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
|
suppressMessages(requirePackagesOrSkip("kknn"))
test_that("predict", {
inds = multiclass.train.inds
data = multiclass.df
formula = multiclass.formula
wl.lda = makeLearner("classif.lda", predict.type = "prob")
cm2 = train(makeLearner("classif.lda"), multiclass.task, subset = inds)
cp2 = predict(cm2, newdata = data[inds, ])
cp2b = predict(cm2, newdata = data[inds, -5])
requirePackagesOrSkip("MASS", default.method = "load")
ext2 = MASS::lda(formula, data = data[inds, ])
pred2 = predict(ext2, newdata = data[inds, ])$class
expect_equal(cp2$data$response, pred2)
expect_equal(cp2b$data$response, pred2)
cm3 = train(wl.lda, multiclass.task, subset = inds)
cp3 = predict(cm3, newdata = data[multiclass.test.inds, ])
ext3 = MASS::lda(formula, data = data[inds, ])
pred3 = predict(ext3, newdata = data[multiclass.test.inds, ])$class
prob3 = predict(ext3, newdata = data[multiclass.test.inds, ])$post
expect_equal(cp3$data$response, pred3)
expect_equal(prob3, as.matrix(getPredictionProbabilities(cp3, colnames(prob3))))
expect_true(is.numeric(getPredictionProbabilities(cp3, "setosa")))
expect_equal(colnames(getPredictionProbabilities(cp3, c("setosa", "versicolor"))), c("setosa", "versicolor"))
expect_equal(colnames(getPredictionProbabilities(cp3, c("versicolor", "setosa"))), c("versicolor", "setosa"))
cp4 = predict(cm3, task = multiclass.task, subset = multiclass.test.inds)
expect_equal(cp4$data$response, pred3)
expect_equal(cp4$data$truth, data[multiclass.test.inds, multiclass.target])
expect_equal(cp4$data$id, multiclass.test.inds)
df3 = as.data.frame(cp3)
df4 = as.data.frame(cp4)
expect_equal(df3, df4[, -1])
cm5 = train(wl.lda, binaryclass.task, subset = binaryclass.train.inds)
cp5a = predict(cm5, task = binaryclass.task, subset = binaryclass.test.inds)
cp5b = predict(cm5, task = binaryclass.task, subset = binaryclass.test.inds)
cp5c = setThreshold(cp5b, 0)
cp5d = setThreshold(cp5b, 1)
cp5e = predict(cm5, task = binaryclass.task, subset = 1)
expect_equal(cp5a$data$response, cp5b$data$response)
f1 = factor(rep(getTaskDesc(binaryclass.task)$positive, length(binaryclass.test.inds)),
levels = getTaskClassLevels(binaryclass.task))
expect_equal(cp5c$data$response, f1)
f2 = factor(rep(getTaskDesc(binaryclass.task)$negative, length(binaryclass.test.inds)),
levels = getTaskDesc(binaryclass.task)$class.levels)
expect_equal(cp5d$data$response, f2)
expect_true(setequal(levels(cp5e$data$response), c("M", "R")))
})
test_that("predict works with type = se", {
lrn = makeLearner("regr.lm", predict.type = "se")
mod = train(lrn, regr.task)
p = predict(mod, regr.task)
expect_equal(colnames(p$data), c("id", "truth", "response", "se"))
})
test_that("predict works with strange class labels", {
df = binaryclass.df
levels(df[, binaryclass.target]) = c(-1, 1)
task = makeClassifTask(data = df, target = binaryclass.target)
mod = train(makeLearner("classif.lda", predict.type = "prob"), task = task)
p = predict(mod, task = task)
expect_equal(colnames(p$data), c("id", "truth", "prob.-1", "prob.1", "response"))
})
test_that("predict correctly propagates exception in predictLearner", {
capture.output(expect_error(holdout("classif.__mlrmocklearners__1", multiclass.task), "foo"))
})
test_that("predict works with newdata / subset", {
mod = train(makeLearner("classif.lda"), multiclass.task)
p1 = predict(mod, newdata = multiclass.df, subset = 1:10)
expect_equal(nrow(p1$data), 10)
p2 = predict(mod, newdata = multiclass.df, subset = c(rep(TRUE, 10)))
expect_equal(getPredictionResponse(p1), getPredictionResponse(p2))
})
test_that("predict preserves rownames", {
data = multiclass.df
rownames(data) = rev(seq_len(nrow(data)))
task = makeClassifTask(data = data, target = multiclass.target)
# kknn (or its mlr intergration) seems to NOT preserve rownames, issue 142 was reported
mod = train("classif.kknn", task = task)
nd = data[1:2, ]
p = predict(mod, task = task, subset = 1:2)
expect_equal(rownames(as.data.frame(p)), as.character(c(nrow(data), nrow(data) - 1L)))
p = predict(mod, newdata = nd, subset = 2)
expect_equal(rownames(as.data.frame(p)), as.character(nrow(data) - 1L))
})
test_that("setThreshold does not produce NAs for extreme thresholds", {
# we had bug / issue 168 here
data(GermanCredit, package = "caret")
credit.task = makeClassifTask(data = GermanCredit, target = "Class")
lrn = makeLearner("classif.rpart", predict.type = "prob")
mod = train(lrn, credit.task)
p1 = predict(mod, task = credit.task)
p2 = setThreshold(p1, 0)
expect_true(!any(is.na(p2$data$response)))
})
test_that("predict.threshold", {
td = getTaskDesc(binaryclass.task)
lrn = makeLearner("classif.lda", predict.type = "prob", predict.threshold = 0)
r = holdout(lrn, binaryclass.task)
expect_true(all(r$pred$data$response == td$positive))
lrn = makeLearner("classif.lda", predict.type = "prob", predict.threshold = 1)
r = holdout(lrn, binaryclass.task)
expect_true(all(r$pred$data$response == td$negative))
lrn = makeLearner("classif.lda", predict.type = "prob",
predict.threshold = c(setosa = 1000000000, virginica = 0, versicolor = 100000))
r = holdout(lrn, multiclass.task)
expect_true(all(r$pred$data$response == "virginica"))
# now with wrapper
lrn1 = makeLearner("classif.lda")
lrn2 = makeFilterWrapper(lrn1, fw.method = "anova.test", fw.perc = 0.1)
lrn2 = setPredictType(lrn2, "prob")
lrn2 = setPredictThreshold(lrn2, 0)
r = holdout(lrn2, binaryclass.task)
expect_true(all(r$pred$data$response == td$positive))
})
test_that("predict doesn't warn if 'on.learner.error' is 'quiet'", {
lrn = makeLearner("classif.qda", predict.type = "prob",
config = list(on.learner.error = "quiet"))
mod = train(lrn, iris.task, subset = c(1L, 51L, 101L))
expect_true(inherits(mod, "FailureModel"))
expect_warning(predict(mod, multiclass.task), NA)
})
test_that("predict works with data.table as newdata", {
lrn = makeLearner("classif.qda")
mod = train(lrn, iris.task)
expect_warning(predict(mod, newdata = data.table(iris)),
regexp = "Provided data for prediction is not a pure data.frame but from class data.table, hence it will be converted.")
})
|
