2023年11月25日

Python代写｜Assignment 1 – FIT5222 Planning and automated reasoning

这是一篇来自澳洲的关于计算机测试的计算机代写

 

Question 1

Construct and analyze a binary classifier from a simulated dataset using your SID and the mlbench package.

  1. [4 marks] Simulate a single dataset of n = 500 observations completing the code below replacing <insert SID here> with your SID respectively and changing the eval = FALSE setting to eval = TRUE in the R code chunk. Then, inspect the q2.dat using the head command and verify the dimension of the data.frame and that there are 2 numeric features and a single factor variable of class labels. Note: You should explicitly verify the data type is numeric or factor or inspect the class of each column.
library(mlbench)
set.seed(5003)
simulated.data <- mlbench.2dnormals(n = 500, sd = 2)
q2.dat <- as.data.frame(simulated.data)
head(q2.dat)
##          x.1        x.2 classes
## 1  2.3110637 -2.4235455       1
## 2 -2.1471013 -6.8373623       2
## 3 -3.4813172 -1.2873004       2
## 4 -1.4097769  2.1890729       2
## 5  0.1110965  0.7407209       1
## 6  0.1862293  2.3522997       1
dim(q2.dat)
## [1] 500   3
# Any of the below are acceptable
lapply(q2.dat, class)
## $x.1
## [1] "numeric"
## 
## $x.2
## [1] "numeric"
## 
## $classes
## [1] "factor"
str(q2.dat)
## 'data.frame':    500 obs. of  3 variables:
##  $ x.1    : num  2.311 -2.147 -3.481 -1.41 0.111 ...
##  $ x.2    : num  -2.424 -6.837 -1.287 2.189 0.741 ...
##  $ classes: Factor w/ 2 levels "1","2": 1 2 2 2 1 1 1 1 2 1 ...
  1. [6 marks] Split the simulated data into 75% training and 25% test data set using caret::createDataPartition or otherwise. Then fit a logistic regression model to explain the classes response using the two features on the training data. Predict the classes on the test data using your fitted logistic regression model and the threshold for classification using the esimated probability of being in the positive class at 0.5. Compute the accuracy on the test set in this situation.
train.ind <- caret::createDataPartition(q2.dat[["classes"]], p = 0.75)[[1]]
training.data <- q2.dat[train.ind, ]
test.data <- q2.dat[-train.ind, ]
trained.model <- glm(classes ~ ., data = training.data, family = binomial(link = "logit"))
predicted.classes <- ifelse(predict(trained.model, newdata = test.data) > 0.5, 2, 1)
accuracy <- mean(predicted.classes == test.data[["classes"]])
accuracy
## [1] 0.8225806
  1. Create a scatter plot of the data in the test set and colour each point by its true class label. Draw the linear decision boundary generated by the logistic regression model fitted above. Comment on how the accuracy computed on the test set in part c. relates to your plot and decision boundary.
plot(x.2 ~ x.1, data = test.data, col = classes)
betas <- coef(trained.model)
abline(a = -betas[1]/betas[3], b = -betas[2]/betas[3], lty = "dotted")

The model will classify points in the bottom left of the plot (below the boundary) as being in the positive class and points in the top right (above the boundary) as the negative class. These classifications are correct 82.2580645% of the time.

Question 2

Consider the estimation of density of the duration of a geyser eruptions (in minutes). Provided is a messy and clean dataset these geyser eruption durations in the files clean-s1-22-q1.rds and messy-s1-22-q2.rds respectively. The readRDS commands below load the data using the native data format in R.

messy.duration <- readRDS('messy-s1-22-q2.rds')
clean.duration <- readRDS('clean-s1-22-q2.rds')

Suppose only the messy dataset was only available initially and requires cleaning by removing the negative and missing (NA) values. The goal here is the clean the messy dataset and then provide an analysis of the stability of the bandwidths in the density algorithm.

  1. [4 marks] Some geyser eruptions were recorded incorrectly with a negative duration or were coded as missing (coded as NA). Using relevant R code, verify that messy.duration is a numeric vector with 341 observations. Also count the number of observations that are negative and the number that are coded as missing (i.e. NA).
length(messy.duration)
## [1] 341
n.missing <- sum(is.na(messy.duration))
n.negative <- sum(messy.duration < 0, na.rm = TRUE)
c(`n missing` = n.missing, `n negative` = n.negative)
##  n missing n negative 
##         22         20
  1. [ marks] Create a new vector called my.cleaned.duration which uses the messy.duration vector and cleans it by removing the negative values or values that are coded as NA. Verify that your created vector is the same as the clean.duration dataset using a call to all.equal or identical.
my.cleaned.duration <- messy.duration[!is.na(messy.duration) & messy.duration > 0]
identical(my.cleaned.duration, clean.duration)
## [1] TRUE

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