s3 bucket copy object to another bucket, python example

 code

def copy_s3_object(s3_resource, source_bucket_name, source_key, target_bucket_name, target_key):

copy_source = {'Bucket': source_bucket_name, 'Key': source_key}

s3_resource.meta.client.copy(copy_source, target_bucket_name, target_key)

s3_resource = boto3.resource('s3')

copy_s3_object(s3_resource, source_bucket_name, source_key, target_bucket_name, target_key)

.

aws s3 get all object more than 1000 python example code

simply to use paginator instance

example code

paginator = s3_client.get_paginator('list_objects_v2')

pages = paginator.paginate(Bucket='bucket', Prefix='folder1/')

for page in pages:

for obj in page['Contents']:

print(obj['Key'])

.

sci sparse -> tuple list -> sci sparse

 refer to below source code

source code start

from scipy.sparse import csr_matrix

#sci sparse to tuple list

c = A2.tocoo() #A2 is scipy.sparse.csr.csr_matrix

in_edge_idx = list(zip(c.row, c.col)) #make tuple list

#tuple list to sci sparse

two_list = list(map(list, zip(*in_edge_idx))) #tuple 2 tow list of list [[1,2,3], [2,3,4]]

rows = np.array(two_list[0]) #rows

cols = np.array(two_list[1]) #cols

data_num = len(rows) #number of edge

data = np.ones( data_num ) #edge value

dim = len(x_data) #N x N adj

#sci sparse -> tuple list -> sci sparse

re_edge_idx = csr_matrix((data, (rows, cols)), shape=(dim, dim))

print('in', A2, type(A2))

print('re', re_edge_idx, type(re_edge_idx))

#origin A2 and re-generated edge index same?

print( (A2!=re_edge_idx).nnz==0 )

source code end

VS code SSH fails to connect: Connecting was canceled

Type this one in server side.

rm -rf ~/.vscode-server/

draw roc curve using python sklearn, Matplotlib

import matplotlib.pyplot as plt

from sklearn.metrics import roc_auc_score, average_precision_score

from sklearn import metrics

gt = [1, 0, 1, 0, 1, 1] #origin

pre = [0.9, 0.5, 0.8, 0.4, 0.5, 0.8] #predict

fpr, tpr, thresholds = metrics.roc_curve(gt, pre)

roc_auc = metrics.auc(fpr, tpr)

fig, ax = plt.subplots(figsize=(10,7))

ax.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)

ax.plot(np.linspace(0, 1, 100),

np.linspace(0, 1, 100),

label='baseline',

linestyle='--')

plt.title('Receiver Operating Characteristic Curve', fontsize=18)

plt.ylabel('TPR', fontsize=16)

plt.xlabel('FPR', fontsize=16)

plt.legend(fontsize=12) 

print gpu memory status in python

*install pynvml

https://pypi.org/project/pynvml/

pip install pynvml

*use below code in python code

from pynvml import *
nvmlInit()
h = nvmlDeviceGetHandleByIndex(0)
info = nvmlDeviceGetMemoryInfo(h)
print(f'total    : {info.total}')
print(f'free     : {info.free}')
print(f'used     : {info.used}')

remove duplicated tuple item in list (python code)

 

print(tuple_list)

tuple_list = [ tuple(sorted(tuple_list[i])) for i in range(len(tuple_list))]

tuple_list = list(set(tuple_list))

print(tuple_list)

before:

[(0, 0), (0, 1), (0, 3), (1, 0), (1, 1), (1, 2), (2, 1), (2, 2), (2, 3), (3, 0), (3, 2), (3, 3)]

After:
[(0, 1), (1, 2), (0, 0), (3, 3), (2, 3), (2, 2), (0, 3), (1, 1)]