Learning Tasks

Learning tasks encapsulate the data set and further relevant information about a machine learning problem, for example the name of the target variable for supervised problems.

Task types and creation

The tasks are organized in a hierarchy, with the generic Task at the top. The following tasks can be instantiated and all inherit from the virtual superclass Task:

To create a task, just call make<TaskType>, e.g., makeClassifTask. All tasks require an identifier (argument id) and a data.frame (argument data). If no ID is provided it is automatically generated using the variable name of the data. The ID will be later used to name results, for example of benchmark experiments, and to annotate plots. Depending on the nature of the learning problem, additional arguments may be required and are discussed in the following sections.

Regression

For supervised learning like regression (as well as classification and survival analysis) we, in addition to data, have to specify the name of the target variable.

data(BostonHousing, package = "mlbench")
regr.task = makeRegrTask(id = "bh", data = BostonHousing, target = "medv")
regr.task
#> Supervised task: bh
#> Type: regr
#> Target: medv
#> Observations: 506
#> Features:
#> numerics  factors  ordered 
#>       12        1        0 
#> Missings: FALSE
#> Has weights: FALSE
#> Has blocking: FALSE

As you can see, the Task records the type of the learning problem and basic information about the data set, e.g., the types of the features (numeric vectors, factors or ordered factors), the number of observations, or whether missing values are present.

Creating tasks for classification and survival analysis follows the same scheme, the data type of the target variables included in data is simply different. For each of these learning problems some specifics are described below.

Classification

For classification the target column has to be a factor.

In the following example we define a classification task for the BreastCancer data set and exclude the variable Id from all further model fitting and evaluation.

data(BreastCancer, package = "mlbench")
df = BreastCancer
df$Id = NULL
classif.task = makeClassifTask(id = "BreastCancer", data = df, target = "Class")
classif.task
#> Supervised task: BreastCancer
#> Type: classif
#> Target: Class
#> Observations: 699
#> Features:
#> numerics  factors  ordered 
#>        0        4        5 
#> Missings: TRUE
#> Has weights: FALSE
#> Has blocking: FALSE
#> Classes: 2
#>    benign malignant 
#>       458       241 
#> Positive class: benign

In binary classification the two classes are usually referred to as positive and negative class with the positive class being the category of greater interest. This is relevant for many performance measures like the true positive rate or ROC curves. Moreover, mlr, where possible, permits to set options (like the decision threshold or class weights) and returns and plots results (like class posterior probabilities) for the positive class only.

makeClassifTask by default selects the first factor level of the target variable as the positive class, in the above example benign. Class malignant can be manually selected as follows:

classif.task = makeClassifTask(id = "BreastCancer", data = df, target = "Class", positive = "malignant")

Survival analysis

Survival tasks use two target columns. For left and right censored problems these consist of the survival time and a binary event indicator. For interval censored data the two target columns must be specified in the "interval2" format (see Surv).

data(lung, package = "survival")
lung$status = (lung$status == 2) # convert to logical
surv.task = makeSurvTask(data = lung, target = c("time", "status"))
surv.task
#> Supervised task: lung
#> Type: surv
#> Target: time,status
#> Events: 165
#> Observations: 228
#> Features:
#> numerics  factors  ordered 
#>        8        0        0 
#> Missings: TRUE
#> Has weights: FALSE
#> Has blocking: FALSE

The type of censoring can be specified via the argument censoring, which defaults to "rcens" for right censored data.

Multilabel classification

In multilabel classification each object can belong to more than one category at the same time.

The data are expected to contain as many target columns as there are class labels. The target columns should be logical vectors that indicate which class labels are present. The names of the target columns are taken as class labels and need to be passed to the target argument of makeMultilabelTask.

In the following example we get the data of the yeast data set, extract the label names, and pass them to the target argument in makeMultilabelTask.

yeast = getTaskData(yeast.task)

labels = colnames(yeast)[1:14]
yeast.task = makeMultilabelTask(id = "multi", data = yeast, target = labels)
yeast.task
#> Supervised task: multi
#> Type: multilabel
#> Target: label1,label2,label3,label4,label5,label6,label7,label8,label9,label10,label11,label12,label13,label14
#> Observations: 2417
#> Features:
#> numerics  factors  ordered 
#>      103        0        0 
#> Missings: FALSE
#> Has weights: FALSE
#> Has blocking: FALSE
#> Classes: 14
#>  label1  label2  label3  label4  label5  label6  label7  label8  label9 
#>     762    1038     983     862     722     597     428     480     178 
#> label10 label11 label12 label13 label14 
#>     253     289    1816    1799      34

See also the tutorial page on multilabel classification.

Cluster analysis

As cluster analysis is unsupervised, the only mandatory argument to construct a cluster analysis task is the data. Below we create a learning task from the data set mtcars.

data(mtcars, package = "datasets")
cluster.task = makeClusterTask(data = mtcars)
cluster.task
#> Unsupervised task: mtcars
#> Type: cluster
#> Observations: 32
#> Features:
#> numerics  factors  ordered 
#>       11        0        0 
#> Missings: FALSE
#> Has weights: FALSE
#> Has blocking: FALSE

Cost-sensitive classification

The standard objective in classification is to obtain a high prediction accuracy, i.e., to minimize the number of errors. All types of misclassification errors are thereby deemed equally severe. However, in many applications different kinds of errors cause different costs.

In case of class-dependent costs, that solely depend on the actual and predicted class labels, it is sufficient to create an ordinary ClassifTask.

In order to handle example-specific costs it is necessary to generate a CostSensTask. In this scenario, each example is associated with an individual cost vector of length with denoting the number of classes. The -th component indicates the cost of assigning to class . Naturally, it is assumed that the cost of the intended class label is minimal.

As the cost vector contains all relevant information about the intended class , only the feature values and a cost matrix, which contains the cost vectors for all examples in the data set, are required to create the CostSensTask.

In the following example we use the iris data and an artificial cost matrix (which is generated as proposed by Beygelzimer et al., 2005):

df = iris
cost = matrix(runif(150 * 3, 0, 2000), 150) * (1 - diag(3))[df$Species,]
df$Species = NULL

costsens.task = makeCostSensTask(data = df, cost = cost)
costsens.task
#> Supervised task: df
#> Type: costsens
#> Observations: 150
#> Features:
#> numerics  factors  ordered 
#>        4        0        0 
#> Missings: FALSE
#> Has blocking: FALSE
#> Classes: 3
#> y1, y2, y3

For more details see the page about cost-sensitive classification.

Further settings

The Task help page also lists several other arguments to describe further details of the learning problem.

For example, we could include a blocking factor in the task. This would indicate that some observations "belong together" and should not be separated when splitting the data into training and test sets for resampling.

Another option is to assign weights to observations. These can simply indicate observation frequencies or result from the sampling scheme used to collect the data. Note that you should use this option only if the weights really belong to the task. If you plan to train some learning algorithms with different weights on the same Task, mlr offers several other ways to set observation or class weights (for supervised classification). See for example the tutorial page about training or function makeWeightedClassesWrapper.

Accessing a learning task

We provide many operators to access the elements stored in a Task. The most important ones are listed in the documentation of Task and getTaskData.

To access the task description that contains basic information about the task you can use:

getTaskDesc(classif.task)
#> $id
#> [1] "BreastCancer"
#> 
#> $type
#> [1] "classif"
#> 
#> $target
#> [1] "Class"
#> 
#> $size
#> [1] 699
#> 
#> $n.feat
#> numerics  factors  ordered 
#>        0        4        5 
#> 
#> $has.missings
#> [1] TRUE
#> 
#> $has.weights
#> [1] FALSE
#> 
#> $has.blocking
#> [1] FALSE
#> 
#> $class.levels
#> [1] "benign"    "malignant"
#> 
#> $positive
#> [1] "malignant"
#> 
#> $negative
#> [1] "benign"
#> 
#> attr(,"class")
#> [1] "ClassifTaskDesc"    "SupervisedTaskDesc" "TaskDesc"

Note that task descriptions have slightly different elements for different types of Tasks. Frequently required elements can also be accessed directly.

## Get the ID
getTaskId(classif.task)
#> [1] "BreastCancer"

## Get the type of task
getTaskType(classif.task)
#> [1] "classif"

## Get the names of the target columns
getTaskTargetNames(classif.task)
#> [1] "Class"

## Get the number of observations
getTaskSize(classif.task)
#> [1] 699

## Get the number of input variables
getTaskNFeats(classif.task)
#> [1] 9

## Get the class levels in classif.task
getTaskClassLevels(classif.task)
#> [1] "benign"    "malignant"

Moreover, mlr provides several functions to extract data from a Task.

## Accessing the data set in classif.task
str(getTaskData(classif.task))
#> 'data.frame':    699 obs. of  10 variables:
#>  $ Cl.thickness   : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 5 5 3 6 4 8 1 2 2 4 ...
#>  $ Cell.size      : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 1 4 1 8 1 10 1 1 1 2 ...
#>  $ Cell.shape     : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 1 4 1 8 1 10 1 2 1 1 ...
#>  $ Marg.adhesion  : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 1 5 1 1 3 8 1 1 1 1 ...
#>  $ Epith.c.size   : Ord.factor w/ 10 levels "1"<"2"<"3"<"4"<..: 2 7 2 3 2 7 2 2 2 2 ...
#>  $ Bare.nuclei    : Factor w/ 10 levels "1","2","3","4",..: 1 10 2 4 1 10 10 1 1 1 ...
#>  $ Bl.cromatin    : Factor w/ 10 levels "1","2","3","4",..: 3 3 3 3 3 9 3 3 1 2 ...
#>  $ Normal.nucleoli: Factor w/ 10 levels "1","2","3","4",..: 1 2 1 7 1 7 1 1 1 1 ...
#>  $ Mitoses        : Factor w/ 9 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 5 1 ...
#>  $ Class          : Factor w/ 2 levels "benign","malignant": 1 1 1 1 1 2 1 1 1 1 ...

## Get the names of the input variables in cluster.task
getTaskFeatureNames(cluster.task)
#>  [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear"
#> [11] "carb"

## Get the values of the target variables in surv.task
head(getTaskTargets(surv.task))
#>   time status
#> 1  306   TRUE
#> 2  455   TRUE
#> 3 1010  FALSE
#> 4  210   TRUE
#> 5  883   TRUE
#> 6 1022  FALSE

## Get the cost matrix in costsens.task
head(getTaskCosts(costsens.task))
#>      y1        y2         y3
#> [1,]  0 1589.5664  674.44434
#> [2,]  0 1173.4364  828.40682
#> [3,]  0  942.7611 1095.33713
#> [4,]  0 1049.5562  477.82496
#> [5,]  0 1121.8899   90.85237
#> [6,]  0 1819.9830  841.06686

Note that getTaskData offers many options for converting the data set into a convenient format. This especially comes in handy when you integrate a new learner from another R package into mlr. In this regard function getTaskFormula is also useful.

Modifying a learning task

mlr provides several functions to alter an existing Task, which is often more convenient than creating a new Task from scratch. Here are some examples.

## Select observations and/or features
cluster.task = subsetTask(cluster.task, subset = 4:17)

## It may happen, especially after selecting observations, that features are constant.
## These should be removed.
removeConstantFeatures(cluster.task)
#> Removing 1 columns: am
#> Unsupervised task: mtcars
#> Type: cluster
#> Observations: 14
#> Features:
#> numerics  factors  ordered 
#>       10        0        0 
#> Missings: FALSE
#> Has weights: FALSE
#> Has blocking: FALSE

## Remove selected features
dropFeatures(surv.task, c("meal.cal", "wt.loss"))
#> Supervised task: lung
#> Type: surv
#> Target: time,status
#> Events: 165
#> Observations: 228
#> Features:
#> numerics  factors  ordered 
#>        6        0        0 
#> Missings: TRUE
#> Has weights: FALSE
#> Has blocking: FALSE

## Standardize numerical features
task = normalizeFeatures(cluster.task, method = "range")
summary(getTaskData(task))
#>       mpg              cyl              disp              hp        
#>  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
#>  1st Qu.:0.3161   1st Qu.:0.5000   1st Qu.:0.1242   1st Qu.:0.2801  
#>  Median :0.5107   Median :1.0000   Median :0.4076   Median :0.6311  
#>  Mean   :0.4872   Mean   :0.7143   Mean   :0.4430   Mean   :0.5308  
#>  3rd Qu.:0.6196   3rd Qu.:1.0000   3rd Qu.:0.6618   3rd Qu.:0.7473  
#>  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
#>       drat              wt              qsec              vs        
#>  Min.   :0.0000   Min.   :0.0000   Min.   :0.0000   Min.   :0.0000  
#>  1st Qu.:0.2672   1st Qu.:0.1275   1st Qu.:0.2302   1st Qu.:0.0000  
#>  Median :0.3060   Median :0.1605   Median :0.3045   Median :0.0000  
#>  Mean   :0.4544   Mean   :0.3268   Mean   :0.3752   Mean   :0.4286  
#>  3rd Qu.:0.7026   3rd Qu.:0.3727   3rd Qu.:0.4908   3rd Qu.:1.0000  
#>  Max.   :1.0000   Max.   :1.0000   Max.   :1.0000   Max.   :1.0000  
#>        am           gear             carb       
#>  Min.   :0.5   Min.   :0.0000   Min.   :0.0000  
#>  1st Qu.:0.5   1st Qu.:0.0000   1st Qu.:0.3333  
#>  Median :0.5   Median :0.0000   Median :0.6667  
#>  Mean   :0.5   Mean   :0.2857   Mean   :0.6429  
#>  3rd Qu.:0.5   3rd Qu.:0.7500   3rd Qu.:1.0000  
#>  Max.   :0.5   Max.   :1.0000   Max.   :1.0000

For more functions and more detailed explanations have a look at the data preprocessing page.

Example tasks and convenience functions

For your convenience mlr provides pre-defined Tasks for each type of learning problem. These are also used throughout this tutorial in order to get shorter and more readable code. A list of all Tasks can be found in the Appendix.

Moreover, mlr's function convertMLBenchObjToTask can generate Tasks from the data sets and data generating functions in package mlbench.