This package aims to enable the use of classifiers in boosting family algorithms, apart from decision trees and other classical weak classifiers. The current scope of the package includes Real AdaBoost and Discrete AdaBoost, as well as multiclass versions of these methods, the SAMME and SAMME.R algorithms.
I am aware that there have been attempts to create frameworks that allow using custom functions in boosting (e.g. boostr). However, they are too complex to my taste. I haven’t seen a simple-to-use package that handles different boosting methods. What can be done simply in Python, why can’t it be done in R? That is my point. The package is still in the starting phase (a baby package). Things on my mind:
booster functionThere is a function for each method in the package. But the main
function is the booster. By using this function it will be
possible to access everything. Let me give you information about the
features of this function and how to use it. First of all, 2 different
AdaBoost methods can be applied with the booster function:
Real AdaBoost and Discrete AdaBoost. Let us use glass0 dataset in
imbalance package and prepare a train and test dataset.
test dataset is not mandatory.
library(rbooster)
cv_sampler <- function(y, train_proportion) {
unlist(lapply(unique(y), function(m) sample(which(y==m), round(sum(y==m))*train_proportion)))
}
library(imbalance)
data <- glass0
p <- ncol(data) - 1
x <- data[,1:p]
y <- data[, p + 1]
train_i <- cv_sampler(y, 0.9)
x_train <- x[train_i,]
y_train <- y[train_i]
x_test <- x[-train_i,]
y_test <- y[-train_i]This is a two-class dataset. Let us use decision tree to build Discrete and Real AdaBoost models.
m_discrete <- booster(x_train = x_train,
y_train = y_train,
classifier = "rpart",
method = "discrete",
x_test = x_test,
y_test = y_test,
weighted_bootstrap = FALSE,
max_iter = 20,
lambda = 1,
print_detail = TRUE,
print_plot = TRUE,
bag_frac = 0.8,
p_weak = 4)
#> 1 Train err:0.04167, Test err:0.18182, Weighted err:0.04167
#> 2 Train err:0.04167, Test err:0.18182, Weighted err:0.07195
#> 3 Train err:0.00521, Test err:0.04545, Weighted err:0.04014
#> 4 Train err:0.00521, Test err:0.04545, Weighted err:0.11524
#> 5 Train err:0.01042, Test err:0.09091, Weighted err:0.07580
#> 6 Train err:0.01042, Test err:0.09091, Weighted err:0.13923
#> 7 Train err:0.00521, Test err:0.09091, Weighted err:0.08863
#> 8 Train err:0.00000, Test err:0.13636, Weighted err:0.05526
#> 9 Train err:0.00000, Test err:0.09091, Weighted err:0.06539
#> 10 Train err:0.00000, Test err:0.09091, Weighted err:0.18110
#> 11 Train err:0.00000, Test err:0.09091, Weighted err:0.12768
#> 12 Train err:0.00000, Test err:0.09091, Weighted err:0.24721
#> 13 Train err:0.00000, Test err:0.09091, Weighted err:0.06630
#> 14 Train err:0.00000, Test err:0.09091, Weighted err:0.10293
#> 15 Train err:0.00000, Test err:0.09091, Weighted err:0.07404
#> 16 Train err:0.00000, Test err:0.04545, Weighted err:0.08221
#> 17 Train err:0.00000, Test err:0.09091, Weighted err:0.12928
#> 18 Train err:0.00000, Test err:0.04545, Weighted err:0.14301
#> 19 Train err:0.00000, Test err:0.09091, Weighted err:0.13722
#> 20 Train err:0.00000, Test err:0.04545, Weighted err:0.08173
m_real <- booster(x_train = x_train,
y_train = y_train,
classifier = "rpart",
method = "real",
x_test = x_test,
y_test = y_test,
weighted_bootstrap = FALSE,
max_iter = 20,
lambda = 1,
print_detail = TRUE,
print_plot = TRUE,
bag_frac = 0.8,
p_weak = 4)
#> 1 Train err:0.07812, Test err:0.31818, Weighted err:0.07812
#> 2 Train err:0.05208, Test err:0.22727, Weighted err:0.01914
#> 3 Train err:0.04167, Test err:0.27273, Weighted err:0.07696
#> 4 Train err:0.01562, Test err:0.27273, Weighted err:0.26836
#> 5 Train err:0.02083, Test err:0.22727, Weighted err:0.02338
#> 6 Train err:0.01562, Test err:0.22727, Weighted err:0.46096
#> 7 Train err:0.01042, Test err:0.22727, Weighted err:0.02315
#> 8 Train err:0.00000, Test err:0.13636, Weighted err:0.02416
#> 9 Train err:0.00521, Test err:0.13636, Weighted err:0.13217
#> 10 Train err:0.00000, Test err:0.04545, Weighted err:0.10323
#> 11 Train err:0.00000, Test err:0.09091, Weighted err:0.05400
#> 12 Train err:0.00000, Test err:0.04545, Weighted err:0.12940
#> 13 Train err:0.00000, Test err:0.00000, Weighted err:0.07897
#> 14 Train err:0.00000, Test err:0.09091, Weighted err:0.09409
#> 15 Train err:0.00000, Test err:0.04545, Weighted err:0.19163
#> 16 Train err:0.00000, Test err:0.09091, Weighted err:0.00126
#> 17 Train err:0.00000, Test err:0.09091, Weighted err:0.11718
#> 18 Train err:0.00000, Test err:0.13636, Weighted err:0.02881
#> 19 Train err:0.00000, Test err:0.09091, Weighted err:0.15983
#> 20 Train err:0.00000, Test err:0.09091, Weighted err:0.15628
We can obtain predictions on x_test like:
head(m_discrete$test_prediction)
#> [1] "positive" "positive" "positive" "positive" "positive" "positive"
head(m_real$test_prediction)
#> [1] "positive" "positive" "positive" "positive" "positive" "positive"
table(y_test, m_discrete$test_prediction)
#>
#> y_test negative positive
#> negative 14 1
#> positive 0 7
table(y_test, m_real$test_prediction)
#>
#> y_test negative positive
#> negative 13 2
#> positive 0 7However x_test is used to validate on
y_test. So without y_test, x_test
is ignored. x_test and y_test can be set to
NULL, they are not mandatory for model to be established.
You can use the usual predict function.
pred_discrete <- predict(object = m_discrete, newdata = x_test, type = "pred")
pred_real <- predict(object = m_real, newdata = x_test, type = "pred")
all(pred_discrete == m_discrete$test_prediction)
#> [1] TRUE
all(pred_discrete == m_discrete$test_prediction)
#> [1] TRUEIn predict function type can be “pred” or
“prob”. “prob” will gives class probability matrix.
prob_discrete <- predict(object = m_discrete, newdata = x_test, type = "prob")
head(prob_discrete)
#> negative positive
#> [1,] 2.808763e-02 0.9719124
#> [2,] 1.922510e-02 0.9807749
#> [3,] 2.669226e-01 0.7330774
#> [4,] 1.115012e-09 1.0000000
#> [5,] 6.798746e-06 0.9999932
#> [6,] 1.947661e-03 0.9980523Let us use Glass dataset in mlbench for multiclass classification example.
library(mlbench)
data(Glass)
data <- Glass
p <- ncol(data) - 1
x <- data[,1:p]
y <- data[, p + 1]
train_i <- cv_sampler(y, 0.9)
x_train <- x[train_i,]
y_train <- y[train_i]
x_test <- x[-train_i,]
y_test <- y[-train_i]
par(mfrow = c(1,2))
m_discrete <- booster(x_train = x_train,
y_train = y_train,
classifier = "rpart",
method = "discrete",
x_test = x_test,
y_test = y_test,
weighted_bootstrap = FALSE,
max_iter = 20,
lambda = 1,
print_detail = FALSE,
print_plot = TRUE,
bag_frac = 0.8,
p_weak = p)
m_real <- booster(x_train = x_train,
y_train = y_train,
classifier = "rpart",
method = "real",
x_test = x_test,
y_test = y_test,
weighted_bootstrap = FALSE,
max_iter = 20,
lambda = 0.1,
print_detail = FALSE,
print_plot = TRUE,
bag_frac = 1,
p_weak = p)
#> Warning in bb(x_train = x_train, y_train = y_train, classifier = classifier, :
#> Learning stopped at iteration 16
invisible(dev.off())
pred_discrete <- predict(object = m_discrete, newdata = x_test, type = "pred")
pred_real <- predict(object = m_real, newdata = x_test, type = "pred")
table(y_test, pred_discrete)
#> pred_discrete
#> y_test 1 2 3 5 6 7
#> 1 6 1 0 0 0 0
#> 2 3 4 1 0 0 0
#> 3 1 0 1 0 0 0
#> 5 0 0 0 1 0 1
#> 6 0 0 0 0 1 0
#> 7 0 0 0 0 0 3
table(y_test, pred_real)
#> pred_real
#> y_test 1 2 3 5 6 7
#> 1 7 0 0 0 0 0
#> 2 2 4 2 0 0 0
#> 3 2 0 0 0 0 0
#> 5 0 0 0 1 0 1
#> 6 0 0 0 0 1 0
#> 7 0 0 0 0 0 3There is a major reason people use decision trees in boosting. It
fits very quick. Let us try a pre-ready classifier Discrete Naive Bayes,
“dnb”, in rbooster.
par(mfrow = c(1,2))
m_discrete <- booster(x_train = x_train,
y_train = y_train,
classifier = "dnb",
method = "discrete",
x_test = x_test,
y_test = y_test,
weighted_bootstrap = FALSE,
max_iter = 250,
lambda = 1,
print_detail = FALSE,
print_plot = TRUE,
bag_frac = 0.5,
p_weak = 4)
m_real <- booster(x_train = x_train,
y_train = y_train,
classifier = "dnb",
method = "real",
x_test = x_test,
y_test = y_test,
weighted_bootstrap = FALSE,
max_iter = 250,
lambda = 1e-4,
print_detail = FALSE,
print_plot = TRUE,
bag_frac = 0.2,
p_weak = 4)
invisible(dev.off())
pred_discrete <- predict(object = m_discrete, newdata = x_test, type = "pred")
pred_real <- predict(object = m_real, newdata = x_test, type = "pred")
table(y_test, pred_discrete)
#> pred_discrete
#> y_test 1 2 3 5 6 7
#> 1 2 2 3 0 0 0
#> 2 2 5 0 0 0 1
#> 3 1 1 0 0 0 0
#> 5 0 0 0 1 0 1
#> 6 0 0 0 0 1 0
#> 7 0 0 0 0 0 3
table(y_test, pred_real)
#> pred_real
#> y_test 1 2 3 5 6 7
#> 1 6 1 0 0 0 0
#> 2 4 4 0 0 0 0
#> 3 2 0 0 0 0 0
#> 5 0 0 0 1 0 1
#> 6 0 0 0 0 1 0
#> 7 0 0 0 0 0 3I needed to play with lambda, bag_frac and p_weak a lot to find a good setting so that “dnb” fits. It is still not quite successful. Methods other that decision trees require care.
Classifier function requires x_train, y_train, weights as mandatory. Output of Classifier function will be the input for Predictor function. Predictor function requires model, which is the output of Classifier, x_new, which is the train features, and type inputs. type can only be “pred” or “prob”. “pred” must give a class vector of predictions, and “prob” must give a matrix of class probabilities. “pred” is mandatory for both Real and Discrete AdaBoost. However, Discrete AdaBoost does not require type “prob”. Let me prepare a made up Classifier function.
classifier_lm <- function(x_train, y_train, weights, ...){
y_train_code <- c(-1,1)
y_train_coded <- sapply(levels(y_train), function(m) y_train_code[(y_train == m) + 1])
y_train_coded <- y_train_coded[,1]
if (is.null(weights)) {
weights <- rep(1, length(y_train))
}
model <- lm.wfit(x = as.matrix(cbind(1,x_train)), y = y_train_coded, w = weights)
return(list(coefficients = model$coefficients,
levels = levels(y_train)))
}
predictor_lm <- function(model, x_new, type = "pred", ...) {
coef <- model$coefficients
levels <- model$levels
fit <- as.matrix(cbind(1, x_new))%*%coef
probs <- 1/(1 + exp(-fit))
probs <- data.frame(probs, 1 - probs)
colnames(probs) <- levels
if (type == "pred") {
preds <- factor(levels[apply(probs, 1, which.max)], levels = levels, labels = levels)
return(preds)
}
if (type == "prob") {
return(probs)
}
}This classifier will convert y_train into (-1,1) codes and predictions will be transformed into the range (0,1) using logit transformation. These values will be used as probabilities and class with the higher probabilitiy will be selected as prediction. Since this is a linear classification method, it should work well with boosting. Let us see.
data <- glass0
p <- ncol(data) - 1
x <- data[,1:p]
y <- data[, p + 1]
train_i <- cv_sampler(y, 0.9)
x_train <- x[train_i,]
y_train <- y[train_i]
x_test <- x[-train_i,]
y_test <- y[-train_i]
par(mfrow = c(1,2))
m_discrete <- booster(x_train = x_train,
y_train = y_train,
classifier = classifier_lm,
predictor = predictor_lm,
method = "discrete",
x_test = x_test,
y_test = y_test,
weighted_bootstrap = FALSE,
max_iter = 600,
lambda = 2,
print_detail = FALSE,
print_plot = TRUE,
bag_frac = 0.4,
p_weak = 4)
m_real <- booster(x_train = x_train,
y_train = y_train,
classifier = classifier_lm,
predictor = predictor_lm,
method = "real",
x_test = x_test,
y_test = y_test,
weighted_bootstrap = FALSE,
max_iter = 200,
lambda = 0.1,
print_detail = FALSE,
print_plot = TRUE,
bag_frac = 1,
p_weak = 4)
#> Warning in bb(x_train = x_train, y_train = y_train, classifier = classifier, :
#> Learning stopped at iteration 45
invisible(dev.off())
pred_discrete <- predict(object = m_discrete, newdata = x_test, type = "pred")
pred_real <- predict(object = m_real, newdata = x_test, type = "pred")
table(y_test, pred_discrete)
#> pred_discrete
#> y_test negative positive
#> negative 14 1
#> positive 1 6
table(y_test, pred_real)
#> pred_real
#> y_test negative positive
#> negative 13 2
#> positive 2 5It fits but not quite there. print_plot can be set to
FALSE. In that case, the same plot can be generated using
plot function.
par(mfrow = c(1,2))
plot(m_discrete)
plot(m_real)
invisible(dev.off())Pre-ready weak classifiers are “rpart”, “gnb”, “dnb”, “earth” and “glm” which are CART, Gaussian Naive Bayes, Discrete Naive Bayes, Multivariate Adaptive Regression Splines from earth package and logistic regression respectively. “glm” is not capable of multiclass classification. booster object can be used with prediction, plot, and print functions. For other details about booster object and its outputs, please see help document.