def load_results(experiment_root):
# load the model (mlp_best.pkl)
model_file = os.path.join(experiment_root, 'mlp_best.pkl');
with log_timing(log, 'loading model from {}'.format(model_file)):
model = serial.load(model_file);
# load train
train_yaml_file = os.path.join(experiment_root, 'train.yaml');
train_yaml = load_yaml_template(train_yaml_file);
# fix dataset path
localizer = PathLocalizer();
train_yaml = localizer.localize_yaml(train_yaml);
with log_timing(log, 'loading train from {}'.format(train_yaml_file)):
train = load_yaml(train_yaml)[0];
return train, model;
def load_results(experiment_root):
# load the model (mlp_best.pkl)
model_file = os.path.join(experiment_root, 'mlp_best.pkl')
with log_timing(log, 'loading model from {}'.format(model_file)):
model = serial.load(model_file)
# load train
train_yaml_file = os.path.join(experiment_root, 'train.yaml')
train_yaml = load_yaml_template(train_yaml_file)
# fix dataset path
localizer = PathLocalizer()
train_yaml = localizer.localize_yaml(train_yaml)
with log_timing(log, 'loading train from {}'.format(train_yaml_file)):
train = load_yaml(train_yaml)[0]
return train, model
config = load_config_file(args.config)
else:
config = empty_config()
if not hasattr(config, 'random_seed'):
random_seed = random.randint(0, 100)
config.random_seed = random_seed
log.debug('using random seed {}'.format(random_seed))
# load optional localizer
if args.localizer is not None:
localizer_class = args.localizer
else:
localizer_class = config.get(
'localizer_class',
'deepthought.datasets.rwanda2013rhythms.PathLocalizer')
# for compatibility with old code
localizer = load_class(localizer_class)
# localize settings
config = localizer.localize_config(config)
# apply settings
train_yaml = train_yaml % config
# localize YAML
train_yaml = localizer.localize_yaml(train_yaml)
train, _ = load_yaml(train_yaml)
train.main_loop()
def run_job(job_id,
meta_job_path,
yaml_template_file,
base_config_path,
hyper_params,
cache_path=None):
# ConstrainedGPEIOptChooser requires NaN or inf to recognize constraints
# BAD_SOLUTION_RETURN_VALUE = np.inf;
BAD_SOLUTION_RETURN_VALUE = 1
# TODO: nice-to-have: make logging a little nicer
init_logging(pylearn2_loglevel=logging.INFO)
for key, value in hyper_params.items():
hyper_params[key] = value[0]
log.debug('{} = {}'.format(key, hyper_params[key]))
base_config = load_config_file(base_config_path)
# fix dataset path
localizer_class = base_config.get(
'localizer_class',
'deepthought.datasets.rwanda2013rhythms.PathLocalizer')
# for compatibility with old code
localizer = load_class(localizer_class)
base_config = localizer.localize_config(base_config)
if not hasattr(base_config, 'random_seed') \
and not hasattr(hyper_params, 'random_seed'):
random_seed = random.randint(0, 100)
hyper_params['random_seed'] = random_seed
log.debug('using random seed {}'.format(random_seed))
param_str = ''
for key in sorted(hyper_params.keys()): # deterministic order
param_str += '_{}_{}'.format(key, hyper_params[key])
verbose_job_id = str(job_id) + param_str
base_config.verbose_job_id = verbose_job_id
if cache_path is None:
cache_path = os.path.join(meta_job_path, 'cache')
job_output_path = os.path.join(meta_job_path, 'output', str(job_id))
output_path = os.path.join(cache_path,
convert_to_valid_filename(param_str))
# check whether cached result already exists
model = None
failed_file = os.path.join(output_path, 'failed')
if os.path.exists(output_path):
# create a link to job-id
symlink(output_path,
job_output_path,
override=True,
ignore_errors=True)
# using cached result
model_file = os.path.join(output_path, 'mlp.pkl')
if os.path.exists(model_file):
try:
with log_timing(
log,
'loading cached model from {}'.format(model_file)):
model = serial.load(model_file)
channels = model.monitor.channels
except Exception as e:
log.error('unexpected exception loading model from {}: {} \n{}'\
.format(model_file, e, traceback.format_exc()))
else:
# if mlp.pkl is missing but mlp-best.pkl is there, then it was a bad configuration
if os.path.exists(failed_file):
log.info('cache contains \'failed\' flag')
return BAD_SOLUTION_RETURN_VALUE
if model is None:
# output_path = os.path.join(
# meta_job_path,
# 'output',
# convert_to_valid_filename(verbose_job_id)
# );
# needs to go here to get the internal reference resolved
base_config.output_path = output_path
# sanity check of structural parameters:
if not structural_param_check(
merge_params(base_config, hyper_params),
raise_error=False,
):
touch(failed_file, mkdirs=True)
# set marker
return BAD_SOLUTION_RETURN_VALUE
ensure_dir_exists(output_path)
symlink(output_path,
job_output_path,
override=True,
ignore_errors=True)
yaml = flatten_yaml(
yaml_file_path=yaml_template_file,
base_config=base_config,
hyper_params=hyper_params,
)
save_yaml_file(yaml_str=yaml,
yaml_file_path=os.path.join(output_path, 'train.yaml'))
with log_timing(log, 'loading yaml for job {}'.format(job_id)):
train = load_yaml(yaml)[0]
with log_timing(log, 'running job {} '.format(job_id)):
try:
train.main_loop()
except Exception as e:
log.error('unexpected exception during training: {} \n{}'\
.format(e, traceback.format_exc()))
touch(failed_file, mkdirs=True)
# set marker
return BAD_SOLUTION_RETURN_VALUE
channels = train.model.monitor.channels
# directly analyze the model from the train object
best_results = _extract_best_results(
channels=channels,
mode='misclass',
check_dataset='valid',
check_channels=['_y_misclass'],
)
best_epochs = _get_best_epochs(best_results)
best_epoch = best_epochs[-1]
# take last entry -> more stable???
datasets = ['train', 'valid', 'test', 'post']
measures = ['_y_misclass', '_objective', '_nll']
print 'results for job {}'.format(job_id)
for measure, dataset in itertools.product(measures, datasets):
channel = dataset + measure
if channel in channels:
value = float(channels[channel].val_record[best_epoch])
print '{:>30} : {:.4f}'.format(channel, value)
# return float(channels['test_y_misclass'].val_record[best_epoch]);
return float(channels['valid_y_misclass'].val_record[best_epoch])
# load optional settings
if args.config is not None:
config = load_config_file(args.config);
else:
config = empty_config();
if not hasattr(config, 'random_seed'):
random_seed = random.randint(0, 100);
config.random_seed = random_seed;
log.debug('using random seed {}'.format(random_seed))
# load optional localizer
if args.localizer is not None:
localizer_class = args.localizer;
else:
localizer_class = config.get('localizer_class',
'deepthought.datasets.rwanda2013rhythms.PathLocalizer'); # for compatibility with old code
localizer = load_class(localizer_class);
# localize settings
config = localizer.localize_config(config);
# apply settings
train_yaml = train_yaml % config;
# localize YAML
train_yaml = localizer.localize_yaml(train_yaml);
train, _ = load_yaml(train_yaml);
train.main_loop();
def run_job(job_id, meta_job_path, yaml_template_file, base_config_path, hyper_params, cache_path=None):
# ConstrainedGPEIOptChooser requires NaN or inf to recognize constraints
# BAD_SOLUTION_RETURN_VALUE = np.inf;
BAD_SOLUTION_RETURN_VALUE = 1;
# TODO: nice-to-have: make logging a little nicer
init_logging(pylearn2_loglevel=logging.INFO);
for key, value in hyper_params.items():
hyper_params[key] = value[0];
log.debug('{} = {}'.format(key, hyper_params[key]));
base_config = load_config_file(base_config_path);
# fix dataset path
localizer_class = base_config.get('localizer_class',
'deepthought.datasets.rwanda2013rhythms.PathLocalizer'); # for compatibility with old code
localizer = load_class(localizer_class);
base_config = localizer.localize_config(base_config);
if not hasattr(base_config, 'random_seed') \
and not hasattr(hyper_params, 'random_seed'):
random_seed = random.randint(0, 100);
hyper_params['random_seed'] = random_seed;
log.debug('using random seed {}'.format(random_seed))
param_str = '';
for key in sorted(hyper_params.keys()): # deterministic order
param_str += '_{}_{}'.format(key, hyper_params[key]);
verbose_job_id = str(job_id) + param_str;
base_config.verbose_job_id = verbose_job_id;
if cache_path is None:
cache_path = os.path.join(meta_job_path, 'cache');
job_output_path = os.path.join(meta_job_path, 'output', str(job_id));
output_path = os.path.join(
cache_path,
convert_to_valid_filename(param_str)
);
# check whether cached result already exists
model = None;
failed_file = os.path.join(output_path, 'failed');
if os.path.exists(output_path):
# create a link to job-id
symlink(output_path, job_output_path, override=True, ignore_errors=True);
# using cached result
model_file = os.path.join(output_path, 'mlp.pkl');
if os.path.exists(model_file):
try:
with log_timing(log, 'loading cached model from {}'.format(model_file)):
model = serial.load(model_file);
channels = model.monitor.channels;
except Exception as e:
log.error('unexpected exception loading model from {}: {} \n{}'\
.format(model_file, e, traceback.format_exc()));
else:
# if mlp.pkl is missing but mlp-best.pkl is there, then it was a bad configuration
if os.path.exists(failed_file):
log.info('cache contains \'failed\' flag');
return BAD_SOLUTION_RETURN_VALUE;
if model is None:
# output_path = os.path.join(
# meta_job_path,
# 'output',
# convert_to_valid_filename(verbose_job_id)
# );
# needs to go here to get the internal reference resolved
base_config.output_path = output_path;
# sanity check of structural parameters:
if not structural_param_check(
merge_params(base_config, hyper_params),
raise_error=False,
):
touch(failed_file, mkdirs=True); # set marker
return BAD_SOLUTION_RETURN_VALUE;
ensure_dir_exists(output_path);
symlink(output_path, job_output_path, override=True, ignore_errors=True);
yaml = flatten_yaml(
yaml_file_path = yaml_template_file,
base_config = base_config,
hyper_params = hyper_params,
);
save_yaml_file(
yaml_str = yaml,
yaml_file_path = os.path.join(output_path, 'train.yaml')
);
with log_timing(log, 'loading yaml for job {}'.format(job_id)):
train = load_yaml(yaml)[0];
with log_timing(log, 'running job {} '.format(job_id)):
try:
train.main_loop();
except Exception as e:
log.error('unexpected exception during training: {} \n{}'\
.format(e, traceback.format_exc()));
touch(failed_file, mkdirs=True); # set marker
return BAD_SOLUTION_RETURN_VALUE;
channels = train.model.monitor.channels;
# directly analyze the model from the train object
best_results = _extract_best_results(
channels=channels,
mode='misclass',
check_dataset='valid',
check_channels=['_y_misclass'],
);
best_epochs = _get_best_epochs(best_results);
best_epoch = best_epochs[-1]; # take last entry -> more stable???
datasets = ['train', 'valid', 'test', 'post'];
measures = ['_y_misclass', '_objective', '_nll'];
print 'results for job {}'.format(job_id);
for measure,dataset in itertools.product(measures,datasets):
channel = dataset+measure;
if channel in channels:
value = float(channels[channel].val_record[best_epoch]);
print '{:>30} : {:.4f}'.format(channel, value);
# return float(channels['test_y_misclass'].val_record[best_epoch]);
return float(channels['valid_y_misclass'].val_record[best_epoch]);