bin/env/python
import collections
import os
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sb
import pandas as pd
import pathlib as pl
# from . import files
from . import amm_results2 as res
# from . import amm_methods as ameth
# sb.set_context('poster')
# sb.set_con... | bolt-master | experiments/python/amm_figs2.py |
bolt-master | experiments/python/__init__.py | |
#!/bin/env/python
from . import amm, vq_amm
METHOD_EXACT = 'Exact'
METHOD_SCALAR_QUANTIZE = 'ScalarQuantize'
METHOD_SKETCH_SQ_SAMPLE = 'SketchSqSample'
METHOD_SVD = 'SVD' # truncated SVD run on the matrix at test time
METHOD_FD_AMM = 'FD-AMM'
METHOD_COOCCUR = 'CooccurSketch'
METHOD_PCA = 'PCA' # PCA projection, wit... | bolt-master | experiments/python/amm_methods.py |
#!/usr/bin/env python
from __future__ import print_function
import numpy as np
import pprint
microbench_output = \
"""
ncodebooks = 4
amm bolt N, D, M, ncodebooks: 10000, 512, 10, 4 (5x20): 7.574 (4.225e+07/s), 7.582 (4.221e+07/s), 7.584 (4.219e+07/s), 7.587 (4.218e+07/s), 7.579 (4.222e+07/s),
amm bolt N, D, M,... | bolt-master | experiments/python/amm_results.py |
#!/usr/bin/env python
import itertools
import numpy as np
from sklearn import cluster
from scipy import signal
# import types
import kmc2 # state-of-the-art kmeans initialization (as of NIPS 2016)
from joblib import Memory
_memory = Memory('.', verbose=0)
# ========================================================... | bolt-master | experiments/python/utils.py |
#!/usr/bin/env python
import numpy as np
import numba
import zstandard as zstd # pip install zstandard
# ================================================================ Funcs
def nbits_cost(diffs, signed=True):
"""
>>> [nbits_cost(i) for i in [0, 1, 2, 3, 4, 5, 7, 8, 9]]
[0, 2, 3, 3, 4, 4, 4, 5, 5]
... | bolt-master | experiments/python/compress.py |
#!/usr/bin/env python
from __future__ import print_function
import os
import numpy as np
import pandas as pd
from io import StringIO
from . import amm_methods as methods
from joblib import Memory
_memory = Memory('.', verbose=1)
pd.options.mode.chained_assignment = None # suppress stupid warning
RESULTS_DIR = o... | bolt-master | experiments/python/amm_results2.py |
#!/usr/bin/env python
import abc
import numpy as np
from . import vquantizers as vq
from . import amm
KEY_NLOOKUPS = 'nlookups'
class VQMatmul(amm.ApproxMatmul, abc.ABC):
def __init__(self, ncodebooks, ncentroids=None):
self.ncodebooks = ncodebooks
self.ncentroids = (self._get_ncentroids() if n... | bolt-master | experiments/python/vq_amm.py |
#!/bin/env/python
"""utility functions for running experiments"""
from __future__ import print_function, absolute_import
import datetime
import os
import itertools
import warnings
import numpy as np
import pandas as pd
import sys
import sklearn
# from sklearn.model_selection import StratifiedKFold
from python.file... | bolt-master | experiments/python/pyience.py |
#!/usr/bin/env python
import functools
import matplotlib.pyplot as plt
import numpy as np
import os
from scipy.stats.stats import pearsonr
import seaborn as sb
import time
from collections import namedtuple
# import datasets
import files
import product_quantize as pq
import pyience as pyn
from datasets import neigh... | bolt-master | experiments/python/main.py |
#!/bin/env/python
import copy
import numpy as np
from functools import reduce
import numba
from sklearn.decomposition import PCA
from sklearn import linear_model
from . import subspaces as subs
from joblib import Memory
_memory = Memory('.', verbose=0)
# def bucket_id_to_new_bucket_ids(old_id):
# i = 2 * old_i... | bolt-master | experiments/python/clusterize.py |
#!/usr/bin/env python
from __future__ import division, absolute_import
import abc
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sb
from . import product_quantize as pq
from . import subspaces as subs
from . import clusterize
from .utils import kmeans
# =======================================... | bolt-master | experiments/python/vquantizers.py |
#!/bin/env/python
import os
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sb
import pandas as pd
import pathlib as pl
# from . import files
from . import amm_results as res
from . import amm_methods as ameth
sb.set_context('poster')
# sb.set_context('talk')
# sb.set_cmap('tab10')
RESULTS_DIR... | bolt-master | experiments/python/amm_figs.py |
#!/usr/bin/env python
import time
import numpy as np
from .utils import kmeans, orthonormalize_rows, random_rotation
from joblib import Memory
_memory = Memory('.', verbose=0)
# ================================================================ PQ
@_memory.cache
def learn_pq(X, ncentroids, nsubvects, subvect_len, m... | bolt-master | experiments/python/product_quantize.py |
#!/bin/env/python
import os
import shutil
def ls(dir='.'):
return os.listdir(dir)
def is_hidden(path):
return os.path.basename(path).startswith('.')
def is_visible(path):
return not is_hidden(path)
def join_paths(dir, contents):
return [os.path.join(dir, f) for f in contents]
def files_matchi... | bolt-master | experiments/python/datasets/files.py |
#!/bin/env python
from __future__ import absolute_import, division, print_function
import numpy as np
import os
import PIL
from PIL import Image
from PIL import ImageOps # can't just do PIL.ImageOps for some reason
from . import files
# ================================ TODO rm duplicate code from imagenet.py
# ... | bolt-master | experiments/python/datasets/image_utils.py |
#!/bin/env python
from __future__ import print_function
import numpy as np
import os
import warnings
import h5py
from sklearn.datasets import load_digits
import keras
from keras.preprocessing import image
# from python import imagenet, svhn, caltech
# from python.datasets import caltech
from . import imagenet
from .... | bolt-master | experiments/python/datasets/image_classify.py |
#!/bin/env python
from __future__ import division, print_function
import numpy as np
# import pyedflib as edf # pip install pyedflib
# import mne
from . import paths
from . import files
ECG_DIR = paths.UCD_ECG
NUM_RECORDINGS = 25
def main():
pass
print("ecg dir: ", ECG_DIR)
fpaths = files.list_files(... | bolt-master | experiments/python/datasets/ucddb.py |
#!/usr/env/python
import os
DATASETS_DIR = os.path.expanduser("~/Desktop/datasets/")
def to_path(*args):
return os.path.join(DATASETS_DIR, *args)
# straightforward datasets
MSRC_12 = to_path('MSRC-12', 'origData')
UCR = to_path('ucr/UCRArchive_2018')
UCR_INFO = to_path('ucr/DataSummary.csv')
UWAVE = to_path('... | bolt-master | experiments/python/datasets/paths.py |
#!/bin/env python
import os
import numpy as np
from sklearn.datasets.samples_generator import make_blobs
from joblib import Memory
_memory = Memory('.', verbose=1)
DATA_DIR = os.path.expanduser('~/Desktop/datasets/nn-search')
join = os.path.join
class Random:
UNIFORM = 'uniform'
GAUSS = 'gauss'
WALK = ... | bolt-master | experiments/python/datasets/neighbors.py |
#!/usr/bin/env python
import os
# import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sb
from joblib import Memory
from . import paths
from . import files
_memory = Memory('./')
def _list_csvs(directory):
return files.list_files(directory, endswith=... | bolt-master | experiments/python/datasets/ampds.py |
#!/bin/env python
from __future__ import absolute_import, division, print_function
import numpy as np
import os
import PIL
import pickle
import psutil # pip install psutil
import shutil
import sys # just for stderr for warnings
# import warnings
from PIL import Image
from python import files
from python import im... | bolt-master | experiments/python/datasets/imagenet.py |
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