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()