Question

我一直在尝试并行化我的代码，因为目前我正在使用双循环来记录结果。我一直试图看看如何在R中使用SNOW和doParallel软件包来做到这一点。

如果您想要一个可复制的示例，请使用

residual_anomalies <- matrix(sample(c('ANOMALY','NO SIGNAL'),300,replace=T),nrow=100)

而不是使用这三行

inputfile <- paste0("simulation_",i,"_",metrics[k],"_US.csv")
data <- residuals(inputfile)

residual_anomalies <- conceptdrift(data,length=10,threshold=.05)

在嵌套的for循环中。整个代码如下。

source("GetMetrics.R")
source("slowdrift_resampling_vectorized.R")

metrics <- unique(metrics)
num_metrics <- length(metrics)

f1_scores_table_raw = data.frame(matrix(ncol=10,nrow=46))
f1_scores_table_pred = data.frame(matrix(ncol=10,nrow=46))

rownames(f1_scores_table_raw) <- metrics
colnames(f1_scores_table_raw) <- paste0("Sim",1:10)

rownames(f1_scores_table_pred) <- metrics
colnames(f1_scores_table_pred) <- paste0("Sim",1:10)


for(k in 1:num_metrics){

  for(i in 1:10){
    #inputfile <- paste0("simulation_",i,"_",metrics[k],"_US.csv")
    #data <- residuals(inputfile)

    #residual_anomalies <- conceptdrift(data,length=10,threshold=.05)

    #the above is how I get the data frame but I'll create another one for reproducibility.
    residual_anomalies <- as.data.frame(matrix(sample(c('ANOMALY','NO SIGNAL'),300,replace=T),nrow=100))
    names(residual_anomalies) <- c("Raw_Anomaly","Prediction_Anomaly","True_Anomaly")

    #calculate precision and recall for an F1 score

    #first for raw data

    counts <- ifelse(rowSums(residual_anomalies[c("Raw_Anomaly","True_Anomaly")]=='ANOMALY')==2,1,0)
    correct_detections <- sum(counts)

    total_predicted = sum(residual_anomalies$Raw_Anomaly =='ANOMALY')
    total_actual = sum(residual_anomalies$True_Anomaly =='ANOMALY')

    raw_precision = correct_detections / total_predicted
    raw_recall = correct_detections / total_actual

    f1_raw = 2*raw_precision*raw_recall / (raw_precision+raw_recall)

    #then for prediction (DLM,ESP,MLR) data

    counts <- ifelse(rowSums(residual_anomalies[c("Prediction_Anomaly","True_Anomaly")]=='ANOMALY')==2,1,0)
    correct_detections <- sum(counts)

    total_predicted = sum(residual_anomalies$Prediction_Anomaly =='ANOMALY')
    total_actual = sum(residual_anomalies$True_Anomaly =='ANOMALY')

    pred_precision = correct_detections / total_predicted
    pred_recall = correct_detections / total_actual

    f1_pred = 2*pred_precision*pred_recall / (pred_precision+pred_recall)

    f1_scores_table_raw[[k,i]] <- f1_raw
    f1_scores_table_pred[[k,i]] <- f1_pred
  }

}

之前，我使用％dopar％在外环上使用foreach，但我遇到的问题是我一直没有找到问题'％dopar％'。我应该并行化两个循环还是仅仅一个？

我也知道foreach会创建一个列表并将其存储到变量中，但是我还能在其foreach循环中存储其他变量吗？例如，我仍然想将数据记录到我的f1_scores_table_raw和f1_scores_table_pred数组中。

谢谢！

Answer 1

如果在循环级别之间使用%:%运算符，Foreach将自动处理此问题（请参阅“嵌套”小插图）：

require(foreach)
# Register parallel backend

foreach (k = 1:num_metrics) %:% # nesting operator
  foreach (i = 1:10) %dopar% {
    # code to parallelise
}

如何在R中并行化双循环？

1 个答案: