GitHub Repository: debakarr/machinelearning
Path: blob/master/Part 8 - Deep Learning/Artificial Neural Networks/[R] Artificial Neural Network.ipynb
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Kernel: R

Artificial Neural Network

Like in Python, we will not have to use our own resource to train the ANN. Here we will use h2o package which connect to a server and train the ANN on that. More importantly h2o have parameter tunning feature which automatically tune the parameter as per requirement.

Data preprocessing

In [1]:

# Importing the dataset
dataset = read.csv('Churn_Modelling.csv')

In [2]:

head(dataset, 5)

Out[2]:

In [3]:

dataset = dataset[4:14]
dataset = dataset[-c(3)]

In [4]:

head(dataset, 5)

Out[4]:

In [5]:

# Encoding the categorical variables as factors
dataset$Geography = as.numeric(factor(dataset$Geography,
                                      levels = c('France', 'Spain', 'Germany'),
                                      labels = c(1, 2, 3)))

In [6]:

# Splitting the dataset into the Training set and Test set
# install.packages('caTools')
library(caTools)
set.seed(1234)
split = sample.split(dataset$Exited, SplitRatio = 0.8)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)

In [7]:

# Feature Scaling
training_set[-10] = scale(training_set[-10])
test_set[-10] = scale(test_set[-10])

In [8]:

head(training_set, 10)

Out[8]:

In [9]:

head(test_set, 10)

Out[9]:

Fiting ANN to the Training set

In [10]:

# install.packages('h2o')

In [11]:

library(h2o)
h2o.init(nthreads = -1)

Out[11]:

----------------------------------------------------------------------

Your next step is to start H2O:
    > h2o.init()

For H2O package documentation, ask for help:
    > ??h2o

After starting H2O, you can use the Web UI at http://localhost:54321
For more information visit http://docs.h2o.ai

----------------------------------------------------------------------


Attaching package: ‘h2o’

The following objects are masked from ‘package:stats’:

    cor, sd, var

The following objects are masked from ‘package:base’:

    &&, %*%, %in%, ||, apply, as.factor, as.numeric, colnames,
    colnames<-, ifelse, is.character, is.factor, is.numeric, log,
    log10, log1p, log2, round, signif, trunc

H2O is not running yet, starting it now...

Note:  In case of errors look at the following log files:
    /tmp/Rtmp8aN7Ch/h2o_baka_started_from_r.out
    /tmp/Rtmp8aN7Ch/h2o_baka_started_from_r.err


Starting H2O JVM and connecting: ....... Connection successful!

R is connected to the H2O cluster: 
    H2O cluster uptime:         5 seconds 838 milliseconds 
    H2O cluster version:        3.16.0.2 
    H2O cluster version age:    27 days  
    H2O cluster name:           H2O_started_from_R_baka_fmq912 
    H2O cluster total nodes:    1 
    H2O cluster total memory:   0.82 GB 
    H2O cluster total cores:    4 
    H2O cluster allowed cores:  4 
    H2O cluster healthy:        TRUE 
    H2O Connection ip:          localhost 
    H2O Connection port:        54321 
    H2O Connection proxy:       NA 
    H2O Internal Security:      FALSE 
    H2O API Extensions:         XGBoost, Algos, AutoML, Core V3, Core V4 
    R Version:                  R version 3.4.2 (2017-09-28) 

In [12]:

classifier = h2o.deeplearning(y = 'Exited', 
                              training_frame = as.h2o(training_set),
                              activation = 'Rectifier',
                              hidden = c(6, 6),
                              epochs = 100,
                              train_samples_per_iteration = -2)

Out[12]:

  |======================================================================| 100%
  |======================================================================| 100%

Predicting the Test set results

In [13]:

prob_pred = h2o.predict(classifier, 
                        newdata = as.h2o(test_set[-10]))
y_pred = (prob_pred > 0.5)

# Convert h2o object to vector
y_pred = as.vector(y_pred)

Out[13]:

  |======================================================================| 100%
  |======================================================================| 100%

In [14]:

head(y_pred, 20)

Out[14]:

In [15]:

head(test_set[, 10], 20)

Out[15]:

Making the Confusion Matrix

In [16]:

cm = table(test_set[, 10], y_pred)
cm

Out[16]:

   y_pred
       0    1
  0 1530   63
  1  218  189

Calculating Accuracy

In [17]:

(cm[1, 1] + cm[2, 2]) / 2000

Out[17]:

Disconnect from server

In [18]:

h2o.shutdown()

Out[18]:

Are you sure you want to shutdown the H2O instance running at http://localhost:54321/ (Y/N)? y