bs4.FeatureNotFound: Couldn't find a tree builder with the features you requested: lxml. Do you need to install a parser library?

 pip install lxml

get parameter from parameter store of aws system manager, python example code

firstly create parameter in parameter store of AWS system manager 

Then get the key from this code.

code

import boto3

ssm = boto3.client('ssm')

parameter = ssm.get_parameter(Name='/project/key_endpoint', WithDecryption=True)

print(parameter['Parameter']['Value'])

.

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)