In this vignette we demonstrate an application of the rSAFE
package to the HR_data
set. The dataset contains information from a Human Resources department about employees and may be used in the classification task of predicting whether an employee is likely to leave the company. The data comes from the Kaggle competition “Human Resources Analytics” and is available in breakDown
and rSAFE
packages.
library(rSAFE)
head(HR_data)
#> satisfaction_level last_evaluation number_project average_monthly_hours
#> 1 0.38 0.53 2 157
#> 2 0.80 0.86 5 262
#> 3 0.11 0.88 7 272
#> 4 0.72 0.87 5 223
#> 5 0.37 0.52 2 159
#> 6 0.41 0.50 2 153
#> time_spend_company work_accident left promotion_last_5years sales salary
#> 1 3 0 1 0 sales low
#> 2 6 0 1 0 sales medium
#> 3 4 0 1 0 sales medium
#> 4 5 0 1 0 sales low
#> 5 3 0 1 0 sales low
#> 6 3 0 1 0 sales low
As explanatory variables we use all available in the dataset except for which specifies department in which the employee works for.
<- HR_data[, colnames(HR_data) != "sales"] data
In order to ensure the final errors are computed on the data which has not been seen by an appropriate model, we divide our data as follows:
data1
- the data which initial black-box and white-box models are fitted to,data2
- the data used to create an explainer
and a safe_extractor
for black-box model from the previous point, serving also as a test data,data3
- the data for which transformations and feature selection are performed, used also as a training set for new models,data4
- the data used as a test set for the new models, allowing to compare the results. Before splitting the data, we first shuffle rows to ensure they are evenly distributed.set.seed(111)
<- data[sample(1:nrow(data)),]
data
<- data[1:4000,]
data1 <- data[4001:8000,]
data2 <- data[8001:12000,]
data3 <- data[12001:14999,] data4
In this example we decide to use the GBM model as a black-box - it will serve us as a surrogate.
<- 10000
n.trees library(gbm)
set.seed(111)
<- gbm(left ~ ., data = data1, distribution = "bernoulli", n.trees = n.trees) model_xgb1
We also create an explainer
object that will be used later to create new variables. For classification problems we need to specify predict_function
- a function that may be used for model predictions and returns a single numerical value for each observation.
library(DALEX)
<- function(model, x) predict(model, x, type = "response", n.trees = n.trees)
predict_function <- explain(model_xgb1, data = data2[,-7], y = data2$left, predict_function = predict_function, verbose = FALSE) explainer_xgb1
Now, we create a safe_extractor
object using rSAFE
package and our surrogate model. Setting the argument verbose=FALSE
stops progress bar from printing.
<- safe_extraction(explainer_xgb1, penalty = 20, verbose = FALSE) safe_extractor
Now, let’s print summary for the new object we have just created.
print(safe_extractor)
#> Variable 'satisfaction_level' - selected intervals:
#> (-Inf, 0.48]
#> (0.48, 0.9193878]
#> (0.9193878, Inf)
#> Variable 'last_evaluation' - selected intervals:
#> (-Inf, 0.42]
#> (0.42, 0.57]
#> (0.57, 0.8]
#> (0.8, Inf)
#> Variable 'number_project' - selected intervals:
#> (-Inf, 4]
#> (4, Inf)
#> Variable 'average_monthly_hours' - selected intervals:
#> (-Inf, 115.6122]
#> (115.6122, 160]
#> (160, 214]
#> (214, Inf)
#> Variable 'time_spend_company' - selected intervals:
#> (-Inf, 3]
#> (3, Inf)
#> Variable 'work_accident' - no transformation suggested.
#> Variable 'promotion_last_5years' - no transformation suggested.
#> Variable 'salary' - created levels:
#> high -> high
#> low, medium -> low_medium
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 = "last_evaluation")
For factor variables we can observe in which order levels have been merged and what is the optimal clustering:
plot(safe_extractor, variable = "salary")
Now we can use our safe_extractor
object to create new categorical features in the given dataset.
<- safely_transform_data(safe_extractor, data3, verbose = FALSE) data3_trans
salary | satisfaction_level | last_evaluation | number_project | average_monthly_hours | time_spend_company | work_accident | left | promotion_last_5years | satisfaction_level_new | last_evaluation_new | number_project_new | average_monthly_hours_new | time_spend_company_new | salary_new |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
low | 0.78 | 0.77 | 2 | 177 | 4 | 0 | 0 | 0 | (0.48, 0.9193878] | (0.57, 0.8] | (-Inf, 4] | (160, 214] | (3, Inf) | low_medium |
low | 0.87 | 0.94 | 5 | 219 | 5 | 0 | 1 | 0 | (0.48, 0.9193878] | (0.8, Inf) | (4, Inf) | (214, Inf) | (3, Inf) | low_medium |
medium | 0.85 | 1.00 | 5 | 244 | 2 | 0 | 0 | 0 | (0.48, 0.9193878] | (0.8, Inf) | (4, Inf) | (214, Inf) | (-Inf, 3] | low_medium |
medium | 0.24 | 0.46 | 7 | 224 | 5 | 0 | 1 | 0 | (-Inf, 0.48] | (0.42, 0.57] | (4, Inf) | (214, Inf) | (3, Inf) | low_medium |
low | 0.81 | 0.49 | 4 | 285 | 4 | 0 | 0 | 0 | (0.48, 0.9193878] | (0.42, 0.57] | (-Inf, 4] | (214, Inf) | (3, Inf) | low_medium |
high | 0.72 | 0.84 | 2 | 173 | 2 | 1 | 0 | 0 | (0.48, 0.9193878] | (0.8, Inf) | (-Inf, 4] | (160, 214] | (-Inf, 3] | high |
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.
<- safely_select_variables(safe_extractor, data3_trans, which_y = "left", verbose = FALSE)
selected_variables <- data3_trans[,c("left", selected_variables)]
data3_trans_sel print(selected_variables)
#> [1] "work_accident" "promotion_last_5years"
#> [3] "salary" "satisfaction_level_new"
#> [5] "last_evaluation_new" "number_project_new"
#> [7] "average_monthly_hours_new" "time_spend_company_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:
left | work_accident | promotion_last_5years | salary | satisfaction_level_new | last_evaluation_new | number_project_new | average_monthly_hours_new | time_spend_company_new |
---|---|---|---|---|---|---|---|---|
0 | 0 | 0 | low | (0.48, 0.9193878] | (0.57, 0.8] | (-Inf, 4] | (160, 214] | (3, Inf) |
1 | 0 | 0 | low | (0.48, 0.9193878] | (0.8, Inf) | (4, Inf) | (214, Inf) | (3, Inf) |
0 | 0 | 0 | medium | (0.48, 0.9193878] | (0.8, Inf) | (4, Inf) | (214, Inf) | (-Inf, 3] |
1 | 0 | 0 | medium | (-Inf, 0.48] | (0.42, 0.57] | (4, Inf) | (214, Inf) | (3, Inf) |
0 | 0 | 0 | low | (0.48, 0.9193878] | (0.42, 0.57] | (-Inf, 4] | (214, Inf) | (3, Inf) |
0 | 1 | 0 | high | (0.48, 0.9193878] | (0.8, Inf) | (-Inf, 4] | (160, 214] | (-Inf, 3] |
Now, we perform transformations on another data that will be used later to compare models performance.
<- safely_transform_data(safe_extractor, data4, verbose = FALSE)
data4_trans <- data4_trans[,c("left", selected_variables)] data4_trans_sel
Let’s fit the models to data containing newly created columns. We consider a generalized linear model (glm) as a white-box model.
<- glm(left ~ ., data = data3_trans_sel, family = binomial())
model_lr2 set.seed(111)
<- gbm(left ~ ., data = data3_trans_sel, distribution = "bernoulli", n.trees = n.trees) model_xgb2
Moreover, we create a glm model based on original data in order to check if our methodology improves results.
<- glm(left ~ ., data = data1, family = binomial()) model_lr1
Final step is the comparison of all four models we have created. For each of them we make predictions on the relevant test set, i.e. we use model_lr1
and model_xgb1
to predict the output for data2
and model_lr2
and model_xgb2
to predict the output for data4
.
<- round(predict(model_lr1, data2, type = "response"))
pred_lr1 <- round(predict(model_xgb1, data2, n.trees = n.trees, type = "response"))
pred_xgb1 <- round(predict(model_lr2, data4_trans_sel, type = "response"))
pred_lr2 <- round(predict(model_xgb2, data4_trans_sel, n.trees = n.trees, type = "response")) pred_xgb2
The performance of the models may then be evaluated based on confusion matrices with relative percentages obtained via the confusion_matrix
function:
<- function(y_true, y_pred) {
confusion_matrix <- data.frame(pred_0 = c(sum(y_true==0 & y_pred==0)/sum(y_true==0),
cm sum(y_true==1 & y_pred==0)/sum(y_true==1)),
pred_1 = c(sum(y_true==0 & y_pred==1)/sum(y_true==0),
sum(y_true==1 & y_pred==1)/sum(y_true==1)))
<- apply(cm, MARGIN = 2, function(x) round(x, 2))
cm rownames(cm) <- c("actual_0", "actual_1")
cm }
GBM models give the following results:
predicted 0 | predicted 1 | |
---|---|---|
actual 0 | 0.97 | 0.03 |
actual 1 | 0.09 | 0.91 |
rSAFE
transformations applied:predicted 0 | predicted 1 | |
---|---|---|
actual 0 | 0.93 | 0.07 |
actual 1 | 0.27 | 0.73 |
The model trained on original data has higher predictive power - feature transformations have caused the loss of valuable information which was apparently used by the first model. However, within logistic regression models the significant improvement can be observed:
predicted 0 | predicted 1 | |
---|---|---|
actual 0 | 0.92 | 0.08 |
actual 1 | 0.63 | 0.37 |
rSAFE
transformations applied:predicted 0 | predicted 1 | |
---|---|---|
actual 0 | 0.93 | 0.07 |
actual 1 | 0.27 | 0.73 |
The initial logistic regression model has difficulties with “true negatives” - for employees that actually quit it very often predicts the opposite. On the other hand, model_lr2
which was trained on the set of features modified by the SAFE algorithm has much better accuracy.