下面是random_split方法的内部实现: defrandom_split(dataset,lengths):ifsum(lengths)!=len(dataset):raiseValueError("Sum of input lengths does not equal the length of the input dataset!")indices=torch.randperm(sum(lengths)).tolist()return[Subset(dataset,indices[offset-length:offset])foroffset,length...
1importos2#import cv23importrandom4importsys5fromrandomimportrandint6importshutil78deffileExist(path1):9ifos.path.exists(path1):10return11else:12try:13os.mkdir(path1)#创建单层文件夹14exceptException as e:15os.makedirs(path1)#创建多层文件夹161718defsplit_dataset(root_path, new_path, ratio=0.7...
importrandom#数据集拆分函数: 将列表 full_list按比例ratio(随机)划分为3个子列表sublist_1、sublist_2、sublist_3defdata_split(full_list, ratio, shuffle=False): n_total=len(full_list) offset0= int(n_total *ratio[0]) offset1= int(n_total * ratio[1]) offset2= int(n_total * ratio[2])...
from torch.utils.tensorboard.writer import SummaryWriter from torch.utils.data import random_split from sklearn.model_selection import train_test_split class mydataset(Dataset): def __init__(self): # 读取加载数据 data=pd.read_csv("600519.csv",encoding='gbk') self._x=torch.tensor(np.array(...
fromsklearn.datasetsimportload_irisfromsklearn.model_selectionimporttrain_test_splitfromsklearn.linear_modelimportLogisticRegressionfromsklearn.metricsimportaccuracy_scoreiris=load_iris()X=iris.dataY=iris.targetprint("Size of Dataset{}".format(len(X)))logreg=LogisticRegression()x_train,x_test,y_train...
data = pd.read_csv('dataset.csv') # 定义特征和目标变量 X = data.drop('target', axis=1) # 特征 y = data['target'] # 目标变量 # 将数据集拆分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2. random_state=42) ...
This is because dataset splitting is random by default. The result differs each time you run the function. However, this often isn’t what you want. Sometimes, to make your tests reproducible, you need a random split with the same output for each function call. You can do that with the...
随机性控制:通过np.random.seed()保证可复现性 四、进阶优化技巧 4.1 参数调优指南 参数影响推荐值 k 样本多样性 3-10(根据特征维度调整) ratio 平衡程度 根据IR(不平衡比)动态计算 4.2 改进变种算法 Borderline-SMOTE:聚焦边界样本 ADASYN:根据密度分布自动调整 SVM-SMOTE:使用SVM决策边界指导 五、实际应用案例 ...
正如我们上面所说的,随机森林和袋装决策树之间的关键区别是对树的创建方式的一个小的改变,这里在get_split()函数中。 完整的例子如下所示。 代码语言:js AI代码解释 # Random Forest Algorithm on Sonar Dataset from randomimportseed from randomimportrandrange ...
| | A random forest is a meta estimator that fits a number of decision tree | classifiers on various sub-samples of the dataset and uses averaging to | improve the predictive accuracy and control over-fitting. | The sub-sample size is controlled with the `max_samples` parameter if | `...