-
Notifications
You must be signed in to change notification settings - Fork 0
/
week15_assi7_sol2.r
412 lines (328 loc) · 15.2 KB
/
week15_assi7_sol2.r
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
# # -*- coding: utf-8 -*-
# """Week15 Assi7 Sol2.ipynb
#
# Automatically generated by Colaboratory.
#
# Original file is located at
# https://colab.research.google.com/drive/1_Et_v9mmzBrmbE8jR1XTGHR_m1d5DieY
# """
###########################################################################
## Week-15, Homework-7, Sol-2
## Sreya Dhar
## Created: Dec 09, 2020
## Edited: Dec 16, 2020
###########################################################################
rm(list=ls())
## installing all the libraries in R kernel
# install.packages("corrplot")
# install.packages("forecast")
# install.packages("zoo")
# install.packages("rsample")
# install.packages("leaps")
# install.packages("car")
# install.packages("caret")
# install.packages("ROCR")
# install.packages("PerformanceAnalytics")
# install.packages("funModeling")
# install.packages("hrbrthemes")
# install.packages("ggthemes")
# install.packages("GGally")
# install.packages("glmnet")
# install.packages("ISLR")
# install.packages("kableExtra")
# install.packages("broom")
# install.packages("knitr")
# install.packages("psych")
# install.packages("aod")
# install.packages("epiDisplay")
# install.packages("e1071")
# install.packages("class")
#
# install.packages("rpart.plot")
# install.packages("party")
# install.packages("partykit")
# install.packages("rattle")
## importing the libraries in R kernel
library(ggplot2)
library(dplyr)
library(tidyverse)
library(tidyr)
library(corrplot)
library(repr)
library(reshape2)
library(forecast)
library(zoo)
library(rsample)
library(gplots)
library(ROCR)
library(class)
library(readr)
library(leaps)
library(car)
library(PerformanceAnalytics)
library(funModeling)
library(gridExtra)
library(caret)
library(MASS)
library(Hmisc)
library(hrbrthemes)
library(GGally)
library(glmnet)
library(pROC)
library(psych)
library(aod)
library(epiDisplay)
library(e1071)
library(ggthemes)
library(kableExtra)
library(broom)
library(knitr)
library(devtools)
library(rpart) #for trees
library(rattle) # Fancy tree plot This is a difficult library to install (https://gist.github.com/zhiyzuo/a489ffdcc5da87f28f8589a55aa206dd)
library(rpart.plot) # Enhanced tree plots
library(RColorBrewer) # Color selection for fancy tree plot
library(party) # Alternative decision tree algorithm
library(partykit) # Convert rpart object to BinaryTree
library(randomForest)
library(viridis)
library(tree)
library(factoextra)
library(pca3d)
library(rgl)
library(rrr)
# if (!requireNamespace('BiocManager', quietly = TRUE))
# install.packages('BiocManager')
#
# BiocManager::install('PCAtools')
## set directory ##
setwd("C:/File G/EAS 506 Statistical Mining I/Week 15/Assignment-7")
## converting the RData file into .csv file
load("C:/File G/EAS 506 Statistical Mining I/Week 15/Assignment-7/pendigits.RData")
write.csv(pendigits,'pendigits.csv')
pendigits <- read.csv("pendigits.csv", header = TRUE)
digits <- pendigits[,-c(1,19,37)]
digits_final <- digits[, c(1:17)]
digits_features <- digits[,-c(17:34)]
digits_class <- digits[,17]
GGally::ggcorr(digits_final)+theme_bw()
glimpse(digits_final)
names(digits_final)
status(digits_final)
profiling_num(digits_final)
describe(digits_final)
summary(digits_final)
plot_num(digits_final)
# corrplot(digits_final, method = "circle", type = "lower")
rrr(digits_features, digits_features, type = "pca")$goodness_of_fit
rank_trace(digits_features, digits_features, type = "cva")
rank_trace(digits_features, digits_features, type = "cva", plot = FALSE)
rank_trace(digits_features, digits_features, type = "pca")
rank_trace(digits_features, digits_features, type = "pca", plot = FALSE)
args(pairwise_plot)+theme_bw()
pairwise_plot(digits_features, digits_class, type = "pca")
pairwise_plot(digits_features, digits_class, type = "pca", pair_x = 2, pair_y = 4)
rrr(digits_features, digits_features, type = "pca", rank = 10)
# args(pca_allpairs_plot)
# pca_allpairs_plot(digits_features, rank = 3, class_labels = digits_class)
## variance of 16 predictor variables ##
vars <- apply(digits_features, 2, var)
vars
an############### PCA on Whole dataset ################
pca_dig <- prcomp(digits_features, scale = TRUE)
sum_pca_dig <- summary(pca_dig)
sum_pca_dig
options(repr.plot.width=4, repr.plot.height=4, repr.plot.res = 200)
par(mfrow=c(1,1))
fviz_eig(pca_dig, title="Explained variance in whole dataset", xlab="Components")
pca_dig
pairsplot(pca_dig)
### eigen values from PCA on whole dataset
eig_val <- get_eigenvalue(pca_dig)
eig_val
# biplot(pca_dig)
# pca2d(pca_dig, group=digits_class, legend="topleft")
pca2d(pca_dig, group=digits_class, biplot=TRUE, biplot.vars=3, legend=TRUE)
pca3d(pca_dig, group=digits_class, show.ellipses=FALSE,
ellipse.ci=0.95, show.plane=FALSE, legend="topleft")
### extracting first 5 components from PCA on train and test set #########
digits_dat<- digits[,-c(18:34)]
pred_pca_tr <- pca_dig$x[,1:5]
resp_pca_tr <- digits_dat[,17]
dat_pca_tr <- cbind(pred_pca_tr,resp_pca_tr )
data_pca_tr <- data.frame(dat_pca_tr)
pairs.panels(data_pca_tr[,1:5], main = "Pairs plot on PCA components, unclassed on digitclasses", pch = 21, bg = c("blue", "green")[unclass(data_pca_tr$resp_pca_tr)], hist.col="red")
## splitting the dataset into train and test sets
set.seed(4444) ## seeding the sampling
data_split_pca <- initial_split(data_pca_tr, prop = 0.75) ## spliting the data by library 'rsample'
data_train_pca <- training(data_split_pca)
data_test_pca <- testing(data_split_pca)
### knn of PCA comps ####
accuracy = function(actual, predicted) { ## defining accuracy function
mean(actual == predicted)}
error = function(actual, predicted) { ## defining error function
mean(actual != predicted)}
iter_k = c(1,3,5,7,9,13,15) ## giving k-values
accu_pca_te = rep(x = 0, times = length(iter_k))
accu_pca_tr = rep(x = 0, times = length(iter_k))
error_pca_te = rep(x = 0, times = length(iter_k))
error_pca_tr = rep(x = 0, times = length(iter_k))
for(i in seq_along(iter_k)) {
pred_pca_tr = knn( train = data_train_pca[,1:5],
test = data_train_pca[,1:5],
cl = data_train_pca$resp_pca_tr,
k = iter_k[i])
accu_pca_tr[i] = accuracy(pred_pca_tr,data_train_pca$resp_pca_tr) ## accuracy from knn on train set
error_pca_tr[i] = error( pred_pca_tr, data_train_pca$resp_pca_tr) ## error from knn on train set
} ## error from knn train set
for(i in seq_along(iter_k)) {
pred_pca_te = knn( train = data_train_pca[,1:5],
test = data_test_pca[,1:5],
cl = data_train_pca$resp_pca_tr,
k = iter_k[i])
accu_pca_te[i] = accuracy(pred_pca_te, data_test_pca$resp_pca_tr) ## accuracy from knn on test set
error_pca_te[i] = error(pred_pca_te, data_test_pca$resp_pca_tr) ## error from knn on test set
} ## error from knn test set
error_pca_train <- mean(data_train_pca$resp_pca_tr != pred_pca_tr)
error_pca_test <- mean(data_test_pca$resp_pca_tr != pred_pca_te)
print(paste('Accuracy of train set from linear regression',(1-error_pca_train)*100,'%'))
print(paste('Accuracy of test set from linear regression',(1-error_pca_test)*100,'%'))
c(error_pca_te, error_pca_tr)
####################### Comparing classification accuracy and errror in knn from PCA components #########################
options(repr.plot.width=8, repr.plot.height=8, repr.plot.res = 200)
par(mfrow=c(2,2))
# plot accuracy vs choice of k on Training set
plot(iter_k, accu_pca_tr*100, type = "b",col = "blue", cex = 1, pch = 20, lwd = 2, lty = 2,
xlab = "k, number of neighbors", ylim=c(90,100),
ylab = "classification accuracy, %", main = "Accuracy of Training set")
abline(v = which.max(accu_pca_tr),y = max(accu_pca_tr)*100, type = "l", col = "red", lwd = 2, lty = 2)
abline(x = which.max(accu_pca_tr),h = max(accu_pca_tr)*100, type = "l", col = "black", lty = 2)
# plot accuracy vs choice of k on Test set
plot(iter_k, accu_pca_te*100, type = "b", col = "blue", cex = 1, pch = 20, lwd = 2,
xlab = "k, number of neighbors", ylab = "classification accuracy, %",
main = "Accuracy of Test set")
abline(v = 3,y = max(accu_pca_te)*100, type = "l", col = "red", lwd = 2, lty = 2)
abline(x = which.max(accu_pca_te),h = max(accu_pca_te)*100, type = "l", col = "black", lty = 2)
# plot accuracy vs choice of k on Training set
plot(iter_k, error_pca_tr*100, type = "b",col = "blue", cex = 1, pch = 20, lwd = 2,
xlab = "k, number of neighbors",
ylab = "classification error, %", main = "classification error of Training set")
abline(v = which.min(error_pca_tr),y = min(error_pca_tr)*100, type = "l", col = "red", lwd = 2, lty = 2)
abline(x = which.min(error_pca_tr),h = min(error_pca_tr)*100, type = "l", col = "black", lty = 2)
# # plot accuracy vs choice of k on Test set
plot(iter_k, error_pca_te*100, type = "b", col = "blue", cex = 1, pch = 20, lwd = 2,lty = 2,
xlab = "k, number of neighbors", ylab = "classification error, %",
main = "classification error of Test set")
abline(v = 3,y = min(error_pca_te)*100, type = "l", col = "red", lwd = 2, lty = 2)
abline(x = which.min(error_pca_te),h = min(error_pca_te)*100, type = "l", col = "black", lty = 2)
## KNN=3 on pca components ##
pred_pca_tr = knn( train = data_train_pca[,1:5],
test = data_train_pca[,1:5],
cl = data_train_pca$resp_pca_tr,
k = 5)
pred_pca_te = knn( train = data_train_pca[,1:5],
test = data_test_pca[,1:5],
cl = data_train_pca$resp_pca_tr,
k = 5)
confusionMatrix(table(actual =data_train_pca$resp_pca_tr, prediction = pred_pca_tr))
confusionMatrix(table(actual =data_test_pca$resp_pca_tr, prediction = pred_pca_te))
################## Preparing the data for Analysis ########################
## splitting the dataset into train and test sets
set.seed(4444) ## seeding the sampling
data_split <- initial_split(digits_final, prop = 0.75) ## spliting the data by library 'rsample'
data_train <- training(data_split)
data_test <- testing(data_split)
# ##############################################################
# ###################### PCA ###################################
# ############# applying pca on train data #####################
#
# pca_tr <- prcomp(data_train, scale = TRUE)
# sum_pca_tr <- summary(pca_tr)
# sum_pca_tr
#
# ############# applying pca on train data #####################
# pca_te <- prcomp(data_test[,1:12], scale = TRUE)
# sum_pca_te <- summary(pca_te)
# sum_pca_te
#
# options(repr.plot.width=4, repr.plot.height=4, repr.plot.res = 200)
# par(mfrow=c(1,1))
# fviz_eig(pca_tr, title="Explained variance in train set", xlab="Components")
#
# pca_tr
#
# ### eigen values from PCA
# eig_val_tr <- get_eigenvalue(pca_tr)
# eig_val_tr
###################################################################
############### Knn prediction on the dataset #####################
###################################################################
accuracy = function(actual, predicted) { ## defining accuracy function
mean(actual == predicted)}
error = function(actual, predicted) { ## defining error function
mean(actual != predicted)}
iter_k = c(1,3,5,7,9,13,15) ## giving k-values
accu_pca_te = rep(x = 0, times = length(iter_k))
accu_pca_tr = rep(x = 0, times = length(iter_k))
error_pca_te = rep(x = 0, times = length(iter_k))
error_pca_tr = rep(x = 0, times = length(iter_k))
for(i in seq_along(iter_k)) {
pred_pca_tr = knn( train = data_train[,1:16],
test = data_train[,1:16],
cl = data_train$V17,
k = iter_k[i])
accu_pca_tr[i] = accuracy(pred_pca_tr,data_train$V17) ## accuracy from knn on train set
error_pca_tr[i] = error( pred_pca_tr, data_train$V17) ## error from knn on train set
} ## error from knn train set
for(i in seq_along(iter_k)) {
pred_pca_te = knn( train = data_train[,1:16],
test = data_test[,1:16],
cl = data_train$V17,
k = iter_k[i])
accu_pca_te[i] = accuracy(pred_pca_te, data_test$V17) ## accuracy from knn on test set
error_pca_te[i] = error(pred_pca_te, data_test$V17) ## error from knn on test set
} ## error from knn test set
error_pca_train <- mean(data_train$Cover_Type != pred_pca_tr)
error_pca_test <- mean(data_test$Cover_Type != pred_pca_te)
print(paste('Accuracy of train set from linear regression',(1-error_pca_train)*100,'%'))
print(paste('Accuracy of test set from linear regression',(1-error_pca_test)*100,'%'))
c(error_pca_te, error_pca_tr)
####################### Comparing classification accuracy and errror in knn from PCA components #########################
options(repr.plot.width=8, repr.plot.height=8, repr.plot.res = 200)
par(mfrow=c(2,2))
# plot accuracy vs choice of k on Training set
plot(iter_k, accu_pca_tr*100, type = "b",col = "blue", cex = 1, pch = 20, lwd = 2, lty = 2,
xlab = "k, number of neighbors",
ylab = "classification accuracy, %", main = "Accuracy of Training set")
abline(v = which.max(accu_pca_tr),y = max(accu_pca_tr)*100, type = "l", col = "red", lwd = 2, lty = 2)
abline(x = which.max(accu_pca_tr),h = max(accu_pca_tr)*100, type = "l", col = "black", lty = 2)
# plot accuracy vs choice of k on Test set
plot(iter_k, accu_pca_te*100, type = "b", col = "blue", cex = 1, pch = 20, lwd = 2,
xlab = "k, number of neighbors", ylab = "classification accuracy, %",
main = "Accuracy of Test set")
abline(v = 3,y = max(accu_pca_te)*100, type = "l", col = "red", lwd = 2, lty = 2)
abline(x = which.max(accu_pca_te),h = max(accu_pca_te)*100, type = "l", col = "black", lty = 2)
# plot accuracy vs choice of k on Training set
plot(iter_k, error_pca_tr*100, type = "b",col = "blue", cex = 1, pch = 20, lwd = 2,
xlab = "k, number of neighbors",
ylab = "classification error, %", main = "classification error of Training set")
abline(v = which.min(error_pca_tr),y = min(error_pca_tr)*100, type = "l", col = "red", lwd = 2, lty = 2)
abline(x = which.min(error_pca_tr),h = min(error_pca_tr)*100, type = "l", col = "black", lty = 2)
# # plot accuracy vs choice of k on Test set
plot(iter_k, error_pca_te*100, type = "b", col = "blue", cex = 1, pch = 20, lwd = 2,lty = 2,
xlab = "k, number of neighbors", ylab = "classification error, %",
main = "classification error of Test set")
abline(v = 3,y = min(error_pca_te)*100, type = "l", col = "red", lwd = 2, lty = 2)
abline(x = which.min(error_pca_te),h = min(error_pca_te)*100, type = "l", col = "black", lty = 2)
## KNN=9 on pca components of ##
pred_pca_tr = knn( train = data_train[,1:16],
test = data_train[,1:16],
cl = data_train$V17,
k = 3)
pred_pca_te = knn( train = data_train[,1:16],
test = data_test[,1:16],
cl = data_train$V17,
k = 3)
confusionMatrix(table(actual =data_train$V17, prediction = pred_pca_tr))
confusionMatrix(table(actual =data_test$V17, prediction = pred_pca_te))
## end ##