wlinInspire

import os
import pickle
import caffe
import cv2
import numpy as np


# ======================================================================================================================
# Off load params for each net in a pickle file
# ======================================================================================================================

wlinInspire

# Connect DB function
connect_db <- function() {

  pgsql <- RJDBC::JDBC("com.amazon.redshift.jdbc42.Driver",
                       "./jdbc_driver/RedshiftJDBC42-1.2.16.1027.jar",
                       "`")

  host <- "redshift-cluster-1.abc1234.us-east-2.redshift.amazonaws.com"
  port <- '5439'
  user <- 'abc1234'

wlinInspire

.First <- function(){

  # Set Java Heap Space
  options(java.parameters = "-Xmx25g")
  
  # Load common packages
  library(ggplot2)
  library(tidyverse)
  library(data.table)

wlinInspire

library(survival)
library(data.table)
library(ggplot2)

data = read.csv('https://raw.githubusercontent.com/IBM/invoke-wml-using-cognos-custom-control/master/data/Telco-Customer-Churn.csv')
setDT(data)

churn_data <- data[, churn_flag := ifelse(Churn == 'Yes', 1, 0)]

km_curve <- survfit(Surv(tenure, churn_flag) ~ 1, data=churn_data)

wlinInspire

library(survival)
library(data.table)
library(ggplot2)

data = read.csv('https://raw.githubusercontent.com/IBM/invoke-wml-using-cognos-custom-control/master/data/Telco-Customer-Churn.csv')
setDT(data)

churn_data <- data[, churn_flag := ifelse(Churn == 'Yes', 1, 0)]

km_curve <- survfit(Surv(tenure, churn_flag) ~ 1, data=churn_data)

wlinInspire

library(survival)
library(data.table)
library(ggplot2)

data = fread('https://raw.githubusercontent.com/IBM/invoke-wml-using-cognos-custom-control/master/data/Telco-Customer-Churn.csv')

churn_data <- data[, .(churn_flag = ifelse(Churn == 'Yes', 1, 0), tenure)]
km_curve <- survfit(Surv(tenure, churn_flag) ~ 1, data=churn_data)

# Calculate Survival and Churn Rate

wlinInspire

library(survival)
library(data.table)
library(ggplot2)

data <- fread('https://raw.githubusercontent.com/IBM/invoke-wml-using-cognos-custom-control/master/data/Telco-Customer-Churn.csv')

churn_data <- data[, .(churn_flag = ifelse(Churn == 'Yes', 1, 0), tenure)]
km_curve <- survfit(Surv(tenure, churn_flag) ~ 1, data=churn_data)

# Calculate Survival and Churn Rate

wlinInspire

h2o_start <- function() {
    h2o::h2o.init(nthreads = -1, enable_assertions = FALSE, min_mem_size = '8g',
                  strict_version_check = FALSE, port = 54321)
}

h2o_stop <- function() {
    h2o::h2o.shutdown(prompt = FALSE)
}

wlinInspire

cc <- function(df) {
    print(df %>% is.na() %>% colSums())
}

cd <- function(df) {
    print(df %>% is.na() %>% colMeans())
}

wlinInspire

quick_date <- function(x, …) {
    if (anyDuplicated(x)) {
        ux <- unique(x)
        idx <- match(x, ux)
        y <- as.Date.character(ux, …)
        return(y[idx])
    }
    as.Date.character(x, …)
}