class BaysOptimizer(object):
def __init__(self, name_dataset='Freebase15k', sampling="uniform", args=None):
"""store the information of database"""
model_name = args.model.lower()
self.args = args
self.knowledge_graph = KnowledgeGraph(dataset=name_dataset, negative_sample=sampling)
hyper_params = None
try:
self.model_obj = getattr(importlib.import_module(model_path + ".%s" % modelMap[model_name]),
modelMap[model_name])
self.config_obj = getattr(importlib.import_module(config_path), configMap[model_name])
hyper_params = getattr(importlib.import_module(hyper_param_path), hypMap[model_name])()
except ModuleNotFoundError:
print("%s not implemented! Select from: %s" % (model_name,
' '.join(map(str, modelMap.values()))))
config = self.config_obj()
config.data=name_dataset
# config.set_dataset(name_dataset)
self.trainer = Trainer(model=self.model_obj(config), debug=self.args.debug, tuning=True)
self.search_space = self.define_search_space(hyper_params)
def define_search_space(self, hyper_params):
space = {k: hp.choice(k, v) for k, v in hyper_params.__dict__.items() if not k.startswith('__') and not callable(k)}
return space
def optimize(self):
space = self.search_space
trials = Trials()
best_result = fmin(fn=self.get_loss, space=space, algo=tpe.suggest, max_evals=2, trials=trials)
columns = list(space.keys())
results = pd.DataFrame(columns=['iteration'] + columns + ['loss'])
for idx, trial in enumerate(trials.trials):
row = []
row.append(idx)
translated_eval = space_eval(self.search_space, {k: v[0] for k, v in trial['misc']['vals'].items()})
for k in columns:
row.append(translated_eval[k])
row.append(trial['result']['loss'])
results.loc[idx] = row
path = self.trainer.config.result / self.trainer.model.model_name
path.mkdir(parents=True, exist_ok=True)
results.to_csv(str(path / "trials.csv"), index=False)
print(results)
print('Found Golden Setting:')
pprint(space_eval(space, best_result))
def get_loss(self, params):
self.trainer.config.L1_flag = params['L1_flag']
self.trainer.config.batch_size = params['batch_size']
self.trainer.config.epochs = params['epochs']
self.trainer.config.hidden_size = params['hidden_size']
self.trainer.config.learning_rate = params['learning_rate']
self.trainer.config.margin = params['margin']
self.trainer.config.disp_result = False
self.trainer.config.disp_summary = False
self.trainer.config.save_model = False
self.trainer.config.debug = True
self.trainer.config.test_num = 1000
self.trainer.build_model()
self.trainer.summary_hyperparameter()
loss = self.trainer.tune_model()
# loss = self.trainer.train_model(tuning=True)
return {'loss': loss, 'status': STATUS_OK}
class BaysOptimizer(object):
"""Bayesian optimizer class for tuning hyperparameter.
This class implements the Bayesian Optimizer for tuning the
hyper-parameter.
Args:
args (object): The Argument Parser object providing arguments.
name_dataset (str): The name of the dataset.
sampling (str): sampling to be used for generating negative triples
Examples:
>>> from pykg2vec.config.hyperparams import KGETuneArgParser
>>> from pykg2vec.utils.bayesian_optimizer import BaysOptimizer
>>> model = Complex()
>>> args = KGETuneArgParser().get_args(sys.argv[1:])
>>> bays_opt = BaysOptimizer(args=args)
>>> bays_opt.optimize()
"""
def __init__(self, args=None):
"""store the information of database"""
model_name = args.model.lower()
self.args = args
self.knowledge_graph = KnowledgeGraph(dataset=args.dataset_name,
negative_sample=args.sampling)
hyper_params = None
try:
self.model_obj = getattr(
importlib.import_module(model_path +
".%s" % modelMap[model_name]),
modelMap[model_name])
self.config_obj = getattr(importlib.import_module(config_path),
configMap[model_name])
hyper_params = getattr(importlib.import_module(hyper_param_path),
hypMap[model_name])()
except ModuleNotFoundError:
print("%s not implemented! Select from: %s" %
(model_name, ' '.join(map(str, modelMap.values()))))
config = self.config_obj()
config.data = args.dataset_name
self.trainer = Trainer(model=self.model_obj(config),
debug=self.args.debug,
tuning=True)
self.search_space = self.define_search_space(hyper_params)
self.max_evals = self.args.max_number_trials if not self.args.debug else 1
def define_search_space(self, hyper_params):
"""Function to perform search space addition"""
space = {
k: hp.choice(k, v)
for k, v in hyper_params.__dict__.items()
if not k.startswith('__') and not callable(k)
}
return space
def optimize(self):
"""Function that performs bayesian optimization"""
space = self.search_space
trials = Trials()
best_result = fmin(fn=self.get_loss,
space=space,
algo=tpe.suggest,
max_evals=self.max_evals,
trials=trials)
columns = list(space.keys())
results = pd.DataFrame(columns=['iteration'] + columns + ['loss'])
for idx, trial in enumerate(trials.trials):
row = []
row.append(idx)
translated_eval = space_eval(
self.search_space,
{k: v[0]
for k, v in trial['misc']['vals'].items()})
for k in columns:
row.append(translated_eval[k])
row.append(trial['result']['loss'])
results.loc[idx] = row
path = self.trainer.config.result / self.trainer.model.model_name
path.mkdir(parents=True, exist_ok=True)
results.to_csv(str(path / "trials.csv"), index=False)
print(results)
print('Found Golden Setting:')
pprint(space_eval(space, best_result))
def get_loss(self, params):
"""Function that defines and acquires the loss"""
self.trainer.config.L1_flag = params['L1_flag']
self.trainer.config.batch_size = params['batch_size']
self.trainer.config.epochs = params['epochs']
if 'hidden_size' in params:
self.trainer.config.hidden_size = params['hidden_size']
if 'ent_hidden_size' in params:
self.trainer.config.ent_hidden_size = params['ent_hidden_size']
if 'rel_hidden_size' in params:
self.trainer.config.rel_hidden_size = params['rel_hidden_size']
self.trainer.config.learning_rate = params['learning_rate']
self.trainer.config.margin = params['margin']
self.trainer.config.disp_result = False
self.trainer.config.disp_summary = False
self.trainer.config.save_model = False
self.trainer.config.debug = True
self.trainer.config.test_num = 1000
self.trainer.build_model()
self.trainer.summary_hyperparameter()
loss = self.trainer.tune_model()
# loss = self.trainer.train_model(tuning=True)
return {'loss': loss, 'status': STATUS_OK}
class BaysOptimizer(object):
"""Bayesian optimizer class for tuning hyperparameter.
This class implements the Bayesian Optimizer for tuning the
hyper-parameter.
Args:
args (object): The Argument Parser object providing arguments.
name_dataset (str): The name of the dataset.
sampling (str): sampling to be used for generating negative triples
Examples:
>>> from pykg2vec.config.hyperparams import KGETuneArgParser
>>> from pykg2vec.utils.bayesian_optimizer import BaysOptimizer
>>> model = Complex()
>>> args = KGETuneArgParser().get_args(sys.argv[1:])
>>> bays_opt = BaysOptimizer(args=args)
>>> bays_opt.optimize()
"""
def __init__(self, args=None):
"""store the information of database"""
if args.model.lower() in [
"tucker", "tucker_v2", "conve", "convkb", "proje_pointwise"
]:
raise Exception(
"Model %s has not been supported in tuning hyperparameters!" %
args.model)
model_name = args.model.lower()
self.args = args
self.knowledge_graph = KnowledgeGraph(
dataset=args.dataset_name, custom_dataset_path=args.dataset_path)
hyper_params = None
try:
self.model_obj = getattr(
importlib.import_module(model_path +
".%s" % modelMap[model_name]),
modelMap[model_name])
self.config_obj = getattr(importlib.import_module(config_path),
configMap[model_name])
hyper_params = getattr(importlib.import_module(hyper_param_path),
hypMap[model_name])()
except ModuleNotFoundError:
print("%s not implemented! Select from: %s" % \
(model_name, ' '.join(map(str, modelMap.values()))))
from pykg2vec.config.config import KGEArgParser
kge_args = KGEArgParser().get_args([])
kge_args.dataset_name = args.dataset_name
config = self.config_obj(kge_args)
self.trainer = Trainer(model=self.model_obj(config),
debug=self.args.debug,
tuning=True)
self.search_space = hyper_params.search_space
self.max_evals = self.args.max_number_trials if not self.args.debug else 1
def optimize(self):
"""Function that performs bayesian optimization"""
trials = Trials()
self.best_result = fmin(fn=self.get_loss,
space=self.search_space,
trials=trials,
algo=tpe.suggest,
max_evals=self.max_evals)
columns = list(self.search_space.keys())
results = pd.DataFrame(columns=['iteration'] + columns + ['loss'])
for idx, trial in enumerate(trials.trials):
row = []
row.append(idx)
translated_eval = space_eval(
self.search_space,
{k: v[0]
for k, v in trial['misc']['vals'].items()})
for k in columns:
row.append(translated_eval[k])
row.append(trial['result']['loss'])
results.loc[idx] = row
path = self.trainer.config.result / self.trainer.model.model_name
path.mkdir(parents=True, exist_ok=True)
results.to_csv(str(path / "trials.csv"), index=False)
print(results)
print('Found Golden Setting:')
pprint(space_eval(self.search_space, self.best_result))
def return_best(self):
"""Function to return the best hyper-parameters"""
return space_eval(self.search_space, self.best_result)
def get_loss(self, params):
"""Function that defines and acquires the loss"""
# copy the hyperparameters to trainer config and hyperparameter set.
for key, value in params.items():
self.trainer.config.__dict__[key] = value
self.trainer.config.hyperparameters[key] = value
# configure common setting for a tuning training.
self.trainer.config.disp_result = False
self.trainer.config.disp_summary = False
self.trainer.config.save_model = False
self.trainer.config.test_num = 1000
if self.args.debug:
self.trainer.config.epochs = 1
self.trainer.config.hyperparameters['epochs'] = 1
# start the trial.
self.trainer.build_model()
self.trainer.summary_hyperparameter()
loss = self.trainer.tune_model()
return {'loss': loss, 'status': STATUS_OK}