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performance-evaluation.Rmd
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performance-evaluation.Rmd
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---
title: "Performance Evaluation"
description: |
This R script evaluates GBM/CNN performance by bootstrapping across 500 iterations of the independent test set.
author:
- first_name: "Colin"
last_name: "Magdamo"
affiliation: Massachusetts General Hospital
affiliation_url: https://www.massgeneral.org/neurology/research/mind-data-science-lab
orcid_id: 0000-0001-8965-4630
output:
distill::distill_article:
toc: true
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(eval = FALSE)
```
# Dependencies
Load requisite packages and define directories.
```{r load-packages, message=FALSE, warning=FALSE}
library(data.table)
library(ROCR)
library(pROC)
library(purrr)
library(glmnet)
library(dbplyr)
library(PRROC)
library(rms)
```
# Read Data
Read test set classification probabilities from gradient boosting machines (GBM) and convolutional neural network (CNN) model output. To comply with the bootstrapping function, datasets must have a column called `labels` (has true labels) and column called `predictions` (has predicted probability).
```{r read-data}
# parse GBM data
gbm <- readRDS('data/AD vs. CTRL GBM Results.rds')
# parse astrocyte GBM
gbm_astrocyte <- gbm[['Astrocyte']]$Scores[,2:3]
setnames(gbm_astrocyte,c('Condition','Alzheimer'),c('labels','predictions'))
gbm_astrocyte[,labels := as.integer(labels) - 1]
# parse microglia GBM
gbm_microglia <- gbm[['Microglia']]$Scores[,2:3]
setnames(gbm_microglia,c('Condition','Alzheimer'),c('labels','predictions'))
gbm_microglia[,labels := as.integer(labels) - 1]
# read astrocyte CNN
cnn_astrocyte <- fread('data/Astrocyte CNN TestSetResults.csv')[,c('TrueLabel','ProbabilityAD')]
setnames(cnn_astrocyte,c('TrueLabel','ProbabilityAD'),c('labels','predictions'))
cnn_astrocyte[,labels := ifelse(labels == 1,0,1)]
# read microglia CNN
cnn_microglia <- fread('data/Microglia CNN TestSetResults.csv')[,c('TrueLabel','ProbabilityAD')]
setnames(cnn_microglia,c('TrueLabel','ProbabilityAD'),c('labels','predictions'))
cnn_microglia[,labels := ifelse(labels == 1,0,1)]
# create named list
all_experiments <- list(gbm_astrocyte = gbm_astrocyte, gbm_microglia = gbm_microglia,
cnn_astrocyte = cnn_astrocyte,cnn_microglia = cnn_microglia)
```
# Compute Model Performance
Function to compute model performance and calculate 95% confidence intervals by boostrapping across 500 iterations of the independent test set.
```{r compute-performance}
compute_model_performance <- function(model_name,model_preds,metrics = c('acc', 'ppv', 'npv', 'sens', 'spec', 'f','auc','aucpr'),threshold_criteria = 'max_accuracy',marginal_thresholds = NULL){
# prediction object ROCR expects; if it is NOT ROCR class prediction then make model_preds one
if(!'prediction' %in% class(model_preds)) {
ROCR_prediction <- prediction(model_preds$predictions,model_preds$labels)
} else {ROCR_prediction <- model_preds}
# first, check if there are any threshold dependent metrics
if(any(!metrics %in% c('auc','aucpr'))){
acc <- performance(ROCR_prediction,'acc')
acc <- data.table(cutoffs = acc@x.values[[1]],accuracy = acc@y.values[[1]])
if(threshold_criteria == 'max_accuracy'){
# pick cutoff that yields max accuracy
threshold <- acc[acc[,.I[which.max(accuracy)]],cutoffs]
}
# must input all_experiments_performance_metrics_cast if using this threshold_criteria
if(threshold_criteria == 'from_marginal'){
# merge back threshold from marginal model
colnames_strip <- names(stratifed_result_indices)
from_marginal_model <- gsub(paste(colnames_strip,collapse = "|"),"",model_name)
from_marginal_model <- gsub('_\\b',"",from_marginal_model)
threshold <- marginal_thresholds[model_name == from_marginal_model,Threshold]
# need to get threshold closest to this ON the data in strata
threshold <- acc[cutoffs <= threshold,][order(-cutoffs)][1,cutoffs] # get threshold within acc object immediately BEFORE this threshold or equal to if its
}
}
# build DT for results from threshold independent metrics
ROCR_results_no_threshold <- map_dfr(metrics[metrics %in% c('auc','aucpr')],~data.table(Measure = .x,
Value = performance(ROCR_prediction,.x)@y.values[[1]],
model_name = model_name,
threshold_criteria = threshold_criteria,
threshold = 'Measure Independent of Threshold'))
# threshold dependent methods
# index y value where x value equal to threshold selected from threshold criteria
ROCR_results_threshold <- map_dfr(metrics[!metrics %in% c('auc','aucpr')],~data.table(Measure = .x,
Value = performance(ROCR_prediction,.x)@y.values[[1]][which(performance(ROCR_prediction,.x)@x.values[[1]] == threshold)],
model_name = model_name,
threshold_criteria = threshold_criteria,
threshold = threshold
))
# add CIs, sample 300 times w replacement and take .025 slices each end
set.seed(5281995)
seeds <- sample.int(10000000,500)
# for 1:500, compute CI for each measure
# takes a replicate index and data to resample, resamples w replacement, computes all specified measures for each replicate
boot_CIs <- function(i,data_to_resample){
# new seed for each replicate
set.seed(seed = seeds[[i]]);
# extract just preds and labels from ROCR object
data <- data.table(predictions = data_to_resample@predictions[[1]],labels = data_to_resample@labels[[1]])
# resample w replacement
data <- data[sample(1:nrow(data),replace = T),]
# make a new prediction object
ROCR_prediction <- prediction(data$predictions,data$labels)
if(any(!metrics %in% c('auc','aucpr'))){
acc <- performance(ROCR_prediction,'acc')
acc <- data.table(cutoffs = acc@x.values[[1]],accuracy = acc@y.values[[1]])
if(threshold_criteria == 'max_accuracy'){
# pick cutoff that yields max accuracy
threshold <- acc[acc[,.I[which.max(accuracy)]],cutoffs]
}
}
ROCR_results_no_threshold <- map_dfr(metrics[metrics %in% c('auc','aucpr')],~data.table(Measure = .x,
Value = performance(ROCR_prediction,.x)@y.values[[1]],
model_name = model_name,
threshold_criteria = threshold_criteria,
threshold = 'Measure Independent of Threshold',
replicate = i))
# threshold dependent methods, index y vals where x val equal to threshold selected from threshold criteria
ROCR_results_threshold <- map_dfr(metrics[!metrics %in% c('auc','aucpr')],~data.table(Measure = .x,
Value = performance(ROCR_prediction,.x)@y.values[[1]][which(performance(ROCR_prediction,.x)@x.values[[1]] == threshold)],
model_name = model_name,
threshold_criteria = threshold_criteria,
threshold = threshold,
replicate = i))
rbindlist(list(ROCR_results_threshold,ROCR_results_no_threshold),fill = TRUE)
}
# get results of each replicate
bootstrap_runs <- map_dfr(1:500,~boot_CIs(.x,data_to_resample = ROCR_prediction))
# cast so each row is a replicate, each col is a measure
bootstrap_runs_cast <- dcast(bootstrap_runs,model_name+threshold_criteria+replicate~Measure,value.var = 'Value')
# get quantiles accross measure cols
bootstrap_aggregation <- imap_dfr(bootstrap_runs_cast[,4:ncol(bootstrap_runs_cast)],~data.table(Measure = .y,CI_l = quantile(.x,.025,na.rm = T),CI_u = quantile(.x,.975,na.rm = T)))
# bind metrics together
final_metrics <- rbindlist(list(ROCR_results_threshold,ROCR_results_no_threshold),fill = TRUE)
final_metrics[bootstrap_aggregation,on=.(Measure),`:=` (CI_l = i.CI_l,CI_u = i.CI_u)]
return(final_metrics)
}
```
Map previously defined function.
```{r map-compute}
all_experiments_performance_metrics<- imap_dfr(all_experiments, ~compute_model_performance(model_name = .y, model_preds = .x))
```