ledell

# Set API Key
Sys.setenv(SIGOPT_API_TOKEN="HERE")

# Start a local H2O cluster for training models
library(h2o)
h2o.init(nthreads = -1)

# Load a dataset
data(iris)
y <- "Species"

mrecos

### ------- Load Packages ---------- ###
library("purrr")
library("future")
library("dplyr")
library("randomForest")
library("rsample")
library("ggplot2")
library("viridis")
### ------- Helper Functions for map() ---------- ###
# breaks CV splits into train (analysis) and test (assessmnet) sets

JohnMount

#!/bin/bash

# on local
pempath="$1"
ec2target="$2"

ssh -T -i "${pempath}" -o UserKnownHostsFile=/dev/null -o StrictHostKeyChecking=no ubuntu@${ec2target} << 'EOBLOCK'
# on remote machine
sudo apt-get -y update
sudo apt-get -y upgrade

doraneko94

from sklearn.metrics import roc_auc_score
from math import sqrt

def roc_auc_ci(y_true, y_score, positive=1):
    AUC = roc_auc_score(y_true, y_score)
    N1 = sum(y_true == positive)
    N2 = sum(y_true != positive)
    Q1 = AUC / (2 - AUC)
    Q2 = 2*AUC**2 / (1 + AUC)
    SE_AUC = sqrt((AUC*(1 - AUC) + (N1 - 1)*(Q1 - AUC**2) + (N2 - 1)*(Q2 - AUC**2)) / (N1*N2))

aditya-malte

sellorm

render_with_jobs <- function(){
  rstudioapi::verifyAvailable()
  jobs_file <- tempfile(tmpdir = "/tmp", fileext = ".R")
  rmd_to_render <- rstudioapi::selectFile(caption = "Choose an Rmd file...",
                                          filter = "Rmd files (*.Rmd)")
  if (is.null(rmd_to_render)){
    stop("You must choose an Rmd file to proceed!")
  }
  cat(paste0('rmarkdown::render("', rmd_to_render, '")'), file = jobs_file)
  rstudioapi::jobRunScript(path = jobs_file,