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Python实现数据集划分(训练集和测试集)

作者:Python小丸子

这篇文章主要为大家详细介绍了Python是如何实现数据集划分的,分为训练集和测试集,文中的实现方法讲解详细,感兴趣的小伙伴可以了解一下

前面是分部讲解,完整代码在最后。

导入模块 :

import os
from shutil import copy, rmtree
import random

创建文件夹 :

def make_file(file_path: str):
    if os.path.exists(file_path):
        rmtree(file_path)
    os.makedirs(file_path)

划分数据集的比例,本文是0.1:验证集的数量占总数据集的10%比如填0.1就是验证集的数量占总数据集的10%

random.seed(0)  
split_rate = 0.1 

数据集的存放:新建一个数据文件夹,将划分的数据集存放进去

data_path = r'D:\chengxu\data\caodi'  # 数据集存放的地方
data_root = r'D:\chengxu\data\cd'  # 这里是生成的训练集和验证集所处的位置,这里设置的是在当前文件夹下。
data_class = [cla for cla in os.listdir(data_path)]
print("数据的种类分别为:")
print(data_class)  # 输出数据种类

建立训练集文件夹:

train_data_root = os.path.join(data_root, "train")  # 训练集的文件夹名称为 train
make_file(train_data_root)
for num_class in data_class:
    make_file(os.path.join(train_data_root, num_class))

建立测试集文件夹:

val_data_root = os.path.join(data_root, "val")  # 验证集的文件夹名称为 val
make_file(val_data_root)
for num_class in data_class:
    make_file(os.path.join(val_data_root, num_class))

划分数据:

for num_class in data_class:
    num_class_path = os.path.join(data_path, num_class)
    images = os.listdir(num_class_path)
    num = len(images)
    val_index = random.sample(images, k=int(num * split_rate))  # 随机抽取图片
    for index, image in enumerate(images):
        if image in val_index:
            # 将划分到验证集中的文件复制到相应目录
            data_image_path = os.path.join(num_class_path, image)
            val_new_path = os.path.join(val_data_root, num_class)
            copy(data_image_path, val_new_path)
        else:
            # 将划分到训练集中的文件复制到相应目录
            data_image_path = os.path.join(num_class_path, image)
            train_new_path = os.path.join(train_data_root, num_class)
            copy(data_image_path, train_new_path)
    print("\r[{}] split_rating [{}/{}]".format(num_class, index + 1, num), end="")  # processing bar
    print()
print("       ")
print("       ")
print("划分成功")

完整代码:

import os
from shutil import copy, rmtree
import random
 
 
def make_file(file_path: str):
    if os.path.exists(file_path):
 
        rmtree(file_path)
    os.makedirs(file_path)
 
random.seed(0) 
 
# 将数据集中10%的数据划分到验证集中
split_rate = 0.1  
data_path = r'D:\chengxu\data\caodi'  # 数据集存放的地方,建议在程序所在的文件夹下新建一个data文件夹,将需要划分的数据集存放进去
data_root = r'D:\chengxu\data\cd'  # 这里是生成的训练集和验证集所处的位置,这里设置的是在当前文件夹下。
 
data_class = [cla for cla in os.listdir(data_path)]
print("数据的种类分别为:")
print(data_class)  
# 建立保存训练集的文件夹
train_data_root = os.path.join(data_root, "train")  # 训练集的文件夹名称为 train
make_file(train_data_root)
for num_class in data_class:
    # 建立每个类别对应的文件夹
    make_file(os.path.join(train_data_root, num_class))
 
# 建立保存验证集的文件夹
val_data_root = os.path.join(data_root, "val")  # 验证集的文件夹名称为 val
make_file(val_data_root)
for num_class in data_class:
    # 建立每个类别对应的文件夹
    make_file(os.path.join(val_data_root, num_class))
 
for num_class in data_class:
    num_class_path = os.path.join(data_path, num_class)
    images = os.listdir(num_class_path)
    num = len(images)
 
    val_index = random.sample(images, k=int(num * split_rate))  # 随机抽取图片
    for index, image in enumerate(images):
        if image in val_index:
          
            data_image_path = os.path.join(num_class_path, image)
            val_new_path = os.path.join(val_data_root, num_class)
            copy(data_image_path, val_new_path)
        else:
      
            data_image_path = os.path.join(num_class_path, image)
            train_new_path = os.path.join(train_data_root, num_class)
            copy(data_image_path, train_new_path)
    print("\r[{}] split_rating [{}/{}]".format(num_class, index + 1, num), end="")  # processing bar
    print()
 
print("       ")
print("       ")
print("划分成功")

到此这篇关于Python实现数据集划分(训练集和测试集)的文章就介绍到这了,更多相关Python数据集划分内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家!

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