Note
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Tutorial 5: Combining Multiple Datasets into a Single Dataset#
# 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)
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79%|██████████████████████████████ | 58.7M/74.3M [01:13<00:18, 854kB/s]
79%|██████████████████████████████ | 58.8M/74.3M [01:14<00:19, 794kB/s]
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87%|█████████████████████████████████▏ | 64.9M/74.3M [01:21<00:11, 825kB/s]
88%|█████████████████████████████████▎ | 65.0M/74.3M [01:21<00:12, 768kB/s]
88%|█████████████████████████████████▎ | 65.1M/74.3M [01:21<00:12, 720kB/s]
88%|█████████████████████████████████▍ | 65.2M/74.3M [01:21<00:10, 841kB/s]
88%|█████████████████████████████████▍ | 65.3M/74.3M [01:22<00:10, 814kB/s]
88%|█████████████████████████████████▍ | 65.4M/74.3M [01:22<00:11, 758kB/s]
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89%|████████████████████████████████▊ | 65.7M/74.3M [01:22<00:08, 1.05MB/s]
89%|█████████████████████████████████▋ | 65.9M/74.3M [01:22<00:08, 976kB/s]
89%|█████████████████████████████████▊ | 66.0M/74.3M [01:22<00:08, 927kB/s]
89%|█████████████████████████████████▊ | 66.1M/74.3M [01:22<00:09, 861kB/s]
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89%|█████████████████████████████████▉ | 66.2M/74.3M [01:23<00:10, 779kB/s]
89%|█████████████████████████████████▉ | 66.4M/74.3M [01:23<00:09, 802kB/s]
89%|█████████████████████████████████▉ | 66.4M/74.3M [01:23<00:10, 748kB/s]
90%|██████████████████████████████████ | 66.6M/74.3M [01:23<00:08, 857kB/s]
90%|██████████████████████████████████ | 66.7M/74.3M [01:23<00:09, 822kB/s]
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90%|██████████████████████████████████▎ | 67.0M/74.3M [01:24<00:08, 845kB/s]
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91%|██████████████████████████████████▋ | 67.8M/74.3M [01:25<00:09, 685kB/s]
91%|██████████████████████████████████▋ | 67.9M/74.3M [01:25<00:09, 705kB/s]
92%|██████████████████████████████████▊ | 68.0M/74.3M [01:25<00:08, 718kB/s]
92%|██████████████████████████████████▊ | 68.1M/74.3M [01:25<00:08, 726kB/s]
92%|██████████████████████████████████▉ | 68.2M/74.3M [01:25<00:08, 696kB/s]
92%|██████████████████████████████████▉ | 68.3M/74.3M [01:25<00:07, 784kB/s]
92%|██████████████████████████████████▉ | 68.4M/74.3M [01:25<00:07, 773kB/s]
92%|███████████████████████████████████ | 68.5M/74.3M [01:26<00:07, 766kB/s]
92%|███████████████████████████████████ | 68.6M/74.3M [01:26<00:07, 724kB/s]
92%|███████████████████████████████████▏ | 68.7M/74.3M [01:26<00:07, 731kB/s]
93%|███████████████████████████████████▏ | 68.8M/74.3M [01:26<00:07, 771kB/s]
93%|███████████████████████████████████▏ | 68.9M/74.3M [01:26<00:07, 724kB/s]
93%|███████████████████████████████████▎ | 69.0M/74.3M [01:26<00:07, 732kB/s]
93%|███████████████████████████████████▎ | 69.1M/74.3M [01:26<00:06, 848kB/s]
93%|███████████████████████████████████▍ | 69.2M/74.3M [01:26<00:06, 818kB/s]
93%|██████████████████████████████████▌ | 69.4M/74.3M [01:27<00:04, 1.02MB/s]
94%|███████████████████████████████████▌ | 69.5M/74.3M [01:27<00:05, 948kB/s]
94%|███████████████████████████████████▌ | 69.6M/74.3M [01:27<00:05, 882kB/s]
94%|███████████████████████████████████▋ | 69.7M/74.3M [01:27<00:05, 877kB/s]
94%|███████████████████████████████████▋ | 69.9M/74.3M [01:27<00:04, 911kB/s]
94%|███████████████████████████████████▊ | 70.0M/74.3M [01:27<00:05, 862kB/s]
94%|███████████████████████████████████▊ | 70.0M/74.3M [01:27<00:05, 829kB/s]
94%|███████████████████████████████████▉ | 70.1M/74.3M [01:27<00:05, 805kB/s]
95%|███████████████████████████████████▉ | 70.3M/74.3M [01:28<00:04, 826kB/s]
95%|████████████████████████████████████ | 70.4M/74.3M [01:28<00:04, 795kB/s]
95%|████████████████████████████████████ | 70.5M/74.3M [01:28<00:04, 856kB/s]
95%|████████████████████████████████████ | 70.6M/74.3M [01:28<00:04, 824kB/s]
95%|████████████████████████████████████▏ | 70.7M/74.3M [01:28<00:04, 838kB/s]
95%|████████████████████████████████████▏ | 70.8M/74.3M [01:28<00:04, 780kB/s]
95%|████████████████████████████████████▎ | 70.9M/74.3M [01:28<00:04, 801kB/s]
96%|████████████████████████████████████▎ | 71.0M/74.3M [01:29<00:04, 785kB/s]
96%|████████████████████████████████████▍ | 71.1M/74.3M [01:29<00:04, 775kB/s]
96%|████████████████████████████████████▍ | 71.2M/74.3M [01:29<00:03, 805kB/s]
96%|████████████████████████████████████▌ | 71.3M/74.3M [01:29<00:03, 862kB/s]
96%|████████████████████████████████████▌ | 71.4M/74.3M [01:29<00:03, 826kB/s]
96%|████████████████████████████████████▌ | 71.5M/74.3M [01:29<00:03, 768kB/s]
96%|████████████████████████████████████▋ | 71.6M/74.3M [01:29<00:03, 716kB/s]
97%|████████████████████████████████████▋ | 71.7M/74.3M [01:29<00:03, 696kB/s]
97%|███████████████████████████████████▊ | 71.9M/74.3M [01:30<00:02, 1.05MB/s]
97%|████████████████████████████████████▊ | 72.0M/74.3M [01:30<00:02, 972kB/s]
97%|████████████████████████████████████▉ | 72.1M/74.3M [01:30<00:02, 902kB/s]
97%|████████████████████████████████████▉ | 72.3M/74.3M [01:30<00:02, 955kB/s]
98%|████████████████████████████████████ | 72.5M/74.3M [01:30<00:01, 1.08MB/s]
98%|████████████████████████████████████▏| 72.6M/74.3M [01:30<00:01, 1.13MB/s]
98%|████████████████████████████████████▏| 72.7M/74.3M [01:30<00:01, 1.05MB/s]
98%|█████████████████████████████████████▎| 72.8M/74.3M [01:31<00:01, 998kB/s]
98%|█████████████████████████████████████▎| 72.9M/74.3M [01:31<00:01, 927kB/s]
98%|█████████████████████████████████████▍| 73.1M/74.3M [01:31<00:01, 942kB/s]
99%|█████████████████████████████████████▍| 73.2M/74.3M [01:31<00:01, 878kB/s]
99%|█████████████████████████████████████▍| 73.3M/74.3M [01:31<00:01, 817kB/s]
99%|█████████████████████████████████████▌| 73.3M/74.3M [01:31<00:01, 760kB/s]
99%|█████████████████████████████████████▌| 73.4M/74.3M [01:31<00:01, 749kB/s]
99%|█████████████████████████████████████▋| 73.6M/74.3M [01:31<00:00, 895kB/s]
99%|█████████████████████████████████████▋| 73.7M/74.3M [01:32<00:00, 962kB/s]
99%|█████████████████████████████████████▊| 73.8M/74.3M [01:32<00:00, 899kB/s]
100%|█████████████████████████████████████▊| 73.9M/74.3M [01:32<00:00, 855kB/s]
100%|█████████████████████████████████████▉| 74.0M/74.3M [01:32<00:00, 824kB/s]
100%|█████████████████████████████████████▉| 74.1M/74.3M [01:32<00:00, 767kB/s]
100%|█████████████████████████████████████▉| 74.2M/74.3M [01:32<00:00, 795kB/s]
0%| | 0.00/74.3M [00:00<?, ?B/s]
100%|██████████████████████████████████████| 74.3M/74.3M [00:00<00:00, 529GB/s]
score time samples ... dataset pipeline codecarbon_task_name
0 0.628709 2.470331 768.0 ... CustomDataset3 MDM
1 0.542075 5.426785 1356.0 ... CustomDataset3 MDM
2 0.617500 0.371004 120.0 ... CustomDataset3 MDM
3 0.642500 0.379192 120.0 ... CustomDataset3 MDM
[4 rows x 13 columns]
Total running time of the script: (3 minutes 13.760 seconds)
Estimated memory usage: 905 MB
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