Source code for

.. module:: CDataSplitterKFold
   :synopsis: K-Fold splitting

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

from sklearn.model_selection import KFold

from secml.array import CArray
from import CDataSplitter

[docs]class CDataSplitterKFold(CDataSplitter): """K-Folds dataset splitting. Provides train/test indices to split data in train and test sets. Split dataset into 'num_folds' consecutive folds (with shuffling). Each fold is then used a validation set once while the k - 1 remaining fold form the training set. Parameters ---------- num_folds : int, optional Number of folds to create. Default 3. This correspond to the size of tr_idx and ts_idx lists. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, is the RandomState instance used by np.random. Attributes ---------- class_type : 'kfold' Examples -------- >>> from import CDataset >>> from import CDataSplitterKFold >>> ds = CDataset([[1,2],[3,4],[5,6]],[1,0,1]) >>> kfold = CDataSplitterKFold(num_folds=3, random_state=0).compute_indices(ds) >>> print(kfold.num_folds) 3 >>> print(kfold.tr_idx) [CArray(2,)(dense: [0 1]), CArray(2,)(dense: [0 2]), CArray(2,)(dense: [1 2])] >>> print(kfold.ts_idx) [CArray(1,)(dense: [2]), CArray(1,)(dense: [1]), CArray(1,)(dense: [0])] """ __class_type = 'kfold' def __init__(self, num_folds=3, random_state=None): super(CDataSplitterKFold, self).__init__( num_folds=num_folds, random_state=random_state)
[docs] def compute_indices(self, dataset): """Compute training set and test set indices for each fold. Parameters ---------- dataset : CDataset Dataset to split. Returns ------- CDataSplitter Instance of the dataset splitter with tr/ts indices. """ # Resetting indices self._tr_idx = [] self._ts_idx = [] sk_splitter = KFold(n_splits=self.num_folds, shuffle=True, random_state=self.random_state) # We take sklearn indices (iterators) and map to list of CArrays for train_index, test_index in \ sk_splitter.split(dataset.X.get_data()): train_index = CArray(train_index) test_index = CArray(test_index) self._tr_idx.append(train_index) self._ts_idx.append(test_index) return self