4-3 训练数据集，测试数据集

判断机器学习算法的性能

改进：训练和测试数据集的分离，train test split

但这种方式也有它的问题，后面其它小节会讲到

准备iris数据

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets

iris = datasets.load_iris()
X = iris.data
y = iris.target

X.shape为(150, 4)，y.shape为(150,)

train_test_split

注意1：本例中训练数据集的为如下： 因此按顺序取前多少个样本不会有很好的效果，要先对数据乱序化

注意2：本例中X和y是分离的，但它们不能分别乱序化。乱序化的同时要保证样本和标签是对应的。

在Notebook实现

shuffle_indexes = np.random.permutation(len(X))
test_ratio = 0.2
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]

将train_test_split封装成函数

import numpy as np

def train_test_split(X, y, test_ratio=0.2, seed=None):
    """将X和y按照test_ratio分割成X_train，X_test，y_train，y_test"""

    assert X.shape[0] == y.shape[0], "the size of X must be equal to the size of y"
    assert 0.0 <= test_ratio <= 1.0, "test_ration must be valid"

    if seed:
        np.random.seed(seed)

    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, X_test, y_train, y_test

KNN结合train_test_split计算分类准确度

X_train, X_test, y_train, y_test = train_test_split(X, y)
my_Knn_clf = kNNClassifier(k = 3)    # kNNClassifier在上一节实现
my_Knn_clf.fit(X_train, y_train)
y_predict = my_Knn_clf.predict(X_test)
accuracy = sum(y_predict == y_test) / len(y_test)

sklearn中的train_test_split

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)