-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathexampleSceneMLR.R
192 lines (147 loc) · 7.36 KB
/
exampleSceneMLR.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
library("mlr")
library("BBmisc")
library("mldr")
library("ParamHelpers")
set.seed(1729)
# wczytanie danych prawidlowe
scene <- read.arff("scene")
data = scene[["dataframe"]]
target = labels(scene[["attributes"]])
labels = target[scene[["labelIndices"]]]
feats = setdiff(target, labels)
new_data = data[feats]
logic_data <- lapply(data[labels], as.logical)
# stworzenie zadania
scene.task = makeMultilabelTask(id = "multi", data = cbind(new_data,logic_data), target = labels)
scene.task
# podzial danych na testowe i trenujace
n = getTaskSize(scene.task)
train.set = seq(1, n, by = 2)
test.set = seq(2, n, by = 2)
#stworzenie klasyfikatora
lrn.rfsrc = makeLearner("multilabel.randomForestSRC")
lrn.rfsrc
# uczenie => powstaje model
modRandomForestSRC = train(lrn.rfsrc, scene.task, subset = train.set)
# predykcja dla zestawu testowego
predictRandomForestSRC = predict(modRandomForestSRC, scene.task, subset=test.set)
#stworzenie klasyfikatorów binarnych
# drzewo decyzyjne
binary.tree = makeLearner("classif.rpart")
# Naiwny Bayes
binary.naiveBayes = makeLearner("classif.naiveBayes")
# Maszyna wektorów nosnych
binary.svm = makeLearner("classif.svm")
# Lalncuch klasyfikatorow dla binarnych
lrn.cc.tree = makeMultilabelClassifierChainsWrapper(binary.tree)
lrn.cc.naiveBayes = makeMultilabelClassifierChainsWrapper(binary.naiveBayes)
lrn.cc.svm = makeMultilabelClassifierChainsWrapper(binary.svm)
# uczenie => powstaja modele dla chain wrapper
mod.cc.tree = train(lrn.cc.tree, scene.task, subset = train.set)
mod.cc.naiveBayes = train(lrn.cc.naiveBayes, scene.task, subset = train.set)
mod.cc.svm= train(lrn.cc.svm, scene.task, subset = train.set)
# predykcja dla zestawu testowego
predict.cc.tree = predict(mod.cc.tree, task = scene.task, subset = test.set)
predict.cc.naiveBayes = predict(mod.cc.naiveBayes, task = scene.task, subset = test.set)
predict.cc.svm = predict(mod.cc.svm, task = scene.task, subset = test.set)
###### Nested Stacking Wrapper
lrn.ns.tree = makeMultilabelNestedStackingWrapper(binary.tree, order = NULL, cv.folds = 2)
lrn.ns.naiveBayes = makeMultilabelNestedStackingWrapper(binary.naiveBayes, order = NULL, cv.folds = 2)
lrn.ns.svm = makeMultilabelNestedStackingWrapper(binary.svm, order = NULL, cv.folds = 2)
# uczenie => powstaje model dla nested stacking
mod.ns.tree = train(lrn.ns.tree, scene.task, subset = train.set)
mod.ns.naiveBayes = train(lrn.ns.naiveBayes, scene.task, subset = train.set)
mod.ns.svm = train(lrn.ns.svm, scene.task, subset = train.set)
# predykcja dla zestawu testowego
predict.ns.tree = predict(mod.ns.tree, task = scene.task, subset = test.set)
predict.ns.naiveBayes = predict(mod.ns.naiveBayes, task = scene.task, subset = test.set)
predict.ns.svm = predict(mod.ns.svm, task = scene.task, subset = test.set)
listMeasures("multilabel")
measuresList = list(multilabel.hamloss, multilabel.subset01, multilabel.f1, multilabel.acc)
# pomiary parametrów
performance(predictRandomForestSRC, measures = measuresList)
performance(predict.cc.tree, measures = measuresList)
performance(predict.ns.tree, measures = measuresList)
performance(predict.cc.naiveBayes, measures = measuresList)
performance(predict.ns.naiveBayes, measures = measuresList)
performance(predict.cc.svm, measures = measuresList)
performance(predict.ns.svm, measures = measuresList)
# resampling
rdesc = makeResampleDesc("CV", iters = 10)
resampleRansdomForestSRC = resample(lrn.rfsrc, scene.task, rdesc, measures = measuresList)
resample.cc.tree = resample(lrn.cc.tree, scene.task, rdesc, measures = measuresList)
resample.ns.tree = resample(lrn.ns.tree, scene.task, rdesc, measures = measuresList)
resample.cc.naiveBayes = resample(lrn.cc.naiveBayes, scene.task, rdesc, measures = measuresList)
resample.ns.naiveBayes = resample(lrn.ns.naiveBayes, scene.task, rdesc, measures = measuresList)
resample.cc.svm = resample(lrn.cc.svm, scene.task, rdesc, measures = measuresList)
resample.ns.svm = resample(lrn.ns.svm, scene.task, rdesc, measures = measuresList)
mesaure.values <- resampleRansdomForestSRC[["measures.test"]]
mesaure.values.cc.tree <- resample.cc.tree[["measures.test"]]
mesaure.values.ns.tree <- resample.ns.tree[["measures.test"]]
mesaure.values.cc.naiveBayes <- resample.cc.naiveBayes[["measures.test"]]
mesaure.values.ns.naiveBayes <- resample.ns.naiveBayes[["measures.test"]]
mesaure.values.cc.svm <- resample.cc.svm[["measures.test"]]
mesaure.values.ns.svm <- resample.ns.svm[["measures.test"]]
hamloss_mean = mean(mesaure.values[,2])
subset_mean = mean(mesaure.values[,3])
f1_mean = mean(mesaure.values[,4])
acc_mean = mean(mesaure.values[,5])
hamloss_cc_mean.tree = mean(mesaure.values.cc.tree[,2])
subset_cc_mean.tree = mean(mesaure.values.cc.tree[,3])
f1_cc_mean.tree = mean(mesaure.values.cc.tree[,4])
acc_cc_mean.tree = mean(mesaure.values.cc.tree[,5])
hamloss_ns_mean.tree = mean(mesaure.values.ns.tree[,2])
subset_ns_mean.tree = mean(mesaure.values.ns.tree[,3])
f1_ns_mean.tree = mean(mesaure.values.ns.tree[,4])
acc_ns_mean.tree = mean(mesaure.values.ns.tree[,5])
hamloss_cc_mean.naiveBayes = mean(mesaure.values.cc.naiveBayes[,2])
subset_cc_mean.naiveBayes = mean(mesaure.values.cc.naiveBayes[,3])
f1_cc_mean.naiveBayes = mean(mesaure.values.cc.naiveBayes[,4])
acc_cc_mean.naiveBayes = mean(mesaure.values.cc.naiveBayes[,5])
hamloss_ns_mean.naiveBayes = mean(mesaure.values.ns.naiveBayes[,2])
subset_ns_mean.naiveBayes = mean(mesaure.values.ns.naiveBayes[,3])
f1_ns_mean.naiveBayes = mean(mesaure.values.ns.naiveBayes[,4])
acc_ns_mean.naiveBayes = mean(mesaure.values.ns.naiveBayes[,5])
hamloss_cc_mean.svm = mean(mesaure.values.cc.svm[,2])
subset_cc_mean.svm = mean(mesaure.values.cc.svm[,3])
f1_cc_mean.svm = mean(mesaure.values.cc.svm[,4])
acc_cc_mean.svm = mean(mesaure.values.cc.svm[,5])
hamloss_ns_mean.svm = mean(mesaure.values.ns.svm[,2])
subset_ns_mean.svm = mean(mesaure.values.ns.svm[,3])
f1_ns_mean.svm = mean(mesaure.values.ns.svm[,4])
acc_ns_mean.svm = mean(mesaure.values.ns.svm[,5])
counts <- matrix(0,7,4)
counts[1,1] = hamloss_mean
counts[1,2] = subset_mean
counts[1,3] = f1_mean
counts[1,4] = acc_mean
counts[2,1] = hamloss_cc_mean.tree
counts[2,2] = subset_cc_mean.tree
counts[2,3] = f1_cc_mean.tree
counts[2,4] = acc_cc_mean.tree
counts[3,1] = hamloss_ns_mean.tree
counts[3,2] = subset_ns_mean.tree
counts[3,3] = f1_ns_mean.tree
counts[3,4] = acc_ns_mean.tree
counts[4,1] = hamloss_cc_mean.naiveBayes
counts[4,2] = subset_cc_mean.naiveBayes
counts[4,3] = f1_cc_mean.naiveBayes
counts[4,4] = acc_cc_mean.naiveBayes
counts[5,1] = hamloss_ns_mean.naiveBayes
counts[5,2] = subset_ns_mean.naiveBayes
counts[5,3] = f1_ns_mean.naiveBayes
counts[5,4] = acc_ns_mean.naiveBayes
counts[6,1] = hamloss_cc_mean.svm
counts[6,2] = subset_cc_mean.svm
counts[6,3] = f1_cc_mean.svm
counts[6,4] = acc_cc_mean.svm
counts[7,1] = hamloss_ns_mean.svm
counts[7,2] = subset_ns_mean.svm
counts[7,3] = f1_ns_mean.svm
counts[7,4] = acc_ns_mean.svm
counts
barplot(counts, main="Miary jakosci dla klasyfikacji zbioru 'Scene'",
col=c("black","darkgray","lightgray", "darkgreen", "lightgreen","darkblue", "lightblue"),
names.arg=c("Hamming Loss", "Subset", "F1","Accuracy"), beside=TRUE,legend=TRUE)
legend("bottomright",legend=c("random forest","chain wrapper tree","nested stacking wrapper tree","chain wrapper Naive Bayes","nested stacking wrapper Naive Bayes"
,"chain wrapper SVM","nested stacking wrapper SVM"), fill=c("black","darkgray","lightgray", "darkgreen", "lightgreen","darkblue", "lightblue"))