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Windows-preconfiguration.md

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Installing on windows

Prerequisites

You need to have Python and Pip and git installed.

Installing libraries with Pip

# Installs required Python Pip packages
pip install -r requirements.txt

If some libraries fail to install in a batch with the requirements.txt file, just install one by one.

Installing the custom library

On a Unix system, all you have to do is run the following command to install the custom library the project relies on:

# Install a custom helper library
pip install git+https://github.com/JeffMv/jmm-util-libs.git@v0.1.2.9.0

On Windows, this simple command does not seem to work. So you need to manually install the library.

Manually installing the custom library

Download and unarchive https://codeload.github.com/JeffMv/jmm-util-libs/zip/v0.1.2.9.0 somewhere. Inside the archive is a folder named jmm-util-libs-0.1.2.9.0 . It contains a folder named jmm with all the library's files. You will want to place this jmm directory in the directory where your python packages are installed.

You need to find where your python libraries are installed. To do that, launch python and run the following:

import numpy
numpy

This will print the location of the numpy library.

<module 'numpy' from 'C:\\PATH\\TO\\PYTHON\\INSTALL\\lib\\site-packages\\numpy\\__init__.py'>

Open the Explorer in the path C:\PATH\TO\PYTHON\INSTALL\lib\site-packages and copy the jmm directory in there. Once you have done that, the library will be available to the project files.

Note: if you use a virtual environment, you will of course need to place the jmm in the site-packages directory of the virtual environment.

Installation / running

This repo is a specific snapshot of another project. On its own, it aims to generate features that are aimed to be used by a data scientist.

Installation

# Installs required Python Pip packages
pip install -r requirements.txt

# Install a custom helper library
pip install -U git+https://github.com/JeffMv/jmm-util-libs.git@v0.1.2.9.0

Running

Included data are from the triomagic lottery, which is a pick 1 out of 10 balls for each column lottery. You could substitute this dataset with one of a lottery with similar settings (pick 1 out of 10 for each column) and it would generate the features.

The graphs in the repo were generated with the such a setting. Especially, it used the univ-length-over10.tsv file, which is based on analyzing the number of different numbers that appeared in the last frame of 10 draws.

python eulolib/featuresUpdater.py --makeFeatures --gameId=triomagic --draws="data/example-inputs/TrioMagic-results.txt" --saveDir="triomagic"
# it writes in the input directory under the subfolder "triomagic"

The generated folders are for each different column. You can remove the top 2 lines under the header of univ-length-over10.tsv and feed the file to an auto-model solution like RapidMiner's AutoModel to get the same kind of graphs that are shown in this repo.

IMPORTANT NOTE:

The other files named univ-ecarts-over10-andSupa20.tsv, univ-effectifs-over10-andSupa20.tsv or univ-parity-over10.tsv are on development so do not use them. I only created them as a stub for extending further. The only usable computated file are those called univ-length-over10.tsv.