Benchmark multi-model/multi-view models.

Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the gallery for the big picture.

mmbench.workflow.embedding.benchmark_latent_exp(dataset, datasetdir, configfile, outdir, dtype='full', missing_modalities=None)[source]¶

Retrieve the learned latent space of different models using a N samplings scheme.

Parameters

dataset : str

the dataset name: euaims or hbn.

datasetdir : str

the path to the dataset associated data.

configfile : str

the path to the config file descibing the different models to compare. This configuration file is a Python (*.py) file with a dictionary named ‘_models’ containing the different model settings. Keys of this dictionary are the model names, each beeing described with a model getter function ‘get’ and associated kwargs ‘get_kwargs’, as weel as an evaluation function ‘eval’ and associated kwargs ‘eval_kwargs’. The getter and evaluation functions are defined in the ‘mmbench.model’ module.

outdir : str

the destination folder.

dtype : str, default ‘full’

the data type: ‘complete’ or ‘full’.

missing_modalities : list, default None

remove data from missing modalities.

Notes

We need to extend this procedure to CV models.