In sharechanxd/myNBpackage: Bios625 homework4 (Title Case)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Load data and package

library(myNBpackage)
library(e1071)
data("iris")
# train data
x=iris[c(1:40,51:90,101:140),-5]
y=iris[c(1:40,51:90,101:140),5]

# test data without labels
testx = iris[c(41:50,91:100,141:150),-5]

The usage of the this package

The usage of myNBpackage is to allow users to build naive bayes classifier with discretization and Gaussian estimation and predict the new data's labels.

Discretization

We could run disc_train_data and disc_test_data to transform continuous X column to categorical form.

v = disc_train_data(x,y)
x_dis = v$discredata
v$cutp
head(x_dis)

After this, we could discrete test data with the same paradigm.

testx_dis = disc_test_data(testx,v$cutp)
head(testx_dis)

But actually, with our package, there is no need to do discretization outside, we could just do as follows:

m2_2 = myNaiveBayes(x,y,discre = TRUE)

We can use the naiveBayes() function from e1071 to verify this function

m1_2 = naiveBayes(x_dis,y)
r1_2 = predict(m1_2,testx_dis)

#our functions
m2_2 = myNaiveBayes(x,y,discre = TRUE)
r2_2 = predict_your_model(m2_2,testx,'class')

all.equal(r1_2,r2_2)

Predicted Result: Class and Raw

With different pred_type you assign, the output result would be different.

# Output class result
m1 = naiveBayes(x,y)
r1 = predict(m1,testx)
m2 = myNaiveBayes(x,y)
r2 = predict_your_model(m2,testx,'class')
all.equal(r1,r2)
head(r2)

#Output raw probs
m1 = naiveBayes(x,y)
r1 = predict(m1,testx,'raw')
m2 = myNaiveBayes(x,y)
r2 = predict_your_model(m2,testx,'raw')
# To avoid floating precision issues
all.equal(round(r1,2),round(r2,2))
head(r2)

About the print function

You could get prior distribution info from Y and conditional probabilities from Gaussian estimation or Bayes formula.

m2 = myNaiveBayes(x,y)
print_my_naiveBayes(m2)

Try on the data including categorical Xi

The dataset iris contains only continuous variables. And we will test on a new simulated dataset to check whether we could deal with categorical variables correctly.

# Build a new data frame
data_new = data.frame(
  size=c("Big","Small","Big","Big","Small","Small"),
  weight=c("light","heavy","light","light","heavy","light"),
  color=c("Red","Red","Red","Green","Red","Green"),
  expensive = c(FALSE,FALSE,TRUE,TRUE,FALSE,FALSE),
  taste=c(TRUE,TRUE,FALSE,FALSE,FALSE,TRUE)
)
x=data_new[,-5]
y=data_new[,5]
testx = data.frame(
  size=c("Big","Small"),
  weight=c("heavy","light"),
  color=c("Green","Red"),
  expensive = c(FALSE,TRUE)
)

m1 = naiveBayes(x,y)
r1 = predict(m1,testx)
m2 = myNaiveBayes(x,y)
r2 = predict_your_model(m2,testx,'class')
all.equal(factor(r1,levels = c("TRUE", "FALSE")),r2)

The results by using this package matches the outcomes from base function and other established packages.

Comparisons for efficiency

library(bench)
library(ggplot2)

# Generate large dataset
set.seed(1)
mydata = list()
for (i in 1:50) {
  mean_r = sample(1:100,1)
  sd_r = sample(1:50,1)
  mydata[[i]] = rnorm(5000,mean_r,sd_r)
}

mydata = data.frame(mydata)
names(mydata)=c(1:50)
y = factor(rep(1:10,400))
x = mydata[1:4000,]
testx = mydata[4001:5000,]

m1 = naiveBayes(x,y)
m2 = myNaiveBayes(x,y)

# Predict test
bm_predict = bench::mark(predict(m1,testx), predict_your_model(m2,testx,'class'))
print(bm_predict)
plot(bm_predict)

We could see that our method is not so efficient as original function and would cost more memory. But the gap is tiny on big data and users could build Naive Bayes Model more flexible with our package considering discretization.

sharechanxd/myNBpackage documentation built on Dec. 23, 2021, 1:21 a.m.