-
Notifications
You must be signed in to change notification settings - Fork 632
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
4 changed files
with
397 additions
and
397 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,152 +1,152 @@ | ||
| # | ||
| # test_that('single tree', { | ||
| # skip_on_cran() | ||
| # library(caret) | ||
| # library(C50) | ||
| # | ||
| # set.seed(1) | ||
| # tr_dat <- twoClassSim(200) | ||
| # te_dat <- twoClassSim(200) | ||
| # | ||
| # set.seed(2) | ||
| # class_trim <- train(Class ~ ., data = tr_dat, | ||
| # method = "C5.0", | ||
| # tuneGrid = data.frame(trials = 1, | ||
| # model = "tree", | ||
| # winnow = FALSE), | ||
| # trControl = trainControl(method = "none", | ||
| # classProbs = TRUE, | ||
| # trim = TRUE)) | ||
| # | ||
| # set.seed(2) | ||
| # class_notrim <- train(Class ~ ., data = tr_dat, | ||
| # method = "C5.0", | ||
| # tuneGrid = data.frame(trials = 1, | ||
| # model = "tree", | ||
| # winnow = FALSE), | ||
| # trControl = trainControl(method = "none", | ||
| # classProbs = TRUE, | ||
| # trim = FALSE)) | ||
| # | ||
| # expect_equal(predict(class_trim, te_dat), | ||
| # predict(class_notrim, te_dat)) | ||
| # | ||
| # expect_equal(predict(class_trim, te_dat, type = "prob"), | ||
| # predict(class_notrim, te_dat, type = "prob")) | ||
| # | ||
| # expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) | ||
| # }) | ||
| # | ||
| # test_that('single rule', { | ||
| # skip_on_cran() | ||
| # library(caret) | ||
| # library(C50) | ||
| # | ||
| # set.seed(1) | ||
| # tr_dat <- twoClassSim(200) | ||
| # te_dat <- twoClassSim(200) | ||
| # | ||
| # set.seed(2) | ||
| # class_trim <- train(Class ~ ., data = tr_dat, | ||
| # method = "C5.0", | ||
| # tuneGrid = data.frame(trials = 1, | ||
| # model = "rules", | ||
| # winnow = FALSE), | ||
| # trControl = trainControl(method = "none", | ||
| # classProbs = TRUE, | ||
| # trim = TRUE)) | ||
| # | ||
| # set.seed(2) | ||
| # class_notrim <- train(Class ~ ., data = tr_dat, | ||
| # method = "C5.0", | ||
| # tuneGrid = data.frame(trials = 1, | ||
| # model = "rules", | ||
| # winnow = FALSE), | ||
| # trControl = trainControl(method = "none", | ||
| # classProbs = TRUE, | ||
| # trim = FALSE)) | ||
| # | ||
| # expect_equal(predict(class_trim, te_dat), | ||
| # predict(class_notrim, te_dat)) | ||
| # | ||
| # expect_equal(predict(class_trim, te_dat, type = "prob"), | ||
| # predict(class_notrim, te_dat, type = "prob")) | ||
| # | ||
| # expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) | ||
| # }) | ||
| # | ||
| # test_that('boosted tree', { | ||
| # skip_on_cran() | ||
| # library(caret) | ||
| # library(C50) | ||
| # | ||
| # set.seed(1) | ||
| # tr_dat <- twoClassSim(200) | ||
| # te_dat <- twoClassSim(200) | ||
| # | ||
| # set.seed(2) | ||
| # class_trim <- train(Class ~ ., data = tr_dat, | ||
| # method = "C5.0", | ||
| # tuneGrid = data.frame(trials = 5, | ||
| # model = "tree", | ||
| # winnow = FALSE), | ||
| # trControl = trainControl(method = "none", | ||
| # classProbs = TRUE, | ||
| # trim = TRUE)) | ||
| # | ||
| # set.seed(2) | ||
| # class_notrim <- train(Class ~ ., data = tr_dat, | ||
| # method = "C5.0", | ||
| # tuneGrid = data.frame(trials = 5, | ||
| # model = "tree", | ||
| # winnow = FALSE), | ||
| # trControl = trainControl(method = "none", | ||
| # classProbs = TRUE, | ||
| # trim = FALSE)) | ||
| # | ||
| # expect_equal(predict(class_trim, te_dat), | ||
| # predict(class_notrim, te_dat)) | ||
| # | ||
| # expect_equal(predict(class_trim, te_dat, type = "prob"), | ||
| # predict(class_notrim, te_dat, type = "prob")) | ||
| # | ||
| # expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) | ||
| # }) | ||
| # | ||
| # test_that('boosted rule', { | ||
| # skip_on_cran() | ||
| # library(caret) | ||
| # library(C50) | ||
| # | ||
| # set.seed(1) | ||
| # tr_dat <- twoClassSim(200) | ||
| # te_dat <- twoClassSim(200) | ||
| # | ||
| # set.seed(2) | ||
| # class_trim <- train(Class ~ ., data = tr_dat, | ||
| # method = "C5.0", | ||
| # tuneGrid = data.frame(trials = 5, | ||
| # model = "rules", | ||
| # winnow = FALSE), | ||
| # trControl = trainControl(method = "none", | ||
| # classProbs = TRUE, | ||
| # trim = TRUE)) | ||
| # | ||
| # set.seed(2) | ||
| # class_notrim <- train(Class ~ ., data = tr_dat, | ||
| # method = "C5.0", | ||
| # tuneGrid = data.frame(trials = 5, | ||
| # model = "rules", | ||
| # winnow = FALSE), | ||
| # trControl = trainControl(method = "none", | ||
| # classProbs = TRUE, | ||
| # trim = FALSE)) | ||
| # | ||
| # expect_equal(predict(class_trim, te_dat), | ||
| # predict(class_notrim, te_dat)) | ||
| # | ||
| # expect_equal(predict(class_trim, te_dat, type = "prob"), | ||
| # predict(class_notrim, te_dat, type = "prob")) | ||
| # | ||
| # expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) | ||
| # }) | ||
|
|
||
| test_that('single tree', { | ||
| skip_on_cran() | ||
| library(caret) | ||
| library(C50) | ||
|
|
||
| set.seed(1) | ||
| tr_dat <- twoClassSim(200) | ||
| te_dat <- twoClassSim(200) | ||
|
|
||
| set.seed(2) | ||
| class_trim <- train(Class ~ ., data = tr_dat, | ||
| method = "C5.0", | ||
| tuneGrid = data.frame(trials = 1, | ||
| model = "tree", | ||
| winnow = FALSE), | ||
| trControl = trainControl(method = "none", | ||
| classProbs = TRUE, | ||
| trim = TRUE)) | ||
|
|
||
| set.seed(2) | ||
| class_notrim <- train(Class ~ ., data = tr_dat, | ||
| method = "C5.0", | ||
| tuneGrid = data.frame(trials = 1, | ||
| model = "tree", | ||
| winnow = FALSE), | ||
| trControl = trainControl(method = "none", | ||
| classProbs = TRUE, | ||
| trim = FALSE)) | ||
|
|
||
| expect_equal(predict(class_trim, te_dat), | ||
| predict(class_notrim, te_dat)) | ||
|
|
||
| expect_equal(predict(class_trim, te_dat, type = "prob"), | ||
| predict(class_notrim, te_dat, type = "prob")) | ||
|
|
||
| expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) | ||
| }) | ||
|
|
||
| test_that('single rule', { | ||
| skip_on_cran() | ||
| library(caret) | ||
| library(C50) | ||
|
|
||
| set.seed(1) | ||
| tr_dat <- twoClassSim(200) | ||
| te_dat <- twoClassSim(200) | ||
|
|
||
| set.seed(2) | ||
| class_trim <- train(Class ~ ., data = tr_dat, | ||
| method = "C5.0", | ||
| tuneGrid = data.frame(trials = 1, | ||
| model = "rules", | ||
| winnow = FALSE), | ||
| trControl = trainControl(method = "none", | ||
| classProbs = TRUE, | ||
| trim = TRUE)) | ||
|
|
||
| set.seed(2) | ||
| class_notrim <- train(Class ~ ., data = tr_dat, | ||
| method = "C5.0", | ||
| tuneGrid = data.frame(trials = 1, | ||
| model = "rules", | ||
| winnow = FALSE), | ||
| trControl = trainControl(method = "none", | ||
| classProbs = TRUE, | ||
| trim = FALSE)) | ||
|
|
||
| expect_equal(predict(class_trim, te_dat), | ||
| predict(class_notrim, te_dat)) | ||
|
|
||
| expect_equal(predict(class_trim, te_dat, type = "prob"), | ||
| predict(class_notrim, te_dat, type = "prob")) | ||
|
|
||
| expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) | ||
| }) | ||
|
|
||
| test_that('boosted tree', { | ||
| skip_on_cran() | ||
| library(caret) | ||
| library(C50) | ||
|
|
||
| set.seed(1) | ||
| tr_dat <- twoClassSim(200) | ||
| te_dat <- twoClassSim(200) | ||
|
|
||
| set.seed(2) | ||
| class_trim <- train(Class ~ ., data = tr_dat, | ||
| method = "C5.0", | ||
| tuneGrid = data.frame(trials = 5, | ||
| model = "tree", | ||
| winnow = FALSE), | ||
| trControl = trainControl(method = "none", | ||
| classProbs = TRUE, | ||
| trim = TRUE)) | ||
|
|
||
| set.seed(2) | ||
| class_notrim <- train(Class ~ ., data = tr_dat, | ||
| method = "C5.0", | ||
| tuneGrid = data.frame(trials = 5, | ||
| model = "tree", | ||
| winnow = FALSE), | ||
| trControl = trainControl(method = "none", | ||
| classProbs = TRUE, | ||
| trim = FALSE)) | ||
|
|
||
| expect_equal(predict(class_trim, te_dat), | ||
| predict(class_notrim, te_dat)) | ||
|
|
||
| expect_equal(predict(class_trim, te_dat, type = "prob"), | ||
| predict(class_notrim, te_dat, type = "prob")) | ||
|
|
||
| expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) | ||
| }) | ||
|
|
||
| test_that('boosted rule', { | ||
| skip_on_cran() | ||
| library(caret) | ||
| library(C50) | ||
|
|
||
| set.seed(1) | ||
| tr_dat <- twoClassSim(200) | ||
| te_dat <- twoClassSim(200) | ||
|
|
||
| set.seed(2) | ||
| class_trim <- train(Class ~ ., data = tr_dat, | ||
| method = "C5.0", | ||
| tuneGrid = data.frame(trials = 5, | ||
| model = "rules", | ||
| winnow = FALSE), | ||
| trControl = trainControl(method = "none", | ||
| classProbs = TRUE, | ||
| trim = TRUE)) | ||
|
|
||
| set.seed(2) | ||
| class_notrim <- train(Class ~ ., data = tr_dat, | ||
| method = "C5.0", | ||
| tuneGrid = data.frame(trials = 5, | ||
| model = "rules", | ||
| winnow = FALSE), | ||
| trControl = trainControl(method = "none", | ||
| classProbs = TRUE, | ||
| trim = FALSE)) | ||
|
|
||
| expect_equal(predict(class_trim, te_dat), | ||
| predict(class_notrim, te_dat)) | ||
|
|
||
| expect_equal(predict(class_trim, te_dat, type = "prob"), | ||
| predict(class_notrim, te_dat, type = "prob")) | ||
|
|
||
| expect_less_than(object.size(class_trim)-object.size(class_notrim), 0) | ||
| }) |
Oops, something went wrong.