utiml: Utilities for multi-label learning

1. Introduction

The general prupose of utiml is be an alternative to processing multi-label in R. The main methods available on this package are organized in the groups:

• Classification methods
• Evaluation methods
• Pre-process utilities
• Sampling methods
• Threshold methods

The utiml package needs of the mldr package to handle multi-label datasets. It will be installed together with the utiml¹.

The installation process is similar to other packages available on CRAN:

install.packages("utiml")

After installed, you can now load the utiml package (The mldr package will be also loaded):

library("utiml")

## Loading required package: mldr

## Loading required package: parallel

## Loading required package: ROCR

The utiml brings two multi-label datasets. A synthetic toy dataset called toyml and a real world dataset called foodtruck. To understand how to load your own dataset, we suggest the read of mldr documentation. The toyml contains 100 instances, 10 features and 5 labels, its prupose is to be used for small tests and examples.

head(toyml)

##        att1      att2      att3      att4     att5      att6      att7
## 1 -0.150258  0.000461  0.237302  0.004333 0.086273  0.611953 -0.040632
## 2  0.219093  0.023877  0.038309 -0.041287 0.013978  0.277978  0.147673
## 3  0.137491  0.042125  0.011613  0.066545 0.388947 -0.312591 -0.163133
## 4 -0.318716 -0.054081  0.005198  0.085436 0.660657  0.011783  0.096005
## 5  0.004815  0.659007  0.023343 -0.135839 0.063470 -0.207688  0.091519
## 6  0.336280 -0.140629 -0.032099 -0.365930 0.004982  0.124665 -0.133950
##       iatt8     iatt9    ratt10 y1 y2 y3 y4 y5
## 1 -0.215861  0.447483  0.611953  1  1  0  1  0
## 2 -0.592199 -0.164926  0.277978  1  1  0  1  0
## 3 -0.426994 -0.564884 -0.312591  1  1  0  1  0
## 4 -0.526278  0.505936  0.011783  1  1  0  0  1
## 5  0.170262  0.389038 -0.207688  1  1  0  0  0
## 6  0.652938  0.961077  0.124665  1  1  0  0  0

The foodtruck contains different types of cousines to be predicted from user preferences and habits. The dataset has 12 labels:

foodtruck$labels

##                 index count       freq     IRLbl   SCUMBLE SCUMBLE.CV
## street_food        22   295 0.72481572  1.000000 0.1249889  1.0276904
## gourmet            23   120 0.29484029  2.458333 0.1396873  0.7994104
## italian_food       24    43 0.10565111  6.860465 0.2059097  0.4101859
## brazilian_food     25    72 0.17690418  4.097222 0.1463292  0.7305315
## mexican_food       26    41 0.10073710  7.195122 0.2491880  0.2759161
## chinese_food       27    16 0.03931204 18.437500 0.2831969  0.3981316
## japanese_food      28    36 0.08845209  8.194444 0.2113363  0.5936371
## arabic_food        29    25 0.06142506 11.800000 0.2840999  0.3441985
## snacks             30    67 0.16461916  4.402985 0.1526898  0.5780729
## healthy_food       31    33 0.08108108  8.939394 0.2138170  0.5414302
## fitness_food       32    30 0.07371007  9.833333 0.2268195  0.5120827
## sweets_desserts    33   154 0.37837838  1.915584 0.1730439  0.5959228

In the following section, an overview of how to conduct a multi-label experiment are explained. Next, we explores each group of methods and its particularity.

2. Overview

After load the multi-label dataset some data processing may be necessary. The pre-processing methods are utilities that manipulate the mldr datasets. Suppose that we want to normalize the attributes values (between 0 and 1), we can do:

mytoy <- normalize_mldata(toyml)

Next, we want to stratification the dataset in two partitions (train and test), containing 65% and 35% of instances respectively, then we can do:

ds <- create_holdout_partition(mytoy, c(train=0.65, test=0.35), "iterative")
names(ds)

## [1] "train" "test"

Now, the ds object has two elements ds$train and ds$test, where the first will be used to create a model and the second to test the model. For example, using the Binary Relevance multi-label method with the base algorithm Random Forest², we can do:

brmodel <- br(ds$train, "RF", seed=123)
prediction <- predict(brmodel, ds$test)

The prediction is an object of class mlresult that contains the probability (also called confidence or score) and the bipartitions values:

head(as.bipartition(prediction))

##    y1 y2 y3 y4 y5
## 8   0  1  0  1  0
## 10  1  1  0  1  0
## 12  0  1  0  1  0
## 14  0  1  0  1  0
## 18  0  1  0  1  0
## 19  0  1  0  0  0

head(as.probability(prediction))

##       y1    y2    y3    y4    y5
## 8  0.196 0.948 0.046 0.904 0.216
## 10 0.588 0.956 0.006 0.610 0.104
## 12 0.188 0.942 0.090 0.698 0.248
## 14 0.384 0.850 0.040 0.798 0.280
## 18 0.292 0.838 0.416 0.640 0.296
## 19 0.088 0.844 0.316 0.472 0.086

head(as.ranking(prediction))

##    y1 y2 y3 y4 y5
## 8   4  1  5  2  3
## 10  3  1  5  2  4
## 12  4  1  5  2  3
## 14  3  1  5  2  4
## 18  5  1  3  2  4
## 19  4  1  3  2  5

A threshold strategy can be applied:

newpred <- rcut_threshold(prediction, 2)
head(newpred)

##    y1 y2 y3 y4 y5
## 8   0  1  0  1  0
## 10  0  1  0  1  0
## 12  0  1  0  1  0
## 14  0  1  0  1  0
## 18  0  1  0  1  0
## 19  0  1  0  1  0

Now we can evaluate the models and compare if the use of the MCUT threshold improved the results:

result <- multilabel_evaluate(ds$tes, prediction, "bipartition")
thresres <- multilabel_evaluate(ds$tes, newpred, "bipartition")

round(cbind(Default=result, RCUT=thresres), 3)

##                 Default  RCUT
## F1                0.681 0.718
## accuracy          0.557 0.581
## hamming-loss      0.223 0.211
## macro-AUC         0.565 0.565
## macro-F1          0.480 0.434
## macro-precision   0.587 0.490
## macro-recall      0.463 0.448
## micro-AUC         0.814 0.814
## micro-F1          0.711 0.730
## micro-precision   0.706 0.714
## micro-recall      0.716 0.746
## precision         0.710 0.714
## recall            0.752 0.810
## subset-accuracy   0.143 0.114

Details of the labels evaluation can be obtained using:

result <- multilabel_evaluate(ds$tes, prediction, "bipartition", labels=TRUE)
result$labels

##          AUC        F1  accuracy    balacc precision    recall TP TN FP FN
## y1 0.7133333 0.2857143 0.8571429 0.5833333 0.5000000 0.2000000  1 29  1  4
## y2 0.6620370 0.8852459 0.8000000 0.5625000 0.7941176 1.0000000 27  1  7  0
## y3 0.6494253 0.2500000 0.8285714 0.5660920 0.5000000 0.1666667  1 28  1  5
## y4 0.2859848 0.6923077 0.5428571 0.4204545 0.6428571 0.7500000 18  1 10  6
## y5 0.5166667 0.2857143 0.8571429 0.5833333 0.5000000 0.2000000  1 29  1  4

3. Pre-processing

The pre-processing methods were developed to facilitate some operations with the multi-label data. Each pre-processing method receives a mldr dataset and returns other mldr dataset. You can use them as needed.

Here, an overview of the pre-processing methods:

# Fill sparse data
mdata <- fill_sparse_mldata(toyml)

# Remove unique attributes
mdata <- remove_unique_attributes(toyml)

# Remove the attributes "iatt8", "iatt9" and "ratt10"
mdata <- remove_attributes(toyml, c("iatt8", "iatt9", "ratt10"))

# Remove labels with less than 10 positive or negative examples
mdata <- remove_skewness_labels(toyml, 10)

# Remove the labels "y2" and "y3"
mdata <- remove_labels(toyml, c("y2", "y3"))

# Remove the examples without any labels
mdata <- remove_unlabeled_instances(toyml)

# Replace nominal attributes
mdata <- replace_nominal_attributes(toyml)

# Normalize the predictive attributes between 0 and 1
mdata <- normalize_mldata(mdata)

4. Sampling

# Create a subset of toyml dataset with the even instances and the first five attributes
mdata <- create_subset(toyml, seq(1, 100, 2), 1:5)

# Create a subset of toyml dataset with the ten first instances and all attributes
mdata <- create_subset(toyml, 1:10)

# Create a random subset of toyml dataset with 30 instances and 6 attributes
mdata <- create_random_subset(toyml, 30, 6)

# Create a random subset of toyml dataset with 7 instances and all attributes
mdata <- create_random_subset(toyml, 7)

# Create two equal partitions using the 'iterative' method
toy <- create_holdout_partition(toyml, c(train=0.5, test=0.5), "iterative")
## toy$train and toy$test is a mldr object

# Create three partitions using the 'random' method
toy <- create_holdout_partition(toyml, c(a=0.4, b=0.3, c=0.3))
## Use toy$a, toy$b and toy$c

# Create two partitions using the 'stratified' method
toy <- create_holdout_partition(toyml, c(0.6, 0.4), "stratified")
## Use toy[[1]] and toy[[2]] 

results <- cv(foodtruck, br, base.algorith="SVM", cv.folds=5, 
              cv.sampling="stratified", cv.measures="example-based", 
              cv.seed=123)

round(results, 4)

##              F1        accuracy    hamming-loss       precision          recall 
##          0.5191          0.4408          0.1519          0.6982          0.4810 
## subset-accuracy 
##          0.2580

results <- cv(toyml, "rakel", base.algorith="RF", cv.folds=10, cv.results=TRUE,
              cv.sampling="random", cv.measures="example-based")

#Multi-label results
round(results$multilabel, 4)

##           F1 accuracy hamming-loss precision recall subset-accuracy
##  [1,] 0.6500   0.5500         0.22    0.7500 0.6667             0.3
##  [2,] 0.6633   0.5583         0.26    0.7000 0.6833             0.2
##  [3,] 0.6233   0.5000         0.28    0.6833 0.6833             0.1
##  [4,] 0.7800   0.6833         0.16    0.8000 0.8167             0.4
##  [5,] 0.6467   0.5083         0.30    0.6500 0.7833             0.1
##  [6,] 0.6433   0.5167         0.24    0.6500 0.7333             0.1
##  [7,] 0.6400   0.5167         0.26    0.7000 0.6500             0.1
##  [8,] 0.7200   0.6083         0.24    0.7833 0.7500             0.3
##  [9,] 0.5233   0.4000         0.32    0.6667 0.5000             0.0
## [10,] 0.8067   0.7000         0.14    0.8500 0.8500             0.3

#Labels results
round(sapply(results$labels, colMeans), 4)

##            y1   y2   y3     y4   y5
## accuracy 0.82 0.76 0.78 0.6000 0.83
## balacc    NaN  NaN  NaN 0.4355 0.50
## TP       0.10 7.30 0.10 6.0000 0.00
## TN       8.10 0.30 7.70 0.0000 8.30
## FP       0.20 1.90 0.40 3.1000 0.00
## FN       1.60 0.50 1.80 0.9000 1.70

# Create 3-fold object
kfcv <- create_kfold_partition(toyml, k=3, "iterative")
result <- lapply(1:3, function (k) {
  toy <- partition_fold(kfcv, k)
  model <- br(toy$train, "RF")
  predict(model, toy$test)
})

# Create 5-fold object and use a validation set
kfcv <- create_kfold_partition(toyml, 5, "stratified")
result <- lapply(1:5, function (k) {
  toy <- partition_fold(kfcv, k, has.validation=TRUE)
  model <- br(toy$train, "RF")
  
  list(
    validation = predict(model, toy$validation),
    test = predict(model, toy$test)
  )
})

5. Classification Methods

The multi-label classification is a supervised learning task that seeks to learn and predict one or more labels together. This task can be grouped in: problem transformation and algorithm adaptation. Next, we provide more details about the methods and their specifities.

#Classifier chain with a specific chain
ccmodel <- cc(toyml, "RF", chain = c("y5", "y4", "y3", "y2", "y1"))

# Ensemble with 5 models using 60% of sampling and 75% of attributes
ebrmodel <- ebr(toyml, "C5.0", m = 5, subsample=0.6, attr = 0.75)

# Specific parameters for SVM
brmodel <- br(toyml, "SVM", gamma = 0.1, scale=FALSE)

# Specific parameters for KNN
ccmodel <- cc(toyml, "KNN", c("y5", "y4", "y3", "y2", "y1"), k=5)

# Specific parameters for Random Forest
ebrmodel <- ebr(toyml, "RF", 5, 0.6, 0.75, proximity=TRUE, ntree=100)

# Predict the BR model
result <- predict(brmodel, toyml)

# Specific parameters for KNN
result <- predict(ccmodel, toyml, kernel="triangular", probability = FALSE)

model <- mlknn(toyml, k=3)
pred <- predict(model, toyml)

# Running Binary Relevance method using 2 cores
brmodel <- br(toyml, "SVM", cores=2)
prediction <- predict(brmodel, toyml, cores=2)

# Running Binary Relevance method using 2 cores
brmodel <- br(toyml, "SVM", cores=2, seed=1984)
prediction <- predict(brmodel, toyml, seed=1984, cores=2)

results <- cv(toyml, method="ecc", base.algorith="RF", subsample = 0.9, attr.space = 0.9, cv.folds=5, cv.cores=2)

6. Thresholds

The threshold methods receive a mlresult object and return a new mlresult, except for scut that returns the threshold values. These methods, change mainly the bipartitions values using the probabilities values.

# Use a fixed threshold for all labels 
newpred <- fixed_threshold(prediction, 0.4)

# Use a specific threshold for each label 
newpred <- fixed_threshold(prediction, c(0.4, 0.5, 0.6, 0.7, 0.8))

# Use the MCut approch to define the threshold
newpred <- mcut_threshold(prediction)

# Use the PCut threshold
newpred <- pcut_threshold(prediction, ratio=0.65)

# Use the RCut threshold
newpred <- rcut_threshold(prediction, k=3)

# Choose the best threshold values based on a Mean Squared Error 
thresholds <- scut_threshold(prediction, toyml, cores = 2)
newpred <- fixed_threshold(prediction, thresholds)

#Predict only the labelsets present in the train data
newpred <- subset_correction(prediction, toyml)

7. Evaluation

To evaluate multi-label models you can use the method multilabel_evaluate. There are two ways of call this method:

toy <- create_holdout_partition(toyml)
brmodel <- br(toy$train, "SVM")
prediction <- predict(brmodel, toy$test)

# Using the test dataset and the prediction
result <- multilabel_evaluate(toy$test, prediction)
print(round(result, 3))

##                F1          accuracy average-precision               clp 
##             0.737             0.631             0.816             0.400 
##          coverage      hamming-loss         macro-AUC          macro-F1 
##             2.133             0.207             0.510             0.347 
##   macro-precision      macro-recall       margin-loss         micro-AUC 
##             0.307             0.400             1.200             0.756 
##          micro-F1   micro-precision      micro-recall               mlp 
##             0.748             0.767             0.730             0.600 
##         one-error         precision      ranking-loss            recall 
##             0.267             0.767             0.219             0.761 
##   subset-accuracy               wlp 
##             0.267             0.600

# Build a confusion matrix
confmat <- multilabel_confusion_matrix(toy$test, prediction)
result <- multilabel_evaluate(confmat)
print(confmat)

## Multi-label Confusion Matrix
## 
## Absolute Matrix:
## -------------------------------------
##              Expected_1 Expected_0 TOTAL
## Prediction_1         46         14    60
## Predicion_0          17         73    90
## TOTAL                63         87   150
## 
## Proportinal Matrix:
## -------------------------------------
##              Expected_1 Expected_0 TOTAL
## Prediction_1      0.307      0.093   0.4
## Predicion_0       0.113      0.487   0.6
## TOTAL             0.420      0.580   1.0
## 
## Label Matrix
## -------------------------------------
##    TP FP FN TN Correct Wrong  %TP  %FP  %FN  %TN %Correct %Wrong MeanRanking
## y1  0  0  3 27      27     3 0.00 0.00 0.10 0.90     0.90   0.10        3.70
## y2 22  8  0  0      22     8 0.73 0.27 0.00 0.00     0.73   0.27        1.00
## y3  0  0  6 24      24     6 0.00 0.00 0.20 0.80     0.80   0.20        3.43
## y4 24  6  0  0      24     6 0.80 0.20 0.00 0.00     0.80   0.20        2.00
## y5  0  0  8 22      22     8 0.00 0.00 0.27 0.73     0.73   0.27        4.87
##    MeanScore
## y1      0.19
## y2      0.80
## y3      0.20
## y4      0.64
## y5      0.13

The confusion matrix summarizes a lot of data, and can be merged. For example, using a k-fold experiment:

kfcv <- create_kfold_partition(toyml, k=3)
confmats <- lapply(1:3, function (k) {
  toy <- partition_fold(kfcv, k)
  model <- br(toy$train, "RF")
  multilabel_confusion_matrix(toy$test, predict(model, toy$test))
})
result <- multilabel_evaluate(merge_mlconfmat(confmats))

Its possible choose which measures will be computed:

# Example-based measures
result <- multilabel_evaluate(confmat, "example-based")
print(names(result))

## [1] "F1"              "accuracy"        "hamming-loss"    "precision"      
## [5] "recall"          "subset-accuracy"

# Subset accuracy, F1 measure and hamming-loss
result <- multilabel_evaluate(confmat, c("subset-accuracy", "F1", "hamming-loss"))
print(names(result))

## [1] "F1"              "hamming-loss"    "subset-accuracy"

# Ranking and label-basedd measures
result <- multilabel_evaluate(confmat, c("label-based", "ranking"))
print(names(result))

##  [1] "average-precision" "coverage"          "macro-AUC"        
##  [4] "macro-F1"          "macro-precision"   "macro-recall"     
##  [7] "margin-loss"       "micro-AUC"         "micro-F1"         
## [10] "micro-precision"   "micro-recall"      "one-error"        
## [13] "ranking-loss"

# To see all the supported measures you can try
multilabel_measures()

##  [1] "F1"                "accuracy"          "all"              
##  [4] "average-precision" "bipartition"       "clp"              
##  [7] "coverage"          "example-based"     "hamming-loss"     
## [10] "label-based"       "label-problem"     "macro-AUC"        
## [13] "macro-F1"          "macro-based"       "macro-precision"  
## [16] "macro-recall"      "margin-loss"       "micro-AUC"        
## [19] "micro-F1"          "micro-based"       "micro-precision"  
## [22] "micro-recall"      "mlp"               "one-error"        
## [25] "precision"         "ranking"           "ranking-loss"     
## [28] "recall"            "subset-accuracy"   "wlp"

8. How to Contribute

The utiml repository is available on (https://github.com/rivolli/utiml). If you want to contribute with the development of this package, contact us and you will be very welcome.

Please, report any bugs or suggestions on CRAN mail or git hub page.