devtools::load_all()
#> ℹ Loading autocompboost
# Use adult task from OpenML:
task = tsk("oml", task_id = 7592)
#> INFO [23:36:06.354] Retrieving JSON {url: `https://www.openml.org/api/v1/json/task/7592`, authenticated: `FALSE`}
#> INFO [23:36:06.669] Retrieving JSON {url: `https://www.openml.org/api/v1/json/data/1590`, authenticated: `FALSE`}
#> INFO [23:36:06.763] Retrieving ARFF {url: `https://api.openml.org/data/v1/download/1595261/adult.arff`, authenticated: `FALSE`}
# Remove rows with missings:
task$filter(which(complete.cases(task$data())))
# Train compboost learner:
set.seed(31415)
cboost = lrn("classif.compboost", predict_type = "prob", show_output = FALSE,
learning_rate = 0.1, add_deeper_interactions = TRUE, use_components = FALSE,
stop_epsylon_for_break = 0, stop_patience = 3L, df = 2,
ncores = 4L)
cboost$train(task)
#> Number of groups in 'sex' is smaller than the degrees of freedom 4. Setting 'df = 2' for feature 'sex'.
#> Start risk: 0.378 test: 0.3681
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#> Use suppressPackageStartupMessages() to eliminate package startup
#> messages
## How much risk was explained by which stage:
rstages = cboost$getRiskStages()
knitr::kable(rstages)| stage | value | explained | percentage | iterations |
|---|---|---|---|---|
| featureless | 0.5616 | 0.0000 | 0.0000 | 0 |
| univariate | 0.3290 | 0.2326 | 1.2668 | 9039 |
| pairwise-interactions | 0.3195 | 0.0095 | 0.0516 | 10000 |
| deep-interactions | 0.3780 | -0.0585 | -0.3184 | 500 |
#rstages = rstages[-1, ]
#rstages$stage = factor(rstages$stage, levels = rstages$stage)
#ggplot(rstages, aes(x = "", y = percentage, fill = stage)) +
# geom_bar(stat = "identity") +
# theme(legend.position = "bottom") +
# coord_flip() +
# scale_y_reverse() +
# xlab("") +
# ylab("") +
# ggtitle("Explained risk per stage") +
# labs(fill = "") +
# ggsci::scale_fill_uchicago()## Feature importance
cboost$model$univariate$calculateFeatureImportance()
#> baselearner risk_reduction
#> 8 marital.status_category 0.0883953
#> 5 education.num_spline 0.0517469
#> 2 capital.gain_spline 0.0322855
#> 10 occupation_category 0.0186904
#> 12 relationship_category 0.0176585
#> 7 hours.per.week_spline 0.0073175
#> 1 age_spline 0.0062982
#> 3 capital.loss_spline 0.0048907
#> 14 workclass_category 0.0021600
#> 13 sex_category 0.0012537
#> 9 native.country_category 0.0009638
#> 11 race_category 0.0003954
#> 4 education_category 0.0003378
#> 6 fnlwgt_spline 0.0002094
vip = cboost$model$univariate$calculateFeatureImportance(aggregate_bl_by_feat = TRUE)
plotFeatureImportance(cboost$model$univariate)
# Visualize top 4 vars:
fnms = vip$feature[seq_len(4)]
ggs = lapply(fnms, function(nm) plotPEUni(cboost$model$univariate, nm))
library(patchwork)
Reduce("+", ggs)
vip_int = cboost$model$interactions$calculateFeatureImportance()
knitr::kable(vip_int)| baselearner | risk_reduction | |
|---|---|---|
| 2 | age_capital.loss_tensor | 0.0037 |
| 1 | age_capital.gain_tensor | 0.0031 |
| 8 | capital.loss_hours.per.week_tensor | 0.0018 |
| 6 | capital.gain_occupation_tensor | 0.0006 |
| 4 | age_fnlwgt_tensor | 0.0000 |
| 5 | age_hours.per.week_tensor | 0.0000 |
| 3 | age_education.num_tensor | 0.0000 |
| 7 | capital.loss_fnlwgt_tensor | 0.0000 |
plotTensor(cboost$model$interactions, vip_int$baselearner[1]) + theme_minimal()
plotTensor(cboost$model$interactions, vip_int$baselearner[2]) + theme_minimal()
plotTensor(cboost$model$interactions, vip_int$baselearner[3]) + theme_minimal()
plotTensor(cboost$model$interactions, vip_int$baselearner[4]) + theme_minimal()
library(dplyr, quietly = TRUE)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:autocompboost':
#>
#> between, cross_join, first, last, matches
#> The following object is masked from 'package:testthat':
#>
#> matches
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
# "new observation"
newdata = task$data()[1234,]
newdata$class = NULL
knitr::kable(t(newdata))| age | 42 |
| capital.gain | 0 |
| capital.loss | 0 |
| education | Assoc-acdm |
| education.num | 12 |
| fnlwgt | 478373 |
| hours.per.week | 40 |
| marital.status | Divorced |
| native.country | United-States |
| occupation | Prof-specialty |
| race | White |
| relationship | Not-in-family |
| sex | Female |
| workclass | Private |
plotIndividualContributionAC(cboost, newdata)