Source code for discrimintools.datasets.load_wine
# -*- coding: utf-8 -*-
from pandas import read_excel
from pathlib import Path
#set directory
DATASETS_DIR = Path(__file__).parent / "data"
[docs]
def load_wine(element="train"):
"""
Bordeaux Wine dataset
Parameters
----------
element : str, default = 'train'
The dataset to load. Possible values are:
- 'train' for training dataset.
- 'test' for testing dataset.
Returns
-------
wine : DataFrame of shape (n_samples, n_columns)
The Bordeaux wine dataset.
References
----------
[1] Michel Tenenhaus (1996), « Méthodes statistiques en gestion », Dunod.
[2] Ricco Rakotomalala (2008), « `Analyse discriminante descriptive - vins de Bordeaux <https://eric.univ-lyon2.fr/ricco/tanagra/fichiers/fr_Tanagra_Canonical_Discriminant_Analysis.pdf>`_ », Université Lumière Lyon 2.
[3] Ricco Rakotomalala (2011), « `Analyse factorielle discriminante - Diaporama <https://eric.univ-lyon2.fr/ricco/cours/slides/analyse_discriminante_descriptive.pdf>`_ », Université Lumière Lyon 2.
[4] 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_wine
>>> from discrimintools import CANDISC
>>> D = load_wine("train") # load training data
>>> y, X = D["Quality"], D.drop(columns=["Quality"]) # split into X and y
>>> clf = CANDISC(classes=("bad","medium","good"))
>>> clf.fit(X,y)
CANDISC(classes=("bad","medium","good"))
"""
if element == "train":
wine = read_excel(DATASETS_DIR/"wine.xlsx",header=0,index_col=0,sheet_name="Feuil1")
elif element == "test":
wine = read_excel(DATASETS_DIR/"wine.xlsx",header=0,index_col=0,sheet_name="Feuil2")
else:
raise ValueError("'element' should be one of 'train', 'test'.")
wine.__doc__ = """
Bordeaux Wine dataset
"""
return wine
