Practical Tuning Series - Build an Automated Machine Learning System

Implement a simple automated machine learning system.

Authors

Marc Becker

Theresa Ullmann

Michel Lang

Bernd Bischl

Jakob Richter

Martin Binder

Published

March 11, 2021

Scope

This is the third part of the practical tuning series. The other parts can be found here:

In this post, we implement a simple automated machine learning (AutoML) system which includes preprocessing, a switch between multiple learners and hyperparameter tuning. For this, we build a pipeline with the mlr3pipelines extension package. Additionally, we use nested resampling to get an unbiased performance estimate of our AutoML system.

Prerequisites

We load the mlr3verse package which pulls in the most important packages for this example.

library(mlr3verse)

We initialize the random number generator with a fixed seed for reproducibility, and decrease the verbosity of the logger to keep the output clearly represented. The lgr package is used for logging in all mlr3 packages. The mlr3 logger prints the logging messages from the base package, whereas the bbotk logger is responsible for logging messages from the optimization packages (e.g. mlr3tuning ).

set.seed(7832)
lgr::get_logger("mlr3")$set_threshold("warn")
lgr::get_logger("bbotk")$set_threshold("warn")

In this example, we use the Pima Indians Diabetes data set which is used to to predict whether or not a patient has diabetes. The patients are characterized by 8 numeric features and some have missing values.

task = tsk("pima")

Branching

We use three popular machine learning algorithms: k-nearest-neighbors, support vector machines and random forests.

learners = list(
  lrn("classif.kknn", id = "kknn"),
  lrn("classif.svm", id = "svm", type = "C-classification"),
  lrn("classif.ranger", id = "ranger")
)

The PipeOpBranch allows us to specify multiple alternatives paths. In this graph, the paths lead to the different learner models. The selection hyperparameter controls which path is executed i.e., which learner is used to fit a model. It is important to use the PipeOpBranch after the branching so that the outputs are merged into one result object. We visualize the graph with branching below.

graph =
  po("branch", options = c("kknn", "svm", "ranger")) %>>%
  gunion(lapply(learners, po)) %>>%
  po("unbranch")
graph$plot(html = FALSE)

Alternatively, we can use the ppl()-shortcut to load a predefined graph from the mlr_graphs dictionary. For this, the learner list must be named.

learners = list(
  kknn = lrn("classif.kknn", id = "kknn"),
  svm = lrn("classif.svm", id = "svm", type = "C-classification"),
  ranger = lrn("classif.ranger", id = "ranger")
)

graph = ppl("branch", lapply(learners, po))

Preprocessing

The task has missing data in five columns.

round(task$missings() / task$nrow, 2)
diabetes      age  glucose  insulin     mass pedigree pregnant pressure  triceps 
    0.00     0.00     0.01     0.49     0.01     0.00     0.00     0.05     0.30

The pipeline "robustify" function creates a preprocessing pipeline based on our task. The resulting pipeline imputes missing values with PipeOpImputeHist and creates a dummy column (PipeOpMissInd) which indicates the imputed missing values. Internally, this creates two paths and the results are combined with PipeOpFeatureUnion. In contrast to PipeOpBranch, both paths are executed. Additionally, "robustify" adds PipeOpEncode to encode factor columns and PipeOpRemoveConstants to remove features with a constant value.

graph = ppl("robustify", task = task, factors_to_numeric = TRUE) %>>%
  graph
plot(graph, html = FALSE)

We could also create the preprocessing pipeline manually.

gunion(list(po("imputehist"),
  po("missind", affect_columns = selector_type(c("numeric", "integer"))))) %>>%
  po("featureunion") %>>%
  po("encode") %>>%
  po("removeconstants")
Graph with 5 PipeOps:
              ID         State        sccssors          prdcssors
      imputehist <<UNTRAINED>>    featureunion                   
         missind <<UNTRAINED>>    featureunion                   
    featureunion <<UNTRAINED>>          encode imputehist,missind
          encode <<UNTRAINED>> removeconstants       featureunion
 removeconstants <<UNTRAINED>>                             encode

Graph Learner

We use as_learner() to create a GraphLearner which encapsulates the pipeline and can be used like a learner.

graph_learner = as_learner(graph)

The parameter set of the graph learner includes all hyperparameters from all contained learners. The hyperparameter ids are prefixed with the corresponding learner ids. The hyperparameter branch.selection controls which learner is used.

as.data.table(graph_learner$param_set)[, .(id, class, lower, upper, nlevels)]
                                              id    class lower upper nlevels
 1:           removeconstants_prerobustify.ratio ParamDbl     0     1     Inf
 2:         removeconstants_prerobustify.rel_tol ParamDbl     0   Inf     Inf
 3:         removeconstants_prerobustify.abs_tol ParamDbl     0   Inf     Inf
 4:       removeconstants_prerobustify.na_ignore ParamLgl    NA    NA       2
 5:  removeconstants_prerobustify.affect_columns ParamUty    NA    NA     Inf
 6:                    imputehist.affect_columns ParamUty    NA    NA     Inf
 7:                                missind.which ParamFct    NA    NA       2
 8:                                 missind.type ParamFct    NA    NA       4
 9:                       missind.affect_columns ParamUty    NA    NA     Inf
10:                  imputesample.affect_columns ParamUty    NA    NA     Inf
11:                                encode.method ParamFct    NA    NA       5
12:                        encode.affect_columns ParamUty    NA    NA     Inf
13:          removeconstants_postrobustify.ratio ParamDbl     0     1     Inf
14:        removeconstants_postrobustify.rel_tol ParamDbl     0   Inf     Inf
15:        removeconstants_postrobustify.abs_tol ParamDbl     0   Inf     Inf
16:      removeconstants_postrobustify.na_ignore ParamLgl    NA    NA       2
17: removeconstants_postrobustify.affect_columns ParamUty    NA    NA     Inf
18:                                       kknn.k ParamInt     1   Inf     Inf
19:                                kknn.distance ParamDbl     0   Inf     Inf
20:                                  kknn.kernel ParamFct    NA    NA      10
21:                                   kknn.scale ParamLgl    NA    NA       2
22:                                 kknn.ykernel ParamUty    NA    NA     Inf
23:                             kknn.store_model ParamLgl    NA    NA       2
24:                                svm.cachesize ParamDbl  -Inf   Inf     Inf
25:                            svm.class.weights ParamUty    NA    NA     Inf
26:                                    svm.coef0 ParamDbl  -Inf   Inf     Inf
27:                                     svm.cost ParamDbl     0   Inf     Inf
28:                                    svm.cross ParamInt     0   Inf     Inf
29:                          svm.decision.values ParamLgl    NA    NA       2
30:                                   svm.degree ParamInt     1   Inf     Inf
31:                                  svm.epsilon ParamDbl     0   Inf     Inf
32:                                   svm.fitted ParamLgl    NA    NA       2
33:                                    svm.gamma ParamDbl     0   Inf     Inf
34:                                   svm.kernel ParamFct    NA    NA       4
35:                                       svm.nu ParamDbl  -Inf   Inf     Inf
36:                                    svm.scale ParamUty    NA    NA     Inf
37:                                svm.shrinking ParamLgl    NA    NA       2
38:                                svm.tolerance ParamDbl     0   Inf     Inf
39:                                     svm.type ParamFct    NA    NA       2
40:                                 ranger.alpha ParamDbl  -Inf   Inf     Inf
41:                ranger.always.split.variables ParamUty    NA    NA     Inf
42:                         ranger.class.weights ParamUty    NA    NA     Inf
43:                               ranger.holdout ParamLgl    NA    NA       2
44:                            ranger.importance ParamFct    NA    NA       4
45:                            ranger.keep.inbag ParamLgl    NA    NA       2
46:                             ranger.max.depth ParamInt     0   Inf     Inf
47:                         ranger.min.node.size ParamInt     1   Inf     Inf
48:                              ranger.min.prop ParamDbl  -Inf   Inf     Inf
49:                               ranger.minprop ParamDbl  -Inf   Inf     Inf
50:                                  ranger.mtry ParamInt     1   Inf     Inf
51:                            ranger.mtry.ratio ParamDbl     0     1     Inf
52:                     ranger.num.random.splits ParamInt     1   Inf     Inf
53:                           ranger.num.threads ParamInt     1   Inf     Inf
54:                             ranger.num.trees ParamInt     1   Inf     Inf
55:                             ranger.oob.error ParamLgl    NA    NA       2
56:                 ranger.regularization.factor ParamUty    NA    NA     Inf
57:               ranger.regularization.usedepth ParamLgl    NA    NA       2
58:                               ranger.replace ParamLgl    NA    NA       2
59:             ranger.respect.unordered.factors ParamFct    NA    NA       3
60:                       ranger.sample.fraction ParamDbl     0     1     Inf
61:                           ranger.save.memory ParamLgl    NA    NA       2
62:          ranger.scale.permutation.importance ParamLgl    NA    NA       2
63:                             ranger.se.method ParamFct    NA    NA       2
64:                                  ranger.seed ParamInt  -Inf   Inf     Inf
65:                  ranger.split.select.weights ParamUty    NA    NA     Inf
66:                             ranger.splitrule ParamFct    NA    NA       3
67:                               ranger.verbose ParamLgl    NA    NA       2
68:                          ranger.write.forest ParamLgl    NA    NA       2
69:                             branch.selection ParamFct    NA    NA       3
                                              id    class lower upper nlevels

Tune the pipeline

We will only tune one hyperparameter for each learner in this example. Additionally, we tune the branching parameter which selects one of the three learners. We have to specify that a hyperparameter is only valid for a certain learner by using depends = branch.selection == <learner_id>.

# branch
graph_learner$param_set$values$branch.selection =
  to_tune(c("kknn", "svm", "ranger"))

# kknn
graph_learner$param_set$values$kknn.k =
  to_tune(p_int(3, 50, logscale = TRUE, depends = branch.selection == "kknn"))

# svm
graph_learner$param_set$values$svm.cost =
  to_tune(p_dbl(-1, 1, trafo = function(x) 10^x, depends = branch.selection == "svm"))

# ranger
graph_learner$param_set$values$ranger.mtry =
  to_tune(p_int(1, 8, depends = branch.selection == "ranger"))

# short learner id for printing
graph_learner$id = "graph_learner"

We define a tuning instance and select a random search which is stopped after 20 evaluated configurations.

instance = tune(
  tuner = tnr("random_search"),
  task = task,
  learner = graph_learner,
  resampling = rsmp("cv", folds = 3),
  measure = msr("classif.ce"),
  term_evals = 20
)

The following shows a quick way to visualize the tuning results.

autoplot(instance, type = "marginal",
  cols_x = c("x_domain_kknn.k", "x_domain_svm.cost", "ranger.mtry"))

Final Model

We add the optimized hyperparameters to the graph learner and train the learner on the full dataset.

learner = as_learner(graph)
learner$param_set$values = instance$result_learner_param_vals
learner$train(task)

The trained model can now be used to make predictions on new data. A common mistake is to report the performance estimated on the resampling sets on which the tuning was performed (instance$result_y) as the model’s performance. Instead we have to use nested resampling to get an unbiased performance estimate.

Nested Resampling

We use nested resampling to get an unbiased estimate of the predictive performance of our graph learner.

graph_learner = as_learner(graph)
graph_learner$param_set$values$branch.selection =
  to_tune(c("kknn", "svm", "ranger"))
graph_learner$param_set$values$kknn.k =
  to_tune(p_int(3, 50, logscale = TRUE, depends = branch.selection == "kknn"))
graph_learner$param_set$values$svm.cost =
  to_tune(p_dbl(-1, 1, trafo = function(x) 10^x, depends = branch.selection == "svm"))
graph_learner$param_set$values$ranger.mtry =
  to_tune(p_int(1, 8, depends = branch.selection == "ranger"))
graph_learner$id = "graph_learner"

inner_resampling = rsmp("cv", folds = 3)
at = AutoTuner$new(
  learner = graph_learner,
  resampling = inner_resampling,
  measure = msr("classif.ce"),
  terminator = trm("evals", n_evals = 10),
  tuner = tnr("random_search")
)

outer_resampling = rsmp("cv", folds = 3)
rr = resample(task, at, outer_resampling, store_models = TRUE)

We check the inner tuning results for stable hyperparameters. This means that the selected hyperparameters should not vary too much. We might observe unstable models in this example because the small data set and the low number of resampling iterations might introduce too much randomness. Usually, we aim for the selection of stable hyperparameters for all outer training sets.

extract_inner_tuning_results(rr)

Next, we want to compare the predictive performances estimated on the outer resampling to the inner resampling. Significantly lower predictive performances on the outer resampling indicate that the models with the optimized hyperparameters overfit the data.

rr$score()[, .(iteration, task_id, learner_id, resampling_id, classif.ce)]
   iteration task_id          learner_id resampling_id classif.ce
1:         1    pima graph_learner.tuned            cv  0.2265625
2:         2    pima graph_learner.tuned            cv  0.2382812
3:         3    pima graph_learner.tuned            cv  0.2656250

The aggregated performance of all outer resampling iterations is essentially the unbiased performance of the graph learner with optimal hyperparameter found by random search.

rr$aggregate()
classif.ce 
 0.2434896

Applying nested resampling can be shortened by using the tune_nested()-shortcut.

graph_learner = as_learner(graph)
graph_learner$param_set$values$branch.selection =
  to_tune(c("kknn", "svm", "ranger"))
graph_learner$param_set$values$kknn.k =
  to_tune(p_int(3, 50, logscale = TRUE, depends = branch.selection == "kknn"))
graph_learner$param_set$values$svm.cost =
  to_tune(p_dbl(-1, 1, trafo = function(x) 10^x, depends = branch.selection == "svm"))
graph_learner$param_set$values$ranger.mtry =
  to_tune(p_int(1, 8, depends = branch.selection == "ranger"))
graph_learner$id = "graph_learner"

rr = tune_nested(
  tuner = tnr("random_search"),
  task = task,
  learner = graph_learner,
  inner_resampling = rsmp("cv", folds = 3),
  outer_resampling = rsmp("cv", folds = 3),
  measure = msr("classif.ce"),
  term_evals = 10,
)

Resources

The mlr3book includes chapters on pipelines and hyperparameter tuning. The mlr3cheatsheets contain frequently used commands and workflows of mlr3.