aflansburg

# List all provisioned IP addresses for a subnet
aws ec2 describe-network-interfaces \
--filters Name=subnet-id,Values=<subnet id> \
| jq -r '.NetworkInterfaces[].PrivateIpAddress' | sort

aflansburg

# want to decode the output of the `kubectl get secret secname` command
# and specifically the `auth` value
# when we maybe don't know the value of the key corresponding to a container registry
# {
#  "auths": {
#    "https://index.docker.io/v1/": {
#      "username": "smittywerbenjagermanjensen",
#      "password": "sup3rs3cr3t",
#      "email": "swjmjensen12345@example.com",
#      "auth": "c21pdHR5d2VyYmVuamFnZXJtYW5qZW5zZW46c3VwM3JzM2NyM3QK"

aflansburg

Keybase proof

I hereby claim:

• I am aflansburg on github.
• I am aflansburg (https://keybase.io/aflansburg) on keybase.
• I have a public key ASDRD9OAl-WrrItDis5f65_Hcj4uVQIz9hP0_8jKRtOz6wo

To claim this, I am signing this object:

aflansburg

{
  "exclusions": [
    "email",
    "firstName",
    "lastName",
    "phoneNumber",
    "street1",
    "street2",
    "city",
    "state",

aflansburg

/* Finds text content in some element and redacts it
   with the provided mask value
*/
function redactContent(textValue, tagType, maskValue){
  for (const tag of document.querySelectorAll(tagType)) {
    if (tag.textContent.includes(textValue)) {
        tag.textContent = maskValue;
    }}
}

aflansburg

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
	<key>Ansi 0 Color</key>
	<dict>
		<key>Alpha Component</key>
		<real>1</real>
		<key>Blue Component</key>
		<real>0.13725490868091583</real>

aflansburg

# import initial libs to do EDA
import pandas as pd
import numpy as np
import random

# viz libs
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches

%matplotlib inline

aflansburg

{
  "Ansi 5 Color" : {
    "Red Component" : 0.022819328308105468,
    "Color Space" : "sRGB",
    "Blue Component" : 0.8666666666666667,
    "Alpha Component" : 1,
    "Green Component" : 0.83805519318199617
  },
  "Tags" : [

aflansburg

# import time - not the abstract construct of 'time'
# but rather a library built into Python for
# dealing with time
from time import time

# ML stuff
ada_tuned_clf = AdaBoostClassifier(random_state=1)

# some canned params for hypertuning
parameters = {

aflansburg

# runtime info based on solution below and fit_time results of the gridsearchcv return object
# based on a response on StackExchange Data Science - Naveen Vuppula
# https://datascience.stackexchange.com/a/93524/41883
# from time import time
def gridsearch_runtime(grid_obj, X_train, y_train):
  '''
  Parameters:
    grid_obj: GridSearchCV return object that has not yet been fit to training data
    X_train: split training data independent variables
    y_train: split training data containing dependent variable