# --------------------------- Benchmark configuration --------------------------------

# Dataset Configuration

dataset = "zuco2"

rootdir = "../data/train/"

heldout_dir = "../data/test"

heldout_subjects = ["XBB", "XDT", "XLS", "XPB", "XSE", "XTR", "XWS", "XAH", "XBD", "XSS"]

subjects = ['YAC', 'YAG', 'YAK', 'YDG', 'YDR', 'YFR', 'YFS', 'YHS', 'YIS', 'YLS', 'YMD', 'YRK', 'YRP', 'YSD', 'YSL', 'YTL'] # exclude YMH, - YRH, YMS

# Feature Set Configuration: Choose from various feature sets for the benchmark

feature_sets = ["electrode_features_all", "sent_gaze_sacc", "sent_gaze_sacc_eeg_means"] # default

"""

Other possible feature_sets are commented below:

Electrode features: ["electrode_features_theta", "electrode_features_alpha", "electrode_features_beta", "electrode_features_gamma", "electrode_features_all"]

EEG mean values: ["theta_mean", "alpha_mean", "beta_mean", "gamma_mean", "eeg_means"]

Eye tracking: ["fixation_number", "omission_rate", "reading_speed", 'sent_gaze', \

"mean_sacc_dur", "max_sacc_velocity", "mean_sacc_velocity", "max_sacc_dur", "max_sacc_amp", "mean_sacc_amp", 'sent_saccade', 'sent_gaze_sacc']

Combined: ["sent_gaze_eeg_means", "sent_gaze_sacc_eeg_means"]

"""

# Submission Configuration

create_submission = True

# --------------------------- Other configurations --------------------------------

# Experiment Setup

seed = 1

runs = 1

# Experiment Modifications

bootstrap = False # Use only for datasplits where you have the true labels to create CIs

n_bootstraps = 10 # total bootstrap samples are multiplied by #runs

# PCA Preprocessing Configuration

pca_preprocessing = False # can be used as dimensionality reduction for electrode features

explained_variance = 0.95 # median amount of variance to be explained by PCA

# Plotting Configuration

plot_top_electrodes = False

plot_all_subjects_features = False

plot_explained_variance = False

plot_electrode_weights_pca = False

# Data Label Configuration

randomized = False # Randomize labels as a sanity check; default = False

# Save results npy

save_prediction_npy = False

# Task Configuration

class_task = 'tasks-cross-subj'

# SVM Model Configuration

kernel = 'linear' # only linear kernel allows for analysis of coefficients

# EEG information

chanlocs = ['E2', 'E3', 'E4', 'E5', 'E6', 'E7', 'E9', 'E10', 'E11', 'E12', 'E13', 'E15', 'E16', 'E18', 'E19', 'E20',

'E22',

'E23', 'E24', 'E26', 'E27', 'E28', 'E29', 'E30', 'E31', 'E33', 'E34', 'E35', 'E36', 'E37', 'E38', 'E39',

'E40',

'E41', 'E42', 'E43', 'E44', 'E45', 'E46', 'E47', 'E50', 'E51', 'E52', 'E53', 'E54', 'E55', 'E57', 'E58',

'E59',

'E60', 'E61', 'E62', 'E64', 'E65', 'E66', 'E67', 'E69', 'E70', 'E71', 'E72', 'E74', 'E75', 'E76', 'E77',

'E78',

'E79', 'E80', 'E82', 'E83', 'E84', 'E85', 'E86', 'E87', 'E89', 'E90', 'E91', 'E92', 'E93', 'E95', 'E96',

'E97',

'E98', 'E100', 'E101', 'E102', 'E103', 'E104', 'E105', 'E106', 'E108', 'E109', 'E110', 'E111', 'E112',

'E114',

'E115', 'E116', 'E117', 'E118', 'E120', 'E121', 'E122', 'E123', 'E124']