scientisttools : Python library for multidimensional analysis

About scientisttools

scientisttools is a Python package dedicated to multivariate Exploratory Data Analysis.

Why use scientisttools?

• It performs classical principal component methods :
  • Principal Components Analysis (PCA)
  • Principal Components Analysis with partial correlation matrix (PPCA)
  • Weighted Principal Components Analysis (WPCA)
  • Expectation-Maximization Principal Components Analysis (EMPCA)
  • Exploratory Factor Analysis (EFA)
  • Classical Multidimensional Scaling (CMDSCALE)
  • Metric and Non - Metric Multidimensional Scaling (MDS)
  • Correspondence Analysis (CA)
  • Multiple Correspondence Analysis (MCA)
  • Factor Analysis of Mixed Data (FAMD)
  • Multiple Factor Analysis (MFA)
• In some methods, it allowed to add supplementary informations such as supplementary individuals and/or variables.
• It provides a geometrical point of view, a lot of graphical outputs.
• It provides efficient implementations, using a scikit-learn API.

Those statistical methods can be used in two ways :

• as descriptive methods ("datamining approach")
• as reduction methods in scikit-learn pipelines ("machine learning approach")

scientisttools also performs some algorithms such as clustering analysis and discriminant analysis.

• Clustering analysis:
  • Hierarchical Clustering on Principal Components (HCPC)
  • Variables Hierarchical Clustering Analysis (VARHCA)
  • Variables Hierarchical Clustering Analysis on Principal Components (VARHCPC)
  • Categorical Variables Hierarchical Clustering Analysis (CATVARHCA)
• Discriminant Analysis
  • Canonical Discriminant Analysis (CANDISC)
  • Linear Discriminant Analysis (LDA)
  • Discriminant with qualitatives variables (DISQUAL)
  • Discriminant Correspondence Analysis (DISCA)
  • Discriminant with mixed data (DISMIX)
  • Stepwise Discriminant Analysis (STEPDISC) (only backward elimination is available).

Notebooks are availabled.

Installation

Dependencies

scientisttools requires

Python >=3.10
Numpy >= 1.23.5
Matplotlib >= 3.5.3
Scikit-learn >=  1.2.2
Pandas >= 1.5.3
mapply >= 0.1.21
Plotnine >= 0.10.1
Plydata >= 0.4.3

User installation

You can install scientisttools using pip :

pip install scientisttools

Tutorial are available

https://github.com/enfantbenidedieu/scientisttools/blob/master/ca_example2.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/classic_mds.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/efa_example.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/famd_example.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/ggcorrplot.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/mca_example.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/mds_example.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/partial_pca.ipynb
https://github.com/enfantbenidedieu/scientisttools/blob/master/pca_example.ipynb

Author

Duvérier DJIFACK ZEBAZE (duverierdjifack@gmail.com)