Combining ROCR Performance Objects

Understanding the Problem

In this article, we will explore how to combine performance objects created using the ROCR package in R. ROCR is a popular package for evaluating the performance of classification models, and its performance objects are powerful tools for visualizing and analyzing model results.

The problem arises when we have multiple performance objects, each containing auc or fpr/tpr values for different classes. These objects also have results for multiple test runs, making it challenging to combine them and calculate global averages. In this article, we will explore a solution using ROCR’s prediction function and some creative data manipulation techniques.

Background

Before diving into the solution, let’s briefly review how performance objects are created in ROCR. A performance object is created by calling the performance() function on a classification model, specifying the predictor variables and response variable(s). The resulting object contains various metrics for evaluating model performance, such as auc, fpr, and tpr.

ROCR also provides functions for manipulating and combining performance objects, such as combine.perf.objects(). However, these functions can be limited in their capabilities, especially when working with multiple performance objects.

Solution Overview

Our solution involves creating a single prediction object that combines the predictions of all individual performance objects. We will use ROCR’s prediction function to achieve this.

Step 1: Recreating Prediction Objects

To combine performance objects, we need to recreate the prediction object for each class. This can be done using the prediction() function in ROCR.

# Load necessary libraries
library(ROCR)

# Create individual performance objects (not shown here)
cls1.perf.obj <- ...
cls2.perf.obj <- ...
...
cls5.perf.obj <- ...

# Recreate prediction objects for each class
global.prediction <- 
  prediction(
    c(cls1.likelihood, cls2.likelihood, ..., cls5.likelihood),
    c(duplicate.cols(cls1.labels, ncol(cls1.likelihood)),
      duplicate.cols(cls2.labels, ncol(cls2.likelihood)),
      ..., 
      duplicate.cols(cls5.labels, ncol(cls5.likelihood))),
    label.ordering = c(FALSE, TRUE)
  )

In this code snippet, we first create individual performance objects (not shown here) for each class. We then recreate the prediction object using the prediction() function, passing in the likelihoods and labels for each class.

The duplicate.cols() function is used to build a data frame of repeating labels, which is necessary for creating the global prediction object.

Step 2: Combining Performance Objects

Now that we have recreated the prediction objects for each class, we can combine them using ROCR’s combine.perf.objects() function (although this approach may not be straightforward).

However, our solution involves creating a single prediction object that combines all individual predictions. We can achieve this by directly manipulating the data within the prediction object.

Step 3: Calculating Global Averages

Once we have combined the prediction objects, we can calculate global averages for metrics such as auc using ROCR’s performance() function.

# Calculate global average for auc metric
global.auc <- performance(global.prediction, "auc")

This code snippet calculates the global average for the auc metric across all classes and predictions.

Step 4: Plotting Results

Finally, we can plot our results using a library like ggplot2.

# Load necessary libraries
library(ggplot2)

# Plot global averages for auc metric
ggplot(global.auc, aes(x = cut)) + 
  geom_col() +
  labs(title = "Global Averages", x = "Cut", y = "AUC")

This code snippet creates a bar plot of the global average auc values across all classes and predictions.

Conclusion

Combining ROCR performance objects can be challenging, especially when working with multiple objects. However, by using creative data manipulation techniques and leveraging ROCR’s prediction function, we can create a single prediction object that combines all individual predictions. This approach allows us to calculate global averages for metrics such as auc and visualize our results in a meaningful way.

Note: The above code snippets are just examples of how you could implement the solution described in this article. The actual implementation may vary depending on your specific use case.

Last modified on 2023-08-24