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[R] replace uses of T and F with TRUE and FALSE (#5778)

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* [R-package] replace uses of T and F with TRUE and FALSE

* enable linting

* Remove skip

Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
This commit is contained in:
James Lamb
2020-06-11 11:08:02 +01:00
committed by GitHub
parent cb7f7e542c
commit c35be9dc40
15 changed files with 32 additions and 33 deletions
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2
demo/data/gen_autoclaims.R
View File

@@ -14,5 +14,5 @@ data$STATE = as.factor(data$STATE)
data$CLASS = as.factor(data$CLASS)
data$GENDER = as.factor(data$GENDER)
data.dummy <- dummy.data.frame(data, dummy.class='factor', omit.constants=T);
data.dummy <- dummy.data.frame(data, dummy.class='factor', omit.constants=TRUE);
write.table(data.dummy, 'autoclaims.csv', sep=',', row.names=F, col.names=F, quote=F)

4
demo/kaggle-otto/otto_train_pred.R
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@@ -1,8 +1,8 @@
require(xgboost)
require(methods)
train = read.csv('data/train.csv',header=TRUE,stringsAsFactors = F)
test = read.csv('data/test.csv',header=TRUE,stringsAsFactors = F)
train = read.csv('data/train.csv',header=TRUE,stringsAsFactors = FALSE)
test = read.csv('data/test.csv',header=TRUE,stringsAsFactors = FALSE)
train = train[,-1]
test = test[,-1]

16
demo/kaggle-otto/understandingXGBoostModel.Rmd
View File

@@ -30,8 +30,8 @@ require(xgboost)
require(methods)
require(data.table)
require(magrittr)
train <- fread('data/train.csv', header = T, stringsAsFactors = F)
test <- fread('data/test.csv', header=TRUE, stringsAsFactors = F)
train <- fread('data/train.csv', header = T, stringsAsFactors = FALSE)
test <- fread('data/test.csv', header=TRUE, stringsAsFactors = FALSE)
```
> `magrittr` and `data.table` are here to make the code cleaner and much more rapid.
@@ -42,13 +42,13 @@ Let's explore the dataset.
dim(train)
# Training content
train[1:6,1:5, with =F]
train[1:6,1:5, with =FALSE]
# Test dataset dimensions
dim(test)
# Test content
test[1:6,1:5, with =F]
test[1:6,1:5, with =FALSE]
```
> We only display the 6 first rows and 5 first columns for convenience
@@ -70,7 +70,7 @@ According to its description, the **Otto** challenge is a multi class classifica
```{r searchLabel}
# Check the content of the last column
train[1:6, ncol(train), with = F]
train[1:6, ncol(train), with = FALSE]
# Save the name of the last column
nameLastCol <- names(train)[ncol(train)]
```
@@ -86,7 +86,7 @@ For that purpose, we will:
```{r classToIntegers}
# Convert from classes to numbers
y <- train[, nameLastCol, with = F][[1]] %>% gsub('Class_','',.) %>% {as.integer(.) -1}
y <- train[, nameLastCol, with = FALSE][[1]] %>% gsub('Class_','',.) %>% {as.integer(.) -1}
# Display the first 5 levels
y[1:5]
@@ -95,7 +95,7 @@ y[1:5]
We remove label column from training dataset, otherwise **XGBoost** would use it to guess the labels!
```{r deleteCols, results='hide'}
train[, nameLastCol:=NULL, with = F]
train[, nameLastCol:=NULL, with = FALSE]
```
`data.table` is an awesome implementation of data.frame, unfortunately it is not a format supported natively by **XGBoost**. We need to convert both datasets (training and test) in `numeric` Matrix format.
@@ -163,7 +163,7 @@ Each *split* is done on one feature only at one value.
Let's see what the model looks like.
```{r modelDump}
model <- xgb.dump(bst, with.stats = T)
model <- xgb.dump(bst, with.stats = TRUE)
model[1:10]
```
> For convenience, we are displaying the first 10 lines of the model only.