predict_one_vs_rest_TSP: Predict sample class based on one-vs-rest multiclass top...

In NourMarzouka/multiclassPairs: Build MultiClass Pair-Based Classifiers using TSPs or RF

Predict sample class based on one-vs-rest multiclass top score pairs classifier

Description

predict_one_vs_rest_TSP predicts sample class based on one-vs-rest multiclass top score pairs classifier classifier

Usage

predict_one_vs_rest_TSP(classifier,
                        Data,
                        tolerate_missed_genes = TRUE,
                        weighted_votes = TRUE,
                        classes,
                        verbose = TRUE)

Arguments

classifier	classifier as a OnevsrestScheme_TSP object, generated by train_one_vs_rest_TSP function
Data	a matrix, dataframe, ExpressionSet, or data_object generated by ReadData function. Samples as columns and row as features/genes.
tolerate_missed_genes	logical. TRUE means that if a gene in the classifier is missed in the data then this rule will be not considered in the prediction. If tolerate_missed_genes=TRUE then the user should keep an eye on the left rules. In some cases when the missed genes are too many then no enough rules left for a good prediction.
weighted_votes	logical indicates if the rules will be treated equally or be weighted by their scores. weighted_votes=TRUE is useful to break vote ties between classes.
classes	optional vector with the names of the classes. This will be used to order the columns of the ouput dataframe. In case not all classes in the classifier is mentioned in the vector, then these classes will be excluded from the prediction.
verbose	a logical value indicating whether processing messages will be printed or not. Default is TRUE.

Value

returns dataframe with classes votes, ties, and final prediction

Author(s)

Nour-al-dain Marzouka <nour-al-dain.marzouka at med.lu.se>

Examples

# random data
Data <- matrix(runif(10000), nrow=100, ncol=100,
               dimnames = list(paste0("G",1:100), paste0("S",1:100)))

# labels
L <- sample(x = c("A","B","C"), size = 100, replace = TRUE)

# study/platform
P <- sample(c("P1","P2"), size = 100, replace = TRUE)

object <- ReadData(Data = Data,
             Labels = L,
                   Platform = P)

# not to run
# switchBox package from Bioconductor is needed
# Visit their website or install switchBox package using:
# if(!requireNamespace("switchBox", quietly = TRUE)){
#       if (!requireNamespace('BiocManager', quietly = TRUE)) {
#       install.packages('BiocManager')
#      }
#      BiocManager::install('switchBox')", call. = FALSE)
#  }

# filtered_genes <- filter_genes_TSP(data_object = object,
#                                  filter = "one_vs_rest",
#                                  platform_wise = FALSE,
#                                  featureNo = 10,
#                                  UpDown = TRUE,
#                                  verbose = FALSE)

# training
# classifier <- train_one_vs_rest_TSP(data_object = object,
#                              filtered_genes = filtered_genes,
#                              k_range = 2:50,
#                              include_pivot = FALSE,
#                              one_vs_one_scores = FALSE,
#                              platform_wise_scores = FALSE,
#                              seed = 1234,
#                              verbose = FALSE)

# results <- predict_one_vs_rest_TSP(classifier = classifier,
#                                   Data = object,
#                                   tolerate_missed_genes = TRUE,
#                                   weighted_votes = TRUE,
#                                   verbose = FALSE)

# Confusion Matrix and Statistics on training data
#  caret::confusionMatrix(data = factor(results$max_score, levels = unique(L)),
#                         reference = factor(L, levels = unique(L)),
#                         mode="everything")

# plot_binary_TSP(Data = object, classes=c("A","B","C"),
#                 classifier = classifier,
#                 prediction = results,
#                 title = "Test")

NourMarzouka/multiclassPairs documentation built on May 3, 2023, 7:20 p.m.