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Partitioning the Wine dataset is open-source dataset that is available from the UCI machine learning repository into train and test dataset.
Using the pandas library, we will directly read in the open source Wine dataset from the UCI machine learning repository:
import pandas as pd import numpy as np df_wine = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data', header=None) df_wine.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash','Alcalinity of ash', 'Magnesium','Total phenols', 'Flavanoids', 'Nonflavanoid phenols','Proanthocyanins','Color intensity', 'Hue','OD280/OD315 of diluted wines','Proline'] print('Class labels', np.unique(df_wine['Class label']))
Class labels [1 2 3]
A convenient way to randomly partition this dataset into a separate test and training dataset is to use the train_test_split function from scikit-learn's cross_validation submodule
>>> from sklearn.cross_validation import train_test_split >>> X, y = df_wine.iloc[:, 1:].values, df_wine.iloc[:, 0].values >>> X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
First, we assigned the NumPy array representation of feature columns 1-13 to the variable X , and we assigned the class labels from the first column to the variable y . Then, we used the train_test_split function to randomly split X and y into
separate training and test datasets. By setting test_size=0.3 we assigned 30 percent of the wine samples to X_test and y_test , and the remaining 70 percent of the samples were assigned to X_train and y_train , respectively.