def compare_brains_in_regions(brain_regions, regions_to_voxels):
for region_name, voxels in regions_to_voxels[i].items():
if region_name in best_brain_regions:
if region_name not in brain_regions:
brain_regions[region_name] = []
voxels = [v for v in voxels if v in brain_fs[i].get_selected_indexes()]
brain_regions[region_name].append(brains[-1][:, voxels])
for region_name in brain_regions:
if min(map(len, brain_regions[region_name])) > 0:
x_brain, C_brain = get_dists(brain_regions[region_name])
klz_brain, labels_brain = compute_dist_of_dists(x_brain, C_brain, brain_labels)
print(region_name, '\t', np.mean(klz_brain), '\t', np.std(klz_brain))
plot(klz_brain, labels_brain)
all_embeddings = []
all_labels = []
for key in embeddings.keys():
all_embeddings += embeddings[key][1:]
all_labels += labels[key][1:]
#x, C = get_dists(brains + all_embeddings)
#klz, prz, labels_ = compute_dist_of_dists(x, C, brain_labels + all_labels)
#plot(prz, brain_regions_labels)
#klz = np.asarray(klz)
#print(klz.shape)
#print(brain_regions_labels)
import csv
x, C = get_dists(all_embeddings)
klz, prz, labels_ = compute_dist_of_dists(x, C, all_labels)
with open('bert_com_sim_prz_' + '.csv', 'w') as f:
writer = csv.writer(f)
writer.writerow(all_labels)
writer.writerows(prz)
print(all_labels)
plot(prz, all_labels)
half = int(len(all_labels) / 2)
for i in np.arange(half):
print(all_labels[i].split("_")[-1],
all_labels[i + half].split("_")[-1], prz[i][i + half])
#x, C = get_dists(brains + all_embeddings)
#klz, prz, labels_ = compute_dist_of_dists(x, C, brain_labels + all_labels)
#plot(prz, brain_regions_labels)
#klz = np.asarray(klz)
#print(klz.shape)
#print(brain_regions_labels)
for context_size in np.arange(7):
output = str(context_size) + ' '
for key in embeddings:
print(key, embeddings[key][context_size].shape)
embedding_key = key + '_' + str(context_size)
x, C = get_dists(brains + [embeddings[key][context_size]])
klz, prz, labels_ = compute_dist_of_dists(
x, C, brain_labels + [embedding_key])
output += str(np.mean(prz[8, 0:8])) + ' '
print(output)
"""
x, C = get_dists(brain_regions)
klz, prz, labels_ = compute_dist_of_dists(x, C, brain_regions_labels)
region_sim_dic = {}
for region in best_brain_regions:
region_sim_dic[region] = []
for i in np.arange(len(brain_regions_labels)):
if brain_regions_labels[i].endswith(region):
for j in np.arange(len(brain_regions_labels)):
region_sim_dic[region].append(klz[i][j])
open(os.path.join(hparams.emb_save_dir,
str(i) + '_Brain'), 'rb'))
for b in blocks:
brains[-1].extend(a['brain_activations'][b][11 + delay:-3])
brains[-1] = np.asarray(brains[-1])
brain_fs[i].fit(brains[-1])
original_selected_voxels = brain_fs[i].get_selected_indexes()
print(len(original_selected_voxels))
brains[-1] = brains[-1][:, original_selected_voxels]
brain_labels.append('brain_' + str(i))
print(brains[-1].shape)
print('avg brain shape:', )
x, C = get_dists(brains + all_embeddings)
klz, prz, labels_ = compute_dist_of_dists(x, C, brain_labels + all_labels)
plot(prz, labels_)
klz = np.asarray(klz)
print(klz.shape)
import csv
with open('klz_' + str(delay) + '.csv', "w") as f:
writer = csv.writer(f)
writer.writerows(klz)
with open('prz_' + str(delay) + '.csv', "w") as f:
writer = csv.writer(f)
writer.writerows(prz)
#Get brain regions:
labels[embedding_key].append(embedding_key + '_context_' +
str(context_size))
print("####################################")
all_embeddings_per_context_length = {}
all_labels_per_context_length = {}
for context_size in np.arange(7):
all_embeddings_per_context_length[context_size] = []
all_labels_per_context_length[context_size] = []
for key in embeddings.keys():
all_embeddings_per_context_length[context_size] += [
embeddings[key][context_size]
]
all_labels_per_context_length[context_size] += [
labels[key][context_size]
]
import csv
for context_size in np.arange(7):
x, C = get_dists(all_embeddings_per_context_length[context_size])
klz, prz, labels_ = compute_dist_of_dists(
x, C, all_labels_per_context_length[context_size])
print(context_size, ':', np.mean(prz))
with open('com_sim_prz_' + str(context_size) + '.csv', 'w') as f:
writer = csv.writer(f)
writer.writerow(all_labels_per_context_length[context_size])
writer.writerows(prz)
plot(prz, all_labels_per_context_length[context_size])
#print(klz.shape)
#print(brain_regions_labels)
print(brain_regions_labels)
for context_size in np.arange(1):
print('context_size:', context_size)
for bi in np.arange(len(brain_regions_labels)):
print(brain_regions_labels[bi])
output = brain_regions_labels[bi][0] + ' '
for key in embeddings:
print(key, embeddings[key][context_size].shape)
embedding_key = key + '_' + str(context_size)
x, C = get_dists([brain_regions[bi]] +
[embeddings[key][context_size]])
klz, prz, labels_ = compute_dist_of_dists(
x, C, brain_regions_labels[bi] + [embedding_key])
output += str(np.mean(prz[1, 0])) + ' '
print(output)
"""
x, C = get_dists(brain_regions)
klz, prz, labels_ = compute_dist_of_dists(x, C, brain_regions_labels)
region_sim_dic = {}
for region in best_brain_regions:
region_sim_dic[region] = []
for i in np.arange(len(brain_regions_labels)):
if brain_regions_labels[i].endswith(region):
for j in np.arange(len(brain_regions_labels)):
region_sim_dic[region].append(klz[i][j])
selected_voxels = [v for v in original_selected_voxels if voxel_to_regions[i][v] in best_brain_regions]
brains[-1] = brains[-1][:, selected_voxels]
brain_labels.append('brain_' + str(i))
print(brains[-1].shape)
#x, C = get_dists(brains + all_embeddings)
#klz, prz, labels_ = compute_dist_of_dists(x, C, brain_labels + all_labels)
#plot(prz, brain_regions_labels)
#klz = np.asarray(klz)
#print(klz.shape)
#print(brain_regions_labels)
x, C = get_dists(brain_regions)
klz, prz, labels_ = compute_dist_of_dists(x, C, brain_regions_labels)
region_sim_dic = {}
for region in best_brain_regions:
region_sim_dic[region] = []
for i in np.arange(len(brain_regions_labels)):
if brain_regions_labels[i].endswith(region):
for j in np.arange(len(brain_regions_labels)):
region_sim_dic[region].append(klz[i][j])
for key in region_sim_dic:
print(key," ", np.mean(region_sim_dic[key]), len(region_sim_dic[key]))
#import csv
# with open('whole_selected_klz_'+str(delay)+'.csv', 'w') as f:
normal_random_1000 = np.random.standard_normal(
size=(all_embeddings[0].shape[0], 1000))
randoms = [
uniform_random_300, normal_random_300, uniform_random_100,
normal_random_100, uniform_random_1000, normal_random_1000
]
random_labels = [
'uniform_random_300', 'normal_ransom_300', 'uniform_random_100',
'normal_random_100', 'uniform_random_1000', 'normal_random_1000'
]
print("brain shapes:", brain_regions[0].shape)
x, C = get_dists(brain_regions + all_embeddings + randoms)
klz, prz, labels_, p_vals = compute_dist_of_dists(
x, C, brain_regions_labels + all_labels + random_labels)
plot(prz, labels_)
plot(p_vals, labels_)
klz = np.asarray(klz)
print(klz.shape)
dic2save = {"klz": klz, "prz": prz, "labels_": labels_, "p_vals": p_vals}
np.save(
'_'.join(list(map(str, FLAGS.blocks))) +
"rsa_results_all_brain_regions_" + str(delay) + "_" +
selecting_feature, dic2save)
x, C = get_dists(brains + all_embeddings + randoms)
klz, prz, labels_, p_vals = compute_dist_of_dists(
x, C, brain_labels + all_labels + random_labels)
