setred: General Interface for SETRED model
In SSLR: Semi-Supervised Classification, Regression and Clustering Methods

Description Usage Arguments Details Value References Examples

SETRED (SElf-TRaining with EDiting) is a variant of the self-training classification method (as implemented in the function selfTraining) with a different addition mechanism. The SETRED classifier is initially trained with a reduced set of labeled examples. Then, it is iteratively retrained with its own most confident predictions over the unlabeled examples. SETRED uses an amending scheme to avoid the introduction of noisy examples into the enlarged labeled set. For each iteration, the mislabeled examples are identified using the local information provided by the neighborhood graph.

setred(
  dist = "Euclidean",
  learner,
  theta = 0.1,
  max.iter = 50,
  perc.full = 0.7,
  D = NULL
)

`dist`	A distance function or the name of a distance available in the `proxy` package to compute. Default is "Euclidean" the distance matrix in the case that `D` is `NULL`.
`learner`	model from parsnip package for training a supervised base classifier using a set of instances. This model need to have probability predictions (or optionally a distance matrix) and it's corresponding classes.
`theta`	Rejection threshold to test the critical region. Default is 0.1.
`max.iter`	maximum number of iterations to execute the self-labeling process. Default is 50.
`perc.full`	A number between 0 and 1. If the percentage of new labeled examples reaches this value the self-training process is stopped. Default is 0.7.
`D`	A distance matrix between all the training instances. This matrix is used to construct the neighborhood graph. Default is NULL, this means the method create a matrix with dist param

SETRED initiates the self-labeling process by training a model from the original labeled set. In each iteration, the learner function detects unlabeled examples for which it makes the most confident prediction and labels those examples according to the pred function. The identification of mislabeled examples is performed using a neighborhood graph created from the distance matrix. Most examples possess the same label in a neighborhood. So if an example locates in a neighborhood with too many neighbors from different classes, this example should be considered problematic. The value of the theta argument controls the confidence of the candidates selected to enlarge the labeled set. The lower this value is, the more restrictive is the selection of the examples that are considered good. For more information about the self-labeled process and the rest of the parameters, please see selfTraining.

(When model fit) A list object of class "setred" containing:

model: The final base classifier trained using the enlarged labeled set.
instances.index: The indexes of the training instances used to train the model. These indexes include the initial labeled instances and the newly labeled instances. Those indexes are relative to x argument.
classes: The levels of y factor.
pred: The function provided in the pred argument.
pred.pars: The list provided in the pred.pars argument.

Ming Li and ZhiHua Zhou.
Setred: Self-training with editing.
In Advances in Knowledge Discovery and Data Mining, volume 3518 of Lecture Notes in Computer Science, pages 611-621. Springer Berlin Heidelberg, 2005. ISBN 978-3-540-26076-9. doi: 10.1007/11430919 71.

library(tidyverse)
library(tidymodels)
library(caret)
library(SSLR)

data(wine)

set.seed(1)
train.index <- createDataPartition(wine$Wine, p = .7, list = FALSE)
train <- wine[ train.index,]
test  <- wine[-train.index,]

cls <- which(colnames(wine) == "Wine")

#% LABELED
labeled.index <- createDataPartition(wine$Wine, p = .2, list = FALSE)
train[-labeled.index,cls] <- NA

#We need a model with probability predictions from parsnip
#https://tidymodels.github.io/parsnip/articles/articles/Models.html
#It should be with mode = classification

#For example, with Random Forest
rf <-  rand_forest(trees = 100, mode = "classification") %>%
  set_engine("randomForest")


m <- setred(learner = rf,
            theta = 0.1,
            max.iter = 2,
            perc.full = 0.7) %>% fit(Wine ~ ., data = train)


#Accuracy
predict(m,test) %>%
  bind_cols(test) %>%
  metrics(truth = "Wine", estimate = .pred_class)



#Another example, with dist matrix

distance <- as.matrix(proxy::dist(train[,-cls], method ="Euclidean",
                                  by_rows = TRUE, diag = TRUE, upper = TRUE))

m <- setred(learner = rf,
            theta = 0.1,
            max.iter = 2,
            perc.full = 0.7,
            D = distance) %>% fit(Wine ~ ., data = train)

#Accuracy
predict(m,test) %>%
  bind_cols(test) %>%
  metrics(truth = "Wine", estimate = .pred_class)