Loading required libraries

• h2o – for Machine Learning
• tidyverse – for Data Manipulation

library(h2o)
library(tidyverse)

Initializing H2O Cluster

h2o::h2o.init()

Reading Input Files (Data)

train <- read_csv("~/Documents/R Codes/Kannada-MNIST/train.csv")
test <- read_csv("~/Documents/R Codes/Kannada-MNIST/test.csv")
valid <- read_csv("~/Documents/R Codes/Kannada-MNIST/Dig-MNIST.csv")
submission <- read_csv("~/Documents/R Codes/Kannada-MNIST//sample_submission.csv")

Checking the shape / dimension of the dataframe

dim(train)

784 Pixel Values + 1 Label denoting what digit it’s.

Label Count

train  %>% count(label)

Visualizing the Kannada MNIST Digits

# visualize the digits
par(mfcol=c(6,6))

par(mar=c(0, 0, 3, 0), xaxs='i', yaxs='i')

for (idx in 1:36) { 

im<-matrix((train[idx,2:ncol(train)]), nrow=28, ncol=28)

im_numbers <- apply(im, 2, as.numeric)

image(1:28, 1:28, im_numbers, col=gray((0:255)/255), main=paste(train$label[idx]))
}

Converting R dataframe to H2O object which is required by H2O functions

train_h <- as.h2o(train)
test_h <- as.h2o(test)
valid_h <- as.h2o(valid)

Converting our numeric target variable into a factor for the algorithm to perform Classification

train_h$label <- as.factor(train_h$label)
valid_h$label <- as.factor(valid_h$label)

Explanatory and Response Variables

x <- names(train)[-1]
y <- 'label'

AutoML in Action

aml <- h2o::h2o.automl(x = x, 
                       y = y,
                       training_frame = train_h,
                       nfolds = 3,
                       leaderboard_frame = valid_h,
                       max_runtime_secs = 1000)

nfolds denotes the number of folds for cross-validation and max_runtime_secs represents the maximum amount of time the AutoML process can go on.

AutoML Leaderboard

Leaderboard is where the AutoML lists the top performing Models.

aml@leaderboard

Prediction and Submission

pred <- h2o.predict(aml, test_h)  

submission$label <- as.vector(pred$predict)

#write_csv(submission, "submission_automl.csv")

Submission (for Kaggle)

write_csv(submission, "submission_automl.csv")

This is currently a playground Competition on Kaggle. So, this submission file can be submitted to this competition. Based on the above parameters the submission scored 0.90720 in the public leaderboard. 0.90 score in an MNIST Classification is close to nothing, but I hope this code snippet can serve as quick starter template for anyone attempting to begin with AutoML.

References

• Kannada MNIST H2O AutoML in R - Kaggle Notebook
• Kannada-MNIST Kaggle Competition
• H2O AutoML Docs

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