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Part_time_general_w_time.py
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def Cat_all(sub):
suffics = { 1:'no_max',
2:'max_wo_head',
3:'max_w_head'}
sensor = ('meg','grad','mag')
delta_T = 50
participant_arr=list(Part_info.keys())
participant=participant_arr[sub-1]
Category = {
"11":"move",
"12":"still",
"21":"big",
"22":"small",
"31":"nat",
"32":"man"}
def run_time_general(mod, cat, Category, participant, delta_T, sens,xx,method):
cat_1 = mod+'/'+Category[str(cat)]
cat_2 = mod+'/'+Category[str(cat+1)]
train_1 = epochs[cat_1].copy()
train_2 = epochs[cat_2].copy()
mod_test = 'p' if mod == 'w' else 'w'
cat_1 = mod_test+'/'+Category[str(cat)]
cat_2 = mod_test+'/'+Category[str(cat+1)]
test_1 = epochs[cat_1].copy()
test_2 = epochs[cat_2].copy()
epochs_train = mne.concatenate_epochs([train_1 ,train_2])
epochs_train = epochs_train.copy().apply_baseline(baseline=(-0.1, 0))
X_train = epochs_train.get_data(picks=sens)
num_conditions, num_channels, num_timepoints = X_train.shape
X_train_lagged = np.zeros((num_conditions, num_channels*num_lags, num_timepoints-num_lags))
for i in range(num_timepoints-num_lags):
lagged_data = np.concatenate([X_train[:, :, i+j] for j in range(num_lags)], axis=1)
X_train_lagged[:, :, i] = lagged_data.copy()
tr_1=np.unique(train_1.events[:,2])
tr_2=np.unique(train_2.events[:,2])
merged_events = mne.merge_events(epochs_train.events, tr_1, 1)
merged_events = mne.merge_events(merged_events, tr_2, 2)
epochs_train.events=merged_events
y_train = merged_events[:,2]
epochs_test = mne.concatenate_epochs([test_1 ,test_2])
epochs_test = epochs_test.copy().apply_baseline(baseline=(-0.1, 0))
X_test = epochs_test.get_data(picks=sens)
num_conditions, num_channels, num_timepoints = X_test.shape
X_test_lagged = np.zeros((num_conditions, num_channels*num_lags, num_timepoints-num_lags))
for i in range(num_timepoints-num_lags):
lagged_data = np.concatenate([X_test[:, :, i+j] for j in range(num_lags)], axis=1)
X_test_lagged[:, :, i] = lagged_data.copy()
tst_1=np.unique(test_1.events[:,2])
tst_2=np.unique(test_2.events[:,2])
merged_events = mne.merge_events(epochs_test.events, tst_1, 1)
merged_events = mne.merge_events(merged_events, tst_2, 2)
epochs_test.events=merged_events
y_test = merged_events[:,2]
clf = make_pipeline(Vectorizer(),StandardScaler(),
LinearModel(sklearn.svm.SVC(kernel = 'linear')))
time_decod = SlidingEstimator(clf, n_jobs=-1, scoring='roc_auc', verbose=True)
time_decod.fit(X_train_lagged, y_train)
scores = time_decod.score(X_test_lagged, y_test)
row_idx = (mod=='p')*3 + (cat//10-1)
scores_all[row_idx, :] = scores
times_all[:] = epochs_test.times[0:(num_timepoints-num_lags)]
fig=plt.figure(row_idx)
plt.plot(times_all, scores, label='score')
plt.axhline(.5, color='k', linestyle='--', label='chance')
plt.xlabel('Times')
plt.ylabel('AUC') # Area Under the Curve
plt.legend()
plt.axvline(.0, color='k', linestyle='-')
plt.title(cat_1+' vc '+cat_2+method+'_train_'+mod)
filename_fig = op.join(path_to_save, 'train_'+mod+'_'+Category[str(cat)]+'VC'+Category[str(cat+1)]+method +'.png')
fig.savefig(filename_fig, dpi=600)
#plt.close(fig)
time_gen = GeneralizingEstimator(clf, n_jobs=-1, scoring='roc_auc',
verbose=True)
time_gen.fit(X_train_lagged, y_train)
scores_timegen = time_gen.score(X_test_lagged, y_test)
row_idx = (mod=='p')*3 + (cat//10-1)
scores_gen_all[row_idx, :,:] = scores_timegen
times_gen_all[:] = epochs_test.times[0:(num_timepoints-num_lags)]
fig, ax = plt.subplots(1, 1)
plt.imshow(scores_timegen, interpolation='nearest', origin='lower', cmap='RdBu_r',
vmin=0, vmax=1)
ax.set_xlabel('Times Test (ms)' + '/' + mod_test)
ax.set_ylabel('Times Train (ms)' + '/' + mod)
ax.set_title('Time generalization ' + Category[str(cat)]+'VC'+Category[str(cat+1)])
plt.axvline(0, color='k')
plt.axhline(0, color='k')
plt.colorbar()
filename_fig = op.join(path_to_save, 'Time generalization ' + Category[str(cat)]+'VC'+Category[str(cat+1)]+'_'+method +'.png')
fig.savefig(filename_fig, dpi=600)
#plt.close(fig)
for xx in [2]:
for sens in ['meg']:
method = '_' + suffics[xx] + '_' + sens +'_w_T_'+str(delta_T)
result_all_path='/rds/projects/k/kowalcau-opm-recordings/cross_modal_project/Across_participants/Time_generalization_wT/'+ suffics[xx]+'/'
data_path ='/rds/projects/k/kowalcau-opm-recordings/cross_modal_project/'+participant+'/'
result_path=data_path+'proccessed/' + suffics[xx]
path_to_save=result_path + '/Time_gen_w_time/'
data_name='full'
if not os.path.exists(result_all_path):
os.makedirs(result_all_path)
if not os.path.exists(result_path):
os.makedirs(result_path)
if not os.path.exists(path_to_save):
os.makedirs(path_to_save)
path_file = os.path.join(result_path, data_name + '_' + suffics[xx] + '_supertrials-right.fif')
epochs_raw = mne.read_epochs(path_file, preload=True, verbose=True)
epochs_raw.event_id=events_id
epochs=epochs_raw.copy().filter(1,30).crop(tmin=-0.1, tmax=0.7)
T_full = epochs.resample(500).times
num_lags = round(delta_T/(T_full[2]-T_full[1])/1000)
scores_all = np.zeros((len(['w', 'p']) * len([11, 21, 31]), len(T_full)-num_lags))
times_all = np.zeros(len(T_full)-num_lags)
scores_gen_all = np.zeros((len(['w', 'p']) * len([11, 21, 31]), len(T_full)-num_lags,len(T_full)-num_lags))
times_gen_all = np.zeros(len(T_full)-num_lags)
results = Parallel(n_jobs=-1)(delayed(run_time_general)(mod, cat, Category, participant, delta_T, sens, xx, method) for mod in ['w', 'p'] for cat in [11, 21, 31])
np.save(result_all_path + 'scores_gen_move_big_nat_W_P_' + str(Part_info[participant]) + method , scores_all)
np.save(result_all_path + 'times_' + method , times_all)
np.save(path_to_save + 'scores_move_big_nat_W_P_' + str(Part_info[participant]) + method , scores_gen_all)
np.save(path_to_save+ 'times_' + method , times_gen_all)
if __name__ == "__main__":
import sys
import os
import numpy as np
import mne
import matplotlib
import sys
#matplotlib.use('Qt5Agg')
import matplotlib.pyplot as plt
from mne.preprocessing import annotate_muscle_zscore
import os.path as op
from mne.preprocessing import ICA
from mne.datasets import sample
from mne.decoding import (SlidingEstimator, GeneralizingEstimator, Scaler,
cross_val_multiscore, LinearModel, get_coef,
Vectorizer, CSP)
import sklearn.svm
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, make_scorer, roc_auc_score
from init_y import *
from joblib import delayed, Parallel
Cat_all(int(sys.argv[1]))