Source code for

.. module:: DataLoader
   :synopsis: Load and save a dataset to/from disk

.. moduleauthor:: Marco Melis <>
.. moduleauthor:: Ambra Demontis <>

from abc import ABCMeta, abstractmethod

from secml.array import CArray
from secml.core import CCreator

[docs]class CDataLoader(CCreator, metaclass=ABCMeta): """Interface for Dataset loaders.""" __super__ = 'CDataLoader'
[docs] @abstractmethod def load(self, *args, **kwargs): """Loads a dataset. This method should return a `.CDataset` object. """ raise NotImplementedError( "Please implement a `load` method for class {:}" "".format(self.__class__.__name__))
# TODO: GENERALIZE THIS FUNCTION AND PUT IT INTO CARRAY @staticmethod def _remove_all_zero_features(patterns): """ Return patterns with only non zero features Parameters ---------- patterns : CArray Array with scattered data containing features which are all zero for each pattern Returns ------- patterns : CArray Array with scattered data without features which are all zero for each pattern idx_mapping : CArray Mapping of features's indices to original data. Examples -------- >>> from import CDataLoader >>> patterns = CArray([[1,0,2], [4,0,5]]) >>> patterns, mapping = CDataLoader._remove_all_zero_features(patterns) >>> print(patterns) CArray([[1 2] [4 5]]) >>> print(mapping) CArray([0 2]) """ all_feat_num = patterns.shape[1] all_orig_feat_idx = CArray.arange(start=0, stop=all_feat_num) # found indices that are really features (not zero for every pattern) nnz_elem_idx = patterns.nnz_indices idx_feat_presents = CArray(nnz_elem_idx[1]).unique() # return ds without features that are all zero and non zero old idx return patterns[:, idx_feat_presents], \ all_orig_feat_idx[idx_feat_presents]