The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
In this example we will use titanic_imputed data to show some examples for the ArenaR library.
We will create a random forest model that will predict chances of survival and then use Arena to do global exploration of this model.
Let's see the data.
library("dplyr")
library("DALEX")
head(titanic_imputed)
Arena offers the possibility to explore any ML model. We will start this example with one model and global explanations.
We'll use the ranger package for this example. With its help, we will build a random forest model.
library("ranger")
titanic_rf <- ranger(survived ~ .,
data = titanic_imputed,
probability = TRUE,
classification = TRUE)
ArenaR, like all packages from the DrWhy family, works on unified model wrappers. We will create them with the explain function from the DALEX package.
library("DALEX")
titanic_ex <- explain(
titanic_rf,
y = titanic_imputed$survived,
data = titanic_imputed)
We are ready to create an arena for comparison and exploration of machine learning models.
First, we need to create an empty space to explore models with the use of the create_arena function.
Then we can add models to it with the push_model function.
We have one model, so let's add it to the arena!
library("arenar")
titanic_ar <- create_arena(live = TRUE) %>%
push_model(titanic_ex)
For the pushed model a set of global explanations is calculated. Such as the Variables importance and Partial Dependence Plots / Accumulated Local Effects for each variable.
We are ready to work with the model interactively.
We can execute the arena object using run_server function.
It will turn R into a server serving data and use the dashboard https://arena.drwhy.ai/ to explore the data.
run_server(titanic_ar)
The browser will open an interactive tool for model exploration.
Arena allows you to explore the ML model for any instance. To the model built in the previous chapter we will add explanations for three new observations.
The arena also supports the exploration of the model at the level of explanations for individual instances. Let's first prepare a data set with three new passengers.
passangers <- data.frame(
class = factor(c("1st", "3rd", "1st"), levels = c("1st", "2nd", "3rd", "deck crew",
"engineering crew", "restaurant staff", "victualling crew")),
gender = factor(c("male", "male", "female"), levels = c("female", "male")),
age = c(8, 42, 12),
sibsp = c(0, 0, 0),
parch = c(0, 0, 0),
fare = c(72, 10, 50),
embarked = factor(c("Southampton", "Belfast", "Belfast"), levels = c("Belfast",
"Cherbourg","Queenstown","Southampton")))
rownames(passangers) = c("Johny D", "Henry", "Mary")
passangers
class gender age sibsp parch fare embarked
Johny D 1st male 8 0 0 72 Southampton
Henry 3rd male 42 0 0 10 Belfast
Mary 1st female 12 0 0 50 Belfast
Let's add these observations to the arena with the push_observations functions.
titanic_ar <- titanic_ar %>%
push_observations(passangers)
For these new observations a set of local explanations is calculated. Such as the Break Down, Shapley values and Ceteris Paribus for each variable.
The updated arena object can be viewed again by running the run_server function on it.
run_server(titanic_ar)
The browser will open an interactive tool for model exploration.
The most important feature of the Arena is the ability to compare any number of ML models regardless of their complexity and internal structure.
We will use the model created in the previous section to demonstrate this functionality.
For the titanic data let's build a gradient boosting model and a generalized linear model. Together with the ranger model, these are three models with a completely different structures. This will make comparing them much more interesting.
The linear model is additive, the gradient boosting model can have deep interactions. Let's build these models and then compare them.
titanic_glm <- glm(survived ~ ., data = titanic_imputed, family = "binomial")
library("gbm")
titanic_gbm <- gbm(survived ~ ., data = titanic_imputed, n.trees = 500)
Since these models have different structures, we need to standardize the way we can access them. We will use the explain function for this.
titanic_egb <- explain(titanic_gbm,
y = titanic_imputed$survived,
data = titanic_imputed)
titanic_elm <- explain(titanic_glm,
y = titanic_imputed$survived,
data = titanic_imputed)
We can add more models to the Arena with the push_model function. It is very easy.
titanic_ar <- titanic_ar %>%
push_model(titanic_egb) %>%
push_model(titanic_elm)
The updated arena object can be viewed again by running the run_server function on it.
run_server(titanic_ar)
The browser will open an interactive tool for model exploration.
In the above example, we called create_arena(live = TRUE) so all the necessary explanations were calculated on the spot when they were needed.
However, this requires a working R in the backend.
You can also run the arena in serverless mode. Just initialize it with the parameter create_arena(live = FALSE). In this case all important statistics will be pre-calculated and the arena can be used in serverless mode.
Here is the full example that shows how to use Arena in the this mode.
create_arena() %>%
push_model(titanic_ex) %>%
push_model(titanic_egb) %>%
push_model(titanic_elm) %>%
push_observations(passangers) %>%
upload_arena()
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.