Setup Guide for Coding Machine Learning Methods in Python

In order to implement the algorithms seen in class and work on the projects, we’ll be using Python notebooks. This first lab will serve as an introduction to the Python language, the environment we are going to be using, and how to do basic vector and matrix manipulations.

Once you have the notebook system up and running, if you are new to the numpy library, you can try to do the exercises in the Matlab introduction pdf - but using numpy.

The environment

Python distribution: Anaconda

We will be using the Anaconda distribution to run Python 3, as it is easy to install and comes with most packages we will need. To install Anaconda, go to the download page and get the Python installer for your OS - make sure to use the newer version 3.x, not 2.x. Follow the instructions of the installer and you’re done.

Warning! The installer will ask you if you want to add Anaconda to your path. Your default answer should be yes, unless you have specific reasons not to want this.

Development Environment

During the course, we will use Jupyter Notebooks, which is a great tool for exploratory and interactive programming and in particular for data analysis. Notebooks are browser based, and you start it on your localhost by typing jupyter notebook in the console. Notebooks are already available by default by Anaconda. The interface is pretty intuitive, but they are a few tweaks and shortcuts that will make your life easier, which we’ll detail in the next section. You can of course ask any of the TAs for help on using the Notebooks.

The Notebook System

For additional resources on how the notebook system works, we recommend

• The Jupyter notebook beginner’s guide
• The official documentation

Examples

We provide you with an example of a notebook for this first lab, but if you want to see some more examples already, feel free to take a look at

• The introductory notebooks available at Try Jupiter. It spawns an instance of the Jupyter Notebook, which won’t save any of your changes.
  Note: it might not be available if their server is under too much load.
• A gallery of interesting IPython Notebooks by the Ipython Notebook team
• Another gallery of notebooks

Tips & Tricks

There are a few handy commands that you should start every notebook with

# Plot figures in the notebook (instead of a new window)
%matplotlib notebook # or %matplotlib inline

# Automatically reload modules
%load_ext autoreload
%autoreload 2        

# The usual imports
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

Keyboard shortcuts

• Adding cells
  
  • a adds an empty cell above the selected one,
  • b adds it below.
• Running code
  
  • Enter enters the edition mode of the currently selected cell.
  • Shift-Enter runs the current cell and goes to the next one.
  • Ctrl-Enter runs the currect cell and leave it selected.

• Autocompletion

  • Tab pops up the Autocompletion when you are in the middle of writing a function call/class name and shows the arguments of the function being called when used after an opening parenthesis.
  • Shift-Tab pops up the help/documentation of the function its used on

• For a complete list of shortcuts, go to help > keyboard shortcuts

Python

We will be working in Python. If you already have been introduced to Python, feel free to skip this section. If you come from another background, you might want to take some tutorials in addition to this lab in the next week to feel comfortable with it. You do not need to become an expert in Python, but you should be confortable with the general syntax, some of the idiosyncrasies of Python and know how to do basic vector and matrix algebra. For the last part, we will be using NumPy, a library we will introduce later.

For a nice introduction to Python, you should take a look at the Python tutorial. Here are some reading recommendations:

• Skim through Sections 1-3 to get an idea of the Python syntax if you never used it

• Pay a little more attention to Section 4, especially

  • Section 4.2 on for loops, as they behave like foreach by default, which may be disturbing if you are more acoutumed to coding in lower level languages.
  • Section 4.7 on functions, default argument values and named arguments, as they are a real pleasure to use (compared to traditional, order-based arguments) once you are used to it.
• Section 5 on Data Structures, especially how to use Lists, Dictionnaries and Tuples if you have not used a language with those concepts before
• You can keep Sections 6-9 on Modules, IO, Exceptions and Objects for later - when you know you will be needing it.
• Section 10 on the standard library and the standard library index are worth a quick scroll to see what’s available.
• Do not bother with Sections 11-16 for now.

Here are some additionnal ressources on Python:

• Python’s standard library reference
• Debugging and profiling
• If you want to, some exercises are available at learnpython.org

NumPy and Vector Calculations

Our npprimer.ipynb notebook as part of the first lab has some useful commands and exercises to help you get started with NumPy.

If you are familiar with Matlab, a good starting point is this guide. Be careful that we will use way more the array structure compared to the matrix structure.

A good and probably more complete reference is this one.

Installation FAQ

Other shell. If you are using another shell (e.g. zsh on Mac OSX), after installing Anaconda you still need to add the installed software to your path, that is to add it to the correct profile of your shell. To do so, run the following commands in your terminal touch ~/.bash_profile; open ~/.bash_profile. It will open your bash profile where you’ll see the line that was added by the Python installer. Copy it. Then touch ~/.zshrc; open ~/.zshrc, that will open the profile for zsh, you can paste the line at the bottom of the file.)

Alternative Python IDEs. While we recommend plain Jupyter Notebooks, if you are more comfortable using a more traditional IDE, you can give PyCharm a try. Your EPFL email gives you access to the free educational version. You should keep this option in mind if you need a full fledged debugger to find a nasty bug.

And of course, as a third alternative, you can always use a decent text editor and run your code from the console or any plugin. Keep in mind that the TAs might not be able to help you with your setup if you go down this path.

Additional References

A good Python and NumPy Tutorial from CS231n, Stanford. (WARNING : Uses Python 2 and not 3)