def test_index_dict(self):
labels = np.asarray([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
dict_a = {
"variable_one": np.random.randn(10),
"variable_two": np.random.randn(10, 10),
}
dict_a_1 = PhotonDataHelper.index_dict(dict_a, labels == 0)
dict_a_2 = PhotonDataHelper.index_dict(dict_a, labels == 1)
self.assertEqual(len(dict_a_1["variable_one"]), 5)
self.assertEqual(dict_a_2["variable_two"].shape, (5, 10))
def transform(self, X, y=None, **kwargs):
"""
Generates "new samples" by computing the mean between all or n_draws pairs of existing samples and appends them to X
The target for each new sample is computed as the mean between the constituent targets
:param X: data
:param y: targets (optional)
:param draw_limit: in case the full number of combinations is > 10k, how many to draw?
:param rand_seed: sets seed for random sampling of combinations (for reproducibility only)
:return: X_new: X and X_augmented; (y_new: the correspoding targets)
"""
logger.debug("Pairing " + str(self.draw_limit) + " samples...")
# ensure class balance in the training set if balance_classes is True
unique_classes = np.unique(y)
n_pairs = list()
for label in unique_classes:
if self.balance_classes:
n_pairs.append(self.draw_limit - np.sum(y == label))
else:
n_pairs.append(self.draw_limit)
# run get_samples for each class independently
X_extended = list()
y_extended = list()
kwargs_extended = dict()
for label, limit in zip(unique_classes, n_pairs):
X_new_class, y_new_class, kwargs_new_class = self._return_samples(
X[y == label],
y[y == label],
PhotonDataHelper.index_dict(kwargs, y == label),
generator=self.generator,
distance_metric=self.distance_metric,
draw_limit=limit,
rand_seed=self.random_state,
)
X_extended.extend(X_new_class)
y_extended.extend(y_new_class)
# get the corresponding kwargs
if kwargs:
kwargs_extended = PhotonDataHelper.join_dictionaries(
kwargs_extended, kwargs_new_class)
return X_extended, y_extended, kwargs_extended
