twodlearn.datasets.unbalanced module

class twodlearn.datasets.unbalanced.DfUnbalancedDataset(datasets)

Bases: twodlearn.datasets.unbalanced.UnbalancedDataset

columns

dtypes

filter_by_size(minimum_size)

get_stats()

mapper

Decorator used to specify an optional property inside a model. The decorator works similar to @property, but the specified method correponds to the initialization of the property

maps

Decorator used to specify an optional property inside a model. The decorator works similar to @property, but the specified method correponds to the initialization of the property

sample(n_samples, replace=False)

class twodlearn.datasets.unbalanced.UnbalancedDataset(datasets)

Bases: object

property datasets

filter_by_size(minimum_size)

classmethod from_data(data_x, data_y, DatasetClass=<class 'twodlearn.datasets.base.Dataset'>)

get_stats()

property histogram

property labels

mapper

Decorator used to specify an optional property inside a model. The decorator works similar to @property, but the specified method correponds to the initialization of the property

n_samples

next_batch(batch_size)

normalizer

Decorator used to specify an optional property inside a model. The decorator works similar to @property, but the specified method correponds to the initialization of the property

sample(n_samples, replace=False)

shuffle()

split(values, shuffle=True)

Split the dataset according to the percentages provided in values

Parameters

• values (list) – percentages used for splitting the dataset

• shuffle (bool) – true if shuffle the dataset before splitting (default True)

Returns

list with the new datasets

Return type

list(UnbalancedDataset)