Loading a dataset

Loading a built-in dataset

Loading one of the datasets offered natively by DeepMTP In the example above, the multi-label classification dataset is loaded my one of the built-in functions offered by DeepMTP. More specifically the available functions are the following:

load_process_MLC()

The user can load the multi-label classification datasets available in the MULAN repository. The different datasets can be accessed by specifying a valid name in the dataset_name parameter.:

data_mlc = load_process_MLC(dataset_name='bibtex', variant='undivided', features_type='dataframe')
train_mlc, val_mlc, test_mlc, data_info_mlc = data_process(data_mlc, validation_setting='B', verbose=True)

load_process_MTR()

The user can load the multivariate regression datasets available in the MULAN repository. The different datasets can be accessed by specifying a valid name in the dataset_name parameter.:

mtr_data = load_process_MTR(dataset_name='enb', features_type='dataframe')
train_mtr, val_mtr, test_mtr, data_info_mtr = data_process(mtr_data, validation_setting='B', verbose=True)

load_process_MTL()

The user can load the multi-task learning dataset dog, a crowdsourcing dataset first introduced in Liu et al. More specifically, the dataset contains 800 images of dogs who have been partially labelled by 52 annotators with one of 5 possible breeds. To modify this multi-class problem to a binary problem, we modify the task so that the prediction involves the correct or incorrect labelling by the annotator. In a future version of the software another dataset of the same type will be added.:

train_mtl, val_mtl, test_mtl, data_info_mtl = data_process(mtl_data, validation_setting='B', verbose=True)
train_mtl, val_mtl, test_mtl, data_info_mtl = data_process(mtl_data, validation_setting='B', verbose=True)

load_process_MC()

The user can load the matrix completion dataset MovieLens 100K, a movie rating prediction dataset available by the the GroupLens lab that contains 100k ratings from 1000 users on 1700 movies. In a future version of the software larger versions of the movielens dataset will be added:

dp_data = load_process_DP(dataset_name='ern')
train_dp, val_dp, test_dp, data_info_dp = data_process(dp_data, validation_setting='D', verbose=True)

load_process_DP()

The user can load dyadic prediction datasets available here. These are four different biological network datasets (ern, srn, dpie, dpii) which can be accessed by specifying one of the four keywords in the dataset_name parameter.:

mc_data = load_process_MC(dataset_name='ml-100k')
train_mc, val_mc, test_mc, data_info_mc = data_process(mc_data, validation_setting='A', verbose=True)

Loading a custom dataset

In the most abstract view of a multi-target prediction problem there are three at most datasets that can be needed. These include the interaction matrix, the instance features, and the target features. When accounting for a train, val, test split the total number raises to 9 possible data sources. To group this info and avoid passing 9 different parameters in the data_process function, the framework uses a single dictionary with 3 key-value pairs {‘train’:{}, ‘val’:{}, ‘test’:{}}. The values should also be a dictionaries with 3 key-value pairs {‘y’:{}, ‘X_instance’:{}, ‘X_target’:{}}. When combined the dictionary can have the following form: {‘train’:{}, ‘val’:{}, ‘test’:{}}:

data = {
    'train': {
        'y': ,
        'X_instance': ,
        'X_target': ,
    },
    'val': {
        'y': ,
        'X_instance': ,
        'X_target': ,
    },
    'test': {
        'y': ,
        'X_instance': ,
        'X_target': ,
    },
}