def stack(*datasets):
"""
Assuming that the datasets have same structure, stacks data, targets and other info
:param datasets:
:return: stacked dataset
"""
return Dataset(
data=vstack([d.data for d in datasets]),
target=stack_or_concat([d.target for d in datasets]),
sample_info=np.concatenate([d.sample_info for d in datasets]),
info={
k: [d.info.get(k, None) for d in datasets]
for k in merge_dicts(*[d.info for d in datasets])
},
)
def redivide_data(
datasets,
partition_proportions=None,
shuffle=False,
filters=None,
maps=None,
balance_classes=False,
rand=None,
):
"""
Function that redivides datasets. Can be use also to shuffle or filter or map examples.
:param rand:
:param balance_classes: # TODO RICCARDO
:param datasets: original datasets, instances of class Dataset (works with get_data and get_targets for
compatibility with mnist datasets
:param partition_proportions: (optional, default None) list of fractions that can either sum up to 1 or less
then one, in which case one additional partition is created with
proportion 1 - sum(partition proportions).
If None it will retain the same proportion of samples found in datasets
:param shuffle: (optional, default False) if True shuffles the examples
:param filters: (optional, default None) filter or list of filters: functions with signature
(data, target, index) -> boolean (accept or reject the sample)
:param maps: (optional, default None) map or list of maps: functions with signature
(data, target, index) -> (new_data, new_target) (maps the old sample to a new one,
possibly also to more
than one sample, for data augmentation)
:return: a list of datasets of length equal to the (possibly augmented) partition_proportion
"""
rnd = get_rand_state(rand)
all_data = vstack([get_data(d) for d in datasets])
all_labels = stack_or_concat([get_targets(d) for d in datasets])
all_infos = np.concatenate([d.sample_info for d in datasets])
N = all_data.shape[0]
if partition_proportions: # argument check
partition_proportions = list([partition_proportions] if isinstance(
partition_proportions, float) else partition_proportions)
sum_proportions = sum(partition_proportions)
assert sum_proportions <= 1, (
"partition proportions must sum up to at most one: %d" %
sum_proportions)
if sum_proportions < 1.0:
partition_proportions += [1.0 - sum_proportions]
else:
partition_proportions = [
1.0 * get_data(d).shape[0] / N for d in datasets
]
if shuffle:
if sp and isinstance(all_data, sp.sparse.csr.csr_matrix):
raise NotImplementedError()
# if sk_shuffle: # TODO this does not work!!! find a way to shuffle these matrices while
# keeping compatibility with tensorflow!
# all_data, all_labels, all_infos = sk_shuffle(all_data, all_labels, all_infos)
# else:
permutation = np.arange(all_data.shape[0])
rnd.shuffle(permutation)
all_data = all_data[permutation]
all_labels = np.array(all_labels[permutation])
all_infos = np.array(all_infos[permutation])
if filters:
if sp and isinstance(all_data, sp.sparse.csr.csr_matrix):
raise NotImplementedError()
filters = as_list(filters)
data_triple = [(x, y, d)
for x, y, d in zip(all_data, all_labels, all_infos)]
for fiat in filters:
data_triple = [
xy for i, xy in enumerate(data_triple)
if fiat(xy[0], xy[1], xy[2], i)
]
all_data = np.vstack([e[0] for e in data_triple])
all_labels = np.vstack([e[1] for e in data_triple])
all_infos = np.vstack([e[2] for e in data_triple])
if maps:
if sp and isinstance(all_data, sp.sparse.csr.csr_matrix):
raise NotImplementedError()
maps = as_list(maps)
data_triple = [(x, y, d)
for x, y, d in zip(all_data, all_labels, all_infos)]
for _map in maps:
data_triple = [
_map(xy[0], xy[1], xy[2], i)
for i, xy in enumerate(data_triple)
]
all_data = np.vstack([e[0] for e in data_triple])
all_labels = np.vstack([e[1] for e in data_triple])
all_infos = np.vstack([e[2] for e in data_triple])
N = all_data.shape[0]
assert N == all_labels.shape[0]
calculated_partitions = reduce(
lambda v1, v2: v1 + [sum(v1) + v2],
[int(N * prp) for prp in partition_proportions],
[0],
)
calculated_partitions[-1] = N
print(
"datasets.redivide_data:, computed partitions numbers -",
calculated_partitions,
"len all",
N,
end=" ",
)
new_general_info_dict = {}
for data in datasets:
new_general_info_dict = {**new_general_info_dict, **data.info}
if balance_classes:
new_datasets = []
forbidden_indices = np.empty(0, dtype=np.int64)
for d1, d2 in zip(calculated_partitions[:-1],
calculated_partitions[1:-1]):
indices = np.array(
get_indices_balanced_classes(d2 - d1, all_labels,
forbidden_indices))
dataset = dl.Dataset(
data=all_data[indices],
target=all_labels[indices],
sample_info=all_infos[indices],
info=new_general_info_dict,
)
new_datasets.append(dataset)
forbidden_indices = np.append(forbidden_indices, indices)
test_if_balanced(dataset)
remaining_indices = np.array(
list(set(list(range(N))) - set(forbidden_indices)))
new_datasets.append(
dl.Dataset(
data=all_data[remaining_indices],
target=all_labels[remaining_indices],
sample_info=all_infos[remaining_indices],
info=new_general_info_dict,
))
else:
new_datasets = [
dl.Dataset(
data=all_data[d1:d2],
target=all_labels[d1:d2],
sample_info=all_infos[d1:d2],
info=new_general_info_dict,
) for d1, d2 in zip(calculated_partitions,
calculated_partitions[1:])
]
print("DONE")
return new_datasets