"""
=====================
Within Session SSVEP
=====================

This Example shows how to perform a within-session SSVEP analysis on the
MAMEM dataset 3, using a CCA pipeline.

The within-session evaluation assesses the performance of a classification
pipeline using a 5-fold cross-validation. The reported metric (here, accuracy)
is the average of all fold.
"""

# Authors: Sylvain Chevallier <sylvain.chevallier@uvsq.fr>
#
# License: BSD (3-clause)

import warnings

import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.pipeline import make_pipeline

import moabb
from moabb.datasets import Kalunga2016
from moabb.evaluations import WithinSessionEvaluation
from moabb.paradigms import SSVEP
from moabb.pipelines import SSVEP_CCA


warnings.simplefilter(action="ignore", category=FutureWarning)
warnings.simplefilter(action="ignore", category=RuntimeWarning)
moabb.set_log_level("info")

###############################################################################
# Loading Dataset
# ---------------
#
# Load 2 subjects of Kalunga2016 dataset

subj = [1, 3]
dataset = Kalunga2016()
dataset.subject_list = subj

###############################################################################
# Choose Paradigm
# ---------------
#
# We select the paradigm SSVEP, applying a bandpass filter (3-15 Hz) on
# the data and we keep only the first 3 classes, that is stimulation
# frequency of 13Hz, 17Hz and 21Hz.

paradigm = SSVEP(fmin=10, fmax=40, n_classes=3)

##############################################################################
# Create Pipelines
# ----------------
#
# Use a Canonical Correlation Analysis classifier

interval = dataset.interval
freqs = paradigm.used_events(dataset)

pipeline = {}
pipeline["CCA"] = make_pipeline(SSVEP_CCA(n_harmonics=3))

##############################################################################
# Get Data (optional)
# -------------------
#
# To get access to the EEG signals downloaded from the dataset, you could
# use `dataset.get_data(subjects=[subject_id])` to obtain the EEG under
# MNE format, stored in a dictionary of sessions and runs.
# Otherwise, `paradigm.get_data(dataset=dataset, subjects=[subject_id])`
# allows to obtain the EEG data in scikit format, the labels and the meta
# information. In `paradigm.get_data`, the EEG are preprocessed according
# to the paradigm requirement.

# sessions = dataset.get_data(subjects=[3])
# X, labels, meta = paradigm.get_data(dataset=dataset, subjects=[3])

##############################################################################
# Evaluation
# ----------
#
# The evaluation will return a DataFrame containing a single AUC score for
# each subject and pipeline.

overwrite = True  # set to True if we want to overwrite cached results

evaluation = WithinSessionEvaluation(
    paradigm=paradigm, datasets=dataset, suffix="examples", overwrite=overwrite
)
results = evaluation.process(pipeline)

print(results.head())

##############################################################################
# Plot Results
# ----------------
#
# Here we plot the results, indicating the score for each subject

plt.figure()
sns.barplot(data=results, y="score", x="session", hue="subject", palette="viridis")

##############################################################################
# And the computation time in seconds

plt.figure()
ax = sns.barplot(data=results, y="time", x="session", hue="subject", palette="Reds")
ax.set_ylabel("Time (s)")
plt.show()