def test_if_negative_evidence_is_distributed(self):
stratifier = IterativeStratification(n_splits=2, order=1)
y = np.matrix([[0, 0], [1, 0], [0, 1], [1, 1]])
rows, rows_used, all_combinations, per_row_combinations, samples_with_combination, folds = \
stratifier._prepare_stratification(y)
stratifier._distribute_positive_evidence(rows_used, folds, samples_with_combination, per_row_combinations)
self.assertFalse(rows_used[0])
stratifier._distribute_negative_evidence(rows_used, folds)
self.assertTrue(rows_used[0])
def test_if_negative_evidence_is_distributed(self):
stratifier = IterativeStratification(n_splits=2, order=1)
y = np.matrix([[0, 0], [1, 0], [0, 1], [1, 1]])
rows, rows_used, all_combinations, per_row_combinations, samples_with_combination, folds = \
stratifier._prepare_stratification(y)
stratifier._distribute_positive_evidence(rows_used, folds,
samples_with_combination,
per_row_combinations)
self.assertFalse(rows_used[0])
stratifier._distribute_negative_evidence(rows_used, folds)
self.assertTrue(rows_used[0])
def test_if_positive_evidence_does_not_include_negative_evidence(self):
stratifier = IterativeStratification(n_splits=2, order=1)
y = np.matrix([[0, 0], [1, 0], [0, 1], [1, 1]])
rows, rows_used, all_combinations, per_row_combinations, samples_with_combination, folds = \
stratifier._prepare_stratification(y)
stratifier._distribute_positive_evidence(rows_used, folds, samples_with_combination, per_row_combinations)
self.assertFalse(rows_used[0])
self.assertTrue(rows_used[1])
self.assertTrue(rows_used[2])
self.assertTrue(rows_used[3])
for combination, samples in stratifier.desired_samples_per_combination_per_fold.items():
for desire in samples:
self.assertEqual(desire, 0)
def test_if_variables_are_initialized_correctly(self):
stratifier = IterativeStratification(n_splits=2, order=1)
y = np.matrix([[0, 0], [1, 0], [0, 1], [1, 1]])
rows, rows_used, all_combinations, per_row_combinations, samples_with_combination, folds = \
stratifier._prepare_stratification(y)
self.assertEqual(stratifier.n_samples, 4)
self.assertEqual(stratifier.n_labels, 2)
self.assertEqual(len(rows), 4)
self.assertEqual(len(rows_used), 4)
self.assertEqual(len(stratifier.percentage_per_fold), 2)
self.assertEqual(len(stratifier.desired_samples_per_fold), 2)
self.assertEqual(len(folds), 2)
self.assertTrue(not any(rows_used.values()))
self.assertFalse(any(rows_used.values()))
self.assertEqual(stratifier.order, 1)
for d in stratifier.percentage_per_fold:
self.assertEqual(d, 1 / 2.0)
for d in stratifier.desired_samples_per_fold:
self.assertEqual(d, y.shape[0] / 2.0)
self.assertEqual(len(all_combinations), 2)
self.assertEqual(len(per_row_combinations[0]), 0)
self.assertEqual(len(per_row_combinations[1]), 1)
self.assertEqual(len(per_row_combinations[2]), 1)
self.assertEqual(len(per_row_combinations[3]), 2)
self.assertEqual(len(samples_with_combination), 2)
self.assertEqual(
len(stratifier.desired_samples_per_combination_per_fold), 2)
for combination, samples in samples_with_combination.items():
self.assertEqual(len(set(combination)), 1)
self.assertEqual(len(samples), 2)
for combination, desirability in stratifier.desired_samples_per_combination_per_fold.items(
):
self.assertEqual(len(set(combination)), 1)
self.assertEqual(len(desirability), 2)
for desire in desirability:
self.assertEqual(desire, 1.0)
def test_if_positive_evidence_does_not_include_negative_evidence(self):
stratifier = IterativeStratification(n_splits=2, order=1)
y = np.matrix([[0, 0], [1, 0], [0, 1], [1, 1]])
rows, rows_used, all_combinations, per_row_combinations, samples_with_combination, folds = \
stratifier._prepare_stratification(y)
stratifier._distribute_positive_evidence(rows_used, folds,
samples_with_combination,
per_row_combinations)
self.assertFalse(rows_used[0])
self.assertTrue(rows_used[1])
self.assertTrue(rows_used[2])
self.assertTrue(rows_used[3])
for combination, samples in stratifier.desired_samples_per_combination_per_fold.items(
):
for desire in samples:
self.assertEqual(desire, 0)
def test_if_variables_are_initialized_correctly(self):
stratifier = IterativeStratification(n_splits=2, order=1)
y = np.matrix([[0, 0], [1, 0], [0, 1], [1, 1]])
rows, rows_used, all_combinations, per_row_combinations, samples_with_combination, folds = \
stratifier._prepare_stratification(y)
self.assertEqual(stratifier.n_samples, 4)
self.assertEqual(stratifier.n_labels, 2)
self.assertEqual(len(rows), 4)
self.assertEqual(len(rows_used), 4)
self.assertEqual(len(stratifier.percentage_per_fold), 2)
self.assertEqual(len(stratifier.desired_samples_per_fold), 2)
self.assertEqual(len(folds), 2)
self.assertTrue(not any(rows_used.values()))
self.assertFalse(any(rows_used.values()))
self.assertEqual(stratifier.order, 1)
for d in stratifier.percentage_per_fold:
self.assertEqual(d, 1 / 2.0)
for d in stratifier.desired_samples_per_fold:
self.assertEqual(d, y.shape[0] / 2.0)
self.assertEqual(len(all_combinations), 2)
self.assertEqual(len(per_row_combinations[0]), 0)
self.assertEqual(len(per_row_combinations[1]), 1)
self.assertEqual(len(per_row_combinations[2]), 1)
self.assertEqual(len(per_row_combinations[3]), 2)
self.assertEqual(len(samples_with_combination), 2)
self.assertEqual(len(stratifier.desired_samples_per_combination_per_fold), 2)
for combination, samples in samples_with_combination.items():
self.assertEqual(len(set(combination)), 1)
self.assertEqual(len(samples), 2)
for combination, desirability in stratifier.desired_samples_per_combination_per_fold.items():
self.assertEqual(len(set(combination)), 1)
self.assertEqual(len(desirability), 2)
for desire in desirability:
self.assertEqual(desire, 1.0)