def make_study(restart=False):
''' Make a study, deleting one if it already exists '''
try:
if restart:
print(f'About to delete {storage}:{name}, you have 5 seconds to intervene!')
sc.timedsleep(5.0)
op.delete_study(storage=storage, study_name=name)
except:
pass
output = op.create_study(storage=storage, study_name=name, load_if_exists=not(restart))
return output
def fix_broken_study(_study: optuna.study.Study, name: str, storage: str,
direction: str, sampler: optuna.samplers.BaseSampler):
"""
This method removes broken trials, which are those
that failed to complete 1 epoch before slurm (or something else) killed the job
and returned NAN or NONE.
Failure to remove these trails leads to a error when optuna tries to update the
parameters. This is because these trails only have "NoneType" data associated
with them, but we need numerical data (e.g. the loss value) to update parameters.
"""
if len(_study.trials) == 0:
return _study, []
trials = []
removed = []
for trial in _study.trials:
if len(trial.intermediate_values) == 0:
trials.append(trial)
continue
step, intermediate_value = max(trial.intermediate_values.items())
if intermediate_value is not None:
trials.append(trial)
else:
removed.append(trial.number + 1)
if len(removed) == 0:
return _study, []
# Delete the current study
optuna.delete_study(study_name=name, storage=storage)
# Create a new one in its place
if isinstance(direction, str):
study_fixed = optuna.create_study(study_name=name,
storage=storage,
direction=direction,
sampler=sampler,
load_if_exists=False)
else:
study_fixed = optuna.multi_objective.create_study(study_name=name,
storage=storage,
directions=direction,
sampler=sampler,
load_if_exists=False)
# Add the working trials to the new study
for trial in trials:
study_fixed.add_trial(trial)
return study_fixed, removed
def calcula_resultados(self) -> List[Resultado]:
"""
Retorna, para cada fold, o seu respectivo resultado
"""
self._resultados = []
self.arr_validacao_por_fold = [] #experimentos de validacao por fold
#seed para mater a reprodutibilidade dos experimentos
np.random.seed(1)
## Para cada fold
for i, fold in enumerate(self.folds):
##1. Caso haja um metodo de otimizacao, obtenha o melhor metodo com ele
if (self.ClasseObjetivoOtimizacao is not None):
try:
if not self.load_if_exists:
optuna.delete_study(
study_name=f"{self.nom_experimento}_fold_{i}",
storage=f'sqlite:///resultados/optuna_studies.db')
except KeyError:
pass
study = optuna.create_study(
study_name=f"{self.nom_experimento}_fold_{i}",
sampler=self.sampler,
direction="maximize",
storage=f'sqlite:///resultados/optuna_studies.db',
load_if_exists=self.load_if_exists)
objetivo_otimizacao = self.ClasseObjetivoOtimizacao(
fold, self.preproc_method)
study.optimize(objetivo_otimizacao, self.num_trials)
#obtem o melhor metodo da otimizacao
best_method = objetivo_otimizacao.arr_evaluated_methods[
study.best_trial.number]
self.studies_per_fold.append(study)
else:
#caso contrario, o metodo, atributo da classe Experimento (sem modificações) é usado
best_method = self.ClasseObjetivoOtimizacao.ml_method_default
##2. adiciona em resultados o resultado predito usando o melhor metodo
resultado = best_method.eval(self.preproc_method, fold.df_treino,
fold.df_data_to_predict,
fold.col_classe)
print(resultado.macro_f1)
self._resultados.append(resultado)
return self._resultados
def test_delete_study(storage_mode: str) -> None:
with StorageSupplier(storage_mode) as storage:
# Test deleting a non-existing study.
with pytest.raises(KeyError):
delete_study(study_name="invalid-study-name", storage=storage)
# Test deleting an existing study.
study = create_study(storage=storage, load_if_exists=False)
delete_study(study_name=study.study_name, storage=storage)
# Test failed to delete the study which is already deleted.
with pytest.raises(KeyError):
delete_study(study_name=study.study_name, storage=storage)
def test_delete_study(storage_mode: str) -> None:
with StorageSupplier(storage_mode) as storage:
# Get storage object because delete_study does not accept None.
storage = get_storage(storage=storage)
assert storage is not None
# Test deleting a non-existing study.
with pytest.raises(KeyError):
delete_study("invalid-study-name", storage)
# Test deleting an existing study.
study = create_study(storage=storage, load_if_exists=False)
delete_study(study.study_name, storage)
# Test failed to delete the study which is already deleted.
with pytest.raises(KeyError):
delete_study(study.study_name, storage)
def test_delete_study(storage_mode, cache_mode):
# type: (str, bool) -> None
with StorageSupplier(storage_mode, cache_mode) as storage:
# Get storage object because delete_study does not accept None.
storage = optuna.storages.get_storage(storage=storage)
assert storage is not None
# Test deleting a non-existing study.
with pytest.raises(ValueError):
optuna.delete_study("invalid-study-name", storage)
# Test deleting an existing study.
study = optuna.create_study(storage=storage, load_if_exists=False)
optuna.delete_study(study.study_name, storage)
# Test failed to delete the study which is already deleted.
if not isinstance(study._storage, optuna.storages.InMemoryStorage):
# Skip `InMemoryStorage` because it just internally initializes trials and so on.
with pytest.raises(ValueError):
optuna.delete_study(study.study_name, storage)
def optimize(self) -> TuningResult:
"""
Method performs a hyperparameter optimization run according to the selected HPO-method.
:return: result: TuningResult
TuningResult-object that contains the results of this optimization run.
"""
# Select the specified HPO-tuning method
if self.hpo_method == 'CMA-ES':
this_optimizer = CmaEsSampler(seed=self.random_seed)
elif self.hpo_method == 'TPE':
this_optimizer = TPESampler(seed=self.random_seed)
elif self.hpo_method == 'RandomSearch':
this_optimizer = RandomSampler(seed=self.random_seed)
else:
raise Exception('Unknown HPO-method!')
# Create a study object and specify the optimization direction
study_name = 'hpo_study'
study_storage = 'sqlite:///hpo.db'
# Optimize on the predefined n_func_evals and measure the wall clock times
# start_time = time.time()
self.times = [] # Initialize a list for saving the wall clock times
# Delete old study objects ('fresh start') >> otherwise the old results will be included
try:
optuna.delete_study(study_name, study_storage)
except:
print('No old optuna study objects found!')
# Use a warmstart configuration?
if self.do_warmstart == 'Yes':
try:
# Create a new study
warmstart_study = optuna.create_study(direction='minimize',
storage=study_storage,
study_name=study_name,
load_if_exists=False)
# Retrieve the warmstart hyperparameters for the ML-algorithm
warmstart_params = self.get_warmstart_configuration()
# Initialize a dictionary for the warmstart HP-configuration
warmstart_dict = {}
# Iterate over all hyperparameters of this ML-algorithm's tuned HP-space and append the default values
# to the dictionary
for i in range(len(self.hp_space)):
this_param = self.hp_space[i].name
this_warmstart_value = warmstart_params[this_param]
# For some HPs (e.g. max_depth of RF) the default value is None, although their typical dtype is
# different (e.g. int)
if this_warmstart_value is None and type(
self.hp_space[i]) == skopt.space.space.Integer:
# Try to impute these values by the mean value
this_warmstart_value = int(
0.5 *
(self.hp_space[i].low + self.hp_space[i].high))
# Add the warm start HP-value to the dictionary
warmstart_dict[this_param] = this_warmstart_value
# Enqueue a trial with the warm start HP-values
warmstart_study.enqueue_trial(params=warmstart_dict)
# Optimize to ensure that the warm start configuration is evaluated first (e.g. for parallel processes)
warmstart_study.optimize(func=self.objective, n_trials=1)
# Set flag to indicate that a warmstart took place
did_warmstart = True
except:
print('Warmstarting optuna failed!')
# Set flag to indicate that NO warmstart took place
did_warmstart = False
# No warmstart requested
else:
# Set flag to indicate that NO warmstart took place
did_warmstart = False
# Create a new study or reload the warmstart study (if available and requested)
study = optuna.create_study(sampler=this_optimizer,
direction='minimize',
study_name=study_name,
storage=study_storage,
load_if_exists=True)
# If a warm start took place, reduce the number of remaining function evaluations to ensure comparability
# (equal budgets)
if did_warmstart:
n_func_evals = self.n_func_evals - 1
else:
n_func_evals = self.n_func_evals
# Start the optimization
try:
study.optimize(func=self.objective,
n_trials=n_func_evals,
n_jobs=self.n_workers)
run_successful = True
# Algorithm crashed
except:
# Add a warning here
run_successful = False
# If the optimization run was successful, determine the optimization results
if run_successful:
# Create a TuningResult-object to store the optimization results
# Transformation of the results into a TuningResult-Object
all_trials = study.get_trials()
best_configuration = study.best_params
best_val_loss = study.best_value
start_times = [] # Start time of each trial
finish_times = [] # Finish time of each trial
# evaluation_ids = [] # Number the evaluations / iterations of this run
unsorted_losses = [] # Loss of each iteration
unsorted_configurations = () # HP-configuration of each iteration
# Number the evaluations / iterations of this run
evaluation_ids = list(range(1, len(all_trials) + 1))
for i in range(len(all_trials)):
start_times.append(all_trials[i].datetime_start)
finish_times.append(all_trials[i].datetime_complete)
# evaluation_ids.append(all_trials[i].number)
unsorted_losses.append(all_trials[i].value)
unsorted_configurations = unsorted_configurations + (
all_trials[i].params, )
abs_start_time = min(start_times) # start time of the first trial
unsorted_timestamps = []
for i in range(len(start_times)):
this_time = finish_times[
i] - abs_start_time # time difference to the start of the first trial
this_timestamp = this_time.total_seconds(
) # conversion into float value
unsorted_timestamps.append(this_timestamp)
wall_clock_time = max(unsorted_timestamps)
ids = list(range(1, len(all_trials) + 1))
temp_dict = {
'ids': ids,
'timestamps [finished]': unsorted_timestamps,
'losses': unsorted_losses,
'configurations': unsorted_configurations,
}
unsorted_df = pd.DataFrame.from_dict(data=temp_dict)
unsorted_df.set_index('ids', inplace=True)
# Sort DataFrame according to timestamps (necessary for multiprocessing)
sorted_df = unsorted_df.sort_values(by=['timestamps [finished]'],
ascending=True,
inplace=False)
timestamps = list(sorted_df['timestamps [finished]'])
losses = list(sorted_df['losses'])
configurations = tuple(sorted_df['configurations'])
# Optuna uses full budgets for its HPO methods
budget = [100.0] * len(losses)
# Compute the loss on the test set for the best found configuration
test_loss = self.train_evaluate_ml_model(params=best_configuration,
cv_mode=False,
test_mode=True)
# Run not successful (algorithm crashed)
else:
evaluation_ids, timestamps, losses, configurations, best_val_loss, best_configuration, wall_clock_time, \
test_loss, budget = self.impute_results_for_crash()
# Pass the results to a TuningResult-object
result = TuningResult(evaluation_ids=evaluation_ids,
timestamps=timestamps,
losses=losses,
configurations=configurations,
best_val_loss=best_val_loss,
best_configuration=best_configuration,
wall_clock_time=wall_clock_time,
test_loss=test_loss,
successful=run_successful,
did_warmstart=did_warmstart,
budget=budget)
return result
import optuna # pip install optuna optuna.create_study() optuna.load_study() optuna.delete_study() optuna.copy_study() optuna.get_all_study_summaries() optuna.TrialPruned trial = Trial(study, trial_id) trial.datetime_start trial.distributions trial.number trial.params trial.system_attrs trial.user_attrs trial.report(value, step) trial.set_system_attr(key, value) trial.set_user_attr(key, value) trial.should_prune() trial.suggest_categorical(name, choices) trial.suggest_discrete_uniform(name, low, high, q) trial.suggest_float(name, low, high, *, ?step, ?log) trial.suggest_int(name, low, high, ?step, ?log) trial.suggest_loguniform(name, low, high) trial.suggest_uniform(name, low, high) #@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ import tensorflow as tf from tf import keras
psql_url = f"postgresql://{DBUSER}@trans-db-01/{DBNAME}?options=-c%20search_path={DBSCHEMA}"
engine = create_engine(psql_url)
if __name__ == "__main__":
outcome_type = snakemake.wildcards["outcome_type"]
model_type = snakemake.wildcards["model_type"]
outcome_variable = snakemake.wildcards["outcome_variable"]
study_name = f"{outcome_type}__{model_type}__{outcome_variable}"
feature_subset = make_feature_subset(outcome_type)
storage = optuna.storages.RDBStorage(url=psql_url, engine_kwargs={"pool_size": 0})
try:
optuna.delete_study(storage=storage, study_name=study_name)
except:
pass
study = optuna.create_study(
storage=storage, study_name=study_name, direction="minimize",
pruner=optuna.pruners.HyperbandPruner(min_resource=15, reduction_factor=3),
sampler=optuna.samplers.TPESampler(multivariate=True, seed=42)
)
study.set_user_attr("contributors", ["benkaa"])
n_jobs = snakemake.threads
n_trials = snakemake.params.n_optuna_trials // n_jobs
def optimize(n_trials):
def make_study():
try:
op.delete_study(storage=storage, study_name=name)
except:
pass
return op.create_study(storage=storage, study_name=name)
# Check if the study record already exists.
try:
optuna.load_study(
study_name=study_name,
storage=storage,
#direction = direction,
sampler=sampler)
except KeyError: # The study name was not in storage, can proceed
pass
except:
if args_dict["override"]:
message = f"Removing the study that exists in storage {storage}."
optuna.delete_study(study_name=study_name,
storage=storage,
direction=direction,
sampler=sampler)
else:
message = f"The study {study_name} already exists in storage and reload was False."
message += f" Delete it from {storage}, and try again or rerun this script"
message += f" with the flag: --override 1"
raise OSError(message)
# Create a new study in the storage object
if single_objective:
create_study = optuna.create_study(study_name=study_name,
storage=storage,
direction=direction,
sampler=sampler)
else:
create_study = optuna.multi_objective.study.create_study(