data/examples/tutorials/tutorial_5_combining_datasets.py

"""
=============================================================
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)