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adaboost_src_label.py
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adaboost_src_label.py
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"""Doc string Here."""
import mne
from mne.minimum_norm import (apply_inverse_epochs, read_inverse_operator)
import socket
import numpy as np
# import matplotlib.pyplot as plt
from sklearn.ensemble import AdaBoostClassifier
# from sklearn.tree import DecisionTreeClassifier
# from sklearn.metrics import confusion_matrix
from sklearn.cross_validation import StratifiedKFold
from sklearn.preprocessing import scale
# import seaborn as sns
# Setup paths and prepare raw data
hostname = socket.gethostname()
if hostname == "Wintermute":
# data_path = "/home/mje/mnt/caa/scratch/"
data_path = "/home/mje/Projects/malthe_alpha_project/data/"
n_jobs = 1
else:
data_path = "/projects/MINDLAB2015_MEG-CorticalAlphaAttention/scratch/"
n_jobs = 1
subjects_dir = data_path + "fs_subjects_dir/"
fname_inv = data_path + '0001-meg-oct-6-inv.fif'
fname_epochs = data_path + '0001_p_03_filter_ds_ica-mc_tsss-epo.fif'
fname_evoked = data_path + "0001_p_03_filter_ds_ica-mc_raw_tsss-ave.fif"
snr = 1.0 # Standard assumption for average data but using it for single trial
lambda2 = 1.0 / snr ** 2
method = "dSPM" # use dSPM method (could also be MNE or sLORETA)
# load labels
labels = mne.read_labels_from_annot('0001', parc='PALS_B12_Lobes',
# regexp="Bro",
subjects_dir=subjects_dir)
labels_occ = [labels[9], labels[10]]
# Load data
inverse_operator = read_inverse_operator(fname_inv)
epochs = mne.read_epochs(fname_epochs)
epochs.crop(0, 1.1)
epochs.resample(200)
stcs_ent_left = apply_inverse_epochs(epochs["ent_left"], inverse_operator,
lambda2, method, pick_ori="normal")
stcs_ent_right = apply_inverse_epochs(epochs["ent_right"], inverse_operator,
lambda2, method, pick_ori="normal")
stcs_ctl_left = apply_inverse_epochs(epochs["ctl_left"], inverse_operator,
lambda2, method, pick_ori="normal")
#src_ctl_l = np.asarray([stc.data.reshape(-1) for stc in stcs_ctl_left])
#src_ent_l = np.asarray([stc.data.reshape(-1) for stc in stcs_ent_left])
#data_ctl_l = np.squeeze(np.asarray(
# mne.extract_label_time_course(stcs_ctl_left,
# labels_occ[1],
# inverse_operator["src"],
# mode="pca_flip")))
#
#data_ent_l = np.squeeze(np.asarray(
# mne.extract_label_time_course(stcs_ent_left,
# labels_occ[1],
# inverse_operator["src"],
# mode="pca_flip")))
data_ent_l = np.asarray([stc.in_label(labels_occ[1]).data.reshape(-1)
for stc in stcs_ent_left])
data_ent_r = np.asarray([stc.in_label(labels_occ[1]).data.reshape(-1)
for stc in stcs_ent_right])
# data_ctl_l = [stc.in_label(labels_occ[1]).data.reshape(-1)
# for stc in stcs_ctl_left]
X = np.vstack([data_ent_l, data_ent_r]) # data for classiication
# Classes for X
y = np.concatenate([np.zeros(len(data_ent_l)), np.ones(len(data_ent_r))])
# Setup classificer
bdt = AdaBoostClassifier(algorithm="SAMME.R",
n_estimators=1000)
n_folds = 10 # number of folds used in cv
cv = StratifiedKFold(y, n_folds=n_folds)
scores = np.zeros(n_folds) # aaray to save scores
feature_importance = np.zeros(X.shape[1]) # array to save features
for ii, (train, test) in enumerate(cv):
bdt.fit(X[train], y[train])
y_pred = bdt.predict(X[test])
y_test = y[test]
scores[ii] = np.sum(y_pred == y_test) / float(len(y_test))
feature_importance += bdt.feature_importances_
feature_importance_std = scale(feature_importance)
feature_importance /= (ii + 1) # create average importance
# # create mask to avoid division error
# feature_importance = np.ma.masked_array(feature_importance,
# feature_importance == 0)
# # normalize scores for visualization purposes
# feature_importance /= feature_importance.std(axis=1)[:, None]
# feature_importance -= feature_importance.mean(axis=1)[:, None]
vertices = [np.array([], int), stc.in_label(labels_occ[1]).rh_vertno]
shape = stcs_ent_left[0].in_label(labels_occ[1]).shape
stc_feat = mne.SourceEstimate(feature_importance.reshape(shape), vertices=vertices,
tmin=0, tstep=stc.tstep,
subject='0001')
stc_feat.save(data_path + "stc_adaboost_feature_label_LvR")
stc_feat_std = mne.SourceEstimate(feature_importance_std.reshape(shape),
vertices=vertices,
tmin=0, tstep=stc.tstep,
subject='0001')
stc_feat_std.save(data_path + "stc_adaboost_feature_label_std_LvR")
np.savetxt(data_path + "adaboost_label_scores_LvR.csv", scores, delimiter=",")
# scores_10 = cross_val_score(bdt, X, y, cv=10, n_jobs=1, verbose=False)