pratos

Build a docker image

• Make sure you are in the folder that contains the Dockerfile
• If your folder is /my-docker-image/, there should be 2 files in your folder:

  /my-docker-image
  |
  |---Dockerfile
  |---requirements.txt
  

pratos

Creating multiple subplots to display numpy images:

def create_subplots(images, labels):
    plt.subplot()
    for _ in range(0, len(images)):
        
        plt.rcParams["figure.figsize"] = [10,20]
        plt.subplot(len(images)/2,len(images)/5, _+1)
        plt.xticks([]), plt.yticks([])
        plt.imshow(npimg, cmap = 'gray')

pratos

require(GGally)
lm.plt <- function(data, mapping, ...){
   plt <- ggplot(data = data, mapping = mapping) + 
    geom_point(shape = 20, alpha = 0.7, color = 'darkseagreen') +
    geom_smooth(method=loess, fill="red", color="red") +
    geom_smooth(method=lm, fill="blue", color="blue") +
    theme_minimal()
  return(plt)
}

pratos

list.of.packages <- c("ggplot2", "parallel", "tidyverse", "pROC", "caret", "corrplot", "doParallel", "dummies", "futile.logger")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]

# Check whether the packages listed are installed or not
# If not then they are installed
if(length(new.packages)) {
  print("Installing new packages")
  install.packages(new.packages, repos = "http://cran.us.r-project.org")
}

pratos

source("./src/load_package.R")
flog.info("Loading the German Credit Card Dataset")
# Load Dataset

german_credit <- read.table("./assets/data/german.data", fileEncoding="UTF-8" , dec = ",")
head(german_credit)

flog.info("Renaming the Columns")
colnames(german_credit) <- c('status', 'duration', 'credit_history', 'purpose', 'credit_amount', 'savings_account', 'employment', 'installment_rate','status_sex', 'guarantors', 'residence', 'property', 'age', 'other_installment', 'housing', 'existing_credits', 'job', 'maintainence_people','telephone', 'foreign', 'rating')

pratos

source("./src/eda.R")
# Importing the intermediate data

flog.info("Loading the intermediate data")
german_credit <- readRDS("./assets/intermediate-files/intermediate_german_data.rds")

german_credit$rating <- ifelse(german_credit$rating == 1, "good", "bad")

# Checking for missing values
# unlist(lapply(german_credit, function(x) sum(is.na(x))))

pratos

#setwd("~/difference-engine/docker-for-data-science-r/")
source("./src/fe-train.R")

set.seed(42)

# Parallelizing the modelling
# NOTE: Try not to use all the cores
doParallel::registerDoParallel(parallel::detectCores() - 2)

# Write the ML Code here

pratos

FROM rocker/tidyverse

RUN apt-get update && apt-get -y upgrade && apt-get install -y \
	build-essential libssl-dev libffi-dev libxml2-dev libcurl4-openssl-dev

RUN mkdir /home/rstudio/data /home/rstudio/models

VOLUME ['/home/rstudio/data', '/home/rstudio/models']

RUN Rscript -e "install.packages(c('dummy', 'corrplot', 'pROC'), dependencies=TRUE)"

pratos

FROM rocker/r-base

RUN apt-get update && apt-get -y upgrade && apt-get install -y \
	build-essential libssl-dev libffi-dev libxml2-dev libcurl4-openssl-dev

RUN Rscript -e "install.packages(c('caret', 'tidyverse', 'gbm', 'pROC', 'corrplot', 'doParallel', 'dummies', 'futile.logger'), dependencies=TRUE)"

ENV INSTALL_PATH /germancc

RUN mkdir -p $INSTALL_PATH

pratos

def timeit(method):
    def timing(*args, **kwargs):
        timings = []
        print("Running this 1000 loops, for benchmarking")
        
        for i in range(1000):  
            start = time.time()
            result = method(*args, **kwargs)
            end = time.time()