def main():
main_dir = os.path.split(os.getcwd())[0]
result_dir = main_dir + '/results'
# args = sys.argv[1:]
# experiment = args[0]
# nor_method = args[1]
# clf_method = args[2]
# source_id = int(args[3])
# target_id = int(args[4])
# op_function = args[5] # 'lpl1' 'l1l2'
metric_xst = 'h'
experiment = 'fmrir'
nor_method = 'no'
clf_method = 'logis'
source_id = 1
target_id = 2
op_function = 'l1l2' # 'lpl1' 'l1l2'
if experiment == 'fmril':
source = fmril
elif experiment == 'fmrir':
source = fmrir
else:
source = meg
result_dir = result_dir + '/{}'.format(experiment)
y_target = source.y_target
subjects = np.array(source.subjects)
x_data = source.x_data_pow if experiment == 'meg' else source.x_data
x_indi = source.x_indi_pow if experiment == 'meg' else source.x_indi
x = x_indi if nor_method == 'indi' else x_data
indices_sub = fucs.split_subjects(subjects)
nb_subs = len(indices_sub)
i = source_id
j = target_id
xs = x[indices_sub[i]]
ys = y_target[indices_sub[i]]
xt = x[indices_sub[j]]
yt = y_target[indices_sub[j]]
note = '{}s_{}t_{}_{}_{}_sinkhorn_{}_{}'.format(i, j, experiment,
nor_method, clf_method,
op_function, metric_xst)
accs = fucs.get_accuracy(xs, ys, xt, yt, clf_method='logis')
print(accs)
def main():
main_dir = os.path.split(os.getcwd())[0]
result_dir = main_dir + '/results'
# args = sys.argv[1:]
# experiment = args[0]
# nor_method = args[1]
# clf_method = args[2]
# source_id = int(args[3])
# target_id = int(args[4])
# op_function = args[5] # 'lpl1' 'l1l2'
metric_xst = "cityblock"
experiment = 'fmril'
nor_method = 'no'
clf_method = 'svm'
source_id = 1
target_id = 2
op_function = 'l1l2' # 'lpl1' 'l1l2'
if experiment == 'fmril':
source = fmril
elif experiment == 'fmrir':
source = fmrir
else:
source = meg
result_dir = result_dir + '/{}'.format(experiment)
y_target = source.y_target
subjects = np.array(source.subjects)
x_data = source.x_data_pow if experiment == 'meg' else source.x_data
x_indi = source.x_indi_pow if experiment == 'meg' else source.x_indi
x = x_indi if nor_method == 'indi' else x_data
indices_sub = fucs.split_subjects(subjects)
nb_subs = len(indices_sub)
i = source_id
j = target_id
xs = x[indices_sub[i]]
ys = y_target[indices_sub[i]]
xt = x[indices_sub[j]]
yt = y_target[indices_sub[j]]
note = '{}s_{}t_{}_{}_{}_sinkhorn_{}_{}'.format(i, j, experiment,
nor_method, clf_method,
op_function, metric_xst)
print(note)
# cityblock sqeuclidian,
best_xst, ot_params = fucs.pairsubs_sinkhorn_lables(xs,
ys,
xt,
ot_method=op_function,
metric=metric_xst,
clf_method=clf_method)
# train on xst, test on xt
ot_accs = fucs.get_accuracy(best_xst, ys, xt, yt, clf_method=clf_method)
ot_score = np.mean(ot_accs)
print('ot source data predicts on original target, accuracy', ot_score,
ot_accs)
# train on xs, test on xt
ori_accs = fucs.get_accuracy(xs, ys, xt, yt, clf_method=clf_method)
ori_score = np.mean(ori_accs)
print('original source data predicts on original target, accuracy',
ori_score, ori_accs)
# train on xs indi-nor, test on xt indi-nor
x_base = x_indi.copy()
xs_base = x_base[indices_sub[i]]
ys_base = y_target[indices_sub[i]]
xt_base = x_base[indices_sub[j]]
yt_base = y_target[indices_sub[j]]
base_accs = fucs.get_accuracy(xs_base,
ys_base,
xt_base,
yt_base,
clf_method=clf_method)
base_score = np.mean(base_accs)
print(
'indi-normalized source predicts on indi-normalized target, accuracy',
base_score, base_accs)
pair_params = {}
pair_params['source'] = i
pair_params['target'] = j
pair_params['ori_score'] = round(ori_score, 4)
pair_params['ori_accs'] = ori_accs
pair_params['ot_accs'] = ot_accs
pair_params['ot_score'] = round(ot_score, 4)
pair_params['base_accs'] = base_accs
pair_params['base_score'] = round(base_score, 4)
pair_params['params'] = ot_params
ttest = stats.ttest_rel(base_accs, ot_accs)
pair_params['ttest'] = (ttest.pvalue, ttest.statistic)
print('pair_params', pair_params)
# if ttest.statistic > 0 and ttest.pvalue < 0.05:
# joblib.dump(pair_params, result_dir+'/big_ori/{}_pair_params.pkl'.format(note))
# elif ttest.statistic > 0 and ttest.pvalue >= 0.05:
# joblib.dump(pair_params, result_dir + '/big_ori_non_significant/{}_pair_params.pkl'.format(note))
# elif ttest.statistic <= 0 and ttest.pvalue >= 0.05:
# joblib.dump(pair_params, result_dir + '/big_ot_non_significant/{}_pair_params.pkl'.format(note))
# else:
# joblib.dump(pair_params, result_dir + '/big_ot/{}_pair_params.pkl'.format(note))
print(
'-----------------------------------------------------------------------------------------'
)
print(best_xst, xs)
# print(scores_params)
# note = '{}s_{}ts_{}_{}_{}_sinkhorn_{}_{}'.format(i, len(target_ids), experiment,
# nor_method, clf_method, op_function, metric_xst)
# joblib.dump(scores_params, result_dir + '/{}_score_params.pkl'.format(note))
# print('unsuccessful transport', unsec)
print(time.strftime('%Y-%m-%d %A %X %Z', time.localtime(time.time())))
def main():
main_dir = os.path.split(os.getcwd())[0]
result_dir = main_dir + '/results'
args = sys.argv[1:]
experiment = args[0]
nor_method = args[1]
clf_method = args[2]
source_id = int(args[3])
target_id = int(args[4])
op_function = args[5] # 'lpl1' 'l1l2' 'maplin'
# experiment = 'fmril'
# nor_method = 'no'
# clf_method = 'svm'
# source_id = 3
# target_id = 2
# op_function = 'maplin' # 'lpl1' 'l1l2'
if experiment == 'fmril':
source = fmril
elif experiment == 'fmrir':
source = fmrir
else:
source = meg
result_dir = result_dir + '/{}'.format(experiment)
y_target = source.y_target
subjects = np.array(source.subjects)
x_data = source.x_data_pow if experiment == 'meg' else source.x_data
x_indi = source.x_indi_pow if experiment == 'meg' else source.x_indi
x = x_indi if nor_method == 'indi' else x_data
indices_sub = fucs.split_subjects(subjects)
i = source_id
j = target_id
xs = x[indices_sub[i]]
ys = y_target[indices_sub[i]]
xt = x[indices_sub[j]]
yt = y_target[indices_sub[j]]
note = '{}s_{}t_{}_{}_{}_{}_circulaire'.format(i, j, experiment,
nor_method, clf_method, op_function)
print(note)
# cityblock sqeuclidian, minkowski
reg, eta = fucs.pairsubs_circular(xs, ys, xt, yt, ot_method=op_function, clf_method=clf_method)
print('best reg and eta', reg, eta)
if op_function == 'l1l2':
transport = ot.da.SinkhornL1l2Transport(reg_e=reg, reg_cl=eta, norm='max')
elif op_function == 'lpl1':
transport = ot.da.SinkhornLpl1Transport(reg_e=reg, reg_cl=eta, norm='max')
elif op_function == 'maplin':
transport = ot.da.MappingTransport(kernel="linear", mu=reg, eta=eta, bias=True, norm='max')
else:
print('Warning: need to choose among "l1l2", "lpl1" and "maplin"', op_function)
# train on xst, test on xt
transport.fit(Xs=xs, Xt=xt, ys=ys)
xst = transport.transform(Xs=xs)
ot_accs = fucs.get_accuracy(xst, ys, xt, yt, clf_method=clf_method)
ot_score = np.mean(ot_accs)
print('train on ot source data, predict on original target, accuracy', ot_score, ot_accs)
# train on xs, test on xt
ori_accs = fucs.get_accuracy(xs, ys, xt, yt, clf_method=clf_method)
ori_score = np.mean(ori_accs)
print('train on original source data, predict on original target, accuracy', ori_score, ori_accs)
# train on xs indi-nor, test on xt indi-nor
x_base = x_indi.copy()
xs_base = x_base[indices_sub[i]]
ys_base = y_target[indices_sub[i]]
xt_base = x_base[indices_sub[j]]
yt_base = y_target[indices_sub[j]]
base_accs = fucs.get_accuracy(xs_base, ys_base, xt_base, yt_base, clf_method=clf_method)
base_score = np.mean(base_accs)
print('base: indi-normalized source predicts on indi-normalized target, accuracy', base_score, base_accs)
pair_params = {}
pair_params['source'] = i
pair_params['target'] = j
pair_params['ori_score'] = round(ori_score, 4)
pair_params['ori_accs'] = ori_accs
pair_params['ot_accs'] = ot_accs
pair_params['ot_score'] = round(ot_score, 4)
pair_params['base_accs'] = base_accs
pair_params['base_score'] = round(base_score, 4)
pair_params['params'] = (reg, eta)
ttest_base_ot = stats.ttest_rel(base_accs,ot_accs)
pair_params['ttest_base_ot'] = (ttest_base_ot.pvalue, ttest_base_ot.statistic)
ttest_ori_ot = stats.ttest_rel(ori_accs, ot_accs)
pair_params['ttest_ori_ot'] = (ttest_ori_ot.pvalue, ttest_ori_ot.statistic)
print('pair_params', pair_params)
if ttest_base_ot .statistic > 0:
joblib.dump(pair_params, result_dir+'/big_base/{}_pair_params.pkl'.format(note))
else:
joblib.dump(pair_params, result_dir + '/small_base/{}_pair_params.pkl'.format(note))
if ttest_ori_ot .statistic > 0:
joblib.dump(pair_params, result_dir+'/big_ori/{}_pair_params.pkl'.format(note))
else:
joblib.dump(pair_params, result_dir + '/small_ori/{}_pair_params.pkl'.format(note))
# if ttest.statistic > 0 and ttest.pvalue < 0.05:
# joblib.dump(pair_params, result_dir+'/big_ori/{}_pair_params.pkl'.format(note))
# elif ttest.statistic > 0 and ttest.pvalue >= 0.05:
# joblib.dump(pair_params, result_dir + '/big_ori_non_significant/{}_pair_params.pkl'.format(note))
# elif ttest.statistic <= 0 and ttest.pvalue >= 0.05:
# joblib.dump(pair_params, result_dir + '/big_ot_non_significant/{}_pair_params.pkl'.format(note))
# else:
# joblib.dump(pair_params, result_dir + '/big_ot/{}_pair_params.pkl'.format(note))
print('-----------------------------------------------------------------------------------------')
# print(scores_params)
# note = '{}s_{}ts_{}_{}_{}_sinkhorn_{}_{}'.format(i, len(target_ids), experiment,
# nor_method, clf_method, op_function, metric_xst)
# joblib.dump(scores_params, result_dir + '/{}_score_params.pkl'.format(note))
# print('unsuccessful transport', unsec)
print(time.strftime('%Y-%m-%d %A %X %Z', time.localtime(time.time())))
def main():
main_dir = os.path.split(os.getcwd())[0]
result_dir = main_dir + '/results'
# args = sys.argv[1:]
# experiment = args[0]
# nor_method = args[1]
# clf_method = args[2]
# source_id = int(args[3])
# target_id = int(args[4])
# op_function = args[5] # 'lpl1' 'l1l2'
metric_xst = "h"
experiment = 'newfmri'
nor_method = 'no'
clf_method = 'svm'
source_id = 1
target_id = 2
op_function = 'l1l2' # 'lpl1' 'l1l2'
if experiment == 'fmril':
source = fmril
elif experiment == 'fmrir':
source = fmrir
elif experiment == 'meg':
source = meg
else:
source = newfmri
result_dir = result_dir + '/{}'.format(experiment)
y_target = source.y_target
subjects = np.array(source.subjects)
x_data = source.x_data_pow if experiment == 'meg' else source.x_data
x_indi = source.x_indi_pow if experiment == 'meg' else source.x_indi
x = x_indi if nor_method == 'indi' else x_data
indices_sub = fucs.split_subjects(subjects)
nb_subs = len(indices_sub)
pca = PCA(n_components=144)
i = source_id
j = target_id
xs = x[indices_sub[i]]
xs_pca = pca.fit_transform(xs)
ys = y_target[indices_sub[i]]
xt = x[indices_sub[j]]
xt_pca = pca.fit_transform(xt)
yt = y_target[indices_sub[j]]
note = 'new_pca_h_{}s_{}t_{}_{}_{}_{}'.format(i, j, experiment, nor_method,
clf_method, op_function)
print(note)
# cityblock sqeuclidian, minkowski
best_xst, ot_params, params_acc = fucs.pairsubs_sinkhorn_lables_h(
xs, ys, xt, ot_method=op_function, clf_method=clf_method)
# train on xst, test on xt
ot_accs = fucs.get_accuracy(best_xst, ys, xt, yt, clf_method=clf_method)
ot_score = np.mean(ot_accs)
print('ot source data predicts on original target, accuracy', ot_score,
ot_accs)
# train on xs, test on xt
ori_accs = fucs.get_accuracy(xs, ys, xt, yt, clf_method=clf_method)
ori_score = np.mean(ori_accs)
print('original source data predicts on original target, accuracy',
ori_score, ori_accs)
# train on xs indi-nor, test on xt indi-nor
x_base = x_indi.copy()
xs_base = x_base[indices_sub[i]]
ys_base = y_target[indices_sub[i]]
xt_base = x_base[indices_sub[j]]
yt_base = y_target[indices_sub[j]]
base_accs = fucs.get_accuracy(xs_base,
ys_base,
xt_base,
yt_base,
clf_method=clf_method)
base_score = np.mean(base_accs)
print(
'base: indi-normalized source predicts on indi-normalized target, accuracy',
base_score, base_accs)
pair_params = {}
pair_params['source'] = i
pair_params['target'] = j
pair_params['ori_score'] = round(ori_score, 4)
pair_params['ori_accs'] = ori_accs
pair_params['ot_accs'] = ot_accs
pair_params['ot_score'] = round(ot_score, 4)
pair_params['base_accs'] = base_accs
pair_params['base_score'] = round(base_score, 4)
pair_params['params'] = ot_params
pair_params['params_acc'] = params_acc
ttest_base_ot = stats.ttest_rel(base_accs, ot_accs)
pair_params['ttest'] = (ttest_base_ot.pvalue, ttest_base_ot.statistic)
print('pair_params', pair_params)
ttest_ori_ot = stats.ttest_rel(ori_accs, ot_accs)
pair_params['ttest_ori_ot'] = (ttest_ori_ot.pvalue, ttest_ori_ot.statistic)
if ttest_base_ot.statistic > 0:
joblib.dump(pair_params,
result_dir + '/big_base/{}_pair_params.pkl'.format(note))
else:
joblib.dump(pair_params,
result_dir + '/small_base/{}_pair_params.pkl'.format(note))
if ttest_ori_ot.statistic > 0:
joblib.dump(pair_params,
result_dir + '/big_ori/{}_pair_params.pkl'.format(note))
else:
joblib.dump(pair_params,
result_dir + '/small_ori/{}_pair_params.pkl'.format(note))
reg_coor = np.log10(params_acc['params'])
zero_coor = reg_coor[params_acc['acc'] == 0]
other_coor = reg_coor[params_acc['acc'] != 0]
plt.figure(figsize=(9, 5))
plt.subplot(1, 2, 1)
plt.plot(zero_coor[:, 0], zero_coor[:, 1], '+r', label='acc is 0.5')
plt.plot(other_coor[:, 0], other_coor[:, 1], 'ob', label='acc not 0.5')
plt.legend(loc=0)
plt.title(note)
plt.subplot(1, 2, 2)
accs = params_acc['acc'].reshape((6, 6))
accs2 = np.empty((accs.T.shape))
for k in range(accs.shape[0]):
accs2[:, k] = accs[k][::-1]
plt.imshow(accs2)
plt.colorbar()
plt.title('imshow')
plt.savefig(main_dir + '/figs/acc_imshow_{}.png'.format(note))
print(
'-----------------------------------------------------------------------------------------'
)
# print(scores_params)
# note = '{}s_{}ts_{}_{}_{}_sinkhorn_{}_{}'.format(i, len(target_ids), experiment,
# nor_method, clf_method, op_function, metric_xst)
# joblib.dump(scores_params, result_dir + '/{}_score_params.pkl'.format(note))
# print('unsuccessful transport', unsec)
print(time.strftime('%Y-%m-%d %A %X %Z', time.localtime(time.time())))