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
|
context("Calculation Tests")
data(testDF)
glmModel <- glm(y ~ ., data=testDF, family="binomial")
Preds <- predict(glmModel, type = 'response')
test_that("logLoss returns correct values", {
expect_equal(logLoss(testDF$y, Preds), 0.1546854, tolerance = .000001)
expect_equal(logLoss(testDF$y, Preds, 'poisson'), 0.6910357, tolerance = .000001)
expect_equal(logLoss(glmModel), 0.1546854, tolerance = .000001)
})
test_that("auc returns correct values", {
expect_equal(auc(testDF$y, Preds), 0.9872666, tolerance = .000001)
expect_equal(auc(c(testDF$y,testDF$y), c(Preds, Preds)), 0.9872666, tolerance = .000001)
expect_equal(auc(glmModel), 0.9872666, tolerance = .000001)
})
test_that("gini returns correct values", {
expect_equal(gini(testDF$y, Preds), 0.9745332, tolerance = .000001)
expect_equal(gini(c(testDF$y,testDF$y), c(Preds, Preds)), 0.9745332, tolerance = .000001)
expect_equal(gini(glmModel), 0.9745332, tolerance = .000001)
})
test_that("rmse returns correct values", {
expect_equal(rmse(testDF$y, Preds), 0.2188343, tolerance = .000001)
expect_equal(rmse(glmModel), 0.2188343, tolerance = .000001)
})
test_that("mse returns correct values", {
expect_equal(mse(testDF$y, Preds), 0.04788846, tolerance = .000001)
expect_equal(mse(glmModel), 0.04788846, tolerance = .000001)
})
test_that("ppv returns correct values", {
expect_equal(ppv(testDF$y, Preds, .5), 0.9365079, tolerance = .000001)
expect_equal(precision(testDF$y, Preds, .5), 0.9365079, tolerance = .000001)
})
test_that("npv returns correct values", {
expect_equal(npv(testDF$y, Preds, .5), 0.9189189, tolerance = .000001)
})
test_that("specificity returns correct values", {
tempTab <- table(testDF$y, Preds > .5)
SPC <- tempTab[1,1]/sum(tempTab[1,])
expect_equal(specificity(testDF$y, Preds, .5), SPC, tolerance = .000001)
expect_equal(tnr(testDF$y, Preds, .5), SPC, tolerance = .000001)
})
test_that("sensitivity returns correct values", {
expect_equal(recall(testDF$y, Preds, .5), 0.9516129, tolerance = .000001)
expect_equal(sensitivity(testDF$y, Preds, .5), 0.9516129, tolerance = .000001)
expect_equal(tpr(testDF$y, Preds, .5), 0.9516129, tolerance = .000001)
})
test_that("f1 score returns correct values", {
expect_equal(f1Score(testDF$y, Preds, .5), 0.944, tolerance = .000001)
})
test_that("f1 score and F score agree with beta 1 (default value)", {
expect_equal(f1Score(testDF$y, Preds, .5), fScore(testDF$y, Preds, .5, 1), tolerance = .000001)
})
test_that("mcc returns correct values", {
expect_equal(mcc(testDF$y, Preds, .5), 0.8508762, tolerance = .000001)
})
test_that("brier returns correct values", {
expect_equal(brier(testDF$y, Preds), 0.04788846, tolerance = .000001)
expect_equal(brier(glmModel), 0.04788846, tolerance = .000001)
})
test_that("mae returns correct values", {
expect_equal(mae(testDF$y, Preds), 0.09440662, tolerance = .000001)
expect_equal(mae(glmModel), 0.09440662, tolerance = .000001)
})
test_that("msle returns correct values", {
expect_equal(msle(testDF$y, Preds), 0.02318011, tolerance = .000001)
expect_equal(msle(glmModel), 0.02318011, tolerance = .000001)
})
test_that("rmsle returns correct values", {
expect_equal(rmsle(testDF$y, Preds), 0.1522501, tolerance = .000001)
expect_equal(rmsle(glmModel), 0.1522501, tolerance = .000001)
})
test_that("rmsle returns correct values", {
A <- c(rep(1, 63), rep(0, 31))
B <- c(rep(1, 61), rep(0, 25), rep(1, 6), rep(0, 2))
tab <- table(A, B)
a = tab[2,2]
b = tab[2,1]
c = tab[1,2]
d = tab[1,1]
marginA = ((a + b)*(a + c))/(a + b + c + d)
marginB = ((c + d)*(b + d))/(a + b + c + d)
Pe = (marginA + marginB)/(a + b + c + d)
Po = (a + d)/(a + b + c + d)
manualKappa = (Po - Pe)/(1 - Pe)
expect_equal(kappa(A, B), manualKappa, tolerance = .000001)
})
|
