Basic H2O for Python Meetup with Eric Eckstrand, 07.08.15.

1. Basic H2O for
Python
Eric C. Eckstrand

2. Agenda
1. Getting H2O & Documentation
2. Basic Architecture
3. Loading Data
4. Data Exploration & Munging
5. Model Building
6. Model Saving & Loading

3. Getting H2O & Docs
1. pip install h2o
2. http://h2o.ai/download/
a. Bleeding Edge (link)
b. Install in Python (tab)
c. pip install http://h2o-release.s3.amazonaws.
com/h2o/master/3066/Python/h2o-3.1.0.3066-py2.py3-none-any.whl
3. build h2o (https://github.com/h2oai/h2o-3#4-building-h2o-3)
a. pip install h2o-py/dist/h2o-3.1.0.99999-py2.py3-none-any.whl
4. http://docs.h2o.ai/ -> H2O 3.0 -> Python Users (link) -> Python docs (link)

4. Basic Architecture
local machine
Python
>>> import h2o

5. Basic Architecture
local machine
Python
>>> import h2o
>>> h2o.init()
H2O JVM
ip=localhost, port=54321

6. Basic Architecture
local machine
Python
>>> import h2o
>>> h2o.init(ip=”172.16.2.181”, port=54321)
H2O JVM
ip=172.16.2.181, port=54321
remote machine

7. Basic Architecture
local machine
Python
>>> import h2o
>>> h2o.init(ip=”172.16.2.181”, port=54321) H2O JVM
H2O JVM
H2O JVM
H2O JVM
H2O JVM

8. Load Data into H2O JVM
1. Iris dataset
a. 150 rows x 5 columns
b. Sepal Width, Sepal Length, Petal Width, Petal
Length, and Species (Verginica, Setosa, Versicolor)
2. Methods
a. h2o.upload_file
b. h2o.import_frame
c. h2o.H2OFrame

9. Load Data into H2O JVM
my laptop: /Users/ece/0xdata/h2o-dev/smalldata/iris/iris.csv
Python
>>> import h2o
>>> h2o.init()
>>> iris_H2OFrame = h2o.upload_file
(“/Users/ece/0xdata/h2o-dev/smalldata/iris/iris.csv”)
>>> iris_H2OFrame = h2o.import_frame
(“/Users/ece/0xdata/h2o-dev/smalldata/iris/iris.csv”)
H2O JVM
ip=localhost, port=54321

10. Load Data into H2O JVM
my laptop: /Users/ece/0xdata/h2o-dev/smalldata/iris/iris.csv
Python
>>> import h2o
>>> h2o.init(ip=”172.16.2.181”, port=54321)
>>>
>>> iris_H2OFrame = h2o.upload_file
(“/Users/ece/0xdata/h2o-dev/smalldata/iris/iris.csv”)
>>>
>>> iris_H2OFrame = h2o.import_frame
(“/home/eric/iris.csv”)
H2O JVM
ip=172.16.2.181, port=54321
server room: /home/eric/iris.csv

11. Exploration & Munging
my laptop: /Users/ece/0xdata/h2o-dev/smalldata/iris/iris.csv
Python
>>> import h2o
>>> h2o.init()
>>> iris_H2OFrame = h2o.upload_file
(“/Users/ece/0xdata/h2o-dev/smalldata/iris/iris.csv”)
H2O JVM
ip=localhost, port=54321Frame
150 x 5

12. Exploration & Munging
1. show, dim, nrow, ncol, head, tail, col_names, setNames
2. indexing
3. summary statistics
a. mean, median, min, max, sd
4. categorical columns
a. levels
5. cut, group_by
6. ndarray <-> DataFrame <-> H2OFrame

13. Model-Building
1. H2O K-means
a. h2o_model = h2o.kmeans(x=iris_H2OFrame[:,0:4],
k=3)
b. h2o_model.centers()
2. Scikit Learn
a. from sklearn.cluster import KMeans
b. sk_model = KMeans(n_clusters=3)
c. sk_model.fit(iris_DataFrame.iloc[:,0:4])
d. sk_model.cluster_centers_

14. Model Saving & Loading
1. path = h2o.save_model(h2o_model,"
/Users/ece/")
2. saved_model = h2o.load_model(str(path))
3. saved_model.centers()

15. Questions?