yoshipon

nrimsky

import torch as t
from torch.autograd import grad
from scipy.sparse.linalg import LinearOperator, eigsh
import numpy as np

def get_hessian_eigenvectors(model, loss_fn, train_data_loader, num_batches, device, n_top_vectors, param_extract_fn):
    """
    model: a pytorch model
    loss_fn: a pytorch loss function
    train_data_loader: a pytorch data loader

kumagi

Copyright 2023 Hiroki Kumazaki

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, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR O

atksh

import warnings
warnings.filterwarnings('ignore')

import pymc3 as pm
from pymc3.distributions.dist_math import bound
import theano.tensor as tt
import theano
import numpy as np
np.random.seed(seed=32)

abikoushi

narrowlyapplicable

twiecki

import dask
import dask.array as da
import dask.dataframe as dd
import sparse

@dask.delayed(pure=True)
def corr_on_chunked(chunk1, chunk2, corr_thresh=0.9):
    return sparse.COO.from_numpy((np.dot(chunk1, chunk2.T) > corr_thresh))
        
def chunked_corr_sparse_dask(data, chunksize=5000, corr_thresh=0.9):

IshitaTakeshi

*.csv

genkuroki

jbburant

## GOAL:
## re-create a figure similar to Fig. 2 in Wilson et al. (2018), 
## Nature 554: 183-188. Available from:
## https://www.nature.com/articles/nature25479#s1
##
## combines a boxplot (or violin) with the raw data, by splitting each
## category location in two (box on left, raw data on right)


# initial set-up ----------------------------------------------------------