---
title: "nonet_ensemble Clustering with nonet_plot"
output: html_vignette
vignette: >
  %\VignetteIndexEntry{nonet ensemble Clustering with nonet plot}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---
### nonet provides ensemble capabilities for Clustering problems. 

Below example shows the step by step implementation of nonet_ensemble and nonet_plot functions in the context of clustering.
We have used Bank Note authentication data set to predict the output class variable using Cluster package because it provides the probability of the input point to be in a specific cluster.
Predictions from first GMM and second GMM model are being used as inputs to the nonet_ensemble in the list form.

Let's start:

#### Load the required libraries


```{r}
library(caret)
library(ggplot2)
library(ClusterR)
library(nonet)
```

##### Setting the seed
```{r}
set.seed(1001)
```

#### Load the banknote_authentication dataset and explore it.
```{r}
dataframe <- data.frame(banknote_authentication)
```

We can see above that class variable has int datatype, we need to convert it into factor.

#### Converting datatype of class variable into factors.

```{r}
dataframe$class <- as.factor(dataframe$class)
```

### First GMM Model

#### Splitting the data into train and test.
```{r}
#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,]
```

#### Exploring the dimensions of trainSet and testSet
```{r}
dim(trainSet); dim(testSet)
```

```{r}
str(trainSet)
```

```{r}
str(testSet)
```
#Feature selection using rfe in caret
```{r}
control <- rfeControl(functions = rfFuncs,
                   method = "repeatedcv",
                   repeats = 3,
                   verbose = FALSE)
```

```{r}
outcomeName<-'class'
predictors<-c("variance", "curtosis", "entropy")
```


```{r}
head(trainSet[,predictors])
```
```{r}
head(trainSet[,outcomeName])
```

#### Model Training

```{r}
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)          

```

#### #### Predictions using first GMM
```{r}
predict_clustering_first <- predict_GMM(trainSet[,predictors], gmm_first$centroids, gmm_first$covariance_matrices, gmm_first$weights) 
head(predict_clustering_first$cluster_proba[, 2])
```
#### Converting probability into classes
```{r}
predict_cluster_first_class <- as.factor(ifelse(predict_clustering_first$cluster_proba[, 2] >= "0.5", "1", "0"))
head(predict_cluster_first_class)
```

```{r}
head(predict_clustering_first$cluster_labels)
```

### Second GMM Model

#### Spliting training set into two parts based on outcome: 75% and 25%
```{r}

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

#### Exploring the dimensions of trainSet and testSet
```{r}
dim(trainSet); dim(testSet)
```

```{r}
str(trainSet)
```

```{r}
str(testSet)
```
#### Feature selection using rfe in caret
```{r}
control <- rfeControl(functions = rfFuncs,
                   method = "repeatedcv",
                   repeats = 3,
                   verbose = FALSE)
```

```{r}
outcomeName<-'class'
predictors<-c("skewness", "curtosis", "entropy")
```

```{r}
head(trainSet[,predictors])
```
```{r}
head(trainSet[,outcomeName])
```


#### Model Training: Second
```{r}
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)          

```


#### Predictions using Second GMM
```{r}
predict_clustering_Second <- predict_GMM(trainSet[,predictors], gmm_second$centroids, gmm_second$covariance_matrices, gmm_second$weights) 
head(predict_clustering_Second$cluster_proba[, 2])
```
#### Converting Prediction Probabilities into classes
```{r}
predict_cluster_Second_class <- as.factor(ifelse(predict_clustering_Second$cluster_proba[, 2] >= "0.5", "1", "0"))
head(predict_cluster_Second_class)
```

```{r}
head(predict_clustering_Second$cluster_labels)
```

#### Create the stack of predictions
```{r}
Stack_object <- list(predict_clustering_first$cluster_proba[, 2], predict_clustering_Second$cluster_proba[, 2])
```
#### Applying naming to the Stack_object
```{r}
names(Stack_object) <- c("Cluster_first", "Cluster_second")
```

#### nonet_ensemble
Now we need to apply the nonet_ensemble method by supplying list object and best model name as input.
Note that We have not provided training or test outcome labels to compute the weights in the weighted average ensemble method, which is being used inside the none_ensemble. Thus it uses best models prediction to compute the weights in the weighted average ensemble.
```{r}
prediction_nonet <- nonet_ensemble(Stack_object, "Cluster_second")
```

#### Result Plotting: nonet_plot

Results can be plotted using the nonet_plot function.
nonet_plot is being designed to provided different plot_type options to the user so that one can plot different visualization based on their needs.

#### Creating the list of cluster probabilities
```{r}
Prediction_data <- list(prediction_nonet, predict_clustering_first$cluster_proba[, 2], predict_clustering_Second$cluster_proba[, 2])
```


#### Applying name to the predictions
```{r}
names(Prediction_data) <- c("pred_nonet", "pred_clust_first", "pred_clust_second")
```

#### Converting list object into dataframe
```{r}
Prediction_dataframe <- data.frame(Prediction_data)
head(Prediction_dataframe)
```

##### nonet_plot for nonet_ensemble model's predictions in histogram
```{r, warning = FALSE}
plot_first <- nonet_plot(Prediction_dataframe$pred_nonet, Prediction_dataframe$pred_clust_first, Prediction_dataframe, plot_type = "hist")
```

```{r}
plot_first
```


##### nonet_plot for the first GMM model's predictions in histogram
```{r, warning = FALSE}
plot_second <- nonet_plot(Prediction_dataframe$pred_clust_first,  Prediction_dataframe$pred_clust_second, Prediction_dataframe, plot_type = "hist")
plot_second
```

##### nonet_plot for the Second GMM model's predictions in histogram
```{r, warning = FALSE}
plot_third <- nonet_plot(Prediction_dataframe$pred_clust_second,  Prediction_dataframe$pred_clust_first, Prediction_dataframe, plot_type = "hist")
plot_third
```


### Conclusion
 
Above it can be seen that nonet_ensemble and nonet_plot can serve in a way that one do not need to worry about the outcome variables labels to compute the weights of weighted average ensemble solution.