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voxel_selection_parallel.py
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voxel_selection_parallel.py
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import errno
import numpy as np
import scipy.io as scio
import sys
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Ridge
from utils import load_pickle, disc_pr, load_data_meta, extract_sent_embed
import argparse
import glob
import string
import os
def mkdir_p(path):
try:
os.makedirs(path)
except OSError as exc: # Python >2.5
if exc.errno == errno.EEXIST and os.path.isdir(path):
pass
else:
raise
def load_embeddings(data_file):
with open(data_file, 'r') as fd:
lines = fd.readlines()
words = [l.strip().split()[0] for l in lines]
d = [map(float, l.strip().split()[1:]) for l in lines]
dims = len(d[0])
vec_dict = dict(zip(words, d))
return vec_dict, dims
def parse_voxels(loaded_mat):
concepts = loaded_mat['keyConcept'].shape[0]
return {
loaded_mat['keyConcept'][c][0][0]: loaded_mat['examples'][c]
for c in range(concepts)
}
def parse_meta(loaded_mat):
voxels2neigh = loaded_mat['meta']['voxelsToNeighbours'][0][0]
nneigh = loaded_mat['meta']['numberOfNeighbors'][0][0].flatten()
return voxels2neigh, nneigh
def parse_meta1(meta):
voxels2neigh = np.expand_dims(meta['voxelsToNeighbours'], axis=0)[0]
nneigh = np.expand_dims(meta['numberOfNeighbours'], axis=0)[0]
return voxels2neigh, nneigh
def load_voxels(mat_file):
loaded = scio.loadmat(mat_file)
vxs = parse_voxels(loaded)
voxels2neigh, nneigh = parse_meta(loaded)
return vxs, voxels2neigh, nneigh
def search_light(v, nneigh, voxels2neigh):
neigh = voxels2neigh[v, :nneigh[v]] - 1
assert np.all(neigh >= 0)
return neigh
def create_X_y(v, nneigh, voxels2neigh, semantic_vectors, mri_vectors):
neigh = search_light(v, nneigh, voxels2neigh)
X, y = [], []
for concept in semantic_vectors.keys():
X.append(mri_vectors[concept][neigh])
y.append(semantic_vectors[concept])
X, y = np.array(X), np.array(y)
return X, y
def split_folds(X, y, k=10):
k_fold = KFold(k)
X_cv_train = []
y_cv_train = []
X_cv_test = []
y_cv_test = []
for train_idx, test_idx in k_fold.split(X, y):
X_cv_train.append(X[train_idx])
y_cv_train.append(y[train_idx])
X_cv_test.append(X[test_idx])
y_cv_test.append(y[test_idx])
return X_cv_train, y_cv_train, X_cv_test, y_cv_test
def voxel_scores(mri_vectors, semantic_vectors, meta):
alpha = 1.0
n_folds = 10
voxels2neigh, nneigh = parse_meta1(meta)
semantic_dims = next(iter(semantic_vectors.values())).shape[0]
num_voxels = voxels2neigh.shape[0]
num_concepts = len(mri_vectors.keys())
scores = np.zeros((semantic_dims, num_voxels))
for v in range(num_voxels):
print('Voxel {} of {}'.format(v, num_voxels))
X, y = create_X_y(
v, nneigh, voxels2neigh,
semantic_vectors, mri_vectors)
X_cv_train, y_cv_train, X_cv_test, y_cv_test = split_folds(
X, y, k=n_folds)
dot_sum = np.zeros(semantic_dims)
for fold in range(len(X_cv_train)):
X_tr = (StandardScaler(with_mean=True, with_std=True)
.fit_transform(X_cv_train[fold]))
y_tr = (StandardScaler(with_mean=True, with_std=True)
.fit_transform(y_cv_train[fold]))
X_te = (StandardScaler(with_mean=True, with_std=True)
.fit_transform(X_cv_test[fold]))
ridge = Ridge(alpha=alpha, normalize=False).fit(X_tr, y_tr)
y_pred = ridge.predict(X_te)
y_pred_z = (StandardScaler(with_mean=True, with_std=True)
.fit_transform(y_pred))
dot_sum += (y_pred_z * y_cv_test[fold]).sum(axis=0)
scores[:, v] = dot_sum / float(num_concepts)
return scores
#def calcu
def load_exp1(data_dir):
w2vec_dict = load_pickle('./stimuli/word2vec.pkl')
exp_id = int((data_dir.split('/')[-2]).split('_')[0][-1])
assert exp_id == 1
fld = data_dir
# Run one participant
data_files = sorted(glob.glob(fld + '/*'))
dt_fls_grouped = [tuple(data_files[i:i + 2]) for i in
range(0, len(data_files), 2)]
print(fld)
disc_pr()
# for every file wordcloud pictures and sentences cases
for data_group in dt_fls_grouped:
data_dict, metadata = load_data_meta(data_group)
word_dict = dict()
for word, _ in data_dict.items():
word_dict[word] = w2vec_dict[word]
yield data_group[0], data_dict, word_dict, metadata
def load_exp23(data_dir):
exp_id = int((data_dir.split('/')[-2]).split('_')[0][-1])
assert exp_id == 2 or exp_id == 3
fld = data_dir
# Run one participant
data_files = sorted(glob.glob(fld + '/*'))
dt_fls_grouped = [tuple(data_files[i:i + 2]) for i in
range(0, len(data_files), 2)]
# for every file here data and meta
for data_group in dt_fls_grouped:
print('\t{}'.format(data_group))
data_dict, metadata = load_data_meta(data_group)
word_dict = dict()
for sent, _ in data_dict.items():
word_dict[sent] = extract_sent_embed(sent)
yield data_group[0], data_dict, word_dict, metadata
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-i', '-data_dir', dest="data_dir", required=True)
args = parser.parse_args()
print(args.data_dir)
# assert 'data_processed' not in args.data_dir, 'You should rename your {} to data_processed'.format(args.data_dir)
exp = int((args.data_dir.split('/')[-2]).split('_')[0][-1])
assert exp == 1 or exp == 2 or exp == 3
assert 'exp' in args.data_dir.split('/')[-2]
if exp == 1:
data_gen = load_exp1(args.data_dir)
disc_pr()
# how to access a generator silly boy :*
for x in data_gen:
print(x[0])
out_file = os.path.join('./', 'voxels_scores', '{}.npy'.format(x[0].split('.')[0]))
out_dir = '/'.join(out_file.split('/')[:-1])
mkdir_p(out_dir)
vscores = voxel_scores(x[1], x[2], x[3])
np.save(out_file, vscores)
elif exp == 2 or exp == 3:
load_exp23(args.data_dir)
else:
raise ValueError("Illegal value for data folder .Select from{1,2,3}")