Machine Learning with H2O - Tutorial 1: Data in H2O with R

In [1]:

    

# Start and connect to a local H2O cluster
suppressPackageStartupMessages(library(h2o))
h2o.init(nthreads = -1)


    

    




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

Note:  In case of errors look at the following log files:
    /tmp/RtmpBf35BI/h2o_joe_started_from_r.out
    /tmp/RtmpBf35BI/h2o_joe_started_from_r.err


Starting H2O JVM and connecting: .. Connection successful!

R is connected to the H2O cluster: 
    H2O cluster uptime:         2 seconds 553 milliseconds 
    H2O cluster version:        3.10.4.4 
    H2O cluster version age:    5 days  
    H2O cluster name:           H2O_started_from_R_joe_esh642 
    H2O cluster total nodes:    1 
    H2O cluster total memory:   5.21 GB 
    H2O cluster total cores:    8 
    H2O cluster allowed cores:  8 
    H2O cluster healthy:        TRUE 
    H2O Connection ip:          localhost 
    H2O Connection port:        54321 
    H2O Connection proxy:       NA 
    H2O Internal Security:      FALSE 
    R Version:                  R version 3.3.2 (2016-10-31) 

In [2]:

    

# Method 1 - Import data from a local CSV file
data_from_csv = h2o.importFile("winequality-white.csv")
head(data_from_csv, 5)


    

    




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






    






fixed acidity
volatile acidity
citric acid
residual sugar
chlorides
free sulfur dioxide
total sulfur dioxide
density
pH
sulphates
alcohol
quality

	
7.0   
0.27  
0.36  
20.7  
0.045 
45    
170   
1.0010
3.00  
0.45  
 8.8  
6     
	
6.3   
0.30  
0.34  
 1.6  
0.049 
14    
132   
0.9940
3.30  
0.49  
 9.5  
6     
	
8.1   
0.28  
0.40  
 6.9  
0.050 
30    
 97   
0.9951
3.26  
0.44  
10.1  
6     
	
7.2   
0.23  
0.32  
 8.5  
0.058 
47    
186   
0.9956
3.19  
0.40  
 9.9  
6     
	
7.2   
0.23  
0.32  
 8.5  
0.058 
47    
186   
0.9956
3.19  
0.40  
 9.9  
6     

In [3]:

    

# Method 2 - Import data from the web
data_from_web = h2o.importFile("https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv")
head(data_from_web, 5)


    

    




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






    






fixed acidity
volatile acidity
citric acid
residual sugar
chlorides
free sulfur dioxide
total sulfur dioxide
density
pH
sulphates
alcohol
quality

	
7.0   
0.27  
0.36  
20.7  
0.045 
45    
170   
1.0010
3.00  
0.45  
 8.8  
6     
	
6.3   
0.30  
0.34  
 1.6  
0.049 
14    
132   
0.9940
3.30  
0.49  
 9.5  
6     
	
8.1   
0.28  
0.40  
 6.9  
0.050 
30    
 97   
0.9951
3.26  
0.44  
10.1  
6     
	
7.2   
0.23  
0.32  
 8.5  
0.058 
47    
186   
0.9956
3.19  
0.40  
 9.9  
6     
	
7.2   
0.23  
0.32  
 8.5  
0.058 
47    
186   
0.9956
3.19  
0.40  
 9.9  
6     

In [4]:

    

# Method 3 - Convert R data frame into H2O data frame

## Import Wine Quality data using R
wine_df = read.csv('winequality-white.csv', sep = ';')
head(wine_df, 5)


    

    






fixed.acidity
volatile.acidity
citric.acid
residual.sugar
chlorides
free.sulfur.dioxide
total.sulfur.dioxide
density
pH
sulphates
alcohol
quality

	
7.0   
0.27  
0.36  
20.7  
0.045 
45    
170   
1.0010
3.00  
0.45  
 8.8  
6     
	
6.3   
0.30  
0.34  
 1.6  
0.049 
14    
132   
0.9940
3.30  
0.49  
 9.5  
6     
	
8.1   
0.28  
0.40  
 6.9  
0.050 
30    
 97   
0.9951
3.26  
0.44  
10.1  
6     
	
7.2   
0.23  
0.32  
 8.5  
0.058 
47    
186   
0.9956
3.19  
0.40  
 9.9  
6     
	
7.2   
0.23  
0.32  
 8.5  
0.058 
47    
186   
0.9956
3.19  
0.40  
 9.9  
6     

In [5]:

    

## Convert R data frame into H2O data frame
data_from_df = as.h2o(wine_df)
head(data_from_df, 5)


    

    




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






    






fixed.acidity
volatile.acidity
citric.acid
residual.sugar
chlorides
free.sulfur.dioxide
total.sulfur.dioxide
density
pH
sulphates
alcohol
quality

	
7.0   
0.27  
0.36  
20.7  
0.045 
45    
170   
1.0010
3.00  
0.45  
 8.8  
6     
	
6.3   
0.30  
0.34  
 1.6  
0.049 
14    
132   
0.9940
3.30  
0.49  
 9.5  
6     
	
8.1   
0.28  
0.40  
 6.9  
0.050 
30    
 97   
0.9951
3.26  
0.44  
10.1  
6     
	
7.2   
0.23  
0.32  
 8.5  
0.058 
47    
186   
0.9956
3.19  
0.40  
 9.9  
6     
	
7.2   
0.23  
0.32  
 8.5  
0.058 
47    
186   
0.9956
3.19  
0.40  
 9.9  
6