Benchmark multi-model/multi-view models.

Note

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mmbench.dataset.get_data(dataset, datasetdir, modalities, dtype='complete', test_size=0.2, residualize=False, random_state=42)[source]¶

Load the train/test data.

Parameters

dataset : str

the dataset name: euaims or hbn.

datasetdir : str

the path to the dataset associated data.

modalities : list of str

the modalities to load.

dtype : str, default ‘complete’

the data type: ‘complete’, ‘full’.

test_size : float, default=0.2

should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split.

residualize : bool, default False

optionaly residualize the image data.

random_state : int, default 42

controls the shuffling applied to the data before applying the split.

Returns

data : dict of DataFrame

the loaded data for each modality.

meta_df : DataFrame

the associated meta information.

tensors : dict of Tensors

the splitted input data (train, test).

train_indices : list of int

the train indices.

test_indices : list of int

the test indices.