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
采用这个方法:from torch.utils.data import random_split 1. dataset = BagDataset(transform) train_size = int(0.9 * len(dataset)) # 整个训练集中,百分之90为训练集 test_size = len(dataset) - train_size train_dataset, test_dataset = random_split(dataset, [train_size, test_size]) # 划分训...
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...
data_split #构建n个子集 def get_subsamples(dataSet,n): subDataSet=[] for i in range(n): index=[] #每次都重新选择k个 索引 for k in range(len(dataSet)): #长度是k index.append(np.random.randint(len(dataSet))) #(0,len(dataSet)) 内的一个整数 subDataSet.append(dataSet[index,:]) ...
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])...
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) ...
Split Your Dataset With scikit-learn's train_test_split() In this quiz, you'll test your understanding of how to use the train_test_split() function from the scikit-learn library to split your dataset into subsets for unbiased evaluation in machine learning. ...
http://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/ 2.6 statsmodels 资料地址:http://www.statsmodels.org/stable/index.html 2.7 tushare 资料地址:http://www.statsmodels.org/stable/index.html
cancer-wisconsin/wdbc.data',header=None)# Breast Cancer Wisconsin datasetX,y=df.values[:,2:],df.values[:,1]encoder=LabelEncoder()y=encoder.fit_transform(y)>>>encoder.transform(['M','B'])array([1,0])X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.2,random_state...