eXtreme RuleFit

Travis build status




RuleFit (described in Friedman & Popescu) is a clever model combining tree ensembles and linear models. The goal is to produce a model with comparable performance to a tree ensemble, with the interpretability of a linear model.

The general algorithm follows:

Specifics to xrf

Comparison to existing packages

pre is a package on CRAN for fitting prediction rule ensembles, and rulefit is another alternative on hosted on github. xrf improves on some aspects of these by: * Usually building more accurate models at fixed number of parameters * Usually building models faster * Building models that predict from missing data and new factor-levels * Providing a more concise and limited interface * Tested & actively maintained, fewer bugs

On the last point, as of April 2019, both packages fail to even build a model on the census income example below due to bugs.


Here we predict whether an individual’s income is greater than $50,000 using census data.


# grabbing data from uci
census_income_text <- getURL('https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data')
census_income <- read.csv(textConnection(census_income_text), header=F, stringsAsFactors = F)
colnames(census_income) <- c('age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status',
                            'occupation', 'relationship', 'race', 'sex', 'capital_gain', 'capital_loss',
                            'hours_per_week', 'native_country', 'above_50k')
m_xrf <- xrf(above_50k ~ ., census_income, family = 'binomial', 
             xgb_control = list(nrounds = 100, max_depth = 3))


Here we employ out-of-the-box RuleFit from pre & xrf, as well as raw xgboost & glmnet models.

library(rulefit) # installed via devtools::install_git("https://github.com/Zelazny7/rulefit")

auc <- function (prediction, actual) {
  stopifnot(length(unique(actual)) == 2)
  stopifnot(length(prediction) == length(actual))
  mann_whit <- wilcox.test(prediction ~ actual)$statistic
  unname(1 - mann_whit/(sum(actual) * as.double(sum(!actual))))

census_income <- census_income %>%
  # pre is picky about data types 
  mutate_if(is.character, as.factor) %>%
    above_50k = as.character(above_50k) == ' >50K'

train_ix <- sample(nrow(census_income), floor(nrow(census_income) * .66))
census_train <- census_income[train_ix, ]
census_test <- census_income[-train_ix, ]
census_mat <- model.matrix(above_50k ~ ., census_income)
census_train_mat <- census_mat[train_ix, ]
census_test_mat <- census_mat[-train_ix, ]

# note, as of 2019-04-17, the pre example fails to work (with an error for a new level in model.frame). as such, the below comparison is not one to one
system.time(m_pre <- pre(above_50k ~ ., na.omit(census_train), 
                         family = 'binomial', ntrees = 100, maxdepth = 3, tree.unbiased = TRUE))
# note, as of 2019-04-25, this example fails by attempting to access names() of a sparse matrix (seems it should be using colnames())
  m_gbm <- gbm.fit(census_train_mat, census_train$above_50k, distribution="bernoulli", interaction.depth=3, shrinkage=0.1, verbose = FALSE)
  rf_plan <- rulefit(m_gbm, n.trees=10)
  m_rf <- train(rf_plan, census_train_mat, y = census_train$above_50k, family="binomial")
system.time(m_xrf <- xrf(above_50k ~ ., census_train, family = 'binomial', 
             xgb_control = list(nrounds = 100, max_depth = 3)))
m_xgb <- xgboost(census_train_mat, census_train$above_50k, max_depth = 3, nrounds = 100, objective = 'binary:logistic')
m_glm <- cv.glmnet(census_train_mat, census_train$above_50k, alpha = 1)

auc(predict(m_pre, census_test), census_test$above_50k)
auc(predict(m_xrf, census_test), census_test$above_50k)
auc(predict(m_glm, newx = census_test_mat, s = 'lambda.min'), census_test$above_50k)
auc(predict(m_xgb, newdata = census_test_mat, s = 'lambda.min'), census_test$above_50k)

With results (on a 2018 Macbook Pro, 16Gb Memory, 6 core i7, mojave):

Model Time (s)
xrf 73
pre 211*

On the test set:

Model AUC
xrf .924
pre .906*
xgboost .926
glmnet .892

De-overlapping rules

Overlapped rules occur when two or more rules belonging to the same subspace are not in mutual exclusion; de-overlapping guarantees that all rules belonging to the same subspace are in mutual exclusion. For example, the rules:

belong to the same subspace but are not in mutual exclusion (e.g. at age=45 & income=45,000). They can be de-overlapped to, for example:

which is one of infinite possible de-overlappings; this de-overlapping is ideal because * it is small * it allows all “effects” from the original 2 rules to be exactly captured (i.e. none of the boundaries are broken)

The following example is somewhat contrived (in that it only uses one feature), but demonstrates how de-overlapping can prove useful in interpreting your model. To de-overlap the derived ruleset, simply specify deoverlap=TRUE:

m_xrf_overlap <- xrf(above_50k ~ capital_gain, census_income, family = 'binomial', 
                     xgb_control = list(nrounds = 100, max_depth = 1), deoverlap = FALSE)
m_xrf_deoverlap <- xrf(above_50k ~ capital_gain, census_income, family = 'binomial', 
                       xgb_control = list(nrounds = 100, max_depth = 1), deoverlap = TRUE)
coef_overlap <- coef(m_xrf_overlap, lambda = 'lambda.1se')
coef_deoverlap <- coef(m_xrf_deoverlap, lambda = 'lambda.1se')

Looking at the overlapped model:

coef_overlap %>%
 filter(coefficient_lambda.1se != 0) %>%
   coefficient_lambda.1se        term               rule
1            3.757519e+00 (Intercept)               <NA>
2           -1.811956e+00        r0_1  capital_gain<5119
3            3.465927e-09        r0_2 capital_gain>=5119
4           -3.586284e+00        r1_1  capital_gain<7074
5            4.433387e-09        r1_2 capital_gain>=7074
6           -2.026456e+00       r10_1  capital_gain<4244
7            1.402758e-09       r10_2 capital_gain>=4244
8           -1.842004e+00       r11_1  capital_gain<3048
9            1.423088e-10       r11_2 capital_gain>=3048
10           2.758517e+00       r14_1  capital_gain<3120
11          -1.295516e-10       r14_2 capital_gain>=3120
12           8.893485e-01       r50_1  capital_gain<5316
13          -1.447534e-10       r50_2 capital_gain>=5316
14           5.068947e-01       r59_1  capital_gain<4401
15          -4.496979e-11       r59_2 capital_gain>=4401
16          -8.325515e-03       r86_1  capital_gain<7566
17           1.895608e-11       r86_2 capital_gain>=7566

Notice that the rules are not in exclusion. To understand the impact of capital gain on income, we have to add up no less than 7 coefficients for any value of capital gain. Matters are made more confusing by effectively 0 coefficients on many of the ‘>=’ rules, likely a numerical issue in the LASSO as a result of the substantial collinearity.

Now for the de-overlapped model:

coef_deoverlap %>%
 filter(coefficient_lambda.1se != 0) %>%
  coefficient_lambda.1se              term                                   rule
1              -1.007898       (Intercept)                                   <NA>
2              -0.345907 X1_capital_gain_1                      capital_gain<3048
3               2.366051 X1_capital_gain_2 capital_gain>=3048 & capital_gain<3120
4              -1.520116 X1_capital_gain_3 capital_gain>=3120 & capital_gain<4244
5               1.728206 X1_capital_gain_4 capital_gain>=4244 & capital_gain<4401
6               2.888030 X1_capital_gain_6 capital_gain>=5119 & capital_gain<5316
7               3.206441 X1_capital_gain_8 capital_gain>=7074 & capital_gain<7566
8               3.927737 X1_capital_gain_9                     capital_gain>=7566

How slick is that! We have:

Effects are immediately available by doing a lookup in the exclusive rules. This is a great win for interpretability.

As mentioned above, this example is contrived in that it uses depth=1 trees (i.e. conjunctions of size 1). As depth increases, interpretability can suffer regardless de-overlapping if the final ruleset is non-sparse. However, for certain problems, particularly small depth or sparse effects, de-overlapping can be a boon for interpretability.