Welcome to svmloader’s documentation!

svmloader is a simplist but very fast python module (written in cython) to load sparse data written at libsvm format.

It is not functionnaly equivalent to sklearn.datasets.load_svmlight_file, and handle only the simplest cases. labels are supposed to be of integer type, and data is parsed as numpy.float64 type.

svmloader.load_svmfile(filename, nfeatures=None, zero_based=True)

Load a sparse matrix from filename at svmlib format.

Parameters:
  • filename (str) – the file name
  • nfeatures (int) – the number of columns (infered from file if is None)
  • zero_based (bool) – indicates if columns indexes are zero-based or one-based
Returns:

(labels, sparse_matrix) tuple

Return type:

(numpy.ndarray, scipy.sparse.csr_matrix)

svmloader.load_svmfiles(filenames, zero_based=True)

Load a sparse matrix list from list of filenames at svmlib format. The number of features will be infered from the maximum indice found on all files.

Parameters:
  • filenames (list) – the list of files names
  • zero_based (bool) – indicates if columns indexes are zero-based or one-based
Returns:

a list [labels_0, matrix_0, .., labels_n, matrix_n]