Note
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Tutorial 5: Creating a dataset class#
# Author: Gregoire Cattan
#
# https://github.com/plcrodrigues/Workshop-MOABB-BCI-Graz-2019
from pyriemann.classification import MDM
from pyriemann.estimation import ERPCovariances
from sklearn.pipeline import make_pipeline
from moabb.datasets import Cattan2019_VR
from moabb.datasets.braininvaders import BI2014a
from moabb.datasets.compound_dataset import CompoundDataset
from moabb.datasets.utils import blocks_reps
from moabb.evaluations import WithinSessionEvaluation
from moabb.paradigms.p300 import P300
Initialization#
This tutorial illustrates how to use the CompoundDataset to: 1) Select a few subjects/sessions/runs in an existing dataset 2) Merge two CompoundDataset into a new one 3) … and finally use this new dataset on a pipeline (this steps is not specific to CompoundDataset)
Let’s define a paradigm and a pipeline for evaluation first.
paradigm = P300()
pipelines = {}
pipelines["MDM"] = make_pipeline(ERPCovariances(estimator="lwf"), MDM(metric="riemann"))
Creation a selection of subject#
We are going to great two CompoundDataset, namely CustomDataset1 & 2. A CompoundDataset accepts a subjects_list of subjects. It is a list of tuple. A tuple contains 4 values:
the original dataset
the subject number to select
the sessions. It can be:
a session name (‘0’)
a list of sessions ([‘0’, ‘1’])
None to select all the sessions attributed to a subject
the runs. As for sessions, it can be a single run name, a list or None` (to select all runs).
class CustomDataset1(CompoundDataset):
def __init__(self):
biVR = Cattan2019_VR(virtual_reality=True, screen_display=True)
runs = blocks_reps([0, 2], [0, 1, 2, 3, 4], biVR.n_repetitions)
subjects_list = [
(biVR, 1, "0VR", runs),
(biVR, 2, "0VR", runs),
]
CompoundDataset.__init__(
self,
subjects_list=subjects_list,
code="CustomDataset1",
interval=[0, 1.0],
)
class CustomDataset2(CompoundDataset):
def __init__(self):
bi2014 = BI2014a()
subjects_list = [
(bi2014, 4, None, None),
(bi2014, 7, None, None),
]
CompoundDataset.__init__(
self,
subjects_list=subjects_list,
code="CustomDataset2",
interval=[0, 1.0],
)
Merging the datasets#
We are now going to merge the two CompoundDataset into a single one. The implementation is straight forward. Instead of providing a list of subjects, you should provide a list of CompoundDataset. subjects_list = [CustomDataset1(), CustomDataset2()]
class CustomDataset3(CompoundDataset):
def __init__(self):
subjects_list = [CustomDataset1(), CustomDataset2()]
CompoundDataset.__init__(
self,
subjects_list=subjects_list,
code="CustomDataset3",
interval=[0, 1.0],
)
Evaluate and display#
Let’s use a WithinSessionEvaluation to evaluate our new dataset. If you already new how to do this, nothing changed: The CompoundDataset can be used as a normal dataset.
datasets = [CustomDataset3()]
evaluation = WithinSessionEvaluation(
paradigm=paradigm, datasets=datasets, overwrite=False, suffix="newdataset"
)
scores = evaluation.process(pipelines)
print(scores)
CustomDataset3-WithinSession: 0%| | 0/4 [00:00<?, ?it/s]
CustomDataset3-WithinSession: 25%|██▌ | 1/4 [00:07<00:22, 7.39s/it]
CustomDataset3-WithinSession: 50%|█████ | 2/4 [00:14<00:14, 7.24s/it]
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100%|██████████████████████████████████████| 46.4M/46.4M [00:00<00:00, 190GB/s]
CustomDataset3-WithinSession: 75%|███████▌ | 3/4 [00:29<00:10, 10.59s/it]
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100%|██████████████████████████████████████| 74.3M/74.3M [00:00<00:00, 248GB/s]
CustomDataset3-WithinSession: 100%|██████████| 4/4 [00:57<00:00, 17.62s/it]
CustomDataset3-WithinSession: 100%|██████████| 4/4 [00:57<00:00, 14.38s/it]
score time samples ... n_sessions dataset pipeline
0 0.530000 0.360842 120.0 ... 1 CustomDataset3 MDM
1 0.557500 0.352545 120.0 ... 1 CustomDataset3 MDM
2 0.619863 2.003942 768.0 ... 1 CustomDataset3 MDM
3 0.539310 4.412965 1356.0 ... 1 CustomDataset3 MDM
[4 rows x 9 columns]
Total running time of the script: (0 minutes 58.530 seconds)
Estimated memory usage: 611 MB