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文库首页人工智能机器学习用python调用H2O.ai实现机器学习建模和AutoML

用python调用H2O.ai实现机器学习建模和AutoML

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用python调用H2O.ai实现机器学习建模和AutoML, Artificial Intelligence Made Easy with H2O.ai – Towards Data Science.

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Artificial Intelligence Made Easy

withH2O.ai

A Comprehensive Guide to Modeling with

H2O.ai and AutoML inPython

ByIshaanDey&ElyseLee

you’re anything like my dad, you’ve worked in IT for decades but

have only tangentially touched data science. Now, your new C-

something-O wants you to fire up a data analytics team and work

with new a set of buzzwords you’ve only vaguely heard about at

conferences. Or perhaps you’re a developer at a fast-moving startup

and have spent weeks finalizing an algorithm, only to be stymied by

issues with deploying the model onto your web application for real

time use. For both cases, H2O.ai is definitely a solution worth looking

into.

Why H2O.ai?

H2O.ai positions itself as a software package that streamlines the

machine learning process through its open source package H2O and

AutoML. While products such as H2O Driverless AI allow end users

Ishaan Dey

Jun 13

12 min read

Photo Source: ShutterStock

to completely automate the process without a single line of code,

most users (like me) want at least some degree of customizability

with their model development.

H2O.ai shines here for a few reasons:

It streamlines the process for development into an intuitive

workflow,

Trains models faster than popular packages like sci-kit learn,

and

Makes moving a model into production far simpler with Java

objects

In short, H2O certainly delivers a fast and accessible machine

learning platform for large datasets that is equipped with user-

friendly and high-performing tools.

Overview

Getting Started

Building a Standalone Model

Finding the Best Model using AutoML

Model Deployment

The good news is that much of H2O in Python is similar to what you

may be familiar with using sci-kit learn functions. H2O’s data

structures are fairly analogous to Pandas, and the workflow for

specifying, fitting, and evaluating models are similar. Using a

financial credit dataset from the UCI repository, we’ll use H2O.ai to

predict the probability that an individual will default on their next

payment.

•

Note: If you’re interested in just the AutoML part, skip the ‘Building a

Hello, World! Model’ section, which gets into the nitty gritty for

developing a single model. You can come back there and use it more

as a reference guide, as well as the H2O documentation links

provided.

You can follow along using the code snippets in the post or the

interactive Python Notebook linked below.

GitHub:https://github.com/elyselee/H2O

Walkthrough/blob/master/H2O%20Walkthrough.ipynb

Getting Started with H2OPython

Setting Up Dependencies

As much as I wish I could say implementation is just as easy as

throwing out another pip install command, it’s a fair bit more

involved than that.

To begin, head over to the H2O stable link here, and download the

zip file containing the most recent version. Follow the commands

below to finish installing the package.

cd ~/Downloads

unzip h2o-3.25.0.4698.zip

cd h2o-3.25.0.4698

java -jar h2o.jar

As of writing this article (June 2019), H2O only supports Java SE

Runtime Environment Versions 8–11. You can check your version

using the java -version command. If you have Java SDK 12, you’ll

have to uninstall and downgrade to Java SDK 11 to maintain

compatibility with H2O. To do so, execute the following command:

/usr/libexec/java_home -V in terminal. Copy the pathname it

returns, and use the following command to uninstall: sudo rm -rf

pathname . Head over to the Oracle JDK 11 download site, create an

account, and follow the instructions there to install.

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