skmultilearn.dataset module

skmultilearn.dataset.available_data_sets()[source]

Lists available data sets and their variants

Returns:available datasets and their variants with the key pertaining to the (set_name, variant_name) and values include md5 and file name on server
Return type:dict[(set_name, variant_name)] -> [md5, file_name]
skmultilearn.dataset.clear_data_home(data_home=None)[source]

Delete all the content of the data home cache.

Parameters:data_home (str (default is None)) – the path to the directory in which scikit-multilearn data sets should be stored.
skmultilearn.dataset.download_dataset(set_name, variant, data_home=None)[source]

Downloads a data set

Parameters:
Returns:

path to the downloaded data set file on disk
Return type:

str
skmultilearn.dataset.get_data_home(data_home=None, subdirectory='')[source]

Return the path of the scikit-multilearn data dir.

This folder is used by some large dataset loaders to avoid downloading the data several times.

By default the data_home is set to a folder named 'scikit_ml_learn_data' in the user home folder.

Alternatively, it can be set by the 'SCIKIT_ML_LEARN_DATA' environment variable or programmatically by giving an explicit folder path. The '~' symbol is expanded to the user home folder.

If the folder does not already exist, it is automatically created.

Parameters:
  • data_home (str (default is None)) – the path to the directory in which scikit-multilearn data sets should be stored, if None the path is generated as stated above
  • subdirectory (str, default '') – return path subdirectory under data_home if data_home passed or under default if not passed
Returns:

the path to the data home
Return type:

str
skmultilearn.dataset.load_dataset(set_name, variant, data_home=None)[source]

Loads a selected variant of the given data set

Parameters:
  • set_name (str) – name of set from available_data_sets()
  • variant (str) – variant of the data set
  • data_home (default None, str) – custom base folder for data, if None, default is used
Returns:

the loaded multilabel data set variant in the scikit-multilearn format, see data_sets
Return type:

dict
skmultilearn.dataset.load_dataset_dump(filename)[source]

Loads a compressed data set dump

Parameters:filename (str) – path to dump file, if without .bz2 ending, the .bz2 extension will be appended.
Returns:
  • X (array_like, numpy.matrix or scipy.sparse matrix, shape=(n_samples, n_features)) – input feature matrix
  • y (array_like, numpy.matrix or scipy.sparse matrix of {0, 1}, shape=(n_samples, n_labels)) – binary indicator matrix with label assignments
  • names of attributes (List[str]) – list of attribute names for X columns
  • names of labels (List[str]) – list of label names for y columns
skmultilearn.dataset.load_from_arff(filename, label_count, label_location='end', input_feature_type='float', encode_nominal=True, load_sparse=False, return_attribute_definitions=False)[source]

Method for loading ARFF files as numpy array

Parameters:
  • filename (str) – path to ARFF file
  • labelcount (integer) – number of labels in the ARFF file
  • endian (str {"big", "little"} (default is "big")) – whether the ARFF file contains labels at the beginning of the attributes list (“start”, MEKA format) or at the end (“end”, MULAN format)
  • input_feature_type (numpy.type as string (default is "float")) – the desire type of the contents of the return ‘X’ array-likes, default ‘i8’, should be a numpy type, see http://docs.scipy.org/doc/numpy/user/basics.types.html
  • encode_nominal (bool (default is True)) – whether convert categorical data into numeric factors - required for some scikit classifiers that can’t handle non-numeric input features.
  • load_sparse (boolean (default is False)) – whether to read arff file as a sparse file format, liac-arff breaks if sparse reading is enabled for non-sparse ARFFs.
  • return_attribute_definitions (boolean (default is False)) – whether to return the definitions for each attribute in the dataset
Returns:

  • X (scipy.sparse.lil_matrix of input_feature_type, shape=(n_samples, n_features)) – input feature matrix
  • y (scipy.sparse.lil_matrix of {0, 1}, shape=(n_samples, n_labels)) – binary indicator matrix with label assignments
  • names of attributes (List[str]) – list of attribute names from ARFF file
skmultilearn.dataset.save_dataset_dump(input_space, labels, feature_names, label_names, filename=None)[source]

Saves a compressed data set dump

Parameters:
  • input_space (array-like of array-likes) – Input space array-like of input feature vectors
  • labels (array-like of binary label vectors) – Array-like of labels assigned to each input vector, as a binary indicator vector (i.e. if 5th position has value 1 then the input vector has label no. 5)
  • feature_names (array-like,optional) – names of features
  • label_names (array-like, optional) – names of labels
  • filename (str, optional) – Path to dump file, if without .bz2, the .bz2 extension will be appended.
skmultilearn.dataset.save_to_arff(X, y, label_location='end', save_sparse=True, filename=None)[source]

Method for dumping data to ARFF files

Parameters:
  • X (array_like, numpy.matrix or scipy.sparse matrix, shape=(n_samples, n_features)) – input feature matrix
  • y (array_like, numpy.matrix or scipy.sparse matrix of {0, 1}, shape=(n_samples, n_labels)) – binary indicator matrix with label assignments
  • label_location (string {"start", "end"} (default is "end")) – whether the ARFF file will contain labels at the beginning of the attributes list (“start”, MEKA format) or at the end (“end”, MULAN format)
  • save_sparse (boolean) – Whether to save in ARFF’s sparse dictionary-like format instead of listing all zeroes within file, very useful in multi-label classification.
  • filename (str or None) – Path to ARFF file, if None, the ARFF representation is returned as string
Returns:

the ARFF dump string, if filename is None
Return type:

str or None