Please read this README.md file to learn more details of this project.
# Importing libraries and set up the work environment
library(knitr)
library(readxl)
library(tidyverse)
library(janitor)
library(rpart)
library(nnet)
library(scales)knitr::opts_chunk$set(cache = TRUE,
warning = FALSE,
echo = TRUE,
message = FALSE,
dpi = 180,
fig.width = 6,
fig.height = 4)
theme_set(theme_classic())
Loading the data files, assigning the header names and counting to check fro class imabalance.
spam_train <- read_csv("spam_train.csv",
col_names = FALSE)
spam_test <- read_csv("spam_test.csv",
col_names = FALSE)
header_spam <- c("make", "address", "all", "3d", "our", "over", "remove",
"internet","order", "mail", "receive", "will",
"people", "report", "addresses","free", "business",
"email", "you", "credit", "your", "font","000","money",
"hp", "hpl", "george", "650", "lab", "labs",
"telnet", "857", "data", "415", "85", "technology", "1999",
"parts","pm", "direct", "cs", "meeting", "original", "project",
"re","edu", "table", "conference", ";", "(", "[", "!", "$", "#",
"CAPAVE", "CAPMAX", "CAPTOT","type")
colnames(spam_train) <- header_spam
colnames(spam_test) <- header_spam
# Checking for class imbalance
spam_train %>%
count(type)
# Important Functions
pct_accuracy <- function(y_hat, y_test, threshold){
y_hat[y_hat > threshold] <- 1
y_hat[y_hat <= threshold] <- 0
correct <- y_hat == y_test
pct_accurate <- sum(correct)/length(correct)
return(pct_accurate)
}
# Missclasification RAtes - Missed in previous HWs
get_misclassification_rates <- function(model, threshold){
y_hat <- predict(model, spam_test_proc)
y_hat[y_hat > threshold] <- 1
y_hat[y_hat <= threshold] <- 0
correct <- y_hat == y_test
correct_spam <- correct[y_test == 1]
correct_nonspam <- correct[y_test == 0]
misclassification_rate <- 1 - sum(correct)/length(correct)
spam_misclassification_rate <- 1 - sum(correct_spam)/length(correct_spam)
nonspam_misclassification_rate <- 1 - sum(correct_nonspam)/length(correct_nonspam)
return(c(misclassification_rate,
spam_misclassification_rate,
nonspam_misclassification_rate))
}
Scaling the data using tidymodels and splitting into training and testing set. Included an example of data pre-processed.
set.seed(415)
# Scaling Data using Tidymodels
spam_train_rec <- recipe(type ~ ., data = spam_train) %>%
step_scale(all_predictors()) %>% # Normaliing the data
step_zv(all_predictors()) %>% # Eliminating Varibales with zero variance
prep(retain = TRUE)
# Response label
y_test <- spam_test %>%
select(type)
y_train <- spam_train %>%
select(type)
# Splitting data
spam_train_proc <- bake(spam_train_rec, new_data = spam_train) %>%
select(-type)
spam_test_proc <- bake(spam_train_rec, new_data = spam_test) %>%
select(-type)
head(spam_train_proc, 10)
Fitting on the training set one hidden layer neural networks with 1, 2,…, 10 hidden units and different sets of starting values for the weights (obtain in this way one model for each number of units). We will know which structural model performs best at classifying on the test set.
First Model - Hidden Neural Layers
set.seed(415)
# Parameters
num_neurons <- seq(1:10)
num_reps <- 10
wt_rang = 0.3
threshold <- 0.5
accuracies <- c()
for(size in num_neurons){
sum_accuracy <- 0
for(i in c(1:num_reps)){
set.seed(415)
model <- nnet(
spam_test_proc, y_train, size=num_neurons[size],
linout = FALSE, softmax = FALSE,
censored = 100, rang = wt_rang, decay = 0,
maxit = 100, trace = FALSE, Hess = FALSE
)
y_hat <- predict(model, spam_test_proc)
sum_accuracy <- sum_accuracy + pct_accuracy(y_hat, y_test, threshold)
}
accuracies <- c(accuracies, sum_accuracy/num_reps)
}
#get the num_neurons corresponding with model that produced highest average accuracy
best_performing <- which.max(accuracies)
best_num_neurons <- num_neurons[best_performing]
The best number of hidden layer neurons is 4 which provides an accuracy of 94% in the testing set.
Choosing the optimal regularization (weight decay for parameters 0,0.1,…,1) for the structural model found above by averaging your estimators of the misclassification error on the test set. The average will be over 10 runs with different starting values.
set.seed(415)
# Parameters
decays <- seq(0, 1, .1) # Adding weight decay (0.1, 1)
num_neurons <- seq(1:10) # Same layer as before
num_reps <- 10
wt_rang = 0.5
threshold <- 0.5
accuracies <- c()
for(decay in decays){
sum_accuracy <- 0
for(i in c(1:num_reps)){
set.seed(415)
model <- nnet(
spam_train_proc, y_train, size=best_num_neurons,
linout = FALSE, softmax = FALSE,
censored = FALSE, skip = FALSE, rang = wt_rang, decay = decay ,
maxit = 100, Hess = FALSE, trace = FALSE
)
y_hat <- predict(model, spam_test_proc)
sum_accuracy <- sum_accuracy + pct_accuracy(y_hat, y_test, threshold)
}
accuracies <- c(accuracies, sum_accuracy/num_reps)
}
# Highest average accuracy by neuron and decay.
best_performing <- which.max(accuracies)
best_decay <- decays[best_performing]
The best performing model is achieved using 0.1 as the best weight decay.
#get misclassification rates for our best chosen parameters
model <- nnet(
spam_train_proc, y_train, size= best_num_neurons,
linout = FALSE, entropy = FALSE, softmax = FALSE,
censored = FALSE, skip = FALSE, rang = wt_rang, decay = best_decay,
maxit = 100, Hess = FALSE, trace = FALSE
)
misclassification_rates <- get_misclassification_rates(model, threshold)#get misclassification rates for our best chosen parameters
Best Weight Decay: 0.1 Best Number of Layer Neurons: 4 Missclasification rate of the model: 4% Spam misscalsification rate: 6% Ham Missclasification Rate: 3% (c) As in the previous homework
misclassified good emails to be less than 1%.
set.seed(415)
#for a given model
find_threshold <- function(model){
thresholds <- seq(0,1,0.01)
y_hat <- predict(model, spam_test_proc)
for(thresh in thresholds){
y_hat_nonspam <- y_hat[y_test == 0]
y_hat_nonspam[y_hat_nonspam > thresh] <- 1
y_hat_nonspam[y_hat_nonspam <= thresh] <- 0
nonspam_misclassification_rate <- sum(y_hat_nonspam)/length(y_hat_nonspam)
if(nonspam_misclassification_rate <= 0.01) {break}
}
return(thresh)
}
# parameters
num_neurons <- seq(1:10)
weight_decays <- seq(0,1,.1)
wt_rang = 0.5
#fitting the modee to specified parameters
models <- list()
for(i in c(1:length(num_neurons))){
for(j in c(1:length(decays))){
model <- nnet(
spam_train_proc, y_train, size=num_neurons[i],
linout = FALSE, entropy = FALSE, softmax = FALSE,
censored = FALSE, skip = FALSE, rang = wt_rang, decay = decays[j],
maxit = 100, Hess = FALSE, trace = FALSE
)
models[[paste(i,j,sep="_")]] <- model
}
}
# < 1% nonspam misclassification rate for each model
model_thresholds <- lapply(models, function(x) {find_threshold(x)})
#get the overall, spam, and non-spam misclassification rates at each threshold
misclassification_rates <- mapply(get_misclassification_rates, models, model_thresholds)
#find the model with the lowest overall misclassification rate
#(using forced < 1% nonspam threshold)
best_model_idx <- which.min(misclassification_rates[1,])
best_model <- models[[best_model_idx]]
best_misclassification_rates <- misclassification_rates[,best_model_idx]
Reporting on the best neural net model:
Best model accuracy for Ham emails: (< 1% missclasification)
Hidden layer neurons: 8 Weight Decay: 0.3
Overall Missclasification Rate: 5%
Spam Email Missclasification Rate: 11%
Ham Email Missclasification Rate: 1%
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