" class="reference-link">triplot

Introduction

The triplot package provides tools for exploration of machine learning
predictive models. It contains an instance-level explainer called
predict_aspects (AKA aspects_importance), that is able to explain
the contribution of the whole groups of explanatory variables.
Furthermore, package delivers functionality called triplot - it
illustrates how the importance of aspects (group of predictors) change
depending on the size of aspects.

Key functions:

predict_triplot() and model_triplot() for instance- and
data-level summary of automatic aspect importance grouping,
predict_aspects() for calculating the feature groups importance
(called aspects importance) for a selected observation,
group_variables() for grouping of correlated numeric features into
aspects.

The triplot package is a part of DrWhy.AI universe.
More information about analysis of machine learning models can be found
in the Explanatory Model Analysis. Explore, Explain and Examine
Predictive Models e-book.

Installation

# from CRAN:
install.packages("triplot")
# from GitHub (development version):
# install.packages("devtools")
devtools::install_github("ModelOriented/triplot")

Overview

triplot shows, in one place:

the importance of every single feature,
hierarchical aspects importance,
order of grouping features into aspects.

We can use it to investigate the instance level importance of
features (using predict_aspects() function) or to illustrate the
model level importance of features (using model_parts() function
from DALEX package). triplot can be only used on numerical features.
More information about this functionality can be found in triplot
overview.

Basic triplot for a model

To showcase triplot, we will choose apartments dataset from DALEX,
use it’s numeric features to build a model, create DALEX
explainer,
use model_triplot() to calculate the triplot object and then plot it
with the generic plot() function.

Import `apartments` and train a linear model

library("DALEX")
apartments_num <- apartments[,unlist(lapply(apartments, is.numeric))]
model_apartments <- lm(m2.price ~ ., data = apartments_num)

Create an explainer

explain_apartments <- DALEX::explain(model = model_apartments, 
                              data = apartments_num[, -1],
                              y = apartments_num$m2.price,
                              verbose = FALSE)

Create a triplot object

set.seed(123)
library("triplot")
tri_apartments <- model_triplot(explain_apartments)
plot(tri_apartments) + 
  patchwork::plot_annotation(title = "Global triplot for four variables in the linear model")

The left panel shows the global importance of individual variables.
Right panel shows global correlation structure visualized by
hierarchical clustering. The middle panel shows the importance of groups
of variables determined by the hierarchical clustering.

At the model level, surface and floor have the biggest
contributions. But we also know that Number of rooms and surface are
strongly correlated and together have strong influence on the model
prediction.Construction year has small influence on the prediction, is
not correlated with number of rooms nor surface variables. Adding
construction year to them, only slightly increases the importance of
this group.

Basic triplot for an observation

Afterwards, we are building triplot for single instance and it’s
prediction.

(new_apartment <- apartments_num[6, -1])

##   construction.year surface floor no.rooms
## 6              1926      61     6        2

tri_apartments <- predict_triplot(explain_apartments, 
                                  new_observation = new_apartment)
plot(tri_apartments) + 
  patchwork::plot_annotation(title = "Local triplot for four variables in the linear model")

The left panel shows the local importance of individual variables
(similar to LIME). Right panel shows global correlation structure
visualized by hierarchical clustering. The middle panel shows the local
importance of groups of variables (similar to LIME) determined by the
hierarchical clustering.

We can observe that for the given apartment surface has also
significant, positive influence on the prediction. Adding number of rooms, increases its contribution. However, adding construction year
to those two features, decreases the group importance.

We can notice that floor has the small influence on the prediction of
this observation, unlike in the model-level analysis.

Aspect importance for single instance

For this example we use titanic dataset with a logistic regression
model that predicts passenger survival. Features are combined into
thematic aspects.

Importing dataset and building a logistic regression model

set.seed(123)
model_titanic_glm <- glm(survived ~ ., titanic_imputed, family = "binomial")

Manual selection of aspects

aspects_titanic <-
  list(
    wealth = c("class", "fare"),
    family = c("sibsp", "parch"),
    personal = c("age", "gender"),
    embarked = "embarked"
  )

Select an instance

We are interested in explaining the model prediction for the johny_d
example.

(johny_d <- titanic_imputed[2,])

##   gender age class    embarked  fare sibsp parch survived
## 2   male  13   3rd Southampton 20.05     0     2        0

predict(model_titanic_glm, johny_d, type = "response")

##         2 
## 0.1531932

It turns out that the model prediction for this passenger’s survival is
very low. Let’s see which aspects have the biggest influence on it.

We start with DALEX
explainer.

explain_titanic <- DALEX::explain(model_titanic_glm, 
                           data = titanic_imputed,
                           y = titanic_imputed$survived,
                           label = "Logistic Regression",
                           verbose = FALSE)

And use it to call triplot::predict_aspects() function. Afterwards, we
print and plot function results

library("triplot")
ai_titanic <- predict_aspects(x = explain_titanic, 
                              new_observation = johny_d[,-8],
                              variable_groups = aspects_titanic)
print(ai_titanic, show_features = TRUE)

##   variable_groups importance     features
## 2          wealth  -0.122049  class, fare
## 3          family   0.023564 sibsp, parch
## 5        embarked  -0.007929     embarked
## 4        personal   0.004069  age, gender

plot(ai_titanic)

We can observe that wealth (class, fare) variables have the biggest
contribution to the prediction. This contribution is of a negative type.
Personal (age, gender) and Family (sibsp, parch) variables have
positive influence on the prediction, but it is much smaller. Embarked
feature has very small, negative contribution to the prediction.

Learn more

Acknowledgments

Work on this package was financially supported by the NCBR Grant
POIR.01.01.01-00-0328/17 and the NCN Opus grant 2017/27/B/ST6/01307.