## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk $ set( collapse = TRUE, comment = " ", fig.width = 7, fig.height = 7, fig.align = "center" )

## ----include = FALSE----------------------------------------------------------
library( liver )

library( pROC )    

library( ggplot2 )  

## -----------------------------------------------------------------------------
data( churn )       

str( churn )

## -----------------------------------------------------------------------------
set.seed( 5 )

data_sets = partition( data = churn, prob = c( 0.8, 0.2 ) )

train_set = data_sets $ part1
test_set  = data_sets $ part2

actual_test  = test_set $ churn

## -----------------------------------------------------------------------------
formula = churn ~ account.length + voice.plan + voice.messages + intl.plan + intl.mins + 
                  day.mins + eve.mins + night.mins + customer.calls

predict_knn = kNN( formula, train = train_set, test = test_set, k = 8 )

## ----fig.align = 'center', fig.height = 3, fig.width = 3----------------------
conf.mat( predict_knn, actual_test )

conf.mat.plot( predict_knn, actual_test )

## -----------------------------------------------------------------------------
mse( predict_knn, actual_test )

## -----------------------------------------------------------------------------
predict_knn_trans = kNN( formula, train = train_set, test = test_set, k = 8, transform = "minmax" )

## ----fig.show = "hold", fig.align = 'default', out.width = "46%"--------------
conf.mat.plot( predict_knn_trans, actual_test )

conf.mat.plot( predict_knn, actual_test )

## -----------------------------------------------------------------------------
prob_knn = kNN( formula, train = train_set, test = test_set, k = 8, type = "prob" )[ , 1 ]

prob_knn_trans = kNN( formula, train = train_set, test = test_set, transform = "minmax", k = 8, type = "prob" )[ , 1 ]

## ----message = F, fig.align = "center"----------------------------------------
roc_knn = roc( actual_test, prob_knn )
roc_knn_trans = roc( actual_test, prob_knn_trans )

ggroc( list( roc_knn, roc_knn_trans ), size = 0.8 ) + 
    theme_minimal() + ggtitle( "ROC plots with AUC") +
  scale_color_manual( values = c( "red", "blue" ), 
    labels = c( paste( "AUC=", round( auc( roc_knn ), 3 ), "; Raw data; " ),
                paste( "AUC=", round( auc( roc_knn_trans ), 3 ), "; Transformed data" ) ) ) +
  theme( legend.title = element_blank() ) +
  theme( legend.position = c( .7, .3 ), text = element_text( size = 17 ) ) + 
    geom_segment( aes( x = 1, xend = 0, y = 0, yend = 1 ), color = "grey", linetype = "dashed" )

## ----fig.align = "center"-----------------------------------------------------
kNN.plot( formula, train = train_set, test = test_set, transform = "minmax", 
          k.max = 30, set.seed = 3 )