Introduction

In this blog post, we will explore how to create a R output that resembles a confusion matrix table. The problem presented involves two directories, each containing a list of files with similar names but different content. The goal is to calculate the percentage of intersecting and non-intersecting data between the test and model directories.

Background

A confusion matrix is a table used to evaluate the performance of a classification model or algorithm. It consists of four quadrants: true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). The values in each quadrant represent the number of actual instances that fall into each category.

Problem Statement

Given two directories, one containing “model” files and the other containing “test” files, we need to calculate the percentage of intersecting and non-intersecting data between the test and model directories. We will use R as our programming language to achieve this goal.

Solution

To solve this problem, we will create a function called myFun that calculates the intersection and exception percentages for each pair of model and test files. We will then use this function in a loop to calculate these values for all pairs of files in both directories.

Step 1: Create a Function to Calculate Intersection and Exception Percentages

myFun <- function(model, test, datasource) {
  model <- datasource[[model]]
  test <- datasource[[test]]
  paste(rev(mapply(function(x, y) (x/y)*100, 
                   lapply(split(test, test %in% model), length), 
                   length(test))), 
        collapse = "/")
}

This function takes three arguments: model, test, and datasource. It extracts the data for each file from the datasource list and calculates the intersection and exception percentages using the mapply and lapply functions.

Step 2: Create a List of Models and Tests

models <- c("model_1", "model_2", "model_3")
tests <- c("test_1", "test_2", "test_3")
A <- expand.grid(models, tests, stringsAsFactors = FALSE)

This code creates a list of models and tests using the expand.grid function.

Step 3: Create a List of Data Sources

dataList <- mget(c(models, tests))

This code uses the mget function to create a list of data sources for each model and test.

Step 4: Calculate Intersection and Exception Percentages

A$value <- apply(A, 1, function(x) myFun(x[1], x[2], dataList))

This code applies the myFun function to each row in the A data frame and stores the results in a new column called value.

Step 5: Reshape the Data

reshape(A, direction = "wide", idvar = "Var1", timevar = "Var2")

This code reshapes the data from a long form to a wide form.

The final output will be a table with the model and test names as headers and the intersection and exception percentages as values.

Example Output

##               model_1        model_2       model_3
##   test_1     75/25          50/50    62.5/37.5
##   test_2      100         87.5/12.5    50/50

This output shows the intersection and exception percentages for each pair of model and test files.

Conclusion

In this blog post, we explored how to create a R output that resembles a confusion matrix table. We created a function called myFun that calculates the intersection and exception percentages for each pair of model and test files. We then used this function in a loop to calculate these values for all pairs of files in both directories. The final output was a table with the model and test names as headers and the intersection and exception percentages as values.

Last modified on 2024-11-23