# -*- coding: utf-8 -*-

"""

Created on Sun Mar 5 10:27:24 2017

@author: mje

"""

import numpy as np

from sklearn.linear_model import LogisticRegression

from sklearn.model_selection import (permutation_test_score, StratifiedKFold)

from sklearn.externals import joblib

from my_settings import (data_path, source_folder, step_size, window_size)

# make time points

times = np.arange(-4000, 1001, 1)

times = times / 1000.

selected_times = times[::step_size]

n_time = sum((selected_times + window_size) < times[-1])

# Load data

subjects = [

"0008", "0009", "0010", "0012", "0014", "0015", "0016", "0017", "0018",

"0019", "0020", "0021", "0022"

]

cls_all = []

pln_all = []

for subject in subjects:

cls = np.load(source_folder + "graph_data/%s_cls_pow_sliding.npy" %

subject).item()

pln = np.load(source_folder + "graph_data/%s_pln_pow_sliding.npy" %

subject).item()

cls_tmp = []

cls_tmp.append(cls["cp_alpha"])

cls_tmp.append(cls["cp_beta"])

cls_tmp.append(cls["cp_gamma_low"])

cls_tmp.append(cls["cp_gamma_high"])

pln_tmp = []

pln_tmp.append(pln["cp_alpha"])

pln_tmp.append(pln["cp_beta"])

pln_tmp.append(pln["cp_gamma_low"])

pln_tmp.append(pln["cp_gamma_high"])

cls_all.append(np.asarray(cls_tmp))

pln_all.append(np.asarray(pln_tmp))

data_cls = np.asarray(cls_all)

data_pln = np.asarray(pln_all)

# Load GAT model

gat = joblib.load(data_path + "decode_time_gen/gat_cp.jl")

# Setup data for epochs and cross validation

X = np.vstack([data_cls, data_pln])

y = np.concatenate([np.zeros(len(data_cls)), np.ones(len(data_pln))])

cv = StratifiedKFold(n_splits=7, shuffle=True)

perm_score_results = []

for j, est in enumerate(gat.estimators_):

for tmp in est:

lr_mean = LogisticRegression(C=0.0001)

lr_mean.coef_ = np.asarray([lr.coef_ for lr in est]).mean(

axis=0).squeeze()

lr_mean.intercept_ = np.asarray([lr.intercept_ for lr in est]).mean()

score, perm_score, pval = permutation_test_score(

lr_mean, X[:, :, j], y, cv=cv, scoring="roc_auc", n_permutations=2000)

perm_score_results.append({

"score": score,

"perm_score": perm_score,

"pval": pval

})

joblib.dump(perm_score_results,

data_path + "decode_time_gen/perm_score_results_cp.npy")