Note
Go to the end to download the full example code.
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 [00:36<01:50, 36.95s/it]
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34%|████████████▋ | 25.4M/74.3M [00:19<00:04, 10.5MB/s]
39%|██████████████▎ | 28.6M/74.3M [00:19<00:02, 16.0MB/s]
43%|███████████████▉ | 31.9M/74.3M [00:19<00:02, 20.1MB/s]
47%|█████████████████▍ | 35.1M/74.3M [00:19<00:01, 22.7MB/s]
52%|███████████████████▏ | 38.5M/74.3M [00:20<00:01, 25.1MB/s]
56%|████████████████████▌ | 41.2M/74.3M [00:20<00:01, 25.0MB/s]
60%|██████████████████████ | 44.3M/74.3M [00:20<00:01, 25.7MB/s]
64%|███████████████████████▊ | 47.9M/74.3M [00:20<00:00, 27.7MB/s]
68%|█████████████████████████▏ | 50.7M/74.3M [00:20<00:00, 26.9MB/s]
73%|███████████████████████████ | 54.4M/74.3M [00:20<00:00, 28.8MB/s]
77%|████████████████████████████▌ | 57.2M/74.3M [00:20<00:00, 28.0MB/s]
81%|█████████████████████████████▉ | 60.0M/74.3M [00:21<00:01, 14.2MB/s]
84%|██████████████████████████████▉ | 62.2M/74.3M [00:21<00:01, 8.73MB/s]
86%|███████████████████████████████▊ | 63.8M/74.3M [00:22<00:01, 5.93MB/s]
88%|████████████████████████████████▍ | 65.0M/74.3M [00:22<00:01, 4.93MB/s]
89%|████████████████████████████████▊ | 65.9M/74.3M [00:23<00:01, 4.79MB/s]
90%|█████████████████████████████████▏ | 66.7M/74.3M [00:23<00:01, 4.90MB/s]
91%|█████████████████████████████████▌ | 67.4M/74.3M [00:23<00:01, 3.80MB/s]
92%|█████████████████████████████████▊ | 68.0M/74.3M [00:23<00:01, 3.94MB/s]
92%|██████████████████████████████████▏ | 68.5M/74.3M [00:23<00:01, 4.05MB/s]
93%|██████████████████████████████████▍ | 69.0M/74.3M [00:23<00:01, 4.15MB/s]
94%|██████████████████████████████████▋ | 69.5M/74.3M [00:24<00:01, 4.17MB/s]
94%|██████████████████████████████████▉ | 70.0M/74.3M [00:24<00:01, 4.18MB/s]
95%|███████████████████████████████████ | 70.5M/74.3M [00:24<00:00, 4.15MB/s]
95%|███████████████████████████████████▎ | 70.9M/74.3M [00:24<00:00, 4.10MB/s]
96%|███████████████████████████████████▌ | 71.3M/74.3M [00:24<00:00, 4.04MB/s]
97%|███████████████████████████████████▊ | 71.8M/74.3M [00:24<00:00, 3.95MB/s]
97%|███████████████████████████████████▉ | 72.2M/74.3M [00:24<00:00, 3.87MB/s]
98%|████████████████████████████████████▏| 72.6M/74.3M [00:24<00:00, 3.79MB/s]
98%|████████████████████████████████████▎| 72.9M/74.3M [00:24<00:00, 3.69MB/s]
99%|████████████████████████████████████▌| 73.3M/74.3M [00:25<00:00, 3.59MB/s]
99%|████████████████████████████████████▋| 73.7M/74.3M [00:25<00:00, 3.59MB/s]
100%|████████████████████████████████████▉| 74.2M/74.3M [00:25<00:00, 3.83MB/s]
0%| | 0.00/74.3M [00:00<?, ?B/s]
100%|██████████████████████████████████████| 74.3M/74.3M [00:00<00:00, 345GB/s]
CustomDataset3-WithinSession: 100%|██████████| 4/4 [02:30<00:00, 39.39s/it]
CustomDataset3-WithinSession: 100%|██████████| 4/4 [02:30<00:00, 37.52s/it]
score time samples ... n_sessions dataset pipeline
0 0.500000 0.364118 120.0 ... 1 CustomDataset3 MDM
1 0.642500 0.309329 120.0 ... 1 CustomDataset3 MDM
2 0.645327 2.160094 768.0 ... 1 CustomDataset3 MDM
3 0.521336 4.628620 1356.0 ... 1 CustomDataset3 MDM
[4 rows x 9 columns]
Total running time of the script: (2 minutes 31.261 seconds)
Estimated memory usage: 576 MB