In this section you will learn how to generate multi-label classification, where to find real world data and how to load it, and how to prepare your data for classification.

The multi-label classification can be performed on different kinds of data - usually it is done on either artificially generated data for analytical purposes or on real-world data sets stored in ARFF <http://www.cs.waikato.ac.nz/ml/weka/arff.html> files.

## Generating artificial data¶

Scikit-learn’s sklearn.datasets.make_multilabel_classification framework can be used to generate artificial multi-label classification data:

from sklearn.datasets import make_multilabel_classification

# this will generate a
x, y = make_multilabel_classification(sparse = True, n_labels = 5,
return_indicator = 'sparse', allow_unlabeled = False)


## Getting real-world data sets¶

The MULAN package provides a repository of multi-label datasets used in a variety of publications. The data sets are provided in the ARFF format, with labels provided as last elements of the ARFF data frame (little endian). Scikit-multilearn provides support for loading ARFF data files.

The class skmultilearn.dataset.Dataset allows loading data from WEKA, MULAN or MEKA provided data sets in ARFF format. The module depends on liac-arff and is capable of loading sparse and dense represented ARFF data, the X, y are returned as sparse matrices. See the skmultilearn.dataset.Dataset.load_arff_to_numpy() for more information.

Example code for converting ARFF file to data dumps:

from skmultilearn.dataset import Dataset

## some information about the data set
# number of labels
labelcount = 16

# where the labels are located,
# big = at the beginning of the file
endianness = 'little'

# dtype used in the feature space
feature_type = 'float'

# whether the nominal attributes should be encoded as integers
encode_nominal = True

# if False - load dense representation and convert to sparse

labelcount = labelcount,
endian = "big",
input_feature_type = feature_type,
encode_nominal = encode_nominal,

labelcount = labelcount,
endian = "big",
input_feature_type = feature_type,
encode_nominal = encode_nominal,


The scikit-multilearn provided data sets are produced using skmultilearn.dataset.Dataset() class and contain a dictionary with two keys: X, y, containing a data set in the format described above. The data sets are pickle dumps compressed using the bz2 module. They can be loaded using the Dataset class.

## Scikit-multilearn data set helpers¶

For experimental purposes we provide a helper function to quickly load compressed data sources for multi-label classification. The skmultilearn.dataset.Dataset() class can load and save X and y to a compressed file containing a pickle of a dictionary with two keys: X, y, containing the input and output matrices. The data sets are dumped using pickle module and compressed using the bz2 module. They can be loaded using the Dataset class.

from skmultilearn.dataset import Dataset

Dataset.save_dataset_dump(X, y, "path/filename.dump.bz2")



## Cross-validation and train-test splits¶

As Tsoumakas et. al note: in supervised learning, experiments typically involve a first step of distributing the examples of a dataset into two or more disjoint subsets. When training data abound, the holdout method is used to distribute the examples into a training and a test set, and sometimes also into a validation set. When training data are limited, cross-validation is used, which starts by splitting the dataset into a number of disjoint subsets of approximately equal size.

To perform a train-test split for multi-label classification - having X and y - we can use scikit-learn’s sklearn.model_selection.train_test_split:

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)

# learn the classifier
classifier.fit(X_train, y_train)

# predict labels for test data
predictions = classifier.predict(X_test)


As there is no established stratified folding procedure for multi-label classification we can use a traditional k-fold cross-validation approach with sklearn.model_selection.KFold:

from sklearn.model_selection import KFold

# remember to set n_splits and shuffle!
kf = KFold(n_splits=n_splits, random_state=None, shuffle=shuffle)

for train_index, test_index in kf.split(X, y):
# assuming classifier object exists
X_train = X[train_index,:]
y_train = y[train_index,:]

X_test = X[test_index,:]
y_test = y[test_index,:]

# learn the classifier
classifier.fit(X_train, y_train)

# predict labels for test data
predictions = classifier.predict(X_test)


It is noteworthy that traditional k-folding may lead to severe problems with label combination representability across folds, thus if your data set exhibits a strong label co-occurrence structure you might want to use a label-combination based stratified k-fold:

from sklearn.model_selection import StratifiedKFold
from skmultilearn.problem_transform import LabelPowerset

lp = LabelPowerset()

# remember to set n_splits and shuffle!
kf = StratifiedKFold(n_splits=n_splits, random_state=None, shuffle=shuffle)

for train_index, test_index in kf.split(X, lp.transform(y)):

# assuming classifier object exists
X_train = X[train_index,:]
y_train = y[train_index,:]

X_test = X[test_index,:]
y_test = y[test_index,:]

# learn the classifier
classifier.fit(X_train, y_train)

# predict labels for test data
predictions = classifier.predict(X_test)


In the next section you will learn what classification methods are available in scikit-multilearn.