customize_plot_text: Customize textual elements of the plots
In modelplotr: Plots to Evaluate the Business Performance of Predictive Models

Description Usage Arguments Value How to customize textual elements of plots See Also Examples

Function to overrule the default textual elements in the plots, like title, subtitle, axis labels and annotation texts when the highlighting parameter highlight_ntile is specified.

1	customize_plot_text(plot_input = plot_input)

plot_input

Dataframe. Dataframe needs to be created with plotting_scope or else meet required input format.

List with default values for all textual elements of the plots.

All textual parts of the plots can be customized, for instance to translate textual elements to another language or to change the annotation text that is added with the highlight_ntile parameter. Once you have created the plot_input dataframe using plotting_Scope, you can run this customize_plot_text() function. It returns a list, containing all textual elements of the plots, including annotation texts. For instance, run

my_plot_text <- customize_plot_text(plot_input = plot_input)

The list contains plot-specific elements (e.g. ...$cumgains$...)).
Now, you can change the textual elements by overriding the element(s) you want to customize. For instance, if you want to change the textual elements of the gains plot to Dutch:

my_plot_text$gains$plottitle <- 'Cumulatieve Gains grafiek'
my_plot_text$gains$x_axis_label <- 'Deciel'
my_plot_text$gains$y_axis_label <- 'cumulatieve gains'
my_plot_text$cumgains$optimal_gains_label <- 'maximale gains'
my_plot_text$cumgains$minimal_gains_label <- 'minimale gains'
plot_cumgains(custom_plot_text = my_plot_text)

To change the annotation text, use the placeholders starting with '&' to dynamically include:

palaceholder	placeholder value
`&NTL`	ntile specified with parameter `highlight_ntile`.
`&PCTNTL`	Total percentage of dataset selected up until specified ntile.
`&MDL`	Selected model label(s).
`&DS`	Selected dataset label(s).
`&YVAL`	Selected target class (Y-value).
`&VALUE`	The plot specific value at specified ntile. Eg. Cumulative gains, Rumulative lift, Response, Cumulative response, Profit, ROI or Revenue.

For instance, to translate the gains plot annotation text to Dutch:
my_plot_text$cumlift$annotationtext <- "Door &PCTNTL met de hoogste modelkans volgens model &MDL in &DS te selecteren is deze selectie van &YVAL observaties &CUMLIFT keer beter dan een random selectie."
plot_cumlift(highlight_ntile=3,custom_plot_text=my_plot_text)

modelplotr for generic info on the package moddelplotr

vignette('modelplotr')

https://github.com/modelplot/modelplotr for details on the package

https://modelplot.github.io/ for our blog on the value of the model plots

# load example data (Bank clients with/without a term deposit - see ?bank_td for details)
data("bank_td")

# prepare data for training model for binomial target has_td and train models
train_index =  sample(seq(1, nrow(bank_td)),size = 0.5*nrow(bank_td) ,replace = FALSE)
train = bank_td[train_index,c('has_td','duration','campaign','pdays','previous','euribor3m')]
test = bank_td[-train_index,c('has_td','duration','campaign','pdays','previous','euribor3m')]

#train models using caret... (or use mlr or H2o or keras ... see ?prepare_scores_and_ntiles)
# setting caret cross validation, here tuned for speed (not accuracy!)
fitControl <- caret::trainControl(method = "cv",number = 2,classProbs=TRUE)
# random forest using ranger package, here tuned for speed (not accuracy!)
rf = caret::train(has_td ~.,data = train, method = "ranger",trControl = fitControl,
                  tuneGrid = expand.grid(.mtry = 2,.splitrule = "gini",.min.node.size=10))
# mnl model using glmnet package
mnl = caret::train(has_td ~.,data = train, method = "glmnet",trControl = fitControl)

# load modelplotr
library(modelplotr)

# transform datasets and model objects to input for modelplotr
scores_and_ntiles <- prepare_scores_and_ntiles(datasets=list("train","test"),
                         dataset_labels = list("train data","test data"),
                         models = list("rf","mnl"),
                         model_labels = list("random forest","multinomial logit"),
                         target_column="has_td",
                         ntiles=100)

# set scope for analysis (default: no comparison)
plot_input <- plotting_scope(prepared_input = scores_and_ntiles)

# customize all textual elements of plots
mytexts <- customize_plot_text(plot_input = plot_input)
mytexts$cumresponse$plottitle <- 'Expected conversion rate for Campaign XYZ'
mytexts$cumresponse$plotsubtitle <- 'proposed selection: best 15 percentiles according to our model'
mytexts$cumresponse$y_axis_label <- '% Conversion'
mytexts$cumresponse$x_axis_label <- 'percentiles (percentile = 1% of customers)'
mytexts$cumresponse$annotationtext <-
  "Selecting up until the &NTL percentile with model &MDL has an expected conversion rate of &VALUE"
plot_cumresponse(data=plot_input,custom_plot_text = mytexts,highlight_ntile = 15)