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

Objective:

This tutorial demonstrates three different ways to import data into H2O.

Wine Quality Dataset:

Methods:

Import data from a local CSV file.
Import data from the web.
Convert a Pandas data frame into H2O data frame.

Full Technical Reference:

http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/data.html



In [1]:

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









    



Checking whether there is an H2O instance running at http://localhost:54321..... not found.
Attempting to start a local H2O server...
  Java Version: openjdk version "1.8.0_131"; OpenJDK Runtime Environment (build 1.8.0_131-8u131-b11-0ubuntu1.16.04.2-b11); OpenJDK 64-Bit Server VM (build 25.131-b11, mixed mode)
  Starting server from /home/joe/anaconda3/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar
  Ice root: /tmp/tmp2g_oakfh
  JVM stdout: /tmp/tmp2g_oakfh/h2o_joe_started_from_python.out
  JVM stderr: /tmp/tmp2g_oakfh/h2o_joe_started_from_python.err
  Server is running at http://127.0.0.1:54321
Connecting to H2O server at http://127.0.0.1:54321... successful.






    




H2O cluster uptime:
01 secs
H2O cluster version:
3.10.5.2
H2O cluster version age:
10 days 
H2O cluster name:
H2O_from_python_joe_oc2161
H2O cluster total nodes:
1
H2O cluster free memory:
5.210 Gb
H2O cluster total cores:
8
H2O cluster allowed cores:
8
H2O cluster status:
accepting new members, healthy
H2O connection url:
http://127.0.0.1:54321
H2O connection proxy:
None
H2O internal security:
False
Python version:
3.6.1 final



In [2]:

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









    



Parse progress: |█████████████████████████████████████████████████████████| 100%






    






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


            7                0.27          0.36             20.7       0.045                    45                    170    1.001 3          0.45       8.8         6
            6.3               0.3          0.34              1.6       0.049                    14                    132    0.994 3.3        0.49       9.5         6
            8.1               0.28          0.4              6.9       0.05                    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        0.4       9.9         6
            7.2               0.23          0.32              8.5       0.058                    47                    186    0.9956 3.19        0.4       9.9         6








    Out[2]:



In [3]:

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









    



Parse progress: |█████████████████████████████████████████████████████████| 100%






    






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


            7                0.27          0.36             20.7       0.045                    45                    170    1.001 3          0.45       8.8         6
            6.3               0.3          0.34              1.6       0.049                    14                    132    0.994 3.3        0.49       9.5         6
            8.1               0.28          0.4              6.9       0.05                    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        0.4       9.9         6
            7.2               0.23          0.32              8.5       0.058                    47                    186    0.9956 3.19        0.4       9.9         6








    Out[3]:



In [4]:

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

## Import Wine Quality data using Pandas
import pandas as pd
wine_df = pd.read_csv('winequality-white.csv', sep = ';')
wine_df.head(5)









    Out[4]:







  
    
      
      fixed acidity
      volatile acidity
      citric acid
      residual sugar
      chlorides
      free sulfur dioxide
      total sulfur dioxide
      density
      pH
      sulphates
      alcohol
      quality
    
  
  
    
      0
      7.0
      0.27
      0.36
      20.7
      0.045
      45.0
      170.0
      1.0010
      3.00
      0.45
      8.8
      6
    
    
      1
      6.3
      0.30
      0.34
      1.6
      0.049
      14.0
      132.0
      0.9940
      3.30
      0.49
      9.5
      6
    
    
      2
      8.1
      0.28
      0.40
      6.9
      0.050
      30.0
      97.0
      0.9951
      3.26
      0.44
      10.1
      6
    
    
      3
      7.2
      0.23
      0.32
      8.5
      0.058
      47.0
      186.0
      0.9956
      3.19
      0.40
      9.9
      6
    
    
      4
      7.2
      0.23
      0.32
      8.5
      0.058
      47.0
      186.0
      0.9956
      3.19
      0.40
      9.9
      6



In [5]:

    
## Convert Pandas data frame into H2O data frame
data_from_df = h2o.H2OFrame(wine_df)
data_from_df.head(5)









    



Parse progress: |█████████████████████████████████████████████████████████| 100%






    






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


            7                0.27          0.36             20.7       0.045                    45                    170    1.001 3          0.45       8.8         6
            6.3               0.3          0.34              1.6       0.049                    14                    132    0.994 3.3        0.49       9.5         6
            8.1               0.28          0.4              6.9       0.05                    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        0.4       9.9         6
            7.2               0.23          0.32              8.5       0.058                    47                    186    0.9956 3.19        0.4       9.9         6








    Out[5]:

H2O cluster uptime:	01 secs
H2O cluster version:	3.10.5.2
H2O cluster version age:	10 days
H2O cluster name:	H2O_from_python_joe_oc2161
H2O cluster total nodes:	1
H2O cluster free memory:	5.210 Gb
H2O cluster total cores:	8
H2O cluster allowed cores:	8
H2O cluster status:	accepting new members, healthy
H2O connection url:	http://127.0.0.1:54321
H2O connection proxy:	None
H2O internal security:	False
Python version:	3.6.1 final

fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
7	0.27	0.36	20.7	0.045	45	170	1.001	3	0.45	8.8	6
6.3	0.3	0.34	1.6	0.049	14	132	0.994	3.3	0.49	9.5	6
8.1	0.28	0.4	6.9	0.05	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	0.4	9.9	6
7.2	0.23	0.32	8.5	0.058	47	186	0.9956	3.19	0.4	9.9	6