rSAFE package is a model agnostic tool for making an interpretable
white-box model more accurate using alternative black-box model called
surrogate model. Based on the complicated model, such as neural network
or random forest, new features are being extracted and then used in the
process of fitting a simpler interpretable model, improving its overall
The package can be installed from GitHub using the code below:
In this vignette we present an example of an application of the
package in case of regression problems. It is based on
apartmentsTest datasets which come from the
DALEX package but are
also available in the
rSAFE package. We will use these artificial
datasets to predict the price per square meter of an apartment based on
features such as construction year, surface, floor, number of rooms and
district. It should be mentioned that four of these variables are
continuous while the fifth one is categorical.
library(rSAFE) head(apartments) #> m2.price construction.year surface floor no.rooms district #> 1 5897 1953 25 3 1 Srodmiescie #> 2 1818 1992 143 9 5 Bielany #> 3 3643 1937 56 1 2 Praga #> 4 3517 1995 93 7 3 Ochota #> 5 3013 1992 144 6 5 Mokotow #> 6 5795 1926 61 6 2 Srodmiescie
Building a black-box model
First we fit a random forest model to the original
- this is our complex model that will serve us as a surrogate.
library(randomForest) set.seed(111) model_rf1 <- randomForest(m2.price ~ construction.year + surface + floor + no.rooms + district, data = apartments)
Creating an explainer
We also create an
explainer object that will be used later to create
new variables and at the end to compare models performance.
library(DALEX) explainer_rf1 <- explain(model_rf1, data = apartmentsTest[1:3000,2:6], y = apartmentsTest[1:3000,1], label = "rf1", verbose = FALSE) explainer_rf1 #> Model label: rf1 #> Model class: randomForest.formula,randomForest #> Data head : #> construction.year surface floor no.rooms district #> 1001 1976 131 3 5 Srodmiescie #> 1002 1978 112 9 4 Mokotow
Creating a safe_extractor
Now, we create a
safe_extractor object using
rSAFE package and our
surrogate model. Setting the argument
verbose=FALSE stops progress bar
safe_extractor <- safe_extraction(explainer_rf1, penalty = 25, verbose = FALSE)
Now, let’s print summary for the new object we have just created.
print(safe_extractor) #> Variable 'construction.year' - selected intervals: #> (-Inf, 1937] #> (1937, 1992] #> (1992, Inf) #> Variable 'surface' - selected intervals: #> (-Inf, 47] #> (47, 101] #> (101, Inf) #> Variable 'floor' - selected intervals: #> (-Inf, 5] #> (5, Inf) #> Variable 'no.rooms' - selected intervals: #> (-Inf, 3] #> (3, Inf) #> Variable 'district' - created levels: #> Bemowo, Bielany, Ursus, Ursynow, Praga, Wola -> Bemowo_Bielany_Praga_Ursus_Ursynow_Wola #> Zoliborz, Mokotow, Ochota -> Mokotow_Ochota_Zoliborz #> Srodmiescie -> Srodmiescie
We can see transformation propositions for all variables in our dataset.
In the plot below we can see which points have been chosen to be the breakpoints for a particular variable:
plot(safe_extractor, variable = "construction.year")
For factor variables we can observe in which order levels have been merged and what is the optimal clustering:
plot(safe_extractor, variable = "district")
Now we can use our
safe_extractor object to create new categorical
features in the given dataset.
data1 <- safely_transform_data(safe_extractor, apartmentsTest[3001:6000,], verbose = FALSE)
|Bielany||3542||1979||21||6||1||(1937, 1992]||(-Inf, 47]||(5, Inf)||(-Inf, 3]||Bemowo_Bielany_Praga_Ursus_Ursynow_Wola|
|Srodmiescie||5631||1997||107||2||4||(1992, Inf)||(101, Inf)||(-Inf, 5]||(3, Inf)||Srodmiescie|
|Bielany||2989||1994||41||9||2||(1992, Inf)||(-Inf, 47]||(5, Inf)||(-Inf, 3]||Bemowo_Bielany_Praga_Ursus_Ursynow_Wola|
|Ursynow||3822||1968||28||2||2||(1937, 1992]||(-Inf, 47]||(-Inf, 5]||(-Inf, 3]||Bemowo_Bielany_Praga_Ursus_Ursynow_Wola|
|Ursynow||2337||1971||146||3||6||(1937, 1992]||(101, Inf)||(-Inf, 5]||(3, Inf)||Bemowo_Bielany_Praga_Ursus_Ursynow_Wola|
|Ochota||3381||1956||97||8||3||(1937, 1992]||(47, 101]||(5, Inf)||(-Inf, 3]||Mokotow_Ochota_Zoliborz|
We can also perform feature selection if we wish. For each original feature it keeps exactly one of their forms - original one or transformed one.
vars <- safely_select_variables(safe_extractor, data1, which_y = "m2.price", verbose = FALSE) data1 <- data1[,c("m2.price", vars)] print(vars) #>  "surface" "floor" "no.rooms" #>  "construction.year_new" "district_new"
It can be observed that for some features the original form was preferred and for others the transformed one.
Here are the first few rows for our data after feature selection:
Now, we perform transformations on another data that will be used later in explainers:
data2 <- safely_transform_data(safe_extractor, apartmentsTest[6001:9000,], verbose = FALSE)[,c("m2.price", vars)]
Creating white-box models on original and transformed datasets
Let’s fit the models to data containing newly created columns. We consider a linear model as a white-box model.
model_lm2 <- lm(m2.price ~ ., data = data1) explainer_lm2 <- explain(model_lm2, data = data2, y = apartmentsTest[6001:9000,1], label = "lm2", verbose = FALSE) set.seed(111) model_rf2 <- randomForest(m2.price ~ ., data = data1) explainer_rf2 <- explain(model_rf2, data2, apartmentsTest[6001:9000,1], label = "rf2", verbose = FALSE)
Moreover, we create a linear model based on original
dataset and its corresponding explainer in order to check if our
methodology improves results.
model_lm1 <- lm(m2.price ~ ., data = apartments) explainer_lm1 <- explain(model_lm1, data = apartmentsTest[1:3000,2:6], y = apartmentsTest[1:3000,1], label = "lm1", verbose = FALSE)
Comparing models performance
Final step is the comparison of all the models we have created.
mp_lm1 <- model_performance(explainer_lm1) mp_rf1 <- model_performance(explainer_rf1) mp_lm2 <- model_performance(explainer_lm2) mp_rf2 <- model_performance(explainer_rf2)
plot(mp_lm1, mp_rf1, mp_lm2, mp_rf2, geom = "boxplot")
In the plot above we can see that the linear model based on transformed features has generally more accurate predictions that the one fitted to the original dataset.
- Python version of SAFE package
- SAFE article - the article about SAFE algorithm, including benchmark results obtained using Python version of SAFE package
The package was created as a part of master’s diploma thesis at Warsaw University of Technology at Faculty of Mathematics and Information Science by Anna Gierlak.