inst/doc/nonet_ensemble_clustering.R

In nonet: Weighted Average Ensemble without Training Labels

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library(caret)
library(ggplot2)
library(ClusterR)
library(nonet)

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set.seed(1001)

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dataframe <- data.frame(banknote_authentication)

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dataframe$class <- as.factor(dataframe$class)

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#Spliting training set into two parts based on outcome: 75% and 25%
index <- createDataPartition(dataframe$class, p=0.75, list=FALSE)
trainSet <- dataframe[ index,]
testSet <- dataframe[-index,]

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dim(trainSet); dim(testSet)

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str(trainSet)

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str(testSet)

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control <- rfeControl(functions = rfFuncs,
                   method = "repeatedcv",
                   repeats = 3,
                   verbose = FALSE)

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outcomeName<-'class'
predictors<-c("variance", "curtosis", "entropy")

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head(trainSet[,predictors])

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head(trainSet[,outcomeName])

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set.seed(900)
gmm_first <- GMM(trainSet[,predictors], 2, dist_mode = "maha_dist", seed_mode = "random_subset", km_iter = 10, em_iter = 10, verbose = F)          


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predict_clustering_first <- predict_GMM(trainSet[,predictors], gmm_first$centroids, gmm_first$covariance_matrices, gmm_first$weights) 
head(predict_clustering_first$cluster_proba[, 2])

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predict_cluster_first_class <- as.factor(ifelse(predict_clustering_first$cluster_proba[, 2] >= "0.5", "1", "0"))
head(predict_cluster_first_class)

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head(predict_clustering_first$cluster_labels)

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index <- createDataPartition(dataframe$class, p=0.75, list=FALSE)
trainSet <- dataframe[ index,]
testSet <- dataframe[-index,]

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dim(trainSet); dim(testSet)

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str(trainSet)

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str(testSet)

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control <- rfeControl(functions = rfFuncs,
                   method = "repeatedcv",
                   repeats = 3,
                   verbose = FALSE)

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outcomeName<-'class'
predictors<-c("skewness", "curtosis", "entropy")

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head(trainSet[,predictors])

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head(trainSet[,outcomeName])

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set.seed(423)
gmm_second <- GMM(trainSet[,predictors], 2, dist_mode = "maha_dist", seed_mode = "random_subset", km_iter = 10, em_iter = 10, verbose = F)          


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predict_clustering_Second <- predict_GMM(trainSet[,predictors], gmm_second$centroids, gmm_second$covariance_matrices, gmm_second$weights) 
head(predict_clustering_Second$cluster_proba[, 2])

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predict_cluster_Second_class <- as.factor(ifelse(predict_clustering_Second$cluster_proba[, 2] >= "0.5", "1", "0"))
head(predict_cluster_Second_class)

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head(predict_clustering_Second$cluster_labels)

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Stack_object <- list(predict_clustering_first$cluster_proba[, 2], predict_clustering_Second$cluster_proba[, 2])

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names(Stack_object) <- c("Cluster_first", "Cluster_second")

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prediction_nonet <- nonet_ensemble(Stack_object, "Cluster_second")

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Prediction_data <- list(prediction_nonet, predict_clustering_first$cluster_proba[, 2], predict_clustering_Second$cluster_proba[, 2])

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names(Prediction_data) <- c("pred_nonet", "pred_clust_first", "pred_clust_second")

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Prediction_dataframe <- data.frame(Prediction_data)
head(Prediction_dataframe)

## ---- warning = FALSE----------------------------------------------------
plot_first <- nonet_plot(Prediction_dataframe$pred_nonet, Prediction_dataframe$pred_clust_first, Prediction_dataframe, plot_type = "hist")

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plot_first

## ---- warning = FALSE----------------------------------------------------
plot_second <- nonet_plot(Prediction_dataframe$pred_clust_first,  Prediction_dataframe$pred_clust_second, Prediction_dataframe, plot_type = "hist")
plot_second

## ---- warning = FALSE----------------------------------------------------
plot_third <- nonet_plot(Prediction_dataframe$pred_clust_second,  Prediction_dataframe$pred_clust_first, Prediction_dataframe, plot_type = "hist")
plot_third