Getting started¶
Here are some helpful links to help you get started learning H2O-3.
Downloads page¶
To begin, download a copy of H2O-3 from the Downloads page.
Click H2O Open Source Platform or scroll down to the H2O section. Here you have access to the different ways to download H2O-3:
Latest stable: this version is the most recentl alpha release version of H2O-3.
Nightly bleeding edge: this version contains all the latest changes to H2O-3 that haven’t been released officially yet.
Prior releases: this houses all previously released versions of H2O-3.
For first-time users, we recomment downloading the latest alpha release and the default standalone option (the Download and Run tab) as the installation method. Make sure to install Java if it is not already installed.
Note
By default, this setup is open. Follow security guidelines if you want to secure your installation.
Using Flow - H2O-3’s web UI¶
This section describes our web interface, Flow. Flow is similar to IPython notebooks and allows you to create a visual workflow to share with others.
Tutorials of Flow¶
The following examples use H2O Flow. To see a step-by-step example of one of our algorithms in action, select a model type from the following list:
Launch from the command line¶
You can configure H2O-3 when you launch it from the command line. For example, you can specify a different directory for saved Flow data, you could allocate more memory, or you could use a flatfile for a quick configuration of your cluster. See more details about configuring the additional options when you launch H2O-3.
Algorithms¶
This section describes the science behind our algorithms and provides a detailed, per-algorithm view of each model type.
Use cases¶
H2O-3 can handle a wide variety of practical use cases due to its robust catalogue of supported algorithms, wrappers, and machine learning tools. The following are some example problems H2O-3 can handle:
Determining outliers in housing prices based on number of bedrooms, number of bathrooms, access to waterfront, etc. through anomaly detection.
Revealing natural customer segments in retail data to determine which groups are purchasing which products.
Linking multiple records to the same person with probabilistic matching.
Unsampling the minority class for credit card fraud data to handle imbalanced data.
Detecting drift on avocado sales pre-2018 and 2018+ to determine if a model is still relevant for new data.
See our best practice tutorials to further explore the capabilities of H2O-3.
New user quickstart¶
You can follow these steps to quickly get up and running with H2O-3 directly from the H2O-3 repository. These steps will guide you through cloning the repository, starting H2O-3, and importing a dataset. Once you’re up and running, you’ll be better able to follow examples included within this user guide.
In a terminal window, create a folder for the H2O-3 repository:
user$ mkdir ~/Desktop/repos
Change directories to that new folder, and then clone the repository. Notice that the prompt changes when you change directories:
user$ cd ~/Desktop/repos
repos user$ git clone https://github.com/h2oai/h2o-3.git
After the repository is cloned, change directories to the
h2o-3folder:
repos user$ cd h2o-3
h2o-3 user$
Run the following command to retrieve sample datasets. These datasets are used throughout the user guide and within the booklets.
h2o-3 user$ ./gradlew syncSmalldata
At this point, choose whether you want to complete this quickstart in Python or R. Then, run the following corresponding commands from either the Python or R tab:
# By default, this setup is open.
# Follow our security guidelines (https://docs.h2o.ai/h2o/latest-stable/h2o-docs/security.html)
# if you want to secure your installation.
# Before starting Python, run the following commands to install dependencies.
# Prepend these commands with `sudo` only if necessary:
# h2o-3 user$ [sudo] pip install -U requests
# h2o-3 user$ [sudo] pip install -U tabulate
# Start python:
# h2o-3 user$ python
# Run the following commands to import the H2O module:
>>> import h2o
# Run the following command to initialize H2O on your local machine (single-node cluster):
>>> h2o.init()
# If desired, run the GLM, GBM, or Deep Learning demo(s):
>>> h2o.demo("glm")
>>> h2o.demo("gbm")
>>> h2o.demo("deeplearning")
# Import the Iris (with headers) dataset:
>>> path = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_wheader.csv"
>>> iris = h2o.import_file(path=path)
# View a summary of the imported dataset:
>>> iris.summary
# sepal_len sepal_wid petal_len petal_wid class
# 5.1 3.5 1.4 0.2 Iris-setosa
# 4.9 3 1.4 0.2 Iris-setosa
# 4.7 3.2 1.3 0.2 Iris-setosa
# 4.6 3.1 1.5 0.2 Iris-setosa
# 5 3.6 1.4 0.2 Iris-setosa
# 5.4 3.9 1.7 0.4 Iris-setosa
# 4.6 3.4 1.4 0.3 Iris-setosa
# 5 3.4 1.5 0.2 Iris-setosa
# 4.4 2.9 1.4 0.2 Iris-setosa
# 4.9 3.1 1.5 0.1 Iris-setosa
#
# [150 rows x 5 columns]
# <bound method H2OFrame.summary of >
# Download and install R:
# 1. Go to http://cran.r-project.org/mirrors.html.
# 2. Select your closest local mirror.
# 3. Select your operating system (Linux, OS X, or Windows).
# 4. Depending on your OS, download the appropriate file, along with any required packages.
# 5. When the download is complete, unzip the file and install.
# Start R
h2o-3 user$ r
...
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
>
# By default, this setup is open.
# Follow our security guidelines (https://docs.h2o.ai/h2o/latest-stable/h2o-docs/security.html)
# if you want to secure your installation.
# Copy and paste the following commands in R to download dependency packages.
> pkgs <- c("methods", "statmod", "stats", "graphics", "RCurl", "jsonlite", "tools", "utils")
> for (pkg in pkgs) {if (! (pkg %in% rownames(installed.packages()))) { install.packages(pkg) }}
# Run the following command to load the H2O:
> library(h2o)
# Run the following command to initialize H2O on your local machine (single-node cluster) using all available CPUs.
> h2o.init()
# Import the Iris (with headers) dataset.
> path <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris_wheader.csv"
> iris <- h2o.importFile(path)
# View a summary of the imported dataset.
> print(iris)
sepal_len sepal_wid petal_len petal_wid class
----------- ----------- ----------- ----------- -----------
5.1 3.5 1.4 0.2 Iris-setosa
4.9 3 1.4 0.2 Iris-setosa
4.7 3.2 1.3 0.2 Iris-setosa
4.6 3.1 1.5 0.2 Iris-setosa
5 3.6 1.4 0.2 Iris-setosa
5.4 3.9 1.7 0.4 Iris-setosa
4.6 3.4 1.4 0.3 Iris-setosa
5 3.4 1.5 0.2 Iris-setosa
4.4 2.9 1.4 0.2 Iris-setosa
4.9 3.1 1.5 0.1 Iris-setosa
[150 rows x 5 columns]
>