README.md

Autocompboost

Use case

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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Information about the stages:

library(ggplot2, quietly = TRUE)
#> 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()

Univariate model

Feature importance

## 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)

Partial effects

# Visualize top 4 vars:
fnms = vip$feature[seq_len(4)]
ggs = lapply(fnms, function(nm) plotPEUni(cboost$model$univariate, nm))

library(patchwork)

Reduce("+", ggs)

Pairwise interactions

Feature importance

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()

Prediction decomposition

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)