Source code for discrimintools.datasets.load_divay
# -*- coding: utf-8 -*-
from pandas import read_excel
from pathlib import Path
#set directory
DATASETS_DIR = Path(__file__).parent / "data"
[docs]
def load_divay(element = "train"):
"""
Divay dataset
Note
----
12 wines coming from 3 diferent origins (4 wines per origin).
Parameters
----------
element : str, default = 'train'
The dataset to load. Possible values are:
- 'train' for training dataset.
- 'test' for testing dataset.
Returns
-------
divay : DataFrame of shape (n_samples, n_columns)
The divay dataset.
References
----------
[1] Hervé Abdi (2007). `Discriminant correspondence analysis <https://personal.utdallas.edu/~herve/Abdi-DCA2007-pretty.pdf>`_. In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage. pp. 270-275.
[2] Ricco Rakotomalala (2012), `Analyse des correspondances discriminante <https://eric.univ-lyon2.fr/ricco/tanagra/fichiers/fr_Tanagra_Discriminant_Correspondence_Analysis.pdf>`_, Université Lumière Lyon 2.
[3] Ricco Rakotomalala (2020), `Pratique de l'Analyse Discriminante Linéaire <https://hal.science/hal-04868585v1/file/Pratique_Analyse_Discriminante_Lineaire.pdf>`_, Version 1.0, Université Lumière Lyon 2.
Examples
--------
>>> from discrimintools.datasets import load_divay
>>> from discrimintools import DiCA
>>> D = load_divay() # load training data
>>> y, X = D["Region"], D.drop(columns=["Region"]) # split into X and y
>>> clf = DiCA()
>>> clf.fit(X,y)
DiCA()
"""
if element == "train":
divay = read_excel(DATASETS_DIR/"divay.xlsx",sheet_name="Feuil1",header=0,index_col=None)
elif element == "test":
divay = read_excel(DATASETS_DIR/"divay.xlsx",sheet_name="Feuil2",header=0,index_col=None)
else:
raise ValueError("'element' should be one of 'train', 'test'")
#set cocumentation
divay.__doc__ = """
Divay dataset
"""
return divay
