EIX is the set of tools to explore the structure of XGBoost and lightGBM models. It includes functions finding strong interactions and also checking importance of single variables and interactions by usage different measures.
EIX consists several functions to visualize results.
EIX functions require only two parameters: a XGBoost or LightGBM model and data table used as training dataset. The exceptions are the
waterfall function and its plot. The first one requires parameters: a XGBoost model and observation, which prediction has to be explained). These two functions support only XGBoost models. All plots are created with package
ggplot2. Most of them use plot theme
This vignette shows usage of
EIX package. It lets to explain XGBoost prediction model concerning departures of employees from company using HR_data. Dataset was taken from kaggle and consists 14999 observations and 10 variables. The dataset is also available in package
EIX and there it is described more precisely.
#devtools :: install_github("ModelOriented/EIX") library("EIX") set.seed(4) knitr::kable(head(HR_data))
To create correct XGBoost model, remember to change categorical features to factors and next change the data frame to sparse matrix. The categorical features are one-hot encoded.
library("Matrix") sparse_matrix <- sparse.model.matrix(left ~ . - 1, data = HR_data) head(sparse_matrix)
## 6 x 19 sparse Matrix of class "dgCMatrix" ## ## 1 0.38 0.53 2 157 3 . . . . . . . . . 1 . . 1 . ## 2 0.80 0.86 5 262 6 . . . . . . . . . 1 . . . 1 ## 3 0.11 0.88 7 272 4 . . . . . . . . . 1 . . . 1 ## 4 0.72 0.87 5 223 5 . . . . . . . . . 1 . . 1 . ## 5 0.37 0.52 2 159 3 . . . . . . . . . 1 . . 1 . ## 6 0.41 0.50 2 153 3 . . . . . . . . . 1 . . 1 .
EIX uses table, which was generated by
xgboost::xgb.model.dt.tree with information about trees, their nodes and leaves.
library("xgboost") param <- list(objective = "binary:logistic", max_depth = 2) xgb_model <- xgboost(sparse_matrix, params = param, label = HR_data[, left] == 1, nrounds = 50, verbose = FALSE) knitr::kable(head(xgboost::xgb.model.dt.tree(colnames(sparse_matrix),xgb_model)))
xgboost::xgb.importance shows importance of single variables.
EIX adds new measures of variables’ importance and shows also importance of interactions.
lollipop plot is used to visualize the model in such way that the most important variables and interactions are visible.
On the x-axis, there are tree numbers and on the y-axis there is Gain measure for each node. One segment is one tree in the model and each point is one node. On the plot there are all nodes, which are not leaves. Shape of points signifies depth of node. All roots on the plot are connected by a red line. If in the same segment there is a variable with a higher depth above the variable with a lower depth, it means that interaction occurs.
There is opportunity to set a different way of labeling. On the plot we can see the most important variables in roots (horizontal labels), and interactions (vertical labels), this is option
labels = "topAll" which is default. Moreover, there are two additional options:
labels = "roots" - for variables in roots only,
labels = "interactions" for interactions only. The numbers of labels visible on the plot you can change by parametr
threshold (range from 0 to 1, default 0.1).
The plot is on a logarithmic scale because the initial trees usually are the most important. You can change the scale of the plot by setting the parameter
log_scale = FALSE.
#plot(lolli, threshold=0.05) #plot(lolli, labels="roots") #plot(lolli, labels="interactions") #plot(lolli, labels="roots", threshold=0.05) #plot(lolli, labels="interactions",threshold=0.05) #plot(lolli, log_scale = FALSE)
We can consider interactions in two ways. In first approach we can explore all pairs of variable, which occur in the model one above the other. This approach is not the best one, because we cannot distinguish if pair of variables are real interaction or not. In this approach high gain of pair can be a result of high gain of down variable (child). To explore pairs of variables you can generate table with them using function
interactions with parametr
option = "pairs". This table includes Gain measure and number of occurrences of pairs. You can also use the function
plot to visualize Gain measure.
pairs<-interactions(xgb_model, sparse_matrix, option = "pairs") head(pairs)
## Parent Child sumGain frequency ## 1: satisfaction_level number_project 3573.8695 6 ## 2: satisfaction_level time_spend_company 3421.1675 5 ## 3: satisfaction_level satisfaction_level 1078.1480 10 ## 4: last_evaluation average_montly_hours 843.8720 4 ## 5: last_evaluation satisfaction_level 826.7479 6 ## 6: last_evaluation time_spend_company 651.9038 4
interactions plot is a matrix plot with a child from the pair on the x-axis and the parent on the y-axis. The color of the square at the intersection of two variables means value of sumGain measure. The darker square, the higher sumGain of variable pairs. The range of sumGain measure is divided into four equal parts:
very low, low, medium, high.
In second approach, to find strong interactions, we can consider only these pairs of variables, where variable on the bottom (child) has higher gain than variable on the top (parent). We can also create ranking of interactions using function
importance with parameter
option = "interactions". More details in the next section.
interactions<-interactions(xgb_model, sparse_matrix, option = "interactions") head(interactions)
## Parent Child sumGain frequency ## 1: last_evaluation average_montly_hours 745.5943 2 ## 2: last_evaluation satisfaction_level 708.8723 4 ## 3: last_evaluation time_spend_company 634.9984 3 ## 4: satisfaction_level time_spend_company 559.9985 2 ## 5: last_evaluation number_project 390.1898 1 ## 6: average_montly_hours time_spend_company 318.0142 2
For exploring variables’ and interactions’ importance there are three functions in
plot with parameter
radar = TRUE or
radar = FALSE.
EIX package we can compare importance of single variables and interactions. The functions
importance can return three kinds of outputs, depending on the
option = "variables" - it consists only single variables
option = "interactions"- only interactions
option = "both"- output shows importance both single variables and interactions.
option = "both" is not direct connection
option = "variables" and
option = "interactions", because values of variable importance measure, which were in the interactions, are not included in importance of single variable.
EIX the following measures are available:
EIX package gives additionally measures of variables importance for single variable:
importance returns a table with all available importance measures for given option.
The table is sorted by descending value of sumGain.
plot with parameter
radar = FALSE and a result from the
importance function as an argument shows two measures of importance, which can be chosen by
ymeasure parameters. By parameter
top we can decide how many positions will be included in the plot.
importance<-importance(xgb_model, sparse_matrix, option = "both") head(importance)
## Feature sumGain sumCover meanGain meanCover ## 1: satisfaction_level 10040.0 43920 264.10 1156.0 ## 2: time_spend_company 4016.0 19820 267.70 1321.0 ## 3: number_project 3706.0 13940 264.70 995.6 ## 4: last_evaluation 1181.0 15340 90.81 1180.0 ## 5: average_montly_hours 886.0 18190 46.63 957.6 ## 6: last_evaluation:average_montly_hours 745.6 1767 372.80 883.7 ## frequency mean5Gain ## 1: 38 1513.0 ## 2: 15 670.4 ## 3: 14 697.4 ## 4: 13 183.0 ## 5: 19 97.5 ## 6: 2 372.8
#plot(importance, xmeasure = "mean5Gain", ymeasure = "sumGain", top = 15, radar=FALSE)
plot with parameter
radar = TRUE enables to compare different measures of variables and interactions importance on the radar plot from
Bellow I attach the example of radar plot. On the outside of the circle there are names of variables or interactions. Colored lines represent various measures of importance. The positions on the plot are sorted decreasing. The variable with the highest sumGain value is on the right of 12 o'clock. Next the sumGain value decreases in a clockwise direction. On the plot it is possible to change place, where the features names start by parameter
text_start_point (range from 0 to 1, default 0.5), and size of this text by parametrer
#plot(importance, text_start_point = 0.3) #plot(importance, text_size = 4) #plot(importance, top=15)
For single prediction explaining package
EIX uses two packages:
breakDown. The package
xgboostExplainer is a tool to interpreting prediction of xgboost model. The package
EIX uses its code and modifies it to include interactions. The methodology of plot creation comes from package
waterfall returns table with variables’ impact on the prediction of the model. Depending on the parameter
option, the table includes interactions (
option = "interactions"- default) or does not (
option = "variables"). The function
waterfall object as an argument visualizes this table. On the y-axis there are: intercept (it is the probability that random variable from training dataset will be 1), variables (which have an impact on prediction) and final prognosis of the model. On the x-axis there is log-odds of impact each variables.
data <- HR_data[9,] new_observation <- sparse_matrix[9,] wf<-waterfall(xgb_model, new_observation, data, option = "interactions") wf
## contribution ## xgboost: intercept -1.530 ## xgboost: time_spend_company = 5 1.519 ## xgboost: last_evaluation = 1 1.485 ## xgboost: Work_accident = 0 -0.736 ## xgboost: satisfaction_level:time_spend_company = 0.89:5 0.406 ## xgboost: last_evaluation:time_spend_company = 1:5 0.316 ## xgboost: number_project:last_evaluation = 5:1 0.258 ## xgboost: satisfaction_level = 0.89 -0.238 ## xgboost: last_evaluation:average_montly_hours = 1:224 0.227 ## xgboost: number_project = 5 -0.224 ## xgboost: salary = 2 0.166 ## xgboost: average_montly_hours:last_evaluation = 224:1 -0.156 ## xgboost: last_evaluation:satisfaction_level = 1:0.89 0.111 ## xgboost: average_montly_hours:time_spend_company = 224:5 0.098 ## xgboost: time_spend_company:last_evaluation = 5:1 0.095 ## xgboost: average_montly_hours:number_project = 224:5 0.094 ## xgboost: average_montly_hours = 224 -0.048 ## xgboost: satisfaction_level:number_project = 0.89:5 -0.003 ## xgboost: prediction 1.839
#wf<-waterfall(xgb_model, new_observation, data, option = "interactions", baseline = "intercept") #wf #plot(wf)