{"id":25816,"date":"2024-01-23T17:23:54","date_gmt":"2024-01-23T09:23:54","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25816"},"modified":"2024-01-23T17:23:57","modified_gmt":"2024-01-23T09:23:57","slug":"%e7%b3%96%e5%b0%bf%e7%97%85%e9%98%b2%e4%b8%8d%e8%83%9c%e9%98%b2%ef%bc%9f%e5%b0%8f%e6%9e%9c%e5%b8%a6%e4%bd%a0%e7%94%a8caret%e4%b8%80%e9%94%ae%e9%a2%84%e6%b5%8b%ef%bc%81","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25816","title":{"rendered":"\u7cd6\u5c3f\u75c5\u9632\u4e0d\u80dc\u9632\uff1f\u5c0f\u679c\u5e26\u4f60\u7528Caret\u4e00\u952e\u9884\u6d4b\uff01"},"content":{"rendered":"

\u5927\u5bb6\u597d\uff0c\u6211\u662f\u5c0f\u679c\uff01\u4eca\u5929\u6211\u8981\u5411\u5927\u5bb6\u4ecb\u7ecd\u4e00\u4e2a\u795e\u5947\u7684R\u5305\u2014\u2014caret\uff0c\u5b83\u5c06\u5e2e\u52a9\u60a8\u5728\u751f\u7269\u4fe1\u606f\u5b66\u9886\u57df\u53d1\u73b0\u6570\u636e\u7684\u5b9d\u85cf\u3002\u65e0\u8bba\u60a8\u662f\u7814\u7a76\u4eba\u5458\u8fd8\u662f\u751f\u7269\u6570\u636e\u5206\u6790\u7684\u7231\u597d\u8005\uff0c\u8fd9\u4e2aR\u5305\u90fd\u5c06\u6210\u4e3a\u60a8\u7684\u5f97\u529b\u52a9\u624b\u3002<\/p>\n

Caret\uff08Classification And REgression Training\uff09\u662f\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684R\u5305\uff0c\u65e8\u5728\u7b80\u5316\u673a\u5668\u5b66\u4e60\u4e2d\u5206\u7c7b\u548c\u56de\u5f52\u6a21\u578b\u7684\u8bad\u7ec3\u548c\u8bc4\u4f30\u8fc7\u7a0b\u3002\u5b83\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u51fd\u6570\u548c\u5de5\u5177\uff0c\u5e2e\u52a9\u7528\u6237\u8f7b\u677e\u5730\u5904\u7406\u6570\u636e\u3001\u9009\u62e9\u5408\u9002\u7684\u7b97\u6cd5\u3001\u8fdb\u884c\u6a21\u578b\u8bad\u7ec3\u548c\u9884\u6d4b\uff0c\u5e76\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n

Caret\u5305\u7684\u4e3b\u8981\u529f\u80fd\u5305\u62ec\uff1a<\/p>\n

1. \u6570\u636e\u9884\u5904\u7406\uff1aCaret\u63d0\u4f9b\u4e86\u4e00\u7cfb\u5217\u51fd\u6570\u548c\u65b9\u6cd5\uff0c\u7528\u4e8e\u6570\u636e\u7684\u9884\u5904\u7406\uff0c\u5982\u6570\u636e\u7f29\u653e\u3001\u6570\u636e\u6807\u51c6\u5316\u3001\u7f3a\u5931\u503c\u5904\u7406\u7b49\u3002\u8fd9\u4e9b\u51fd\u6570\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u6709\u6548\u5730\u51c6\u5907\u6570\u636e\uff0c\u4ee5\u4fbf\u8fdb\u884c\u540e\u7eed\u7684\u6a21\u578b\u8bad\u7ec3\u548c\u8bc4\u4f30\u3002<\/p>\n

2. \u7b97\u6cd5\u9009\u62e9\u548c\u8bad\u7ec3\uff1aCaret\u652f\u6301\u591a\u79cd\u5206\u7c7b\u548c\u56de\u5f52\u7b97\u6cd5\uff0c\u5305\u62ec\u903b\u8f91\u56de\u5f52\u3001\u652f\u6301\u5411\u91cf\u673a\u3001\u968f\u673a\u68ee\u6797\u3001\u68af\u5ea6\u63d0\u5347\u6811\u7b49\u3002\u7528\u6237\u53ef\u4ee5\u6839\u636e\u4efb\u52a1\u7684\u7279\u70b9\u548c\u6570\u636e\u7684\u6027\u8d28\u9009\u62e9\u5408\u9002\u7684\u7b97\u6cd5\uff0c\u5e76\u4f7f\u7528Caret\u63d0\u4f9b\u7684\u51fd\u6570\u8fdb\u884c\u6a21\u578b\u8bad\u7ec3\u3002<\/p>\n

3. \u6a21\u578b\u8c03\u4f18\uff1aCaret\u63d0\u4f9b\u4e86\u53c2\u6570\u8c03\u4f18\u7684\u529f\u80fd\uff0c\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u627e\u5230\u6700\u4f73\u7684\u6a21\u578b\u53c2\u6570\u7ec4\u5408\u3002\u5b83\u652f\u6301\u81ea\u52a8\u5316\u7684\u53c2\u6570\u641c\u7d22\u548c\u4ea4\u53c9\u9a8c\u8bc1\uff0c\u4ee5\u63d0\u9ad8\u6a21\u578b\u7684\u6027\u80fd\u548c\u6cdb\u5316\u80fd\u529b\u3002<\/p>\n

4. \u6a21\u578b\u8bc4\u4f30\uff1aCaret\u63d0\u4f9b\u4e86\u591a\u79cd\u6027\u80fd\u5ea6\u91cf\u6307\u6807\uff0c\u5982\u51c6\u786e\u7387\u3001\u7cbe\u786e\u7387\u3001\u53ec\u56de\u7387\u3001F1\u5206\u6570\u7b49\uff0c\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\u3002\u6b64\u5916\uff0cCaret\u8fd8\u63d0\u4f9b\u4e86\u53ef\u89c6\u5316\u5de5\u5177\uff0c\u5982ROC\u66f2\u7ebf\u3001\u6df7\u6dc6\u77e9\u9635\u7b49\uff0c\u4ee5\u5e2e\u52a9\u7528\u6237\u66f4\u597d\u5730\u7406\u89e3\u548c\u89e3\u91ca\u6a21\u578b\u7684\u6027\u80fd\u3002<\/p>\n

5. \u6a21\u578b\u96c6\u6210\u548c\u6bd4\u8f83\uff1aCaret\u652f\u6301\u6a21\u578b\u7684\u96c6\u6210\u548c\u6bd4\u8f83\uff0c\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u96c6\u6210\u5b66\u4e60\u65b9\u6cd5\uff08\u5982\u968f\u673a\u68ee\u6797\u3001\u68af\u5ea6\u63d0\u5347\u6811\uff09\u6765\u63d0\u9ad8\u6a21\u578b\u7684\u51c6\u786e\u6027\u548c\u7a33\u5b9a\u6027\u3002\u6b64\u5916\uff0cCaret\u8fd8\u63d0\u4f9b\u4e86\u6a21\u578b\u6027\u80fd\u7684\u7edf\u8ba1\u6bd4\u8f83\u65b9\u6cd5\uff0c\u7528\u6237\u53ef\u4ee5\u65b9\u4fbf\u5730\u6bd4\u8f83\u4e0d\u540c\u6a21\u578b\u7684\u6027\u80fd\u5dee\u5f02\u3002<\/p>\n

Caret\u5305\u7684\u8bbe\u8ba1\u65e8\u5728\u63d0\u4f9b\u7edf\u4e00\u7684\u63a5\u53e3\u548c\u5de5\u4f5c\u6d41\u7a0b\uff0c\u4f7f\u7528\u6237\u80fd\u591f\u66f4\u8f7b\u677e\u5730\u5e94\u7528\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u8fdb\u884c\u5206\u7c7b\u548c\u56de\u5f52\u4efb\u52a1\u3002\u5b83\u51cf\u5c11\u4e86\u7e41\u7410\u7684\u4ee3\u7801\u7f16\u5199\u548c\u91cd\u590d\u7684\u5de5\u4f5c\uff0c\u5e2e\u52a9\u7528\u6237\u66f4\u9ad8\u6548\u5730\u8fdb\u884c\u6570\u636e\u5efa\u6a21\u548c\u5206\u6790\u3002<\/p>\n

\u603b\u4e4b\uff0cCaret\u662f\u4e00\u4e2a\u5f3a\u5927\u800c\u7075\u6d3b\u7684R\u5305\uff0c\u9002\u7528\u4e8e\u5404\u79cd\u673a\u5668\u5b66\u4e60\u4efb\u52a1\u3002\u5b83\u4e3a\u7528\u6237\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u529f\u80fd\u548c\u5de5\u5177\uff0c\u4f7f\u5f97\u6570\u636e\u79d1\u5b66\u5bb6\u548c\u673a\u5668\u5b66\u4e60\u4ece\u4e1a\u8005\u80fd\u591f\u66f4\u52a0\u4fbf\u6377\u5730\u8fdb\u884c\u6a21\u578b\u7684\u8bad\u7ec3\u3001\u4f18\u5316\u548c\u8bc4\u4f30\u3002<\/p>\n

\u5728\u5927\u6570\u636e\u65f6\u4ee3\uff0c\u6211\u4eec\u9762\u5bf9\u7684\u6570\u636e\u91cf\u5e9e\u5927\u3001\u590d\u6742\u591a\u53d8\u3002\u5982\u4f55\u4ece\u4e2d\u63d0\u53d6\u6709\u7528\u7684\u4fe1\u606f\uff0c\u63ed\u793a\u751f\u7269\u4f53\u7684\u5965\u79d8\uff1f\u4f7f\u7528caret\uff0c\u60a8\u5c06\u80fd\u591f\u63a2\u7d22\u6570\u636e\u7684\u795e\u79d8\u9762\u7eb1\uff0c\u63ed\u793a\u9690\u85cf\u5728\u5176\u4e2d\u7684\u89c4\u5f8b\u3002\u53ea\u9700\u8981\u51e0\u884c\u7b80\u5355\u7684R\u4ee3\u7801\uff0c\u6211\u5c31\u80fd\u4e3a\u60a8\u5c55\u793acaret\u7684\u795e\u5947\u4e4b\u5904\u3002\u5c0f\u679c\u6559\u4e00\u4e0b\u5927\u5bb6\u600e\u4e48\u5728R\u8bed\u8a00\u4e2d\u4f7f\u7528caret\u5305\uff0c\u5229\u7528\u968f\u673a\u68ee\u6797\uff0c\u5bf9\u7cd6\u5c3f\u75c5\u8fdb\u884c\u9884\u6d4b\u3002\u5177\u4f53\u8981\u5982\u4f55\u5b9e\u73b0\uff1f \u4eca\u5929\uff0c\u5c0f\u679c\u4ee5\u4ee3\u7801\u548c\u63d0\u4f9b\u7684\u6570\u636e\u4e3a\u4f8b\u8fdb\u884c\u6f14\u793a\u548c\u8bf4\u660e\u3002<\/p>\n

\u4ee3\u7801\u5177\u4f53\u5305\u62ec\uff1a<\/p>\n

Step1 \u8f93\u5165\u6570\u636e\u53ca\u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/p>\n

# \u8fd9\u91cc\u7684”inputdata.txt”\u662f\u81ea\u884c\u51c6\u5907\u7684\u672c\u5730\u6587\u4ef6\uff0c\u5c0f\u679c\u7ed9\u5927\u5bb6\u9644\u5728\u6700\u540e\u3002<\/p>\n

# \u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u793a\u4f8b\uff0c\u5c55\u793a\u5982\u4f55\u4f7f\u7528R\u5305caret\u8fdb\u884c\u7cd6\u5c3f\u75c5\u9884\u6d4b\u6a21\u578b\u7684\u8bad\u7ec3\u548c\u8bc4\u4f30\uff1a<\/p>\n

# \u5b89\u88c5\u548c\u52a0\u8f7d\u6240\u9700\u7684\u5e93<\/p>\n

# \u5b89\u88c5\u5e76\u5bfc\u5165caret\u5305<\/p>\n

install.packages(“caret”)<\/p>\n

library(caret)<\/p>\n

# \u5bfc\u5165\u6570\u636e\u96c6\u4ee5\u53ca\u6570\u636e\u9884\u5904\u7406<\/p>\n

biological_data <- read.table(“D:\/wanglab\/life\/ziyuan\/20230717\/inputdata.txt”,header = T)<\/p>\n

# \u5212\u5206\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6<\/p>\n

set.seed(123)<\/p>\n

trainIndex <- createDataPartition(1:length(biological_data$diabetes), p = 0.8, list = FALSE)<\/p>\n

trainData <- biological_data[trainIndex, ]<\/p>\n

testData <- biological_data[-trainIndex, ]<\/p>\n

Step2\u5efa\u7acb\u6a21\u578b<\/p>\n

# \u5efa\u7acb\u6a21\u578b<\/p>\n

fitControl <- trainControl(method = “repeatedcv”,#method\u786e\u5b9a\u591a\u6b21\u4ea4\u53c9\u68c0\u9a8c\u7684\u62bd\u6837\u65b9\u6cd5<\/p>\n

## 10-fold CV…<\/p>\n

number = 10,#number\u786e\u5b9a\u4e86\u5212\u5206\u7684\u91cd\u6570<\/p>\n

## repeated ten times<\/p>\n

repeats = 10)#repeats\u786e\u5b9a\u4e86\u53cd\u590d\u6b21\u6570<\/p>\n

# \u6a21\u578b\u8bad\u7ec3\u548c\u8c03\u4f18\uff1a\u4f7f\u7528train\u51fd\u6570\u6765\u8bad\u7ec3\u6a21\u578b\uff0c\u5e76\u81ea\u52a8\u8c03\u4f18\u6a21\u578b\u7684\u53c2\u6570\u3002<\/p>\n

# \u901a\u8fc7\u6307\u5b9a\u8bad\u7ec3\u6570\u636e\u3001\u76ee\u6807\u53d8\u91cf\u4ee5\u53ca\u4e4b\u524d\u521b\u5efa\u7684\u8bad\u7ec3\u63a7\u5236\u53c2\u6570\uff0cCaret\u5c06\u4f7f\u7528\u4ea4\u53c9\u9a8c\u8bc1\u6765\u8bad\u7ec3\u548c\u8bc4\u4f30\u6a21\u578b\uff0c\u5e76\u81ea\u52a8\u641c\u7d22\u6700\u4f73\u53c2\u6570\u7ec4\u5408\u3002<\/p>\n

set.seed(825)<\/p>\n

Laplacian <- train(trainData,trainData$diabetes,<\/p>\n

method = “rf”,<\/p>\n

preProcess = NULL,<\/p>\n

weights = NULL,<\/p>\n

metric = ifelse(is.factor(y), “Accuracy”, “RMSE”),<\/p>\n

trControl = trainControl(),<\/p>\n

tuneGrid = NULL,<\/p>\n

tuneLength = 20)<\/p>\n

Laplacian<\/p>\n

## \u91c7\u7528RMSE\u6cd5\u9009\u53d6\u6700\u5c0f\u503c\u7684\u6700\u4f18\u6a21\u578b<\/p>\n

## \u6700\u7ec8\u6a21\u578b\u4f7f\u7528\u7684\u53c2\u6570\u503c\u4e3amtry = 6<\/p>\n

\u6700\u4f18\u6a21\u578b<\/strong><\/p>\n

<\/p>\n

ggplot(Laplacian) + scale_x_log10()<\/p>\n

# \u6570\u636e\u7684\u66f2\u7ebf\u56fe\u8868\u660e\uff0c\u5f53mtry \u5927\u4e8e6\u65f6\uff0c\u6a21\u578b\u6ca1\u6709\u53d8\u5316<\/p>\n

\u6700\u4f18\u6a21\u578b\u9009\u62e9\u7684\u66f2\u7ebf\u56fe<\/strong><\/p>\n

<\/p>\n

Step3\u8fdb\u884c\u9884\u6d4b<\/p>\n

# \u8fdb\u884c\u9884\u6d4b<\/p>\n

predictions <- predict(Laplacian, newdata = testData)# \u5728\u6d4b\u8bd5\u96c6\u4e0a\u8fdb\u884c\u9884\u6d4b<\/p>\n

print(predictions)# \u67e5\u770b\u9884\u6d4b\u7ed3\u679c<\/p>\n

# 2.\u7ed8\u5236\u6df7\u6dc6\u77e9\u9635\u56fe<\/p>\n

# \u63d0\u53d6\u9884\u6d4b\u7ed3\u679c<\/p>\n

predictions_df <- as.data.frame(predictions)<\/p>\n

# \u5c06\u9884\u6d4b\u7ed3\u679c\u548c\u5b9e\u9645\u503c\u5408\u5e76\u4e3a\u6570\u636e\u6846<\/p>\n

result_df <- data.frame(Predicted = ifelse(predictions_df$predict > 0.5, “1”, “0”), Actual = as.vector(testData$diabetes))# \u8f6c\u6362\u4e3a\u4e8c\u5206\u7c7b\u9884\u6d4b\u7ed3\u679c<\/p>\n

Step4\u7ed8\u5236\u6df7\u6dc6\u77e9\u9635\u56fe<\/p>\n

# \u7ed8\u5236\u6df7\u6dc6\u77e9\u9635<\/p>\n

confusion_matrix <- table(result_df$Predicted, result_df$Actual)<\/p>\n

confusion_matrix_plot <- ggplot(as.data.frame(confusion_matrix), aes(x = Var1, y = Var2, fill = Freq)) +<\/p>\n

geom_tile() +<\/p>\n

geom_text(aes(label = Freq), color = “white”, size = 12) +<\/p>\n

labs(title = “Confusion Matrix”) +<\/p>\n

xlab(“Predicted”) +<\/p>\n

ylab(“Actual”) +<\/p>\n

scale_fill_gradient(low = “lightblue”, high = “darkblue”)<\/p>\n

# \u663e\u793a\u6df7\u6dc6\u77e9\u9635\u56fe<\/p>\n

print(confusion_matrix_plot)<\/p>\n

\u6df7\u6dc6\u77e9\u9635\u56fe <\/strong><\/p>\n

<\/p>\n

Step5\u7ed8\u5236AUC\u66f2\u7ebf\u56fe<\/p>\n

# 5.\u7ed8\u5236AUC\u66f2\u7ebf\u56fe<\/p>\n

library(pROC)<\/p>\n

roc <- pROC::roc(as.numeric(result_df$Actual), predictions_df$predict)# \u8ba1\u7b97AUC<\/p>\n

plot(roc, main = “Receiver Operating Characteristic (ROC) Curve”,<\/p>\n

xlab = “1 – Specificity”, ylab = “Sensitivity”,<\/p>\n

print.auc=TRUE,#\u663e\u793aAUC\u9762\u79ef<\/p>\n

print.thres=TRUE)#\u6dfb\u52a0\u622a\u70b9\u548c95%CI)# \u7ed8\u5236AUC\u66f2\u7ebf<\/p>\n

)# \u7ed8\u5236AUC\u66f2\u7ebf<\/p>\n

print(paste(“AUC:”, round(auc(roc), 2)))# \u663e\u793aAUC\u503c\u4e3a1<\/p>\n

# \u8bf7\u786e\u4fdd\u5c06\u8def\u5f84”inputdata.txt”\u66ff\u6362\u4e3a\u5b9e\u9645\u6570\u636e\u96c6\u6587\u4ef6\u7684\u8def\u5f84\uff0c\u5e76\u6839\u636e\u9700\u8981\u8c03\u6574\u6a21\u578b\u53c2\u6570\u548c\u5176\u4ed6\u914d\u7f6e\u3002<\/p>\n

# \u4ee5\u4e0a\u4ee3\u7801\u4f7f\u7528pROC\u5e93\u8ba1\u7b97AUC\uff0c\u5e76\u4f7f\u7528plot\u51fd\u6570\u7ed8\u5236AUC\u66f2\u7ebf\u3002\u6700\u540e\uff0c\u901a\u8fc7\u8c03\u7528auc\u51fd\u6570\u83b7\u53d6AUC\u503c\uff0c\u5e76\u4f7f\u7528print\u51fd\u6570\u663e\u793a\u5728\u56fe\u5f62\u4e0a\u3002<\/p>\n

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