def process_jobs(config):
common_config = config.common;
for job in config.jobs:
log.info('Processing job {} with base {}'.format(job.name, job.base));
job_config = merge_params(common_config, config[job.base]);
log.debug('job overrides: {}'.format(job.overrides));
job_config = merge_params(job_config, job.overrides);
job_config.experiment_root = os.path.join(
config.output_root,
job_config.type,
job.name
);
log.debug('experiment root: {}'.format(job_config.experiment_root));
print job_config;
# try:
if job_config.type == 'cnn':
train_convnet(job_config);
elif job_config.type == 'fftcnn':
train_convnet(job_config);
elif job_config.type == 'sda':
train_mlp(job_config);
else:
log.error('unsupported job type {}'.format(job_config.type));
def process_jobs(config):
common_config = config.common
for job in config.jobs:
log.info('Processing job {} with base {}'.format(job.name, job.base))
job_config = merge_params(common_config, config[job.base])
log.debug('job overrides: {}'.format(job.overrides))
job_config = merge_params(job_config, job.overrides)
job_config.experiment_root = os.path.join(config.output_root,
job_config.type, job.name)
log.debug('experiment root: {}'.format(job_config.experiment_root))
print job_config
# try:
if job_config.type == 'cnn':
train_convnet(job_config)
elif job_config.type == 'fftcnn':
train_convnet(job_config)
elif job_config.type == 'sda':
train_mlp(job_config)
else:
log.error('unsupported job type {}'.format(job_config.type))
def load_datasets_for_subjects(dataset_params, subjects, suffix=''):
datasets = {}
for key, params in dataset_params.items():
if not key in dataset_names:
continue;
params['subjects'] = subjects;
params['name'] = params['name']+suffix;
dataset_config = merge_params(config, params);
dataset, dataset_yaml = load_yaml_file(
os.path.join(os.path.dirname(__file__), 'run', 'dataset_template.yaml'),
params=dataset_config,
);
# log.info('dataset loaded. X={} y={}'.format(dataset.X.shape, dataset.y.shape));
datasets[key+suffix] = dataset;
del dataset, dataset_yaml;
return datasets;
def run_experiment(config, hyper_params, random_seeds):
experiment_root = hyper_params['experiment_root'];
best_acc = -1;
best_results = [np.NAN, np.NAN, np.NAN];
for seed in random_seeds:
hyper_params['random_seed'] = seed;
hyper_params['experiment_root'] = experiment_root + '.' + str(seed);
params = merge_params(config, hyper_params);
if os.path.exists(os.path.join(params.experiment_root, 'mlp.pkl')):
print 'found existing mlp.pkl: {}'.format(params.experiment_root);
else:
print 'no mlp.pkl found at: {}'.format(params.experiment_root);
if not config.get('only_extract_results', False):
train_convnet(params);
try:
values = extract_results(params.experiment_root, mode='misclass');
results = np.multiply(100, [
# 1 - values['test_y_misclass'],
# 1 - values['test_wseq_misclass_rate'],
# 1 - values['test_wtrial_misclass_rate']]);
1 - values['frame_misclass'],
1 - values['sequence_misclass'],
1 - values['trial_misclass']]);
# save the best results
if np.max(results[2]) > best_acc:
best_results = results;
best_acc = np.max(results[2]);
except:
print traceback.format_exc();
results = [np.NAN, np.NAN, np.NAN];
print 'results for seed {}: {}'.format(seed, results);
if params.save_output:
output = extract_output(params, values['best_epoch']);
save(os.path.join(params.experiment_root, 'best_output.pklz'), output);
print 'best results: {}'.format(best_results);
return best_results;
def run_experiment(config, hyper_params, random_seeds):
if config.global_sda == False:
hyper_params = fix_local_sda_config(hyper_params)
experiment_root = hyper_params['experiment_root']
best_acc = -1
best_results = [np.NAN, np.NAN, np.NAN]
for seed in random_seeds:
hyper_params['random_seed'] = seed
hyper_params['experiment_root'] = experiment_root + '.' + str(seed)
params = merge_params(config, hyper_params)
if os.path.exists(os.path.join(params.experiment_root, 'mlp.pkl')):
print 'found existing mlp.pkl: {}'.format(params.experiment_root)
else:
print 'no mlp.pkl found at: {}'.format(params.experiment_root)
if not config.get('only_extract_results', False):
train_mlp(params)
try:
values = extract_results(params.experiment_root, mode='misclass')
results = np.multiply(
100,
[
# 1 - values['test_y_misclass'],
# 1 - values['test_wseq_misclass_rate'],
# 1 - values['test_wtrial_misclass_rate']]);
1 - values['frame_misclass'],
1 - values['sequence_misclass'],
1 - values['trial_misclass']
])
# save the best results
if np.max(results[2]) > best_acc:
best_results = results
best_acc = np.max(results[2])
except:
print traceback.format_exc()
results = [np.NAN, np.NAN, np.NAN]
print 'results for seed {}: {}'.format(seed, results)
print 'best results: {}'.format(best_results)
return best_results
def load_datasets_for_subjects(dataset_params, subjects, suffix=''):
datasets = {}
for key, params in dataset_params.items():
if not key in dataset_names:
continue
params['subjects'] = subjects
params['name'] = params['name'] + suffix
dataset_config = merge_params(config, params)
dataset, dataset_yaml = load_yaml_file(
os.path.join(os.path.dirname(__file__), 'run',
'dataset_template.yaml'),
params=dataset_config,
)
# log.info('dataset loaded. X={} y={}'.format(dataset.X.shape, dataset.y.shape));
datasets[key + suffix] = dataset
del dataset, dataset_yaml
return datasets
with log_timing(log, 'training MLP'):
train.main_loop();
log.info('done');
def get_default_config_path():
return os.path.join(os.path.dirname(__file__),'train_sda_mlp.cfg');
if __name__ == '__main__':
# config = load_config(default_config='../../train_sda.cfg', reset_logging=False);
config = load_config(default_config=get_default_config_path(), reset_logging=False);
hyper_params = {
};
params = merge_params(config, hyper_params);
if not config.get('only_extract_results', False):
train_mlp(params);
scan_for_best_performance(params.experiment_root, 'valid_y_misclass');
scan_for_best_performance(params.experiment_root, 'valid_ptrial_misclass_rate')
values = extract_results(config.experiment_root, mode='misclass');
print np.multiply(100, [
# 1 - values['test_y_misclass'],
# 1 - values['test_wseq_misclass_rate'],
# 1 - values['test_wtrial_misclass_rate']]);
1 - values['frame_misclass'],
log.debug('job overrides: {}'.format(job.overrides))
job_config = merge_params(job_config, job.overrides)
job_config.experiment_root = os.path.join(config.output_root,
job_config.type, job.name)
log.debug('experiment root: {}'.format(job_config.experiment_root))
print job_config
# try:
if job_config.type == 'cnn':
train_convnet(job_config)
elif job_config.type == 'fftcnn':
train_convnet(job_config)
elif job_config.type == 'sda':
train_mlp(job_config)
else:
log.error('unsupported job type {}'.format(job_config.type))
# except:
# log.fatal("Unexpected error:", sys.exc_info());
if __name__ == '__main__':
default_config = os.path.join(os.path.dirname(__file__), 'batch.cfg')
config = load_config(default_config=default_config, reset_logging=False)
config = merge_params(Config(file(default_config)), config)
process_jobs(config)
log.info('done')
def get_default_config_path():
return os.path.join(os.path.dirname(__file__), 'train_sda_mlp.cfg')
if __name__ == '__main__':
# config = load_config(default_config='../../train_sda.cfg', reset_logging=False);
config = load_config(default_config=get_default_config_path(),
reset_logging=False)
hyper_params = {}
params = merge_params(config, hyper_params)
if not config.get('only_extract_results', False):
train_mlp(params)
scan_for_best_performance(params.experiment_root, 'valid_y_misclass')
scan_for_best_performance(params.experiment_root,
'valid_ptrial_misclass_rate')
values = extract_results(config.experiment_root, mode='misclass')
print np.multiply(
100,
[
# 1 - values['test_y_misclass'],
# 1 - values['test_wseq_misclass_rate'],
job_config.experiment_root = os.path.join(
config.output_root,
job_config.type,
job.name
);
log.debug('experiment root: {}'.format(job_config.experiment_root));
print job_config;
# try:
if job_config.type == 'cnn':
train_convnet(job_config);
elif job_config.type == 'fftcnn':
train_convnet(job_config);
elif job_config.type == 'sda':
train_mlp(job_config);
else:
log.error('unsupported job type {}'.format(job_config.type));
# except:
# log.fatal("Unexpected error:", sys.exc_info());
if __name__ == '__main__':
default_config = os.path.join(os.path.dirname(__file__), 'batch.cfg');
config = load_config(default_config=default_config, reset_logging=False);
config = merge_params(Config(file(default_config)), config);
process_jobs(config);
def pair_cross_trial_test(config, pairs=None):
if pairs is None:
pairs = [
[18, 19],
[20, 21],
[22, 23],
[0, 1],
[15, 9],
[16, 17],
[11, 5],
[12, 6],
[2, 3],
[10, 4],
[13, 7],
[14, 8],
]
# config.experiment_root = os.path.join(config.experiment_root, 'cross-trial') ;
accuracy = np.zeros(len(pairs))
results = np.zeros([len(pairs), 3])
for i in xrange(len(pairs)):
test_stimulus_ids = pairs[i]
train_stimulus_ids = set()
for j in xrange(48):
if not j in test_stimulus_ids:
train_stimulus_ids.add(j)
train_stimulus_ids = list(train_stimulus_ids)
log.info('training stimuli: {} \t test stimuli: {}'.format(
train_stimulus_ids, test_stimulus_ids))
hyper_params = {
'experiment_root':
os.path.join(config.experiment_root, 'pair' + str(pairs[i])),
'remove_train_stimulus_ids':
test_stimulus_ids,
'remove_test_stimulus_ids':
train_stimulus_ids,
}
params = merge_params(config, hyper_params)
# dummy values for testing
# results[i] = [i, i*10, i*100.];
# accuracy[i] = 0;
# continue;
if os.path.exists(os.path.join(params.experiment_root, 'mlp.pkl')):
print 'found existing mlp.pkl: {}'.format(params.experiment_root)
else:
print 'no mlp.pkl found at: {}'.format(params.experiment_root)
if not config.get('only_extract_results', False):
train_mlp(params)
values = extract_results(params.experiment_root, mode='misclass')
results[i] = np.multiply(
100,
[
# 1 - values['test_y_misclass'],
# 1 - values['test_wseq_misclass_rate'],
# 1 - values['test_wtrial_misclass_rate']]);
1 - values['frame_misclass'],
1 - values['sequence_misclass'],
1 - values['trial_misclass']
])
accuracy[i] = 100 * (1 - extract_best_result(
params.experiment_root, mode='misclass', check_dataset='test')[0])
print results
print results.mean(axis=0)
print results.max(axis=1)
print accuracy
print accuracy.mean()
return results, accuracy
def pair_cross_trial_test(config, pairs=None):
if pairs is None:
pairs = [
[18, 19],
[20, 21],
[22, 23],
[ 0, 1],
[15, 9],
[16, 17],
[11, 5],
[12, 6],
[ 2, 3],
[10, 4],
[13, 7],
[14, 8],
];
# config.experiment_root = os.path.join(config.experiment_root, 'cross-trial') ;
accuracy = np.zeros(len(pairs))
results = np.zeros([len(pairs),3]);
for i in xrange(len(pairs)):
test_stimulus_ids = pairs[i];
train_stimulus_ids = set();
for j in xrange(48):
if not j in test_stimulus_ids:
train_stimulus_ids.add(j);
train_stimulus_ids = list(train_stimulus_ids);
log.info('training stimuli: {} \t test stimuli: {}'.format(train_stimulus_ids, test_stimulus_ids));
hyper_params = {
'experiment_root' : os.path.join(config.experiment_root, 'pair'+str(pairs[i])),
'remove_train_stimulus_ids' : test_stimulus_ids,
'remove_test_stimulus_ids' : train_stimulus_ids,
};
params = merge_params(config, hyper_params);
# dummy values for testing
# results[i] = [i, i*10, i*100.];
# accuracy[i] = 0;
# continue;
if os.path.exists(os.path.join(params.experiment_root, 'mlp.pkl')):
print 'found existing mlp.pkl: {}'.format(params.experiment_root);
else:
print 'no mlp.pkl found at: {}'.format(params.experiment_root);
if not config.get('only_extract_results', False):
train_mlp(params);
values = extract_results(params.experiment_root, mode='misclass');
results[i] = np.multiply(100, [
# 1 - values['test_y_misclass'],
# 1 - values['test_wseq_misclass_rate'],
# 1 - values['test_wtrial_misclass_rate']]);
1 - values['frame_misclass'],
1 - values['sequence_misclass'],
1 - values['trial_misclass']]);
accuracy[i] = 100 * (1 - extract_best_result(params.experiment_root, mode='misclass', check_dataset='test')[0]);
print results;
print results.mean(axis=0);
print results.max(axis=1);
print accuracy;
print accuracy.mean();
return results, accuracy;