In LinlinYin/mlr3vis:

knitr::opts_chunk$set(
  echo = TRUE,
  collapse = TRUE,
  comment = "#>",
  fig.width=7,
  fig.height=7
)

#devtools::load_all("d:/source/mlr3vis/")
library(mlr3)
library(mlr3vis)

mlr: https://mlr.mlr-org.com/articles/tutorial/predict.html

mlr3: https://mlr3book.mlr-org.com/

Learner and Task related

plotLearnerPrediction

This function need learner and task as input. The goal of this function is to show the priediction results with distribution of interested features. It is similar as the function with same name in mlr but it used orginal model in learner (usually used all features) rather than make a new model based on only two interested features. The interestedFeatures were only used for visulization purpose as X axis and Y axis. To show the distribution of prediction result or probability with changes of interested features, grid with different colours were plotted as background. The prediction of grid used orginal model in learner, and interestedFeatures among X and Y axis as data. The median values or most common values for Features used in model but not in interestedFeatures were used to paticipate prediction of the grid background.

Parameters:
learner: need to be trained and predict_type = "prob"
task: task, no requirement
* prob.alpha: FALSE shows prediction result; TRUE shows maximal probability

task = mlr_tasks$get("iris")
learner = mlr_learners$get("classif.rpart")
learner$predict_type = "prob"
learner$train(task)

plotLearnerPrediction(learner=learner,task=task,prob.alpha = FALSE)
plotLearnerPrediction(learner=learner,task=task,prob.alpha = TRUE)

Define otherFeaturesValue for grid prediction in plotLearnerPrediction

As introduced in last section, plotLearnerPrediction used median or most common value for grid prediction if more than the two interested features were used in the model. Here we can define the values as other Features in the model by a list, and the grid prediction will be based on these values. The results will be different (first figure, not defined otherFeaturesValue, use median value; second figure, use defined otherFeaturesValue).

plotLearnerPrediction(learner=learner,task=task,prob.alpha = TRUE,interestedFeatures= c("Petal.Width","Sepal.Length"))
otherFeaturesValue=list(Petal.Length=2.1,Sepal.Width=3.3)
plotLearnerPrediction(learner=learner,task=task,prob.alpha = TRUE,interestedFeatures= c("Petal.Width","Sepal.Length"),otherFeaturesValue=otherFeaturesValue)

Combination of plotLearnerPrediction

It is very interesting to analyze how the distribution of prediction result among different combination of features. Here we found that although there were four features in the learner, they have different roles. "Petal.Length" was used to preidcit "setosa"; "Petal.Width" was used to predict "virginica". "Sepal.Width" and "Sepal.Length" didn't contribute in this model.

I like this figure (go through all combination of features and see how the preidcition results look like) and going to make this as a function too.

p1=plotLearnerPrediction(learner=learner,task=task,prob.alpha = TRUE, interestedFeatures= c("Sepal.Length","Petal.Length"))

p2=plotLearnerPrediction(learner=learner,task=task,prob.alpha = TRUE, interestedFeatures= c("Sepal.Length","Petal.Width"))

p3=plotLearnerPrediction(learner=learner,task=task,prob.alpha = TRUE, interestedFeatures= c("Sepal.Length","Sepal.Width"))

p4=plotLearnerPrediction(learner=learner,task=task,prob.alpha = TRUE, interestedFeatures= c("Petal.Width","Petal.Length"))

p5=plotLearnerPrediction(learner=learner,task=task,prob.alpha = TRUE, interestedFeatures= c("Petal.Width","Sepal.Width"))

p6=plotLearnerPrediction(learner=learner,task=task,prob.alpha = TRUE, interestedFeatures= c("Petal.Width","Sepal.Length"))

ggarrange(p1,p2,p3,p4,p5,p6,common.legend = TRUE,ncol=2,nrow=3)

summaryFeatureInTask

This function shows distribution of features among target groups and their differential p values. Task will be used as input.

summaryFeatureInTask(task)

plotFeatureInTask

This function visualizes distribution of features among target groups, style can be "pairs", "box", "violin" and "dot". Task will be used as input.

plotFeatureInTask(task,style="pairs")
plotFeatureInTask(task,style="box")
plotFeatureInTask(task,style="violin")
plotFeatureInTask(task,style="dot")

Other Features

All introduction for other functions are removed on purpose since I am not sure if they can work with latest version of mlr3 at this time. Will add them back later.

LinlinYin/mlr3vis documentation built on July 7, 2019, 12:11 p.m.