declearn.dataset.split_data
Randomly split a dataset into shards.
The resulting folder structure is:
folder/
└─── data*/
└─── client*/
│ train_data.* - training data
│ train_target.* - training labels
│ valid_data.* - validation data
│ valid_target.* - validation labels
└─── client*/
│ ...
Parameters:
Name | Type | Description | Default |
---|---|---|---|
folder |
str
|
Path to the folder where to add a data folder holding output shard-wise files |
'.'
|
data_file |
Optional[str]
|
Optional path to a folder where to find the data. If None, default to the MNIST example. |
None
|
label_file |
Optional[Union[str, int]]
|
If str, path to the labels file to import, or name of a |
None
|
n_shards |
int
|
Number of shards between which to split the data. |
3
|
scheme |
str
|
Splitting scheme(s) to use. In all cases, shards contain mutually- exclusive samples and cover the full raw training data. - If "iid", split the dataset through iid random sampling. - If "labels", split into shards that hold all samples associated with mutually-exclusive target classes. - If "biased", split the dataset through random sampling according to a shard-specific random labels distribution. |
'iid'
|
perc_train |
float
|
Train/validation split in each client dataset, must be in the ]0,1] range. |
0.8
|
seed |
Optional[int]
|
Optional seed to the RNG used for all sampling operations. |
None
|
Source code in declearn/dataset/_split_data.py
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