训练集 & 测试集

如果拿所有原始数据来训练，存在的问题：

模型很差无法调整；

真实环境难以拿到真实 label；

所以将数据区分为 训练数据 和 测试数据(train test split)；

将训练数据来训练模型；然后用测试数据测试模型；

使用这种方式也存在问题；

python 原生分离 iris 数据集

import numpy as np

from sklearn import datasets

import matplotlib.pyplot as plt

iris = datasets.load_iris()

X = iris.data

y = iris.target

X

'''

(array([[5.1, 3.5, 1.4, 0.2],

[4.9, 3. , 1.4, 0.2], ...

[6.2, 3.4, 5.4, 2.3],

[5.9, 3. , 5.1, 1.8]])

'''

y

'''

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,

0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,

1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,

1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,

2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,

2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])

'''

X.shape, y.shape #((150, 4), (150,))

# shuffle

shuffle_indexes = np.random.permutation(len(X)) # 0--len(X) 的随机排列

shuffle_indexes

# array([ 22, 4, 142, 24, 7, 146, ... 9, 95, 130, 29, 124])

test_ratio = 0.2

test_size = int(len(X) * test_ratio)

test_size # 30

test_indexes = shuffle_indexes[:test_size]

train_indexes = shuffle_indexes[test_size:]

test_indexes

'''

array([ 22, 4, 142, 24, 7, 146, 70, 77, 144, 14, 40, 119, 46, 85, 74, 87, 86, 60, 91, 120, 78, 45, 65, 105, 113, 39, 83, 80, 134, 16])

'''

X_train = X[train_indexes]

y_train = y[train_indexes]

X_test = X[test_indexes]

y_test = y[test_indexes]

X_test.shape, X_train.shape # ((30, 4), (120, 4))

封装 train_test_split 函数

def train_test_split(X, y, test_ratio=0.2, seed=None):

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, y_train, X_test, 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)

train_test_split(*arrays, **options)

X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)