Nothing
## ----echo = FALSE--------------------------------------------------------
library(mldr)
## ----install, eval = FALSE-----------------------------------------------
# install.packages("mldr")
## ----mld_load, eval = FALSE----------------------------------------------
# corel5k <- mldr("corel5k")
## ----mld_load_k, eval = FALSE--------------------------------------------
# corel5k <- mldr("corel5k", label_amount = 374)
## ----mld_load_meka, eval = FALSE-----------------------------------------
# imdb <- mldr("imdb", use_xml = FALSE)
## ----custom_mld, eval = FALSE--------------------------------------------
# df <- data.frame(matrix(rnorm(1000), ncol = 10))
# df$Label1 <- c(sample(c(0,1), 100, replace = TRUE))
# df$Label2 <- c(sample(c(0,1), 100, replace = TRUE))
# mymldr <- mldr_from_dataframe(df, labelIndices = c(11, 12), name = "testMLDR")
## ----summary-------------------------------------------------------------
summary(birds)
## ----measures------------------------------------------------------------
emotions$measures$num.attributes
genbase$measures$scumble
## ----labels--------------------------------------------------------------
birds$labels
## ----plot, fig.width=4, fig.height=4-------------------------------------
plot(emotions, type = "LH")
## ----plot_layout, fig.width=8, fig.height=11-----------------------------
layout(matrix(c(1,1,6,1,1,3,5,5,3,2,4,7), 4, 3, byrow = TRUE))
plot(emotions, type = "LC")
plot(emotions, type = "LH")
plot(emotions, type = "LB")
plot(emotions, type = "CH")
plot(emotions, type = "LSB")
plot(emotions, type = "AT")
plot(emotions, type = "LSH")
## ----plot_lc_custom, fig.width=4, fig.height=4---------------------------
plot(genbase, labelIndices = genbase$labels$index[1:11])
## ----transforms----------------------------------------------------------
emotionsbr <- mldr_transform(emotions, type = "BR")
emotionslp <- mldr_transform(emotions, type = "LP", emotions$labels$index[1:4])
## ----transf_eval, eval = FALSE-------------------------------------------
# emo_lp <- mldr_transform(emotions, "LP")
# library(RWeka)
# classifier <- IBk(classLabel ~ ., data = emo_lp, control = Weka_control(K = 10))
# evaluate_Weka_classifier(classifier, numFolds = 5)
## ----filter--------------------------------------------------------------
emotions$measures$num.instances
emotions[emotions$dataset$.SCUMBLE > 0.01]$measures$num.instances
## ----decoupling, eval = FALSE--------------------------------------------
# mldbase <- mld[.SCUMBLE <= mld$measures$scumble]
#
# # Samples with coocurrence of highly imbalanced labels
# mldhigh <- mld[.SCUMBLE > mld$measures$scumble]
# majIndexes <- mld$labels[mld$labels$IRLbl < mld$measures$meanIR,"index"]
#
# # Deactivate majority labels
# mldhigh$dataset[, majIndexes] <- 0
# joined <- mldbase + mldhigh # Join the instances without changes with the filtered ones
## ----equals--------------------------------------------------------------
emotions[1:10] == emotions[20:30]
emotions == birds
## ----evaluation----------------------------------------------------------
# Get the true labels in emotions
predictions <- as.matrix(emotions$dataset[,emotions$labels$index])
# and introduce some noise
predictions[sample(1:593, 100),sample(1:6, 100, replace = TRUE)] <- sample(0:1, 100,
replace = TRUE)
# then evaluate the predictive performance
res <- mldr_evaluate(emotions, predictions)
str(res)
## ----eval_plot, eval = FALSE---------------------------------------------
# plot(res$ROC, main = "ROC curve for emotions") # Plot ROC curve
## ----gui, eval = FALSE---------------------------------------------------
# mldrGUI()
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