ML4T Software Installation
Contents
Draft
This page is being updated for the Fall 2017 semester, and is currently in draft mode. This notice will be removed once this page has been finalized.
Attention
The information on this page is for those who are interested to have a Python development environment on their own machine. Keep in mind that even if you set up your own environment, your code still MUST run correctly on the GT servers, so it is very important that you ensure that you have access to them. Please see ML4T_Software_Setup for information on how to use those servers, and how to check out the code scaffolding for the projects.
Overview
The assignments in this class are in Python (version 2.7), and rely heavily on a few important libraries. These libraries are under active development, which unfortunately means there can be some compatibility issues between versions. This isn't an issue if you use the provided servers, but if you want to work from your local machine it is very important to make sure you have exactly the same library versions. To that end, here is a list of each library and its version number, provided in the pip freeze format:
cycler==0.10.0 functools32==3.2.3.post2 matplotlib==2.0.2 numpy==1.13.1 pandas==0.20.3 py==1.4.34 pyparsing==2.2.0 pytest==3.2.1 python-dateutil==2.6.1 pytz==2017.2 scipy==0.19.1 six==1.10.0 subprocess32==3.2.7
If you are familiar with pip and virtualenv you can use this to create a virtualenv for this class which matches those version numbers. If not, this script should accomplish the same thing: File:Ml4t-setup.py. This script requires pip which is provided by default on both macOS and Ubuntu, and comes packaged with the standard Python install for Windows. Certain backends for matplotlib may require additional libraries be installed in a platform specific way.
Important note: We use a specific, static dataset for this course, which we will provide. If you download your own data from Yahoo (or elsewhere), you will get wrong answers on assignments.
