def extract_output(experiment_root): train, model = load_results(experiment_root); # get the datasets with their names from the monitor for key, dataset in train.algorithm.monitoring_dataset.items(): # process each dataset with log_timing(log, 'processing dataset \'{}\''.format(key)): y_real, y_pred, output = process_dataset(model, dataset) save(os.path.join(experiment_root, 'cache', key+'_output.pklz'), (y_real, y_pred, output));
def extract_output(experiment_root): train, model = load_results(experiment_root) # get the datasets with their names from the monitor for key, dataset in train.algorithm.monitoring_dataset.items(): # process each dataset with log_timing(log, 'processing dataset \'{}\''.format(key)): y_real, y_pred, output = process_dataset(model, dataset) save(os.path.join(experiment_root, 'cache', key + '_output.pklz'), (y_real, y_pred, output))
import logging logger = logging.getLogger() logger.setLevel(logging.DEBUG) import os import deepthought DATA_PATH = os.path.join(deepthought.DATA_PATH, 'rwanda2013rhythms') MODEL_PATH = os.path.join(deepthought.OUTPUT_PATH, 'nips2014', 'models', 'h0') OUTPUT_PATH = os.path.join(deepthought.OUTPUT_PATH, 'nips2014', 'figures', 'h0') print 'data path : {}'.format(DATA_PATH) print 'model path : {}'.format(MODEL_PATH) print 'output path: {}'.format(OUTPUT_PATH) # test with subject 4 # WARNING: code seems to be broken due to library update! from deepthought.experiments.nips2014.scripts.generate_plots import load_results from deepthought.pylearn2ext.util import process_dataset path4 = os.path.join(MODEL_PATH, '4', 'best') train, model = load_results(path4) dataset = train.algorithm.monitoring_dataset['test'] y_real, y_pred, output = process_dataset(model, dataset) # subject 4 analysis from deepthought.experiments.nips2014.scripts.generate_plots import multi_level_accuracy_analysis multi_level_accuracy_analysis(y_real, y_pred) from deepthought.pylearn2ext.util import aggregate_classification t_real, t_pred, t_predf, t_predp = aggregate_classification( dataset.trial_partitions, y_real, y_pred, output) multi_level_accuracy_analysis(t_real, t_pred)
logger.setLevel(logging.DEBUG) import os; import deepthought; DATA_PATH = os.path.join(deepthought.DATA_PATH, 'rwanda2013rhythms'); MODEL_PATH = os.path.join(deepthought.OUTPUT_PATH, 'nips2014', 'models', 'h0'); OUTPUT_PATH = os.path.join(deepthought.OUTPUT_PATH, 'nips2014', 'figures', 'h0'); print 'data path : {}'.format(DATA_PATH); print 'model path : {}'.format(MODEL_PATH); print 'output path: {}'.format(OUTPUT_PATH); # test with subject 4 # WARNING: code seems to be broken due to library update! from deepthought.experiments.nips2014.scripts.generate_plots import load_results; from deepthought.pylearn2ext.util import process_dataset; path4 = os.path.join(MODEL_PATH, '4', 'best'); train, model = load_results(path4); dataset = train.algorithm.monitoring_dataset['test']; y_real, y_pred, output = process_dataset(model, dataset); # subject 4 analysis from deepthought.experiments.nips2014.scripts.generate_plots import multi_level_accuracy_analysis; multi_level_accuracy_analysis(y_real, y_pred); from deepthought.pylearn2ext.util import aggregate_classification t_real, t_pred, t_predf, t_predp = aggregate_classification(dataset.trial_partitions, y_real, y_pred, output); multi_level_accuracy_analysis(t_real, t_pred);