hparams.root, hparams.emb_save_dir,
hparams.text_encoder + "_" + hparams.embedding_type + "_" +
hparams.context_mode + "_" + str(hparams.past_window) + "-" +
str(hparams.future_window) + "_onlypast-" + str(hparams.only_past))
print("brain data dir: ", hparams.brain_data_dir)
print("saving dir: ", saving_dir)
TextEncoderDic = {
'bert': BertEncoder(hparams),
}
tf.logging.set_verbosity(tf.logging.INFO)
# Define how we want to read the brain data
print("1. initialize brain data reader ...")
brain_data_reader = HarryPotterReader(data_dir=hparams.brain_data_dir)
# Define how we want to computationaly represent the stimuli
print("2. initialize text encoder ...")
stimuli_encoder = TextEncoderDic[hparams.text_encoder]
# Explain Brain object with no mapper
print("4. initialize Explainer...")
explain_brain = ExplainBrain(hparams, brain_data_reader, stimuli_encoder,
None)
# Load the brain data
tf.logging.info('Loading brain data ...')
time_steps, brain_activations, stimuli, start_steps, end_steps = explain_brain.load_brain_experiment(
)
if __name__ == '__main__':
hparams = FLAGS
print("roots", hparams.root)
hparams.brain_data_dir = os.path.join(hparams.root, hparams.brain_data_dir)
hparams.emb_save_dir = os.path.join(hparams.root, hparams.emb_save_dir)
saving_dir = os.path.join(
hparams.root, hparams.emb_save_dir,
hparams.text_encoder + "_" + hparams.embedding_type + "_" +
hparams.context_mode + "_" + str(hparams.past_window) + "-" +
str(hparams.future_window) + "_onlypast-" + str(hparams.only_past))
print("brain data dir: ", hparams.brain_data_dir)
print("saving dir: ", saving_dir)
brain_data_reader = HarryPotterReader(data_dir=hparams.brain_data_dir)
story_features = np.load('story_features.npy').item()
delay = 2 #2,0,3,1
blocks = [1, 2, 3, 4]
selected_steps = {}
selecting_feature = 'quote'
voxel_to_regions = {}
regions_to_voxels = {}
brain_fs = {}
for subject in [1, 2, 3, 4, 5, 6, 7, 8]:
voxel_to_regions[subject], regions_to_voxels[
subject] = brain_data_reader.get_voxel_to_region_mapping(
subject_id=subject)
brain_fs[subject] = VarianceFeatureSelection()