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训练数据集与测试数据集切分

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train test split 训练数据集与测试数据集 切分

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import numpy as np
from sklearn import datasets

# http://sklearn.lzjqsdd.com/modules/generated/sklearn.datasets.load_iris.html
# iris数据集(鸢尾花)
iris = datasets.load_iris()

X = iris.data
y = iris.target

自定义 切分函数

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def train_test_split(X, y, test_ratio=0.2, seed=None):
    """
    将数据X和y，按照test_ratio分割成X_train, y_train; X_test, y_test
    """
    if seed:
        np.random.seed(seed)

    # 对索引的乱序
    shuffle_indexes = np.random.permutation(len(X))

    test_size = int(len(X) * test_ratio)

    test_indexes = shuffle_indexes[:test_size]
    train_indexes = shuffle_indexes[test_size:]

    X_train = X[train_indexes]
    y_train = y[train_indexes]

    X_test = X[test_indexes]
    y_test = y[test_indexes]

    return X_train, y_train, X_test, y_test

sklearn中的train_test_split

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from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=666)

# random_state 随机种子