Terascale Statistics School

Introduction

This GitHub open-source repository contains jupyter notebooks that feature machine learning examples associated with lectures at the Terascale Statistics School in Germany.

Dependencies

The notebooks in this package depend on several well-known Python modules, all well-engineered and free.

modules	description
pandas	data table manipulation, often with data loaded from csv files
numpy	array manipulation and numerical analysis
matplotlib	a widely used plotting module for producing high quality plots
imageio	photo-quality image display module
scikit-learn	easy to use machine learning toolkit
pytorch	a powerful, flexible, machine learning toolkit
scipy	scientific computing
sympy	an excellent symbolic mathematics module
iminuit	an elegant wrapper around the venerable CERN minimizer Minuit
emcee	an MCMC module
tqdm	progress bar
joblib	module to save and load Python object
importlib	importing and re-importing modules

Installation

The simplest way to install these Python modules is first to install miniconda (a slim version of Anaconda) on your laptop by following the instructions at:

https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html

I recommend installing miniconda3, which comes pre-packaged with Python 3.

Software release systems such as Anaconda (conda for short) make it possible to have several separate self-consistent named environments on a single machine. For example, you may need to use Python 3.7.5 and an associated set of compatible Python modules and at other times you may need to use Python 3.9.13 with modules that require that particular version of Python. If you install software without using environments there is the danger that the software on your machine will eventually become inconsistent. Anaconda and its lightweight companion miniconda provide a way, for example, to have software environment on your machine that is consistent with Python 3.7.5 and another that is consistent with Python 3.9.13.

Of course, like anything humans make, miniconda3 is not perfect. There are times when the only solution is to remove an environment using

conda env remove -n <name>

where <name> is the name of the environment and rebuild it by reinstalling the desired Python modules.

Miniconda3

After installing miniconda3, it is a good idea to update conda using the command

conda update conda

Step 1

Assuming conda is properly installed and initialized on your machine (say, your laptop), you can create an environment, here called terascale

conda create --name terascale

and activate it using the command

conda activate terascale

The environment need be created only once, but you must activate it whenever you create a new terminal window.

Step 2

With the environment activated, you can now install root, python, numpy, etc. For example, the following command installs the ROOT package from CERN

	conda install –c conda-forge root

If all goes well, this will install a recent version of the ROOT as well as a compatible version of Python and several Python modules including numpy.

Step 3

Now install pytorch, matplotlib, scikit-learn, etc.

	conda install –c conda-forge pytorch
	conda install –c conda-forge matplotlib
	conda install –c conda-forge scikit-learn
	conda install –c conda-forge pandas
	conda install –c conda-forge sympy
	conda install –c conda-forge imageio
	conda install –c conda-forge jupyter

Step 4

The command git is needed to download the Terascale package from GitHub. If git is not on your machine, it can be installed using the command

	conda install –c conda-forge git

To install Terascale do

  	cd 
	mkdir tutorials
	cd tutorials
	git clone https://github.com/hbprosper/Terascale

In the above, the package Terascale has been downloaded into a directory called tutorials.

Step 5

Open a new terminal window, navigate to the directory containing Terascale and run the jupyter notebook in that window (in blocking mode, that is, without "&" at the end of the command)

	jupyter notebook

If all goes well, the jupyter notebook will appear in your default web browser and the terminal window will be blocked. In your browser, navigate to the Terascale directory and under the Files menu item, click on the notebook test.ipynb and execute it. This notebook tries to import several Python modules. If it does so without complaints, you are ready to try the other notebooks.

Machine Learning Examples

notebook	description
test.ipynb	Test import ofrequired Python moduels
hzz4l_sklearn	Boosted Decision Trees (BDT) with AdaBoost: classification of Higgs boson events
hzz4l_pytorch	Deep Neural Network (DNN): classification of Higgs boson events
sdss_autoencoder	Autoencoder: map SDSS galaxy/quasar data to 1D
mnist_cnn	Convolutional Neural Network (CNN): classification of MNIST digits
taylor_series_transformer	Transformer Neural Network (TNN): example of symbolic Taylor series expansion