discrimintools 0.1.0

Python package dedicated to Discriminant Analysis (DA) distributed under the MIT License

discrimintools : Python library for Discriminant Analysis (DA)

discrimintools is an open source Python package dedicated to Discriminant Analysis (DA) distributed under the MIT License.

Contents

1. Overview

2. Installation

• 2.1 Global environmen
• 2.2 Virtual environment
• 2.3 Version
• 2.4 Dependencies

3. Example

4. Documentation

5. About us

• 5.1 Authors
• 5.2 Feedbacks
• 5.3 Citing discrimintools

Overview

Discriminant analysis is a classification problem, where two or more groups or clusters or populations are known a priori and one or more new observations are classified into one of the known populations based on the measured characteristics.

discrimintools provides functions for:

1. Discriminant Analysis (DA):

   • Canonical Discriminant Analysis - CANDISC
   • Discriminant Correspondence Analysis - DiCA
   • Discriminant Analysis (linear & quadractic) - DISCRIM
   • Stepwise Discriminant Analysis (backward & forward) - STEPDISC

2. Factor Analysis (FA):

   • General Factor Analysis (PCA, MCA & FAMD) - GFA
   • Mixed Principal Component Analysis - MPCA

3. Regularized Discriminant Analysis (RDA):

   • Partial Least Squares for Classification - CPLS
   • General Factor Analysis Linear Discriminant Analysis (PCADA, DISQUAL & DISMIX) - GFALDA
   • Discriminant Analysis on Mixed Predictors - MDA
   • Partial Least Squares Discriminant Analysis - PLSDA
   • Partial Least Squares Logistic Regression - PLSLOGIT
   • Partial Least Squares Linear Discriminant Analysis - PLSDA

Installation

Global environment

You can directly install discrimintools using pip :

pip install discrimintools

or set a virtual environment.

Virtual environment

Install the 64-bit version of Python 3, for instance from the official website. Now create a virtual environment (venv) and install discrimintools.

The virtual environment is optional but strongly recommended, in order to avoid potential conflicts with other packages.

PS C:\> python -m venv discrimintools-env # create virtual env
PS C:\> discrimintools-env\Scripts\activate  # activate
PS C:\> pip install -U discrimintools  # install discrimintools

Version

In order to check your installation, you can use.

import discrimintools
print(discrimintools.__version__)

Using an isolated environment such as pip venv or conda makes it possible to install a specific version of discrimintools with pip and conda and its dependencies independently of any previously installed Python packages.

You should always remember to activate the environment of your choice prior to running any Python command whenever you start a new terminal session.

Dependencies

discrimintools is compatible with python version which supports both dependencies :

Packages	Version
statsmodels	0.14.6
scikit-learn	1.8.0
openpyxl	3.1.5
tabulate	0.9.0
plotnine	0.15.1
adjustText	1.3.0

Example

We performs a linear discriminant analysis with alcools dataset.

from discrimintools.datasets import load_alcools
from discrimintools import DISCRIM
D = load_alcools() # load training data
y, X = D['TYPE'], D.drop(columns=['TYPE']) # split into X and y
clf = DISCRIM()
clf.fit(X,y)

Documentation

The official documentation is hosted on https://discrimintools.readthedocs.io.

About Us

Authors

discrimintools is developed and maintained by Duvérier DJIFACK ZEBAZE, the founder of djifacklab (Djifack Laboratory of Mathematics, Statistics and Economics books and packages production using Python Programming Language).

The djifacklab laboratory maintains others python librairies such as scientisttools, scientistmetrics, scientistshiny, scientisttseries and ggcorrplot.

Feedbacks

If you have found discrimintools useful in your work, research, or company, please let us know by writing to email [e-mail].

Citing discrimintools

If discrimintools has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing it using the following BibTeX format:

@misc{DJIFACK ZEBAZE_2024, 
    url = {https://github.com/enfantbenidedieu/discrimintools}, 
    title = {discrimintools: a Python library for Discriminant Analysis}
    author = {DJIFACK ZEBAZE, Duvérier}, 
    year = {2024}
}