Note
This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the user guide for the big picture.
nidl.datasets.BaseImageDataset¶
- class nidl.datasets.BaseImageDataset(root, patterns, channels, subject_in_patterns, split='train', targets=None, target_mapping=None, transforms=None, mask=None, withdraw_subjects=None)[source]¶
Bases:
BaseDatasetScalable neuroimaging dataset that uses files.
- Parameters:
- root: str
the location where are stored the data.
- patterns: str or list of str
the relative locations of the images to be loaded.
- channels: str or list of str, default=None
the name of the channels.
- subject_in_patterns: int or list of int
the folder level where the subject identifiers can be retrieved.
- split: str, default ‘train’
define the split to be considered.
- targets: str or list of str, default=None
the dataset will also return these tabular data.
- target_mapping: dict, default None
optionaly, define a dictionary specifying different replacement values for different existing values. See pandas DataFrame.replace documentation for more information.
- transforms: callable, default None
a function that can be called to augment the input images.
- mask: str, default None
optionnaly, mask the input data using this numpy array.
- withdraw_subjects: list of str, default None
optionaly, provide a list of subjects to remove from the dataset.
- Raises:
- FileNotFoundError
If the mandatorry input files are not found.
- KeyError
If the mandatory key are not found.
- UserWarning
If missing data are found.
Notes
A ‘participants.tsv’ file containing subject information (including the requested targets) is expected at the root. A ‘<split>.tsv’ file containg the subject to include is expected at the root. The general idea is not to copy all your data in the root folder but rather use a single symlink per project (if you are working with aggregated data). To enforce reproducibility you can check if the content of each file is persistent using the get_checksum method.